The Clinical Journal of Pain-article summary

Sleep and health-related factors in overweight and obese rural women in a randomized controlled trial

Marcia Y. Shade1 • Ann M. Berger1 • Paul J. Dizona1 •

Bunny J. Pozehl2 • Carol H. Pullen1

Received: April 18, 2015 / Accepted: November 25, 2015 / Published online: December 11, 2015

� Springer Science+Business Media New York 2015

Abstract This secondary analysis describes sleep and

health-related factors in healthy overweight and obese mid-

life and older rural women enrolled in the’’ Women Weigh-

In for Wellness’’ randomized clinical trial. The aim of the

trial was to promote healthy behaviors and weight-loss. We

analyzed demographic, anthropometric, and biomarker

variables, self-reported measurements of sleep disturbance

and pain interference, and objective 24-h sleep/wake pat-

terns at baseline, 6 months, and the change over time.

Although self-reported sleep disturbance reflected normal

sleep, pain interference was slightly higher than normal.

There were associations between higher self-reported sleep

disturbance, pain interference and several other variables.

Women who achieved 5 % or more weight loss exhibited

positive associations between sleep, pain, and health-re-

lated factors. Weight loss and lower pain predicted lower

self-reported sleep disturbance. Our results suggest that

overweight and obese rural women who adopt healthy

behaviors and achieve weight loss also may experience

improved sleep and other health benefits.

Clinical trial # NCT01307644.

Keywords Overweight � Sleep � Pain � Middle-aged � Rural � Women

Introduction

The worldwide prevalence of obesity has increased dra-

matically. Over two-thirds of adults in the United States

(US) are considered overweight and one-third are consid-

ered obese (Centers for Disease Control, 2014; Ogden

et al., 2014). The US has reported higher rates of obesity

than other countries, in rural areas, and in women (Befort

et al., 2012; Hartz et al., 2007; Organization for Economic

Cooperation and Development, 2012). Higher obesity

trends have been paralleled by reports of short sleep

duration and are likely to contribute to poor health out-

comes of Americans (Centers for Disease Control and

Prevention (CDC), 2011; Patel et al., 2008; Singh et al.,

2005; Vgontzas et al., 2014).

Sleep duration of 7–9 h each night is recommended for

optimal health (Hirshkowitz et al., 2015; Watson et al.,

2015). Prior evidence has suggested that short self-reported

sleep duration and poor sleep quality are risk factors for the

development of obesity; but trends have been most con-

sistent in children (Beccuti & Pannain, 2011; Grandner,

2012; Hung et al., 2013; Nielsen et al., 2011). In adults,

short self-reported sleep duration has been associated with

several factors such as female gender, older age, higher

BMI, waist circumference, and hypertension (Befort et al.,

2012; Di Milia et al., 2013; Hartz et al., 2007; St-Onge

et al., 2010; Theorell-Haglow et al., 2012). Regardless of

previous sleep patterns, American women who reported

sleeping 7 h or less duration per night were more likely to

be obese (Anic et al., 2010).

Among those living in rural America, short self-reported

sleep disturbance was associated with obesity-related risk

factors such as less physical activity, low intake of healthy

foods, and high fat and fast food consumption (Stamatakis

& Brownson, 2008). In addition, short self-reported sleep

& Marcia Y. Shade marcia.shade@unmc.edu

1 College of Nursing-Omaha, University of Nebraska Medical

Center, 985330 Nebraska Medical Center, Omaha, NE

68198, USA

2 College of Nursing-Lincoln, University of Nebraska Medical

Center, 1230 O Street, Suite 131, Lincoln, NE 68588, USA

123

J Behav Med (2016) 39:386–397

DOI 10.1007/s10865-015-9701-y

duration of 6 h or less was found to be significantly asso-

ciated with higher body mass index (BMI) in men and

women living in rural Iowa (Kohatsu et al., 2006). The risk

of insomnia symptoms has been reported to be increased

among individuals in rural areas with poor health, older

age, female gender, low income and education (Hartz et al.,

2007). Rural health disparities, behavior patterns, culture,

socioeconomic, and environmental factors may be associ-

ated with shorter sleep duration and obesity trends (Bennett

et al., 2011; Crosby et al., 2012; Jones et al., 2009;

Whinnery et al., 2014). Additional examination of sleep in

overweight and obese mid-life and older rural women is

warranted as this population is underrepresented.

In addition to obesity, self-reported short sleep duration

and sleep disturbances have been associated with inflam-

mation, cardiometabolic disease risk, and hypertension

(Bansil et al., 2011; Grandner et al., 2012, 2013). The

relationship of sleep with increased risk of cardiometabolic

disease is particularly important in women due to high

mortality rates from heart disease (Go et al., 2014). Women

who self-reported short sleep duration of 6 h or less per

night had a greater risk of hypertension; obesity may

potentiate this relationship (Gangwisch et al., 2013; Guo

et al., 2013). Pain also may have a significant relationship

with sleep and weight status. A recent survey reported that

adults who experienced pain slept less and had poorer sleep

quality than those without pain (Knutson, 2015). In obese

individuals, pain has been associated with chemical

mediators and difficulty sleeping (Okifuji & Hare, 2015).

We are only aware of one study that found an association

between self-reported sleep disturbance and pain in over-

weight and obese women (Wachholtz et al., 2009). Closer

observation may show that sleep could be associated with

pain and health among rural women who are overweight or

obese.

Objectively measured short sleep duration was found to

be associated with higher BMI and greater waist circum-

ference in adults, but other studies reported these rela-

tionships were not maintained over time (Appelhans et al.,

2013; Evans et al., 2011; Lauderdale et al., 2009; Mezick

et al., 2014; Moraes et al. 2013). The relationship between

objectively measured sleep and weight status remains

unclear. Further examination of the longitudinal relation-

ship between sleep and obesity is needed.

Sleep behavior has been reported to influence quality of

life, body function and performance (Czeisler, 2015; Lee

et al., 2009). Likewise, improving healthy behaviors may

influence sleep and overall health. Alfaris et al. (2015)

reported significantly higher self-reported sleep duration

and sleep quality in obese men and women with 5 % or

more weight loss while enrolled in a behavioral weight loss

intervention. A slightly different relationship was reported

by Thomson et al. (2012); they found that higher self-re-

ported sleep quality and quantity increased the likelihood

of weight loss by 33 % in overweight and obese women

enrolled in a weight loss program. In contrast, Moderate-

intensity exercise, stretching, and behavioral weight loss

programs have reduced BMI but did not significantly

change sleep quality in adults (O’Brien et al., 2012;

Tworoger et al., 2003). Weak associations were found

between self-reported shorter time to fall asleep and weight

loss (O’Brien et al., 2012). Additional evidence is needed

to clarify the relationships between weight loss, sleep

quality, duration and other health factors. The purpose of

this paper is to describe self-reported and objective mea-

surements of sleep and the relationships between sleep,

pain, and demographic and health-related factors in over-

weight and obese mid-life and older rural women enrolled

in a randomized controlled trial.

The specific aims were to:

1. Characterize self-reported sleep disturbance, pain

interference, and health-related factors.

2. Describe objective indirect measurements of sleep.

3. Observe relationships between sleep, pain, and health-

related factors (total sample and in non-achievers and

achievers of weight loss).

4. Identify the predictors of change in self-reported sleep

disturbance.

Methods

The ‘‘Women Weigh-In for Wellness’’ trial was designed

to promote healthy eating, physical activity, and weight

loss. The detailed protocol for the study has been published

(Hageman et al., 2011). The randomized clinical trial was

investigator-initiated and community-based and approved

by the Institutional Review Board at a Midwestern US

University. All participants provided written informed

consent prior to study enrollment. The clinical trial eval-

uated the effectiveness of theory-based web-delivered

interventions for promoting healthy eating and activity

among mid-life and older rural women with the goal of

achieving 5–10 % weight loss and weight maintenance

over a 30-month period. There were three arms of the

intervention: interactive web site, interactive web site plus

a peer-led discussion board, and interactive web site plus

email counseling. An interactive web site was available to

all women that provided weekly messages, opportunities to

monitor and post goals, calories, fat grams, weight in

pounds, and physical activity behaviors. Individualized

feedback of the results of the assessment visits were posted

online. At baseline and 6 months, actigraphy measure-

ments were obtained as well as other behavioral, biomarker

and anthropometric measurements. This secondary analysis

J Behav Med (2016) 39:386–397 387

123

examined data at baseline, 6 months and the change over

time. These times represent Phase 1, called the Weight

Loss phase, of the 30 month study.

Sample and setting

A total of 301 overweight and obese mid-life and older

rural women enrolled. Eligible individuals resided in large,

small, or isolated rural areas as defined by Rural Urban

Commuting Area (RUCA) codes (Hart & Casey, 2012).

The women resided in one of 16 rural counties in a Mid-

western state in the US (Hageman et al., 2011).

Inclusion and exclusion criteria

Women were included in the study if they: (a) were

40–69 years of age; (b) overweight or Class I and II obese

(BMI 28–39.9) or BMI 40–45 with physician clearance;

(c) able to speak and read English; (d) able to communicate

over the telephone, (e) made a commitment to lose weight

through changing eating and physical activity behaviors;

(f) able to use a computer with minimal assistance to access

the internet and complete electronic forms; (g) made a

commitment to access the website as required by the research

intervention; (h) had or were willing to obtain an email

account; (i) had access to a DVD player; (j) able to walk

without assistance (including cane, crutches, walker, oxy-

gen); (k) answered ‘no’ to all questions on the Physical

Activity Readiness Questionnaire (PAR-Q) or obtained

clearance from their physician to become more active and

participate in the assessment of physical activity biomarkers

(Cardinal, 1997); and l) resided within approximately

60-mile radius of the research site in a Midwestern state.

Women were excluded from participating if: (a) diag-

nosed with Type 1 diabetes, (b) diagnosed with Type 2

diabetes and required insulin, (c) had a C 10 % weight loss

in last 6 months; (d) were currently enrolled in a weight

loss management program; (e) taking medications that

affect weight loss or weight gain; (f) enrolled in or

undergoing a formal program of cardiac rehabilitation; and

(g) other physical or medical restrictions that would pre-

clude following the minimum recommendations for mod-

erate physical activity and healthy eating.

Measurements

Demographic/anthropometric/biomarkers

Assessments were gathered using reliable and valid mea-

sures. Demographic and health history data were obtained

via an investigator developed questionnaire. Age and

menopausal status were measured by self-report items on the

health history questionnaire. Participants were asked ‘‘Have

you gone through menopause?’’ and ‘‘If yes, at what age did

you go through menopause?’’ We also collected anthropo-

metric measurements. Height and weight were collected

using The Tanita Model Composition Analyzer, Scale,

Printer and Height Rod [TBF-215, Tanita Corporation of

America, Inc.]. Prior to assessment women fasted, refrained

from exercise, alcohol use, and voided. BMI was calculated

as weight in kilograms divided by height in meters-squared.

A weight loss of 5 % or more represents a significant change

of weight (Wing, Lang, Wadden, Safford, 2011). Partici-

pants who did not achieve weight loss of at least 5 % or more

were considered non-achievers and those who did were

achievers. Waist circumference was measured in inches by

placing a snug tape in a horizontal plane around the abdomen

at the level of the iliac crest at the end of expiration; the

average of two measurements was recorded (US Department

of Health and Human Services, 1998).

Blood pressure was collected using the e-sphyg 2 [9002]

Automatic Sphygmanometer [American Diagnostic Corpo-

ration, Hauppauge, NY]. The women did not consume caf-

feine or participate in exercise or smoking prior to blood

pressure assessment. Blood pressure was assessed using

standard technique after 5 min of quiet sitting (Perloff et al.,

1993). A minimum of two blood pressure measurements

separated by at least 30 s were obtained. Systolic and dias-

tolic blood pressures were recorded as the mean of the two

measurements within 5 mmHg (Perloff et al., 1993).

Self-reported sleep disturbance and pain interference

Self-reported sleep disturbance and pain interference were

each measured from the respective four item subscale of the

Patient Reported Outcomes Measurement System (PRO-

MIS-29v1.0) and interpreted from participants’ T-scores

based on general population norms. A T-score of 50 repre-

sents the mean score in the general population and a higher

PROMIS T-score represents poorer sleep or more pain

interference (Gershon et al., 2010). Sleep disturbance items

assessed the participant’s self-reported perceptions of sleep

quality, depth, and restoration over 7 days. The measure-

ment included five-point Likert-style choices (ranging from

very poor to very good) in response to the participant’s sleep

quality, if sleep was refreshing, difficulty falling asleep, as

well as problems with sleep (Gershon et al., 2010). The

PROMIS sleep disturbance subscale demonstrated an alpha

reliability of .87 at baseline and 6 months.

Four self-reported pain interference subscale items

measured self-reported consequences of pain and the extent

of how pain hinders engagement in all activities over

7 days. The measurement included five-point Likert-style

choices (ranging from not at all to very much) in response

to the participant’s pain interference with activities (Ger-

shon et al., 2010). The PROMIS pain interference subscale

388 J Behav Med (2016) 39:386–397

123

demonstrated an alpha reliability of .93 at baseline and .97

at 6 months.

Objective indirect sleep

The ActiGraph wGT3X [ActiGraph, Pensacola, FL] was

used to collect indirect measurements of 24-h sleep/wake

patterns. We examined four objective sleep parameters:

total sleep time (TST), number of awakenings, wake after

sleep onset (WASO), and percent sleep. The TST is the

number of minutes of actual sleep while in bed and the

number of awakenings is the number of wake counts after

the onset of sleep. The WASO minutes are the amount of

time spent awake after sleep has begun. Percent sleep is

ratio of TST to the amount of time spent in bed. Normal

values are considered 7–9 h for TST, less than 6 number of

awakenings, less than 30 min for WASO, and a percent

sleep of 85 % or higher. Values not within normal limits

may indicate sleep disturbance (Berger et al., 2005). Most

studies measure objective sleep parameters by wrist-worn

actigraphy; this study used hip-worn placement. As illus-

trated in Fig. 1, the actigraph monitor was worn on the

dominant hip. Women were instructed on proper wear of

the ActiGraph and wore it for 24-h a day. Women com-

pleted a sleep diary that was used to identify bedtime and

out-of-bed times during analysis.

An addendum study was implemented on a sub-sample

of 26 participants who were recruited to compare sleep

parameter measurements using wrist and hip actigraphy.

Results indicated mostly reliable and correlated measures

between the sites. High correlations were found for three

variables (TST, WASO, and percent awake) between wrist

and hip measures (r = .93, .85, and .87 respectively); but

not for number of awakenings (r = .39, p = .083).

Data analysis

As shown in the consort diagram, we were unable to use

data from 32 participants due to attrition and 48 partici-

pants due to lack of actigraphy data. A minimum number

of 4 nights of wear were required for analyzing sleep/wake

data. We compared the baseline data of the 80 women

without actigraphy data with the 221 women in the analysis

and found no significant differences between study vari-

ables. We analyzed n = 221 participants who had 4 nights

of actigraphy measurements, complete sleep diaries and

PROMIS questionnaires from baseline and 6 months

(Fig. 2).

Descriptive statistics were used for aims 1 and 2 to

determine the mean, range, percent, and standard deviations

of demographic, anthropometric, and biomarker variables at

baseline and 6 months. The change variable from baseline to

6 months was calculated for weight, BMI, waist circumfer-

ence, and blood pressures such that an increase in those

variables is represented positively and a decrease is repre-

sented negatively. Pearson correlations were used for aims 3

and 4 to examine the associations between study variables.

Weight loss was treated as a single dichotomous variable

with non-achievers being labeled 0 and achievers labelled as

1. The authors used a two-tailed level of significance of

p = .01 to examine the relationships between non-achievers

and achievers of weight loss. Due to the exploratory nature of

this study, an overly stringent p value may conceal potential

relationships that merit further study. Multiple linear

regression modeling was used in aim 4 on the change scores

to determine if specific factors contributed to a meaningful

amount of influence on self-reported sleep disturbance. Our

primary predictors were weight change and pain interfer-

ence; and we controlled for age and arthritis because they

were potential confounders. This was done to determine if

weight change had a meaningful influence on self-reported

sleep disturbance. A two-tailed level of significance was

designated at p = .05 and SPSS v22 statistical software

package was used.

Results

Baseline characteristics

Baseline characteristics are shown in Table 1. Of the par-

ticipants 97 % were Caucasian and 84 % married; 43 %

had completed some college; 68 % were employed full-

time, and over 90 % had health insurance. The mean age

was 54.5 years and most women were post-menopausal.

The self-reported sleep disturbance score and actigraphy

measurements reflected normal sleep characteristics. Pain

interference scores were slightly higher than normal.Fig. 1 Hip-worn actigraphy

J Behav Med (2016) 39:386–397 389

123

Characteristics: overall and in non-achievers

and achievers

Characteristics at 6 months and the change over time of the

221 women are displayed in Table 2. The self-reported

sleep disturbance scores, pain interference scores, and

actigraphy variables did not change considerably, but the

anthropometric and biomarker variables decreased from

baseline to 6 months. Table 2 also shows characteristics

and significant changes in women who were non-achievers

(53 %) and achievers (47 %) of 5 % or more weight loss.

Overall relationships at baseline, 6-months,

and change scores in the total sample

Significant relationships were found at baseline. Higher

self-reported sleep disturbance scores were associated only

with higher pain interference scores (r = .252, p\ .05). We also found associations between the higher pain

interference score and higher weight (r = .214, p\ .05) and BMI (r = .218, p\ .05). Objectively measured sleep was not associated with any other study variables.

Table 3 shows the relationships of the variables at

6 months. Weak associations were found between higher

self-reported sleep disturbance scores and higher blood

pressures. Higher sleep disturbance was moderately asso-

ciated with higher pain interference scores. Pain interfer-

ence scores were associated with most of the

anthropometric and biomarker variables. Higher pain

interference scores had weak to moderate associations with

older age, higher weight, BMI, waist circumference, and

systolic, but not diastolic blood pressure. Relationships also

were found between objective sleep variables. Weak

associations were observed between higher objective

Fig. 2 Consort diagram

390 J Behav Med (2016) 39:386–397

123

WASO minutes and higher pain interference scores. Higher

WASO minutes and number of awakenings were associ-

ated with higher BMI and blood pressure. Lower percent

sleep had weak associations with higher weight, BMI, and

pain interference scores.

We observed relationships between change scores in

self-reported sleep disturbance and pain interference and

the change in anthropometric and biomarker measures.

There were weak positive associations between the change

in self-reported sleep disturbance score and change in

weight (r = .202, p\ .05), BMI (r = .211, p\ .05), waist circumference (r = .169, p\ .05), and diastolic blood pressure (r = .137, p\ .05). Positive associations also were found between the change in pain interference scores

and several of the same variables including change in

weight (r = .185, p\ .05), BMI (r = .205, p\ .05), waist

Table 1 Sleep, pain, and anthropometric, biomarker, characteristics as measured by self-report (PROMIS) and actigraphy at baseline (N = 301)

Variable Baseline data

Secondary analysis

N = 221

Baseline data

Not in secondary analysis

N = 80

N M (SD) N M(SD)

Sleep Disturbance Score (S) 221 49.0 (7.2) 80 48.9 (6.8)

Pain Interference Score (S) 220 51.6 (7.2) 80 54.1 (7.3)

Total sleep time (A) 221 455.1 (42.8) 80 458.9 (44.5)

Wake after sleep onset (A) 221 17.5 (10.2) 80 22.6 (12.8)

Number of awakenings (A) 221 4.8 (2.2) 80 5.9 (2.8)

% Sleep (A) 221 96.3 % (2.1) 80 94.9 % (2.4)

Age (years) 221 54.5 (7.0) 80 52.3 (6.2)

Weight (lbs) 221 205.4 (28.7) 80 210.4 (28.8)

Body Mass Index 221 34.6 (4.2) 80 35.4 (4.2)

Waist circumference (in.) 221 42.8 (4.3) 80 42.9 (4.3)

Arthritis 221 27.6 % 80 23.8 %

Systolic BP 221 123.1 (11.8) 80 123.0 (12.5)

Diastolic BP 221 76.9 (7.8) 80 76.4 (8.2)

A actigraph, S self-report

No significant differences were found between groups

Table 2 Sleep, pain, and anthropometric, biomarker characteristics as measured by self-report (PROMIS) and actigraphy at 6 months, and change over time (N = 221)

Variable 6-months Base to

6 months change

Base to 6 month change

in non-achievers

Base to 6 month

change in achievers

N M (SD) N M (SD) N M (SD) N M (SD)

Sleep Disturbance Score (S)a 220 48.8 (6.9) 220 -.2 (6.3) 117 1.3 (6.4) 103 -1.9 (5.8)

Pain Interference Score (S)a 220 51.7 (8.5) 219 .1 (7.4) 116* 1.4 (7.2) 103 -1.3 (7.3)

Total sleep time (A) 221 457.3 (43.6) 221 2.2 (38.2) 118 4.0 (37.8) 103 .4 (38.9)

Wake after sleep onset (A) 221 17.1 (10.1) 221 -.4 (11.4) 118 .4 (12.3) 103 -1.2 (10.2)

Number of awakenings (A) 221 4.5 (2.3) 221 -.3 (2.1) 118 .1 (2.3) 103 -.8 (1.9)

% Sleep (A) 221 96.4 % (2.0) 221 .7 % (2.3) 118 0 (2.5) 103 .2 (2.1)

Weight (lbs)a 221 193.7 (31.5) 221 -11.7 (13.3) 118 -1.9 (5.6) 103 -22.95 (10.3)

Body Mass Indexa 221 32.6 (4.8) 221 -2.0 (2.2) 118 -.3 (.9) 103 -3.8 (1.6)

Waist circumference (inches)a 221 40.4 (4.7) 221 -2.4 (2.9) 118 -.8 (2.3) 103 -4.2 (2.3)

Systolic BPa 221 120.4 (13.6) 221 -2.7 (11.2) 118 .9 (9.6) 103 -6.9 (11.2)

Diastolic BPa 221 75.6 (4.8) 221 -1.2 (6.4) 118 1.3 (5.4) 103 -4.0 (6.3)

A actigraph, S self-report

* Denotes that participant did not answer a pain item a Denotes significant changes between Non-achievers and Achievers from baseline to 6 months

J Behav Med (2016) 39:386–397 391

123

circumference (r = .153, p\ .05), and systolic blood pressure (r = .152, p\ .05).

Next, we evaluated the relationships between the change

scores in objective sleep measurements and the change in

anthropometric and biomarker measures. Change in total

sleep time was not associated with either the change in

BMI or weight. Weak positive associations were found

between the number of awakenings per actigraphy and

changes in weight (r = .167, p\ .05), BMI (r = .171, p\ .05), waist circumference (r = .173, p\ .05), and both systolic (r = .175, p\ .05) and diastolic (r = .208, p\ .05) blood pressures.

Relationships among non-achievers and achievers

of 5 % weight loss

Table 4 shows the relationships in non-achievers and

achievers at 6 months. In non-achievers, higher self-re-

ported sleep disturbance and self-reported sleep quality

scores were moderately positively associated with higher

pain scores. We also found that higher pain interference

scores had a weak association with higher WASO minutes.

In achievers, we found weak associations between lower

self-reported sleep quality and disturbance scores and

lower pain interference scores. Several weak to moderate

associations were observed between lower pain interfer-

ence scores and weight loss, and smaller waist circumfer-

ence. Objectively measured lower WASO minutes, number

of awakenings, and higher sleep percent had weak associ-

ations with lower BMI. Achievers also demonstrated weak

relationships between higher objectively measured percent

sleep and weight loss.

A regression model significantly predicted lower self-

reported sleep disturbance scores. Table 5 shows higher

weight loss and lower pain interference as significant pre-

dictors of lower self-reported sleep disturbance at

6 months. Approximately 6 % of the variance in the

change in self-reported sleep disturbance was accounted

for by the change in pain interference, weight, age, and

arthritis. Age and arthritis were non-significant predictors

of change in self-reported sleep disturbance when holding

constant change in weight and pain interference. Both

change in weight and change in pain interference had

similar importance to the model. For every pound lost, a

corresponding drop of .163 was seen in the self-reported

sleep disturbance score when holding constant age,

arthritis, and change in pain. In addition, for every one unit

drop in pain interference, a corresponding drop of .142 was

seen in the self-reported sleep disturbance score when

holding constant age, arthritis, and change in weight.

Discussion

This secondary analysis describes self-reported and

objective sleep and their relationships with health-related

factors in 221 mid-life, overweight and obese rural women

who participated in the ‘‘Women Weigh-In for Wellness’’

clinical trial. This is the first known report of concurrent

self-reported and objective measurement of sleep parame-

ters in healthy, mid-life, overweight and obese rural

women. We will now discuss these findings in relationship

to each specific aim and provide implications for research

and practice.

Table 3 Correlations between sleep, pain, anthropometric, and biomarker variables at 6 months (N = 221)

Variable Age Weight Body

Mass

Index

Waist

circumference

Systolic

BP

Diastolic

BP

Pain

interference

Sleep quality (S) .109 -.124 -.105 -.087 -.111 -.141* -.347**

Sleep Disturbance Score (S) -.050 .099 .080 .087 .136* .139* .434**

Pain Interference Score (S) .147* .260** .228** .258** .166* .054 –

Total sleep time (A) .030 -.029 -.044 -.011 .050 .060 .062

Wake after sleep onset (A) .033 .126 .168* .095 .142* .099 .146*

Number of awakenings (A) .073 .122 .177** .110 .167* .150* .113

% Sleep (A) -.029 -.135* -.172* -.106 -.125 -.075 -.140*

Age – -.098 -.070 .014 .069 -.147* .147*

Weight (lbs.) -.098 – .871** .846** .334** .288** .260**

Body Mass Index -.070 .871** – .814** .339** .317** .228**

Systolic BP .069 .334** .339** .257** – .689** .166*

Diastolic BP -.147* .288** .317** .276** .689** – .054

A actigraph, S, self-report

* p\ .05 (2-tailed); ** p\ .01 (2-tailed)

392 J Behav Med (2016) 39:386–397

123

Women self-reported sleep disturbance similar to norms

and self-reported higher pain interference. While the

women did not have high sleep disturbance, our findings

were complementary to previous reports of overweight or

obese women exhibiting pain (Wachholtz et al., 2009).

Overall, women were not hypertensive according to the

current JNC-8 guidelines (James et al., 2014). Aside from

being overweight and obese, women had fairly normal self-

reported sleep, pain, and biomarker characteristics. ‘‘The

Women Weigh-in for Wellness’’ trial may have had a

positive influence on health-related characteristics by

lowering weight, BMI, waist circumference, and blood

pressures in some women.

We were surprised to find that the objective indirect

measurements of sleep reflected very good sleep at baseline

and 6 months. There was not much room for improvement

of sleep duration; actigraphy results revealed that over

7 days, women were sleeping a mean duration of over 7� h. The sleep duration was in accordance with current rec-

Table 4 Correlations between sleep, pain, anthropometric, and biomarker variables in non-achievers (n = 118) and achievers (n = 103) at 6 months

Variable Age Weight Body Mass Index

Non-achiever Achiever Non-achiever Achiever Non-achiever Achiever

Sleep quality (S) .073 .168 -.015 -.207 -.054 -.101

Sleep Disturbance Score (S) -.027 -.094 .016 .105 .029 .029

Pain Interference Score (S) .188 .085 .184 .263** .155 .212

Total sleep time (A) .04 .014 -.010 -.120 -.111 -.051

Wake after sleep onset (A) .104 -.057 .020 .216 .071 .261**

Number of Awakenings (A) .141 -.018 .017 .150 .038 .258**

% Sleep (A) -.099 .054 -.017 -.248** -.078 -.270**

Age – – -.096 -.166 -.096 -.104

Weight (lbs) -.096 -.166 – – .811** .863**

Body Mass Index -.096 -0.104 .811** .863** – –

Systolic BP .066 .054 .339** .168 .346** .165

Diastolic BP -.206 -.11 .270** .131 .325** .129

Variable Waist circumference Systolic BP Diastolic BP Pain interference

Non-achiever Achiever Non-achiever Achiever Non-achiever Achiever Non-achiever Achiever

Sleep quality (S) .041 -.173 -.103 -.082 -.193 -.032 -.340** -.339**

Sleep Disturbance Score (S) -.002 .089 .155 .057 .187 .019 .462** .371**

Pain Interference Score (S) .129 .315** .173 .090 .042 -.011 – –

Total sleep time (A) .015 -.100 .078 -.009 .086 .004 .145 -.055

Wake After sleep onset (A) .009 .140 .165 .091 .099 .071 .244** .009

Number of awakenings (A) -.016 .145 .195 .081 .157 .085 .197 -.031

% Sleep (A) -.008 -.176 -.139 -.090 -.064 -.063 -.218 -.038

Age -.005 -.009 .066 .054 -.206 -.110 .188 .085

Weight (lbs) .736** .880** .339** .168 .270** .131 .184 .263**

Body Mass Index .681** .842** .346** .165 .325** .129 .155 .212

Systolic BP .217 .118 – – .650** .695** .173 .090

Diastolic BP .239** .137 .650** .695** – – .042 -.011

A actigraph, S self-report

** p\ .01 (2-tailed) due to potential collinearity between variables

Table 5 Ordinary least squares regression predicting change from baseline to 6 months in self-reported sleep disturbance (PROMIS)

(N = 221)

Predictor variables Unstandardized

B

Standardized

b coefficient T

Age (baseline) -.045 -.051 -.719

Arthritis (baseline) -1.068 -.078 -1.089

Weight change .163 .173 2.489*

Pain interference change .142 .171 2.476*

Adjusted R2 = .061, F(4, 214) = 4.316 p = .002

*p\ .05 (2-tailed)

J Behav Med (2016) 39:386–397 393

123

ommendations. Women in our sample were mid-life and

older, with the majority employed. These women may have

had scheduled day activities and maintained regular sleep

habits. Also, a ceiling effect could have occurred in the

indirect measurement of sleep. Prior studies collected

longitudinal data over years whereas this study included

data at baseline and 6 months. Considering data from

30 months was not yet available, we are unable to project if

the women will maintain good sleep duration.

Overall, we found consistent relationships among higher

self-reported sleep disturbance with higher pain interfer-

ence. Pain interference was revealed to be a noteworthy

variable as it also was associated with multiple anthropo-

metric, biomarker and ultimately objective indirect sleep

variables. Pain could be associated with sleep because

these centers may have reciprocal relationships within the

brain (Moldofsky, 2001). Women who suffer from dis-

turbed sleep may report pain, and when experiencing pain

may report disturbed sleep (Finan et al., 2013).

Previous studies also reported associations between self-

reported sleep duration and BMI or weight (Di Milia et al.,

2013; Kohatsu et al., 2006; St-Onge et al., 2010; Theorell-

Haglow et al., 2012). Although we did not measure self-

reported sleep duration, we found higher self-reported

sleep disturbance was associated with higher blood pres-

sures (Bansil et al., 2011; Fang et al., 2012; Grandner et al.,

2012; Guo et al., 2013). Our findings are consistent with

several studies that did not support the immediate or lon-

gitudinal relationship between objectively measured short

sleep duration and higher BMI or weight (Appelhans et al.,

2013; Evans et al., 2011; Lauderdale et al., 2009; Mezick

et al., 2014; Moraes et al., 2013). Over the 6 months,

change scores reflected improved sleep and pain that were

associated with lower weight, BMI, waist circumference,

and blood pressure.

Women who achieved 5 % or more weight loss had

improved sleep, pain interference, and blood pressure. Our

analysis adds to the evidence supporting the association of

better self-reported sleep quality and duration with weight

loss (Alfaris et al., 2015; O’Brien et al., 2012; Thomson

et al., 2012). Weight loss and lower pain interference were

predictors of lower self-reported sleep disturbance. Weight

loss could lower pain and contribute to less disruption in

sleep. Women with less sleep disruption may sequentially

show lower blood pressure. These results support the

relationship between sleep and weight change; sleep may

be a factor in achieving weight loss, or vice versa. Thus, it

is important for obese or overweight women to adopt

healthy sleep, diet, and physical activity to improve or

maintain health status.

There were strengths of this secondary analysis. This

was the first study to examine self-reported sleep and

objective indirect measurement of sleep duration and its

association with health factors in healthy obese and over-

weight mid-life and older rural women. The parent study

used reliable and valid instruments. Our secondary analysis

assessed both self-reported sleep disturbance with the

PROMIS and an objective indirect measurement of sleep.

Our analysis is not without limitations. Even though this

analysis included data from 73 % of the original sample;

selection bias could have occurred. The main limitation of

this study was the positioning of the actigraph on the hip.

Zinkhan et al. (2014) recently reported differences in

agreement between hip-worn actigraph, polysomnogram,

and the wrist-worn device. This limitation affects validity

of the results using number of awakenings and comparison

to other studies using wrist actigraphy. Women were not

screened for diagnoses of sleep apnea or other sleep dis-

orders. Obesity is a risk-factor for obstructive sleep apnea

and can result in sleep disturbance (Knutson et al., 2012).

Weight loss may not improve sleep if a sleep disorder is

present. Medications were not recorded; however women

were excluded if they took medications that influenced

weight. This study did not collect a medication history to

confirm the use of medications that influence sleep.

Conclusion

Sleep quantity and quality influence health. This secondary

analysis was performed to provide a description of sleep

and its association with health factors in a sample of rural

overweight and obese mid-life and older women. The

results are promising support for interventions aimed at

communities to improve health and sleep behaviors.

Grandner (2012) suggests that the science of sleep could

benefit from interventions that are clearly conceptualized

and performed at the community level. Other research

implications include the need for consistent use of reliable

and valid self-reported and objective measures of sleep

quality and duration. Studies need to confirm the norms for

sleep variables per actigraphy in mid-life and older women.

Self-reported sleep is often over or under-estimated and

data collected in epidemiological studies may not corre-

spond to objective indirect measures of sleep using actig-

raphy (Girschik et al., 2012). Self-reported sleep duration

has shown negative agreement with objectively measured

sleep/wake parameters (Girschik et al., 2012). We observed

that self-reported sleep disturbance was not significantly

correlated with objectively measured sleep. Self-report

quality measures complement, but are not equivalent to

quantify actigraphy parameters such as sleep duration (total

sleep time), WASO, and number of awakenings. Thus,

examining sleep quantity, quality, and disturbance using

self-reported and objective measures is recommended

(Grandner, 2012; Madsen et al., 2015). Measuring sleep by

394 J Behav Med (2016) 39:386–397

123

both methods provides a well-rounded depiction of sleep’s

association with being overweight or obese and with weight

loss or gain. Science could benefit from more studies of mid-

life and older women living in rural and small communities

and specifically target ethnicities other than Caucasian in

which obesity/overweight and hypertension are prevalent.

Intervention studies are needed that specifically focus on the

impact of weight loss on sleep and pain.

Health professionals need to translate research evidence

on sleep into practice. Implications for practice include

screening, assessment, and management of sleep disorders

and disturbances, pain, and blood pressure in overweight

and obese women. Health professionals need to educate

and encourage non-pharmacologic and/or pharmacologic

management of sleep disturbances and pain as indicated.

Clinicians need to encourage and monitor healthy lifestyle

behaviors, paying particular attention to evidence based

strategies appropriate for rural women to promote weight

loss through healthy eating, physical activity, and sleep.

Acknowledgments Support: NINR/NIH 1R01NR010589.

Compliance with ethical standards

Conflict of interest Marcia Y. Shade, Ann M. Berger, Paul J. Dizona, Bunny J. Pozehl, and Carol H. Pullen declare that they have

no conflict of interest.

Human and animal rights and Informed consent All procedures followed were in accordance with ethical standards of the responsible

committee on human experimentation (institutional and national) and

with the Helsinki Declaration of 1975, as revised in 2000. Informed

consent was obtained from all patients for being included in the study.

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