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Sleep and weight loss in low-income overweight or obese postpartum women

Sleep and weight loss in low-income overweight or obese postpartum women Background: We conducted secondary data analyses to examine the associations between sleep duration, sleep quality, sleep disturbance and ≥ 5% of weight loss in low-income overweight or obese postpartum women enrolled in a community-based lifestyle behavior intervention study aimed at prevention of weight gain. Methods: Participants were recruited from the Special Supplemental Nutrition Program for Women, Infants, and Children in Michigan. The Pittsburgh Sleep Quality Index was used to assess sleep duration, sleep quality, and sleep disturbance. All participants were assessed and weighed at baseline (T1, 569 participants), 4-month (T2, 367 participants) , and 7-month from T1 (T3, 332 participants). Descriptive statistics and mixed-effects regression analysis were performed. Results: Participants reported longer sleep duration (p = 0.048), better sleep quality (p = 0.003) and less sleep disturbance (p < 0.001) over time. There were no significant mean body weight changes at T2 and T3. However, a significantly higher proportion of women lost ≥5% of body weight at T3 (23.1%) than T2 (12.5%, p = 0.001). Sleep duration, quality, and disturbance were not significantly associated with ≥5% of weight loss. Conclusion: Improvements in sleep duration, sleep quality and sleep disturbance over time were not associated with ≥5% of weight loss in low-income overweight or obese postpartum women. Trial registration: Clinical Trials NCT01839708; retrospectively registered February 28, 2013. Keywords: Low-income women, Sleep, Obesity, Postpartum Background cancer [6], type 2 diabetes, cardiovascular disease, and The prevalence of obesity in American women has sig- nontraumatic death [7]. Also, there is a strong linear as- nificantly increased between 2005 and 2014 [1]. One age sociation between longer duration of being overweight group contributing to this significant linear trend is or obese and incidence of obesity-related cancer [8], women at child-bearing age (young women), especially all-cause mortality [9], cardiovascular disease [10], and for those who are low-income [2]. Approximately 50% of type 2 diabetes [11]. Therefore, it is critically important young, low-income women are overweight or obese to help young adults to lose weight. Evidence is clear prior to becoming pregnant [3]. These women are at a that even a loss of ≥5% of weight reduces risk of cardio- high risk for persistent obesity and major weight gain vascular diseases (e.g., by improving glucose, insulin, tri- later in life, most likely related to excessive gestational glyceride [12, 13], systolic and diastolic blood pressure, weight gain (gaining more than the Institute of Medicine HDL cholesterol) [13] and cancer mortality [14]. pregnancy weight gain guideline) [4] and significant Identification of risk factors associated with weight postpartum weight retention (retaining at least 22 lbs. at gain may potentially inform weight loss intervention ef- 1-year postpartum) [5]. forts to reduce the ongoing obesity epidemic and im- Weight gain of as little as 11 lbs. during young adult- prove public health. Derangement of sleep is one such hood increases risk of overweight- or obesity-related risk factor suggested by prior studies, which may be fur- ther characterized by problems with sleep duration, sleep quality, or sleep disturbance. The association be- * Correspondence: chang.1572@osu.edu 1 tween short sleep duration and weight gain has recently College of Nursing, The Ohio State University, 1585 Neil Avenue, Columbus, OH 43210, USA received increased attention in prospective longitudinal Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Chang et al. BMC Obesity (2019) 6:12 Page 2 of 7 studies. A recent meta-analysis that included 11 pro- and ≥ 5% of weight loss in low-income overweight or spective cohort studies in adults aged 18 to 72 years obese postpartum women enrolled in a community-based found that short sleep duration was associated with lifestyle behavior intervention study aimed at prevention weight gain [15]. Consistent with results of the of weight gain. Although sleep was not a target of the meta-analysis, a systematic review that included 3 pro- behavior intervention, examining such associations spective cohort studies and 1 cross-sectional study re- maystill beinformativefor designing futureinterven- ported that short sleep duration was associated with tions in this population. postpartum weight retention [16]. Also, three other pro- spective cohort studies consistently reported the associ- Methods ation between short sleep duration and weight gain in Participants were recruited from the Special Supplemental adults [17–19]. Given the growing evidence that short Nutrition Program for Women, Infants, and Children sleep duration is a potential risk factor for weight gain in (WIC) in Michigan between September 2012 and January adults, it seems logical to examine the association between 2015. WIC is a federally funded program that serves sleep duration and weight loss. However, no prospective low-income pregnant, breastfeeding and postpartum cohort studies have yet been conducted in overweight or women and children (0–5 years) in the US. To be eli- obese adults. Only two lifestyle behavior weight loss inter- gible to participate in this study, women had to be vention studies for middle-aged overweight or obese non-pregnant, 18–39 years old, between 6 weeks and adults have examined such association. These studies have 4.5 years postpartum, non-Hispanic Black (Black) or consistently shown that increased sleep duration was asso- White(White),freeof type1or 2diabetes, andover- ciated with weight loss [20, 21]. However, it is unknown weight or obese (body mass index [BMI] between 25.0 whether the positive results found in middle-aged over- and 39.9 kg/m ). Height and weight were measured weight or obese adults could be applied to low-income during recruitment and were used to calculate BMI. overweight or obese postpartum women. Details about the study recruitment and eligibility have The association between sleep quality and weight loss been previously described [25]. The study procedure has also received little attention, with only three weight was approved and monitored by Michigan Department loss intervention studies for middle-aged overweight or of Health and Human Services and Michigan State obese adults investigating this association. These studies University Institutional Review Boards. have yielded mixed results [20–22]. While two studies reported that improved sleep quality was associated with Randomization and intervention successful weight loss at 3–4 months [20] and at 6 A more complete description of the community-based months from baseline [22], one study did not find such lifestyle behavior intervention study aimed at prevention association at the end of a 6-month intervention or at of weight gain has been previously published [26]. 12-month and 18-month follow ups [21]. Few longitu- Briefly, participants (N = 569) were randomized in a 2:1 dinal prospective studies have investigated an association ratio to an intervention (n = 387) or comparison group between sleep disturbance and weight loss in overweight (n = 182). During the 16-week intervention, the interven- or obese adults. These studies have consistently reported tion group viewed 10 intervention videos at home: a no association between sleep disturbance and weight video every week (weeks 1–4) followed by every other loss in middle-aged women who enrolled in a weight week (weeks 6–16). The video topics did not include any loss intervention study [23] and in adults enrolled in a contents related to sleep but included stress manage- 30-year prospective cohort study [24]. ment (4 videos), healthy eating (5 videos), and physical Sleep duration, sleep quality, and sleep disturbance are activity (1 video). Intervention participants also dialed in potentially modifiable risk factors for obesity. However, to 10 peer support group teleconferences led by commu- only a limited number of studies have investigated the as- nity peer educators and WIC professionals who were sociations between sleep duration, sleep quality, sleep dis- trained in motivational interviewing and group facilita- turbance and weight loss in overweight or obese adults. tion. The comparison group received printed educational Also, these studies mainly focused on middle-aged obese messages on stress management, healthy eating, and adults. Our review of literature suggests a knowledge gap physical activity and no contents related to sleep. We in these associations for low-income overweight or obese assessed all participants’ body weight (in person, primary postpartum women. To design weight loss intervention outcome) at baseline (T1), immediately after the 16-week studies for low-income overweight or obese postpartum intervention (T2, 4 months from T1), and at a 3-month women, it is critically important to understand the associ- follow up (T3, 7 months from T1). We did not detect any ation between sleep and weight loss. The objective of this significant differences between the intervention and com- secondary data analysis was to examine the associations parison groups in mean body weight at T2 or T3 [27]. between sleep duration, sleep quality, sleep disturbance Prior to conducting this secondary analysis study, we Chang et al. BMC Obesity (2019) 6:12 Page 3 of 7 investigated whether there were significant differences in dichotomized outcomes (sleep duration, ≥ 7 h/per night sleep duration, sleep quality, and sleep disturbance be- [adequate or longer sleep duration] vs. < 7 h/per night tween the intervention and comparison groups. Since we [shorter sleep duration]; ≥ 5% weight loss from baseline, did not find any differences between groups, in this paper yes vs. no) and mixed-effects linear regression for con- we presented results that combined all study participants tinuous outcomes (sleep quality, sleep disturbance, body regardless of their group assignments. weight, and weight change from baseline – absolute (or mean) change and % change). We then examined Measurements whether weight change across time was related to sleep. Demographics As the ≥5% weight loss from baseline was the only Participants self-reported their birthday (to calculate weight outcome that had significant change across time, age) and their youngest child’s birthday (to calculate we compared women with < 5% weight loss to those postpartum period). They also provided information on with ≥5% weight loss from baseline in sleep duration, race/ethnicity, smoking, education, employment, and sleep quality, and sleep disturbance. Chi-square statistics current breastfeeding status (yes/no). were performed for categorical variables (sleep duration) and two-sample t-tests were applied for continuous vari- Body weight ables (sleep quality and sleep disturbance). We used SAS Participants returned to the WIC office where they were 9.4® (SAS Institute, Cary, NC) for the statistical analysis. recruited to have body weight measured. Body weight All tests were two-sided at a significance level of 0.05. was measured using an electronic digital scale (Seca 869, Germany) to the nearest 0.2 lbs. while participants wore Results light clothing and no shoes. Table 1 presents demographic characteristics of the study cohort at each time point of data collection. At Sleep duration, sleep quality, and sleep disturbance baseline, the mean age of participants was 28.5 ± 5.03 We used subscales from the Pittsburgh Sleep Quality years, and the mean time from last delivery was 1.72 ± Index, which has established validity and reliability for 1.28 years. Approximately 79% of participants were each subscale [28], to measure sleep duration, quality non-Hispanic White; 67% had at least some college edu- and disturbance. Participants were instructed to provide cation; 46% were employed part-or-full time. Also, most responses that reflected the majority of days and nights participants did not breastfeed (84%), were non-smokers in the past month. For sleep duration (1 item), partici- (74%) and were obese (64%). In spite of sample attrition pants were asked “How many hours of actual sleep did (T1 = 569 participants, T2 = 367 participants, and T3 = you get? This may be different from the number of 332 participants), there were no significant differences in hours you spend in bed.” Response options range from demographic characteristics across the 3 time points. more than 7 h (0), between 6 and 7 h (1), between 5 and Table 2 shows change in sleep duration, sleep quality, 6 h (2), and less than 5 h (3). For sleep quality (1 item), sleep disturbance, and body weight (mean and % change) they were asked to rate their overall sleep quality. over time. Participants reported significant improvements Response options range from very good (0) to very bad in sleep duration (p = 0.048), sleep quality (p =0.003) and (3). For sleep disturbance (9 items), participants were sleep disturbance over time (p < 0.001). Although there asked to respond to a list of questions asking about fac- were no significant changes in mean body weight across tors contributing to their trouble sleeping. Response op- the 3 time points, there was a trend of decreasing mean tions range from none in the last month (0) to three or body weight over time. A significantly higher proportion more times a week (3). The overall sleep disturbance of participants lost ≥5% of body weight at T3 (23.1%) than score was the mean of the 9 responses. A higher score T2 (12.5%; p =0.001). indicates more sleep disturbance. Table 3 presents results of comparison between women with < 5% and those with ≥5% weight loss from Statistical analysis baseline by sleep duration, sleep quality and sleep dis- Descriptive statistics were used to summarize the sample turbance. Improvements in sleep duration, sleep quality at each time point on baseline characteristics and out- and sleep disturbance were not associated with ≥5% of comes, including sleep duration, sleep quality, sleep dis- weight loss at T2 or T3. turbance, and weight outcomes (weight in pounds, % weight change from T1, and whether or not achieving Discussion ≥5% weight loss from T1). We compared the baseline This is the first study to examine the associations be- demographic characteristics across time (T1 to T3). We tween sleep duration, sleep quality, sleep disturbance, also examined the overall trajectories of outcomes across and ≥ 5% weight loss in low-income overweight or obese time using mixed-effects logistic regression for postpartum women using prospective longitudinal data. Chang et al. BMC Obesity (2019) 6:12 Page 4 of 7 Table 1 Demographic Characteristics of Low-Income Overweight or Obese Postpartum Women Characteristics Mean ± SD or N (%) P-value T1 (N = 569) T2 (N = 367) T3 (N = 332) Age (years) 28.54 ± 5.03 29.17 ± 4.91 29.42 ± 4.86 1.00 Postpartum period (years) 1.72 ± 1.28 1.73 ± 1.27 1.74 ± 1.28 0.98 Race Black 121 (21.27) 68 (18.53) 63 (18.98) 0.53 White 448 (78.73) 299 (81.47) 269 (81.02) Education High School or less 187 (32.86) 116 (31.61) 102 (30.72) 0.80 At least some college 382 (67.14) 251 (68.39) 230 (69.28) Employment Employed (FT/PT/Self) 262 (46.05) 161 (43.87) 139 (41.87) 0.66 Unemployed 118 (20.74) 66 (17.98) 61 (18.37) Other (homemaker/student/other) 189 (33.22) 140 (38.15) 132 (39.76) Current smoker No 421 (73.99) 296 (80.65) 260 (78.31) 0.12 Yes 148 (26.01) 71 (19.35) 72 (21.69) Current breastfeeding No 475 (83.48) 290 (79.02) 267 (80.42) 0.76 Yes 94 (16.52) 77 (20.98) 65 (19.58) Body mass index (BMI) 32.04 ± 4.29 31.77 ± 4.35 31.60 ± 4.30 1.00 BMI category Overweight (BMI 25–29.9 kg/m ) 205 (36.03) 142 (38.69) 131 (39.46) 0.83 Obese I (BMI 30–34.9 kg/m ) 209 (36.73) 131 (35.69) 122 (36.75) Obese II (BMI 35.0–39.9 kg/m ) 155 (27.24) 94 (25.61) 79 (23.80) Randomization Intervention group 387 (68.01) 236 (64.31) 213 (64.16) 0.80 Comparison group 182 (31.99) 131 (35.69) 119 (35.84) T1: baseline, T2: 4 months from T1, T3: 7 months from T1. FT: employed full-time, PT: employed part-time, self = self-employed P-values were derived using mixed-effects linear regression modeling for continuous variables (age, postpartum period, BMI) and mixed-effects logistic regression for categorical variables (race, education, employment, current smoker, current breastfeeding, BMI category, and randomization). We did not present coefficient or odds ratio estimates from the regression models since the descriptive statistics (Mean ± SD and %) in the table are more straightforward to illustrate the extent of change across time We observed that improvements in sleep duration, sleep positive change remained at T3. However, our results quality, and sleep disturbance over time (T2 and T3) also revealed that the comparison group had a signifi- were not associated with ≥5% of weight loss. One pos- cant reduction in stress at T3 [30]. Finally, it is possible sible explanation of the overall non-significant findings that as children grow older, they might sleep better [31] is related to the low magnitude of changes in sleep dur- and require less attention from their caregivers at night. ation, sleep quality, and sleep disturbance. This means Also, they might sleep apart from their mothers, which that though overall amount of sleep duration, sleep qual- was associated with better sleep in mothers [32]. ity, and sleep disturbance improved significantly over Our finding showing no association between sleep time, the changes were relatively small; and sleep quality duration and ≥ 5% weight loss is inconsistent with find- and sleep disturbances remained poor. Also, the overall ings of a previous weight loss intervention study [22]. improvement in sleep might have been impacted by our The inconsistency might have related to the study sam- intervention that included stress management. A recent ple. Whereas we focused on postpartum women whose study of pregnant women has reported that higher levels sleep is often dependent on different factors (e.g., chil- of stress were associated with poor sleep [29]. In our dren’s sleep), the prior studies focused on middle-aged intervention study, we found that the intervention group adults. Similarly, our findings on sleep quality and had a significant reduction in stress at T2 and the weight loss were consistent with one study of Chang et al. BMC Obesity (2019) 6:12 Page 5 of 7 Table 2 Change in Sleep Duration, Sleep Quality, Sleep Disturbance, and Body Weight across Time Mean ± SD or N (%) P-value T1 (N = 569) T2 (N = 367) T3 (N = 332) Sleep Duration < 7 h 457 (80.32%) 249 (73.67%) 230 (73.95%) 0.048 ≥ 7 h 112 (19.68) 89 (26.33%) 81 (26.05%) Sleep quality 1.44 ± 0.78 1.35 ± 0.82 1.30 ± 0.75 0.003 Sleep disturbance 1.42 ± 0.58 1.26 ± 0.67 1.21 ± 0.67 < 0.001 Weight 190.8 ± 29.85 188.5 ± 31.22 187.9 ± 32.07 0.389 Weight change from T1 n/a −.08 ± 9.72 −.72 ± 12.46 0.218 % Weight change from T1 n/a −0.1% ± 5.1% −0.4% ± 6.7% 0.253 Having ≥ 5% weight loss from T1 No n/a 300 (87.4%) 246 (76.9%) 0.001 Yes n/a 43 (12.5%) 74 (23.1%) a b T1: baseline, T2: 4 months from T1, T3: 7 months from T1. The lower score, the better sleep quality. The higher score, the more sleep disturbance P-values were derived using mixed-effects linear regression modeling for continuous variables (sleep quality, sleep disturbance, weight, weight change from T1, and % weight change from T1) and using mixed-effects logistic regression for categorical variables (sleep duration and having ≥5% weight loss from T1). We did not present coefficient or odds ratio estimates from the regression models since the descriptive statistics (Mean ± SD and %) in the table are more straightforward to illustrate the extent of change across time Table 3 Comparison of Women with < 5% Vs. ≥ 5% Weight Loss from Baseline by Sleep Duration, Sleep Quality, and Sleep Disturbance Weight loss, T2 vs. T1 Weight loss, T3 vs. T1 $ $ <5% ≥ 5% P-value <5% ≥ 5% P-value N (row %) N (row %) Sleep duration Baseline ≥ 7 h 61 (85.92) 10 (14.08) 0.658 50 (73.53) 18 (26.47) 0.819 < 7 h 239 (87.87) 33 (12.13) 196 (77.78) 56 (22.22) Follow-up ≥ 7 h 78 (88.64) 10 (11.36) 0.852 63 (79.75) 16 (20.25) 0.288 < 7 h 203 (87.88) 28 (12.12) 168 (75.00) 56 (25.00) Change from baseline Kept ≥7 h 36 (87.80) 5 (12.20) 0.783 31 (79.49) 8 (20.51) 0.184 Increased to ≥7 h 42 (89.36) 5 (10.64) 32 (80.00) 8 (20.00) Kept < 7 h 183 (87.98) 25 (12.02) 153 (76.50) 47 (23.50) Decreased to < 7 h 20 (86.96) 3 (13.04) 15 (62.50) 9 (37.50) Mean ± SD Mean ± SD Sleep quality Baseline 1.42 ± 0.75 1.40 ± 0.85 0.822 1.44 ± 0.74 1.46 ± 0.83 0.871 Follow-up 1.35 ± 0.81 1.16 ± 0.75 0.171 1.32 ± 0.77 1.24 ± 0.72 0.388 Change from T1 −0.07 ± 0.85 −0.29 ± 0.65 0.131 −0.10 ± 0.82 − 0.24 ± 0.76 0.224 Sleep disturbance Baseline 1.41 ± 0.58 1.44 ± 0.59 0.764 1.40 ± 0.58 1.49 ± 0.58 0.277 Follow-up 1.27 ± 0.65 1.23 ± 0.68 0.724 1.21 ± 0.67 1.28 ± 0.63 0.411 Change from T1 −0.14 ± 0.70 −0.21 ± 0.51 0.458 −0.19 ± 0.72 −0.20 ± 0.55 0.883 a b T1: baseline, T2: 4 months from T1, T3: 7 months from T1. The lower score, the better sleep quality. The higher score, the more sleep disturbance P-values were derived using Chi-square statistics for categorical variables (sleep duration) and two-sample t-tests for continuous variables (sleep quality and sleep disturbance) Chang et al. BMC Obesity (2019) 6:12 Page 6 of 7 middle-aged adults [23], but not another study of Abbreviations BMI: Body Mass Index; WIC: The Special Supplemental Nutrition Program for middle-aged adults [20]. We are unable to explain the Women, Infants, and Children. discrepancy. Finally, our finding indicating no associ- ation between sleep disturbance and weight loss was Acknowledgements Not applicable. consistent with two prior studies [23, 24]. There are strengths of the present research. Our find- Funding ings were based on analysis of a prospective longitudinal The full trial of the community-based lifestyle behavior intervention study aimed at prevention of weight gain was supported by Grant dataset that included a homogeneous sample of Number R18-DK-083934. However, the design, analysis and not writing low-income overweight or obese postpartum women. of this manuscript was not supported by the grant. The content is solely Also, body weight was measured in person rather than the responsibility of the authors and does not necessarily represent the official views of the National Institute of Diabetes and Digestive and based on self-report. Although sleep was not targeted as Kidney Disease or the National Institutes of Health. a behavior for intervention in the intervention study, we used validated instruments to assess sleep duration, Availability of data and materials The datasets generated and/or analyzed during the current study are not sleep quality, and sleep disturbance that appropriately publicly available because we are in the stage of data analysis to answer measure these characteristics as they occur in this popu- other research questions but are available from the corresponding author on lation. Even so, several limitations of the study need to a reasonable request. be considered when interpreting our findings. First, this Authors’ contributions was a secondary data analysis. The level of power to de- MC and AT conceptualized the present study. AT conducted data analysis. tect any significant differences in the measurements that MC and AT interpreted results, drafted, and critically revised the manuscript. JS and DTW involved in critical revision. All the authors have read and we chose is unknown but could be lower than desired. approved the final manuscript. Second, we used self-reported sleep data over an ex- tended period of time (the past month). Though these Authors’ information Mei-Wei Chang, PhD, is an Associate Professor; Alai Tan, PhD, is an Associate measures have proven helpful in predicting weight loss Professor; Jonathan Schaffir, MD, is an Associate Professor; Duane T. in some studies, asking respondents to report sleep dur- Wegener, PhD, is a Professor. ation, sleep quality, and sleep disturbance over an ex- Ethics approval and consent to participate tended period could lead to biases in over-emphasizing Participation was voluntary. Participants provided written consent prior to recent experience rather than actually characterizing the participating in the study if they met the study criteria and understood the sleep experience over the requested period. If recent ex- study requirements. This study was approved and monitored by Michigan Department of Health and Human Services and Michigan State University periences are more variable than the requested trends Institutional Review Boards. in sleep experience over time, a measure bias toward recent experience could produce weaker associations Consent for publication Not applicable. with longer-termoutcomessuchasweightlossthan would a measure that more faithfully captures the Competing interests longer-term trend in sleep experience. Third, the The authors declare that they have no competing interests. dropout rate across each time point of the study was substantial, though the demographic characteristics of Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in the sample remained consistent over each time point published maps and institutional affiliations. of studydatacollection. Fourth, the length of the study may have been too short to demonstrate clinic- Author details College of Nursing, The Ohio State University, 1585 Neil Avenue, Columbus, ally significant results. Finally, the mean changes in OH 43210, USA. Department of Obstetrics & Gynecology, The Ohio State weight and sleep scores were small, even when statis- University, 370 W. 9th Avenue Columbus, Columbus, OH 43210, USA. tically significant. It is possible that larger amounts of Department of Psychology, The Ohio State University, 1835 Neil Avenue, Columbus, OH 43210, USA. weight loss may be observed with larger changes in sleep behaviors. Received: 31 October 2018 Accepted: 21 February 2019 Conclusions References Our findings show that improvements in sleep dur- 1. Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Ogden CL. Trends in ation, sleep quality, and sleep disturbance were not obesity among adults in the United States, 2005 to 2014. 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Sleep and weight loss in low-income overweight or obese postpartum women

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Copyright © 2019 by The Author(s).
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Medicine & Public Health; Endocrinology; Public Health
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10.1186/s40608-019-0236-6
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Abstract

Background: We conducted secondary data analyses to examine the associations between sleep duration, sleep quality, sleep disturbance and ≥ 5% of weight loss in low-income overweight or obese postpartum women enrolled in a community-based lifestyle behavior intervention study aimed at prevention of weight gain. Methods: Participants were recruited from the Special Supplemental Nutrition Program for Women, Infants, and Children in Michigan. The Pittsburgh Sleep Quality Index was used to assess sleep duration, sleep quality, and sleep disturbance. All participants were assessed and weighed at baseline (T1, 569 participants), 4-month (T2, 367 participants) , and 7-month from T1 (T3, 332 participants). Descriptive statistics and mixed-effects regression analysis were performed. Results: Participants reported longer sleep duration (p = 0.048), better sleep quality (p = 0.003) and less sleep disturbance (p < 0.001) over time. There were no significant mean body weight changes at T2 and T3. However, a significantly higher proportion of women lost ≥5% of body weight at T3 (23.1%) than T2 (12.5%, p = 0.001). Sleep duration, quality, and disturbance were not significantly associated with ≥5% of weight loss. Conclusion: Improvements in sleep duration, sleep quality and sleep disturbance over time were not associated with ≥5% of weight loss in low-income overweight or obese postpartum women. Trial registration: Clinical Trials NCT01839708; retrospectively registered February 28, 2013. Keywords: Low-income women, Sleep, Obesity, Postpartum Background cancer [6], type 2 diabetes, cardiovascular disease, and The prevalence of obesity in American women has sig- nontraumatic death [7]. Also, there is a strong linear as- nificantly increased between 2005 and 2014 [1]. One age sociation between longer duration of being overweight group contributing to this significant linear trend is or obese and incidence of obesity-related cancer [8], women at child-bearing age (young women), especially all-cause mortality [9], cardiovascular disease [10], and for those who are low-income [2]. Approximately 50% of type 2 diabetes [11]. Therefore, it is critically important young, low-income women are overweight or obese to help young adults to lose weight. Evidence is clear prior to becoming pregnant [3]. These women are at a that even a loss of ≥5% of weight reduces risk of cardio- high risk for persistent obesity and major weight gain vascular diseases (e.g., by improving glucose, insulin, tri- later in life, most likely related to excessive gestational glyceride [12, 13], systolic and diastolic blood pressure, weight gain (gaining more than the Institute of Medicine HDL cholesterol) [13] and cancer mortality [14]. pregnancy weight gain guideline) [4] and significant Identification of risk factors associated with weight postpartum weight retention (retaining at least 22 lbs. at gain may potentially inform weight loss intervention ef- 1-year postpartum) [5]. forts to reduce the ongoing obesity epidemic and im- Weight gain of as little as 11 lbs. during young adult- prove public health. Derangement of sleep is one such hood increases risk of overweight- or obesity-related risk factor suggested by prior studies, which may be fur- ther characterized by problems with sleep duration, sleep quality, or sleep disturbance. The association be- * Correspondence: chang.1572@osu.edu 1 tween short sleep duration and weight gain has recently College of Nursing, The Ohio State University, 1585 Neil Avenue, Columbus, OH 43210, USA received increased attention in prospective longitudinal Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Chang et al. BMC Obesity (2019) 6:12 Page 2 of 7 studies. A recent meta-analysis that included 11 pro- and ≥ 5% of weight loss in low-income overweight or spective cohort studies in adults aged 18 to 72 years obese postpartum women enrolled in a community-based found that short sleep duration was associated with lifestyle behavior intervention study aimed at prevention weight gain [15]. Consistent with results of the of weight gain. Although sleep was not a target of the meta-analysis, a systematic review that included 3 pro- behavior intervention, examining such associations spective cohort studies and 1 cross-sectional study re- maystill beinformativefor designing futureinterven- ported that short sleep duration was associated with tions in this population. postpartum weight retention [16]. Also, three other pro- spective cohort studies consistently reported the associ- Methods ation between short sleep duration and weight gain in Participants were recruited from the Special Supplemental adults [17–19]. Given the growing evidence that short Nutrition Program for Women, Infants, and Children sleep duration is a potential risk factor for weight gain in (WIC) in Michigan between September 2012 and January adults, it seems logical to examine the association between 2015. WIC is a federally funded program that serves sleep duration and weight loss. However, no prospective low-income pregnant, breastfeeding and postpartum cohort studies have yet been conducted in overweight or women and children (0–5 years) in the US. To be eli- obese adults. Only two lifestyle behavior weight loss inter- gible to participate in this study, women had to be vention studies for middle-aged overweight or obese non-pregnant, 18–39 years old, between 6 weeks and adults have examined such association. These studies have 4.5 years postpartum, non-Hispanic Black (Black) or consistently shown that increased sleep duration was asso- White(White),freeof type1or 2diabetes, andover- ciated with weight loss [20, 21]. However, it is unknown weight or obese (body mass index [BMI] between 25.0 whether the positive results found in middle-aged over- and 39.9 kg/m ). Height and weight were measured weight or obese adults could be applied to low-income during recruitment and were used to calculate BMI. overweight or obese postpartum women. Details about the study recruitment and eligibility have The association between sleep quality and weight loss been previously described [25]. The study procedure has also received little attention, with only three weight was approved and monitored by Michigan Department loss intervention studies for middle-aged overweight or of Health and Human Services and Michigan State obese adults investigating this association. These studies University Institutional Review Boards. have yielded mixed results [20–22]. While two studies reported that improved sleep quality was associated with Randomization and intervention successful weight loss at 3–4 months [20] and at 6 A more complete description of the community-based months from baseline [22], one study did not find such lifestyle behavior intervention study aimed at prevention association at the end of a 6-month intervention or at of weight gain has been previously published [26]. 12-month and 18-month follow ups [21]. Few longitu- Briefly, participants (N = 569) were randomized in a 2:1 dinal prospective studies have investigated an association ratio to an intervention (n = 387) or comparison group between sleep disturbance and weight loss in overweight (n = 182). During the 16-week intervention, the interven- or obese adults. These studies have consistently reported tion group viewed 10 intervention videos at home: a no association between sleep disturbance and weight video every week (weeks 1–4) followed by every other loss in middle-aged women who enrolled in a weight week (weeks 6–16). The video topics did not include any loss intervention study [23] and in adults enrolled in a contents related to sleep but included stress manage- 30-year prospective cohort study [24]. ment (4 videos), healthy eating (5 videos), and physical Sleep duration, sleep quality, and sleep disturbance are activity (1 video). Intervention participants also dialed in potentially modifiable risk factors for obesity. However, to 10 peer support group teleconferences led by commu- only a limited number of studies have investigated the as- nity peer educators and WIC professionals who were sociations between sleep duration, sleep quality, sleep dis- trained in motivational interviewing and group facilita- turbance and weight loss in overweight or obese adults. tion. The comparison group received printed educational Also, these studies mainly focused on middle-aged obese messages on stress management, healthy eating, and adults. Our review of literature suggests a knowledge gap physical activity and no contents related to sleep. We in these associations for low-income overweight or obese assessed all participants’ body weight (in person, primary postpartum women. To design weight loss intervention outcome) at baseline (T1), immediately after the 16-week studies for low-income overweight or obese postpartum intervention (T2, 4 months from T1), and at a 3-month women, it is critically important to understand the associ- follow up (T3, 7 months from T1). We did not detect any ation between sleep and weight loss. The objective of this significant differences between the intervention and com- secondary data analysis was to examine the associations parison groups in mean body weight at T2 or T3 [27]. between sleep duration, sleep quality, sleep disturbance Prior to conducting this secondary analysis study, we Chang et al. BMC Obesity (2019) 6:12 Page 3 of 7 investigated whether there were significant differences in dichotomized outcomes (sleep duration, ≥ 7 h/per night sleep duration, sleep quality, and sleep disturbance be- [adequate or longer sleep duration] vs. < 7 h/per night tween the intervention and comparison groups. Since we [shorter sleep duration]; ≥ 5% weight loss from baseline, did not find any differences between groups, in this paper yes vs. no) and mixed-effects linear regression for con- we presented results that combined all study participants tinuous outcomes (sleep quality, sleep disturbance, body regardless of their group assignments. weight, and weight change from baseline – absolute (or mean) change and % change). We then examined Measurements whether weight change across time was related to sleep. Demographics As the ≥5% weight loss from baseline was the only Participants self-reported their birthday (to calculate weight outcome that had significant change across time, age) and their youngest child’s birthday (to calculate we compared women with < 5% weight loss to those postpartum period). They also provided information on with ≥5% weight loss from baseline in sleep duration, race/ethnicity, smoking, education, employment, and sleep quality, and sleep disturbance. Chi-square statistics current breastfeeding status (yes/no). were performed for categorical variables (sleep duration) and two-sample t-tests were applied for continuous vari- Body weight ables (sleep quality and sleep disturbance). We used SAS Participants returned to the WIC office where they were 9.4® (SAS Institute, Cary, NC) for the statistical analysis. recruited to have body weight measured. Body weight All tests were two-sided at a significance level of 0.05. was measured using an electronic digital scale (Seca 869, Germany) to the nearest 0.2 lbs. while participants wore Results light clothing and no shoes. Table 1 presents demographic characteristics of the study cohort at each time point of data collection. At Sleep duration, sleep quality, and sleep disturbance baseline, the mean age of participants was 28.5 ± 5.03 We used subscales from the Pittsburgh Sleep Quality years, and the mean time from last delivery was 1.72 ± Index, which has established validity and reliability for 1.28 years. Approximately 79% of participants were each subscale [28], to measure sleep duration, quality non-Hispanic White; 67% had at least some college edu- and disturbance. Participants were instructed to provide cation; 46% were employed part-or-full time. Also, most responses that reflected the majority of days and nights participants did not breastfeed (84%), were non-smokers in the past month. For sleep duration (1 item), partici- (74%) and were obese (64%). In spite of sample attrition pants were asked “How many hours of actual sleep did (T1 = 569 participants, T2 = 367 participants, and T3 = you get? This may be different from the number of 332 participants), there were no significant differences in hours you spend in bed.” Response options range from demographic characteristics across the 3 time points. more than 7 h (0), between 6 and 7 h (1), between 5 and Table 2 shows change in sleep duration, sleep quality, 6 h (2), and less than 5 h (3). For sleep quality (1 item), sleep disturbance, and body weight (mean and % change) they were asked to rate their overall sleep quality. over time. Participants reported significant improvements Response options range from very good (0) to very bad in sleep duration (p = 0.048), sleep quality (p =0.003) and (3). For sleep disturbance (9 items), participants were sleep disturbance over time (p < 0.001). Although there asked to respond to a list of questions asking about fac- were no significant changes in mean body weight across tors contributing to their trouble sleeping. Response op- the 3 time points, there was a trend of decreasing mean tions range from none in the last month (0) to three or body weight over time. A significantly higher proportion more times a week (3). The overall sleep disturbance of participants lost ≥5% of body weight at T3 (23.1%) than score was the mean of the 9 responses. A higher score T2 (12.5%; p =0.001). indicates more sleep disturbance. Table 3 presents results of comparison between women with < 5% and those with ≥5% weight loss from Statistical analysis baseline by sleep duration, sleep quality and sleep dis- Descriptive statistics were used to summarize the sample turbance. Improvements in sleep duration, sleep quality at each time point on baseline characteristics and out- and sleep disturbance were not associated with ≥5% of comes, including sleep duration, sleep quality, sleep dis- weight loss at T2 or T3. turbance, and weight outcomes (weight in pounds, % weight change from T1, and whether or not achieving Discussion ≥5% weight loss from T1). We compared the baseline This is the first study to examine the associations be- demographic characteristics across time (T1 to T3). We tween sleep duration, sleep quality, sleep disturbance, also examined the overall trajectories of outcomes across and ≥ 5% weight loss in low-income overweight or obese time using mixed-effects logistic regression for postpartum women using prospective longitudinal data. Chang et al. BMC Obesity (2019) 6:12 Page 4 of 7 Table 1 Demographic Characteristics of Low-Income Overweight or Obese Postpartum Women Characteristics Mean ± SD or N (%) P-value T1 (N = 569) T2 (N = 367) T3 (N = 332) Age (years) 28.54 ± 5.03 29.17 ± 4.91 29.42 ± 4.86 1.00 Postpartum period (years) 1.72 ± 1.28 1.73 ± 1.27 1.74 ± 1.28 0.98 Race Black 121 (21.27) 68 (18.53) 63 (18.98) 0.53 White 448 (78.73) 299 (81.47) 269 (81.02) Education High School or less 187 (32.86) 116 (31.61) 102 (30.72) 0.80 At least some college 382 (67.14) 251 (68.39) 230 (69.28) Employment Employed (FT/PT/Self) 262 (46.05) 161 (43.87) 139 (41.87) 0.66 Unemployed 118 (20.74) 66 (17.98) 61 (18.37) Other (homemaker/student/other) 189 (33.22) 140 (38.15) 132 (39.76) Current smoker No 421 (73.99) 296 (80.65) 260 (78.31) 0.12 Yes 148 (26.01) 71 (19.35) 72 (21.69) Current breastfeeding No 475 (83.48) 290 (79.02) 267 (80.42) 0.76 Yes 94 (16.52) 77 (20.98) 65 (19.58) Body mass index (BMI) 32.04 ± 4.29 31.77 ± 4.35 31.60 ± 4.30 1.00 BMI category Overweight (BMI 25–29.9 kg/m ) 205 (36.03) 142 (38.69) 131 (39.46) 0.83 Obese I (BMI 30–34.9 kg/m ) 209 (36.73) 131 (35.69) 122 (36.75) Obese II (BMI 35.0–39.9 kg/m ) 155 (27.24) 94 (25.61) 79 (23.80) Randomization Intervention group 387 (68.01) 236 (64.31) 213 (64.16) 0.80 Comparison group 182 (31.99) 131 (35.69) 119 (35.84) T1: baseline, T2: 4 months from T1, T3: 7 months from T1. FT: employed full-time, PT: employed part-time, self = self-employed P-values were derived using mixed-effects linear regression modeling for continuous variables (age, postpartum period, BMI) and mixed-effects logistic regression for categorical variables (race, education, employment, current smoker, current breastfeeding, BMI category, and randomization). We did not present coefficient or odds ratio estimates from the regression models since the descriptive statistics (Mean ± SD and %) in the table are more straightforward to illustrate the extent of change across time We observed that improvements in sleep duration, sleep positive change remained at T3. However, our results quality, and sleep disturbance over time (T2 and T3) also revealed that the comparison group had a signifi- were not associated with ≥5% of weight loss. One pos- cant reduction in stress at T3 [30]. Finally, it is possible sible explanation of the overall non-significant findings that as children grow older, they might sleep better [31] is related to the low magnitude of changes in sleep dur- and require less attention from their caregivers at night. ation, sleep quality, and sleep disturbance. This means Also, they might sleep apart from their mothers, which that though overall amount of sleep duration, sleep qual- was associated with better sleep in mothers [32]. ity, and sleep disturbance improved significantly over Our finding showing no association between sleep time, the changes were relatively small; and sleep quality duration and ≥ 5% weight loss is inconsistent with find- and sleep disturbances remained poor. Also, the overall ings of a previous weight loss intervention study [22]. improvement in sleep might have been impacted by our The inconsistency might have related to the study sam- intervention that included stress management. A recent ple. Whereas we focused on postpartum women whose study of pregnant women has reported that higher levels sleep is often dependent on different factors (e.g., chil- of stress were associated with poor sleep [29]. In our dren’s sleep), the prior studies focused on middle-aged intervention study, we found that the intervention group adults. Similarly, our findings on sleep quality and had a significant reduction in stress at T2 and the weight loss were consistent with one study of Chang et al. BMC Obesity (2019) 6:12 Page 5 of 7 Table 2 Change in Sleep Duration, Sleep Quality, Sleep Disturbance, and Body Weight across Time Mean ± SD or N (%) P-value T1 (N = 569) T2 (N = 367) T3 (N = 332) Sleep Duration < 7 h 457 (80.32%) 249 (73.67%) 230 (73.95%) 0.048 ≥ 7 h 112 (19.68) 89 (26.33%) 81 (26.05%) Sleep quality 1.44 ± 0.78 1.35 ± 0.82 1.30 ± 0.75 0.003 Sleep disturbance 1.42 ± 0.58 1.26 ± 0.67 1.21 ± 0.67 < 0.001 Weight 190.8 ± 29.85 188.5 ± 31.22 187.9 ± 32.07 0.389 Weight change from T1 n/a −.08 ± 9.72 −.72 ± 12.46 0.218 % Weight change from T1 n/a −0.1% ± 5.1% −0.4% ± 6.7% 0.253 Having ≥ 5% weight loss from T1 No n/a 300 (87.4%) 246 (76.9%) 0.001 Yes n/a 43 (12.5%) 74 (23.1%) a b T1: baseline, T2: 4 months from T1, T3: 7 months from T1. The lower score, the better sleep quality. The higher score, the more sleep disturbance P-values were derived using mixed-effects linear regression modeling for continuous variables (sleep quality, sleep disturbance, weight, weight change from T1, and % weight change from T1) and using mixed-effects logistic regression for categorical variables (sleep duration and having ≥5% weight loss from T1). We did not present coefficient or odds ratio estimates from the regression models since the descriptive statistics (Mean ± SD and %) in the table are more straightforward to illustrate the extent of change across time Table 3 Comparison of Women with < 5% Vs. ≥ 5% Weight Loss from Baseline by Sleep Duration, Sleep Quality, and Sleep Disturbance Weight loss, T2 vs. T1 Weight loss, T3 vs. T1 $ $ <5% ≥ 5% P-value <5% ≥ 5% P-value N (row %) N (row %) Sleep duration Baseline ≥ 7 h 61 (85.92) 10 (14.08) 0.658 50 (73.53) 18 (26.47) 0.819 < 7 h 239 (87.87) 33 (12.13) 196 (77.78) 56 (22.22) Follow-up ≥ 7 h 78 (88.64) 10 (11.36) 0.852 63 (79.75) 16 (20.25) 0.288 < 7 h 203 (87.88) 28 (12.12) 168 (75.00) 56 (25.00) Change from baseline Kept ≥7 h 36 (87.80) 5 (12.20) 0.783 31 (79.49) 8 (20.51) 0.184 Increased to ≥7 h 42 (89.36) 5 (10.64) 32 (80.00) 8 (20.00) Kept < 7 h 183 (87.98) 25 (12.02) 153 (76.50) 47 (23.50) Decreased to < 7 h 20 (86.96) 3 (13.04) 15 (62.50) 9 (37.50) Mean ± SD Mean ± SD Sleep quality Baseline 1.42 ± 0.75 1.40 ± 0.85 0.822 1.44 ± 0.74 1.46 ± 0.83 0.871 Follow-up 1.35 ± 0.81 1.16 ± 0.75 0.171 1.32 ± 0.77 1.24 ± 0.72 0.388 Change from T1 −0.07 ± 0.85 −0.29 ± 0.65 0.131 −0.10 ± 0.82 − 0.24 ± 0.76 0.224 Sleep disturbance Baseline 1.41 ± 0.58 1.44 ± 0.59 0.764 1.40 ± 0.58 1.49 ± 0.58 0.277 Follow-up 1.27 ± 0.65 1.23 ± 0.68 0.724 1.21 ± 0.67 1.28 ± 0.63 0.411 Change from T1 −0.14 ± 0.70 −0.21 ± 0.51 0.458 −0.19 ± 0.72 −0.20 ± 0.55 0.883 a b T1: baseline, T2: 4 months from T1, T3: 7 months from T1. The lower score, the better sleep quality. The higher score, the more sleep disturbance P-values were derived using Chi-square statistics for categorical variables (sleep duration) and two-sample t-tests for continuous variables (sleep quality and sleep disturbance) Chang et al. BMC Obesity (2019) 6:12 Page 6 of 7 middle-aged adults [23], but not another study of Abbreviations BMI: Body Mass Index; WIC: The Special Supplemental Nutrition Program for middle-aged adults [20]. We are unable to explain the Women, Infants, and Children. discrepancy. Finally, our finding indicating no associ- ation between sleep disturbance and weight loss was Acknowledgements Not applicable. consistent with two prior studies [23, 24]. There are strengths of the present research. Our find- Funding ings were based on analysis of a prospective longitudinal The full trial of the community-based lifestyle behavior intervention study aimed at prevention of weight gain was supported by Grant dataset that included a homogeneous sample of Number R18-DK-083934. However, the design, analysis and not writing low-income overweight or obese postpartum women. of this manuscript was not supported by the grant. The content is solely Also, body weight was measured in person rather than the responsibility of the authors and does not necessarily represent the official views of the National Institute of Diabetes and Digestive and based on self-report. Although sleep was not targeted as Kidney Disease or the National Institutes of Health. a behavior for intervention in the intervention study, we used validated instruments to assess sleep duration, Availability of data and materials The datasets generated and/or analyzed during the current study are not sleep quality, and sleep disturbance that appropriately publicly available because we are in the stage of data analysis to answer measure these characteristics as they occur in this popu- other research questions but are available from the corresponding author on lation. Even so, several limitations of the study need to a reasonable request. be considered when interpreting our findings. First, this Authors’ contributions was a secondary data analysis. The level of power to de- MC and AT conceptualized the present study. AT conducted data analysis. tect any significant differences in the measurements that MC and AT interpreted results, drafted, and critically revised the manuscript. JS and DTW involved in critical revision. All the authors have read and we chose is unknown but could be lower than desired. approved the final manuscript. Second, we used self-reported sleep data over an ex- tended period of time (the past month). Though these Authors’ information Mei-Wei Chang, PhD, is an Associate Professor; Alai Tan, PhD, is an Associate measures have proven helpful in predicting weight loss Professor; Jonathan Schaffir, MD, is an Associate Professor; Duane T. in some studies, asking respondents to report sleep dur- Wegener, PhD, is a Professor. ation, sleep quality, and sleep disturbance over an ex- Ethics approval and consent to participate tended period could lead to biases in over-emphasizing Participation was voluntary. Participants provided written consent prior to recent experience rather than actually characterizing the participating in the study if they met the study criteria and understood the sleep experience over the requested period. If recent ex- study requirements. This study was approved and monitored by Michigan Department of Health and Human Services and Michigan State University periences are more variable than the requested trends Institutional Review Boards. in sleep experience over time, a measure bias toward recent experience could produce weaker associations Consent for publication Not applicable. with longer-termoutcomessuchasweightlossthan would a measure that more faithfully captures the Competing interests longer-term trend in sleep experience. Third, the The authors declare that they have no competing interests. dropout rate across each time point of the study was substantial, though the demographic characteristics of Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in the sample remained consistent over each time point published maps and institutional affiliations. of studydatacollection. Fourth, the length of the study may have been too short to demonstrate clinic- Author details College of Nursing, The Ohio State University, 1585 Neil Avenue, Columbus, ally significant results. Finally, the mean changes in OH 43210, USA. Department of Obstetrics & Gynecology, The Ohio State weight and sleep scores were small, even when statis- University, 370 W. 9th Avenue Columbus, Columbus, OH 43210, USA. tically significant. It is possible that larger amounts of Department of Psychology, The Ohio State University, 1835 Neil Avenue, Columbus, OH 43210, USA. weight loss may be observed with larger changes in sleep behaviors. Received: 31 October 2018 Accepted: 21 February 2019 Conclusions References Our findings show that improvements in sleep dur- 1. Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Ogden CL. Trends in ation, sleep quality, and sleep disturbance were not obesity among adults in the United States, 2005 to 2014. 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BMC Obesity – Springer Journals

Published: Apr 1, 2019

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