Research Report

Choosing the Best Mattress:
An Experiment in Testing
Whether Individuals
Choose a Bed That Leads to
Improved Sleep
Sean O. Hogan, Jack D. Edinger, Gayle S. Bieler, andAndrew D. Krystal

August 2011

RTI Press

About the Authors
Sean O. Hogan, PhD, is a former
project director in RTI International’s
Survey Research Department. He
participated in the design of thestudy and served as its project
director between 2008 and 2011.
Jack D. Edinger, PhD, is a senior
psychologist at the Veterans Affairs
Medical Center in Durham, North
Carolina.
Gayle S. Bieler, MS, is a senior
statistician at RTI International. Ms.
Bieler led design and implementationof statistical data analysis.
Andrew D. Krystal, MD, is directorof the Quantitative EEG Laboratory
at Duke University Medical Center,and an assistant professor in Duke’s
Department of Psychiatry and
Behavioral Sciences.

RTI Press publication RR-0016-1108
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Suggested Citation

Hogan, S. O., Edinger, J. D., Bieler, G. S., and Krystal, A., D. (2011). Choosing thebest mattress: An experiment in testing whether individuals choose a bed that leads
to improved sleep (RTI Press publication No. RR-0016-1108). Research Triangle
Park, NC: RTI Press. Retrieved [date] from http://www.rti.org/rtipress.

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doi:10.3768/rtipress.2011.rr.0016.1108
www.rti.org/rtipressChoosing the Best Mattress:
An Experiment in Testing Whether
Individuals Choose a Bed That Leads to
Improved Sleep
Sean O. Hogan, Jack D. Edinger, Gayle S. Bieler, andAndrew D. Krystal

Abstract
A comfortable mattress is said to be an essential ingredient in a good night’s
sleep, but we have little understanding of the effects of sleep surface on sleep
outcomes such as daytime drowsiness or energy. Most studies devoted to testing
the effects of sleep surface on sleep have been hampered by methodologicalshortcomings; these include having small numbers of subjects and evaluatinga narrow array of bedding systems. We hypothesized that motion and selfreported measures of sleep quality and outcomes would demonstrate that the
optimal mattress would differ from person to person. We hypothesized that
individuals would be able to select one mattress from among several under
showroom circumstances that would lead to optimal rest. We find that optimal
mattress firmness varies among individuals and is reflected, at least to a degree,by overnight motion. When allowed to test mattresses in a typical showroom
experience, individuals choose a mattress that does not minimize overnight
motion and maximize perceived sleep quality. This suggests that they may not
be receiving the health benefits that come from optimal rest. Therefore, bothmanufacturers and sleep scientists could improve sleep outcomes by testing
ways to help consumers select a mattress.

Contents
Introduction

2

Methods

3

Study Design and Sample

3

Statistical Methods

3

Results

6

Actigraphically Determined
Bed Ranking (Motion Bed
Rank)

6

Sleep Quality Bed Ranking

8

Self-Selected Versus
Actigraphically Determined
Best Bed
9
Discussion

10

References

11

Acknowledgments Inside back cover2

Hogan et al., 2011

Introduction
A growing body of literature indicates the effects
of sleep on health, ability to function, and qualityof life (Alapin et al., 2001; CDC, 2007; Elmenhorst
et al., 2008; Hamilton et al., 2007; Hanel, Dartman,& Shishoo, 1996; NIH, 2003a, 2003b; Roberts,
Roberts, & Duong, 2008; Van Dongen, Maislin,
Mullington, & Dinges, 2003). A comfortable mattressis commonly assumed to be an essential ingredientin a good night’s sleep (Better Sleep Council,
2008b). However, we have little understanding of
the effects of sleep surface on sleep outcomes suchas daytime drowsiness or energy. Moreover, moststudies devoted to testing the effects of sleep surface
on sleep have been hampered by methodologicalshortcomings; these include having small numbersof subjects and evaluating a narrow array of bedding
systems (Lopez-Torrez, Porcar, Solaz, & Romero,2008).
When trying to select a comfortable mattress, or
one that will provide optimal sleep, consumers
confront the decision to choose from among several
mattresses. Sources of information easily accessible
to laypersons (see for example reports by the Better
Sleep Council, 2008b; and Consumer Reports,2005) essentially tell consumers to trust their own
judgment. In other words, consumers are told to basetheir decision on an in-store experience of lying on,
sitting on, and feeling the mattresses. Unfortunately,
the scientific literature is not much more helpful
to health care providers who might wish to offer
guidance to the layperson choosing a mattress. This
literature has been silent on the extent to which
individuals should evaluate a mattress, or whether
they are capable of selecting the mattress that leads
to best outcomes, such as quality of rest, reduced
drowsiness, or increased daytime energy. In addition,
we found that the current literature typically suffersfrom three shortcomings that have deprived experts
and laypersons alike of this knowledge (Krystal,
Edinger, Bieler, Mladsi, & Hogan, 2011). These
deficiencies are that most studies of mattress effects
on sleep have relied on (1) a small number of people

RTI Press

enrolled in the study, (2) a narrow array of test
mattresses,1 and (3) a narrow focus on individuals
suffering from a chronic sleep ailment of some sort.
To help address this deficiency in knowledge, we
recruited a sample of 128 healthy adults (referred toas subjects, participants, or individuals) and asked
them to sleep on an array of seven mattresses for up
to 1 month each. We recorded measures of overnight
motion (with a sensor called an actigraph), andparticipants completed diary reports of sleep qualityand daytime function. According to the collected
data, individuals vary substantially in the degree ofmattress firmness that reduces their morning pain
and optimizes their sleep experience and subsequent
daytime functioning (Krystal et al., 2011). Alongwith validating the methods of measurement, wereported that a slight increase in sleep efficiency(actigraphically measured time devoted to sleep
actually spent sleeping) can lead to improved qualityof sleep as reported in diary observations.
In this, our second report from this study, we buildon the foundation of our earlier paper (Krystal et al.,2011). For this study, we hypothesized that overnight
actigraphic motion measurements (which indicate
sleep and awake periods during the time devoted
to sleep) and self-reported diary measures of sleep
would demonstrate that an optimal mattress could
be identified for an individual and that the “best”
mattress would differ from person to person. We also
hypothesized that changes in measured overnight
motion would coincide with other measures of sleep
and daytime functioning (e.g., as daytime energy anddrowsiness). Finally, we hypothesized that individuals
would be able to rely on conventional shopping
procedures to select one mattress from among several
under showroom circumstances that would lead to
optimal rest. Here we address these questions. As
in the last paper, we relied on a randomized, singleblind, within-subject crossover study examining
multiple levels of mattress firmness in a large sample
of individuals without complaints of pain or sleep
difficulty.

1 In this report, the terms mattress and bed are used interchangeably.

Choosing the Best Mattress

Methods
Study Design and Sample
Our previous paper explains in detail our study
procedures, sample selection, and methods ofmeasurement. We summarize our methods here to
inform readers of the basic elements of our protocol.
We recruited a convenience sample of 128 healthy
adults who lived in the Raleigh-Durham area ofNorth Carolina. Table 1 describes the age, body massindex (BMI), gender, race, and partnership statusof our sample members. None of the subjects had asleep-affecting disease, sleep-disrupting prescription
drug regimen, or lifestyle that is known to interrupt
sleep (e.g., frequent travel, infants to care for,overnight work shift).
Table 1. Descriptive statistics of study sample
Variable

Sample Size

Mean or Percentage Distribution

Age in years

128

40.4 (Range: 24.0–68.0)

BMI

128

25.9 (Range: 17.9–45.0)

Gender128

61% Female
39% Male

Race/ethnicity

128

80.5% White
13.3% African American
5.5% Asian
0.8% Native American

Partner status

128

66% slept with partner
41% were members of couples inthe study (26 couples in study)

BMI = body mass index.

Subjects slept on each of the seven test mattresses
in their own homes. The mattresses were made by
the same manufacturer, ensuring consistency inmaterials and production processes so that the only
difference evaluated would be firmness. The innerspring mattresses ranged in firmness to mirror therange typically found in the US marketplace. Eachof the subjects in this study used each mattress forapproximately 1 month. They were assigned to eachmattress using a Latin square randomization system
so that subjects were not on consecutively firmer orsofter mattresses. Nothing on the mattress would haveindicated to the subject the level of firmness when the
bed arrived at the home.
To measure sleep duration and efficiency, participants
wore a widely accepted monitor called an actigraph3(Ancoli-Israel et al., 2003; Morgenthaler et al., 2007).
The American Academy of Sleep Medicine supports
the use of actigraphic measurement to identify sleep
and wake periods (Littner et al., 2003). To measurepain, sleep quality, daytime drowsiness, and otherparameters, participants entered reports in anelectronic diary.

Self-Selection of Preferred Mattress
Before beginning the in-home part of the sleep
study, we sought to test whether the typical shopping
experience would lead subjects to choose the one
mattress that results in best sleep. We simulated ashowroom environment where all of the subjects triedeach mattress. To maintain the blind study protocol,
we arranged the seven study mattresses in random
order in the simulated showroom. The participants
were blinded to the mattress’s manufacturer,
construction materials, design, and level of firmness.
We asked them to act as though they were in themarket to buy a mattress and to select the one
they preferred. As part of the selection process,we encouraged each participant to “test drive” themattress. They were encouraged to lie on, feel, andevaluate the mattresses. Participants were allowed
as much time as they wanted to make their selection(typically they took 10 to 15 minutes) and were able
to make notes of their observations. We conducted
the mattress self-selection during participant training
in a laboratory located in Research Triangle Park,
North Carolina.

Statistical Methods
We began the analysis by ranking the mattresses(referred to in the rest of this section as “beds”), at theparticipant level, according to their average amountof overnight motion on each bed. This was measured
in terms of the number of minutes during which
the actigraph measured motion during the night,
normalized to 8 hours devoted to sleep. The measureof motion refers to the number of 1-minute intervals
in which the actigraph measured any amount of
motion, during the time from sleep onset to final
arousal, while the subject was lying in bed. We ranked
the beds for each individual from best (coded 1)
to worst (coded 7), where 1 indicates that Bed j(j=1,…,7) has least average actigraphic motion for

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Hogan et al., 2011subject i, and 7 indicates Bed j has greatest actigraphic
motion for subject i. We call the effect resulting fromthis mattress ranking the motion bed rank. Based on
this ranking, a participant’s best motion bed is themattress with the smallest average motion minutes
per night (normalized to 8 hours devoted to sleep).
We used a linear regression model to estimate andcompare average overnight motion within eachmotion bed rank category. This analysis estimates
the degree to which motion (reported in minutes)
was reduced on the best motion bed as comparedwith other beds. To conduct the linear regression
analyses, we analyzed longitudinal data fromall compliant nights on all mattresses from eachparticipant simultaneously in a general linear mixed
model (Diggle, Liang, & Zeger, 1994; Laird & Ware,
1982; Lindsay, 1993). The outcome variable wasovernight motion. The main independent variableof interest—motion bed rank—was modeled as acategorical variable for evaluating the overall motion
bed rank effect and as a continuous variable for
evaluating a trend effect. The MIXED procedure in
SAS Version 9.1.3 (SAS Institute, 2006) was used toaccount for the correlation of mattresses and nights
within participants (Brown & Prescott, 2006; Senn,
2002). Statistical significance refers to p < 0.05, and all
p-values are two-sided.
We used a similar regression modeling approachto evaluate the effect of motion bed rank on eight
key diary outcomes, except in these models wealso adjusted for the participant’s age, gender, BMI,
ordinal day on bed, and time spent in bed (alsoreferred to as time devoted to sleep). The diaryoutcomes are as follows: self-reported sleep time,number of overnight awakenings, minutes awake
overnight, sleep quality, level of restedness at startof day (also referred to as well-restedness at start ofday), pain upon waking (also referred to as morningpain), daytime sleepiness, and daytime energy. Sleep
quality, restedness, morning pain, daytime energy,
and daytime sleepiness are self-reports using a
7-point Likert-type scale from least (1) to greatest(7). Minutes awake were categorized and reported
using an ordinal scale (1=0 minutes, 2=1-15 minutes,
3=16-30 minutes, 4=31-45 minutes, 5=45-60 minutes,
6=60+ minutes). Self-reported sleep time wasrecorded in minutes.

We evaluated the overall effect of motion bed rank
and the trend across bed rank on each of the diaryreported sleep outcomes. We also performed pairwise
comparisons among motion bed ranks (best motion
bed vs. second-best bed; best motion bed vs. averageof all others) and estimated model-adjusted means(also known as least square means) within each motion
bed rank category.
An example of the linear regression equation for one
sleep outcome, self-reported number of awakenings,
is as follows:
Number of Awakeningsijk= β0 + (β1 x Motion Bed Rankij)+ (β2 x Ordinal Day on Bedijk)+ (β3 x Time in Bedijk) + (β4 x Agei)+ (β5 x Genderi) + (β6 x BMIi)+ (β7 x Study Period 1ijk)+ …+ (β12 x Study Period 6ijk) ,where β0— β12 are the regression coefficients to be
estimated. The response and independent variables aredefined as follows:• Self-reported number of overnight awakenings(response variable measured by the diary, forsubject i, bed j, night k).
• Motion bed rank (coded 1–7 for subject i, bed j,modeled as categorical or continuous, depending onwhether the hypothesis is to evaluate an overall
effect of motion bed rank or to evaluate trend acrossmotion bed rank).
• Ordinal day on bed (this measures the acclimation
effect, coded 1 to number of days on bed, modeled
as continuous, for subject i, bed j, night k).
• Time in bed (amount of time devoted to sleep, inminutes, as determined by actigraphy, modeled ascontinuous, for subject i, bed j, night k).
• Age (in years at entrance into study, modeled ascontinuous, for subject i).
• Gender (coded 1 for males, 0 for females, for subject i).
• BMI (body mass index, or weight/height2, modeled ascontinuous, for subject i).
• Study period (Latin square crossover design variable,
coded 1–7, modeled as categorical, for subject i,
bed j).

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Choosing the Best Mattress

Table 2 presents descriptive statistics for the response
variables used in the regression analyses anddescribes the nature of the variables.
In addition to ranking the beds by overnight motion,
we also ranked the beds within subject by theiraverage self-reported sleep quality on each bed(averaged over the nights that the subject slept on
the bed). Higher values of self-reported sleep quality
indicate better sleep on a 7-point scale. Based on thisranking, a participant’s best sleep quality bed is themattress with the highest average sleep quality score.
We carried out a similar set of regression analyses asprevious, replacing motion bed rank with a ranking
based on self-reported sleep quality. Sleep quality
bed rank is modeled as continuous (one regression
coefficient) for evaluating trend, and modeled as
categorical (six regression coefficients) for all otherhypotheses.

Participants’ Self-Selection of “Best” Mattress
In the final part of this analysis, we turned ourattention to whether the participant’s showroom“test drive” provided a means of choosing a mattressthat predicts best sleep for that individual. Morespecifically, we evaluated whether the mattressthat individuals had said they would choose forthemselves agreed with their optimal mattress as
determined by actigraphy and separately by reported
sleep quality. Self-reported sleep quality is based ondiary reports of sleep quality during his or her inhome testing.
To accomplish this, we estimated the kappa measureof agreement2 (Agresti, 2002) between the selfselected bed and the actigraphically determined
best bed for each individual and also between the
self-selected bed and self-reported best sleep quality
bed for each individual. We also estimated the kappa
measure of agreement between the self-selected
bed and the top 3 best motion beds, based on theobserved vs. expected percentage of participants
for which the self-selected bed is among the motion
bed ranks of 1, 2, or 3 for the participant. Finally, we
estimated the mean and median motion bed rank
associated with an individual’s self-selected bed.

2 The kappa statistic measures the extent of agreement between two

raters beyond what would be expected by chance alone.

Table 2. Descriptive statistics for sleep variables
N

Numbermissing

Minimum

Mean

Maximum

Overnight motion in minutes per night (continuous variablenormalized to 8 hours)

16,366

0

0

61.44

316.31Time in bed devoted to sleep (continuous variable in minutes)

16,366032.00

447.291,069.00

Self-reported sleep time (continuous variable in minutes)

15,941425

0433.78888.00

Number of awakenings (continuous)

14,912

1,454

0

0.95

12.00

Minutes awake overnight (categorized from 1 to 6 in 15-minuteincrements, where 1=0 minutes and 6=60+ minutes)

15,059

1,307

1.00

1.78

6.00

Sleep quality (categorical: 1=Not at all; 7=Very good sleep)

16,315

51

1.00

5.08

7.00

Well-restedness in AM (categorical: 1=Not at all; 7=Very well rested)

16,315

51

1.00

4.87

7.00

Morning pain (any type) (categorical: 1=None; 7=Worst imaginable)

16,282

84

1.00

1.56

7.00

Daytime sleepiness (categorical: 1=Not at all; 7=Very sleepy)

15,116

1,250a

1.00

2.83

7.00

Daytime energy (categorical: 1=Not at all; 7=Very energetic)

15,116

1,250a

1.00

4.86

7.00

Variable (type and measure)
Actigraph

Diarya Missing values for Daytime Sleepiness and Daytime Energy indicate lack of an evening diary report on the calendar day immediately following the previous night’s

actigraph data.

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Hogan et al., 2011

As in our prior report, the analyses focused on the
128 participants who successfully completed the study(Krystal et al., 2011). The previous report provides
complete details on our approach to including or
excluding individual observations for analysis.

Results
Actigraphically Determined Bed Ranking(Motion Bed Rank)
The best motion bed was relatively evenly distributed
across mattresses in our sample. Figure 1 depicts
the frequency distribution of the actigraphicallydetermined best bed across mattresses. Table 3
presents the results of the regression modeling(SAS MIXED procedure). The first row of that table
determines the extent to which actigraph-measured
motion per night is lower on the best motion bed
compared to other beds. The results in the columns
labeled Bed Rank 1 through Bed Rank 7 report the(model-adjusted) means for actigraphic and diarymeasures. The Bed Rank 1 column indicates that themean overnight minutes of motion per 8 hours was
slightly more than 54 minutes on the actigraphicallydetermined best bed (Bed Rank 1). The Bed Rank
7 column reports that on average, actigraphic
measurement found nearly 69 minutes of overnight
motion on the worst bed (Bed Rank 7). This is adifference of 15 minutes and is statistically significant(p=0.0001).
Figure 1. Frequency distribution of best motion bed

Number and Percent of Participants

25
15.6%

20
15

14.0%
11.7%

20

18

16.4%
21

14.8%
19

15.6%
20

11.7%
15

15

10
5
0

1

2

3

4
5
Study Beds

6

7

In Table 3, the rightmost column indicates that thebest motion bed on average is associated with 3.26
fewer minutes of motion than the second-best bed.
This difference is statistically significant (p = 0.0001).
This column also reports that the best bed isassociated with 8.3 fewer minutes of motion than
the average of all other beds in the motion ranking(second through seventh), and this, too, is statisticallysignificant (p=0.0001).
Although the differences in total motion are
numerically small, analysis of the effect of motion bed
rank on diary outcomes indicates that the bed withlowest motion was significantly associated with bettersleep quality, better feeling of restedness at the startof the day, improved daytime energy, fewer nighttime
awakenings, and fewer minutes awake.
Self-reported sleep quality was significantly
improved on the bed with the least overnight motion
compared with the bed with the second-lowest
motion (p=0.0048) and with the average of all otherbeds (p=0.0010). In addition, sleep quality decreased
linearly with bed rank (p=0.0001). Average scores forsleep quality ranged from 5.13 on the best motion
bed, to 5.05 on the second-best bed, to 4.99 on theworst bed, out of a Likert-type scale of 1 (not at allgood) to 7 (very good).
Self-reported level of restedness was significantly
improved on the best motion bed compared with theaverage of all other beds (p=0.0193), and restedness
also decreased linearly with bed rank (p = 0.0078).
Average scores for restedness ranged from 4.91 and
4.87 on the best and second-best motion beds to 4.81
on the worst bed, out of a Likert-type scale of 1 (notat all rested) to 7 (very well rested).
Self-reported daytime energy increased linearlywith bed rank, such that daytime energy tended toincrease in beds ranked higher on actigraphic sleep(p=0.0016). However, daytime energy on the best
motion bed was only marginally increased whencompared to the average of all other beds (p=0.0574).
The average score for daytime energy ranged from4.90 and 4.88 on the best and second-best motion
beds to 4.81 on the worst bed, out of a Likert-type
scale of 1 (not at all energetic) to 7 (very energetic).

Choosing the Best Mattress

7

Table 3. Effect of motion bed rank on sleep outcomes
Model-Adjusteda Mean (SE)Overall
Bed Rank
p-value

Rank 1 vs. 2b
Rank 1 vs. All Othersc
Linear Trendd

68.53(1.63)

0.0001-3.26 (0.38) p=0.0001-8.30 (0.29) p=0.0001
2.20 (0.05) p=0.0001

432.69(2.33)

433.59(2.33)

NS

NS
NS-0.36 (0.19) p=0.0595

0.9725(0.0700)

0.9921(0.0701)

1.0493(0.0701)

0.0001-0.08 (0.03) p=0.0039-0.07 (0.02) p=0.0015
0.0175 (0.0039) p=0.0000

1.79(0.05)

1.83(0.05)

1.81(0.05)

1.88(0.05)

0.0001NS-0.08 (0.02) p=0.0002
0.025 (0.004) p=0.0000

5.08(0.07)

5.10(0.07)

5.07(0.07)

5.07(0.07)

4.99(0.07)

0.00010.08 (0.03) p=0.0048
0.07 (0.02) p=0.0010-0.015 (0.004) p=0.0001

4.87(0.07)

4.88(0.07)

4.88(0.07)

4.87(0.07)

4.88(0.07)

4.81(0.07)

0.0347NS
0.05 (0.02) p=0.0193-0.01(0.004) p=0.0078

1.53(0.06)

1.56(0.06)

1.51(0.06)

1.55(0.06)

1.60(0.06)

1.53(0.06)

1.56(0.06)

0.0263NS
NS
NS

Daytime sleepiness

2.78(0.08)

2.82(0.08)

2.81(0.08)

2.85(0.08)

2.76(0.08)

2.77(0.08)

2.90(0.08)

0.0002NS
NS
NS

Daytime energy

4.90(0.08)

4.88(0.08)

4.85(0.08)

4.93(0.08)

4.90(0.08)

4.84(0.08)

4.81(0.08)

0.0001NS
0.04 (0.02) p=0.0574-0.01 (0.003) p=0.0016

Sleep
Response
Variable

Bed
Rank 1

Bed
Rank 2

Bed
Rank 3Bed
Rank 4Bed
Rank 5Bed
Rank 6

Bed
Rank 754.35(1.63)

57.61(1.63)

59.62(1.63)

61.51(1.63)

63.42(1.63)

65.21(1.63)

Sleep time(minutes)

435.03(2.33)

435.01(2.33)

433.05(2.32)

433.65(2.33)

432.01(2.32)

Number of
awakenings

0.9054(0.0702)

0.9886(0.0702)

0.9268(0.0700)

0.9246(0.0701)

Minutes awake
overnight

1.72(0.05)

1.76(0.05)

1.73(0.05)

Sleep quality

5.13(0.07)

5.05(0.07)

Well-restedness

4.91(0.07)

Morning pain
severity

Actigraph
Overnight minutesof motion per
8 hourse
Diary (Self-Report)

BMI = body mass index; NS = not statistically significant (p > 0.05); SE = standard error.
a Diary means within levels of motion bed rank are adjusted for the following model covariates: Ordinal Day in Bed, Time in Bed, Age, Gender, BMI, and Study Period.
b Bed Rank 1 minus Bed Rank 2: Estimated difference, SE, and p-value (2-sided).
c Bed Rank 1 minus average of Bed Ranks 2–7: Estimated difference, SE, and p-value (2-sided).
d Linear trend across Bed Ranks (1=Best, 7=Worst): Estimated slope, SE, and p-value (2-sided).
e Means within levels of motion bed rank are not adjusted for any covariates. They are included as descriptive information.

NOTE: Bed Rank 1 indicates least overnight motion measured by actigraphy, and Bed Rank 7 indicates greatest motion measured by actigraphy. All analyses carriedout using the SAS MIXED procedure.

Self-reported number of nighttime awakenings
were significantly reduced on the best motion bed
compared with second best (p=0.0039) and comparedwith the average of all other beds (p=0.0015). The
reduction in awakenings were also linearly related tomotion bed rank (p<0.0001). The average number ofnighttime awakenings ranged from 0.91 and 0.99 on
the best and second-best motion beds to 1.05 on theworst bed.

Self-reported number of minutes awake overnight
was also significantly reduced on the best motion bed
compared to the average of all other beds (p=0.0002),
and the reduction in minutes awake overnight wasalso linearly related to motion bed rank (p<0.0001).
The average number of minutes awake overnight(categorized in 15-minute increments from 1=0
minutes, 2=1-15 minutes,…, 6=60+ minutes) ranged
from 1.72 and 1.76 on the best and second-best
motion beds to 1.88 on the worst bed.

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Hogan et al., 2011

score of 4.32. The far-right column indicates anaverage increase of 0.19 (3.5 percent improvement) insleep quality between the best and second-best sleep
quality bed (p<0.0001). This column also indicates anincrease of 0.65 (13 percent improvement) in sleep
quality on the best bed compared with the averageof the remaining bed ranks (p<0.0001). Sleep quality
decreased linearly with bed rank (p<0.0001).

Sleep Quality Bed Ranking
Table 4 reports the results of a similar analysis
identifying the “best bed” as determined by the diarymeasure of overnight sleep quality. In the first row of
the table, the columns labeled Bed Rank 1 through
Bed Rank 7 report self-report sleep quality meansalong the Likert scale where 1 is worst sleep quality
and 7 is best. On average, all bed ranks rate above
the midpoint (4), with the best sleep quality bed on
average rated 5.6 on the scale and the second-best
rated 5.41. The lowest rating bed had a sleep quality

Most importantly, there was a significant relationshipbetween sleep quality bed rank and actigraphic

Table 4. Effect of sleep quality bed rank on actigraphic motion and other sleep outcomess
Model-Adjusteda Mean (SE)Overall
Bed Rank
p-value

Rank 1 vs. 2b
Rank 1 vs. All Othersc
Linear Trendd

4.32(0.07)<.00010.19 (0.03) p<0.0001
0.65 (0.02) p<0.0001-0.20 (0.0050) p<0.0001

63.12(1.64)

62.62(1.64)

0.0233-0.43 (0.46) p=0.3501-0.97 (0.34) p=0.0055
0.19 (0.06) p=0.0023

433.08(2.34)

430.98(2.33)

432.36(2.35)

0.0021-2.14 (1.37) p=0.1210
1.04 (1.03) p=0.3156-0.68 (0.18) p=0.0003

0.96(0.07)

0.95(0.07)

1.08(0.07)

1.23(0.07)<.0001-0.07 (0.02) p=0.0168-0.20 (0.02) p<0.0001
0.06 (0.0038) p=<0.0001

1.76(0.05)

1.78(0.05)

1.80(0.05)

1.90(0.05)

1.98(0.05)<.0001-0.02 (0.02) p=0.5018-0.16 (0.02) p<0.0001
0.05 (0.0037) p<0.0001

5.15(0.07)

5.02(0.07)

4.89(0.07)

4.76(0.07)

4.57(0.07)

4.32(0.07)<.0001

0.12 (0.02) p<0.0001
0.49 (0.01) p<0.0001-0.15 (0.0034) p<0.0001

1.34(0.06)

1.42(0.06)

1.51(0.06)

1.48(0.06)

1.59(0.06)

1.70(0.06)

1.86(0.06)<.0001-0.08 (0.02) p=0.0029-0.25 (0.02) p<0.0001
0.08 (0.0036) p<0.0001

Daytimesleepiness

2.64(0.08)

2.71(0.08)

2.76(0.08)

2.83(0.08)

2.82(0.08)

2.96(0.08)

3.00(0.08)<.0001-0.07 (0.03) p=0.0401-0.20 (0.02) p<0.0001
0.06 (0.0045) p<0.0001

Daytime energy

5.04(0.08)

5.02(0.08)

4.93(0.08)

4.89(0.08)

4.79(0.08)

4.76(0.08)

4.63(0.08)<.00010.02 (0.02) p=0.4659
0.20 (0.01) p<0.0001-0.07 (0.0033) p<0.0001

Sleep
Response
Variable

Bed
Rank 1

Bed
Rank 2

Bed
Rank 3Bed
Rank 4Bed
Rank 5Bed
Rank 6

Bed
Rank 75.60(0.07)

5.41(0.07)

5.24(0.07)

5.10(0.07)

4.93(0.07)

4.74(0.07)

61.53(1.64)

61.97(1.64)

62.54(1.64)

62.55(1.64)

62.21(1.64)

Sleep time(minutes)

434.41(2.31)

436.55(2.33)

434.69(2.32)

432.56(2.33)

Number of
awakenings

0.80(0.07)

0.87(0.07)

0.92(0.07)

Minutes awake
overnight

1.66(0.05)

1.68(0.05)

Well-restedness

5.27(0.07)

Morning pain
severity

Sleep Qualitye
Actigraph
Overnight minutesof motion per
8 hours
Diary (Self-Report)

BMI = body mass index; NS = not statistically significant (p > 0.05); SE = standard error.
a Means within levels of Sleep Quality Bed Rank are adjusted for the following model covariates: Ordinal Day in Bed, Time in Bed, Age, Gender, BMI, and Study Period.
b Bed Rank 1 minus Bed Rank 2: Estimated difference, SE, and p-value (2-sided).
c Bed Rank 1 minus average of Bed Ranks 2–7: Estimated difference, SE, and p-value (2-sided).
d Linear trend across Bed Ranks (1=Best, 7=Worst): Estimated slope, SE, and p-value (2-sided).
e Means not adjusted for any covariates. They are included as descriptive information.

NOTE: Bed Rank 1 indicates highest sleep quality measured by diary, and Bed Rank 7 indicates lowest sleep quality measured by diary. All analyses carried out in the
SAS MIXED procedure.

Choosing the Best Mattressmotion (p=0.0233) as well as many diary outcomes,
most notably number of awakenings (p<0.0001),
number of minutes awake overnight (categorized)(p<0.0001), well-restedness (p<0.0001), morning pain
severity (p<0.0001), daytime sleepiness (p<0.0001),
and daytime energy (p<0.0001). After adjustingfor covariates, actigraphic motion minutes was
significantly reduced in the best sleep quality bed
compared to the average of all other bed ranks, andmotion increased linearly with bed rank. The beds
ranked best and second-best for self-reported sleep
quality are also the two beds with the lowest level ofactigraphically measured motion.
The number of awakenings and minutes awake
overnight were both significantly reduced in thebest sleep quality bed compared to the average of
all other bed ranks, with each increasing linearlywith bed rank. The number of awakenings was
also significantly reduced in the best sleep quality
bed compared to second best. Well-restedness anddaytime energy were both significantly improved in
the best sleep quality bed compared to the averageof all other bed ranks, with each decreasing linearlywith bed rank. Well-restedness was also significantly
improved in the best sleep quality bed compared
to second best. Finally, morning pain severity anddaytime sleepiness were both significantly reducedin the best sleep quality bed compared to the second
best bed and compared to the average of all other bed
ranks, with each increasing linearly with bed rank.

Self-Selected Versus Actigraphically
Determined Best Bed
We conducted a series of analyses to determine
whether the mattress that individuals indicated theywould choose for themselves was predictive of theiroptimal mattress (or mattresses) as determinedby either actigraphy or self-reported sleep quality.
In other words, can people predict their optimal
mattress from a typical in-store experience? Results
indicate that standard showroom testing does notlead individuals to select the bed that will provide
their best sleep as measured by either self-reported
sleep quality or actigraphic measurement over an
extended period of time. Although the self-selected
bed varied in our sample (see Figure 1), it was
associated with a median motion bed rank of only

9

4 out of 7 (i.e., mid-rank), and the same was true
for self-reported sleep quality (Table 5). Consistent
with this, the kappa measure of agreement between
the best motion bed and self-select bed was not
significantly different from 0, indicating no additional
agreement than what would be expected by chancealone (Table 6). Agreement between self-select bed
and the top 3 best motion beds (motion bed rank of
1, 2, or 3) was also not significantly different from0 (Table 7), as the percentage of participants whose
self-select bed was among the top 3 motion best
beds was only 38 percent (vs. 43 percent expectedby chance alone). This suggests that the customary
showroom “test drive” in fact often leads consumers
to suboptimal mattress selection.
Table 5. Average motion bed rank and self-reported
sleep quality bed rank of self-selected bed
Variable Used
to Determine
Bed Rank
Statistic

Estimated
Bed Rank(1–7)

Lower 95%
Limit

Upper 95%
Limit

Actigraphic
Motiona(in minutes)

Average

4.172

3.826

4.518

Median

4.000

4.000

5.000

Self-Reported
Sleep Quality(coded 1–7)

Average

3.875

3.525

4.225

Median

4.000

3.000

4.000

a Actigraphic motion is number of minutes of recorded overnight motion,

normalized to an 8-hour night.

Table 6. Agreement between self-selected bed andbest motion bed
Methods
Compared

Kappa
Estimate

Lower 95%
Limit

Upper 95%
Limit

Self-Select vs. Best
Motiona Bed-0.0114-0.0795

0.0567a Best motion bed is the mattress firmness with the smallest average overnight

motion minutes per 8 hours.

Table 7. Agreement between self-selected bed and
top 3 best motion beds
Methods
Compared

Kappa
Observed
Expected P-value
Estimate Percentagea Percentageb H0: Kappa=0

Self-Select vs.
Top 3 Best
Motionc Beds

-0.080138.28%

42.86%

0.2904a Observed percentage of people for which self-select bed is among the top 3

best motion beds (motion bed ranks 1, 2, or 3).
b Expected percentage of people for which self-select bed is among the top 3

best motion beds (motion bed ranks 1, 2, or 3).
c The top 3 best motion beds are the three mattress firmness levels with thesmallest average overnight motion minutes per 8 hours.

10

Hogan et al., 2011

Discussion
Our prior report documented that mattress firmness
has significant effects on sleep and daytime function(Krystal et al., 2011). Very low and very high levels of
firmness tended to be associated with relatively worse
sleep, greater morning pain, and poorer daytime
function.
Our results build on those observations. We provide
here six main conclusions from our blinded,
controlled study:
1. Individuals differ as to the degree of mattressfirmness that is associated with their best sleep.
2. Actigraphic activity level has significant utility
for identifying the best mattress for an individual(Table 3).
3. The best bed as measured by actigraphic motion
significantly minimizes self-reported numberof overnight awakenings and minutes awake
overnight, and significantly maximizes selfreported sleep quality and well-restedness in themorning (Table 3).
4. The best bed as determined by self-reported sleep
quality in the morning significantly minimizes
actigraphic motion, self reported number of
awakenings, minutes awake overnight, andmorning pain severity, and maximizes wellrestedness, daytime sleepiness, and daytime energy(Table 4).
5. People are essentially left to chance when trying toselect a mattress using the generally recommended“test drive” on the showroom floor (Tables 5–7).
6. The actigraphically best mattress was welldistributed among the sample of participants andacross the seven mattresses studied.
These data indicate that activity level is not areflection of mattress firmness; rather, it provides
an indication of the mattress that yields optimal
sleep for an individual. This is consistent with ourpreviously reported finding that activity level was
not significantly correlated with mattress firmness(Krystal et al., 2011). Thus, our findings imply that

RTI Press

improving sleep in many individuals by improving
mattress fit is quite possible. Our results also suggestthat actigraphically determined activity level may
have some utility in this regard.
These observations suggest that improving mattress
fit may improve sleep. Pain, daytime sleepiness, andenergy level appear to be affected more by mattressfirmness than by the degree to which firmness issuitable for an individual. This may reflect the factthat we excluded individuals with pain, insomnia,
or daytime sleepiness from this study. Assessing theeffects of mattress fit on these measures in studies that
include such individuals is an important question for
future research.
The mattress that individuals chose as optimal before
the randomized, controlled phase of the study did
not predict either the actigraphically determined
best mattress or the best mattress as determinedby reported sleep quality. This finding raises thepossibility that the ordinary showroom experience
does not lead individuals to select the mattress that
results in best sleep over a more extended period.
This finding should inspire study into ways inwhich consumers can be better equipped to identify
mattresses that lead to optimal sleep, and the healthbenefits that come from better rest.
In summary, our study indicates that optimal
mattress firmness varies among individuals and isreflected, at least to a degree, by actigraphic activity
level. When allowed to test mattresses in a typical
showroom experience, individuals appear to choosemattresses that do not optimize their sleep. Given
that this “test drive” approach is commonplace, itwould seem that most of the general public may
be sleeping on mattresses improperly suited to theindividual owners. This would help explain to someextent why so many Americans are not enjoying thehealth benefits that come from optimal rest. Sleep
science could assist bedding retailers in improving
their customers’ sleep outcomes by developing betterin-store methods of aiding in the mattress selection
process.

Choosing the Best Mattress

11

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Acknowledgments
Sleep to Live Institute of Joplin, Missouri, sponsored this research. Sleep to Live
Institute did not exercise editorial control over the analysis or reporting of theresults. We recognize the contributions of Michael S.S. Lawrence, Jeff Barghout,and Scott Mladsi in the design and implementation of this experiment.

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