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Background: Commercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions. This study aimed to evaluate methods to address missingness in data collected from commercial activity monitors. Methods: This study utilised 1526 days of near complete data from 109 adults participating in a European weight loss maintenance study (NoHoW). We conducted simulation experiments to test a novel scaling methodology (NoHoW method) and alternative imputation strategies (overall/individual mean imputation, overall/individual multiple imputation, Kalman imputation and random forest imputation). Methods were compared for hourly, daily and 14-day physical activity estimates for steps, total daily energy expenditure (TDEE) and time in physical activity categories. In a second simulation study, individual multiple imputation, Kalman imputation and the NoHoW method were tested at different positions and quantities of missingness. Equivalence testing and root mean squared error (RMSE) were used to evaluate the ability of each of the strategies relative to the true data. Results: The NoHoW method, Kalman imputation and multiple imputation methods remained statistically equivalent (p<0.05) for all physical activity metrics at the 14-day level. In the second simulation study, RMSE tended to increase with increased missingness. Multiple imputation showed the smallest RMSE for Steps and TDEE at lower levels of missingness (<19%) and the Kalman and NoHoW methods were generally superior for imputing time in physical activity categories. Conclusion: Individual centred imputation approaches (NoHoW method, Kalman imputation and individual Multiple imputation) offer an effective means to reduce the biases associated with missing data from activity monitors and maximise data retention. [ABSTRACT FROM AUTHOR]

BackgroundBody weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available. ObjectiveThis study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches MethodsIn total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated. ResultsBody weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method. ConclusionsThe decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data.

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/m 2 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., (© 2022 The Authors. Obesity published by Wiley Periodicals LLC on behalf of The Obesity Society.)

Background: 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., Competing Interests: Declaration of competing interest RJS consults for Slimming World through Consulting Leeds, which is a wholly-owned subsidiary of the university of Leeds. Slimming World was a former partner in NoHoW. MMM and GH has previously consulted for Slimming World, who was a former partner in NoHoW project. All other co-authors have no conflicts of interest to declare., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., Trial Registration: ISRCTN Registry ISRCTN88405328; https://www.isrctn.com/ISRCTN88405328., International Registered Report Identifier (irrid): RR2-10.1136/bmjopen-2019-029425., (©Elina Mattila, Susanne Hansen, Lise Bundgaard, Lauren Ramsey, Alice Dunning, Marlene N Silva, Marja Harjumaa, Miikka Ermes, Marta M Marques, Marcela Matos, Sofus C Larsen, Jorge Encantado, Inês Santos, Graham Horgan, Ruairi O'Driscoll, Jake Turicchi, Cristiana Duarte, António L Palmeira, R James Stubbs, Berit Lilienthal Heitmann, Liisa Lähteenmäki. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.01.2022.)

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/m 2 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., Competing Interests: RS consults for Slimming World UK via Consulting Leeds, a wholly owned subsidiary of the University of Leeds. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Larsen, Turicchi, Christensen, Larsen, Jørgensen, Mikkelsen, Horgan, O’Driscoll, Michalowska, Duarte, Scott, Santos, Encantado, Palmeira, Stubbs and Heitmann.)

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., (©Ruairi O'Driscoll, Jake Turicchi, Mark Hopkins, Cristiana Duarte, Graham W Horgan, Graham Finlayson, R James Stubbs. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 04.08.2021.)

Understanding physiological and behavioral responses to energy imbalances is important for the management of overweight/obesity and undernutrition. Changes in body composition and physiological functions associated with energy imbalances provide the structural and functional context in which to consider psychological and behavioral responses. Compensatory changes in physiology and behavior are more pronounced in response to negative than positive energy balances. The physiological and psychological impact of weight loss (WL) occur on a continuum determined by (i) the degree of energy deficit (ED), (ii) its duration, (iii) body composition at the onset of the energy deficit, and (iv) the psychosocial environment in which it occurs. Therapeutic WL and famine/semistarvation both involve prolonged EDs, which are sometimes similar in magnitude. The key differences are that (i) the body mass index (BMI) of most famine victims is lower at the onset of the ED, (ii) therapeutic WL is intentional and (iii) famines are typically longer in duration (partly due to the voluntary nature of therapeutic WL and disengagement with WL interventions). The changes in psychological outcomes, motivation to eat, and energy intake in therapeutic WL are often modest (bearing in mind the nature of the measures used) and can be difficult to detect but are quantitatively significant over time. As WL progresses, these changes become more marked. It appears that extensive WL beyond 10%-20% in lean individuals has profound effects on body composition and physiological function. At this level of WL, there is a marked erosion of psychological functioning, which appears to run in parallel to WL. Psychological resources dwindle and become increasingly focused on alleviating escalating hunger and food seeking behavior. Functional changes in fat-free mass, characterized by catabolism of skeletal muscle and organs may be involved in the drive to eat associated with semistarvation. Higher levels of body fat mass may act as a buffer to protect fat-free mass, functional integrity and limit compensatory changes in energy balance behaviors. The increase in appetite that accompanies therapeutic WL appears to be very different to the intense and all-consuming drive to eat that occurs during prolonged semistarvation. The mechanisms may also differ but are not well understood, and longitudinal comparisons of the relationship between body structure, function, and behavior in response to differing EDs in those with higher and lower BMIs are currently lacking., (© 2021 World Obesity Federation.)

Background: Weight-loss programmes often achieve short-term success though subsequent weight regain is common. The ability to identify predictive factors of regain early in the weight maintenance phase is crucial., Objective: To investigate the associations between short-term weight variability and long-term weight outcomes in individuals engaged in a weight-loss maintenance intervention., Methods: The study was a secondary analysis from The NoHoW trial, an 18-month weight maintenance intervention in individuals who recently lost ≥5% body weight. Eligible participants (n = 715, 64% women, BMI = 29.2 (SD 5.0) kg/m 2 , age = 45.8 (SD 11.5) years) provided body-weight data by smart scale (Fitbit Aria 2) over 18 months. Variability in body weight was calculated by linear and non-linear methods over the first 6, 9 and 12 weeks. These estimates were used to predict percentage weight change at 6, 12, and 18 months using both crude and adjusted multiple linear regression models., Results: Greater non-linear weight variability over the first 6, 9 and 12 weeks was associated with increased subsequent weight in all comparisons; as was greater linear weight variability measured over 12 weeks (up to AdjR 2  = 4.7%). Following adjustment, 6-week weight variability did not predict weight change in any model, though greater 9-week weight variability by non-linear methods was associated with increased body-weight change at 12 (∆AdjR 2  = 1.2%) and 18 months (∆AdjR 2  = 1.3%) and by linear methods at 18 months (∆AdjR 2  = 1.1%). Greater non-linear weight variability measured over 12 weeks was associated with increased weight at 12 (∆AdjR 2  = 1.4%) and 18 (∆AdjR 2  = 2.2%) months; and 12-week linear variability was associated with increased weight at 12 (∆AdjR 2  = 2.1%) and 18 (∆AdjR 2  = 3.6%) months., Conclusion: Body-weight variability over the first 9 and 12 weeks of a weight-loss maintenance intervention weakly predicted increased weight at 12 and 18 months. These results suggest a potentially important role in continuously measuring body weight and estimating weight variability.

There is substantial evidence documenting the effects of behavioural interventions on weight loss (WL). However, behavioural approaches to initial WL are followed by some degree of longer-term weight regain, and large trials focusing on evidence-based approaches to weight loss maintenance (WLM) have generally only demonstrated small beneficial effects. The current state-of-the-art in behavioural interventions for WL and WLM raises questions of (i) how we define the relationship between WL and WLM, (ii) how energy balance (EB) systems respond to WL and influence behaviours that primarily drive weight regain, (iii) how intervention content, mode of delivery and intensity should be targeted to keep weight off, (iv) which mechanisms of action in complex interventions may prevent weight regain and (v) how to design studies and interventions to maximise effective longer-term weight management. In considering these issues a writing team within the NoHoW Consortium was convened to elaborate a position statement, and behaviour change and obesity experts were invited to discuss these positions and to refine them. At present the evidence suggests that developing the skills to self-manage EB behaviours leads to more effective WLM. However, the effects of behaviour change interventions for WL and WLM are still relatively modest and our understanding of the factors that disrupt and undermine self-management of eating and physical activity is limited. These factors include physiological resistance to weight loss, gradual compensatory changes in eating and physical activity and reactive processes related to stress, emotions, rewards and desires that meet psychological needs. Better matching of evidence-based intervention content to quantitatively tracked EB behaviours and the specific needs of individuals may improve outcomes. Improving objective longitudinal tracking of energy intake and energy expenditure over time would provide a quantitative framework in which to understand the dynamics of behaviour change, mechanisms of action of behaviour change interventions and user engagement with intervention components to potentially improve weight management intervention design and evaluation., (© 2021 The Author(s) Published by S. Karger AG, Basel.)

Background: Effective interventions and commercial programmes for weight loss (WL) are widely available, but most people regain weight. Few effective WL maintenance (WLM) solutions exist. The most promising evidence-based behaviour change techniques for WLM are self-monitoring, goal setting, action planning and control, building self-efficacy, and techniques that promote autonomous motivation (e.g., provide choice). Stress management and emotion regulation techniques show potential for prevention of relapse and weight regain. Digital technologies (including networked-wireless tracking technologies, online tools and smartphone apps, multimedia resources, and internet-based support) offer attractive tools for teaching and supporting long-term behaviour change techniques. However, many digital offerings for weight management tend not to include evidence-based content and the evidence base is still limited. The Project: First, the project examined why, when, and how many European citizens make WL and WLM attempts and how successful they are. Second, the project employed the most up-to-date behavioural science research to develop a digital toolkit for WLM based on 2 key conditions, i.e., self-management (self-regulation and motivation) of behaviour and self-management of emotional responses for WLM. Then, the NoHoW trial tested the efficacy of this digital toolkit in adults who achieved clinically significant (≥5%) WL in the previous 12 months (initial BMI ≥25). The primary outcome was change in weight (kg) at 12 months from baseline. Secondary outcomes included biological, psychological, and behavioural moderators and mediators of long-term energy balance (EB) behaviours, and user experience, acceptability, and cost-effectiveness., Impact: The project will directly feed results from studies on European consumer behaviour, design and evaluation of digital toolkits self-management of EB behaviours into development of new products and services for WLM and digital health. The project has developed a framework and digital architecture for interventions in the context of EB tracking and will generate results that will help inform the next generation of personalised interventions for effective self-management of weight and health., (© 2021 The Author(s) Published by S. Karger AG, Basel.)

Context: Weight loss is known to improve health, however the influence of variability in body weight around the overall trajectory on these outcomes is unknown. Few studies have measured body weight frequently enough to accurately estimate the variability component., Objective: To investigate the association of 12-month weight variability and concurrent weight change with changes in health markers and body composition., Methods: This study was a secondary analysis of the NoHoW trial, a 2 × 2 factorial randomised controlled trial promoting evidence-based behaviour change for weight loss maintenance. Outcome measurements related to cardiometabolic health and body composition were taken at 0, 6 and 12 months. Participants were provided with Wi-Fi connected smart scales (Fitbit Aria 2) and asked to self-weigh regularly over this period. Associations of weight variability and weight change with change in outcomes were investigated using multiple linear regression with multiple levels of adjustment in 955 participants., Results: Twelve models were generated for each health marker. Associations between weight variability and changes in health markers were inconsistent between models and showed no evidence of a consistent relationship, with all effects explaining <1% of the outcome, and most 0%. Weight loss was consistently associated with improvements in health and body composition, with the greatest effects seen in percent body fat (R 2  = 10.4-11.1%) followed by changes in diastolic (4.2-4.7%) and systolic (3-4%) blood pressure., Conclusion: Over 12-months, weight variability was not consistently associated with any measure of cardiometabolic health or body composition, however weight loss consistently improved all outcomes., Trial Registration Number: ISRCTN88405328., Competing Interests: All named authors have nothing to declare., (© 2020 The Authors.)

Background: Several 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 Findings: The 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., Conclusion: Our 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 Registration: The trial was registered with the ISRCTN registry (ISRCTN88405328)., Competing Interests: RJS consults for Slimming World UK via Consulting Leeds, a wholly owned subsidiary of the University of Leeds. All other authors have declared that no competing interests exist.

Background: An association between sleep and obesity has been suggested in several studies, but many previous studies relied on self-reported sleep and on BMI as the only adiposity measure. Moreover, a relationship between weight loss history and attained sleep duration has not been thoroughly explored., Design: The study comprised of 1202 participants of the European NoHoW trial who had achieved a weight loss of ≥5% and had a BMI of ≥25 kg/m 2 prior to losing weight. Information was available on objectively measured sleep duration (collected during 14 days), adiposity measures, weight loss history and covariates. Regression models were conducted with sleep duration as the explanatory variable and BMI, fat mass index (FMI), fat-free mass index (FFMI) and waist-hip ratio (WHR) as response variables. Analyses were conducted with 12-month weight loss, frequency of prior weight loss attempts or average duration of weight maintenance after prior weight loss attempts as predictors of measured sleep duration., Results: After adjusting for physical activity, perceived stress, smoking, alcohol consumption, education, sex and age, sleep duration was associated to BMI (P < 0.001), with the highest BMI observed in the group of participants sleeping <6 h a day [34.0 kg/m 2 (95% CI: 31.8-36.1)]. Less difference in BMI was detected between the remaining groups, with the lowest BMI observed among participants sleeping 8-<9 h a day [29.4 kg/m 2 (95% CI: 28.8-29.9)]. Similar results were found for FMI (P = 0.008) and FFMI (P < 0.001). We found no association between sleep duration and WHR. Likewise, we found no associations between weight loss history and attained sleep duration., Conclusion: In an overweight population who had achieved a clinically significant weight loss, short sleep duration was associated with higher BMI, with similar associations for fat and lean mass. We found no evidence of association between weight loss history and attained sleep duration.

A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of activities using acceleration, physiological signals (e.g., heart rate, body temperature, galvanic skin response), and participant characteristics (e.g., sex, age, height, weight, body composition) collected from wearable devices (Fitbit charge 2, Polar H7, SenseWear Armband Mini and Actigraph GT3-x) as potential inputs. By utilising a leave-one-out cross-validation approach in 59 subjects, we investigated the predictive accuracy in sedentary, ambulatory, household, and cycling activities compared to indirect calorimetry (Vyntus CPX). Over all activities, correlations of at least r = 0.85 were achieved by the models. Root mean squared error ranged from 1 to 1.37 METs and all overall models were statistically equivalent to the criterion measure. Significantly lower error was observed for Actigraph and Sensewear models, when compared to the manufacturer provided estimates of the Sensewear Armband (p < 0.05). A high degree of accuracy in EE estimation was achieved by applying non-linear models to wearable devices which may offer a means to capture the energy cost of free-living activities.

Background: Technological advances in remote monitoring offer new opportunities to quantify body weight patterns in free-living populations. This paper describes body weight fluctuation patterns in response to weekly, holiday (Christmas) and seasonal time periods in a large group of individuals engaged in a weight loss maintenance intervention., Methods: Data was collected as part The NoHoW Project which was a pan-European weight loss maintenance trial. Three eligible groups were defined for weekly, holiday and seasonal analyses, resulting in inclusion of 1,421, 1,062 and 1,242 participants, respectively. Relative weight patterns were modelled on a time series following removal of trends and grouped by gender, country, BMI and age., Results: Within-week fluctuations of 0.35% were observed, characterised by weekend weight gain and weekday reduction which differed between all groups. Over the Christmas period, weight increased by a mean 1.35% and was not fully compensated for in following months, with some differences between countries observed. Seasonal patterns were primarily characterised by the effect of Christmas weight gain and generally not different between groups., Conclusions: This evidence may improve current understanding of regular body weight fluctuation patterns and help target future weight management interventions towards periods, and in groups, where weight gain is anticipated., Competing Interests: The authors have declared that no competing interests exist.

Objective: To determine the accuracy of wrist and arm-worn activity monitors' estimates of energy expenditure (EE)., Data Sources: SportDISCUS (EBSCOHost), PubMed, MEDLINE (Ovid), PsycINFO (EBSCOHost), Embase (Ovid) and CINAHL (EBSCOHost)., Design: A random effects meta-analysis was performed to evaluate the difference in EE estimates between activity monitors and criterion measurements. Moderator analyses were conducted to determine the benefit of additional sensors and to compare the accuracy of devices used for research purposes with commercially available devices., Eligibility Criteria: We included studies validating EE estimates from wrist-worn or arm-worn activity monitors against criterion measures (indirect calorimetry, room calorimeters and doubly labelled water) in healthy adult populations., Results: 60 studies (104 effect sizes) were included in the meta-analysis. Devices showed variable accuracy depending on activity type. Large and significant heterogeneity was observed for many devices (I 2 >75%). Combining heart rate or heat sensing technology with accelerometry decreased the error in most activity types. Research-grade devices were statistically more accurate for comparisons of total EE but less accurate than commercial devices during ambulatory activity and sedentary tasks., Conclusions: EE estimates from wrist and arm-worn devices differ in accuracy depending on activity type. Addition of physiological sensors improves estimates of EE, and research-grade devices are superior for total EE. These data highlight the need to improve estimates of EE from wearable devices, and one way this can be achieved is with the addition of heart rate to accelerometry., Prosperoregistration Number: CRD42018085016., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Continuous energy restriction (CER) is purported to be problematic because of reductions in fat-free mass (FFM), compensatory motivation to overeat, and weakened satiety. Intermittent energy restriction (IER) is an alternative behavioral weight loss (WL) strategy that may mitigate some of these limitations., Objective: The objective of the DIVA study was to compare the effects of CER and IER on appetite when the degree of WL (≥5%) is matched., Methods: Women with overweight/obesity (BMI 25.0-34.9 kg/m2; age 18-55 y) were recruited for this controlled-feeding RCT via CER (25% daily energy restriction) or IER (alternating ad libitum and 75% energy restriction days). Probe days were conducted at baseline and post-intervention to assess body composition, ad libitum energy intake and subjective appetite in response to a fixed-energy breakfast, and eating behavior traits. After baseline measurements, participants were allocated to CER (n = 22) or IER (n = 24). Per protocol analyses (≥5% WL within 12 wk) were conducted with use of repeated measures ANOVA., Results: Thirty of 37 completers reached ≥5% WL [CER (n = 18): 6.3 ± 0.8% in 57 ± 16 d, IER (n = 12): 6.6 ± 1.1% in 67 ± 13 d; % WL P = 0.43 and days P = 0.10]. Fat mass [-3.9 (95% CI: -4.3, -3.4) kg] and FFM [-1.3 (95% CI: -1.6, -1.0) kg] were reduced post-WL (P < 0.001), with no group differences. Self-selected meal size decreased post-WL in CER (P = 0.03) but not in IER (P = 0.19). Hunger AUC decreased post-WL (P < 0.05), with no group differences. Satiety quotient remained unchanged and was similar in both groups. Both interventions improved dietary restraint, craving control, susceptibility to hunger, and binge eating (P < 0.001)., Conclusions: Controlled ≥5% WL via CER or IER did not differentially affect changes in body composition, reductions in hunger, and improvements in eating behavior traits. This suggests that neither CER nor IER lead to compensatory adaptations in appetite in women with overweight/obesity. This trial was registered at clinicaltrials.gov as NCT03447600., (Copyright © The Author(s) 2019.)

Background: Dynamic changes in body composition which occur during weight loss may have an influential role on subsequent energy balance behaviors and weight., Objectives: The aim of this article is to consider the effect of proportionate changes in body composition during weight loss on subsequent changes in appetite and weight outcomes at 26 wk in individuals engaged in a weight loss maintenance intervention., Methods: A subgroup of the Diet, Obesity, and Genes (DiOGenes) study (n = 209) was recruited from 3 European countries. Participants underwent an 8-wk low-calorie diet (LCD) resulting in ≥8% body weight loss, during which changes in body composition (by DXA) and appetite (by visual analog scale appetite perceptions in response to a fixed test meal) were measured. Participants were randomly assigned into 5 weight loss maintenance diets based on protein and glycemic index content and followed up for 26 wk. We investigated associations between proportionate fat-free mass (FFM) loss (%FFML) during weight loss and 1) weight outcomes at 26 wk and 2) changes in appetite perceptions., Results: During the LCD, participants lost a mean ± SD of 11.2 ± 3.5 kg, of which 30.4% was FFM. After adjustment, there was a tendency for %FFML to predict weight regain in the whole group (β: 0.041; 95% CI: -0.001, 0.08; P = 0.055), which was significant in men (β: 0.09; 95% CI: 0.02, 0.15; P = 0.009) but not women (β: 0.01; 95% CI: -0.04, 0.07; P = 0.69). Associations between %FFML and change in appetite perceptions during weight loss were inconsistent. The strongest observations were in men for hunger (r = 0.69, P = 0.002) and desire to eat (r = 0.61, P = 0.009), with some tendencies in the whole group and no associations in women., Conclusions: Our results suggest that composition of weight loss may have functional importance for energy balance regulation, with greater losses of FFM potentially being associated with increased weight regain and appetite. This trial was registered at clinicaltrials.gov as NCT00390637., (Copyright © The Author(s) 2020.)

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.

Weight regain following weight loss is common although little is known regarding the associations between amount, rate, and composition of weight loss and weight regain. Forty-three studies (52 groups; n = 2379) with longitudinal body composition measurements were identified in which weight loss (≥5%) and subsequent weight regain (≥2%) occurred. Data were synthesized for changes in weight and body composition. Meta-regression models were used to investigate associations between amount, rate, and composition of weight loss and weight regain. Individuals lost 10.9% of their body weight over 13 weeks composed of 19.6% fat-free mass, followed by a regain of 5.4% body weight over 44 weeks composed of 21.6% fat-free mass. Associations between the amount (P < 0.001) and rate (P = 0.049) of weight loss and their interaction (P = 0.042) with weight regain were observed. Fat-free mass (P = 0.017) and fat mass (P < 0.001) loss both predicted weight regain although the effect of fat-free mass was attenuated following adjustment. The amount (P < 0.001), but not the rate of weight loss (P = 0.150), was associated with fat-free mass loss. The amount and rate of weight loss were significant and interacting factors associated with weight regain. Loss of fat-free mass and fat mass explained greater variance in weight regain than weight loss alone., (© 2019 World Obesity Federation.)