Are motivational and self-regulation factors associated with 12 months' weight regain prevention in the NoHoW study? An analysis of European adults.

PURPOSE: Preventing weight regain can only be achieved by sustained changes in energy balance-related behaviors that are associated with weight, such as diet and physical activity. Changes in motivation and self-regulatory skills can support long-term behavioral changes in the context of weight loss maintenance. We propose that experiencing a supportive climate care is associated with enhanced satisfaction of basic psychological needs, intrinsic goals, and autonomous motivation. These factors are expected to be associate with the utilization of self-regulation skills, leading to more sustained behavior changes and ultimately preventing weight regain. This hypothesis was tested in this ancillary analysis of the NoHoW trial, where the study arms were pooled and followed for 12 months. METHODS: The NoHoW was a three-center, large-scale weight regain prevention full factorial trial. In this longitudinal study, data were collected in adults who lost > 5% weight in the past year (N = 870, complete data only, 68.7% female, 44.10 ± 11.86 years, 84.47 ± 17.03 kg) during their participation in a 12-month digital behavior change intervention. Weight and validated measures of motivational- and self-regulatory skills-related variables were collected at baseline, six- and 12 months. Change variables were used in Mplus' path analytical models informed by NoHoW's logic model. RESULTS: The bivariate correlations confirmed key mediators' potential effect on weight outcomes in the expected causal direction. The primary analysis showed that a quarter of the variance (r2 = 23.5%) of weight regain prevention was achieved via the mechanisms of action predicted in the logic model. Specifically, our results show that supportive climate care is associated with needs satisfaction and intrinsic goal content leading to better weight regain prevention via improvements in self-regulatory skills and exercise-controlled motivation. The secondary analysis showed that more mechanisms of action are significant in participants who regained or maintained their weight. CONCLUSIONS: These results contribute to a better understanding of the mechanisms of action leading to behavior change in weight regain prevention. The most successful participants used only a few intrinsic motivation-related mechanisms of action, suggesting that habits may have been learned. While developing a digital behavior change intervention, researchers and practitioners should consider creating supportive climate care to improve needs satisfaction and intrinsic goal contents. TRIAL REGISTRATION: ISRCTN, ISRCTN88405328 , registered 12/22/2016.

Motivational and self-efficacy reciprocal effects during a 12-month' weight regain prevention program.

OBJECTIVE: Weight regain prevention is a critical public health challenge. Digital behaviour change interventions provide a scalable platform for applying and testing behaviour change theories in this challenging context. This study's goal was to analyse reciprocal effects between psychosocial variables (i.e., needs satisfaction, eating regulation, self-efficacy) and weight over 12 months using data from a large sample of participants engaged in a weight regain prevention trial. METHODS: The NoHoW study is a three-centre, large-scale weight regain prevention trial. Adults who lost >5% of their weight in the past year (N = 1627, 68.7% female, 44.10 ± 11.86 years, 84.47 ± 17.03 kg) participated in a 12-month' digital behaviour change-based intervention. Weight and validated measures of basic psychological needs satisfaction, eating regulation and self-efficacy were collected at baseline, six- and 12 months. Correlational, latent growth models and cross-lagged analysis were used to identify potential reciprocal effects. RESULTS: Baseline higher scores of needs satisfaction and self-efficacy were associated with six- and 12-month' weight loss. Baseline weight was linked to all psychosocial variables at six months, and six-months weight was associated with needs satisfaction and self-efficacy at 12 months. During the 12 months, increases in eating regulation, needs satisfaction and self-efficacy were associated with weight loss over the same period, and reciprocal effects were observed between the variables, suggesting the existence of Weight Management Cycles. CONCLUSIONS: While further studies are needed, during long-term weight regain prevention, weight decrease, needs satisfaction and self-efficacy may lead to Weight Management Cycles, which, if recurrent, may provide sustained prevention of weight regain.

Does the design of the NHS Low-Calorie Diet Programme have fidelity to the programme specification? A documentary review of service parameters and behaviour change content in a type 2 diabetes intervention.

BACKGROUND: NHS England commissioned four independent service providers to pilot low-calorie diet programmes to drive weight loss, improve glycaemia and potentially achieve remission of Type 2 Diabetes across 10 localities. Intervention fidelity might contribute to programme success. Previous research has illustrated a drift in fidelity in the design and delivery of other national diabetes programmes. AIMS: (1) To describe and compare the programme designs across the four service providers; (2) To assess the fidelity of programme designs to the NHS England service specification. METHODS: The NHS England service specification documents and each provider's programme design documents were double-coded for key intervention content using the Template for Intervention Description and Replication Framework and the Behaviour Change Technique (BCT) Taxonomy. RESULTS: The four providers demonstrated fidelity to most but not all of the service parameters stipulated in the NHS England service specification. Providers included between 74% and 87% of the 23 BCTs identified in the NHS specification. Twelve of these BCTs were included by all four providers; two BCTs were consistently absent. An additional seven to 24 BCTs were included across providers. CONCLUSIONS: A loss of fidelity for some service parameters and BCTs was identified across the provider's designs; this may have important consequences for programme delivery and thus programme outcomes. Furthermore, there was a large degree of variation between providers in the presence and dosage of additional BCTs. How these findings relate to the fidelity of programme delivery and variation in programme outcomes and experiences across providers will be examined.

Psychometric properties of the Cystic Fibrosis Eating Attitudes and Behaviours scale (CFEAB) in an adult population.

OBJECTIVES: Individuals with Cystic Fibrosis (CF) may be at an increased risk of developing a range of eating difficulties. Scales designed to measure disordered eating in the general population do not cover CF-specific behaviours resulting in a knowledge gap. The CFEAB was developed as a CF-specific measure assessing eating behaviours and attitudes however little evidence exists regarding its psychometric quality. The aim of this cross-sectional study was to provide a robust assessment of its internal consistency, structural validity, and criterion validity. METHODS: One-hundred and thirty-two people with CF completed self-report scales pertaining to mental health, eating disorders, and the Cystic Fibrosis Eating Attitudes and Behaviours (CFEAB). RESULTS: Results of exploratory structural equation modelling indicated that a three-factor structure produced good fit with the 24-item CFEAB but a purified 12-item CFEAB displayed superior fit and internal consistency. Also, the 12-item scale predicted significant amounts of variance for anxiety, depression, and eating disorders showing enhanced relevance for clinical use. Conclusions These findings add emphasis to the importance of the validation and development of CF-specific measures and the possible inclusion at clinics to help improve CF patient care.

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.

Users' Experiences With the NoHoW Web-Based Toolkit With Weight and Activity Tracking in Weight Loss Maintenance: Long-term Randomized Controlled Trial.

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.

A Theory- and Evidence-Based Digital Intervention Tool for Weight Loss Maintenance (NoHoW Toolkit): Systematic Development and Refinement Study.

BACKGROUND: Many weight loss programs show short-term effectiveness, but subsequent weight loss maintenance is difficult to achieve. Digital technologies offer a promising means of delivering behavior change approaches at low costs and on a wide scale. The Navigating to a Healthy Weight (NoHoW) project, which was funded by the European Union's Horizon 2020 research and innovation program, aimed to develop, test, and evaluate a digital toolkit designed to promote successful long-term weight management. The toolkit was tested in an 18-month, large-scale, international, 2×2 factorial (motivation and self-regulation vs emotion regulation) randomized controlled trial that was conducted on adults with overweight or obesity who lost ≥5% of their body weight in the preceding 12 months before enrollment into the intervention. OBJECTIVE: This paper aims to describe the development of the NoHoW Toolkit, focusing on the logic models, content, and specifications, as well as the results from user testing. METHODS: The toolkit was developed by using a systematic approach, which included the development of the theory-based logic models, the selection of behavior change techniques, the translation of these techniques into a web-based app (NoHoW Toolkit components), technical development, and the user evaluation and refinement of the toolkit. RESULTS: The toolkit included a set of web-based tools and inputs from digital tracking devices (smart scales and activity trackers) and modules that targeted weight, physical activity, and dietary behaviors. The final toolkit comprised 34 sessions that were distributed through 15 modules and provided active content over a 4-month period. The motivation and self-regulation arm consisted of 8 modules (17 sessions), the emotion regulation arm was presented with 7 modules (17 sessions), and the combined arm received the full toolkit (15 modules; 34 sessions). The sessions included a range of implementations, such as videos, testimonies, and questionnaires. Furthermore, the toolkit contained 5 specific data tiles for monitoring weight, steps, healthy eating, mood, and sleep. CONCLUSIONS: A systematic approach to the development of digital solutions based on theory, evidence, and user testing may significantly contribute to the advancement of the science of behavior change and improve current solutions for sustained weight management. Testing the toolkit by using a 2×2 design provided a unique opportunity to examine the effect of motivation and self-regulation and emotion regulation separately, as well as the effect of their interaction in weight loss maintenance.

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 (ß: 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.

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.

Evidence-Based Digital Tools for Weight Loss Maintenance: The NoHoW Project.

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.

The H2020 "NoHoW Project": A Position Statement on Behavioural Approaches to Longer-Term Weight Management.

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.

Validity and reliability of a shorter version of the Geriatric Depression Scale in institutionalized older Portuguese adults.

OBJECTIVES: Depressive symptoms are common in older adults in institutional contexts; however, there is a lack of validated measures for these settings. Identifying depressive symptoms can help clinicians to manage them and to prevent or delay their complications. This study aimed to validate the Geriatric Depression Scale (GDS) in an institutionalized sample of older adults. METHOD: 493 institutionalized older people (73% women) aged 60 or over were evaluated through the GDS, the Mini International Neuropsychiatric Interview (MINI) (depression vs. no depression = 11% vs. 89%), the Geriatric Anxiety Inventory (GAI), the Positive Affect (PA) and Negative Affect (NA) Schedule, and the Satisfaction with Life Scale (SWLS). Test-retest reliability was assessed with 57 older adults. RESULTS: An 8-item version presented a Cronbach's alpha value of .87 with a single factor explaining its variance. The correlations (p < .01) attested the concurrent validity (GAI: r = .76; PA: r = -.22; AN: r = .62; SWLS: r = -.32). Test-retest reliability (6.51 months) was adequate (r = .52). ROC analysis (AUC = .82; sensitivity = 80%; specificity = 77%) and Youden index revealed a cutoff of 5/6 for the diagnosis of depression. CONCLUSION: Results support the validity and the screening capacity of a short version of GDS in institutional contexts. Short screening instruments for depressive symptoms may facilitate their identification, allowing for timely clinical interventions in institutional settings.

Effect of adding a compassion-focused intervention on emotion, eating and weight outcomes in a commercial weight management programme.

This study examined whether adding a compassion-focused light touch digital intervention into a commercial multicomponent weight management programme improved eating behaviour, self-evaluation and weight-related outcomes. The compassion intervention significantly reduced binge eating symptomatology and dropout, and improved psychological adjustment and self-evaluation, but did not affect weight outcomes. Compassion, self-reassurance and reductions in shame and self-criticism mediated the effect of the intervention on reductions of binge eating symptomatology. Negative self-evaluation, binge eating symptomatology, susceptibility to hunger and eating guilt were significant predictors of dropout. Findings suggest that compassion-based digital tools may help participants better manage binge eating symptomatology and self-evaluation in weight management interventions.

Body Fatness Influences Associations of Body Composition and Energy Expenditure with Energy Intake in Healthy Women.

OBJECTIVE: This study aimed to investigate the influence of body fatness on the associations of body composition and energy expenditure (EE) with energy intake (EI). METHODS: Data from 93 women (BMI = 25.5 [SD 4.2] kg/m2 ) recruited for two studies (Study 1, n = 48, BMI = 25.0-34.9 kg/m2 ; Study 2, n = 45, BMI = 18.5-24.9 kg/m2 ) were examined. Body composition, resting metabolic rate (RMR), and test meal EI were assessed during a laboratory probe day. Physical activity, total daily EE (TDEE), and self-reported free-living 24-hour EI were collected during 7 days. RESULTS: In the whole sample, fat-free mass (r = 0.45; P < 0.001), RMR (r = 0.41; P < 0.001), and TDEE (r = 0.39; P < 0.001), but not fat mass (r = 0.17; P = 0.11), were positively associated with free-living 24-hour EI. Body fat percentage moderated the associations of RMR (ß = -1.88; P = 0.02) and TDEE (ß = -1.91; P = 0.03) with mean free-living 24-hour EI. Fat mass was negatively associated with test meal EI only in the leaner group (r = -0.43; P = 0.004), and a weak nonlinear association was observed in the whole sample (r2 = 0.092; P = 0.04). CONCLUSIONS: Body fat percentage appears to moderate the associations between EE and daily EI. Furthermore, the negative association between fat mass and test meal EI observed in leaner individuals was absent in those with higher body fatness. Therefore, higher levels of body fatness may weaken the coupling between EE and EI.

Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study.

BACKGROUND: Body 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. OBJECTIVE: This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches. METHODS: In 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. RESULTS: Body 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. CONCLUSIONS: The 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.

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

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).

Weekly, seasonal and holiday body weight fluctuation patterns among individuals engaged in a European multi-centre behavioural weight loss maintenance intervention.

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.

The drive to eat in homo sapiens: Energy expenditure drives energy intake.

The drive to eat is a component of appetite control, independent of the omnivorous habit of humans, and separate from food choice, satiety and food reward. The drive forms part of the tonic component of appetite and arises from biological needs; it is distinct from episodic aspects of appetite which are heavily influenced by culture and the environment (and which reflect the omnivorous habit). It is proposed that the tonic drive to eat reflects a need state generated by metabolic energy expenditure (EE) required to maintain the functioning and integrity of vital organs. Specifically, the tonic drive is quantitatively associated with fat-free mass (FFM) and resting metabolic rate (RMR). A rational proposition is that high metabolic rate organs (such as heart, liver, kidneys, brain) together with skeletal muscle generate a metabolic need which drives energy intake (EI). The basic phenomenon of a relationship between FFM, RMR and EI, first published in 2011, has been substantially replicated and there are at least 14 concordant published studies carried out in 9 different countries (and 4 continents) with various ethnic groups of lean and obese humans. These studies demonstrate that FFM and RMR represent major determinants of the drive to eat, and this is rational from an evolutionary perspective. The EE of bodily movements through skeletal muscle activity (namely physical activity and exercise) represents another driver which is clearly but more weakly associated with an increase in EI. This account of appetite control, developed within an energy balance framework, is consistent with the apparent inexorable escalation of fatness in individual humans, and for the progressive increase in the prevalence of obesity which, among other factors, reflects the difficulty of managing the biological drive to eat.

Association between objectively measured sleep duration, adiposity and weight loss history.

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/m2 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/m2 (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/m2 (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.

Associations between the proportion of fat-free mass loss during weight loss, changes in appetite, and subsequent weight change: results from a randomized 2-stage dietary intervention trial.

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 (ß: 0.041; 95% CI: -0.001, 0.08; P = 0.055), which was significant in men (ß: 0.09; 95% CI: 0.02, 0.15; P = 0.009) but not women (ß: 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.