Implementation of Digital Vital Sign Monitoring to Decrease Sleep Interruption and Enhance Recovery in Phase II of the PROmoting Sleep, Patient Engagement and Recovery (PROSPER) Project
Phase: PHASE2
Conditions: Sleep
Sponsor: M.D. Anderson Cancer Center

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Title:  Implementation of digital vital sign monitoring to decrease sleep interruption and enhance recovery in Phase II of 
the PROmoting Sleep, Patient Engagement and Recovery (PROSPER) project 
Study Co-Chairs: Vijaya Gottumukkala, Diwakar Balachandran & Eileen Hacker 
Collaborators: Anaeze C. Offodile II, Juliann Truver, Samuel Swanson, Josiah Halm, Kodwo Dickson, George Chang, 
Michael White, Mary Welch, Daniel Mullen, Nicole Vaughan-Adams, Darrell Puckett, Kristofer Jennings, Delmy Vesho 
Important Note: This protocol contains a parent trial (main study) and a substudy component. 
PROSPER (Parent Trial) 
1.0 Objectives 
1.1 Primary Objective: Investigate the impact of our sleep enhancement intervention, which entails a wearable digital 
vital sign monitoring device, on variation in the quality of life of acute care cancer patients 
1.2 Secondary Objectives: 
a.
Assess the impact of our sleep enhancement intervention on acute care utilization e.g. length of stay, 30-day
readmissions
b.
Assess the implementation and feasibility of wearable digital vital signs capture in an inpatient oncologic
setting using validated instruments such as system usability scale (SUS) and Weiner implementation outcomes 
(AIM, FIM, IAM).
c.
Assess the impact, via validated survey instruments, of our sleep enhancement intervention on patient mood,
patient activation and satisfaction with inpatient stay experience.
2.0 Background/Rationale 
Post hospitalization syndrome refers to a generalized increased risk for a range of adverse health related events 
following an acute hospitalization.1 The mechanism is thought to be driven by allostatic and physiologic stress acquired 
during the hospitalization.2 Sleep disruptions, deconditioning from acute illness, loss of personal autonomy, pain, and 
disruption of normal circadian rhythms are prominent examples.2 Numerous reports have documented the prevalence 
and risk of these perturbations. The clinical impact of post-hospitalization syndrome is also being increasingly 
recognized. This includes but is not limited to immune dysfunction, coagulopathy, impaired physical coordination and 
poor wound healing.3–5 According to estimates, up to 20% of Medicare beneficiaries (approx. 2.6 million seniors) are 
readmitted within 30 days for reasons unrelated to the diagnosis that prompted the index admission.6 As a result, 
post-hospitalization syndrome has become the impetus for numerous quality improvement (QI) initiatives that 
promote successful recovery. A prominent example involves a multi-level QI project implemented at Ochsner clinic 
that bundled reduced nighttime noise, passive vital monitoring, delayed morning phlebotomy and use of red-enriched 
lighting after sunset.2 This program was associated with reduced length of stay (LOS), improved readmission rates and 
enhanced sleep quality across a sample of 3425 consecutive medical-surgical patients.2 However, it is unclear whether 
and to what extent these findings are generalizable to a population of acute care cancer patients. Phase I of the 
PROSPER protocol involved the use of various standard of care methods to minimize sleep interruptions during the 
hours or 10pm to 6am including, noise monitoring and reduction strategies, minimizing bedtime medication 
administration, delayed early morning phlebotomy, and use of red-light instead of white light when provider-patient 
interactions were necessary. These interventions were bundled into a “clustered care approach” along with 
eliminating nocturnal vital sign check when possible.  Clustered care has been shown to improve sleep quality, improve 
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patient satisfaction and clinical outcomes. One concern with clustered care regarding avoiding intermittent nocturnal 
vital sign checks is that these sleep interruptions are justified by the need to anticipate and prevent clinical  
deterioration. In this proposal we aim to use an FDA-approved digital vital sign motioning device (ViSi Mobile, Sotera 
Wireless, San Diego CA) to replace manual nurse bedtime vital sign check allowing for vital sign evaluation at the 
clinically appropriate cadence without compromising sleep quality. 
 
This FDA approved digital wearable technology that continuously monitors vital signs has previously been 
demonstrated in peer-reviewed studies to improve outcome measures such as decreased need to call rapid response 
teams, ICU transfer, length of stay, and 30-day mortality.  To our knowledge, the effects of substituting digital 
motoring for manual nocturnal vital sign checks, and the effects of reducing / eliminating sleep interruptions on sleep 
quality and quantity in an acute oncologic setting has not been previously reported.  Our aim is to investigate the 
impact of the Phase II of our “PROSPER”  bundle with the addition of digital passive vital sign monitoring to replace 
routine nocturnal vital sign checks on sleep hygiene (quantity and quality), clinical outcomes (LOS, readmission) 
and patient-centered outcomes (patient experience and satisfaction) in an acute oncologic patient population.  
 
Hypothesis 
We hypothesize that the implementation of our wearable technology intervention will result in a reduced LOS, lower 
30-day readmission rates, and improved patient recovery (sleep quality and quantity), and enhanced subjective 
measures of patient engagement (PAM-13), satisfaction (HCAHPS) and well-being (EQ-5D-5L). 
 
3.0 Research Plan  
3.1 Study Population  
This study will include consented consecutive patients who are admitted to the medical-surgical floor at MD Anderson 
Cancer Center (Texas Medical Center location only) for the conditions and/or surgical procedures outlined below.  
 
3.2 Inclusion Criteria  
Adult patients (> 18 years of age), English-speaking, able to complete consent and survey materials. Patients must be 
admitted to the hospital for at least 3 nights. Patients using pharmacological sleeping aides will be included in the 
study, as this will help us understand if the intervention also helps patients with pre-exisitng sleep disturbances. 
 
3.3 Exclusion Criteria  
Vulnerable populations (pregnancy, incarcerated, history of delirium, suicidal ideation, ischemic stroke with 
measurable neurologic deficit, cerebral palsy, seizures).Patients concurrently enrolled in contact isolation with  SARS-
CoV2 will be excluded. Lastly, hospice or hospice-bound patients will also be excluded. Any patient can remove 
themselves from the study at any point.  
 
Table 1: Study participants 
Participating Surgical Services 
Participating Medical Services 
Colorectal Surgery 
Hospitalist service 
 
3.4 Study Design 
This is a pragmatic, quasi-experimental study wherein patients admitted for any of the aforementioned conditions will 
be allocated into treatment (ViSi mobile plus PROSPER) and control (PROSPER only) based on non-critical care bed 
availability on a floor unit. Specifically, for patients on colorectal service admitted to P10 floor, one side (“unit A”) will 
be designated as an intervention arm while the other (“unit B”) will serve as a control arm. For patients admitted to 
Hospitalist Medicine service; patients on “ unit G21”  will be designated as the intervention arm, and patients admitted 
to “unit G22” as the control arm. Our pseudorandomization scheme is based on the naturally occurring random 
variation in bed assignment by availability across both the intervention and control units on a given floor, or floors at 
the time of patient enrollment. Since bed designation is made by hospital admissions based on availability, it is 
independent of the care delivery processes across the study floors / units. Our approach is intended to reduce the 
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burden on frontline staff, i.e., changing existing workflows to randomize patients and track allocation in a database. It 
also serves to reinforce the embedded intent of our QI work.7 Participants, family members and clinicians will not be 
blinded to the intervention, and given our pragmatic design, we will not attempt to control patients’ communication 
or information-seeking behaviors beyond the pseudo-randomization described above. The intervention wing will 
employ the following initiatives to improve the sleep quality, level of engagement, and overall care journey of patients. 
 
1. Passive digital monitoring of vital signs – Blood pressure, heart rate, respiratory rate, oxygen saturation, and 
temperature – will be wirelessly and passively collected using a U.S. Food and Drug Administration-approved 
wireless technology, which is worn on the wrist and chest (ViSiMobile, Sotera, San Diego, Calif), and recorded 
data will be sent directly to  the  electronic  medical  record (Epic Systems Corporation, Verona,Wis)  at 
protocolized set intervals.  Abnormal vitals will trigger a nursing alert via text message or page. If an in-person 
assessment is warranted, handheld red-spectrum lighting will be used by nursing staff as an alternative to the 
standard overhead white lighting.2  
 
An intervention checklist will be created to ensure consistent adherence and documentation. Monthly feedback 
sessions will be held with the respective care teams to solicit frontline feedback with respect to implementation issues. 
Patients in the control arm will receive routine care per hospital routine.  
 
3.5 Recruitment 
Potential participants admitted to participating floors in the study will be identified in the EMR. Research coordinators 
will approach patients to participate in the study within 1-2 days of admission. The recruitment process will be 
documented and attested by the Investigators and their delegates.  
 
3.6 Informed Consent 
Informed Consent from eligible patients will be obtained following the Clinical Research Informed Consent SOP 4.0. 
 
3.7 Patient Allocation to study groups 
There is generally one patient per room and patient assignment to a room is largely driven by unit census (i.e., bed 
availability) as well as other clinical factors: e.g., need for an isolation room, nursing availability.  All of these factors 
vary independently of the preferences of the patient or research team, reducing the risk for systematic bias. The unit 
of randomization is by pod on a floor. Research coordinators as part of the “on-boarding process” will also provide 
culturally competent instructions and in-person teaching for device use. 
 
3.8 Data collection  
This will entail a blend of Epic medical record abstraction and primary data collection using validated surveys. Example 
of variables to be exstracted from the medical record include patient demographics (e.g., age, sex, race), primary 
diagnosis (cancer type), body mass index (BMI), timing of AM phlebotomy, comorbid conditions (used to compute 
Charlson co-morbidity index), length of stay, and 30-day readmission. To reduce survey burden for patients and ensure 
parity in scope of responses across the 2 groups, the majority of questionnaires will be distributed at two time points 
- at admission and within 24 hours of the date of discharge. All validated surveys will be administered via the hospital-
owned iPad device, via text message or email, or recorded manually. A password-protected database will be used to 
collate the clinical and outcomes data collected. Lastly, all patients will receive a phone call 24 hours after discharge 
to ensure all questions are answered and follow-up care has been arranged.  
 
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The digital device is worn on the wrist, like a watch, and provides 
continuous vital sign monitoring using a three-lead system that 
included electrocardiography (ECG), pulse oximetry, continuous 
noninvasive blood pressure, mean arterial pressure, temperature, 
respiratory rate, heart rate, and pulse rate. Patients will be able to 
view their vital signs on the wrist-worn device’s display screen.  
Pre-programmed alarm parameters for abnormal vital signs will be 
established by the research team in close collaboration with the 
clinical and nursing leadership of each participating service (Table 
1).2 Nurses will be alerted to abnormal vital signs via secure machine 
to nurse messaging via a Sotera app on the iPhone device that is 
carried by each nurse. This workflow is consistent with published 
literature.2 Continuous monitoring data from all patients wearing 
the ViSi Mobile monitor will be recorded directly into the electronic 
medical record at intervals determined by the clinical team. (EPIC, 
Epic Systems, Verona WI) and will also be temporarily stored in an 
on-premise server. The ViSi Mobile data will be periodically 
accessed and downloaded into a virtual private cloud operated by 
Sotera Wireless on Amazon Web Services. This data does not 
include protected healthcare information and is deidentified. It 
includes all vital signs, device data, user interactions, and full 
waveform data, including high frequency ECG, PPG, and 
accelerometer data. This information will be attributed to a unique patient via time stamp and the ViSi Mobile device 
ID of the wrist monitor. The device ID must be recorded for all consenting patients in the EMR. Sotera Wireless 
research staff will access the cloud data and apply signal processing and machine learning algorithms to quantify 
metrics and events from the continuous monitoring data during sleep. MD Anderson staff will annotate events, collect 
patient reported outcome responses (Table 2 and 3). Also, in addition to vitals and alarms the MD Anderson EMR will 
be configured to record VISI Mobile Device ID for each patient placed on continuous monitoring. The device ID will 
allow patient data in the Sotera cloud server to be linked to patient data in the EMR by MD Anderson staff. Sotera will 
have access to designated MD Anderson research staff and clinicians to collaborate on the development of novel 
metrics, graphic reports, and explore how patient care would benefit from clinically meaningful alerts triggered by the 
new information. Access to MD Anderson research staff & clinicians will include a minimum of 4 hours of clinical 
collaboration and consultation on a quarterly (3-month) basis throughout the duration of the proposal. MD Anderson 
staff will have the ability to correlate this new information with EQ-5D scores, PAM-13 scores, HADS scores, Richard 
Campbell Questionnaire responses, and implementation outcome surveys.   
 
3.9 Outcomes  
Patient-Centered Outcomes (Primary): The present study will be powered to the EQ-5D measure at discharge, our 
primary outcome, which allows us to discern variations in self-reported quality-of-life over the course of the 
inpatient care episode that is attributable to our intervention (Table 2) 
 
Table 2: Patient Reported Outcomes 
Primary Outcome 
Instrument 
Duration 
Analysis level 
Assessment Calendar 
Quality of Life 
EQ-5D-5L 
2-5 min 
Patient 
Admit, discharge +/- 1 
day 
Secondary Outcome Instrument 
Duration 
Analysis level 
Assessment Calendar 
Patient Engagement 
 PAM-13 
3 min 
Patient 
Admit, discharge +/- 1 
day 
Patient Mood 
Hospital Anxiety & 
Depression Survey (HADS) 
2-5 min 
Patient 
Admit, discharge +/- 1 
day 
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Patient Satisfaction  
1. HCAHPS 
2. Net Promoter Score 
7 mins 
2 mins 
Floor unit 
Patient 
4x a year (rolling) 
Discharge +/- 1 day 
Sleep Perception 
Richard Campbell Sleep 
Questionnaire (Daily) 
1 min 
Patient 
Admit, discharge +/- 1 
day 
PAM: Patient Activation Measure; EQ-5D-5L: EuroQol – 5 dimension – 5 level; HADS: Hospital Anxiety and Depression Scale; HCAHPS – Hospital 
Consumer Assessment of Healthcare Providers and Systems 
 
Additional  Outcomes:  
a. Acute care utilization: Median length of stay (LOS) in hours, all-cause Emergency Room (ER) visit, and hospital 
readmission within 30 days postoperatively  
 
b. Implementation and feasibility outcomes: We will also perform psychometric assessments, from the perspective 
of patient and nursing stakeholders, of the implementation process and perceived effectiveness associated with 
our passive vital sign monitoring intervention. Three 5-item validated implementation metrics of Acceptability 
(AIM), Appropriateness (IAM), Feasibility (FIM) will be used to monitor and evaluate the adoption of our passive 
vital signs monitoring intervention.8 We will also leverage the system usability scale (SUS) to examine the 
usability and ergonomics of the passive vital sign monitoring platform (Table 3).9 
 
Table 3: Implementation Outcomes 
Outcome 
Instrument 
Duration 
Analysis level 
Assessment Calendar 
Intervention 
usability 
System Usability Scale 
5 mins  
Patient, Nurse 
Admit, discharge +/- 1 
day 
Intervention 
acceptance 
Acceptability of Intervention 
measure (AIM) 
3 min 
Patient, Nurse 
Admit, discharge +/- 1 
day 
Intervention 
feasibility 
Feasibility of Intervention 
Measure (FIM) 
2-5 min 
Patient, Nurse 
Admit, discharge +/- 1 
day 
Intervention 
appropriateness 
Intervention Appropriateness 
Measure (IAM) 
2-5 min 
Provider, 
Nurse 
Admit, discharge +/- 1 
day 
 
c. Secondary patient-reported outcomes: Validated surveys that capture patient mood (HADS), engagement with 
care (PAM), and satisfaction with inpatient stay (HCAHPS, Net promoter score), outlined in Table 2 as well, will 
also be administered to intervention and control patients according through a pre-specified schedule. 
 
d. Sleep interruption and relationship to patient outcomes:  Sleep interruptions between the control and intervention 
arms will be collected electronically from the hospital Electronic Medical Record  
 
3.10 Statistical Analysis Plan 
Means with standard deviations, frequencies and percentages will be used to convey descriptive statistics. Standard 
tests of association (chi-squared, t-tests) will be used for comparing characteristics between the control and 
intervention arm.  Inverse probability weighting using propensity scores modeled from baseline covariates and 
instrumental variable analysis will be the quasi-experimental approach used to minimize any confounding bias from 
our pseudorandomization process. We will model most outcomes with weighted multivariate generalized mixed 
effects regression models, focusing primarily on estimate the average treatment effect.  The effect of unit will be 
modeled using a random intercept term.  Care utilization outcomes will be compared using standard time-to-event 
models and log-rank testing.  Final analyses will be performed when all data has been entered, cleaned, and locked.   
 
All analyses will be based on an intent-to-treat framework.  For missing covariate values, a multiple imputation 
approach will be used.  Outcomes will be assumed to be missing at random; a sensitivity analysis will be conducted to 
assess the robustness of significant findings to different magnitudes of missing data mechanisms. 
 
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3.11 Sample size justification 
Preliminary calculations, based on an estimated conservative change of 10% from baselines scores in QOL, as 
measured by the EQ-5D instrument total score (with a baseline of mean of 13 units and standard deviation of 5 units).  
We expect to detect a significant change with at least 80% power using 250 patients per treatment arm.  To account 
for increased estimation variance due to propensity score-based reweighting (and a small rate of drop-out after 
enrollment), we plan to enroll 350 patients per treatment arm to ensure a reasonable expectation of significance. 
 
4.0 Sub-Study 
This pilot sub-study is intended to compliment the parent trial by generating objective measures of sleep quality 
(sleep time, sleep latency, efficiency, awakenings after sleep onset) and leveraging additional patient-reported 
outcome measures (i.e. PROMIS) in this smaller population of consented patients. The present sub-study is nested 
within the parent trial, i.e. eligible patients for the parent trial will be eligible for the sub-study.  
 
4.1 Hypothesis 
a. Primary Hypothesis: Participants assigned to PROSPER with digital vital sign monitoring will trend toward 
improved objective sleep parameters compared to participants assigned to PROSPER without digital vital sign 
monitoring. 
 
b. Secondary Hypothesis:  Participants assigned to PROSPER with digital vital sign monitoring will trend toward 
fewer sleep disturbances and reduced fatigue compared to participants assigned to PROSPER without digital 
vital sign monitoring. 
 
4.2 Objectives 
a. Primary Objective: Determine the preliminary effects of the PROmoting Sleep, Patient Engagement and 
Recovery (PROSPER) intervention with digital vital sign monitoring (experimental group) compared to 
PROSPER without digital vital sign monitoring (control group) on objective sleep parameters (total sleep time, 
sleep latency [minutes until sleep onset], sleep efficiency, frequency and duration of awakenings after sleep 
onset [WASO], and number of awakenings). 
 
b. Secondary Objective: Determine the preliminary effects of the PROSPER intervention with digital vital sign 
monitoring (experimental group) compared to PROSPER without digital vital sign monitoring (control group) 
on patient reported symptom outcomes (real-time fatigue, fatigue [PROMIS] and sleep disturbance 
[PROMIS]).  
 
4.3 Research Plan 
a. Study Population: In order to participate in the sub-study, participants must be enrolled in the PROSPER trial. 
We plan to enroll 100 participants from the parent trial in this pilot study, 50 participants from each arm of 
the parent trial. The inclusion and exclusion criteria for the sub-study are the same as the parent trial.  
 
b. Inclusion criteria: Adult patients (> 18 years of age), English-speaking and able to complete consent and survey 
materials are eligible to participate. Potential participants must be admitted to the hospital for an anticipated 
minimum of 3 nights.  
 
c. Exclusion criteria: Vulnerable populations (pregnant, incarcerated, history of delirium, suicidal ideation, 
ischemic stroke with measurable neurologic deficit, cerebral palsy, seizures), primary brain tumor/metastases 
patients) are not eligible. Patients concurrently enrolled in contact isolation with SARS-CoV2 will be excluded. 
Lastly, hospice or hospice-bound patients will also be excluded. Any patient can remove themselves from the 
study at any point. 
 
4.4 Study Methods 
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a. Study design: The pilot sub-study will be integrated into the parent trial. This pilot study leverages a two-
group, pre-post test design to conduct preliminary tests for differences between the experimental arm 
(PROSPER intervention with digital vital sign monitoring) and the control arm (PROSPER intervention without 
digital vital sign monitoring) on objective sleep parameters and patient reported sleep disturbances and 
fatigue.  
 
b. Recruitment: Once participants are enrolled in the parent trial, they will be evaluated for inclusion in the pilot 
sub-study based on the availability of the device that measures objective sleep parameters (Actiwatch 
Spectrum Pro®). Thirty wrist actigraphy devices (Actiwatch Spectrum Pro®)  are available for use at any given 
time. Fifty participants from the patient care units designated as the intervention arm for the parent trial will 
be enrolled as treatment patients in the substudy; fifty participants from the patient care units designated as 
the control arm will be enrolled as control patients in the substudy.  Participants will be consecutively enrolled 
in the sub-study as they are enrolled in the parent study although enrollment may pause if wrist actigraphy 
devices are not available. 
 
c. Instrumentation: Objective sleep parameters will be assessed with the Actiwatch Spectrum Pro® during the 
course of hospitalization, consistent with the parent trial. Patient-reported sleep disturbances will be 
measured with the PROMIS Sleep Disturbance Adult Short Form. Patient-reported fatigue will be measured in 
real-time with a one-time global fatigue intensity rating over the course of the hospitalization and the PROMIS 
Fatigue Adult Short Form. The PROMIS instruments will be completed upon enrollment and discharge +/- 1 
day, consistent with the parent trial.  
 
d. Sleep: The Actiwatch Spectrum Pro (Phillips Respironics, Bend, OR) will be used to objectively assess sleep 
parameters. Sleep parameters will be computed using Actiware® software (V. 6.0). Data are stored in one-
minute epochs. The software scores each epoch as wake or sleep. The software assigns rest, sleep, or active 
intervals based on activity counts. The default software settings will be used for the analysis as recommended 
by the manufacturer. Using an automated process, the software determines sleep start by searching for the 
first 10 minutes during which no more than one epoch is scored as “wake.” The sleep parameters produced 
include total sleep time, sleep latency (minutes until sleep onset), sleep efficiency, frequency and duration of 
awakenings after sleep onset (WASO), and number of awakenings. Sleep variables will be scored for each 24-
hour period, and averages over the course of hospitalization will be computed for nighttime sleep alone and 
nighttime sleep plus daytime naps. Wrist actigraphy is highly correlated with polysomnography, the gold 
standard for assessing sleep.1 Compared to polysomnography, wrist actigraphy is highly accurate (86%) and 
sensitive (96%) for detecting sleep.2 It has been successfully used to assess sleep and sleep disturbances across 
a wide range of populations, including people with cancer.3-5 Subjective sleep disturbances will be measured 
using the PROMIS Sleep Disturbance Adult Short Form described in detail below.6 
 
e. Fatigue: Fatigue will be measured with a one-item fatigue intensity scale, using computerized ecological 
momentary assessment (real-time assessment) and the Fatigue Adult Short Form (PROMIS instrument).7 The 
Actiwatch Spectrum Pro (Phillips Respironics, Bend, OR) contains a subjective event marker on the face of 
the activity monitor. This subjective event marker will be used as a real-time, single-item, global, self-report 
scale to measure real-time fatigue intensity. The fatigue intensity scores range from 0 (no fatigue) to 10 
(worst). This rating of fatigue is particularly attractive for use in fatigued people due to ease of use and ability 
to capture repeated real-time measurements of fatigue throughout the day. While numeric intensity ratings 
do not provide information regarding the multidimensional nature of fatigue, this type of scale is suggested 
for use as a fatigue-screening device in clinical situations.8-12 A computerized ecological momentary 
assessment approach to collecting the real-time fatigue data was chosen to allow for improved 
documentation of compliance, avoid the problems with recall inaccuracies, and improve compliance.13 Data 
entered into the subjective event marker are stored in the onboard memory of the wrist actigraphy device. 
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We successfully used this computerized methodology with people with cancer, such as hematopoietic cell 
transplantation patients.14-16 Fatigue will also be measured using the PROMIS Fatigue Adult Short Form.7 The 
Adult Short Forms of the PROMIS instruments, including fatigue, are described together and in detail below.  
 
f. Sleep Disturbance and Fatigue PROMIS Instruments: In concert with the NIH’s move to utilize common data 
elements for research related to symptom science, PROMIS instruments will be used to assess patient-
reported symptoms. We will use the Adult Short Forms for fatigue7 and sleep disturbances.6 These 
instruments are reliable, valid, and precise.17,18 Computerized versions will be used. The PROMIS instruments 
use a 5-point Likert scale and employ 7-day recall. Higher scores indicate higher levels of fatigue and sleep 
disturbances. Scores on all instruments are standardized (M = 50, SD = 10) and normed to a reference (U.S.) 
population; thus, enabling normative comparisons and providing an interpretative context for scores.  
 
 
Table. Variables and Measurement 
Variable 
Measure 
Fatigue  
Patient-Reported Real-Time Assessments over course of hospitalization (Actiwatch 
Spectrum Pro) 
• 
1-item fatigue intensity scale using the subjective event marker of the 
Actiwatch Spectrum Pro 
 
Patient-Reported Questionnaires 
• 
PROMIS Fatigue Adult Short Form 
Sleep 
Disturbances 
Patient-Reported Questionnaires  
• 
PROMIS Sleep Disturbance Adult Short Form 
 
Objective Real-Time Assessment averaged over the time of hospitalization 
(Actiwatch Spectrum Pro) 
• 
Total Sleep Time  
• 
Sleep Latency (minutes until sleep onset)  
• 
Sleep Efficiency 
• 
Frequency and Duration of Awakenings after sleep onset (WASO)  
• 
Number of Awakenings 
 
 
4.4 Data Collection  
Upon enrollment to the pilot sub-study, the wrist actigraph will be placed on the non-dominant wrist. The research 
staff will review real-time fatigue intensity rating procedures and instruct participants to leave the device in place for 
the duration of the hospitalization. Participants will be directed to carry on with normal activities. They will be asked 
to rate the intensity of fatigue at that moment in time on a 0 (no fatigue) to 10 (worst fatigue) scale five times during 
each day (upon awakening, 10:00 am, 2:00 pm, 6:00 pm, and at bedtime). An audible and vibratory alarm on the wrist 
actigraph will sound at preset times (10:00 am, 2:00 pm, and 6:00 pm) to remind participants to complete the real-
time fatigue assessment. They will enter the rating directly into the subjective event marker of the wrist actigraph. 
Because of the expected time variability associated with awakening and going to bed, it will not be possible to program 
the Actiwatch Spectrum Pro® to alarm at these times. Participants will complete the PROMIS Sleep Disturbance and 
Fatigue questionnaires upon enrollment and hospital discharge +/- 1 day, in keeping with the parent trial data 
collection periods.  
 
4.5 Statistical Analysis 
Prior to analysis of the specific aims, we will clean the data, check internal consistency of scales using Crombach’s 
alpha coefficient, and check distributions using both descriptive statistics and graphical displays.   
 
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a. Primary Objective 
For each variable, differences between PROSPER with digital vital sign monitoring (the experimental group) and 
PROSPER without digital vital sign monitoring (the control group) will be calculated and reported with 95% two-
sided confidence intervals. Further exploratory analysis with ANCOVA models will incorporate covariates of 
baseline measures and length of stay to assess the relative efficiency gain of each covariate, as well as potential 
for confounding. 
 
Based on previous work by the investigators15, the confidence interval for the difference between groups should 
have expected lengths of no more than 54 (total sleep time, min), 12 (sleep latency, min), 5 (sleep efficiency, %), 
17 (frequency and duration of awakenings after sleep onset, min), and 5 (number of awakenings). 
 
b. Secondary Objective 
Descriptive data will be computed for the real-time fatigue scores, PROMIS Fatigue, and PROMIS Sleep 
disturbance. Preliminary analysis will be conducted using a two-sided independent samples t-tests comparing 
PROSPER with digital vital sign monitoring (experimental group) compared to PROSPER without digital vital sign 
monitoring (control group) over the course of the hospitalization.   
 
5.0 Risks/Benefits  
The parent study poses minimal risks to patient’s well-being in light of the fact that they will still be receiving standard of 
care per hospital routine that is augmented by novel technologies (i.e., passive vital signs monitoring). Although not 
anticipated, there may also be a minimal risk of emotional distress in patients when answering questions about depression 
and anxiety (HADS scale). However, study participation is completely voluntary, and participants may opt to skip any/all 
questions in the survey that they do not feel comfortable answering. Finally, we believe that any possible risks will be 
counterbalanced by potential benefits to study participants such as new insights, potential to help others, feelings of well-
being and an opportunity to discuss their inpatient care journey. 
 
This pilot sub-study is a minimal risk study. The risk of wearing the wrist actigraph device is minimal but may include skin 
irritation from the wristband. The risks associated with wearing a wrist actigraph are the same as wearing a wristwatch. 
In all of our studies that measured physical activity and sleep with wrist actigraphy, only one subject complained of skin 
irritation from the wristband. The skin irritation was minimal, did not require treatment, and was rectified using a different 
wristband. The subject continued in the study. No other subjects complained of skin irritation. There is minimal risk 
associated with self-reported assessments (fatigue and sleep disturbances). Providing these data may cause subjects some 
emotional discomfort. In our previous studies using these measures, no subjects reported emotional discomfort and/or 
distress.  
  
6.0 Data Confidentiality Plan 
The study team is committed to protecting patient confidentiality at all times and in all circumstances.  No patient 
identifying information will be used in the publication of findings and all information extracted from the medical record 
will be entered onto coded data sheets which will be maintained on Excel spreadsheets stored in approved locations and 
accessible by MDACC employees.  Electronic data will be strictly stored only on password protected institutional 
computers and/or electronic databases behind the MD Anderson firewall and accessible only to the PI and collaborators. 
If needed, hard copies of the coded study will be stored in a locked cabinet in the principal investigator’s or study 
manager’s office. Only the PI and the collaborators will be participating in the collection and analysis of data. No patient 
identifying information will be used in presentation or publication of this material. Upon study termination, all data, 
questionnaires and remaining identifiers will be banked indefinitely in REDCap for future use only in IRB-approved 
research. The technology vendor (Sotera Wireless) will have no access to patient confidential information (name, medical 
history, demographics, treatment history). All the vital sign data captured on the ViSi mobile passive monitoring device 
are captured and transmitted in an anonymized fashion.  
 
7.0 Data Protection 
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A contractual agreement will be reached between Sotera and MD Anderson regard data usage and will be subject to 
approval by MD Anderson legal counsel to specify the following: Unless necessary to use the Products or Professional 
Services, Hospital agrees to not submit to Company: (i) any personally identifiable information; (ii) any protected health 
information regulated by the U.S. Health Insurance Portability and Accountability Act (“HIPAA”) or any similar federal, 
state, or local laws, rules, or regulations; or (iii) any information subject to regulation or protection under the Gramm-
Leach-Bliley Act. Hospital acknowledges that any submission to Company contrary to the foregoing statement is at 
Hospital’s own risk. In performing any Professional Services, Company may receive from Hospital patient healthcare, 
billing, or other confidential patient information (“Patient Information”). Hospital will identify to Company in writing all 
such information when Hospital provides such information to Company and Company will use Patient Information so 
identified by Hospital only as necessary to provide the Professional Services to Hospital. Company will treat Patient 
Information consistently with all applicable laws and regulations and in accordance with the Business Associate Agreement 
entered by the Parties concurrently with this Addendum or immediately prior to Company performing any Service. 
Hospital represents and certifies that Hospital has received appropriate consents to provide such Patient Information to 
Company. Hospital agrees to not interrogate, or extract data from any Company product without written consent from 
Company 
 
8.0 Parent trial References 
1.  
Krumholz HM. Post-Hospital Syndrome — An Acquired, Transient Condition of Generalized Risk. N Engl J Med. 
2013;368(2):100-102. doi:10.1056/NEJMp1212324 
2.  
Milani R V., Bober RM, Lavie CJ, Wilt JK, Milani AR, White CJ. Reducing Hospital Toxicity: Impact on Patient 
Outcomes. Am J Med. Published online 2018. doi:10.1016/j.amjmed.2018.04.013 
3.  
Hoyle NP, Seinkmane E, Putker M, et al. Circadian actin dynamics drive rhythmic fibroblast mobilization during 
wound healing. Sci Transl Med. Published online 2017. doi:10.1126/scitranslmed.aal2774 
4.  
Curtis AM, Bellet MM, Sassone-Corsi P, O’Neill LAJ. Circadian Clock Proteins and Immunity. Immunity. Published 
online 2014. doi:10.1016/j.immuni.2014.02.002 
5.  
Zhang R, Lahens NF, Ballance HI, Hughes ME, Hogenesch JB. A circadian gene expression atlas in mammals: 
Implications for biology and medicine. Proc Natl Acad Sci U S A. Published online 2014. 
doi:10.1073/pnas.1408886111 
6.  
Jencks SF, Williams M V., Coleman EA. Rehospitalizations among patients in the medicare fee-for-service 
program. N Engl J Med. Published online 2009. doi:10.1056/NEJMsa0803563 
7.  
Horwitz LI, Kuznetsova M, Jones SA. Creating a learning health system through rapid-cycle, randomized testing. N 
Engl J Med. Published online 2019. doi:10.1056/NEJMsb1900856 
8.  
Weiner BJ, Lewis CC, Stanick C, Powell BJ, Dorsey CN, Clary AS, Boynton MH, Halko H. Psychometric assessment 
of three newly developed implementation outcome measures. Implement Sci. 2017 Aug 29;12(1):108 
9.  
Bangor, A, Kortum, P., Miller, J., An empirical evaluation of the system usability scale. International Journal of 
Human-Computer Interation. 2008;6(24):574-594. 
 
 Pilot Sub-Study References 
 
1. 
Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak CP. The role of actigraphy in the 
study of sleep and circadian rhythms. Sleep. 2003;26(3):342-392. 
2. 
Marino M, Li Y, Rueschman MN, et al. Measuring sleep: accuracy, sensitivity, and specificity of wrist 
actigraphy compared to polysomnography. Sleep. 2013;36(11):1747-1755. 
3. 
Hacker ED, Ferrans C, Verlen E, et al. Fatigue and physical activity in patients undergoing 
hematopoietic stem cell transplant. Oncol Nurs Forum. 2006;33(3):614-624. 
4. 
Dean GE, Abu Sabbah E, Yingrengreung S, et al. Sleeping with the enemy: sleep and quality of life in 
patients with lung cancer. Cancer Nurs. 2015;38(1):60-70. 
5. 
Hacker ED, Kapella MC, Park C, Ferrans CE, Larson JL. Sleep Patterns During Hospitalization 
Following Hematopoietic Stem Cell Transplantation. Oncol Nurs Forum. 2015;42(4):371-379. 
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6. 
Buysse DJ, Yu L, Moul DE, et al. Development and validation of patient-reported outcome measures for 
sleep disturbance and sleep-related impairments. Sleep. 2010;33(6):781-792. 
7. 
Garcia SF, Cella D, Clauser SB, et al. Standardizing patient-reported outcomes assessment in cancer 
clinical trials: a patient-reported outcomes measurement information system initiative. J Clin Oncol. 
2007;25(32):5106-5112. 
8. 
Mock V. Fatigue management: Evidence and guidelines for practice. Cancer. 2001;92(S6):1699-1701. 
9. 
Glaus A. Assessment of fatigue in cancer and non-cancer patients and in healthy individuals. Support 
Care Cancer. 1993;1:305-315. 
10. 
Berger AM, Abernethy AP, Atkinson A, et al. Cancer-related fatigue. J Natl Compr Canc Netw. 
2010;8(8):904-931. 
11. 
Denlinger CS, Ligibel JA, Are M, et al. Survivorship: fatigue, version 1.2014. J Natl Compr Canc Netw. 
2014;12(6):876-887. 
12. 
National Comprehensive Cancer Network. Cancer-Related Fatigue (Version 1.2019). 
https://www.nccn.org/professionals/physician_gls/pdf/fatigue.pdf. Accessed September 24, 2019. 
13. 
Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR. Patient non-compliance with paper 
diaries. BMJ. 2002;324(7347):1193-1194. 
14. 
Hacker ED, Ferrans CE. Ecological momentary assessment of fatigue in patients receiving intensive 
cancer therapy. J Pain Symptom Manage. 2007;33(3):267-275. 
15. 
Hacker ED, Fink AM, Peters T, Park C, Fantuzzi G, Rondelli D. Persistent Fatigue in Hematopoietic 
Stem Cell Transplantation Survivors. Cancer Nurs. 2017;40(3):174-183. 
16. 
Hacker ED, Kim I, Park C, Peters T. Real-time Fatigue and Free-Living Physical Activity in 
Hematopoietic Stem Cell Transplantation Cancer Survivors and Healthy Controls: A Preliminary 
Examination of the Temporal, Dynamic Relationship. Cancer Nurs. 2017;40(4):259-268. 
17. 
Cella D, Riley W, Stone A, et al. The Patient-Reported Outcomes Measurement Information System 
(PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-
2008. J Clin Epidemiol. 2010;63(11):1179-1194. 
18. 
Rothrock NE, Hays RD, Spritzer K, Yount SE, Riley W, Cella D. Relative to the general US population, 
chronic diseases are associated with poorer health-related quality of life as measured by the Patient-
Reported Outcomes Measurement Information System (PROMIS). J Clin Epidemiol. 2010;63(11):1195-
1204. 
 
 
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Appendix – Surveys 
 
Patient Activation Measure 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Richards-Campbell Sleep Questionnaire 
 
 
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