Consistent sleep onset and maintenance of body weight after weight loss: An analysis of data from the NoHoW trial

Substituting sedentary time with sleep or physical activity and subsequent weight-loss maintenance

OBJECTIVE

In this study, the associations between the substitution of sedentary time with sleep or physical activity at different intensities and subsequent weight-loss maintenance were examined.

METHODS

This prospective study included 1152 adults from the NoHoW trial who had achieved a successful weight loss of ≥5% during the 12 months prior to baseline and had BMI ≥25 kg/m² before losing weight. Physical activity and sleep were objectively measured during a 14-day period at baseline. Change in body weight was included as the primary outcome. Secondary outcomes were changes in body fat percentage and waist circumference. Cardiometabolic variables were included as exploratory outcomes.

RESULTS

Using isotemporal substitution models, no associations were found between activity substitutions and changes in body weight or waist circumference. However, the substitution of sedentary behavior with moderate-to-vigorous physical activity was associated with a decrease in body fat percentage during the first 6 months of the trial (-0.33% per 30 minutes higher moderate-to-vigorous physical activity [95% CI: -0.60% to -0.07%], p = 0.013).

CONCLUSIONS

Sedentary behavior had little or no influence on subsequent weight-loss maintenance, but during the early stages of a weight-loss maintenance program, substituting sedentary behavior with moderate-to-vigorous physical activity may prevent a gain in body fat percentage.

Disparities in sleep duration among American children: effects of race and ethnicity, income, age, and sex

Children in the United States sleep less than the recommended amount and sleep deficiencies may be worse among disadvantaged children. Prior studies that compared sleep time in children of different race/ethnic groups mostly relied on questionnaires or were limited to small sample sizes. Our study takes advantage of the Adolescent Brain Cognitive Development study to compare total sleep time using a week of actigraphy data among American children (n = 4,207, 9 to 13 y old) of different racial/ethnic and income groups. We also assessed the effects of neighborhood deprivation, experience of discrimination, parent's age at child's birth, body mass index (BMI), and time the child fell asleep on sleep times. Daily total sleep time for the sample was 7.45 h and race/ethnicity, income, sex, age, BMI, were all significant predictors of total sleep time. Black children slept less than White children (∼34 min; Cohen's d = 0.95), children from lower income families slept less than those from higher incomes (∼16 min; Cohen's d = 0.44), boys slept less than girls (∼7 min; Cohen's d = 0.18), and older children slept less than younger ones (∼32 min; Cohen's d = 0.91); mostly due to later sleep times. Children with higher BMI also had shorter sleep times. Neither area deprivation index, experience of discrimination, or parent's age at child's birth significantly contributed to sleep time. Our findings indicate that children in the United States sleep significantly less than the recommended amount for healthy development and identifies significant racial and income disparities. Interventions to improve sleep hygiene in children will help improve health and ameliorate racial disparities in health outcomes.

Sleep Disturbance and Changes in Energy Intake and Body Composition During Weight Loss in the POUNDS Lost Trial

To examine associations between sleep disturbance and changes in weight and body composition and the mediating role of changes of appetite and food cravings in the Preventing Overweight Using Novel Dietary Strategies (POUNDS Lost) 2-year weight-loss diet intervention trial, this study included 810 overweight or obese individuals with baseline sleep disturbance assessment who were randomly assigned one of four diets varying in macronutrient composition. Changes in body weight and fat distribution were assessed by DEXA and computed tomography during the 2-year intervention. Participants were asked to provide sleep disturbance levels (no, slight, moderate, or great) at baseline and to recall their sleep disturbances since last visit at 6, 12, 18, and 24 months. Weight loss during the first 6 months was followed by 1.5 years of steady weight regain. Participants with greater sleep disturbance from baseline to 6 months showed significant losses of body weight (Ptrend <0.001) and waist circumference (Ptrend = 0.002) at 6 months, after multivariate adjustment. Compared with individuals without sleep disturbance at all from baseline to 6 months, those with slight, moderate, or great sleep disturbance showed an elevated risk of failure to lose weight (-5% or more loss) at 6 months, when the maximum weight loss was achieved, with an odds ratio of 1.24 (95% CI 0.87, 1.78), 1.27 (95% CI 0.75, 2.13), or 3.12 (95% CI 1.61, 6.03), respectively. In addition, we observed that the repeatedly measured levels of sleep disturbance over 2 years were inversely associated with the overall weight loss rate (weight changes per 6 months) (Ptrend <0.001). Further, sleep disturbances during weight loss from baseline to 6 months and weight regain from 6 months to 24 months were significantly predictive of total fat, total fat mass percent, and trunk fat percent changes during the 2 years. Our results also indicated that food cravings for carbohydrates/starches, fast food fats, and sweets; cravings, prospective consumption, hunger of appetite measurements; and dietary restraint, disinhibition, and hunger subscales measured at 6 months significantly mediated the effects of sleep disturbance on weight loss. In conclusion, our results suggested that more severe sleep disturbance during weight loss was associated with an elevated risk of failure to lose weight during the dietary intervention. Food cravings and eating behaviors may partly mediate these associations.

The Impact of Wearable Technologies in Health Research: Scoping Review

Background

Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.

Objective

In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.

Methods

We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.

Results

We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).

Conclusions

Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

Examining the Role of Exercise Timing in Weight Management: A Review

Many adults cite exercise as a primary strategy for losing weight, yet exercise alone is modestly effective for weight loss and results in variable weight loss responses. It is possible that some of the variability in weight loss may be explained by the time of day that exercise is performed. Few studies have directly compared the effects of exercise performed at different times of the day (i. e., morning versus evening exercise). Results from these existing studies are mixed with some studies demonstrating superior weight and fat mass loss from morning exercise, while other studies have found that evening exercise may be better for weight management. Exercise timing may alter modifiable lifestyle behaviors involved in weight management, such as non-exercise physical activity, energy intake, and sleep. The purpose of this review is to summarize evidence for and against time-of-day dependent effects of exercise on weight management. Although limited, we also review studies that have examined the effect of exercise timing on other lifestyle behaviors linked to body weight regulation. While exercise at any time of day is beneficial for health, understanding whether there is an optimal time of day to exercise may advance personalized treatment paradigms for weight management.

Sleep Health Characteristics among Adults Who Attempted Weight Loss in the Past Year: NHANES 2017-2018

Background: The purpose of this study was to characterize sleep health in adults who attempted weight loss in the prior year. Methods: We analyzed data from the National Health and Nutrition Examination Survey 2017–2018 exam cycle. We included 4837 US adults who did (n = 1919) or did not (n = 2918) attempt weight loss in the past year. Participants self-reported their sleep regularity, satisfaction, sleepiness, timing, and duration, which we defined as “good” based on the prior literature. We characterized sleep health by weight loss attempts status, current BMI and weight change among participants who attempted weight loss. Results: On average, participants reported good sleep health in 3.21 ± 1.14 out of the five sleep domains. A total of 13% of participants had good sleep health in all five domains. The prevalence of sleep regularity (52%) was lowest, and the prevalence of infrequent sleepiness was highest (72%), relative to other sleep domains. In models adjusting for BMI, sleep health was similar in participants who did and did not attempt weight loss. Among adults who attempted weight loss, good sleep health was inversely associated with current BMI and self-reported weight change. Discussion: This study’s findings highlight the importance of considering sleep health when engaging with adults attempting weight loss.

Physical Activity, Sleep, and Sedentary Behavior among Successful Long-Term Weight Loss Maintainers: Findings from a U.S. National Study

Despite adults’ desire to reduce body mass (weight) for numerous health benefits, few are able to successfully lose at least 5% of their starting weight. There is evidence on the independent associations of physical activity, sedentary behaviors, and sleep with weight loss; however, this study provided insight on the combined effects of these behaviors on long-term body weight loss success. Hence, the purpose of this cross-sectional study was to evaluate the joint relations of sleep, physical activity, and sedentary behaviors with successful long-term weight loss. Data are from the 2005–2006 wave of the National Health and Examination Survey (NHANES). Physical activity and sedentary behavior were measured with an accelerometer, whereas sleep time was self-reported. Physical activity and sleep were dichotomized into meeting guidelines (active/not active, ideal sleep/short sleep), and sedentary time was categorized into prolonged sedentary time (4th quartile) compared to low sedentary time (1st–3rd quartiles). The dichotomized behaviors were combined to form 12 unique behavioral combinations. Two-step multivariable regression models were used to determine the associations between the behavioral combinations with (1) long-term weight loss success (≥5% body mass reduction for ≥12-months) and (2) the amount of body mass reduction among those who were successful. After adjustment for relevant factors, there were no significant associations between any of the independent body weight loss behaviors (physical activity, sedentary time, and sleep) and successful long-term weight loss. However, after combining the behaviors, those who were active (≥150 min MVPA weekly), regardless of their sedentary time, were significantly (p < 0.05) more likely to have long-term weight loss success compared to the inactive and sedentary referent group. These results should be confirmed in longitudinal analyses, including investigation of characteristics of waking (type, domain, and context) and sleep (quality metrics) behaviors for their association with long-term weight loss success.

Hair Cortisol Concentration, Weight Loss Maintenance and Body Weight Variability: A Prospective Study Based on Data From the European NoHoW Trial

Several cross-sectional studies have shown hair cortisol concentration to be associated with adiposity, but the relationship between hair cortisol concentration and longitudinal changes in measures of adiposity are largely unknown. We included 786 adults from the NoHoW trial, who had achieved a successful weight loss of ≥5% and had a body mass index of ≥25 kg/m² prior to losing weight. Hair cortisol concentration (pg/mg hair) was measured at baseline and after 12 months. Body weight and body fat percentage were measured at baseline, 6-month, 12-month and 18-month visits. Participants weighed themselves at home ≥2 weekly using a Wi-Fi scale for the 18-month study duration, from which body weight variability was estimated using linear and non-linear approaches. Regression models were conducted to examine log hair cortisol concentration and change in log hair cortisol concentration as predictors of changes in body weight, change in body fat percentage and body weight variability. After adjustment for lifestyle and demographic factors, no associations between baseline log hair cortisol concentration and outcome measures were observed. Similar results were seen when analysing the association between 12-month concurrent development in log hair cortisol concentration and outcomes. However, an initial 12-month increase in log hair cortisol concentration was associated with a higher subsequent body weight variability between month 12 and 18, based on deviations from a nonlinear trend (β: 0.02% per unit increase in log hair cortisol concentration [95% CI: 0.00, 0.04]; P=0.016). Our data suggest that an association between hair cortisol concentration and subsequent change in body weight or body fat percentage is absent or marginal, but that an increase in hair cortisol concentration during a 12-month weight loss maintenance effort may predict a slightly higher subsequent 6-months body weight variability.

CLINICAL TRIAL REGISTRATION

ISRCTN registry, identifier ISRCTN88405328.

Sleep Extension: A Potential Target for Obesity Treatment

PURPOSE OF REVIEW

Sleep and obesity share a bidirectional relationship, and weight loss has been shown to enhance sleep. Aiming to extend sleep on its own or as part of a lifestyle intervention may attenuate health consequences of short sleep. This review highlights several sleep extension approaches, discusses feasibility of each, and summarizes findings relevant to obesity.

RECENT FINDINGS

Sleep extension in response to experimental sleep restriction demonstrates partial rescue of cardiometabolic dysfunction in some but not all studies. Adequate sleep on a nightly basis may be necessary for optimal health. While initial sleep extension interventions in habitually short sleepers have been met with obstacles, preliminary findings suggest that sleep extension or sleep hygiene interventions may improve glycemic control, decrease blood pressure, and enhance weight loss. Sleep extension has the potential to attenuate obesity risk and cardiometabolic dysfunction. There is tremendous opportunity for future research that establishes a minimum threshold for sleep extension effectiveness and addresses logistical barriers identified in seminal studies.