Investigating motivations to eat: Refining and validating a framework of Eating Behaviour Traits in dieters and the general population.

Due to relationships between diet and health including obesity, there is a need to examine the explanatory power of factors that motivate people to (over or under) eat. In a previous investigation, a four-factor subscale-based model of eating behaviour traits (EBTs) was developed which identified individual differences in psychological factors influencing motivations to eat and some residual uncertainties. The current study used a data-driven and theory-driven approach, including individual items to refine and extend previous EBT models. The aim was to examine and validate the domain structure of a framework for EBTs. The analysis used two samples including a representative sample of the UK population (n = 2010, 51% female, 49% male, 18 - 88 years), and members of a weight management program (n = 2317, 96.6% female, 2.8% male, 21 - 84 years), who completed 5 questionnaires including 10 EBTs. The results found some support for a 6-factor model, encompassing reactive eating, negative emotional eating, positive emotional eating, restricted eating, homeostatic eating, and body-food choice congruence (data-driven model) or eating for health (theory-driven model). There were differences between the data-driven model and the theory-driven model regarding the 6th factor. Additionally, the data-driven model did not distinguish between eating for pleasure and reactive eating. The models demonstrated that the eating behaviour factors were significantly associated with BMI category. Overall, this research contributes to a more structured understanding of the dimensions of motivation underlying EBTs, emphasising the utility of this framework for identifying at-risk individuals and tailoring interventions to meet specific individual needs.

Can eating behaviour traits be explained by underlying, latent factors? An exploratory and confirmatory factor analysis.

Eating Behaviour Traits (EBTs) are psychological constructs developed to explain patterns of eating behaviour, including factors that motivate people to (over or under) eat. There is a need to align and clarify their unique contributions and harmonise the understanding they offer for human eating behaviour. Therefore, the current study examined whether 18 commonly cited EBTs could be explained by underlying, latent factors (domains of eating behaviour). An exploratory factor analysis (EFA) was used to identify latent factors, and these factors were validated using a confirmatory factor analysis (CFA). 1279 participants including the general public and members of a weight management programme were included in the analysis (957 females, 317 males, 3 others, 2 prefer not to say), with a mean age of 54 years (median = 57 years, SD = 12.03) and a mean BMI of 31.93 kg/m2 (median = 30.86, SD = 6.00). The participants completed 8 questionnaires which included 18 commonly cited EBTs and the dataset was split at random with a 70/30 ratio to conduct the EFA (n = 893) and CFA (n = 383). The results supported a four-factor model which indicated that EBTs can be organised into four domains: reactive, restricted, emotional, and homeostatic eating. The four-factor model also significantly predicted self-reported BMI and weight change. Future research should test whether this factor structure is replicated in more diverse populations, and including other EBTs, to advance these domains of eating as a unifying framework for studying individual differences in human eating behaviour.

Comparing self-reported energy intake using an online dietary tool with energy expenditure by an activity tracker.

OBJECTIVE: The aim of this study was to compare self-reported total energy intake (TEI) collected using an online multiple-pass 24-h dietary recall tool (Intake24) with total energy expenditure (TEE) estimated from Fitbit Charge 2-improved algorithms in adults from the NoHoW trial (12-mo weight maintenance after free-living weight loss). METHODS: Bland-Altman plots were used to assess the level of agreement between TEI and TEE at baseline and after 12 mo. The ratio of TEI to TEE was also calculated. RESULTS: Data from 1323 participants (71% female) was included in the analysis (mean ± SD: age 45 ± 12 y, body mass index 29.7 ± 5.4 kg/m2, initial weight loss 11.5 ± 6.5 kg). The TEI was lower than TEE on average by 33%, with limits of agreement ranging from -91% to +25%. Men, younger individuals, those with higher body mass index, those with the greater weight loss before enrollment, and those who gained weight during the study underestimated to a greater extent. CONCLUSIONS: These findings contribute to the ongoing research examining the validity of technology-based dietary assessment tools.

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.

Testing motivational and self-regulatory mechanisms of action on device-measured physical activity in the context of a weight loss maintenance digital intervention: A secondary analysis of the NoHoW trial.

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.

Exploratory analysis of reflective, reactive, and homeostatic eating behaviour traits on weight change during the 18-month NoHoW weight maintenance trial.

Behaviour change interventions for weight management have found varied effect sizes and frequent weight re-gain after weight loss. There is interest in exploring whether differences in eating behaviour can be used to develop tailored weight management programs. This secondary analysis of an 18-month weight maintenance randomised controlled trial (RCT) aimed to investigate the association between individual variability in weight maintenance success and change in eating behaviour traits (EBT). Data was analysed from the NoHoW trial (Scott et al., 2019), which was designed to measure processes of change after weight loss of ≥5% body weight in the previous year. The sample included 1627 participants (mean age = 44.0 years, SD = 11.9, mean body mass index (BMI) = 29.7 kg/m2, SD = 5.4, gender = 68.7% women/31.3% men). Measurements of weight (kg) and 7 EBTs belonging to domains of reflective, reactive, or homeostatic eating were taken at 4 time points up to 18-months. Increases in measures of 'reactive eating' (binge eating, p < .001), decreases in 'reflective eating' (restraint, p < .001) and changes in 'homeostatic eating' (unlimited permission to eat, p < .001 and reliance on hunger and satiety cues, p < .05) were significantly and independently associated with concomitant weight change. Differences in EBT change were observed between participants who lost, maintained, or re-gained weight for all EBTs (p < .001) except for one subscale of intuitive eating (eating for physical reasons, p = .715). Participants who lost weight (n = 322) exhibited lower levels of reactive eating and higher levels of reflective eating than participants who re-gained weight (n = 668). EBT domains can identify individuals who need greater support to progress in weight management interventions. Increasing reflective eating and reducing reactive eating may enhance weight management success.

A neurocognitive perspective on the relationship between exercise and reward: Implications for weight management and drug addiction.

The impact of exercise on food reward is increasingly being discussed as an interplay between executive function (EF), homeostasis and mechanisms promoting or undermining intentional behaviour change. Integrating current knowledge of neurocognitive processes encompassing cognitive and affective networks within an energy balance framework will provide a more comprehensive account. Reward circuitry affected by recreational drugs and food overlap. Therefore the underlying processes explaining changes in drug-taking behaviour may offer new insights into how exercise affects the reward value of recreational drugs and food. EF is important for successful self-regulation, and training EF may boost inhibitory control in relation to food- and drug-related reward. Preclinical and clinical observations suggest that reward-seeking can transfer within and between categories of reward. This may have clinical implications beyond exercise improving metabolic health in people with obesity to understanding therapeutic responses to exercise in people with neurocognitive deficits in non-food reward-based decision making such as drug dependence.

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.

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.

Do eating behavior traits predict energy intake and body mass index? A systematic review and meta-analysis.

At present, it is unclear whether eating behavior traits (EBT) predict objectively measured short-term energy intake (EI) and longer-term energy balance as estimated by body mass index (BMI). This systematic review examined the impact of EBT on BMI and laboratory-based measures of EI in adults ( ≥ 18 years) in any BMI category, excluding self-report measures of EI. Articles were searched up until 28th October 2021 using MEDLINE, PsycINFO, EMBASE and Web of Science. Sixteen EBT were identified and the association between 10 EBT, EI and BMI were assessed using a random-effects meta-analysis. Other EBT outcomes were synthesized qualitatively. Risk of bias was assessed with the mixed methods appraisal tool. A total of 83 studies were included (mean BMI = 25.20 kg/m2 , mean age = 27 years and mean sample size = 70). Study quality was rated moderately high overall, with some concerns in sampling strategy and statistical analyses. Susceptibility to hunger (n = 6) and binge eating (n = 7) were the strongest predictors of EI. Disinhibition (n = 8) was the strongest predictor of BMI. Overall, EBT may be useful as phenotypic markers of susceptibility to overconsume or develop obesity (PROSPERO: CRD42021288694).

What goes on in digital behaviour change interventions for weight loss maintenance targeting physical activity: A scoping review.

Objective: To identify the core components of digital behaviour change interventions for weight loss maintenance targeting physical activity, in terms of: (i) behaviour change techniques, (ii) mechanisms of action, (iii) modes of delivery, (iv) dose and (v) tailoring/personalization. In addition, the links between these components were investigated. Methods: A literature search was performed in five electronic databases: PubMed, Embase, CINHAL, PsycINFO and Web of Science. Two reviewers independently screened the identified articles and extracted data related with the study characteristics and behaviour change techniques, mechanism of action, mode of delivery, dose, and tailoring, using standardized classifications whenever available (e.g. behaviour change techniques taxonomy). Results: Seventeen articles reporting 11 original studies were selected. Two studies were protocols, 9 studies presented results for weight change and all but one showed no significant differences between the intervention and control groups. Eight studies (73%) provided adequate information on behaviour change techniques. Five studies (45%) provided partial information about how the behaviour change techniques were linked to mechanisms of action, and only one study (0.9%) described these links for all the techniques. Around half of the studies reported the modes through which behaviour change techniques were delivered. Descriptions of dose were present in most studies, but with minimal information. The use of tailoring or personalization approaches was mentioned in eight studies (73%), but descriptions of what was tailored and how were minimal. Conclusions: The compilation of information regarding intervention components was difficult due to the lack of information and systematization in reporting across papers. This is particularly true for the reporting of the links between behaviour change techniques and the other core intervention components. This information is crucial to help us understand in the context of behaviour change interventions what works or does not work, how it works and why.

Evaluation of the Immediate Effects of Web-Based Intervention Modules for Goals, Planning, and Coping Planning on Physical Activity: Secondary Analysis of a Randomized Controlled Trial on Weight Loss Maintenance.

BACKGROUND: The use of digital interventions can be accurately monitored via log files. However, monitoring engagement with intervention goals or enactment of the actual behaviors targeted by the intervention is more difficult and is usually evaluated based on pre-post measurements in a controlled trial. OBJECTIVE: The objective of this paper is to evaluate if engaging with 2 digital intervention modules focusing on (1) physical activity goals and action plans and (2) coping with barriers has immediate effects on the actual physical activity behavior. METHODS: The NoHoW Toolkit (TK), a digital intervention developed to support long-term weight loss maintenance, was evaluated in a 2 x 2 factorial randomized controlled trial. The TK contained various modules based on behavioral self-regulation and motivation theories, as well as contextual emotion regulation approaches, and involved continuous tracking of weight and physical activity through connected commercial devices (Fitbit Aria and Charge 2). Of the 4 trial arms, 2 had access to 2 modules directly targeting physical activity: a module for goal setting and action planning (Goal) and a module for identifying barriers and coping planning (Barriers). Module visits and completion were determined based on TK log files and time spent in the module web page. Seven physical activity metrics (steps; activity; energy expenditure; fairly active, very active and total active minutes; and distance) were compared before and after visiting and completing the modules to examine whether the modules had immediate or sustained effects on physical activity. Immediate effect was determined based on 7-day windows before and after the visit, and sustained effects were evaluated for 1 to 8 weeks after module completion. RESULTS: Out of the 811 participants, 498 (61.4%) visited the Goal module and 406 (50.1%) visited the Barriers module. The Barriers module had an immediate effect on very active and total active minutes (very active minutes: before median 24.2, IQR 10.4-43.0 vs after median 24.9, IQR 10.0-46.3; P=.047; total active minutes: before median 45.1, IQR 22.9-74.9 vs after median 46.9, IQR 22.4-78.4; P=.03). The differences were larger when only completed Barriers modules were considered. The Barriers module completion was also associated with sustained effects in fairly active and total active minutes for most of the 8 weeks following module completion and for 3 weeks in very active minutes. CONCLUSIONS: The Barriers module had small, significant, immediate, and sustained effects on active minutes measured by a wrist-worn activity tracker. Future interventions should pay attention to assessing barriers and planning coping mechanisms to overcome them. TRIAL REGISTRATION: ISRCTN Registry ISRCTN88405328; https://www.isrctn.com/ISRCTN88405328.

Associations between high-metabolic rate organ masses and fasting hunger: A study using whole-body magnetic resonance imaging in healthy males.

BACKGROUND: Fat-free mass (FFM) has been shown to be positively associated with hunger and energy intake, an association mediated by resting metabolic rate (RMR). However, FFM comprises a heterogeneous group of tissues with distinct metabolic rates, and it remains unknown how specific high-metabolic rate organs contribute to the degree of perceived hunger. OBJECTIVE: To examine whether FFM and its anatomical components were associated with fasting hunger when assessed at the tissue-organ level. DESIGN: Body composition (quantitative magnetic resonance and magnetic resonance imaging), RMR and whole-body glucose oxidation (indirect calorimetry), HOMA-index as a marker of insulin sensitivity, nitrogen balance and fasting hunger (visual analogue scales) were assessed in 21 healthy males (age = 25 ± 3y; BMI = 23.4 ± 2.1 kg/m2) after 3 days of controlled energy balance. RESULTS: FFM (rs = 0.39; p = 0.09), RMR (rs = 0.52; p = 0.02) and skeletal muscle mass (rs = 0.57; p = 0.04), but not fat mass (rs = -0.01; p = 0.99), were positively associated with fasting hunger. The association between the combined mass of high-metabolic rate organs (i.e., brain, liver, kidneys and heart; rs = 0.58; p = 0.006) and fasting hunger was stronger than with FFM as a uniform body component. The strongest individual association was between liver mass and fasting hunger (rs = 0.51; p = 0.02). No associations were observed between glucose parameters, markers of insulin sensitivity and fasting hunger. The encephalic measure, an index of brain-to-body energy allocation, was negatively associated with fasting hunger (rs = -0.51; p = 0.02). CONCLUSIONS: Fasting hunger was more strongly associated with the combined mass of high-metabolic rate organs than with FFM as a uniform body component, highlighting the importance of integrating individual tissue-organ masses and their functional correlates into homeostatic models of human appetite. The association between liver mass and fasting hunger may reflect its role in ensuring the brain's basal energy needs are met.

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.

Fat-Free Mass and Total Daily Energy Expenditure Estimated Using Doubly Labeled Water Predict Energy Intake in a Large Sample of Community-Dwelling Older Adults.

BACKGROUND: Up to 30% of community-based older adults report reduced appetite and energy intake (EI), but previous research examining the underlying physiological mechanisms has focused on the mechanisms that suppress eating rather than the hunger drive and EI. OBJECTIVES: We examined the associations between fat-free mass (FFM), physical activity (PA), total daily energy expenditure (TDEE), and self-reported EI in older adults. METHODS: The present study was a secondary analysis of the Interactive Diet and Activity Tracking in AARP study. Body composition (deuterium dilution), PA (accelerometry), and TDEE (doubly labeled water) were measured in 590 older adults (age, 63.1 ± 5.9 years; BMI, 28.1 ± 4.9 kg/m2). The total daily EI was estimated from a single 24-hour dietary recall (EIsingle; ±1 month of PA and TDEE measurement) and the mean of up to 6 recalls over a 12-month period (EImean), with misreporters classified using the 95% CIs between the EImean and TDEE. RESULTS: After controlling for age and sex, linear regression demonstrated that FFM and TDEE predicted EI when estimated from a single 24-hour dietary recall (P < 0.05), from the mean of up to 6 dietary recalls (P < 0.05), and after the removal of those classified as underreporters (P < 0.001). Age moderated the associations between FFM and EIsingle (P < 0.001), FFM and EImean (P < 0.001), and TDEE with EIsingle (P = 0.016), with associations becoming weaker across age quintiles. CONCLUSIONS: These data suggest that the total daily EI is proportional to the FFM and TDEE, but not fat mass, in older adults. These associations may reflect an underling drive to eat that influences the daily food intake. While the associations between FFM or TDEE and EI existed across all age quintiles, these associations weakened with increasing age.

Fat-Free Mass and Total Daily Energy Expenditure Estimated Using Doubly Labeled Water Predict Energy Intake in a Large Sample of Community-Dwelling Older Adults.

BACKGROUND: Up to 30% of community-based older adults report reduced appetite and energy intake (EI), but previous research examining the underlying physiological mechanisms has focused on the mechanisms that suppress eating rather than the hunger drive and EI. OBJECTIVES: We examined the associations between fat-free mass (FFM), physical activity (PA), total daily energy expenditure (TDEE), and self-reported EI in older adults. METHODS: The present study was a secondary analysis of the Interactive Diet and Activity Tracking in AARP study. Body composition (deuterium dilution), PA (accelerometry), and TDEE (doubly labeled water) were measured in 590 older adults (age, 63.1 ± 5.9 years; BMI, 28.1 ± 4.9 kg/m2). The total daily EI was estimated from a single 24-hour dietary recall (EIsingle; ±1 month of PA and TDEE measurement) and the mean of up to 6 recalls over a 12-month period (EImean), with misreporters classified using the 95% CIs between the EImean and TDEE. RESULTS: After controlling for age and sex, linear regression demonstrated that FFM and TDEE predicted EI when estimated from a single 24-hour dietary recall (P < 0.05), from the mean of up to 6 dietary recalls (P < 0.05), and after the removal of those classified as underreporters (P < 0.001). Age moderated the associations between FFM and EIsingle (P < 0.001), FFM and EImean (P < 0.001), and TDEE with EIsingle (P = 0.016), with associations becoming weaker across age quintiles. CONCLUSIONS: These data suggest that the total daily EI is proportional to the FFM and TDEE, but not fat mass, in older adults. These associations may reflect an underling drive to eat that influences the daily food intake. While the associations between FFM or TDEE and EI existed across all age quintiles, these associations weakened with increasing age.

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.

Free-Living Energy Balance Behaviors Are Associated With Greater Weight Loss During a Weight Loss Program.

Introduction: Free-living movement (physical activity [PA] and sedentary behavior [SB]) and eating behaviors (energy intake [EI] and food choice) affect energy balance and therefore have the potential to influence weight loss (WL). This study explored whether free-living movement and/or eating behaviors measured early (week 3) in a 14-week WL programme or their change during the intervention are associated with WL in women. Methods: In the study, 80 women (M ± SD age: 42.0 ± 12.4 years) with overweight or obesity [body mass index (BMI): 34.08 ± 3.62 kg/m2] completed a 14 week WL program focused primarily on diet (commercial or self-led). Body mass (BM) was measured at baseline, and again during week 2 and 14 along with body composition. Free-living movement (SenseWear Armband) and eating behavior (weighed food diaries) were measured for 1 week during week 3 and 12. Hierarchical multiple regression analyses examined whether early and early-late change in free-living movement and eating behavior were associated with WL. The differences in behavior between clinically significant weight losers (CWL; ≥5% WL) and non-clinically significant weight losers (NWL; ≤ 3% WL) were compared. Results: The energy density of food consumed [ß = 0.45, p < 0.001] and vigorous PA [ß = -0.30, p < 0.001] early in the intervention (regression model 1) and early-late change in light PA [ß = -0.81 p < 0.001], moderate PA [ß = -1.17 p < 0.001], vigorous PA [ß = -0.49, p < 0.001], total energy expenditure (EE) [ß = 1.84, p < 0.001], and energy density of food consumed [ß = 0.27, p = 0.01] (regression model 2) significantly predicted percentage change in BM. Early in the intervention, CWL consumed less energy dense foods than NWL [p = 0.03]. CWL showed a small but significant increase in vigorous PA, whereas NWL showed a slight decrease in PA [p = 0.04]. Conclusion: Both early and early-late change in free-living movement and eating behaviors during a 14 week WL program are predictors of WL. These findings demonstrate that specific behaviors that contribute to greater EE (e.g., vigorous PA) and lower EI (e.g., less energy-dense foods) are related to greater WL outcomes. Interventions targeting these behaviors can be expected to increase the effectiveness of WL programs.

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.