NASA/TM-2008-214560

The Effects of the Mars Exploration Rovers (MER) Work

Schedule Regime on Locomotor Activity Circadian

Rhythms, Sleep and Fatigue

Charles W. DeRoshia

NASA Ames Research Center, Moffett Field, California

Laura C. Colletti

QSS Group Inc.

NASA Ames Research Center, Moffett Field, California

Melissa M. Mallis

NASA Ames Research Center, California

December 2008
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NASA/TM-2008-214560

The Effects of the Mars Exploration Rovers (MER) Work

Schedule Regime on Locomotor Activity Circadian

Rhythms, Sleep and Fatigue

Charles W. DeRoshia

NASA Ames Research Center, Moffett Field, California

Laura C. Colletti

QSS Group Inc.

NASA Ames Research Center, Moffett Field, California

Melissa M. Mallis

NASA Ames Research Center, California

National Aeronautics and

Space Administration

Ames Research Center

Moffett Field, California 94037

December 2008
Authors’ Note

The research for the Mars Exploration Rovers (MER) mission incorporated into this

report was conducted from December 18, 2003, to April 30, 2004. The writing of this

NASA publication was completed in July 2006, at which time it was submitted for

administrative review and approvals.

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prior to July 2006.

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This report is also available in electronic form at http://www.sti.nasa.gov/ and http://ntrs.nasa.gov/
 Contents

INTRODUCTION ................................................................................................................................ 1

METHODS........................................................................................................................................... 3

Participants........................................................................................................................................ 3

Materials........................................................................................................................................... 3

Procedures for Actiwatch Data Acquisition ..................................................................................... 5

Circadian Rhythm Data Analysis ..................................................................................................... 6

RESULTS............................................................................................................................................. 7

MER Operations Workloads............................................................................................................. 7

Adaptation to MER Mars Sol Schedules: Subjective Responses..................................................... 9

Sleep Quality................................................................................................................................... 14

Average Time in Bed per 24-Hour ................................................................................................. 14

Naps................................................................................................................................................ 18

Circadian Rhythm Analysis............................................................................................................ 20

DISCUSSION..................................................................................................................................... 53

Adaptation to the MER Operations Regime................................................................................... 53

Circadian Rhythm Entrainment to the MER Regime ..................................................................... 56

Circasemidian Rhythms.................................................................................................................. 60

Countermeasures to MER Operational Schedule Effects Upon Circadian Rhythms and Sleep .... 62

Suggestions for Future MER Operational Studies.......................................................................... 64

Conclusions......................................................................................................................................... 65

Acknowledgments .............................................................................................................................. 65

REFERENCES ................................................................................................................................... 67

APPENDIX A Schedule for Baseline Data Recording and MER Operations ................................... 75

APPENDIX B Circadian Rhythm Metrics......................................................................................... 79

APPENDIX C Background Questionnaire......................................................................................... 81

Background Questionnaire ......................................................................................................... 81

APPENDIX D Post-Study Questionnaire........................................................................................... 87

APPENDIX E Analysis of Sleep and Circadian Rhythm Metrics from Actigraphy Data ................ 93

Circadian Rhythm Data Analysis ....................................................................................................... 93

APPENDIX F Transitioning from a Mars Day to an Earth Day: Effects on Psychomotor Vigilance

Performance* ................................................................................................................................. 95

APPENDIX G Participant Demographics.......................................................................................... 99
 
1

ABSTRACT

This study assessed human adaptation to a Mars sol by evaluating

sleep metrics obtained by actigraphy and subjective responses in 22

participants, and circadian rhythmicity in locomotor activity in 9

participants assigned to Mars Exploration Rover (MER) operational

work schedules (24.65 hour days) at the Jet Propulsion Laboratory in

2004. During MER operations, increased work shift durations and

reduced sleep durations and time in bed were associated with the

appearance of pronounced 12-hr (circasemidian) rhythms with

reduced activity levels. Sleep duration, workload, and circadian

rhythm stability have important implications for adaptability and

maintenance of operational performance not only of MER operations

personnel but also in space crews exposed to a Mars sol of 24.65

hours during future Mars missions.

INTRODUCTION

This study provided the opportunity to assess human adaptation to a Mars sol by evaluating sleep

quality and circadian rhythmicity in locomotor activity data obtained by actigraphy from workers

assigned to Mars Exploration Rover (MER) operational work schedules during MER operations at

the Jet Propulsion Laboratory (JPL) at Pasadena, California in 2004. Humans normally operate on a

24-hour schedule and exhibit circadian rhythms in many behavioral and physiological factors

including sleep/wake cycles, body temperature, activity, alertness, and neurobehavioral performance

(Minors & Waterhouse, 1990; Colquhoun, 1971). Previous research has shown that sleep loss or a

misalignment of circadian rhythms increases subjective and physiological sleepiness, negative

affect, performance errors, adverse health, and accidents (Akerstedt, 1991; Bonnet, 2000). This was

important in reference to the demands of the Mars Exploration Rover (MER) Surface Operations

mission, which required personnel to perform mission critical tasks on schedules coinciding with a

Mars day (24.65 hour), also referred to as a Mars sol. To adjust to a Mars sol, personnel had to start

the work day 39 minutes (0.65 hour) later each day for three months (e.g., start work at 09:00 and

the next day 09:39). No previous published studies were identified that explored the ability of

personnel to shift work schedules 39 minutes daily for an extended period while being exposed to

outside Earth-based exogenous cues, such as sunlight and social cues, which exhibit a 24.0 hour

periodicity. Actigraphy involves the continuous acquisition of gross motor activity at short time

intervals (e.g., 1-min samples) for several weeks, via an accelerometer mounted in a wrist worn

watch-like device, and therefore provides the opportunity to evaluate circadian rhythmicity in

locomotor activity in an operational environment without intrusive data acquisition procedures.

Thus, an actigraphic ambulatory exploratory study was conducted during the first three months of

MER operations to investigate the sleep patterns of personnel while delaying their work schedule

39-minutes each day. Results of sleep quality indices (e.g., the average time in bed) for this study

were reported as well as case analyses of the circadian rhythm of activity to determine the effect of

working a Mars sol on sleep patterns.

The MER operation schedules were coordinated with Mars daylight times since two rovers were

reliant on sunlight for both electrical power and navigation. The two rovers were in operation in
2

Mars time zones approximately 12 hours apart and the first rover (“ Spirit” or MER-A) landed

January 4th, 2004 at 4:35 UTC (January 23rd, 20:35 PST) and the second rover (“Opportunity” or

MER-B) landed January 25th, 2004 at 5:05 UTC (January 24th, 21:05 PST). Missions were staffed

to coincide with “Mars Time” for approximately 90 days for each rover to maximize scientific gain

during the nominal mission. Detailed mission timelines were established for tactical operations of

the rovers for receiving data, assessing the data, planning and developing a new set of commands for

the next Sol (Bass, Wales, & Shalin, 2005; Parke, 2002).

The Fatigue Countermeasures Group at NASA, Ames Research Center (Oyung 2003) and other

research groups (Ancoli-Israel, et al., 2003) have shown that locomotor activity data obtained by

actigraphy were a useful means of documenting sleep disturbances associated with excessive

workload and fatigue in environments without the need for intrusive or invasive sleep measures such

as electroencephalography. Changes in circadian rhythm actigraph activity metrics (e.g., mean,

amplitude, interdaily stability) have been associated with changes in neuropsychological functioning

(Martin, et al., 2001), health status (Satlin, et al., 1995) and fatigue states (Mormon, et al., 2000) and

may provide a biological basis for psychological distress (Mormon and Waterhouse 2002). Cancer

patients with marked rest/activity circadian rhythms had better quality of life and significantly less

fatigue (Mormon, et al., 2000). The relationship between fatigue and circadian rhythm

desynchronization was also observed in cancer patients receiving chemotherapy, in which fatigue

was associated with circadian rhythm acrophase phase delays and amplitude changes (Liu, et al.,

2005). Therefore, the reliable statistical evaluation of changes in circadian rhythmicity in locomotor

activity provides the opportunity to document physiological changes which may be predictive of

subsequent sleep disturbance and impaired performance in operational work environments. One of

the primary manifestations of fatigue in field operations is circadian rhythm disruption resulting

from transmeridian flight or shift work schedules. Preliminary results of this MER study obtained on

one participant during the MER 24.65 hour work schedule showed decreased circadian rhythm

stability, the appearance of additional rhythms, and pronounced changes in the circadian rhythm

waveform during the MER work shift phase (DeRoshia, et al., 2005). These changes were associated

with a significant decrease in total bedtime in this participant (Colletti, et al., 2004).

Actigraphy data have been used to assess circadian rhythmicity in disease states (Liu, et al., 2005;

Mormon, et al., 2000; Mormon and Waterhouse, 2002; Satlin, et al., 1995; Van Someren, et al.,

1996), aeronautical operations (DeRoshia, et al., 2005; French, et al., 1994), age, gender, and

ethnicity differences (Jean-Louis, et al., 2000a, 2000b) and even spaceflight (Monk, et al., 1999;

Dijk, et al., 2001). While several investigators have evaluated changes in circadian locomotor

activity rhythms as an indicator of changes in health status (Satlin, et al., 1995; Mormon and

Waterhouse 2002), some reports have criticized the suitability of actigraphy data for reliable

estimates of circadian rhythm metrics due to large transient changes in voluntary activity (outliers or

masking effects), the presence of square wave circadian wave forms, which were not appropriate for

harmonic or spectral analysis, and poor goodness of fit of activity time series data by harmonic

analysis (Van Someren, et al., 1996; Bos, et al., 2002). Research was previously conducted to

develop and test mathematical techniques to establish that statistically reliable estimates of circadian

rhythm metrics can be obtained to document circadian rhythm changes associated with fatigue states

and performance decrements in operational environments, and these methods have been applied to

quantify changes in circadian rhythms associated with fatigue states in shift work and operational

transmeridian flight environments (DeRoshia, et al., 2004, 2005) and also to evaluate adaptation to

hyper gravity environments (Holley, et al., 2003). These methods can be valuable for assessing
3

changes in circadian rhythms in operational environments, where it is not feasible to use body

temperature probes or to take regular blood samples to assess circadian rhythmicity. The failure of

other investigators to perform reliable circadian rhythm analysis on actigraphy data probably reflects

differences reported in sampling rates (10 sec to 15 min), the use of raw data with large masking

effects and high sample-to-sample variability, and inappropriate use of mathematical procedures for

circadian rhythm analysis (DeRoshia, et al., 2005).

These techniques outlined by DeRoshia, et al. (2005) were incorporated into the circadian rhythm

analysis methods used in this study. The initial purpose of this report was to evaluate sleep quality

changes derived from actigraphy data while working different schedule types including two weeks

before the rovers landed (baseline), while working a Mars sol (MER rotation) and two weeks after

rotating (MER reduced-rotation, Colletti 2006). Data collection days during the study, which were

not assigned to baseline, MER rotation, or MER reduced-rotation, i.e., days in which subjects were

on nominal work schedules, were designated as transitional days. The rovers lasted over three

months and therefore, a modified rotation with reduced hours between 6:00 and 22:00 was worked

instead of the continuous rotation. The primary purpose of this report was to evaluate changes in

circadian rhythmicity associated with rotating MER schedules and the interrelationship between

circadian rhythm and sleep quality changes. Methodology and results for the actigraphic evaluation

of sleep quality metrics and also subjective assessment of sleep quality were previously reported as

part of a Master’s Thesis project (Colletti 2006).

METHODS

Participants

Thirty of the 250 MER mission personnel, seven females and 23 males, age 21 – 59 (M= 38.2,

SD=11.0) volunteered for this study, which was approved by the NASA Ames Research Institutional

Review Board. The participant demographic information is presented in Appendix G. The

participants were almost equally divided on locality to JPL: sixteen lived local to JPL and thirteen

reported their residences in Mountain, Central, or Eastern Time zones, and one international

participant (GMT-3) volunteered as well. Recruitment occurred with assistance from the MER

Surface Operations managers. Participation was not limited by age, gender or race. No monetary

compensation was given to participants; however, a personal analysis of their sleep history was

made available to them upon request. The volunteer subject group consisted of engineers, who

performed rover operations, scientists, who controlled mission operations, and managers. Baseline

locomotor activity data were collected approximately two weeks before the rover landings in most

subjects, starting on or about December 18, 2003 and data collection ended for all participants by

April 30th, 2004.

Materials

The Actiwatch recorder (AW-64; Mini-Mitter, Inc., Bend, OR; Figure 1) is a small waterproof, wrist

worn device (17 grams) used to measure gross motor activity for estimating sleep. The Actiwatch

was powered by a 3V, 150 mAmp-hr lithium manganese battery that has a lifetime of 180 days.

Each Actiwatch contains an accelerometer capable of sensing motion with a minimal resultant force
4

of 0.01g. Measurements were collected at 32 Hz and amplified, filtered and passed into an analog to

digital converter. The peak value at each second was summed over the minute to create an activity

score that was logged onto the non-volatile memory. The AW-64 also has an event-marker button on

the face for participants to record events such as the bedtime, wake time and periods in which the

Actiwatch was removed.

Figure 1. Participants wore the Actiwatch on the non-dominant wrist for

approximately four months.

Data were downloaded to a computer using a special reader and then Actiware-Sleep scoring

software (version 3.3; Mini-Mitter, Inc., Bend, OR) was used to calculate the sleep measurement,

defined as time in bed, bedtime and wake-up time. The software requires a sleep analysis window to

be set for each sleep episode, which was defined using the event markers pressed at bedtime and

wake time. The technical problems in the reliability of sleep quality assessment by actigraphy are

described in Appendix E . We decided to use the sleep efficiency metric since its reliability was

supported by two publications (Lichstein, et al., 2006; Tworoger, et al., 2005). We selected time in

bed as the primary dependent measure of sleep for group analyses and sleep efficiency, percentage

of actual wake time, bed times and wake-up times were used to further examine sleep disturbances

observed in circadian rhythm analyses of individuals. Time in bed per 24-hour period between the

hours of 00:00 to 23:59 was calculated from the time in bed measure to determine whether personnel

obtained an equivalent amount of sleep per 24-hours during the 24-hour and 24.65-hour work

schedules. This provided a common 24-hour measurement period for the different schedules.

The paper-and-pencil Sleep Log was a daily log with inputs for bed times and awakenings. These

sleep times were used to validate the Actiwatch event markers or mark the sleep times if event

markers were unavailable. Additionally, participants reported the onset and end times for work

schedules, naps, and subjective ratings of alertness and fatigue (“How rested do you feel upon

awakening?” and (“How sleepy do you feel upon attempting to sleep?”) in their daily logs. During

the second half of the study an additional rating scale was added (“Since your wakeup time listed

above, how sleepy were you on average during your waking hours?”). All rating scales ranged from

0 (“Not at all”) to 9 (“Extremely”). In addition, the logs included a line where the subjects could

check the operational work status (“Day off, or “MER”). Participants also marked extended periods

of time in which the Actiwatch was not worn and recorded anomalies such as traveling to different

time zones. These items provided additional information to help interpret the activity data.

Instructions for use were included with the Daily Sleep Log. This study was conducted secondary to

Actiwatch
5

the primary personnel objective, which was to operate the Mars Exploration Rovers. Therefore, this

log was completed by the personnel only if time permitted.

Two paper-and-pencil surveys were administered throughout the protocol: the Background, and

Post-study. The Background Questionnaire was a 30-item survey with questions about participants'

age, gender, family status, sleep profile and commute profile. The Morningness/ Eveningness (M/E)

Questionnaire (Horne & Ostberg, 1976) has been used to determine whether participants normally

feel most alert in the morning or the evening. This questionnaire was important to incorporate into

this study since it has been shown that morningness-eveningness was correlated with circadian

period, wake times (Duffy, et al., 2001), sleep satisfaction, sleep quality (Kato, et al., 2006), and

time in bed (Taillard, et al., 1999). The M/E questionnaire and the background questionnaire

(Appendix C) were administered to all subjects at the beginning of the study. A 28-item paper and

pencil Post-study questionnaire (Appendix D) was administered at the end of the study to determine

the subjective impact of working a Mars sol on personnel. This questionnaire incorporated a mood

test to assess levels of fatigue, irritableness, concentration, energy, and sleepiness. Fatigue was

defined in this report as subjective loss of desire or ability to continue performing and sleepiness was

defined as desire to sleep (Van Dongen and Dinges 2005). A composite fatigue scale was created by

assigning numerical weights to the responses, ranging from “strongly increased (-2)”, “moderately

increased (-1) ”, “neither increased or decreased” (0), “moderately decreased” (1), and “strongly

decreased” (2) for the fatigue, irritableness, and sleepiness scales, and “strongly increased (2)”,

“moderately increased (1) ”, “neither increased or decreased” (0), “moderately decreased” (-1), and

“strongly decreased” (-2), for the concentration and energy scales. The values across the five scales

were summed for each participant to create the composite fatigue scale.

Procedures for Actiwatch Data Acquisition

Upon signing the Consent Form, participants were sent an Actiwatch, Background questionnaire,

M/E questionnaire and Daily Sleep Log two weeks before the MER landing on January 4th, 2004.

Materials were mailed directly to the participants with a self-addressed envelope for immediate

return of the questionnaire and materials at the end of the study.

Actiwatches are usually worn on the non-dominant wrist; however, participants were allowed to

place the Actiwatch on the dominant wrist for the duration of the study, if discomfort occurred, to

reduce participant non-compliance. A review of the literature on actigraph placement found

inconclusive findings with more research required (Ancoli-Israel et al., 2003).

The Actiwatch was worn on the wrist at all times including sleep and wake except during bathing or

high-impact activities to avoid damaging the watch. Upon attempting to sleep or upon awakening,

the participant pushed an event marker on the top of the watch. They also input the sleep/wake time

on the daily sleep log. At regular intervals, the researcher would travel to JPL to exchange

equipment with participants due to the limited memory capacity of the Actiwatch. It would have

been preferable if participants wore the same Actiwatch throughout the protocol; however,

experience during an operational readiness test found the high workload and unusual schedules of

the participants interfered with getting the same Actiwatch back to each participant after processing

without missing one or several sleep cycles. We decided that it was more important to switch

Actiwatches to maintain continuous data collection. Therefore, the participants immediately received

a replacement Actiwatch, when turning in the previous one for downloading, battery change,
6

memory clearing and re-coding. At the end of the protocol, the Post-study questionnaire was

distributed and the materials were picked up or returned via pre-paid delivery service to NASA

Ames Research Center. Daylight savings occurred on Sunday, April 4, 2004, at 0200; however, the

Actiwatch remained on standard time throughout the protocol.

Circadian Rhythm Data Analysis

Mathematical and statistical methods for analysis of the Actiwatch locomotor activity data were

based primarily upon previous verification of these methods for use on locomotor activity data

(DeRoshia, et al., 2004, 2005; Holley, et al., 2003). First, the 1-minute raw data were filtered to

eliminate or reduce the statistical effects of outliers and transient activity anomalies by clipping

activity values > 95% of the sorted activity values >0 (i.e., setting these values to the clipped value).

Robust locally weighted regression (RLWR) was next used to filter and smooth the locomotor

activity data. RLWR provides a locally weighted non-linear curve fit (Cleveland 1979) and was

therefore an ideal procedure for smoothing spiky data with large transients such as locomotor

activity (Holley, et al., 2003). The computer program utilized was derived from the “lowess”

transform provided in SigmaPlot software (2001, SPSS, Inc.). The filter was tested by evaluating

different filter lengths ranging from 30–180 sample points. The optimum filter used a number of

terms equivalent to one hour of data (60 data points, DeRoshia et al., 2005). The filtered data were

then converted to half-hourly means for most circadian rhythm analyses since the data sets collected

in this study were extremely long (maximum of 125 days, 180,000 consecutive 1-minute samples).

Circadian rhythm metrics were extracted by harmonic analysis using the complex demodulates

analysis method (Redmond, et al., 1982; Sing, et al., 1980). The metrics obtained were acrophase

(circadian rhythm peak time derived from harmonic least squares fit), amplitude (circadian rhythm

peak-to-trough range), mesor (mean estimated statistic of the mean, the circadian rhythm means

estimated from harmonic least squares analysis), goodness of fit (correlation between activity data

and fitted harmonic), and percent rhythm (ratio of residual harmonic variance to total data variance).

The latter two metrics provide a measure of circadian rhythm stability, as well as the goodness of fit

by harmonic analysis. In this study, relative amplitude (ratio of the amplitude to the mesor; Satlin, et

al., 1995; Van Someren, et al., 1999) was used in place of the amplitude or mesor metrics since

Actiwatch sensors were found to differ significantly in accelerometer sensitivity and the Actiwatch

sensors are sensitive to changed positions on the wrist. In this study, Actiwatches were exchanged

up to five times due to limited memory capacity of the Actiwatch and since the position of the watch

on the wrist may change slightly after changing watches or as a consequence of removing the watch

temporarily for hygiene. The relative amplitude metric provides a reliable estimate of rhythm

strength independent of changes in amplitude or mesor subsequent to removing or replacing

watches. Circadian rhythm statistical significance was established by Cosinor analysis (Nelson, et

al., 1979), which provides a statistical significance test and 95% confidence limits for acrophase and

amplitude. Circadian rhythm periodicity was evaluated using a non-parametric waveform-based

method (profilogram, Citta 1982) and also by power spectral analysis utilizing a modified

(Powerspec) transform provided in SigmaPlot 2001 software. Since classical power spectral analysis

can result in periodicity artifacts at harmonics of the main circadian rhythm frequency as a

consequence of non-stationary, asymmetric, or non-sinusoidal circadian waveforms, the profilogram

was used primarily to verify the biological reliability of spectral peaks derived from power spectral

analysis. Spectral peaks in the profilogram represent the ratio of the variance of the mean educed

cycle to the total data variance and spectral peaks in the power spectrum represent relative spectral
7

power amplitude at a given frequency. Moving spectral analysis (pergressive, or moving

periodogram analysis, (Kramm 1973) was used to reveal short-term changes in periodicity and nonstationary transient changes in rhythmicity during MER and non-MER regimens. This was

accomplished by employing power spectral analysis stepwise through the data, usually by evaluating

periodicity in consecutive 4-day data blocks. Educed circadian cycles were computed by first

estimating the circadian rhythm period length in a given data segment, using power spectral

methods, averaging data across the total number of cycles, given the input period length, then

reducing the educed cycle data from 1-min to 30-min means. Educed circadian cycle data were used

to evaluate changes in circadian rhythm waveforms during the MER regimes and also to calculate

interdaily stability. Interdaily stability (the ratio of circadian cycle variance at the peak estimated

circadian period length to total variance; Satlin, et al., 1995; Van Someren, et al., 1999; Teicher, et

al., 1997) was computed as a measure of rhythm stability. The Cosinor (Martin, et al., 2001), wave

form based periodograms (Pollak, et al., 2001) and spectral analysis have been used for circadian

rhythm analysis of actigraphy by other investigators.

The rotating MER schedule resulted in the timing of operational MER schedules during the

subjective night in several subjects. Therefore, we deemed it important to compare sleep quality and

circadian rhythmicity in the same participant during day and night shifts wherever possible. Activity

circadian rhythm metrics and waveforms were selected from day shift and night shift times defined

by the activity circadian rhythm acrophase. Day shifts included data where the acrophase occurred

between 11:40 and 19:40 and night shifts included data where the acrophase occurred between 23:40

and 07:40. These time ranges were selected to incorporate at least five days of activity data from as

many subjects as possible.

Baseline data and MER regimes were established by inspecting participant daily work activity logs

and post-study questionnaires, which document MER operations duty days and start and end dates

for MER operations regimes, and also by inspecting plots of Actiwatch actigraph plots and recorded

wake-up times to determine baseline and MER regime start and end dates. This was necessary since

the participants did not maintain complete records of their workload schedules and duty

assignments. The baseline data segments consisted of data recorded before the onset of MER

operational duties in most participants. In other participants, baseline segments were defined as the

start of a sequence of days in which activity onsets were relatively constant. The MER rotating

schedule onsets were defined as the start of a sequence of days in which the participant was

participating in MER operations.

RESULTS

MER Operations Workloads

The MER mission operational workload demands defined the workload schedules of the MER

workforce and the MER operational activities contributed to the locomotor activity levels recorded

by actigraphy. The MER workload schedules include activities performed during the Mars sol and

activities performed between sunset and sunrise on Mars. The activities during the Mars sol included

Mars rover sequence plan reviews, real time rover monitoring, tactical science assessment, and

observation planning. The post-sol activities included end-of-sol science and engineering assessment

meetings, rover activity plan integration and development, and activity sequence development and
8

approval (Bass, et al. 2005). The MER workloads were also categorized as tactical processes, which

involved planning and directing rover operations, and strategic operations, which involved long term

mission activities, resource allocations, coordination of the two rover missions, satellite

communications, determination of rover resource situations, and public outreach activities. The

tactical workload was scheduled on Mars sol time, while the strategic work was done on earth time

(Bass, et al., 2005). The tactical work necessitated a scheduled 10-hour downlink shift staff, who

analyzed received data and made recommendations for rover activity execution, and two scheduled

10-hour uplink shift staffs, who converted the recommendations into spacecraft commands. These

shifts initially took 19 hours to complete; however, by the beginning of the reduced-rotation

operations, the process was reduced to an average of 8 hours by improvements in automation,

increased staff training, and expertise development (Bass, et al., 2005). The success of the MER

mission resulted in the extension of the mission past the scheduled three-month period for about

another 1–4 weeks for most personnel. During this latter period, the rotating MER schedule was

changed to a “reduced MER rotation” schedule, in which personnel were limited to work shifts

between 07:00 and 21:00 hours. The rover Spirit went on the new schedule on 3/29/04, while the

rover Opportunity transitioned on 4/6/04. For the duration of this study, personnel were either

working on a rotating MER schedule based upon a Mars sol (24.65 hour day), or they were working

on their normal job assignments during the approximately two-week data collection period (baseline

period) preceding the rover landings on Mars, working on the reduced MER rotation schedule, or

they were on days off. Since the baseline period, MER reduced rotation schedule and days off were

based upon 24.0 hour days, these days will be referred to as “earth time”. During the MER mission,

five of the 30 study participants were required to switch work shifts between the MER-A and MERB missions, which necessitated a 12-hour shift in their work/rest schedules. The changes in rover

assignments and the transition to the reduced MER rotation schedule may have resulted from

personnel shortages and the lack of sufficient funding to cover the mission extension. The details of

participant baseline data recording, MER operations schedules and experimental notes are provided

in Appendix G.
9

Adaptation to MER Mars Sol Schedules: Subjective Responses

Twenty-seven participants completed the Post-study questionnaire which documented changes in

moods and difficulty during Mars sol MER operations. Five of these participants were excluded

from the evaluation of sleep and fatigue-related responses for the reasons specified in the Sleep

Quality results section, which left 22 subjects for the subjective sleep and fatigue evaluation. Eleven

subjects ( 50%) found it difficult working a Mars sol, six (27%) were neutral and five (23%) found it

easy (Figure 2).

0

2

4

6

8

10

very difficult somewhat

difficult

neutral somewhat

easy

very easy

Number of participants

Figure 2. Frequency of responses to the post-study question “How difficult was it

working a Mars Sol?”.

Open-ended questions about what participants found most challenging about working a Mars sol and

general comments identified:

• Major changes in work shift times due to switching between MER-A and MER-B rovers (5/22,

23%)

• Major changes in rest/activity schedules due to switching between a Mars sol and earth time

(6/22, 27%)

• Working the graveyard shift during the MER rotation (4/22, 18%)

• Social/daily living factors in completing chores, shopping and socializing (or social isolation)

(8/22, 36%)

• Sleeping during the day (3/22, 14%)

• Awakening by children (5/22, 23%)

• Workload fatigue: working long hours for many consecutive days and additional tasks outside

of MER operations (2/22, 9%)

There were many anecdotal comments that did not fall into the above categories but provide further

insight into experiences of participants while working a Mars Sol. One participant commented that

their “schedule was quite easy because I stayed on LST-B daytime. I served as MER-B SOWG chair

or MER-A SUR (which are about the same time-of-day in LST-B time).” Thus, this participant

remained on a consistent Earth schedule by working multiple job responsibilities and therefore

found the scheduling easy. One participant indicated that the inconsistency of work and off day

schedules was a problem in adaptation. However, another participant indicated that multiple days off
10

provided the opportunity to readjust sleep/wake timing for the upcoming MER regime. The changes

in work shift times between MER-A and MER-B operations was considered a major problem by all

four individuals required to perform these shifts and one participant recommended that personnel

should be assigned to a single MER mission. Mission excitement (adrenaline) promoted adaptation

in one participant. Other comments of interest included the deleterious effects of meetings scheduled

at the start or end of MER shifts, and the inadequate lighting during night shifts. Two participants

indicated that they were able to adapt to the MER operations regimes after the initial 2–4 weeks of

working a Mars Sol, after which their mood and sleep times stabilized.

Mood responses as a consequence of MER operations, which were extracted from the post-study

questionnaire, showed a negative impact of working a Mars Sol in most participants (Figure 3). In

particular, 18 participants (82%) indicated that fatigue increased while working MER operations. In

addition, sleepiness (n=14, 64%) increased, while concentration (n=12, 55%) and energy (n=13,

59%) decreased. The composite fatigue score was negative in 18 participants (82%). However,

responses were variable since two participants exhibited positive composite mood scores, in

response to MER operations, while four participants exhibited highly negative composite mood

scores (Figure 4, -6 to -8, indicative of an average of moderately to strongly negative mood

responses across the five mood scales). The best adapters to the MER regime, suggested by the

combined fatigue scores, were participants M310 (+3) and M323 (+1). These participants reported

MER sleep durations of 8 and 9 hours, respectively. The worst adapters were participants M0306 (-

8), M0325 (-7), M0330 (-6), and M0319 (-6). These participants reported MER sleep durations of 5,

8, 6, and 6 hours, respectively. Participant M0306 reported problems with frequent awakenings and

the necessity of attending several meetings immediately before and after MER shifts, which reduced

the time available for sleep. Participant M0330 had a morningness-eveningness score of 66

(moderate morning type) and indicated that he had difficulty adjusting to the longer Mars sol

schedule since he was a morning person. This participant also reported difficulties in switching from

MER-A to MER-B work shifts. Participant M0319 reported difficulty in sleeping in daytime during

the MER rotation.
11

Concentration

0

4

8

12

16

strongly

decreased

(-2)

moderately

decreased 

(-1)

neither 

(0)

moderately

increased 

(1)

strongly

increased

(2)

Number of participants

Fatigue

0

4

8

12

16

strongly

increased

(-2)

moderately

increased

(-1)

neither

(0)

moderately

decreased 

(1)

strongly

decreased

(2)

Number of participants

Irritableness

0

4

8

12

16

strongly

increased

(-2)

moderately

increased

(-1)

neither

(0)

moderately

decreased 

(1)

strongly

decreased

(2)

Number of participants

Energy

0

4

8

12

16

strongly

decreased

(-2)

moderately

decreased 

(-1)

neither 

(0)

moderately

increased 

(1)

strongly

increased

(2)

Number of participants

Sleepiness

0

4

8

12

16

strongly

increased

(-2)

moderately

increased

(-1)

neither

(0)

moderately

decreased 

(1)

strongly

decreased

(2)

Number of participants

Figure 3. Changes in fatigue variables obtained from the post-study questionnaire of

participants while working MER operations.
12

Composite fatigue scores from poststudy questionnaire

-10

-8

-6

-4

-2

0

2

4

M0306

M0325

M0319

M0330

M0305

M0308M0313

M0315

M0316

M0326

M0302M0329

M0307

M0312

M0324

M0303

M0320M0332

M0304

M0331

M0323

M0310

Participant number

Composite fatigue score

Figure 4. Changes in participant composite fatigue scores obtained from the poststudy questionnaire sorted from maximum fatigue responses (left) to minimal fatigue

levels (right). The effects of the MER operations regime on fatigue were highly

variable between subjects. Four subjects showed no change or a reduction in

composite fatigue (M0304, M0331, M0323, M0310) and four subjects showed a

pronounced increase in composite fatigue (scores of -6 to -8, M0330, M0319, M0325,

M0306).

While the subjective questionnaires and daily log did not focus on the stressful aspects of MER

operations workload, participant responses indicative of reactions to stressful conditions were

reported, aside from the workload stresses associated with the MER operations workload. Most

(n=13, 59%) participants indicated that irritability increased during the MER regime. Three

participants reported that social isolation during the MER regime, particularly during night rotation

shifts, was a major challenge since they were isolated from social contacts with friends and family.

Another source of stress expressed by several participants was the difficulty in completing

household chores during MER operations. The challenge of being awakened and attending to

children reported by five participants may have been another source of stress during MER

operations. One participant indicated that social camaraderie, which helped to alleviate stress during

a previous mission operation (1997 Mars Pathfinder), was absent during the MER mission.

Several strategies were employed by the participants to counteract sleep problems and fatigue during

MER operations. The most common strategy was the use of caffeinated beverages (n = 15, 68%).

Other strategies included naps (n = 11, 50%), exercise or walking (n = 7, 32%), days off or vacations

(n = 5, 23%), ensuring 8+ hours of sleep (n = 4, 18%), and the use of eye shades or window shades

(n = 3, 14%). One participant took melatonin to promote sleep. Another participant indicated that

reducing work responsibilities as much as possible was helpful. Although seven participants

indicated that exercise was an important adaptation strategy, one participant indicated that a

reduction in exercise activity was useful to reduce fatigue.

During MER operations, personnel had days off ranging from 1–7 consecutive days. The days off

schedules were determined by MER task subgroups within the MER operations team. The
13

operations teams implemented these schedules differently. One team chose to work four days on

MER operations followed by three days off, while another team worked seven days on with seven

days off (Bass, et al., 2005). Two participants in our study elected to stay on Mars time throughout

MER operations, while the other participants transitioned to earth times on their days off. The

number of consecutive MER operations days ranged from 1–31 (mean = 6.8), while the number of

consecutive days off ranged from 1–11 (mean = 3.0). There were eight cases in which participants

worked 17 or more consecutive days, which represented 9% of the total cases. The ratio of

consecutive MER operations days to days off ranged from 3/3 to 17/5. Work time duration data

records were not completed for all work days by all participants. However, at least five days of data

were available from 17 participants to compare work shift durations between earth time days and

MER operations days. The work time duration data indicated that workload duration significantly

increased from earth time (mean = 8.8 hours, range = 6.4–12.3 hours) to the MER regimes (mean =

10.4 hours, range = 7.7–12.2 hours, paired t-test, t(df = 17) = -4.9, P = 0.001). The subjective sleep

duration data indicated a reduction in sleep time from earth time (mean = 8.2 hours, range = 6–11

hours) to the MER regimes (mean = 6.9 hours, t(df = 21), t = 6.2, P = .000004). The distribution of

work time durations is shown below (Figure 5). Individual work time durations ranged from

0.5–20.5 hours (earth time) to 1.3–23 hours during the MER regimes. During the MER regimes,

58% of shift durations exceeded 10 hours, and 26% of workload durations exceeded 12 hours.

Distribution of work shift durations during earth time and 

MER shifts

0

20

40

60

80

100

120

140

160

180

0-0.99

1-1.99

2-2.99

3-3.99

4-4.99

5-5.99

6-6.99

7-7.99

8-8.99

9-9.99

10-10.99

11-11.99

12-12.99

13-13.99

14-14.99

15-15.99

16-16.99

17-17.9918-18.99

19-19.99

20-20.99

21-21.99

22-22.99

Work shift duration in hours

Number of work shifts

Earth time

MER

Figure 5. Comparison of work shift durations between earth time and MER operations

shifts.
14

Sleep Quality

Six participants were removed from the study due to missing sleep log data (n = 2), inconsistent

sleep log entries compared to actigraphy (n = 1), medical issues (n = 1), international location (n =

1), or family emergencies (n = 1). Actiwatch software cannot accurately predict active sleep from

restful wake without specifying bedtimes. Therefore, sleep quality analyses were computed using the

remaining twenty-four participants out of the original thirty. Five participants that visually appeared

to maintain a sleep/wake schedule that coincided with a Mars sol were selected for circadian rhythm

analyses to examine the effect of working a Mars sol on activity circadian rhythms. Two participants

were selected who shifted back and forth from Mars sol time to earth time on days off. Two

additional participants were selected since they maintained a rotating MER sleep/wake schedule

throughout the MER mission, which included maintenance of the schedule during days off. Four

participants were required to undergo the transition from a MER-A schedule to a MER-B schedule,

which required a 12-hr shift in rest-activity patterns since the MER rovers were located on opposite

sides of the planet. Two of these participants were selected for evaluation of the effects of the MERA to MER-B schedule shift.

The Actiwatch was worn on average 109.2 days (range 49 –136 days). The average number of days

per work schedule was 13.9 days (range 3 –42 days) during baseline, 77.3 days (range 37–100 days)

during MER-rotation and 20.1 days (range 9 –33 days) during the MER modified-rotation schedule.

The start dates for baseline, MER-rotation and MER reduced-rotation schedules were determined by

the post-study questions requesting the start and end dates for working a Mars sol.

Average Time in Bed per 24-Hour

Time in bed was analyzed using SAS (ver. 8.02) and SPSS (ver. 11.5) to determine changes in 24-

hour time in bed. Repeated-measures analyses of variance (ANOVA) were run using SAS PROC

MIXED to allow for missing values within a subject. Analyses investigated time in bed for two

within-subject factors: 1) work schedule (baseline, MER rotation, MER reduced-rotation); and 2)

working or not working during a MER rotation. The MER reduced-rotation was scheduled to be the

recovery period; however, the rovers continued to operate after three months and the MER schedule

was reduced to rotate between 06:00 and 22:00. Sleep periods or days in which the participants were

sick, had no event markers and no sleep log entries, or varied greatly from sleep log entries were

removed from the data set. Additionally, New Year’s Eve (12/31/03) and Daylight Savings day

(04/04/04) were removed if the sleep period varied more than two hours from the preceding and

following two days. Two Actiwatch recordings for participants M0304 and M0313 were missing due

to hardware problems and sleep log entries were used instead.
15

Sleep fractions for baseline, MER rotation and MER reduced-rotation 

work schedules

0

10

20

30

40

50

60

70

80

90

100

19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Hour

Percentage of time in bed at hour

Baseline

MER rotation

MER reduced-rotation

Figure 6. Percentage of sleep episodes (also referred to as sleep fractions) in which

participants were in bed at the time of day specified along the x-axis. Notice the

increase in sleep occurring during normal wake times between 09:00 and 20:00 while

participants worked Mars sols (MER-rotation).

To determine how the timing of sleep changed during the different work schedules, sleep fractions

or percentage of sleep periods in which participants were in bed at each hour of the day were plotted

for the different work schedules in Figure 6 (Lewis and Masterton 1957; Naitoh, et al., 1991). Sleep

from midnight to 04:00 was below 80% during the baseline period, which suggests that other factors

were affecting the sleep schedules of personnel and this period did not represent a true “baseline”.

Sleep during the MER reduced-rotation was above 80% from midnight to 04:00 (range 80 –88%)

suggesting personnel slept more consistently during the night for this work schedule.

While personnel worked the MER-rotation, shifting 39 minutes daily, time in bed decreased from

baseline during the night hours from midnight to 04:00 from 52% to 66%. Time in bed increased

during the day and early evening with a larger percentage of sleep episodes occurring in the late

morning (43% at 08:00) and decreasing across the day until only 14% of the sleep episodes occurred

at 18:00. The highest percentage of sleep was still obtained during normal sleep times from 00:00 to

07:00 and the smallest percentage of sleep episodes occurring during the early evening 16:00–19:00.

The peak time of day in which most sleep episodes occurred was 03:00 for baseline, 05:00 for the

MER-rotation and 02:00 for the MER reduced-rotation.

To determine whether the time in bed obtained by the MER operations personnel differed among the

different schedules (baseline, MER-rotation and MER reduced-rotation) operations, a repeatedmeasures ANOVA was run. Since two participants were missing baseline data due to late

recruitment and one participant stopped wearing the Actiwatch after 49 days before working a MERrotation schedule, their data were included by using mixed-model ANOVA.
16

7.2

7.4

7.6

7.8

8

8.2

8.4

Baseline MER-rotation MER reducedrotation

Average time in bed (h)

Figure 7. Average time in bed for participants across baseline, MER-rotation and MER

reduced-rotation work schedules showed a significant difference between schedules.

Error bars represent standard error of the mean.

Average hours of time in bed for participants across baseline (M = 8.18, SD = 0.70), MER-rotation

(M = 7.81, SD = 0.67) and MER reduced-rotation (M = 7.73, SD = 0.61) work schedules showed a

significant difference in work schedule (F(2, 43) = 7.92, p < .01) with baseline time in bed

significantly higher than the MER-rotation (t(43) = 3.29, p < .01) and MER reduced-rotation

schedules (t(43) = 3.63, p < .001; Figure 7). Time in bed for the MER-rotation and reduced-rotation

schedules were not significantly different (t(43) = 0.41, n.s.). Results suggest that participants

obtained more sleep before the landing of the rovers and 24-hour time in bed while working a MERrotation and reduced-rotation was about the same.

By averaging sleep across each participant, resolution was lost in the data analysis. For example, the

minimum time in bed for each participant across all sleep episodes for a MER-rotation schedule

ranged from 0 to 5 hours and the maximum ranged from 10.7 to 18 hours; yet the average minimum

time in bed averaged across each participant was 6.4 hours and maximum was 9.0. Therefore, the

variability of each participant’s time in bed was also analyzed.

To examine the variability of sleep, the standard deviation of time in bed was used. A significant

main effect was found for the standard deviation of average time in bed for participants across

baseline, MER-rotation and MER reduced-rotation work schedules (F(2, 43) = 8.54, p < .001; Figure

8). The variability of time in bed during the MER-rotation schedule was significantly higher than

baseline (t(43) = -3.60, p < .001) and MER reduced-rotation (t(43) = 3.52, p = .001) schedules. The

variability of time in bed was not significantly different between baseline and the MER reducedrotation schedules (t(43) = -0.12, n.s.). These results suggest that a work schedule that rotates 39

minutes daily causes increased variability in the length of sleep episodes, which could result in

circadian disruption affecting alertness and performance.
17

1.2

1.4

1.6

1.8

2

Baseline MER-rotation MER reducedrotation

Standard deviation of average time in bed

Figure 8. The variability of time in bed for the MER-rotation schedule was

significantly different than baseline and MER reduced-rotation suggesting sleep

durations were more variable while working a Mars Sol. Error bars represent

standard error of the mean.

To determine whether the average sleep duration (time in bed per 24 hours) of personnel working a

Mars sol (MER-rotation schedule) differs between non-working days and working days, a repeatedmeasures ANOVA was run. A day was scored as a work day if the participant worked more than

two hours within the 24 hour period. Only twenty-one participants were included in this analysis due

to removal of three participants with missing work schedules from the sleep log.

Time in bed while working (M = 7.59, SD = 0.62) was significantly different from time in bed while

not working (M = 8.63, SD = 0. 71; F(1, 20) = 78.9, p < .001; Figure 9). These results suggest that

personnel slept more on days off, therefore potentially compensating for sleep lost while working.

Sleeping longer on non-work days was common for all permanent workers; although, night shift

workers tend to sleep less during workdays and sleep more on non-work days compared to day

workers (Tepas and Carvalhais 1990).

The standard deviation of time in bed across participants was also analyzed to determine whether the

variability in sleep duration was different for days working and not working. The repeated-measures

ANOVA on the standard deviation of time in bed while working (M = 1.65, SD = -.37) and not

working (M = 1.75, SD = 0.57) found no significant main effect (F(1, 20) = 0.55, n.s.).
18

7

7.5

8

8.5

9

Work day Non-work day

Average time in bed (hrs)

Figure 9. Comparison of average time in bed during work days and non-work days.

During the MER-rotation, average 24-hour time in bed while working was significantly

less than while not working. These results suggest that personnel slept more on days off

potentially compensating for sleep lost while working a schedule that coincided with

the Mars Sol. Error bars represent standard error of the mean.

Naps

A total of 325 nap periods were identified in 24 participants, which is an average of about one nap

per participant per eight days. Naps tended to occur in clusters during consecutive days in most

participants (Figure 10) and were often associated with large reductions in time in bed (Figure 19e).
19

Total daily nap durations by subject

0

20

40

60

80

100

120

12/18/2003

1/1/2004

1/15/2004

1/29/2004

2/12/20042/26/2004

3/11/2004

3/25/2004

4/8/2004

4/22/2004

Date

Total nap duration (hours)

M0310

M0320

M0304

M0303

M0309

M0325

M0332

M0313

M0302

M0305

M0306

M0307

M0308

M0312

M0315

M0316

M0319

M0322

M0323

M0324

M0326

M0329

M0330

M0331

Figure 10. Total daily nap durations are shown for 24 participants. The ordinate scale

ranges from 0-5 hours total nap duration for each participant. The naps tended to

occur in clusters for most participants.
20

Circadian Rhythm Analysis

A comparison of mean circadian rhythm metrics between a laboratory baseline group (data collected

from participants in research studies by the Fatigue Countermeasures Group [FCG], Human Factors

Division, NASA Ames Research Center, California), the MER study baseline and MER regimes are

shown in Table 1 and the individual circadian rhythm metrics are shown in Appendix B.

TABLE 1. SUMMARY OF CIRCADIAN RHYTHM METRICS (MEANS AND

VARIABILITY) BETWEEN THE LABORATORY BASELINE (FCG DATA), MER

BASELINE, AND MER REGIME

ID N1

PeriodAmplitude%RhythmAcrophase

Degrees Mesor

Relative

Amplitude

Goodness of

fit

Interdaily

Stability

Rhythm

Sig Prob 95% CL

Comparison of circadian rhythm means between laboratory data and MER study data

Lab FCG data2 7 24.04 202.31 47.36 208.11 237.27 0.836 0.68 0.51 3.10E-04 30.56

MER baseline 9 24.08 115.91 53.35 223.30 187.12 0.673 0.75 0.49 5.44E-04 74.56

MER regime 9 24.84 166.66 39.27 284.07 151.50 1.101 0.71 0.50 8.38E-05 33.45

Comparison of circadian rhythm between MER baseline, MER regime and transitional days

Baseline 9 24.08 115.91 53.35 223.30 187.12 0.67 0.75

Transition days 5 24.41 113.55 38.45 285.39 133.60 0.84 0.66

MER regime 9 24.84 166.66 39.27 284.07 151.50 1.10 0.71

Comparison of circadian rhythm variability (standard deviation) between MER baseline, MER regime and transitional days

Baseline 9 34.96 16.10 66.70 44.15 0.20 0.10

Transition days 6 47.06 17.20 61.59 39.04 0.36 0.11

MER regime 9 35.40 13.62 25.70 28.85 0.24 0.09

MER study circadian rhythm statistics: Subjects with strong Circasemidian rhythms

Baseline 24.06 125.30 49.84 231.24 199.34 0.72 0.72 0.43 4.95E-04 69.19

MER regime 24.98 171.91 29.67 298.01 148.52 1.15 0.65 0.51 9.69E-06 38.64

MER study circadian rhythm statistics: Day vs. night MER rotation shifts

Day 100.46 47.24 246.28 16.42 0.68 0.72

Night 111.95 31.63 207.99 13.87 1.02 0.64 1

 Number of participants in analysis, 2Fatigue Countermeasures Group subjects

Circadian rhythm analyses were performed on nine participants for this report. Estimates of

circadian rhythm amplitude, mesor, and relative amplitude were lower in the MER study participants

during earth time (baseline, 115.9, 187.1, and 0.67, respectively) than in our Fatigue

Countermeasures Group laboratory participants (202.3, 237.3, and 0.84, respectively), indicating

that locomotor activity and circadian activity oscillations were lower in the MER participants.

However, comparison of the circadian rhythm metrics of period length, acrophase in degrees,

relative amplitude and interdaily stability revealed no statistically significant differences between the

MER baseline and the laboratory participants (t14<1.8, ns). The mean circadian period length

increased from 24.08 hours (earth time) to 24.84 (MER, paired-t test, t8 = 8.4, P<0.001, P<0.002

with Bonferroni correction). Circadian acrophase ( 223o) and relative amplitude (0.673) both

increased during the MER regimes, (284o and 1.101, respectively, t8 = 3.5, 3.8, P<0.01, P<0.05 with

Bonferroni correction). No statistically significant changes between earth time and MER regimes

occurred in amplitude or interdaily stability. The Morningness-Evening questionnaire (Horne and

Ostberg, 1976) showed a subject response range from 29 (Definitely Evening chronotype) to 72
21

(Definitely morning) among the 24 participants evaluated. In the nine participants in which circadian

rhythmicity was evaluated, the participant scores ranged from 65 (moderate morning) to 32

(moderate evening) A negative correlation with activity circadian rhythm acrophase values during

the baseline regime was obtained for these participants (r = -0.2, df = 8, n.s.). Although this value

was not statistically significant, the acrophase showed an increase with lower morningnesseveningness scores (i.e., higher trend toward an eveningness chronotype), as was found in previous

studies (Horne and Ostberg, 1976; Duffy, et al., 2001).

Activity circadian rhythm goodness of fit estimates averaged 0.75 for the study baseline regimes.

This value was well above goodness of fit estimates reported in a previous study using 5-min

sampling (0.49, Satlin, et al.,1995) and was comparable to the highest estimates reported (0.8-0.95,

Teicher, et al., 1997). Therefore, the methodology employed in processing the raw activity data

(clipping, robust locally weighted regression, followed by conversion from 1-min to 30-min

samples) was fully adequate to provide extraction of reliable activity circadian rhythm wave forms

which could be reliably processed by single harmonic analysis. Since the MER participants only

slept 80% of the time during the 00:00–04:00 interval during baseline, it was possible that the

reduced activity was associated with higher fatigue levels in this group. Activity circadian rhythm

amplitude and mesor were also lower in the MER participants during the MER regime than during

baseline, which may also reflect increased fatigue levels during the MER regime, relative to the

baseline regime. However, relative amplitude was higher during the MER regime, indicating that

robust circadian oscillations were maintained during the MER regime. It was interesting that activity

circadian rhythm amplitude goodness of fit and mesor were actually lower during the transitional

days (intervals between baseline and MER regimes) than during baseline or MER regimes. In

addition, the variability (mean standard deviation) of all the circadian rhythm metrics was higher

during transitional days. This indicates that the degree of circadian rhythm disruption was higher

during transitional days, which may reflect the effects of time zone travel back and forth between

MER operations, or transitions between MER schedules and normal duty or weekend schedules. No

statistically significant differences in circadian metric variability were detected between earth time

and MER regimes, t8<1.7, ns).

Table 2 (Appendix B) indicates that intra-individual variability between subjects was relatively high,

particularly during MER regimes. The differences in circadian periodicity were of particular interest.

While the baseline circadian period lengths clustered around 24.0 (mean = 24.08 hours), seven of

nine period lengths during the MER regime exceeded the expected MER workload schedule of

24.65 hours with a mean period length (24.84 hours) that exceeded the MER schedule by 0.2 hours

per day. We then attempted to elucidate this phenomenon by comparing participants with available

wake-up and work onset and end times with the expected MER 24.65 hour schedule. Figure 11

shows two participants (M0303 and M0310) who had a relatively long circadian period (25.3 hours

and 24.98 hours, respectively) during the MER regime but nonetheless maintained wake-up and

work times according to the expected MER schedule. This indicates that this participant may have

had a free-running activity circadian rhythm which was not entrained to the 24.65 hour MER

schedule despite the maintenance of wake-up and work times which did follow the MER schedule.

Another participant (M0310) also had a long circadian period (24.98 hours), in which the MER work

schedule was maintained but the wake-up times delayed progressively, relative to the MER

schedule, until the wake-up times occurred immediately prior to the work onset times. This

participant may also have had a circadian rhythm which may have been free-running or entrained by

the longer period wake-up time schedule. Of six participants whose wake-up and work times were
22

evaluated with respect to the MER schedule, three participants wake-up times were synchronized

with the MER schedule, two participants wake-up times diverged from the MER schedule, and one

participant (M0313) had wake-up times which were synchronized with the MER schedule during

one MER regime but diverged from the schedule during a second MER regime. Therefore,

individual synchronization of wake-up times with the MER regime was variable and in most cases

the activity circadian rhythm did not appear to be directly entrained to the 24.65 hour MER

schedule. Thus, the circadian rhythm in these participants was not entrained to a schedule that

shifted consistently 39-minutes daily. More likely, their rhythm was modulated by the MER

schedule by relative coordination, in which the circadian rhythm periodicity was modulated, but not

directly synchronized by the MER schedule.

M0303 MER predicted schedule with 

getup and work onset times

0

5

10

15

20

25

30

35

1/1/04

1/3/04

1/5/04

1/7/04

1/9/04

1/11/04

1/13/04

1/15/04

1/17/04

1/19/04

1/21/04

1/23/04

Date

Time of day

MER Getup times MER scheduled time

Work time

Figure 11. Comparison of wake-up and work onset and end times with expected MER

24.65 hour day schedule. The work onset schedule was synchronized to the MER

schedule in M0303 but the wake-up times occurred several hours before the work onset

times until 1/12/04, at which date, the work onset times became synchronized with the

work onset and MER schedules. The wake-up times in M0310 progressively delayed

relative to the MER schedule and the work onset times during this MER operations

time period.

Two participants (M0332, 75 days; M0325, 72 days) stayed on a MER schedule during the

transitional days between MER regimes. However, their wake-up times and activity circadian

rhythm acrophases did not remain constant during this schedule but exhibited transient, nonstationary changes in wake-up times and circadian rhythm acrophases during this phase (Figures

12a–d). These changes coincide with increased napping while transitioning to a Mars Sol. Since

non-stationary changes were evident in these and other MER regimes, we decided to use shorter

MER data segments, in which non-stationary changes in circadian phase, amplitude, and amplitude

were minimal, to perform direct comparisons between baseline and MER regime circadian rhythm

data. Both of these participants lived local to JPL and therefore no traveling occurred between

different time zones.

M0310 MER predicted schedule with 

getup and work onset times

5

7

9

11

13

15

17

19

21

23

25

Date

Time of day

MER Getup times MER scheduled time

Work times
23

M0332 Work schedule

0

12

24

36

48

60

72

84

12/31/03

1/10/04

1/20/04

1/30/04

2/9/04

2/19/04

2/29/04

3/10/04

3/20/04

3/30/04

4/9/04

4/19/04

Date

Time of day (accumulative)

Getup time

MER estimated start time

Work start

Figure 12a. Comparison of wake-up times with work start times and estimated MER

start times in participant M0332, who stayed on a MER schedule for 72 days. Time of

day was accumulated to incorporate transitions at midnight (i.e., 00:00 = 24 hours,

01:00 = 25 hours). Spikes in the plots represent multiple sleep periods (napping) or a

large shift in sleep times, which was indicative of difficulties in adjusting to the Mars

sol. This appears to occur in transitioning to and from a Mars Sol. Transitioning back

to an Earth day required a phase advance.
24

M0332 Activity get-up and circadian rhythm peak times 

during MER regime

0

10

20

30

40

50

60

70

80

1/3/2004

1/10/2004

1/17/2004

1/24/2004

1/31/2004

2/7/2004

2/14/2004

2/21/2004

2/28/2004

3/6/2004

3/13/2004

Date

Time of day (accumulated)

Get-up times Circadian peak times

Figure 12b. Comparison of wake-up times with activity circadian rhythm acrophase

converted to time of day in the same participant plotted in Figure 12a. A large nonstationary phase shift in the activity circadian rhythm was evident on 1/20/04 which

coincides with the shifts in wake-up times in Figure 12a, after which the circadian

rhythm peak time slowly converges with the wake-up times.
25

M0325 work schedule with 

getup times

0

24

48

72

96

120

5-Dec 25-Dec 14-Jan 3-Feb 23-Feb 14-Mar 3-Apr 23-Apr

Date

Time of day (accumulated)

Getup time

MER estimated start time

Work start

Figure 12c. Comparison of wake-up times with work start times and estimated MER

start time in participant M0325. Increased napping occurred during the transition to a

Mars sol; however, increased napping did not occur upon transitioning back to an

Earth day despite the presence of a phase advance in the activity circadian rhythm.
26

M0325 Activity get-up and circadian rhythm 

peak times during MER regime

0

10

20

30

40

50

60

70

1/2/2004

1/9/2004

1/16/2004

1/23/2004

1/30/2004

2/6/2004

2/13/2004

2/20/2004

2/27/2004

3/5/2004

3/12/2004

Date

Time of day (accumulated)

Get-up times Circadian peak times

Figure 12d. Comparison of wake-up times with activity circadian rhythm acrophase in

the same participant shown in Figure 12c. A large non-stationary delay in wake-up

times was evident starting on 1/12/04, followed by a phase advance on 1/28/04. A

subsequent phase shift in the activity circadian rhythm was evident on 1/23/04, after

which the circadian rhythm peak time slowly converges to its original phase position

with the wake-up times.

Changes in activity circadian rhythm periodicity during the MER regime were further evaluated by

moving periodogram analysis (Figures 13a–i). This analysis indicated the appearance of nonstationary changes in circadian rhythm period length and amplitude throughout the MER regimes,

including the progressive lengthening or shortening of circadian periodicity, the occasional

appearance of arrhythmic or bimodal circadian periodicities during four-day segments, and the

appearance of increased power in the 12-hour, or circasemidian rhythm periodicity.
27

Figure 13a. Moving periodogram on locomotor activity data. Power spectra were computed on 4-

day blocks of data incremented four days at a time. The graph starts with MER regime days 1–4 at

the bottom and ends with baseline days 105–108 at the top. During the MER regime, the circadian

rhythm progressively lengthens and shortens, with increasing power evidenced in the 12-hr

(circasemidian) rhythm component.
28

Figure 13b. Moving periodogram on locomotor activity data. Power spectra were computed on 4-

day blocks of data incremented four days at a time. The graph starts with baseline data (days 1–4),

and continues with the MER regime starting at days 15–23. Circadian rhythm stability was

maintained during the MER regime, but spectral power in the 12-hr (circasemidian) rhythmic

component increased while the circadian component decreased.
29

Figure 13c. Moving periodogram on locomotor activity data. Power spectra were computed on 4-

day blocks of data incremented four days at a time. The graph starts with baseline regime (days 1–4)

at the bottom. The MER regime starts on days 13–16. The circadian periodicity progressively

lengthened and shortened, with segments showing circadian instability, with decreased spectral

power and the appearance of bimodal periodicity components, especially during the day 33–36

block, which was associated with circadian acrophase instability. There was also increased spectral

power in the 12-hr (circasemidan) rhythmic component during the latter half of the MER regime.
30

Figure 13d. Moving periodogram on locomotor activity data. Power spectra were computed on 4-

day blocks of data incremented four days at a time. The graph starts with the baseline regime (days

1–4) at the bottom. The MER regime starts on days 13–16. Circadian periodicity was relatively

stable, with progressive lengthening and shortening of the circadian rhythm periodicity.
31

Figure 13e. Moving periodogram on locomotor activity data. Power spectra were computed on 4-

day blocks of data incremented four days at a time. The circadian periodicity progressively

lengthened and shortened, with higher spectral power in the 12-hr (circasemidian) periodicity,

relative to baseline.
32

Figure 13f. Moving periodogram on locomotor activity data. Power spectra were computed on 4-

day blocks of data incremented four days at a time. The circadian periodicity progressively

lengthened and shortened, with higher spectral power in the 12-hr (circasemidian) periodicity,

relative to baseline. After the participant switched from a MER-A to a MER-B schedule (days 33–36)

and from a MER-B to a MER-A schedule (day 59), the subsequent blocks of days (days 37–40 and

61–64) show the appearance of a bimodal circadian rhythm peak with reduced spectral amplitude.
33

Figure 13g. Moving periodogram on locomotor activity data. Power spectra were computed on 4-

day blocks of data incremented four days at a time. The circadian rhythm period progressively

shortened during days 17–32. The circadian rhythm became unstable on days 41–52, and then

lengthened thereafter. Circadian arrhythmia is evident during days 81–84 and days 105–108.
34

Figure 13h. Moving periodogram on locomotor activity data. Power spectra were computed on

4-day blocks of data incremented four days at a time. Relatively large changes in circadian period

length indicate that the circadian rhythm was unstable at the onset of the MER regime (days 17–28)

and also during days 57–72, during which large changes in circadian rhythm acrophase were also

observed. During the MER regime, spectral power increased in the 12-hr (circasemidian)

periodicity, relative to baseline. The circasemidian rhythm had higher spectral power than the

circadian rhythm during days 69–72 and 81–84.
35

Figure 13i. Moving periodogram on locomotor activity data. Power spectra were computed on 4-

day blocks of data incremented four days at a time. The graph starts with the baseline regime days

1–4. The MER regime starts on Days 17–20 and ends on Days 93–96 and was followed by earth

time days starting at days 97–100. During the MER regime, the circadian rhythm progressively

lengthens and shortens, with increasing power evidenced in the 12-hr (circasemidian) rhythm

component. The circadian periodicity progressively lengthens and shortens over a wide range

throughout the MER regime, with occasional regions of circadian rhythmic disruption or

arrhythmia (days 45–48, days 77–80). The relatively large changes in circadian period length with

occasional circadian rhythmic instability may have resulted from this participant switching back

and forth between the rotating MER regime on work days and earth time on days off.
36

The responses of activity circadian periodicity during the MER regimes were highly individually

specific. Some participants exhibit circadian rhythms with varying period lengths that progressively

shorten and lengthen (Figures 13a, 13c, and 13d), some exhibit MER regimes with relatively stable

circadian periodicity (Figures 13b and 13d), and some participants have transient 4-day segments

that exhibit circadian arrhythmia and even two circadian rhythmic components (Figures 13c and

13d). Of particular interest was the appearance of relatively strong circasemidian (circa-12 hour)

periodicities during the MER regime in some participants (Figures 13b and 13c). The important

aspect of this “circasemidian” rhythm was that it was more marked in people who were relatively

sleep deprived and resulted in increased performance errors (Hildebrandt, et al., 1974). The impact

of circasemidian rhythms on circadian rhythmic stability was examined in the educed cycle analysis,

in which four participants with relatively strong circasemidian periodicities during the MER regime

were evaluated (Figure 14). In two participants (Figures 15a and b), the circasemidian rhythm is

stronger (exhibits higher spectral amplitude) than the circadian rhythm.

Activity educed cycles with strong circasemidian rhythms 

during baseline and MER regimes

0

50

100

150

200

250

300

350

400

450

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

Sample number

Mean activity

Baseline Means MER Means

Figure 14. Comparison of activity circadian rhythm educed cycles during baseline and MER

regimes. Represented were the mean +/- standard error of activity data from four participants with

relatively strong circasemidian (circa 12-hour) rhythms. Sample number represents the number of

sampling intervals in the educed cycle (e.g., for baseline data with a period length of 24.0 hours,

there are 48 values and for MER data with a period length of 24.5 hours, there are 49 values).

Therefore, sample 1 corresponds to the mean of the 00:00–00:30 interval, while sample 24

corresponds to the mean of the 12:00–12:30 interval for a 24.0 hour period and 12:30–13:00 for a

24.5 hour period. During the MER regime, activity assumes a bimodal waveform with a significant

decline in activity levels during approximately the 11:00–20:00 time period (sample points 22–40).
37

M0304 Activity power spectra during baseline and MER regimes

0

5

10

15

20

25

30

35

40

37.93

34.7132.00

29.68

27.6825.92

24.38

23.01

21.7920.69

19.69

18.79

17.96

17.2116.5215.88

15.28

14.73

14.22

13.7413.3012.88

12.49

12.1211.77

11.4411.13

10.84

10.56

10.29

10.04

Sample point

Spectral amplitude

Baseline spectral amp MER spectral amp

Figure 15a. Activity power spectra during baseline and MER regimes. In this participant, the

circasemidian rhythm exhibits higher spectral power than the circadian rhythm during the MER

regime.

M0310 Activity power spectra during baseline and MER regimes

0

10

20

30

40

50

60

37.93

33.85

30.57

27.86

25.60

23.68

22.02

20.58

19.32

18.20

17.21

16.3215.52

14.79

14.12

13.52

12.96

12.45

11.98

11.54

11.13

10.75

10.40

10.06

Period length

Spectral amplitude

Baseline MER

Figure 15b. Activity power spectra during baseline and MER regimes. In this participant, the

circasemidian rhythm exhibits higher spectral power than the circadian rhythm during the MER

regime.
38

Differences in circadian and circasemidian periodicities between baseline and MER regimes are

presented in Figures 16a–d. Both power spectra and profilogram analysis show that the activity

circadian rhythm strength was reduced during the MER regime, relative to baseline.

Comparison of MER and baseline activity circadian rhythm power spectra

0

10

20

30

40

50

60

70

80

30.12

29.68

29.26

28.85

28.44

28.05

27.68

27.31

26.95

26.60

26.26

25.92

25.60

25.28

24.98

24.67

24.38

24.09

23.81

23.54

23.27

23.01

22.76

22.51

22.26

22.02

21.79

21.56

21.33

21.11

20.90

20.69

20.48

20.28

20.08

Power spectrum Period length

Mean spectral amplitude

MER

Baseline

Figure 16a. Comparison of activity circadian rhythm spectral power means (+/- standard error)

during baseline in 9 participants (mean = 23.98 hours) and MER regimes (mean = 24.9 hours).

Comparison of MER and baseline activity circadian rhythm profilogram

0.000

0.100

0.200

0.300

0.400

0.500

0.600

20.00

20.5021.00

21.50

22.00

22.5023.0023.5024.0024.50

25.00

25.50

26.0026.5027.00

27.50

28.00

28.5029.0029.50

30.00

Profilogram Period length

Mean variance ratio

MER

Baseline

Figure 16b. Comparison of activity circadian rhythm periodicity (profilogram) means (+/- standard

error) during baseline in 9 participants (mean = 24.17 hours) and MER regime (mean = 24.64 hours).
39

Comparison of activity circasemidian rhythm periodicity during baseline and MER schedules

0

10

20

30

14.03

13.79

13.56

13.34

13.13

12.92

12.72

12.53

12.34

12.15

11.98

11.80

11.64

11.47

11.31

11.16

11.01

10.86

10.72

10.58

10.45

10.32

10.19

10.06

Power spectrum Period length

Mean spectral amplitude

MER

Baseline

Figure 16c. Comparison of activity circasemidian rhythm spectral power means (+/- standard error)

during baseline in 9 participants (mean = 11.91 hours) and MER regimes (mean = 12.41 hours).

Comparison of MER and baseline activity circadian rhythm profilogram

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0.180

10.00

10.50

11.00

11.50

12.00

12.50

13.00

13.50

14.00

Profilogram period length

Mean variance ratio

MER

Baseline

Figure 16d. Comparison of activity circasemidian rhythm periodicity (profilogram) means (+/-

standard error) during baseline in 9 participants (mean = 12.12 hours) and MER regime (mean =

12.51 hours).
40

Both analyses also show that the circasemidian periodicity was substantially stronger during the

MER regime than during the baseline regime (Figures 16c and d). The circasemidian periodicity also

has a longer period length during the MER regime than during the baseline regime. The ratio

between the activity circadian and circasemidian periodicities for baseline and MER regimes for

both power spectra and profilogram methods ranges from 1.99–2.01. This ratio was therefore

relatively constant, regardless of condition or periodicity method. The constant ratio for the power

spectral analysis implies that the circasemidian rhythm was simply a harmonic of the primary

circadian periodicity, but the presence of a strong circasemidian rhythm in the profilogram, which

was a waveform based method independent of harmonic model assumptions, indicates that the

circasemidian periodicity was a real biological periodicity. However the constant ratio between

circadian and circasemidian periodicities indicates that the circasemidian rhythm was controlled or

modulated by the circadian periodicity. When the subjective information on work shift duration and

sleep duration means between earth time and MER regimes were compared in the four participants

with strong circasemidian rhythms to the other participants, no substantial differences were found.

Work shift durations were 10.04 hours for the circasemidian group and 10.38 hours for the other

participants. Sleep durations were somewhat higher in the circasemidian group (7.7 hours) than in

the other participants (6.9 hours). However, the composite fatigue scores showed no reported net

fatigue symptoms in the circasemidian group (+0.7) but pronounced fatigue symptoms in the other

participants (-3.8).

The effects of the MER regime on the activity circadian rhythm waveform are illustrated in Figures

17a–i. The educed activity circadian rhythm cycles exhibit considerable individual variability but

there were distinct waveform pattern differences between baseline and MER regimens. The baseline

cycles exhibit square wave type of waveform while the MER cycles show asymmetric waveforms

with a rhythmic peak early in the subjective day (M0303, M0304), a predominantly bimodal

waveform pattern with a pronounced post-lunch or afternoon decline in activity levels (M0309,

M0310), and a similar waveform shape but with reduced activity levels during the subjective day in

the two participants who maintained the MER schedule (M0332, M0325). Waveforms in which

relatively high activity levels were maintained for a longer period of time during the subjective day

were evident in all participants except M0309.
41

M0303 Comparison of activity circadian rhythm educed 

cycles during baseline and MER schedules

0

50

100

150

200

250

300

350

400

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Sample number

Mean activity

Baseline MER

Figure 17a. Comparison of the activity circadian rhythm waveform means (+/- standard error)

during the baseline and MER regimes. During the MER regime the circadian waveform was more

asymmetric with activity levels declining toward the end of the subjective day.

M0304 Comparison of educed cycle activity circadian 

rhythms during baseline and MER schedules

0

100

200

300

400

500

600

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Sample number

Mean activity

Baseline Days 1-9 MER schedule Days 10-18

Figure 17b. Comparison of the activity circadian rhythm waveform means (+/- standard error)

during the baseline and MER regimes. During the MER regime the circadian waveform was more

asymmetric with an early activity peak and activity levels declining toward the end of the subjective

day.
42

M0309 Comparison of activity circadian rhythm educed 

cycles during baseline and MER schedules

0

100

200

300

400

500

600

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Sample point

Mean activity

MER Baseline

Figure 17c. Comparison of the activity circadian rhythm waveform means (+/- standard error)

during the baseline and MER regimes. During the MER regime the circadian waveform was bimodal

with a substantial decline in activity levels corresponding to afternoon and early evening.

M0332 Comparison of activity circadian rhythm educed 

cycles during baseline and MER schedules

0

50

100

150

200

250

300

350

400

450

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Sample number

Mean activity

Baseline MER 

Figure 17d. Comparison of the activity circadian rhythm waveform means (+/- standard error)

during the baseline and MER regimes. During the MER regime the circadian waveform was similar

to the baseline waveform with a decline in activity levels during the subjective day.
43

M0325 Comparison of activity circadian rhythm educed 

cycles during baseline and MER schedules

0

50

100

150

200

250

300

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Sample point

Mean activity

Baseline MER

Figure 17e. Comparison of the activity circadian rhythm waveform means (+/- standard error)

during the baseline and MER regimes. During the MER regime the circadian waveform was similar

to the baseline waveform with a decline in activity levels during the subjective day.

M0313 Comparison of educed activity circadian rhythm cycles 

during baseline and MER regimes

0

100

200

300

400

500

600

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Sample number

Mean activity

Baseline MER

Figure 17f. Comparison of the activity circadian rhythm waveform means (+/- standard error)

during the baseline and MER regimes. During the MER regime the circadian waveform was similar

to the baseline waveform but the high activity levels extend over a larger proportion of the subjective

day.
44

M0310 Comparison of activity circadian rhythm educed cycles 

between baseline and MER regimes

0

100

200

300

400

500

600

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Sample point

Mean activity

Baseline MER

Figure 17g. Comparison of the activity circadian rhythm waveform means (+/- standard error)

during the baseline and MER regimes. During the MER regime the circadian waveform exhibits a

bimodal waveform with a decline in activity levels during the middle of the subjective day.

M0329 Comparison of activity circadian rhythm educed cycles 

between baseline and MER regimes

0

100

200

300

400

500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Sample number

Mean activity

Baseline Days 102-111 MER schedule Days 17-24

Figure 17h. Comparison of the activity circadian rhythm waveform means (+/- standard error)

during the baseline and MER regimes. During the MER regime the circadian waveform was more

asymmetric with an early activity peak and activity levels declining toward the end of the subjective

day. Relatively high activity levels extend over a larger proportion of the subjective day during the

MER regime.
45

M0320 Comparison of activity circadian rhythm educed cycles 

between baseline and MER regimes

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Sample number

Mean activity

Baseline Days 115-123 MER regime Days 17-24

Figure 17i. Comparison of the activity circadian rhythm waveform means (+/- standard error)

during the baseline and MER regimes. During the MER regime the circadian waveform was more

symmetric, relative to baseline, with a high early activity peak, relative to baseline. Activity levels

were higher during the MER regime, especially during the first half of the subjective day.

The comparison of activity circadian rhythms in four participants between MER regime day shifts

(circadian acrophase peaks between 11:40 and 19:40) and MER regime schedules rotated to night

shifts (circadian acrophase peaks between 23:40 and 07:40) is shown in Figures 18a–d. The

circadian waveform differences were less pronounced between MER day and night shifts than

between baseline and MER regimes but there was a tendency for reduced activity levels during the

middle of the night shift in all four participants. Also, the length of the higher activity levels during

the night shift was extended in 2/4 participants (M303 and M332) such that the ratio of subjective

day to subjective night was larger. Circadian rhythm relative amplitude increased from 0.68 during

day shifts to 1.02 during night shifts but circadian rhythm goodness of fit decreased from 0.72 to

0.64. These changes indicate that the activity circadian rhythm was robust during night shifts but

that the waveform was less symmetric and sinusoidal, relative to day shifts.
46

M0309 Comparison of activity educed cycles during 

day and night shifts

0

100

200

300

400

500

600

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46

Sample number

Mean activity

Day shift Mean Night shift Mean

Figure 18a. Comparison of mean (n = 4) activity circadian rhythm waveform means (+/- standard

error) during MER regime operations occurring during the subjective day (11:40–19:40) and

rotated MER regime operations occurring during the subjective night shift (23:40–07:40). The

circadian waveforms were very similar except for a reduction in activity levels during the middle of

the night shift.

M0303 Comparison of activity educed cycles during 

day and night shifts

0

100

200

300

400

500

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46

Sample number

Mean activity

Day shift Mean Night shift Mean

Figure 18b. Comparison of activity circadian rhythm waveform means (+/- standard error) during

MER regime operations occurring during the subjective day (11:40–19:40) and rotated MER regime

operations occurring during the subjective night shift (23:40–07:40). The circadian waveforms show

a reduction in activity levels during the night shift associated with extension of the duration of the

high activity levels during the night shift.
47

M0332 Comparison of activity educed cycles during 

day and night shifts

0

50

100

150

200

250

300

350

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46

Sample number

Mean activity

Day shift Mean Night shift Mean

Figure 18c. Comparison of activity circadian rhythm waveform means (+/- standard error) during

MER regime operations occurring during the subjective day (11:40–19:40) and rotated MER regime

operations occurring during the subjective night shift (23:40–07:40). The circadian waveforms show

a reduction in activity levels during the night shift associated with extension of the duration of the

high activity levels.

M0325 Comparison of activity educed cycles during 

day shift and night shift

0

50

100

150

200

250

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46

Sample number

Mean activity

Day shift Mean Night shift Mean

Figure 18d. Comparison of activity circadian rhythm waveform means (+/- standard error) during

MER regime operations occurring during the subjective day (11:40–19:40) and rotated MER regime

operations occurring during the subjective night shift (23:40–07:40). The circadian waveforms were

very similar except for a reduction in activity levels during the middle of the night shift and a

reduction of the duration of the higher activity levels during the night shift.
48

The consequences of a shift in MER operational work schedules from a MER-A shift with one Mars

rover to a MER-B shift with the other rover was evaluated by examining the actigraph and

periodicity analysis of activity circadian rhythms from one participant (Figures 19a–e). Since the

second Mars rover was located on the other side of the planet, the switch in work shifts necessitated

a 12-hour shift in the rest-activity schedule for this participant. The schedule shift occurred on days

33–36 of a 12-day MER regime.

M0313 Activity actigraph during MER-A to MER-B switch

0:00

2:00

4:00

6:00

8:00

10:00

12:00

14:00

16:00

18:00

20:00

22:00

0:00

2:00

4:00

6:008:00

10:00

12:0014:00

16:00

18:00

20:0022:00

Time of day

Activity counts

Switch

Switch

Switch

Switch

1/17/04 

1/18/04 

1/19/04 

1/20/04 

1/21/04 MER switch

1/22/04 MER switch

1/23/04 MER switch

1/24/04 MER switch

1/25/04 

1/26/04 

1/27/04 

1/28/04 

Figure 19a. Double raster plot actigraph of raw activity data from participant M0313 during a shift

from a MER operational schedule with one Mars rover lander (MER-A) to another MER schedule

with the other Mars rover (MER-B). The plot shows a major phase delay in the timing of the restactivity cycle followed by extension of activity levels during the 5th day after onset of the schedule

shift.
49

M0313 Activity circadian rhythm power spectra during MER-A to MER-B 

transition

0

20

40

60

80

100

120

37.93

34.42

31.51

29.05

26.95

25.13

23.54

22.14

20.90

19.79

18.79

17.89

17.07

16.32

15.63

15.00

14.42

13.88

13.39

12.92

12.49

12.08

11.70

11.35

11.01

10.69

10.40

10.11

Period length

Spectral amplitude

Baseline 13 days

MER 12 days

Figure 19b. Comparison of activity circadian rhythm power spectra between baseline and a MER

regime in which the participant switched operational MER schedules between Mars rovers (MER-A

to MER-B schedule).

M0313 Activity circadian rhythm profilogram during MER-A to MER-B 

transition

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

10.0011.0012.0013.00

14.00

15.00

16.00

17.0018.0019.0020.00

21.0022.00

23.0024.00

25.0026.0027.00

28.0029.0030.00

31.00

32.0033.0034.00

35.00

36.0037.0038.00

Period length

Variance ratio

Baseline

MER

Figure 19c. Comparison of activity circadian rhythm profilogram periodicity between baseline and

a MER regime in which the participant switched operational MER schedules between Mars rovers

(MER-A to MER-B schedule).

Figure 19d. Plots of actual wake percent and sleep efficiency suggests sleep disturbances occurred

due to the transition between rovers. The participant has missing data on 1/25/04. Bedtimes were

delayed starting from 1/18/04 at 16:00 until 1/26/04 at 6:00.

Percent of sleep episode in wake during the transition from

MER A to MER B from 1/21/04 to 1/24/04

35

30

25

20

15

10

5

0

Actual wake percent

Percent of sleep efficiency during the transition from MER A to 

MER B from 1/21/04 to 1/24/04

100

90

80

70

60

Sleep efficiency
50

M0313 Comparison between time in bed 

and nap durations

0

2

4

6

8

10

12

14

16

12/20/2003

12/27/2003

1/3/2004

1/10/2004

1/17/2004

1/24/2004

1/31/2004

2/7/2004

2/14/2004

2/21/2004

2/28/2004

3/6/2004

3/13/2004

3/20/2004

3/27/2004

4/3/2004

4/10/2004

4/17/2004

Date

Hours

Naps Time in bed

Switch MER-A to MER-B

Switch MER-B to MER-A

Circadian phase shift

M0313 Activity circadian rhythm acrophase

0

200

400

600

800

1000

1200

1400

1600

12/20/2003

12/27/2003

1/3/2004

1/10/2004

1/17/2004

1/24/2004

1/31/2004

2/7/2004

2/14/20042/21/2004

2/28/2004

3/6/2004

3/13/20043/20/2004

3/27/2004

4/3/2004

4/10/20044/17/2004

Date

Degrees

Switch MER-A to MER-B Switch MER-B to MER-A

Phase shift

Figure 19e. Plots of daily time in bed and total daily nap durations (top) and locomotor activity

circadian rhythm acrophase (bottom) show the consequences of a 12-hour work shift change that

occurred when this participant switched from MER-A to MER-B operations during the period of 1/21

to 1/24 and from MER-B to MER-A operations on 2/16. The first MER operations shift was

associated with a pronounced phase advance in the activity circadian rhythm peak time and a drastic

reduction in time in bed. The second MER operations shift was associated with a circadian rhythm

phase delay and reduction in time in bed. Other transient reductions in time in bed on 2/16 and 2/28

were associated with relatively long naps.
51

The periodicity analysis in M0313 showed that extreme circadian rhythm disruption occurred during

the MER schedule switch regime. The power spectrum shows that either the participant was

arrhythmic during this regime or that masking effects resulting from conflict between two different

rest-activity times effectively occluded the rhythm, resulting in the appearance of a bimodal

circadian rhythmic component (Figure 19b). The profilogram shows a weak broad band circadian

periodicity (Figure 19c). The difference between the two periodicity estimates probably reflects the

differential influences of masking effects and harmonic residuals on the spectral output. The

circadian rhythm disruption was accompanied by reduced sleep efficiency and increased wake time

in bed (Figure 19d), and a profound decline in time in bed, which was indicative of significant sleep

loss in this subject (Figure 19e). The disruptive effects of changes in work shifts on circadian

rhythmicity and sleep during MER operations were also evident in participant M0329, in which the

changes in work shifts were associated with large circadian rhythm phase shifts and dramatically

reduced time in bed (Figure 20). The extreme activity circadian rhythm disruption and reduced time

in bed in these participants have deleterious implications for sleep loss, sleep disruption and fatigue

during MER operations.
52

M0329 Comparison between time in bed 

and nap durations

0

2

4

6

8

10

12

1/1/2004

1/8/2004

1/15/2004

1/22/2004

1/29/2004

2/5/2004

2/12/2004

2/19/2004

2/26/2004

3/4/2004

3/11/2004

3/18/2004

3/25/2004

4/1/2004

4/8/2004

4/15/2004

4/22/2004

Date

Hours

Naps Time in bed

Circadian phase shifts

Change in work shift

Switch MER-A to MER-B

M0329 Activity circadian rhythm acrophase

0

200

400

600

800

1000

1200

1/1/2004

1/8/2004

1/15/2004

1/22/2004

1/29/2004

2/5/2004

2/12/2004

2/19/2004

2/26/2004

3/4/2004

3/11/2004

3/18/2004

3/25/2004

4/1/20044/8/2004

4/15/2004

4/22/2004

Date

Degrees

Switch MER-A to MER-B

Change work shift

Phase shift

Figure 20. Plots of daily time in bed and total daily nap durations (top) and locomotor activity

circadian rhythm acrophase (bottom) show the consequences of a 12-hour work shift change that

occurred when this participant switched from MER-A to MER-B operations during the period of

1/21 to 1/24 and also from a large change in work shift times during MER operations on 1/31. The

MER operations shift was associated with a phase delay in the activity circadian rhythm peak time

and a drastic reduction in time in bed. The change in work shift timing was associated with a

circadian rhythm phase delay and pronounced reduction in time in bed.
53

DISCUSSION

Adaptation to the MER Operations Regime

The decision to conduct MER operations on a Mars sol rotation schedule instead of conducting

standard shift rotations on earth time has been documented (Bass, et al., 2005; Parke 2001). This

decision was based upon the experience of the 1997 Mars Pathfinder mission, in which operations

personnel agreed that working on a Mars time schedule had several advantages and disadvantages.

The advantages of Mars time operations were ability to command every Mars sol operation, optimal

time utilization, response to off-nominal situations, and little or no requirement for cross training.

The disadvantages were the requirement for more crews to sustain Mars time operations and the

potential deleterious effects of long duration Mars time operations on operational crew fatigue and

well-being. Mars time operations were selected for the MER operational schedules in this study

since Mars time provided the most efficient staffing solution and a fatigue/stress questionnaire

completed by 43% of the Mars Pathfinder operations personnel indicated that the negative effects of

both fatigue and the Mars time schedule were relatively low (Parke 2001).

The environment in which MER operations personnel were required to work presented challenges

and placed unique demands on human circadian rhythm and sleep physiology that could influence

the capacity of these personnel to adapt and perform during MER operations, where the maintenance

of optimal levels of performance and alertness was critical to the success of the MER mission. In

response, overall adaptation to the MER operational regime was relatively poor, as evidenced by the

subjective report of moderate to strong increases in fatigue in 82% of the participants, and moderate

to strong increases in sleepiness, with decreases in concentration and energy in most participants. In

addition, participants reported several primary challenges to successful adaptation, including

switching between Mars sol and earth time, switching work shift assignments between the two Mars

rovers, working during the subjective night during MER rotations, sleep difficulty during the

subjective day, the adverse effects of changes in the timing of social and daily living activities, and

awakening by children. Participants reported the necessity of adopting several strategies to cope with

the MER operational regime. These strategies primarily included the use of caffeinated beverages,

naps, recovery days off, and exercise to maintain alertness and performance during MER operations.

Naps tended to be clustered over periods of consecutive days in most participants, which could

reflect an adaptive response to cumulative sleep loss and fatigue. There were also relatively long

duration naps associated directly with large reductions in time in bed, in which these naps could

represent a compensatory response to sleep loss. There was large individual variability in the

capacity to adjust to the MER regimes since several participants expressed little or no difficulty in

adaptation. There were several factors which could have affected individual adaptability to the MER

regime, including workload duration, relative number of days off, sleep quality and duration,

circadian rhythm stability, and individual response differences to MER mission stressors. In

addition, there were large differences in the durations of consecutive MER operations work days and

rest days in different participants. Personnel who needed to work from 17–31 consecutive days

without a break would be expected to show higher incidences of accumulated sleep loss, stress, and

fatigue. In general, work shift durations increased 1.6 hours from earth time to MER operations,

while subjective sleep durations decreased by 1.3 hours. The increase in work shift durations during

MER was likely the consequence of the requirement for 10-hour uplink and downlink shifts. Time in

bed also decreased 0.4 hours during MER operations and was associated with higher variability of

sleep episodes. The primary factor differentiating the relatively best adapters from the poorest
54

adapters appeared to be sleep duration. The good adapters reported 8-9 hours of sleep, while the

poorest adapters reported only 5-6 hours of sleep. Poor adaptation was also associated with frequent

awakenings and changes in the timing of work shifts. The importance of this magnitude of sleep

reduction for the maintenance of alertness and performance has been demonstrated in other studies.

Sleep duration restrictions experienced by several MER personnel (i.e., less than 6-hr sleep per

night) raises safety concerns because it has been documented in multiple ground based studies that

such levels of sleep deprivation affect neurobehavioral functioning such as increased reaction times,

memory difficulties, cognitive slowing, and increased lapses of attention (Bonnet 1994, Carskadon

and Dement 1987, Dinges 1992, Dinges and Kribbs 1991, Dinges, et al., 1997, Naitoh 1975, Webb

and Agnew 1965). When restriction continues over successive days, significant decrements in

performance can appear in less than one week (Belenky, et al., 2003, Dinges, et al., 1997, Van

Dongen, et al., 2003). Operational space flight simulation studies (Wright, et al., 1999), have also

demonstrated that such chronic sleep reduction can result in performance decrements, subjective and

objective sleepiness, decreased alertness and sleep disruptions. As a result, operations personnel can

experience fatigue when trying to perform mission critical tasks and experience sleep disruption

during daytime sleep, thus potentially compromising the mission and increasing the risk of accidents

and possible mission failure. A slight reduction of sleep length to 7 hours per night was insufficient

to maintain brain vigilance during schedules which precluded extra time for sleep recovery (Wright,

et al., 2006). Van Dongen (Van Dongen, et al., 2003) has shown that even 8 hours per night may be

insufficient to prevent the buildup of neurobehavioral impairment. Therefore, the reduction of

subjective and objective sleep time during MER operations in our study has unfavorable

implications for the maintenance of personnel well-being and mission performance.

The increase in work shift durations of 1.6 hours from baseline to MER operations was also of

concern since increased workload durations subtract from time available for sleep. A recent survey

showed that 63% of Americans work more than 40 hours per week, with some 40% exceeding the

50-hour a week mark (Sandweiss 2004). The average MER workload of 10.4 hours corresponds to a

5-day workload of 52 hours, which is comparable to the workload for 40% of the population.

However, 26% of the MER workloads exceeded 12 hours in our study. In another study, a week with

12 hour work days was associated with greater exhaustion and lowered sleep quality (Dahlgren

2005). Fatigue also was predicted by high work demands and disturbed sleep (Akerstedt, et al.,

2004). High workload combined with high stress levels increases sleepiness and impairs sleep

(Dahlgren, et al., 2005). Since disturbed sleep was reported by several participants in our study as

the consequence of awakenings due to children and difficulty sleeping during the subjective day,

sleep disturbances may have contributed to fatigue resulting from high workloads during MER

operations. The problems presented by attempts to obtain restful sleep during the subjective day are

common in night work, which is associated with excessive daytime sleepiness, abnormal sleep

duration, and poor/fair health (Paine, et al., 2005). Therefore, high workloads in our study during

MER operations are predictive of potential sleep loss and deterioration in well-being and the

resulting fatigue may have been exacerbated by stress and disturbed sleep. Two participants

indicated that their adaptation improved during about the first month of MER operations. This

adaptation may have resulted from the initial excessively long work MER shift durations. These

work shift durations decreased during the mission due to improvements in automation, training and

experience (Bass, et al., 2005).

Differences in adaptation to the MER regimes between personnel living local to JPL and those who

moved to Pasadena from remote locations were not assessed due to the difficulty of evaluating the
55

effects of all the other uncontrolled variables, such as age, sex, presence of children, and differences

in MER consecutive work days and days off. However, it was reported that the remote personnel had

less difficulty adapting to the MER sol schedule than the JPL local personnel (Bass, et al., 2005).

This conclusion was not documented with specific evidence but was supported by specifying that the

remote personnel had the advantage of having special housing arrangements with black-out shades

and flexible house cleaning services. On the other hand, the JPL local personnel had the additional

stress of managing tasks with their families such as child care and the additional workload of

attending MER institutional meetings and meetings related to other aspects of their professional

work. These demands could have resulted in increased stress levels in the JPL local personnel and

reduced available time for sleep and recovery.

This study focused primarily on assessment of changes in sleep and circadian rhythmicity during

MER operations. However, subjective reports indicated the occurrence of stress related responses in

several participants. These responses were manifested by increases in irritability in most participants

and individual reports of stress related problems with social isolation, lack of social camaraderie,

and awakenings in response to children. The experience of social isolation in this study may be

important since depression, irritability and hostility with some cognitive impairment were commonly

reported during exposure to isolation in Antarctic missions (Palinkas 1988). One of the problems in

subjective evaluations of fatigue is that dependent variables measured in survey responses to

operational duty factors characterized as “stress”, “fatigue”, or “sleepiness” are not orthogonal or

independent. (Galipault 1980; Chatoo, et al., 2006). However, the presence of documented stress

responses in our study, in which several objective indicators of increased workload and reduced

sleep time were obtained, is important to the degree that stress may exacerbate fatigue resulting from

other factors. In flight attendants, end of duty stress was significantly correlated with end fatigue,

start and end sleepiness, start stress, and miles walking required (Galipault 1980). Also, sleep

problems in flight attendants were frequently attributable to family and personal problems, tension,

and emotional stress (Galipault 1980). Physiological interrelationships between fatigue due to sleep

loss and stress have been recently established since poor sleep quality is associated with higher

blood pressure and blood pressure variability (Lanfranchi, et al., 2005). Therefore, it is possible that

perceived stress in certain participants exposed to MER operations may have contributed to their

problems in adaptation to the MER regime.

The reported incidences of increased fatigue and stress problems in the MER participants appear to

be of higher magnitude than the incidences of problems experienced by operations personnel

working on Mars time in the 1997 Mars Pathfinder study, in which subjective responses to

Pathfinder operations were assessed by a post-mission questionnaire (Parke 2001). In this mission

about 70% of the questionnaire respondents worked on the Pathfinder rover and 30% worked

primarily on the Sojourner rover. The negative effects of fatigue and scheduling on work

performance were reported as reduced during the second month of the Pathfinder mission. The

differences in reported fatigue between the MER and Pathfinder studies can be attributed to changes

in workload and days off during the second month of the Pathfinder mission. During the second

month, Mars time work days were reduced from 23 to 11 days, daily workload was reduced from 12

hours/day to 10 hours/day, and days off increased from 5.5 to seven days. Fatigue was lower during

the second month of operations in some workers despite increased workload on Mars time.

Therefore, the increased Mars time workload effects upon fatigue may have been counteracted by

shorter work shift durations and increased time off. The respondents said they could have sustained

Mars time schedules for an additional two months with reduced hours and more days off. Fatigue
56

had significantly less effect on performance at work in the Pathfinder workers than in the Sojourner

workers. This difference was attributed to the fact that Pathfinder workers took nearly twice as many

days off than the Sojourner workers. The implications of the Pathfinder questionnaire are that if the

MER operations personnel had been reduced and they had more days off during the 2nd and 3rd

month of MER operations, their adaptation to the rotating MER schedule would have been

enhanced, with reduced fatigue, sleep loss, and stress. However, this was not possible to MER

operational demands and limited personnel resources. Also, there was no indication in the Pathfinder

report that operations personnel were subjected to the switches in MER rover work shift operations

reported by several MER participants, which could have contributed to circadian rhythm and sleep

disruption.

Circadian Rhythm Entrainment to the MER Regime

The circadian rhythm and sleep homeostatic systems represent two physiological processes which

interact in a dynamic manner to regulate changes in alertness, performance and timing of sleep

(Borbely 1982; Jewett and Kronauer 1999; Van Dongen and Dinges 2000; Broughton 1998). The

circadian component is controlled by an endogenous biological clock, the circadian pacemaker,

which is located in the suprachiasmatic nucleus of the hypothalamus (Klein, et al., 1991). A

predictive mathematical model indicates that a vegetative class of circadian rhythm variables (body

temperature and REM sleep) is controlled by one circadian pacemaker, whereas overt behavioral

rhythms such as activity and sleep-wakefulness, are controlled by another pacemaker (Kronauer, et

al., 1982). Circadian rhythms are entrained or synchronized by periodic environmental

synchronizers, primarily the periodic light-dark cycle associated with the earth’s daily rotation. The

circadian pacemaker modulates waking alertness and performance in a sinusoidal rhythmic fashion

of approximately 24-hr throughout the day. Light information is received via retinal ganglion cells

of the eye and is then transmitted through the hypothalamic tract to the suprachiasmatic nucleus.

Following this neural pathway, light acts as a powerful stimulus in the regulation of circadian

rhythms, contributing to a stable phase relationship between circadian rhythms and the sleep/wake

cycle (Czeisler and Wright 1999). Light pulses can also shift the phase of circadian rhythms to an

earlier (phase advance, if provided shortly after the body temperature circadian rhythm minimum) or

later (phase delay, if provided shortly before the body temperature minimum) time within the

biological day. The degree to which light regulates circadian rhythms is dependent on the duration,

intensity and frequency of light exposure as well as the phase of the circadian rhythms at which the

light is received by the eye (Czeisler and Wright 1999). Research has also suggested that the

wavelength of the light is equally critical, with shorter wavelengths having a greater effect on the

pacemaker than longer wavelengths (Lockley, et al., 2003).

There exists a regular pattern of circadian rhythm peaks and troughs, in alertness and performance,

throughout the 24-hr day, where on a normal 24-hr schedule, performance and alertness variables

reach their low point around 0300-0500 and 1500-1700, Monk, et al., 1996). The circadian trough in

performance and body temperature is associated with increased fatigue, i.e., a decline in arousal,

alertness and reduced motivation (Frazier, et al., 1968; Waterhouse, et al., 2001). The sleep drive, a

homeostatic process of exponential form, is primarily responsible for the timing of sleep and waking

(Borbely 1982; Van Dongen and Dinges 2000). The drive to sleep is at its lowest point in the

morning, upon awakening, and as the day progresses, the drive to sleep increases. Once sleep is

initiated, this drive gradually decreases until awakening. Broughton (1998) suggests that this

represents the output from the interaction between an endogenous brain rhythm, which provides a
57

twice per day increase in sleep propensity due to accumulating wakefulness, and an opposing

circadian arousal process, which counteracts the increasing sleep propensity process later during the

wake phase.

The comprehensive human isolation studies by Wever (1979, 1986) showed that the average human

circadian rhythm period is 25.0 hours. There is large individual variability in individual circadian

rhythm capacity to entrain to non-24 hour zeitgebers. Some individuals can entrain their rhythms to

26.7 hour zeitgebers, some exhibit non-entrained free-running circadian rhythms, and others exhibit

changes in rhythm periodicity dependent upon the phase relationship to the zeitgeber (relative

coordination) (Wever 1986). Wever’s finding of a 25.0 hour innate average circadian periodicity

implies that personnel working on a 24.65 hour MER schedule in this study would have been able to

easily entrain their circadian rhythms to this schedule. However, it was recently reported that the

innate circadian periodicity was much closer to 24 hours than 25 hours (24.18 hours, Czeisler, et al.,

1999). There are two published studies of circadian entrainment to a MER type work-rest schedule,

in which entrainment to a 24.6 hour light-dark cycle was evaluated under controlled laboratory

conditions (Wright, et al., 2001, 2006). In these studies, the melatonin circadian rhythm period

lengths were less than 24.6 hours in all participants, i.e., the daily phase shifts in the melatonin

rhythm were insufficient in low light intensity (ca. 1.5 lux) to maintain a constant phase relationship

to the light-dark cycle and exhibited phase dissociation from the timing of the scheduled sleep

episode. The authors concluded that the circadian rhythms of these participants were not entrained

but showed evidence of relative coordination effects by the light-dark cycle (Wright, et al., 2001).

The entrained participants in these studies also exhibited a change in phase angle in response to the

weak environmental synchronizer and the 24.6 hour day length. The participants in our study also

exhibited a 61 degree increase in phase angle during the MER regime. Such a change in circadian

acrophase following a transition to a non-24-h work-rest schedule reflects an adjustment of the

circadian system to a new zeitgeber periodicity and has been observed in other studies (Wright, et

al., 2006). This change in acrophase could also be attributed in part to a change in time zone after

moving to JPL from their home base (up to three hours time zone difference). More importantly, the

synchronized participants in the Wright, et al. (2001, 2006) studies showed less reduction in total

sleep time than the non-synchronized participants. The non-synchronized participants also exhibited

impairment in cognitive and vigilance performance (Psychomotor Vigilance Task [PVT], Wright, et

al., 2006). These deleterious changes in sleep and performance were attributed to changes in the

phase relationship between internal biological time and scheduled sleep/wake timing which

prevented the circadian system from counteracting the buildup of homeostatic sleep drive across the

day, resulting in worse performance and shorter sleep latencies (Wright, et al., 2006). The reduction

of sleep length in these participants to 7 hours per night was insufficient to maintain brain vigilance

during schedules which precluded extra time for sleep recovery (Wright, et al., 2006). Dissociation

between the circadian pacemaker and the sleep-wake schedule has been associated with the periodic

appearance of fatigue symptoms (Shibui, et al., 1998). In a similar study, a 25-hour day work-rest

schedule resulted in sleep and circadian disruption for 7/16 participants. Circadian rhythm disruption

in these participants was associated with reduced cortisol levels, which may be related to circadian

rhythmic amplitude disruption during rhythmic misalignment (Drake, et al., 2005). However, in a

study using a zeitgeber combination of a 26 hour sleep-wake schedule and a 26 hour light-dark cycle

containing evening bright light, the circadian rhythm of body temperature was entrained in most

participants tested (Eastman and Miescke 1990). This study was conducted in the home environment

of the participants, in which they were exposed to a conflicting 24.0 hour zeitgeber. The periodicity

analysis showed circadian peaks at 24 and 26 hours, attributed to the 24 hour zeitgeber and masking
58

by the 26 hour sleep-wake schedule. The study was similar to the present MER study in that

participants were exposed to an imposed schedule with a differing zeitgeber period length than

experienced in their home environment. The above studies indicate that circadian rhythm

entrainment can occur to an imposed schedule of 24.65 hours, and even to a schedule of 26.0 hours

in most individuals, assuming that zeitgeber strength is sufficient. The studies in which circadian

rhythms failed to entrain to 24.6 and 25.0 hour cycles were characterized by weak zeitgeber (light

intensity) strength and entrainment failed in the melatonin rhythm, which is a marker rhythm for the

core circadian oscillator (Wright, et al., 2001, 2006). However, the rest-activity rhythm was found to

have a wider range of entrainment (20–32 hours) than the core body temperature circadian rhythm

(Wever 1979), and was more sensitive to non-photic entrainment (e.g., social) zeitgebers than was

the body temperature rhythm (Honma, et al., 2003). We recognize that it was also not possible to

validate entrainment in this study due to the potential masking effects upon the activity rhythm by

uncontrolled environmental factors. However, the results from the Wright (Wright, et al., 2001,

2006) studies imply that individuals who fail to entrain to MER-like schedules were more likely to

show reduced sleep time and deterioration in performance, which has unfavorable implications for

the sleep quality and performance of participants in our study. In our study, we observed decreased

time in bed, increased sleep length variability, and increased sleep during the subjective day during

the MER rotation regimes.

Progressive deterioration in the psychomotor vigilance task in this study was documented in 4/10

tested participants following the transition from the MER schedule to an Earth time schedule

(Appendix F, Brandt, et al., 2005). These effects may be attributable to lack of entrainment to the

MER schedules, as in the Wright studies, or may reflect a direct negative impact of the rotating

MER schedule on sleep quality and duration. The changes in circadian rhythm periodicity and

amplitude during MER regimes may also reflect conflicts with the external 24.0 hour social and

light-dark zeitgebers and possible dissociation of the rest-activity rhythm from the core circadian

oscillator due to incomplete entrainment of the circadian oscillatory system and changes in the

relative intensity and timing of exposure to light-dark cycles and social and behavioral zeitgebers.

Behavioral zeitgebers (also referred to as sleep-wake schedule zeitgebers, Eastman and Miescke

1990), such as acoustically transmitted signals (e.g., alarm clock) which signal participants to wake

up or go to bed, can be effective zeitgebers (Wever 1983). These zeitgebers can be even twice as

effective as light-dark cycles (Wever 1986), which require at least three hours per day of bright light

(>3000 lux) (Wever 1985). Changes in wake time were associated with much greater circadian

phase shifts than changes in bedtime, which was most likely due to the associated reduction in

morning light exposure (Burgess and Eastman 2005). It was likely that acoustic zeitgebers (alarm

clock) served as the primary zeitgeber for the activity circadian rhythm in our study since exposure

to ambient light-dark cycles was erratic due to the rotating MER schedule, the uncontrolled exposure

to workplace, home and outside illumination, and the uncontrolled exposure to social interaction

zeitgebers in the workplace and home environments.

Operational demands in missions such as the MER mission, require shifted work schedules and

irregular sleep/wake cycles during operations. These shifts can induce a misalignment between the

phase of the circadian pacemaker and the sleep/wake cycle, resulting in circadian rhythmic

disruption. Subsequently, there is a dissociation between the timing of circadian physiological and

performance rhythms. The consequences, as demonstrated in ground-based studies, can be increased

sleep disruption, malaise, performance errors, uncontrollable sleep periods intruding into waking

hours, a more negative mood and decrements in social interaction, inefficient communication and
59

accidents (Holley, et al., 1981; Winget, et al., 1984). This overall impairment of performance

proficiency results from the shifting of performance times to an unfavorable phase of the

performance circadian rhythm. Certain individuals are more susceptible to sleep loss or to the

debilitating effects of shifted work-rest cycles (Stepanova 1986). In our study, shifted work

schedules resulted from 12-hr shifts in work schedules between the two Mars rovers, changes in job

position schedules during MER operations, and shifts between Mars sol work shifts and earth time

on days off. The maximum degree of activity circadian rhythm disruption in this study was seen in

two participants who switched from a MER-A to a MER-B schedule, which necessitated a 12-hour

shift in rest-activity patterns. The importance of this finding was the association of large circadian

rhythm phase shifts with reduced time in bed and sleep efficiency. The large circadian phase shifts

and circadian rhythm disruption in activity in these participants has serious implications for sleep

disruption, performance deterioration and fatigue during MER operations, similar to that observed in

the ‘jet-lag” response to transmeridian flight (Winget, et al., 1984). The MER rover work shift

transitions of 12 hours were similar to a procedure called “slam shifting,” which involves abrupt

shifts of up to 12-hr used to align the sleep/wake schedules of Space Shuttle and ISS crews upon

docking (Neri, et al., 2003). Since all four participants in this study who were required to perform

MER rover work shift transitions reported that these transitions were a major problem in adaptation

to MER operations and the one participant evaluated showed profound circadian rhythm disruption

during a MER rover transition, this practice has major implications for profound sleep disruption

and performance decrements in MER operational personnel and space crews and should be avoided

if at all possible. The shifts in rest-activity times between Mars sol work shifts and days off

schedules also created adaptation problems for some participants and two participants elected to

remain on a Mars sol schedule throughout MER operations. A preliminary study on two participants

who worked three consecutive 13 hour night shifts per week followed by 4 four consecutive nights

off showed that the one participant who maintained the same sleep/wake cycle on days off slept

nearly two hours more per night than the other participant, who reverted back to normal night sleep

times on days off (Hurlbert, et al., 2005). The implications of this study for the MER operations

regime is that individuals who transition back and forth between Mars sol schedules and earth time

are much more likely to experience chronic sleep loss than individuals who remain on the Mars sol

schedule for the duration of the MER operations.

A number of individual circadian rhythm indices, including morningness-eveningness (Horne and

Ostberg 1976), rhythm phase, amplitude and stability, have been evaluated as predictors of

adaptability to circadian phase shifts (Minors and Waterhouse 1981). These individual differences in

circadian rhythm characteristics suggest that there would be significant differences in the capacity of

a given MER operations personnel to maintain adequate sleep and physiologically adapt to workrest schedule shifts and lengthened sleep/wake cycle schedules. In the Wright studies (Wright, et al.,

2001, 2006), failure to entrain was related to individual differences in intrinsic circadian period

length. Research on circadian chronotypes showed that individuals with peaks in circadian rhythms

that peak relatively early in the day tend to be morning active types and vice versa (Horne and

Ostberg 1976). Additional studies demonstrated that circadian period was correlated with

morningness-eveningnesss,, circadian phase and wake times (Duffy, et al., 2001), individuals with

shorter circadian periods initiate sleep and awaken at later biological times than did individuals with

longer circadian periods (Wright, et al., 2005), there were differences in the regularity of sleep

satisfaction and sleep quality between morning and evening types (Kato, et al., 2006), and

eveningness was associated with a greater need for sleep and less time in bed (Taillard, et al., 1999).

In our study, about half of the participants exhibited entrainment to the MER 24.65 hour schedule,
60

but the rest showed activity circadian periods of about 25 hours, which often diverged from expected

MER schedule onsets, work time onsets and wake-up time onsets. These differences in MER

schedule synchronization, along with the differences observed in circadian rhythm stability and

waveform patterns during the MER regimes may reflect chronotype differences between the

participants, who exhibited the expected wide distribution of chronotypes from morning to evening

types. However, it was not possible to statistically evaluate the effects of chronotype differences in

this study given the differences in participant work schedules, home environments, exposure to

different zeitgeber influences, and the other uncontrolled variables. However, the literature indicates

that individuals with morning active chronotypes would have more difficulty adjusting their

work/rest schedules and synchronizing their circadian rhythms to a longer period day length such as

the MER 24.65-hr day. One participant, who had a moderate morning chronotype, reported

difficulty in adjusting to the MER regime. Individuals who have periods shorter than 24-hr, which is

about 25% of the population, will have the greatest challenges in entraining to a Mars sol (Czeisler,

et al., 2003). An investigation was conducted of the extent to which individual performance

responses to extended duration mission demands were a function of recent sleep/wake history, traitlike neurobehavioral vulnerability and/or sensitization or adaptation as a consequence of previous

exposure (Van Dongen, et al., 2003). The results revealed that there were differences in performance

among individuals but performance responses were not related to sleep/wake history or previous

exposure and there appeared to be trait-like characteristics within the individual for performance

responses. The prospects for identifying individual traits which may be predictive of performance

and physiological responses during operational missions have recently been evaluated, in which

multivariate converging indicators provide a significantly more reliable method for assessing

environmental effects upon performance and health than single indicators (Cowings, et al., 2006).

Circasemidian Rhythms

Several studies have reported the existence of 12-hour rhythms in several variables, including body

temperature (Colquhoun, et al., 1978), melatonin (Maggioni, set al., 1999), slow-wave sleep

(Hayashi, set al., 2002) and operational performance (Hildebrandt, et al., 1974). This rhythm has

been characterized as a bimodal component of the circadian waveform, in which peaks in the

evening and early morning are separated by about 12 hours, with the interval in between referred to

as the post-lunch dip or performance decrement interval (Hildebrandt, et al., 1974), or afternoon nap

zone, in which sleepiness increases (Broughton 1998). The important aspect of this “circasemidian”

rhythm was that it was more marked in people who were relatively sleep deprived and results in

increased performance errors (Hildebrandt, et al., 1974). This rhythm can represent the manifestation

of a bimodal circadian rhythm, but the 12 hour rhythms detected in this study by spectral analysis

could represent the output of circadian period harmonics generated by harmonic analysis of nonsinusoidal and asymmetric circadian activity waveforms, in which the residuals from the harmonic

fit process would generate spectral peaks at submultiples of the circadian period (e.g., at 12, 6, 3

hours, etc). The verification of the 12 hour rhythm observed in several participants primarily during

the MER regime by the waveform-based profilogram method, which was independent of harmonic

waveform assumptions, suggests that the 12 hour rhythms were real biological periodicities.

However, the ratio between the circadian and circasemidian rhythm period lengths detected was 2.0

in nearly all cases during baseline and MER regimes. This indicates that the circasemidian

periodicity was either a harmonic of the circadian period or, more likely, was modulated by the

circadian rhythm, or was the manifestation of a bimodal circadian wave form. A third explanation

for the circasemidian rhythm phenomenon based upon the work of Broughton (1998) suggests that
61

the bimodal 12 hour pattern was not a true rhythm but represented the output from the interaction

between an endogenous brain rhythm, which provides a twice per day increase in sleep propensity

due to accumulating wakefulness, and an opposing circadian arousal process, which counteracts the

increasing sleep propensity process later during the wake phase. In this model, the circasemidian

rhythm would not represent an independent oscillatory system, but would be the result of an

evolutionary process which promotes energy efficiencies by the preferential selection of rhythmic

processes that show simple fixed integer rations such as 2:1 (Broughton 1998). Sleep deprivation

would then amplify the sleep propensity circadian rhythm, resulting in increased sleepiness and

fatigue during the nap zone (Lack and Lushington 1996). The finding of substantially reduced

activity levels in the mean activity educed cycle waveforms during the period from about noon to

early evening in participants with pronounced circasemidian rhythms in this study suggests that the

reduced activity may have resulted from fatigue or increased sleepiness. This suggestion is

supported by previous studies in which increased fatigue was associated with reductions in activity

circadian rhythm amplitude (Liu, et al., 2005; Mormon, et al., 2000). Students who had experienced

sleep loss indicated that they were less likely to participate in daily activities and to be less active

(Engle-Friedman, et al., 2005), which would likely result in lower daily activity levels. However, in

our study, the increased circasemidian rhythm strength was not associated with increased workload

and sleep durations were actually 0.8 hours higher in the circasemidian group. Another unexpected

result was the finding that the four participants analyzed in the strong circasemidian group adapted

well, as evidenced by a positive composite fatigue score. Two of these participants reported that

their adaptation to the MER regime was enhanced by attempting to obtain at least 8-hr sleep per

night. Therefore, the increased circasemidian rhythm strength in these participants may have resulted

not from increased fatigue levels, but from an attempt by these participants to conserve energy

during the MER regimes by reducing activity levels during the MER work shifts. However, the

finding of increased performance errors associated with circasemidian rhythms (Hildebrandt, et al.,

1974) implies that the circasemidian group may have had reduced performance capacity during

MER work shifts. Monk (Monk, et al., 1996) indicated that participants showing a post-lunch

performance dip had higher circadian amplitude than those not showing the dip. Therefore, the postlunch dip is likely linked to individual endogenous circadian rhythm characteristics (Monk, et al.,

1996). Finally, we cannot eliminate the possibility that the activity reductions observed in our study

may be due in part to differences in MER schedule related changes in workload patterns which could

have affected activity levels at certain phases of the MER schedule.

In most participants, the reduced activity levels were associated with an extension of high activity

levels, such that the ratio between subjective day and night activity was higher. Aschoff (1971)

showed a negative correlation between duration of wakefulness and mean activity levels, in which

extended wakefulness was associated with lower activity levels. He attributed this finding to a

homeostatic process, which tends to maintain relatively constant levels of daily activity. The

extensions of the subjective day during the MER shifts would result in increased activity levels. This

would reduce available sleep time and may have resulted in sleep loss and subsequent fatigue in

these individuals, with reduced activity as a compensatory response. This conclusion was supported

by the report of a reduction in total sleep time under night shift conditions in another study (St.

Hilaire and Klerman 2006). Aside from the absence of evidence for increased fatigue in the strong

circasemidian rhythm group, the assumption that reduction in activity levels during the subjective

day, particularly during the post-lunch phase of the activity circadian rhythms, represents evidence

for increased fatigue levels was supported by the subjective mood reports in our study. The mood

data show an increase in subjective fatigue, sleepiness, and irritability during MER operations,
62

relative to baseline. Also, circadian rhythm waveforms became less symmetric and sinusoidal during

night shifts. However, we cannot eliminate the possibility that these waveform changes may be due

in part to differences in MER schedule uncontrolled changes in workload patterns which could have

affected activity levels at certain phases of the MER schedule.

Countermeasures to MER Operational Schedule Effects Upon Circadian

Rhythms and Sleep

Maintenance of the MER schedule throughout the MER operation would likely result in reduced

fatigue and circadian rhythm disruption resulting from shifting back and forth between the MER

schedule and weekend schedules. Circadian period length was significantly longer following

entrainment to a 24.65 hour day than to a 23.5 hour day, which demonstrates an aftereffect of

entrainment on circadian period length (Scheer, et al., 2005) which could have a disruptive effects

on circadian rhythm stability in personnel shifting back and forth between the longer day MER

schedule and the 24.0 hour non-MER work or weekend schedules. Using weekend breaks for fatigue

recovery by sleeping-in later results in circadian rhythm phase delays and longer sleep onset

latencies (Taylor, et al, 2006). Adaptation to the MER regime was facilitated in four participants by

attempting to obtain at least 8-hr sleep per night. A recent study by Monk and Buysse (2006) showed

that advancing bedtime by two hours was associated with increased sleep time and greater daytime

alertness. Vigilance performance was also improved by increasing time in bed by two hours per

night (Wright, et al., 2006). A sleep extension period also resulted in substantial improvements in

daytime alertness, vigilance performance, and mood (Kamdar, et al., 2004). Therefore, MER

personnel could be encouraged to retire to bed earlier or strive to spend more time in bed during

MER regimes to maintain alertness and reduce fatigue levels.

Caffeinated beverages were used by the majority of MER personnel to counteract fatigue effects

during MER operations. Caffeine is successful in overcoming the midday dip in physiological

alertness (Dijk and Edgar 1999). Caffeine stimulates the nervous system, generally taking effect in

15–45 minutes after ingestion and it usually remains active for 3–5 hours, although the effects can

continue for up to 10 hours in sensitive individuals. However, caffeine should not be ingested before

sleep since it interferes with sleep quality (Rosekind, et al., 1996).

Naps were used as a strategy to aid in adaptation to the MER regime by half the participants. Anchor

sleep is sleep obtained on days off during normal night sleep times. Nighttime anchor sleep, in

combination with daytime nap sleep, does increase neurobehavioral functioning (Maislin, et al.,

2001). It is not only the duration of the nap that determines the magnitude of effect on performance

but the placement of the nap within the circadian cycle, as well as the time at which the nap is taken

relative to beginning of the sleep restriction period (Rogers, et al., 2003). A nap reduces the duration

of continuous wakefulness before a work period and can be particularly beneficial before a period of

night work, when the challenge of working through the circadian trough is also a factor (Rosekind,

et al., 1996). The duration of naps is very important since in a recent study of the efficacy of 5 min

to 30 min naps, the 10-minute nap produced immediate improvements in all outcome measures,

including sleep latency, subjective sleepiness, fatigue, vigor, and cognitive performance. However,

the 30-minute nap resulted in a period of impaired alertness and performance immediately after

napping, indicative of sleep inertia, followed by improvements lasting up to 155 minutes after the

nap. Therefore, 10-min naps would be recommended to MER personnel as a restorative measure for
63

fatigue resulting from sleep restriction. Other investigators (Rosekind, et al., 1996) recommend

limiting naps to 45 minutes to avoid disruption of sleep architecture during normal sleep periods.

Exercise was used by one third of the participants in this study as a strategy to aid in adaptation to

the MER regime, although one participant indicated that exercise contributed to fatigue. Although

exercise can have adverse effects on sleep, it can also induce phase shifts in the melatonin circadian

rhythm, in which late afternoon or early evening exercise regimens induced phase advances and late

evening exercise induced phase delays (Buxton, et al., 2003, Eastman, et al., 1995). Therefore,

repeated exposure to appropriately timed exercise sessions during MER operations could be used to

facilitate the adaptation of circadian rhythmicity (Barger, et al., 2004, Buxton, et al., 2003) and thus

serve as a countermeasure to circadian rhythm disruption.

Five participants reported that they used days off to recover from fatigue and sleep loss and adapt

their work/rest times for their return to the MER regime. Repeated sleep loss in humans appears to

be cumulative, as indicated by the tendency to make up for lost sleep on days off (Carskadon and

Dement 1981). The effective use of days off and rest periods to catch up on sleep generally requires

two nights of unrestricted sleep (Rosekind, et al., 1996). The timing of sleep periods is critical since

sleeping-in later on days off can result in circadian rhythm phase delays, which could interfere with

adaptation to the subsequent MER regime (Taylor, et al, 2006). The problem in the MER study was

that the distribution and number of consecutive days off was highly variable, which implies that

sleep recovery and MER schedule adaptation was feasible for some participants but not for other

participants.

The results of this study showed that excessive work shift schedules and work shift transitions

between MER rovers were associated with increased fatigue levels and/or circadian rhythm

disruption. In addition, there was one instance where one of the most extreme morningness

chronotypes was associated with difficulty in adaptation to the longer Mars sol. Therefore, future

MER operations schedules should strive to schedule sufficient personnel to avoid excessive MER

shift durations and eliminate the necessity to require major work shift schedule transitions to

minimize fatigue and reduce the potential for mission operational performance errors. Individuals

with extreme morning chronotypes should be assigned to reduced-rotation MER missions or nonMER rotation duties instead of the full rotation 24.65-hr day missions.

Melatonin skin patches were effective in improving daytime sleep in other studies by increasing

REM sleep and decreasing waking after sleep onset (Aeschback, et al., 2006). Bright blue light

(3450 lux) was effective, even more so than bright white light, in producing phase shifts in circadian

rhythms (Smith, et al., 2006). Such phase advances were enhanced by the combined use of

intermittent morning light and afternoon melatonin doses (Revell, et al., 2005). Such treatments

could be used to promote circadian rhythm phase advances in personnel transitioning from normal

work-rest schedules to a MER regime which starts at an earlier time than the normal work-rest

schedule. Short time (30 min) exposure to natural bright light in the afternoon improved arousal

levels in the afternoon (Kaida, et al., 2005), which suggests that personnel on MER daytime

rotations would benefit from brief sunlight exposures. MER operations personnel who had rotated to

night time shifts were probably exposed to much lower light levels than during daytime shifts. An

increase in bright light exposure during night shifts could be beneficial since bright light exposure

during nighttime has been shown to result in significant improvement in performance and alertness

levels (Campbell and Dawson 1990, Daurat, et al., 1993, Hannon, et al., 1991). Exposure to bright
64

light can also facilitate entrainment to rotating work schedules (Czeisler and Allan 1987). Exposure

to 30-min of natural bright light in the afternoon improved arousal levels (Kaida, et al., 2005). This

treatment could be useful in counteract fatigue and the decline in locomotor activity observed in

several participants during MER operations.

Other countermeasures suggested to improve sleep quality and duration include: a) regular pre-sleep

routines which can condition relaxation in preparation for falling asleep; b) the use of various

physical and mental relaxation techniques such as meditation, autogenic training, yoga, and

progressive muscle relaxation; c) giving priority to sleep time and keeping sleep time as free as

possible from other commitments and activities; and d). sleeping in a dark, quiet room, with the use

of eyeshades to screen unwanted light and earplugs or background white noise to reduce noise

(Rosekind, et al., 1996).

A comprehensive approach in the management of fatigue and alertness for MER operations

personnel needs to be employed, which includes: 1) educational efforts; 2) effective scheduling

policies and procedures; and 3) implementation of specific fatigue remedies and countermeasures.

These workshops can be tailored specifically for MER operations and based on the original Fatigue

Education and Training Module (ETM) developed by the NASA Ames Fatigue Countermeasures

Group (Rosekind, et al., 2002).

Suggestions for Future MER Operational Studies

1. Obtain marker rhythm measures (e.g., body temp, melatonin), if possible and also obtain more

comprehensive pre-and-post MER operations mood tests and surveys to establish potential

predictive factors for MER adaptation.

2. Evaluate light exposure by utilizing Actiwatches with built-in light meters to evaluate the range

and timing of light exposure (Oyung 2003; Heil and Mathis 2002) which could modulate

circadian rhythm entrainment and stability. These devices are ideal for unobtrusive use in field

studies.

3. Evaluate activity rhythms in a control group working in the same environment but not on the

MER rotating schedule.

4. Obtain more complete participant information on work schedules and daily mood and subjective

sleep reports.

5. Incorporate more measurements of cognitive performance levels during MER regimes. This could

be done by incorporating shorter versions of cognitive tests such as a 3-min version of the

psychomotor vigilance task (William, et al., 2006), which could provide test data without

significant intrusion into the operational work schedule of the participant.

6. Evaluate countermeasures such bright light exposure, exercise regimes, and fatigue

countermeasures training to improve sleep quality.

7. Evaluate relative changes in activity levels and circadian activity wave form in individuals

subjected to varying degrees of chronic sleep loss.
65

8. Compare differences in mood, performance, and circadian rhythmicity between participants who

are chronically synchronized to a Mars sol schedule and participants who transition back and

forth between a Mars sol schedule and earth time on days off.

CONCLUSIONS

This study documented reduced sleep durations associated with increased work shift durations and

changes in circadian rhythm stability with reduced activity levels, the appearance of pronounced 12-

hr (circasemidian) rhythms and extended high activity level durations during MER operational

schedules. In response, overall adaptation to the MER operational regime was characterized by

increases in fatigue, sleepiness, and irritability and decreases in concentration and energy in most

participants. In addition, participants reported several primary challenges to successful adaptation,

which necessitated the adoption of several strategies to cope with the MER operational regime. The

assessment of adaptation to the MER rotating schedule by evaluation of circadian rhythm changes in

locomotor activity was important since high circadian rhythm mesor and circadian amplitude were

associated with better neuropsychological function (Martin, et al., 2001) and there is a high

correlation between actigraph and EEG estimates of sleep duration and efficiency (Monk, et al.,

1999). The entrainment limits of the human circadian pacemaker have important implications for the

entrainment not only of MER operations personnel but also in astronauts during normal spaceflight

operations and during future Mars missions, in which space crews will be exposed to a mars sol of

24.65 hours and potential conflicts with ground control personnel working on 24.0 hour earth days.

ACKNOWLEDGMENTS

We thank the Mars Exploration Rover subject participants and management at the Jet Propulsion

Laboratory, Pasadena, CA for their dedication to this study. We also thank Drs. Robert Welch,

William Toscano, Mary Connors and John Caldwell for their constructive comments on the

manuscript. This study was supported by the Human Factors Technology Division, NASA Ames

Research Center, Moffett Field, California.
66
67

REFERENCES

Aesbach, D., B.J. Lochyer, E. Dijk, S. W. Lockley, E.S. Nuwayer, L.D. Nichols and C.A. Czeisler.

Sleep 29: A41, 2006.

Akerstedt, T. Sleepiness at work: effects of irregular work hours. In: Sleep, Sleepiness and

Performance, T.H. Monk, ed., John Wiley & Sons, Chichester, England, pp. 129-152, 1991.

Akerstedt, T., A. Knutsson, P. Westerholm, T. Theorell, L. Alfredsson and G. Kecklund. Mental

fatigue, work and sleep. J. Psychosom. Res. 57: 427-433, 2004.

Ancoli-Israel, S., R. Cole, C. Alessi, M. Chambers, W. Moorcroft and C.P. Pollak. The role of

actigraphy in the study of sleep and circadian rhythms. Sleep 26: 342-392, 2003.

Barger L.K., K.P. Wright, R.J. Hughes, and C.A. Czeisler. Daily exercise facilitates phase delays of

circadian melatonin rhythm in very dim light. Am. J. Physiol. Regul. Integr. Comp. Physiol.

286:R1077-R1084, 2004.

Bass, D.S., R.C. Wales, and V.L. Shalin. Choosing Mars time: Analysis of the Mars Exploration

Rover experience. IEEE Aerospace Conference, Big Sky, Montana, March 2004, paper No.

1162, pp. 4174-4185, 2005.

Blau, A. K. Knoop, J.G. Peter and I. Fietze. Effects of starting night shift work. Sleep 29: A53,

2006.

Bonnet, M.H. Sleep deprivation. In: Principles and Practice of Sleep Medicine, 3rd ed., M. H.

Kryger, T. Roth and W.C. Dement, eds., W.B. Saunders, Philadelphia, pp. 53-71, 2000.

Borbély A.A. A two process model of sleep regulation. Human Neurobiology 1:195–204, 1982.

Brandt, S.L., L.M. Colletti, H. Van Dongen, D.F. Dinges, and M.M. Mallis. Transitioning from a

Mars day to an earth day: Effects on psychomotor vigilance performance. Associated

Professional Sleep Societies, 19th Annual Meeting, Denver, CO, June 18-23, 2005. Sleep 28:

A356.

Brooks, A. and L. Lack. A brief afternoon nap following nocturnal sleep restriction: Which nap

duration is most recuperative? Sleep 29: 830-840, 2006.

Broughton, R.J. SCN controlled circadian arousal and the afternoon “nap zone”. Sleep Res. Online

1: 166-178, 1998.

Burgess, H.J. and C.I. Eastman. Waking up late phase delays the human circadian clock more than

staying up late. Sleep 28: A55, 2005.

Buxton O.M., C.W. Lee, M. L’Hermite-Baleriaux, et al. Exercise elicits phase shifts and acute

alterations of melatonin that vary with circadian phase. Am. J. Physiol. Regul. Integr. Comp.

Physiol. 284:R714-R724, 2003.

Campbell S.S., and D.Dawson Enhancement of nighttime alertness and performance with bright

ambient light. Physiol. Behav. 48:317-320, 1990.

Chattoo, K., J. Shen, D.L. Streiner, S.A. Chung and C.M. Shapiro. Novel scale measuring both

sleepiness and fatigue. Sleep 29: A344, 2006.

Citta, M. Time series analysis: The profilogram method. Experientia 38: 870-871, 1982.
68

Cleveland, W.S. Robust locally weighted regression and scatterplots. J. Am. Stat. Assoc. 74: 829-

836, 1979.

Colletti, L.M. Sleep/wake Cycles of Personnel Working a Mars Day (24.65 h). Masters Thesis, San

Jose State University, San Jose, California, 2006.

Colletti, L.M., C.W. DeRoshia and M.M. Mallis. Sleep quantity of personnel working a 24h 39m

day during MER operations. In: Human Factors Workshop Symposium, NASA, Ames

Research Center, Moffett Field, CA, Oct 18-20, 2004.

Colquhoun, W.P. Circadian variations in mental efficiency. In: Biological Rhythms and Human

Performance, W.P. Colquhoun, ed., Academic Press, London, pp. 39-107, 1971.

Colquhoun, W.P., M.W.P.H. Paine and A. Fort. Circadian rhythm of body temperature during

prolonged undersea voyages. Aviat. Space Environ. Med. 49: 671-678, 1978.

Cowings, P.S., W.B. Toscano, C.W. DeRoshia, et al. Converging Indicators for Assessing Individual

Differences in Adaptation to Extreme Environments: Preliminary Report NASA Technical

Memorandum TM-2006-213491, 2006.

Czeisler., C.A. and J.S. Allan. Acute circadian phase reversal in man via bright light exposure:

Application to jet-lag. Sleep Res. 16: 605, 1987.

Czeisler, C.A., J.F. Duffy, T.L. Shanahan, E.N. Brown, J.F. Mitchell,D.W. Rimmer J.M. Ronda, E.J.

Silva, J.S. Allan, J.S. Emens, et al. Stability, precision, and near 24-hour period of the

human circadian pacemaker. Science 284: 2177-2181, 1999.

Czeisler C.A., C. Gronfier, K.P. Wright, and R.E. Kronauer RE. Circadian entrainment, sleep-wake

regulation and performance during space flight. In: Bioastronautics Investigators’ Workshop

Abstract Volume; 2003 Jan 13–15; Galveston, TX. Houston: National Aeronautics and Space

Administration; p. 214, 2003.

Czeisler, C.A., R.E. Kronauer, J.S. Allan, et al. Bright light induction of strong (type 0) resetting of

the human circadian pacemaker. Science 244: 1328-1333, 1989.

Czeisler C.A. and K.P. Wright Influence of light on circadian rhythmicity. In: Neurobiology of

sleep and circadian rhythms. Turek F.W. and P.C. Zee, eds., New York: Marcel Dekker Inc,

pp. 147-180, 1999.

Dahlgren, A.K. Effects on sleep and fatigue of long work hours. Sleep 28: A353, 2005.

Dahlgren, A.K., G. Kecklund, and T. Akerstedt. Different levels of work-related stress and the

effects on sleep, fatigue and cortisol. Scand. J. Work Environ. Health 31: 277-285, 2005.

Daurat A., Acquirre A., Foret J., et al. Bright light affects alertness and performance rhythms during

a 24-h constant routine. Physiol. Behav.53:929-936, 1993.

DeRoshia, C. W., R. Oyung, L. Colletti, and M. Mallis. Detection of Fatigue States by Analysis of

Circadian Rhythms in Locomotor Activity. Human Factors Workshop Symposium, NASA,

Ames Research Center, Moffett Field, CA, Oct 18-20, 2004.

DeRoshia, C. W., R. Oyung, L. Colletti, and M. Mallis. Detection of Fatigue States by Analysis of

Circadian Rhythms in Locomotor Activity. Associated Professional Sleep Societies, 19th

Annual Meeting, Denver, CO, June 18-23, 2005. Sleep 28:173, 2005.
69

Dijk, D-J. and D. Edgar. Circadian and homeostatic control of wakefulness and sleep. In: Regulation

of Sleep and Circadian Rhythms, F.W. Turek and P.C. Zee, eds., Marcel Dekker, New York,

pp. 11-147, 1999.

Dijk, D., D.F. Neri, J.K. Wyatt, J.M. Ronda, E. Riel, A. Ritz-De Cecco, R.J. Hughes, A. R. Elliott,

G. K. Prisk, J.B. West and C.A. Czeisler. Sleep, performance, circadian rhythms, and lightdark cycles during two space shuttle flights. Am. J. Physiol. Regulatory Integrative Comp.

Physiol. 281: R1647-R1664, 2001.

Dinges D.F., and N.B. Kribbs. Performing while sleepy: effects of experimentally-induced

sleepiness. In: Monk TH, ed. Sleep, sleepiness and performance., T.H. Monk, ed., John

Wiley & Sons ,Chichester, England, pp. 97-128, 1991.

Dinges D.F., Pack F., Williams K., et al. Cumulative sleepiness, mood disturbance, and

psychomotor vigilance performance decrements during a week of sleep restricted to 4-5

hours per night. Sleep 20:267-277, 1997.

Drake, A.L., C.A. Czeisler, and K.P. Wright. Effects of total sleep deprivation and chronic circadian

misalignment on cortisol levels. Sleep 28: A59, 2005.

Duffy, J.F., D.W. Rimmer and C.A. Czeisler. Association of intrinsic circadian period with

morningness-eveningness, usual wake time, and circadian phase. Behav. Neurosci. 115: 895-

899, 2001.

Eastman C.I., Hoese E.K., Youngstedt S.D., and L. Liu. Phase-shifting human circadian rhythms

with exercise during the night shift. Physiol. Behav. 58:1287-1291, 1995.

Eastman, C.I. and K.J. Miescke. Entrainment of circadian rhythms with 26-h bright light and sleepwake schedules. Am. J. Physiol. 259: R1189-1197, 1990.

Engle-Friedman, M., G. Garcia, and L. Hayrapetyan. The expected impact of sleep loss on common

daily activities. Sleep 28: A126, 2005.

Frazier T.W., J.A. Rummel, and H.S. Lipscomb HS. Circadian variability in vigilance performance.

Aerospace Med .39:383-395, 1968.

French, J., R.U. Bisson,K.J. Neville, J. Mitcha, and W.F. Storm. Crew fatigue during simulated, long

duration B-1B bomber missions. Aviat. Space Environ. Med. 65(Suppl.): A1-A6, 1994.

Galipault, J.B. (1980). A study of airline flight attendant sleepiness, fatigue, and stress. (Rep. No.

80-2). Worthington, OH: The Aviation Safety Institute.

Hannon P., Brainard G., Gibson W., et al. Effects of bright wide spectrum illumination on sublingual

temperature and cognitive performance in humans during nighttime hours. Sleep Res.

20:458, 1991.

Hayashi, M., T. Morikawa, and T. Hori. Circasemidian 12h cycle of slow wave sleep under constant

darkness. Clin. Neurophysiol. 113: 1505-1516, 2002.

Heil, D.P. and S.R. Mathis. Characterizing free-living light exposure using a wrist-worn light

monitor. Appl. Ergon. 33: 357-363, 2002.

Hildebrand, G., W. Rohmert, and J. Rutenfranz. 12 & 24 h rhythms in error frequency of locomotive

drivers and the influence of tiredness. Int. J. Chronobiol. 2: 175-180, 1974.
70

Holley, D.C., C.W. DeRoshia, M.M. Moran and C.E. Wade. Chronic centrifugation (hypergravity)

influences the circadian system of the rat. J. Appl. Physiol. 95: 1266-1278, 2003.

Holley D.C., C.M. Winget, .C.W. DeRoshia, et al. Effects of circadian phase alteration on

physiological and psychological variables: implications to pilot performance. Moffett Field,

CA: National Aeronautics and Space Administration; Technical Memorandum 81277, 1981.

Horne, J.A. and O. Ostberg. A self-assessment questionnaire to determine morningness-eveningness

in human circadian rhythms. Intern. J. Chronobiol. 4: 97-110, 1976.

Hurlbert, K.W., E. Roth, and P. Smith. Long term actigraphic assessment of night-shift workers.

Sleep 28: A63, 2005.

Jean-Louis, G., D.F. Kripke, S. Ancoli-Israel, M.R. Klauber, R.S. Sepulveda, M. Mowen, J.D.

Assmus and R.D. Langer. Circadian sleep, illumination and activity patterns in women:

influences of aging and time reference. Physiol. Behav. 68: 347-352, 2000a.

Jean-Louis, G., D.F. Kripke, S. Ancoli-Israel, M.R. Klauber, and R.S. Sepulveda. Sleep duration,

illumination, and activity patterns in a population sample: Effects of gender and ethnicity.

Biol. Psychiat. 47: 921-927, 2000b.

Jewett M.E. and R.E. Kronauer. Interactive mathematical models of subjective alertness and

cognitive throughput in humans. J Biol. Rhythms 14:588–597, 1999.

Kaida, K., M. Takahashi, Y. Otsuka, T. Haratani, A. Nakata, and K. Fukasawa. Indoor exposure to

natural bright light reduces afternoon sleepiness. Sleep 28: A39, 2005.

Kamdar, B.B., K.A. Kaplan, E.J. Kezirian, and W.C. Dement. The impact of extended sleep on

daytime alertness, vigilance, and mood. Sleep Med. 5: 441-448, 2004.

Kato, H., A. Noda, S. Miyata, Y. Morishita, Y. Adachi, A. Iwama, M. Sukegawa, Nl. Maeno, N.

Ozaki and Y. Koike. Sleeping and life habits in morning and evening types of sleep-wake

rhythm. Sleep 29: A57, 2006.

Klein D.C., R.Y. Moore, and S.M. Reppert Suprachiasmatic nucleus: the mind's clock. New York:

Oxford University Press, 1991.

Kramm, K.R. Experimental prediction for theoretical timing map model. J. Interdiscipl. Cycle Res.

4: 361-367, 1973.

Kronauer, R.E., C.A. Czeisler, S.F. Pilato, M.C. Moore-Ede, and E.D. Weitzman. Mathematical

model of the human circadian system with two interacting oscillators. Am. J. Physiol. 242:

R3-R17, 1982.

Lack, L. and K. Lushington. The rhythms of human sleep propensity and core body temperature. J.

Sleep Res. 5: 1-11, 1996.

Lanfranchi, P.A.,M. Pennestri, M. Richard, B. Dube, and P. Montplasir. Cardiovascular correlates of

sleep quality in healthy subjects. Sleep 28: A42, 2005.

Lewis H.E. & Masterton, J.P. (1957). Sleep and wakefulness in the arctic. Lancet, 272, 1262-1266.

Lichstein, K.L., K.C. Stone, J. Donaldson, S.D. Nau, J.P. Soeffing, D. Murray, K.W. Lester, and

R.N. Aguillard. Actigraphy validation with insomnia. Sleep 29: 232-239, 2006.
71

Liu, L., B.A. Parker, M. Marler, S. Johnson, L. Fiorentino, and S. Ancoli-Israel. Increased fatigue in

breast cancer patients was related to desynchronized circadian rhythms during chemotherapy.

Sleep 28: A289, 2005.

Lockley S.W., G.C. Brainard, and C.A.Czeisler. High sensitivity of the human circadian melatonin

rhythm in resetting by short wavelength light. J. Clin. Endocrinol. Metab. 88:4502-4505,

2003.

Maggioni, C., G. Cornelissen, R. Antinozzi, M. Ferrario, A. Grafe and F. Halberg. A half-yearly

aspect of circulating melatonin in pregnancies complicated by intrauterine growth

retardation. Neuroendocrin, Lett. 20: 55-68, 1999.

Maislin G., N.L. Roger N.J. Price, et al. Response surface modeling of the effects of chronic sleep

restriction with and without a diurnal naps. Sleep 24:242-243, 2001.

Martin, J., D.V. Jeste, M.P. Caliguiri, T. Patterson, R. Heaton, and S. Ancoli-Israel. Actigraphic

estimates of circadian rhythms and sleep/wake in older schizophrenia patients. Schizophren.

Res. 47: 77-86, 2001.

Minors D.S. and J.M. Waterhouse. Circadian Rhythms and the Human. Boston: Wright PSG, 1981.

Minors, D.S. and J.M. Waterhouse. Circadian Rhythms in General. In: Occupational Medicine: State

of the art reviews, Shiftwork edition, A.J. Scott, ed., Hanley & Belfus, Philadelphia, pp. 165-

182, 1990.

Mishkin, A. Sojourner: An Insider’s View of the Mars Pathfinder Mission. Berkeley Books, New

York, 2003.

Monk, T.H. and D.J. Buysse. Changes in the sleep, circadian rhythms and daytime alertness of

healthy seniors as induced by a 2h change in bedtime. Sleep 29: A102, 2006.

Monk T.H., D.J. Buysse, C.F. Reynolds, and D.J. Kupfe. Circadian determinants of the postlunch

dip in performance. Chronobiol. Int. 13:123–133, 1996.

Monk, T.H., D.J. Buysse, and L.R. Rose. Wrist actigraphic measures of sleep in space. Sleep 22:

948-953, 1999.

Mormon, M. and J. Waterhouse. Contribution of the rest-activity circadian rhythm to quality of life

in cancer patients. Chronobiol. Intern. 19: 313-323, 2002.

Mormon, M., J. Waterhouse, P. Bleuzen, S. Giacchetti, A. Jami, A. Bogdan, J. Lellouch, J.L. Misset,

Y. Touitou, and F. Levi. Marked 24-hr rest/activity rhythms were associated with better

quality of life, better response, and longer survival in patients with metastatic colorectal

cancer and good performance status. Clin. Cancer Res. 6: 3038-3045, 2000.

Naitoh, P., Banta, G. R., Kelly, T. L., Bower, J., & Burr, R. Sleep logs: Measurement of individual

and operational efficiency (Rep. No. 90-29). Naval Medical Research and Development

Command: Naval Health Research Center; PO Box 85122; San Diego, CA 92186-5122,

1991.

Nelson, W., Y.L. Tong, J. Lee and F. Halberg. Methods for cosinor-rhythmometry. Chronobiol. 6:

305-323, 1979.

Neri D.F.,C.A. Czeisler, D-J. Dijk, et al. Sleep, circadian rhythms and performance during space

shuttle missions. In: The Neurolab Spacelab Mission: Neuroscience research in space:
72

Results from the STS-90, Neurolab Spacelab Mission. NASA/SP-2003-535; 2003, pp. 211-

221, 2003.

Oyung, R. L. Activity and Light Exposure Patterns in Flight Crew. M.S. Thesis, San Jose State

University, 2003.

Paine, S., P.H. Gander, and N. Travier. The early bird gets the worm: Implications of

morningness/eveningness for work, sleep and life. Sleep 28: A61, 2005.

Parke, B.K. Mars Pathfinder Fatigue/Stress Questionnaire Report. Unpublished Report, Ames

Research Center, Moffett Field, CA, 2 p., 2001.

Parke, B.K. Best Practices in Shift Handover: Applications to MER Surface Operations.

Unpublished Report, Ames Research Center, Moffett Field, CA, 26 p., 2002.

Pollak, C.P., W.W. Tryon, H. Nagaraja and R. Dzwonczyk. How accurately does wrist actigraphy

identify the states of sleep and wakefulness. Sleep 24: 957-965, 2001.

Redmond, D.P., H.C. Sing and F.W. Hegge. Biological time series analysis complex demodulation.

In: Rhythmic Aspects of Behavior, F.M. Brown and R.C. Graeber (eds.), Erlbaum, New

Jersey, pp. 429-457, 1982.

Revell, V.L., H.J. Burgess, C.J. Gazda, and C.I. Eastman, Afternoon melatonin and morning

intermittent bright light can help you prepare for eastward jet travel. Sleep 28: A55, 2005.

Rogers N.L., J. Dorrian and D.F. Dinges. Sleep, waking and neurobehavioral performance. Frontiers

in Bioscience Sep 1, 8:S1056-1067, 2003.

Rosekind, M.R., P.H. Gander, K.B. Greogory, et al. Managing fatigue in operational settings. I:

Physiological considerations and countermeasures. J. Behav. Med. 21: 157-165, 1996.

Rosekind M.R., E.L.Co, D.F. Neri, et al. Crew factors in flight operations XIV: alertness

management in regional flight operations education module. Moffett Field, CA: National

Aeronautics and Space Administration Technical Memorandum 2002-211393, 2002.

Sandweiss, L.A. The 40-hour work week-dead or alive? Indiana University Home Pages,

http://www.homepages.indiana.edu/040904/text/workweek.html.

Satlin, A., L. Volicer, E.G. Stopa and D. Harper. Circadian locomotor activity and core-body

temperature rhythms in Alzheimer’s disease. Neurobiol. Aging 16: 765-771, 1995.

Scheer, F.A., K.P. Wright, C. Gronfier, R.E. Kronauer, and C.A. Czeisler, Human circadian period

is influenced by entrainment to non-24 hour schedules. Sleep 28: A72, 2005.

Shibui, K., M. Uchiyama, H. Iwama, S. Ozaki, K. Takahashi, and M. Okawa. Periodic fatigue

symptoms due to desynchronization in a patient with non-24-h sleep-wake syndrome.

Psychiatry Clin. Neurosci. 52: 477-481, 1998.

Signal, L., J. Gale, and P.H. Gander. Actigraphic and subjective measures of sleep: How well do

they measure the sleep of flight crew? Sleep 28: A321, 2005a.

Signal, L., J. Gale, and P.H. Gander. Sleep measurement in flight crew: comparing actigraphic and

subjective estimates in polysomnography. Aviat. Space Environ. Med. 76: 1058-1063,

2005b.

Smith, M.R., V.L. and C.I. Eastman. Intermittent and continuous bright blue=enriched light

augments circadian phase advances. Sleep 29: A52, 2006.
73

Sing, H.C., D.P. Redmond and F.W. Hegge. Multiple complex demodulation: A method for

rhythmic analysis of physiological and biological data. In: Proceeding of the Fourth Annual

Symposium on Computer Applications in Medical Care, IEEE, New York, pp. 151-158,

1980.

Stepanova S.I. Major trends in the use of biological rhythms for cosmonaut selection. [Russian]. In:

Biological aspects of the problem of adaptation (pt II). Moscow: Nauka; pp. 165-171, 1986.

Taillard, J., P. Philip and B. Bioulac. Morningness/eveningness and the need for sleep. J. Sleep Res.

8: 291-295, 1999.

Taylor, A., L.C. Lack, and H.R. Wright. The effect of “sleeping-in” weekend mornings on circadian

rhythms, sleep and daytime sleepiness. Sleep 29: A57, 2006.

Teicher, M.H., C.A. Glod, E. Magnus, D. Harper, G. Benson, K. Krueger and C.E. McGreenery.

Circadian rest-activity disturbances in seasonal affective disorder. Arch. Gen. Psychiat. 54:

124-130, 1997.

Tepas, D. I. & Carvalhais, A. B. Sleep patterns of shiftworkers. Occup.Med., 5, 199-208, 1990.

Tworoger, S.S., S. Davis, M.V. Vitiello, M.J. Lentz, and A. McTiernan. Factors associated with

objective (actigraphic) and subjective sleep quality in young adult women. J. Psychosom.

Res. 59: 1-9, 2005.

Vallieres, A. And C.M. Morin. Actigraphy in the assessment of insomnia. Sleep 26: 902-906, 2003.

Van Dongen H.P.A. and D.F. Dinges. Circadian rhythms in fatigue, alertness and performance. In:

Principles and practice of sleep medicine. Kryger M.H., Roth T., Dement W.C., eds., 3rd ed.,

Philadelphia: Saunders; pp. 391-399, 2000.

Van Dongen, H.P.A., G. Maislin, J. M. Mullington, and D.F. Dinges. The cumulative cost of

additional wakefulness: Dose-response effects on neurobehavioral functions and sleep

physiology from chronic sleep restriction and total sleep deprivation. Sleep 26: 117-126,

2003.

Van Someren, E.J., E.E. Hagebeuk, C. Lijzenga, P. Scheltens, S.E. De Rooij, C. Jonker, A.M. Pot,

M. Mirmiran and D.F. Swaab. Circadian rest-activity rhythm disturbances in Alzheimer’s

disease. Biol. Psychiatry 40: 259-70, 1996.

Van Someren, E.J.W., D.F. Swaab, C.C. Colenda, W. Cohen, W. V. McCall and P.B. Rosenquist.

Chronobiol. Intern. 16: 505-508, 1999.

Waterhouse J.M., D.S. Minors, T. Akerstedt, et al. Rhythms of human performance. Plenum, New

York: Kluver Academic; pp.571-601, 2001.

Wever, R.A. The Circadian System of Man. Springer, New York, 1979.

Wever, R.A. Organization of the human circadian system: Internal interactions. In: Circadian

Aspects of Psychiatry, T.A. Wehr, F.K. Goodwin, eds, Boxwood Press, Los Angeles, pp. 17-

32, 1983.

Wever, R.A. Use of light to treat jet-lag: Differential effects of normal and bright artificial light on

human circadian rhythms. Ann. New York. Acad. Sci. 453: 282-304, 1985.

Williams, J.H., K.M. Fomberstein, S. Niyogi, M.M. Coble, S. Banks, and D.F. Dinges. Development

of a briefer version of the psychomotor vigilance task. Sleep 29: A344, 2006.
74

Winget, C.M., C.W. DeRoshia, C.M. Markley, and D.C. Holley. A review of human physiological

and performance changes associated with desynchronosis of biological rhythms. Aviat.

Space Environ. Med. 55: 1085-1096, 1984.

Wright, K.P., D.J. Frey, M.E. Moritz, C.M. Jung, J. Nguyen, J.J. Rodgers, and A.T. Wertz. Effects

of chronic sleep loss and sleep extension on vigilance performance. Sleep 29: A132, 2006.

Wright, K.P., C. Gronfier, J.F. Duffy and C.A. Czeisler. Intrinsic period and light intensity

determine the phase relationship between melatonin and sleep in humans. J. Biol. Rhythms

20: 168-177, 2005.

Wright, K.P., J.T. Hull, R.J. Hughes, J.M. Ronda, and C.A. Czeisler. Sleep and wakefulness out of

phase with internal biological time impairs learning in humans. J. Cogn. Neurosci. 18: 508-

521, 2006.

Wright K.P., Jewett, M.E., Klerman, E.B., et al. Circadian entrainment, sleep-wake regulation and

neurobehavioral performance under the simulated lighting conditions of long-duration space

missions. Proceedings of the First Biennial Space Biomedical Investigators' Workshop; 1999

Jan 11-13; League City, TX. Houston: National Aeronautics and Space Administration, pp.

567-568, 1999.
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APPENDIX A

Schedule for Baseline Data Recording and MER Operations

Participant M0303

Baseline data 12/18/03–1/2/03 16 days Days 1–16

MER regime 1/3/04–3/17/04 77 days Days 17–91

Earth time 3/18–4/5/04 19 days Days 92–110

Baseline data segment 12/18–12/31/03 14 days Days 1–14

MER data segment 1/4/03–1/17/04 14 days Days 8–31

Participant M0304

Baseline data 12/17/03–1/4/04 19 days Days 1–19

MER regime 1/5/04–3/28/04? 80 days Days 20–99?

Baseline data segment 12/21–1/28/03 8 days Days 4–11

MER data segment 1/5–1/12/04 8 days Days 19–26

Participant M0309

Baseline data 12/20/03–1/2/04 14 days Days 1–14

MER regime 1/3/04–4/5/04 94 days Days 15–108

Baseline data segment 12/20–12/31/03 12 days Days 1–12

MER data segment 1/6/–1/17/04 12 days Days 18–29

MST from 12/19/03 to 1/1/04 Time changes

PST from 1/2/04 to 3/23/04 -1 hr

CST from 3/24/04 to 3/27/04 +2 hr

PST from 3/28/04 to ?

No additional time zone data available

Notes: This person seemed to be impaired by the schedule based on personal correspondences.

Missing post-study data and sleep difficulties data.

Missing data 4/4/04 from 02:00 to 02:59 replaced by data folding. A note referring to missing data

on 1/13/04 was not verified since no missing data blocks were identified on this date.

Participant M0310

Baseline data 12/20/03–1/2/04/03 14 days Days 1–14

MER regime 1/3/04*4/3/04 10 days Days 15–106

Baseline data segment 12/20–12/30/03 11 days Days 1–11

MER data segment 1/4–1/13/04 10 days Days 16–25
76

Participant M0313

Baseline data 12/20/03–1/2/04 14 days Days 1–14

MER data 1/3-3/28/04* 86 days Days 15–100

Baseline data segment 4/13/04–4/24/04 12 days Days 116–127

MER data segment 1/17–1/28/04 12 days Days 29–40

*participant switched from MER-A to MER-B schedule 1/21–1/24/04

 and from MER-B to MER-A on 2/16/04

Time changes

CST from 12/19/03 to 12/20/03

EST from 12/21/03 to 12/26/03

CST from 12/27/03 to 1/1/04

PST from 1/2/04 to 2/19/04

AZT from 2/20/04 to 2/27/04*

*Note: Unsure whether in AZ or PST time zones

PST from 2/28/04

to 3/13/04

CST from 3/14/04 to 3/19/04

PST from 3/20/04 to 4/2/04

PDT from 4/3/04 to 4/24/04

Notes: Switched to MER B on 1/24/04

Switched to MER A on 2/16/04

Sick 2/22-2/25/04 and 3/21/04

Participant M0325*

Baseline data 12/23/03–1/4/03 13 days Days 1–13

MER regime 1/5/04–4/5/04 72 days Days 14–105

Baseline data segment 12/24–12/29/03 6 days Days 2–7

MER data segment 2/15–2/20/04 6 days Days 55–60

*participant remained on MER regime throughout MER operations phase

Time changes

EST from 12/23/03 to 12/30/03

PST from 12/31/03 to 4/3/04

PDT from 4/4/04 to 4/18/04

Notes: This participant stayed on a Mars schedule

Flew to Las Vegas on 3/1/04 for overnight trip

Missing data 4/9/04 02:00–02:59, 2/10/04 10:27-2-12:04, replaced by data folding

Missing data 3/24/04 21:00 to 3/25/04 14:00, replaced by averaging across two adjacent days.
77

Participant M0332

Baseline data: No pre-MER regime baseline data available prior to 1/3/04

MER regime 1/2/04 to 3/19/04 78 days Days 1–78

Post-baseline 3/19/04 to 4/22/04 35 days Days 79–112

Baseline data segment 4/4–4/21/04 18 days Days 94–111

MER data segment 2/1–2/18/04 18 days Days 31– 48

Time changes

PST from 1/3/04 to 4/3/04

PDT from 4/4/04 to 4/22/04

Notes: –Worked overtime on 3/29-3/30/04 which affected sleep pattern. Removed this sleep period

since interested in normal operations.

M0329

Baseline data: No pre-MER regime baseline data available prior to 1/5/04

MER regime 1/5/04–4/10/04 97 days Days 5–101*

Baseline 4/11/04–4/30/04 35 days Days 102–121

Baseline data segment 4/11–4/20/04 10 days Days 102–111

MER data segment 1/5–1/11/04 10 days Days 5–11

*participant switched from MER-A to MER-B schedule 1/21–1/24/04

M0320

Baseline data 12/17–1/3/04 18 days Days 1–18

MER regime 1/4/04–3/20/04 77 days Days 19-95

Post-baseline 4/11/04–4/30/04 35 days Days 102–121

Baseline data segment 4/10–4/18/04 9 days Days 102-111

MER data segment 2/16–2/24/04 9 days Days 62–70
78
79

APPENDIX B

Circadian Rhythm Metrics

Table 2. Circadian Rhythm Metrics (Means) for the Baseline Data for the Fatigue

Countermeasures Group Working a Day Schedule

ID

No.

days

PeriodAmplitude%

Rhythm

Acrophase

Degrees Mesor

Relative

Amplitude

Goodness of

fit

Interdaily

Stability

Rhythm

Sig Prob 95% CL

LC 44 23.97 161.53 38.62 214.20 210.46 0.76 0.60 0.47 3.12E-20 19.14

CWD 19 23.96 89.62 37.40 196.91 110.48 0.80 0.61 0.30 2.97E-11 19.56

DR 11 23.96 107.41 44.12 251.14 139.36 0.78 0.66 0.64 4.80E-09 25.55

SB 11 24.10 143.96 43.74 260.09 186.33 0.80 0.64 0.50 8.20E-06 22.50

S1014 6 24.24 345.64 58.26 174.82 379.98 0.92 0.76 0.58 2.07E-03 53.73

S1008 8 24.10 249.23 53.53 174.87 275.87 0.91 0.72 0.51 4.46E-05 36.72

S1006 14 23.96 318.80 55.85 184.72 358.42 0.90 0.74 0.55 4.46E-05 36.72

Means 24.04 202.31 47.36 208.11 237.27 0.84 0.68 0.51 3.10E-04 30.56

Table 3. Circadian Rhythm Metrics (Means) for MER Personnel at Baseline

before the Rovers Landed

ID

No.

days

PeriodAmplitude%

Rhythm

Acrophase

Degrees Mesor

Relative

Amplitude

Goodness of

fit

Interdaily

Stability

Rhythm

Sig Prob 95% CL

M0303 13 23.96 105.51 28.85 195.71 118.37 0.95 0.60 0.21 1.52E-08 51.41

M0304 8 24.10 117.24 58.31 217.74 215.97 0.57 0.77 0.46 1.96E-03 101.80

M0309 12 24.10 180.58 68.99 258.47 293.83 0.75 0.86 0.53 8.45E-08 78.33

M0332 18 23.80 117.25 46.84 200.02 161.78 0.74 0.73 0.48 6.34E-10 71.17

M0325 6 24.10 41.34 58.40 199.98 75.06 0.58 0.74 0.52 2.21E-03 134.55

M0313 13 24.24 143.40 68.17 207.78 271.87 0.54 0.83 0.55 1.32E-07 36.93

M0310 10 24.10 97.87 43.20 253.05 169.18 0.60 0.66 0.53 1.95E-05 45.25

M0329 7 24.09 124.11 54.00 253.67 190.90 0.66 0.79 0.61 6.71E-05 74.20

M0320 9 24.24 249.95 41.01 306.00 246.75 1.09 0.64 0.51 6.45E-04 77.46

Means 24.08 115.91 53.35 223.30 187.12 0.67 0.75 0.49 5.44E-04 74.56
80

Table 4. Circadian Rhythm Metrics (Means) for Personnel during the MER Regime

ID

No.

days

PeriodAmplitude%

Rhythm

Acrophase

Degrees Mesor

Relative

Amplitude

Goodness of

fit

Interdaily

Stability

Rhythm

Sig Prob 95% CL

M0303 13 25.29 129.40 27.93 240.55 123.82 1.09 0.60 0.32 4.90E-07 52.93

M0304 8 24.83 150.28 25.50 309.69 134.46 1.12 0.62 0.53 2.74E-05 49.03

M0309 12 24.82 256.20 47.28 328.02 198.46 1.30 0.78 0.68 3.82E-08 20.85

M0332 18 24.53 108.04 45.93 225.58 123.82 0.90 0.77 0.51 2.44E-10 21.35

M0325 6 24.68 101.08 49.99 285.63 97.45 1.08 0.75 0.23 3.00E-04 55.74

M0313 9 25.13 238.35 48.72 259.20 227.48 1.06 0.75 0.50 1.60E-06 4.63

M0310 10 24.98 151.76 18.00 313.76 137.33 1.11 0.61 0.51 1.08E-05 31.76

M0329 7 24.58 198.17 50.79 310.15 169.18 1.16 0.77 0.74 3.76E-04 33.75

M0320 9 24.68 164.04 52.81 256.41 195.39 0.85 0.75 0.51 3.80E-05 31.01

Means 24.84 166.66 39.27 284.07 151.50 1.10 0.71 0.50 8.38E-05 33.45

Table 5. Circadian Rhythm Variability (Standard Deviation) for MER Personnel

during Baseline before the Rovers Landed

ID Amplitude

%

Rhythm

Acrophase

Degrees Mesor

Relative

Amplitude

Goodness of

fit

M0303 34.60 11.35 106.92 24.75 0.34 0.11

M0304 24.89 16.01 19.57 53.87 0.12 0.09

M0309 34.59 17.24 41.41 81.41 0.13 0.11

M0332 36.33 17.54 144.26 27.51 0.27 0.10

M0325 36.33 17.54 144.26 27.51 0.27 0.10

M0313 43.29 17.19 25.81 49.44 0.15 0.09

M0310 26.33 14.77 25.58 39.24 0.18 0.11

M0329 18.91 11.71 15.54 23.87 0.16 0.05

Means 33.77 15.95 72.54 43.39 0.21 0.10

Table 6. Circadian Rhythm Variability (Standard Deviation) for

Personnel during the MER Regime

ID Amplitude

%

Rhythm

Acrophase

Degrees Mesor

Relative

Amplitude

Goodness of

fit

M0303 25.42 10.99 38.25 23.51 0.35 0.07

M0304 43.20 22.31 63.14 91.93 0.29 0.16

M0309 28.55 4.35 20.60 15.21 0.18 0.02

M0332 28.72 13.08 16.00 20.25 0.29 0.08

M0325 14.98 10.95 17.37 25.59 0.24 0.05

M0313 43.46 17.85 26.08 20.14 0.23 0.11

M0310 39.21 7.12 15.92 23.16 0.25 0.08

M0329 53.93 15.04 10.64 21.60 0.19 0.08

M0320 33.10 16.69 22.94 26.96 0.18 0.10

Means 34.51 13.15 25.66 29.82 0.25 0.08
81

APPENDIX C

Background Questionnaire

BACKGROUND QUESTIONNAIRE

This background questionnaire is being provided to all those who have

agreed to participate in the MER activity study.

DO NOT WRITE YOUR NAME ON THIS SURVEY. This will

ensure anonymity for you. This survey will be administered to as

many as 40 subjects and will be held in the strictest confidence.

For research purposes only

We very much appreciate your participation.
82

GENERAL SURVEY DIRECTIONS:

I. Please answer all questions, and please be as accurate as possible. All information

is confidential and anonymous.

II. Please mark boxes and lines as follows:

Small boxes should get a checkmark or an “x”:  √ or X

Large boxes should get a number:  204

Lines should get text: 

kids

III. Watch for special instructions relating to a question or set of questions.

A. GENERAL

1. Do you have children? 

yes no

If you answered yes, how many children do you have in each age category:

_____ < 1 yrs old _____ age 1 to 5 _____ age 6 to 13 _____ age 14 to 18 _____ > 18 yrs old

How many children will reside in your place of residence during MER operations? ________

2. Do you have a bed partner or roommate?

yes no

3. According to him or her, or to your never rarely sometimes frequently always don’t

knowledge, how often in the past less than 1–2 times 3–4 times 5–7 times know

3 months have you had: once/week week week week

a. loud snoring 

 

b. long pauses between breaths

 

while sleeping

c. legs twitching or jerking

 

d. episodes of disorientation or

 

confusion during sleep

e. other sleep problems, please describe _____________________________________
83

4. What is your usual amount/frequency daily weekly

of caffeine consumption? (Enter number

of cups of coffee/caffeinated soft drinks)

5. Do you exercise on a regular basis?

If no, skip to Section B. yes no

6. How often do you exercise? less than 1–2 times/ 3–4 times/ over 4 times/

once/week week week week

a. List type(s) of exercise you do: ______________________________________

______________________________________

B. SLEEPING AT HOME

Based on an average night of sleep at home , please give one best answer to each of the following

questions. Use your local 24-hour clock.

7. What time do you usually go to bed?

time, 24-hr clock

8. How long after going to bed do you hr and min

usually fall asleep?

9. How many times on average times

do you wake up?

10. If you wake during the night, what bathroom can’t sleep

most often awakens you? children/spouse noise

(Check ONLY one answer.) other __________________ _______

11. If you wake during the night, on hr and min

average, how long does it take you to

go back to sleep?
84

12. What is the amount of total sleep hr and min

you get on average?

13. On your days off, what time do you

usually get out of bed? Time, 24-hr clock

On your work days, what time do you

usually get out of bed? Time, 24-hr clock

14. On average, how much sleep per 24 hr hr and min

day have you obtained during the past

30 days?

15. How much sleep per 24 hr day do you hr and min

usually need to feel fully alert for the day?

16. Do you consider yourself a morning morning evening

person or evening person? person person

17. At what time of day do you feel most alert? (please shade/circle your alert times below)

0000 0200 0400 0600 0800 1000 1200 1400 1600 1800 2000 2200

18. How often do you take a nap at never rarely sometimes often very often

home? (If “never,” skip to #20.) 1-10 /yr. 1-3 /mo. 1-4 /wk 5-7 /wk

19. On average, how long are your naps? hr and min

20. How often do you have problems never rarely sometimes often very often

problems getting to sleep? 1-10 /yr. 1-3 /mo. 1-4 /wk 5-7 /wk
85

21. How often do you take medication to never rarely sometimes often very often

help you sleep? 1-10 /yr. 1-3 /mo. 1-4 /wk 5-7 /wk

22. How often do you use alcohol to help never rarely sometimes often very often

you sleep? 1-10 /yr. 1-3 /mo. 1-4 /wk 5-7 /wk

23. Overall, what kind of sleeper are you? very poor poor good very good

D. DUTY & HOUSING

24. Is your home local to JPL?

yes no

25. If “no”, what timezone do you typically live? ________

26. Where will you stay during MER operations?

house apartment hotel other

 If you selected “other”, please specify: ____________

27. Typically, how long will it take to commute to hrs mins

 JPL during MER operations?

28. If you had your own choice to schedule begin time end time

your duty day, what time would you

pick to work your shift? (24 hr clock)

E. DEMOGRAPHICS

29. Age? 

18 – 29 30 – 39 40 – 49 50+

30. Gender?

male female
86
87

APPENDIX D

Post-Study Questionnaire

POST-STUDY QUESTIONNAIRE

This questionnaire is being provided to all those who have agreed to

participate in the MER Sleep/Wake Cycles of MER Personnel in

Working a Mars Sol Schedule study, protocol HRII-03-44.

DO NOT WRITE YOUR NAME ON THIS SURVEY. This will

ensure anonymity for you and your company. This survey will be

administered to as many as 40 subjects and will be held in the

strictest confidence.

For research purposes only

We very much appreciate your participation.
88

GENERAL SURVEY DIRECTIONS:

I. Please answer all questions, and please be as accurate as possible. All information

is confidential and anonymous.

II. Please mark boxes and lines as follows:

Small boxes should get a checkmark or an “x”:  √ or X

Large boxes should get a number:  204

Lines should get text: 

kids

III. Answer these questions based on the time period during MER operations. Watch for

special instructions relating to a question or set of questions.

A. GENERAL

 1. Describe typically times and reasons ________________________________

for removing the Actiwatch.

(e.g., to shower at night) ________________________________

 2. On which wrist did you wear non-dominant dominant 

 the Actiwatch? hand hand

 3. Age 

 4. What was your usual amount/frequency daily weekly

of caffeine consumption during the

MER operation? (Enter number of cups

of coffee/caffeinated soft drinks)

5. Did you exercise on a regular basis yes no

 during MER operations?

If no, skip to Section B.

6. How often did you exercise? less than 1–2 times/ 3–4 times/ over 4 times/

once/week week week week

a. List type(s) of exercise you did: ________________________________

________________________________
89

B. SLEEPING DURING MER OPERATIONS

Based on an average night of sleep during MER operations, please give one best answer to each

of the following questions. Use your local 24-hour clock.

7. How long after going to bed did you hr and min

usually fall asleep?

8. How many times on average times

did you wake up?

9. If you woke during the night, what bathroom couldn’t sleep

most often awakened you? children/spouse noise

(Check ONLY one answer.) other __________________ _______

10. If you woke during the night, on hr and min

average, how long did it take you to

go back to sleep?

11. What is the amount of total sleep hr and min

you got on average per 24-hrs

on your days off duty?

12. What is the amount of total sleep hr and min

you got on average per 24-hrs

on your days working a Mars sol?

13. How often did you have problems never rarely sometimes often very often

getting to sleep? 1 or 2 /times 1-3 /mo. 1-4 /wk 5-7 /wk

14. How often did you take a nap? never rarely sometimes often very often

(If “never,” skip to #15.) 1 or 2 /times 1-3 /mo. 1-4 /wk 5-7 /wk

15. On average, how long were your naps? hr and min
90

16. How often did you take medication to never rarely sometimes often very often

help you sleep? (If “never,” skip to #19.) 1 or 2 /times 1-3 /mo. 1-4 /wk 5-7 /wk

17. If you took medication to help you name: __________________

sleep, please specify the medication.

18. Rate the effectiveness of the not at all moderately very

medication. effective effective effective

19. How often did you use alcohol to help never rarely sometimes often very often

you sleep? 1 or 2 /times 1-3 /mo. 1-4 /wk 5-7 /wk

20. On average, how much sleep per 24-hr hr and min

period did you obtained during MER

operations?

21. Overall, what kind of sleeper were you very poor poor good very good

 during MER operations?
91

C. FATIGUE DURING MER OPERATIONS

22. How difficult was it working a very somewhat neutral somewhat very

Mars Sol? difficult difficult easy easy

 

Please explain:

___________________________________________________________________

___________________________________________________________________

___________________________________________________________________

23. What day did you start working a Mars Sol? mm/dd/yy

24. What day did you stop working a Mars Sol? mm/dd/yy

25. Specify how your mood changed during the MER operations:

Mood Strongly

Increased

Moderately

Increased

Neither

Increased Nor

Decreased

Moderately

Decreased

Strongly

Decreased

Fatigue

Irritableness

Concentration

Energy

Sleepiness

26. List in rank order three strategies

that you used to cope with fatigue 1. _____________________________

 during the MER operations.

2. _____________________________

3. _____________________________
92

27. What did you find most challenging about your schedule?

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

28. Please add any additional comments or recommendations about MER scheduling

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________
93

APPENDIX E

Analysis of Sleep and Circadian Rhythm Metrics

from Actigraphy Data

The reliability of actigraphy in extracting reliable sleep metrics is controversial. Some studies have

validated actigraphy for number of awakenings (Lichstein, et al., 2006; Pollak, et al., 2001), total

sleep time (Lichstein, et al., 2006), sleep efficiency (Lichstein, et al., 2006; Tworoger, et al., 2005),

and total wake time (Tworoger, et al., 2005) but actigraphy has been deemed unreliable by other

studies for time in bed (Tworoger, et al., 2005), total sleep time (Vallieres and Morin 2003;

Tworoger, et al., 2005), sleep onset (Tworoger, et al., 2005), sleep efficiency (Vallieres and Morin

2003; Signal, et al., 2005a,b; Pollak, et al., 2001), sleep latency (Signal, et al., 2005a,b) and total

wake time (Vallieres and Morin 2003). The reliability of the sleep efficiency metric was likely

dependent upon the setting of the sensitivity threshold from which actual sleep time was calculated

from the actigraphy data. We decided to use the sleep efficiency metric since its reliability was

supported by two publications (Lichstein, et al., 2006; Tworoger, et al., 2005). We selected time in

bed as the primary dependent measure of sleep for group analyses and sleep efficiency, percentage

of actual wake time, bed times and wake-up times were used to further examine sleep disturbances

observed in circadian rhythm analyses of individuals.

CIRCADIAN RHYTHM DATA ANALYSIS

Previous studies criticized the use of harmonic-based methods, especially single harmonic cosinor

analysis, for the analysis of circadian rhythms in activity, since rest-activity rhythms obtained by

actigraphy were highly non-sinusoidal, and daytime activity levels were considerably longer in

duration than nighttime activity levels, resulting in very poor fits to the data (Teicher, et al., 1997;

Van Someren, et al., 1999). In normal subjects, the goodness of fit was only 0.49 for data sampled

every five minutes (Satlin, et al., 1995). However, in another study, hourly activity measures showed

very good correlations (0.8-0.95) with cosine models, which suggests that data sampling rates may

significantly affect goodness of fit and appropriateness of given data sets for harmonic analysis.

Activity circadian rhythm goodness of fit estimates averaged 0.75 for the baseline regimes in this

study. This value was well above goodness of fit estimates reported in a previous study using 5-min

sampling (0.49, Satlin, et al.,1995) and was comparable to the highest estimates reported (0.8-0.95,

Teicher, et al., 1997). There was no doubt that the utilization of multiple harmonic fits to activity

circadian rhythm data (Van Someren, et al., 1999; Martin, et al., 2001) provides better fits to the data

and probably more reliable estimates of circadian rhythm metrics. However, in this study the

methodology employed in processing the raw activity data (clipping, robust locally weighted

regression, followed by conversion from 1-min to 5-min samples) was fully adequate to provide

extraction of reliable activity circadian rhythm wave forms which could be reliably processed by

single harmonic analysis. In addition, the utilization of non-parametric methods such as the

profilogram periodicity analysis and educed cycle analysis provided means of evaluating changes in

circadian rhythmicity which were free of potential artifacts or misinterpretation of results based

upon the parametric methods.
94
95

APPENDIX F

Transitioning from a Mars Day to an Earth Day: Effects on

Psychomotor Vigilance Performance*

Mars Exploration Rover (MER) Surface Operations personnel were required to complete mission

critical tasks on work schedules coinciding with a Mars sol (day) of 24h and 39m for a minimum of

3 months, while being exposed to 24h Earth-based exogenous cues. Once operations extended

beyond 3 months, personnel were required to revert back to an Earth-day schedule. Research has

shown that changes in circadian rhythms and accumulated sleep loss caused by disruptions in

sleep/wake cycles can lead to reduced daytime alertness and impaired neurobehavioral performance.

The purpose of the current study was to document performance changes associated with

transitioning from a 24h and 39m Mars sol to a 24h Earth day.

Ten participants (8 male, 2 female) MER Surface Operations personnel aged 27–54 (M = 39.7, SD =

8.73) performed the Psychomotor Vigilance Task (PVT), 10-minute simple, visual reaction time

task. Trials were self-administered prior to major sleep periods, when time permitted. Actigraphy

(Minimitter, OR) and sleep logs with work schedules were collected. Participants completed

between 9 and 32 trials over the protocol period. However, three participants did not complete a

sleep log and/or work schedules and therefore, only data from seven participants were analyzed. The

following variables were examined:

• number of lapses during trials and progressive changes thereof over the protocol period

• mixed effects ANOVA with age and recency of work

• subjective reports of symptoms indicative of sleep problems (loud snoring, long pauses

between breaths, and/or leg twitching or jerking)

• sleep/wake estimates obtained from actigraphy (Actiware-Sleep version 3.3) and verified with

sleep log.

Results found that four participants showed degradations over the protocol period reaching levels of

> 4 lapses (RT > 500ms) per trial. Two participants showed progressive degradations in performance

over time [t(21) = 4.7, p < 0.01; t(9) = 3.4, p < 0.01; see Figure 21].

*Brandt, S.L., L.M. Colletti, H. Van Dongen, D.F. Dinges, and M.M. Mallis. Associated Professional

Sleep Societies, 19th Annual Meeting, Denver, CO, June 18-23, 2005. Sleep 28: A356.
96

Figure 21. Raw lapse count on the PVT for participants D and E showing severe,

progressive degradations in performance over the protocol period. Cumulative time

is in hours.

A mixed effects ANOVA suggested that age and recency of work, which was defined as whether the

participant worked in the preceding wake period, did not contribute significantly to performance

impairment [F(1,98) = 1.10, p = .30; F(1,98) = 0.10, p = .75]. Four participants reported subjective

symptoms indicative of sleep problems occurring 1-2 times per week or more (loud snoring, long

pauses between breaths, and/or leg twitching or jerking). Two of these participants were the same

participants who showed progressive degradations in performance over the protocol period (as

shown in Figure 21).

Although only descriptive, Figure 22 demonstrates potential variance in sleep/wake patterns between

an impaired and a non-impaired participant based on available sleep/wake data.

Figure 22. Bed times and wake up times for one impaired participant (G) and one

non-impaired participant (H).

Overall, data indicate wide variability in performance across participants transitioning from a Mars

sol to an Earth day. It is unclear whether the variability was due to transitioning from a Mars sol to

an Earth day or some other factors. Age and recency of work did not significantly affect

performance. There is slight evidence that sleep problems and sleep/wake patterns were contributing

factors to the degraded performance; however, the limited number of samples per day precludes

conclusive statements
97

This unique opportunity to collect data on MER Surface Operations personnel in the field was

limited by operational constraints in this field study. In order to make conclusive statements about

performance changes associated with the transition from a Mars sol to an Earth day, a more

controlled study including baseline and post-study measures as well as better experimenter control

over data acquisition is needed.
98
99

APPENDIX G

Participant Demographics

ID Age

Category

Gender Home

Time Zone

Date

started

MER

rotation

Date

ended

MER

rotation

M0301 30-39 male Pacific 1/3/2004 4/4/2004

M0302 50+ male Pacific 1/8/2004 2/23/2004

M0303 50+ male Central 1/3/2004 3/29/2004

M0304 18-29 female Central 1/4/2004 4/11/2004

M0305 30-39 male Pacific 1/30/2004 4/2/2004

M0306 18-29 female Pacific 1/3/2004 2/8/2004

M0307 50+ male Eastern 1/4/2004 4/5/2004

M0308 18-29 male Pacific 1/15/2004 4/5/2004

M0309 18-29 male Eastern

M0310 18-29 male Mountain 1/3/2004 3/26/2004

M0312 50+ male Arizona 1/2/2004 4/11/2004

M0313 18-29 male Central 1/3/2004 3/29/2004

M0314 40-49 male Eastern

M0315 40-49 male Eastern 1/3/2004 4/15/2004

M0316 18-29 male Arizona 1/3/2004 4/1/2004

M0317 50+ male Pacific 1/2/2004 5/15/2004

M0318 40-49 male Eastern 1/4/2004 4/15/2004

M0319 40-49 male Pacific 1/3/2004 4/12/2004

M0320 18-29 female Pacific 1/28/2004 3/20/2004

M0321 30-39 male Pacific 1/4/2004 3/24/2004

M0322 18-29 female Pacific

M0323 40-49 male Pacific 1/6/2004 3/13/2004

M0324 18-29 female Eastern 1/3/2004 4/9/2004

M0325 30-39 male Pacific 1/5/2004 4/5/2004

M0326 30-39 female Pacific 1/4/2004 3/6/2004

M0329 30-39 male Pacific 1/4/2004 4/10/2004

M0330 30-39 female Pacific 1/21/2004 4/1/2004

M0331 30-39 male Pacific 1/4/2004 4/3/2004

M0332 40-49 male Pacific 1/6/2004 3/29/2004

M0333 30-39 male GMT -3 1/3/2004 3/10/2004

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4. TITLE AND SUBTITLE

The Effects of the Mars Exploration Rovers (MER) Work Schedule

Regime on Locomotor Activity Circadian Rhythms, Sleep and

Fatigue

5a. CONTRACT NUMBER

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6. AUTHOR(S)

Charles W. DeRoshia, Laura C. Colletti, Melissa M. Mallis

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711-70-02

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NASA Ames Research Center

Moffett Field, California 94035-1000

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650-604-1345. An electronic version of this document can be found at http://ntrs.nasa.gov

14. ABSTRACT

This study assessed human adaptation to a Mars sol by evaluating sleep metrics obtained by actigraphy and

subjective responses in 22 participants, and circadian rhythmicity in locomotor activity in 9 participants

assigned to Mars Exploration Rover (MER) operational work schedules (24.65 hour days) at the Jet

Propulsion Laboratory in 2004.

During MER operations, increased work shift durations and reduced sleep durations and time in bed were

associated with the appearance of pronounced 12-hr (circasemidian) rhythms with reduced activity levels.

Sleep duration, workload, and circadian rhythm stability have important implications for adaptability and

maintenance of operational performance not only of MER operations personnel but also in space crews

exposed to a Mars sol of 24.65 hours during future Mars missions.

15. SUBJECT TERMS

Circadian rhythms, Mars Exploration Rover, Sleep, Fatigue, Actigraphy, Locomotor activity, Circasemidian

rhythm

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