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

BackgroundSeveral studies have suggested that reduced sleep duration and quality are associated with an increased risk of obesity and related metabolic disorders, but the role of sleep in long-term weight loss maintenance (WLM) has not been thoroughly explored using prospective data.Methods and findingsThe present study is an ancillary study based on data collected on participants from the Navigating to a Healthy Weight (NoHoW) trial, for which the aim was to test the efficacy of an evidence-based digital toolkit, targeting self-regulation, motivation, and emotion regulation, on WLM among 1,627 British, Danish, and Portuguese adults. Before enrolment, participants had achieved a weight loss of ≥5% and had a BMI of ≥25 kg/m2 prior to losing weight. Participants were enrolled between March 2017 and March 2018 and followed during the subsequent 12-month period for change in weight (primary trial outcome), body composition, metabolic markers, diet, physical activity, sleep, and psychological mediators/moderators of WLM (secondary trial outcomes). For the present study, a total of 967 NoHoW participants were included, of which 69.6% were women, the mean age was 45.8 years (SD 11.5), the mean baseline BMI was 29.5 kg/m2 (SD 5.1), and the mean weight loss prior to baseline assessments was 11.4 kg (SD 6.4). Objectively measured sleep was collected using the Fitbit Charge 2 (FC2), from which sleep duration, sleep duration variability, sleep onset, and sleep onset variability were assessed across 14 days close to baseline examinations. The primary outcomes were 12-month changes in body weight (BW) and body fat percentage (BF%). The secondary outcomes were 12-month changes in obesity-related metabolic markers (blood pressure, low- and high-density lipoproteins [LDL and HDL], triglycerides [TGs], and glycated haemoglobin [HbA1c]). Analysis of covariance and multivariate linear regressions were conducted with sleep-related variables as explanatory and subsequent changes in BW, BF%, and metabolic markers as response variables. We found no evidence that sleep duration, sleep duration variability, or sleep onset were associated with 12-month weight regain or change in BF%. A higher between-day variability in sleep onset, assessed using the standard deviation across all nights recorded, was associated with weight regain (0.55 kg per hour [95% CI 0.10 to 0.99]; P = 0.016) and an increase in BF% (0.41% per hour [95% CI 0.04 to 0.78]; P = 0.031). Analyses of the secondary outcomes showed that a higher between-day variability in sleep duration was associated with an increase in HbA1c (0.02% per hour [95% CI 0.00 to 0.05]; P = 0.045). Participants with a sleep onset between 19:00 and 22:00 had the greatest reduction in diastolic blood pressure (DBP) (P = 0.02) but also the most pronounced increase in TGs (P = 0.03). The main limitation of this study is the observational design. Hence, the observed associations do not necessarily reflect causal effects.ConclusionOur results suggest that maintaining a consistent sleep onset is associated with improved WLM and body composition. Sleep onset and variability in sleep duration may be associated with subsequent change in different obesity-related metabolic markers, but due to multiple-testing, the secondary exploratory outcomes should be interpreted cautiously.Trial registrationThe trial was registered with the ISRCTN registry (ISRCTN88405328)

A secondary analysis of the NoHoW trial

Funding Information: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 643309. The material presented and views expressed here are the responsibility of the author(s) only. The EU Commission takes no responsibility for any use made of the information set out. Funding Information: The NoHoW Trial was funded by the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement number 643309). A detailed description of the NoHoW trial procedures can be found elsewhere ( Marques et al., 2021 ; Scott et al., 2019 ). The trial was registered with the ISRCTN registry (ISRCTN88405328). Ethical approval was granted by all local institutional ethics committees at the Universities of Lisbon (17/2016; 20-Feb-2017), Leeds (17–0082; 27-Feb-2017), and the Capital Region of Denmark (H-16030495; 8-Mar-2017). Participants were assigned to one of four intervention conditions that have access to different theory-based digitally delivered content: Publisher Copyright: © 2022 The AuthorsBackground: To date, few digital behavior change interventions for weight loss maintenance focusing on long-term physical activity promotion have used a sound intervention design grounded on a logic model underpinned by behavior change theories. The current study is a secondary analysis of the weight loss maintenance NoHoW trial and investigated putative mediators of device-measured long-term physical activity levels (six to 12 months) in the context of a digital intervention. Methods: A subsample of 766 participants (Age = 46.2 ± 11.4 years; 69.1% female; original NoHoW sample: 1627 participants) completed all questionnaires on motivational and self-regulatory variables and had all device-measured physical activity data available for zero, six and 12 months. We examined the direct and indirect effects of Virtual Care Climate on post intervention changes in moderate-to-vigorous physical activity and number of steps (six to 12 months) through changes in the theory-driven motivational and self-regulatory mechanisms of action during the intervention period (zero to six months), as conceptualized in the logic model. Results: Model 1 tested the mediation processes on Steps and presented a poor fit to the data. Model 2 tested mediation processes on moderate-to-vigorous physical activity and presented poor fit to the data. Simplified models were also tested considering the autonomous motivation and the controlled motivation variables independently. These changes yielded good results and both models presented very good fit to the data for both outcome variables. Percentage of explained variance was negligible for all models. No direct or indirect effects were found from Virtual Care Climate to long term change in outcomes. Indirect effects occurred only between the sequential paths of the theory-driven mediators. Conclusion: This was one of the first attempts to test a serial mediation model considering psychological mechanisms of change and device-measured physical activity in a 12-month longitudinal trial. The model explained a small proportion of variance in post intervention changes in physical activity. We found different pathways of influence on theory-driven motivational and self-regulatory mechanisms but limited evidence that these constructs impacted on actual behavior change. New approaches to test these relationships are needed. Challenges and several alternatives are discussed. Trial registration: ISRCTN Registry, ISRCTN88405328. Registered December 16, 2016, https://www.isrctn.com/ISRCTN88405328.publishersversionpublishe

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. [ABSTRACT FROM AUTHOR]info:eu-repo/semantics/publishedVersio

Long-term Randomized Controlled Trial

Funding Information: The authors thank Sarah E Scott for her valuable contributions as the trial manager and in the user experience evaluation, and Susana Cunha for her contribution in conducting and reporting the focus groups. This project has received funding from the European Union?s Horizon 2020 research and innovation program under grant agreement number 643309. The material presented and views expressed here are the responsibility of the authors only. The European Union Commission does not take responsibility for any use made of the information set out.Background: Digital behavior change interventions (DBCIs) offer a promising channel for providing health promotion services. However, user experience largely determines whether they are used, which is a precondition for effectiveness. Objective: The primary aim of this study is to evaluate user experiences with the NoHoW Toolkit (TK)—a DBCI that targets weight loss maintenance—over a 12-month period by using a mixed methods approach and to identify the main strengths and weaknesses of the TK and the external factors affecting its adoption. The secondary aim is to objectively describe the measured use of the TK and its association with user experience. Methods: An 18-month, 2×2 factorial randomized controlled trial was conducted. The trial included 3 intervention arms receiving an 18-week active intervention and a control arm. The user experience of the TK was assessed quantitatively through electronic questionnaires after 1, 3, 6, and 12 months of use. The questionnaires also included open-ended items that were thematically analyzed. Focus group interviews were conducted after 6 months of use and thematically analyzed to gain deeper insight into the user experience. Log files of the TK were used to evaluate the number of visits to the TK, the total duration of time spent in the TK, and information on intervention completion. Results: The usability level of the TK was rated as satisfactory. User acceptance was rated as modest; this declined during the trial in all the arms, as did the objectively measured use of the TK. The most appreciated features were weekly emails, graphs, goal setting, and interactive exercises. The following 4 themes were identified in the qualitative data: engagement with features, decline in use, external factors affecting user experience, and suggestions for improvements. Conclusions: The long-term user experience of the TK highlighted the need to optimize the technical functioning, appearance, and content of the DBCI before and during the trial, similar to how a commercial app would be optimized. In a trial setting, the users should be made aware of how to use the intervention and what its requirements are, especially when there is more intensive intervention content.publishersversionpublishe

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

Understanding Body Weight Variability in the Context of Weight Management

In the last 5 years, a strong scientific interest in the role of body weight variability (BWV) in health and disease has re-emerged. Due to varied methodologies, the literature is conflicting, yet generally suggests that BWV could be a significant risk factor for disease and mortality. However, the phenomenon remains inadequately measured and poorly understood. This thesis took two discrete but complementary approaches to: (1) understand the aetiology of a weight cycle and (2) understand the measurement of BWV, and its physiological and psychological correlates, with using high-resolution estimates generated through novel technological and statistical procedures. With regards to (1), two studies examined how the rate, amount and composition of weight loss affect subsequent weight regain and appetite. It was found that the amount and rate of weight loss was directly associated with the magnitude of regain, and that greater proportions of fat-free mass loss predicted greater weight regain and appetite in men but not women. With regards to (2), five studies using data collected from the NoHoW weight loss maintenance trial aimed to (i) improve the measurement of BWV and use this to investigate the: (ii) predictability of weight fluctuations; (iii) impact of BWV on health markers; (iv) impact of short-term BWV on long-term weight management and (v) psychological and behavioural causes and consequences of BWV. Briefly, the main findings were that (a) a greater understanding of the measurement (and associated errors) of BWV was achieved; (b) fluctuations in body weight could be predicted by temporal cues (i.e. weekly cycles or holidays); (c) BWV did not affect health markers over 12-months; (d) greater short-term BWV predicted increased weight at 12-18 months and (e) a range of eating behaviour and psychological traits were identified in the aetiology of BWV. To conclude, a full discussion and recommendations for the future study of BWV were provided

Developing evidence-based behavioural strategies to overcome physiological resistance to weight loss in the general population

Physiological and behavioural systems are tolerant of excess energy intake and responsive to energy deficits. Weight loss (WL) changes body structure, physiological function and energy balance (EB) behaviours, which resist further WL and promote subsequent weight regain. Measuring and understanding the response of EB systems to energy deficits is important for developing evidence-based behaviour change interventions for longer-term weight management. Currently, behaviour change approaches for longer-term WL show modest effect sizes. Self-regulation of EB behaviours (e.g. goal setting, action plans, self-monitoring, relapse prevention plans) and aspects of motivation are important for WL maintenance. Stress management, emotion regulation and food hedonics may also be important for relapse prevention, but the evidence is less concrete. Although much is known about the effects of WL on physiological and psychological function, little is known about the way these dynamic changes affect human EB behaviours. Key areas of future importance include (i) improved methods for detailed tracking of energy expenditure, balance and by subtraction intake, using digital technologies, (ii) how WL impacts body structure, function and subsequent EB behaviours, (iii) how behaviour change approaches can overcome physiological resistance to WL and (iv) who is likely to maintain WL or relapse. Modelling physiological and psychological moderators and mediators of EB-related behaviours is central to understanding and improving longer-term weight and health outcomes in the general population

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Background: Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some evidence indicates that these techniques can be applied to more scalable commercial devices. Objective: This study aims to test the validity and out-of-sample generalizability of algorithms for the prediction of energy expenditure in several wearables (ie, Fitbit Charge 2, ActiGraph GT3-x, SenseWear Armband Mini, and Polar H7) using two laboratory data sets comprising different activities. Methods: Two laboratory studies (study 1: n=59, age 44.4 years, weight 75.7 kg; study 2: n=30, age=31.9 years, weight=70.6 kg), in which adult participants performed a sequential lab-based activity protocol consisting of resting, household, ambulatory, and nonambulatory tasks, were combined in this study. In both studies, accelerometer and physiological data were collected from the wearables alongside energy expenditure using indirect calorimetry. Three regression algorithms were used to predict metabolic equivalents (METs; ie, random forest, gradient boosting, and neural networks), and five classification algorithms (ie, k-nearest neighbor, support vector machine, random forest, gradient boosting, and neural networks) were used for physical activity intensity classification as sedentary, light, or moderate to vigorous. Algorithms were evaluated using leave-one-subject-out cross-validations and out-of-sample validations. Results: The root mean square error (RMSE) was lowest for gradient boosting applied to SenseWear and Polar H7 data (0.91 METs), and in the classification task, gradient boost applied to SenseWear and Polar H7 was the most accurate (85.5%). Fitbit models achieved an RMSE of 1.36 METs and 78.2% accuracy for classification. Errors tended to increase in out-of-sample validations with the SenseWear neural network achieving RMSE values of 1.22 METs in the regression tasks and the SenseWear gradient boost and random forest achieving an accuracy of 80% in classification tasks. Conclusions: Algorithms trained on combined data sets demonstrated high predictive accuracy, with a tendency for superior performance of random forests and gradient boosting for most but not all wearable devices. Predictions were poorer in the between-study validations, which creates uncertainty regarding the generalizability of the tested algorithms