The NoHoW protocol: a multicentre 2×2 factorial randomised controlled trial investigating an evidence-based digital toolkit for weight loss maintenance in European adults

Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity

BACKGROUND

Obesity is considered to be a risk factor for various diseases, and its incidence has tripled worldwide since 1975. In addition to potentially being at risk for adverse health outcomes, people with overweight or obesity are often stigmatised. Behaviour change interventions are increasingly delivered as mobile health (m-health) interventions, using smartphone apps and wearables. They are believed to support healthy behaviours at the individual level in a low-threshold manner.

OBJECTIVES

To assess the effects of integrated smartphone applications for adolescents and adults with overweight or obesity.

SEARCH METHODS

We searched CENTRAL, MEDLINE, PsycINFO, CINAHL, and LILACS, as well as the trials registers ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform on 2 October 2023 (date of last search for all databases). We placed no restrictions on the language of publication.

SELECTION CRITERIA

Participants were adolescents and adults with overweight or obesity. Eligible interventions were integrated smartphone apps using at least two behaviour change techniques. The intervention could target physical activity, cardiorespiratory fitness, weight loss, healthy diet, or self-efficacy. Comparators included no or minimal intervention (NMI), a different smartphone app, personal coaching, or usual care. Eligible studies were randomised controlled trials of any duration with a follow-up of at least three months.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methodology and the RoB 2 tool. Important outcomes were physical activity, body mass index (BMI) and weight, health-related quality of life, self-efficacy, well-being, change in dietary behaviour, and adverse events. We focused on presenting studies with medium- (6 to < 12 months) and long-term (≥ 12 months) outcomes in our summary of findings table, following recommendations in the core outcome set for behavioural weight management interventions.

MAIN RESULTS

We included 18 studies with 2703 participants. Interventions lasted from 2 to 24 months. The mean BMI in adults ranged from 27 to 50, and the median BMI z-score in adolescents ranged from 2.2 to 2.5. Smartphone app versus no or minimal intervention Thirteen studies compared a smartphone app versus NMI in adults; no studies were available for adolescents. The comparator comprised minimal health advice, handouts, food diaries, smartphone apps unrelated to weight loss, and waiting list. Measures of physical activity: at 12 months' follow-up, a smartphone app compared to NMI probably reduces moderate to vigorous physical activity (MVPA) slightly (mean difference (MD) -28.9 min/week (95% confidence interval (CI) -85.9 to 28; 1 study, 650 participants; moderate-certainty evidence)). We are very uncertain about the results of estimated energy expenditure and cardiorespiratory fitness at eight months' follow-up. A smartphone app compared with NMI probably results in little to no difference in changes in total activity time at 12 months' follow-up and leisure time physical activity at 24 months' follow-up. Anthropometric measures: a smartphone app compared with NMI may reduce BMI (MD of BMI change -2.6 kg/m2, 95% CI -6 to 0.8; 2 studies, 146 participants; very low-certainty evidence) at six to eight months' follow-up, but the evidence is very uncertain. At 12 months' follow-up, a smartphone app probably resulted in little to no difference in BMI change (MD -0.1 kg/m2, 95% CI -0.4 to 0.3; 1 study; 650 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in body weight change (MD -2.5 kg, 95% CI -6.8 to 1.7; 3 studies, 1044 participants; low-certainty evidence) at 12 months' follow-up. At 24 months' follow-up, a smartphone app probably resulted in little to no difference in body weight change (MD 0.7 kg, 95% CI -1.2 to 2.6; 1 study, 245 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in self-efficacy for a physical activity score at eight months' follow-up, but the results are very uncertain. A smartphone app probably results in little to no difference in quality of life and well-being at 12 months (moderate-certainty evidence) and in little to no difference in various measures used to inform dietary behaviour at 12 and 24 months' follow-up. We are very uncertain about adverse events, which were only reported narratively in two studies (very low-certainty evidence). Smartphone app versus another smartphone app Two studies compared different versions of the same app in adults, showing no or minimal differences in outcomes. One study in adults compared two different apps (calorie counting versus ketogenic diet) and suggested a slight reduction in body weight at six months in favour of the ketogenic diet app. No studies were available for adolescents. Smartphone app versus personal coaching Only one study compared a smartphone app with personal coaching in adults, presenting data at three months. Two studies compared these interventions in adolescents. A smartphone app resulted in little to no difference in BMI z-score compared to personal coaching at six months' follow-up (MD 0, 95% CI -0.2 to 0.2; 1 study; 107 participants). Smartphone app versus usual care Only one study compared an app with usual care in adults but only reported data at three months on participant satisfaction. No studies were available for adolescents. We identified 34 ongoing studies.

AUTHORS' CONCLUSIONS

The available evidence is limited and does not demonstrate a clear benefit of smartphone applications as interventions for adolescents or adults with overweight or obesity. While the number of studies is growing, the evidence remains incomplete due to the high variability of the apps' features, content and components, which complicates direct comparisons and assessment of their effectiveness. Comparisons with either no or minimal intervention or personal coaching show minor effects, which are mostly not clinically significant. Minimal data for adolescents also warrants further research. Evidence is also scarce for low- and middle-income countries as well as for people with different socio-economic and cultural backgrounds. The 34 ongoing studies suggest sustained interest in the topic, with new evidence expected to emerge within the next two years. In practice, clinicians and healthcare practitioners should carefully consider the potential benefits, limitations, and evolving research when recommending smartphone apps to adolescents and adults with overweight or obesity.

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.

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.

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.

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.

Conference on ‘Obesity and the brain’

The Winter Conference 2021 focused on the theme of obesity and the brain, with symposia on the consequences of obesity on brain function, developments in behavioural change interventions, neurobiology of appetite regulation and obesity treatment, and personalised medicine for obesity prevention and treatment. The first plenary lecture discussed sweetness perceptions and how non-nutritive sweeteners may lead to a mismatch between metabolic signals and reward systems in the brain, and the second plenary lecture presented novel approaches to the treatment of binge-eating disorders. This short report summarises the content of these scientific sessions.

Systematic review of process evaluations of interventions in trials investigating sedentary behaviour in adults

OBJECTIVES

To systematically review and synthesise findings from process evaluations of interventions in trials which measured sedentary behaviour as an outcome in adults to explore: (1) how intervention content, implementation, mechanisms of impact and context influence outcomes and (2) how these interventions are experienced from different perspectives (participants, carers, staff).

DESIGN

Systematic review and narrative synthesis underpinned by the Medical Research Council process evaluation framework.

DATA SOURCES

Databases searches were conducted in March 2019 then updated in May 2020 and October 2021 in: CINAHL, SPORTDiscus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, AMED; EMBASE, PsycINFO, MEDLINE, Web of Science and ProQuest Dissertations & Theses.

ELIGIBILITY CRITERIA

We included: Process evaluations of trials including interventions where sedentary behaviour was measured as an outcome in adults aged 16 or over from clinical or non-clinical populations. We excluded studies if interventions were delivered in educational or workplace settings, or if they were laboratory studies focused on immediate effects of breaking sitting.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers extracted and coded data into a framework and assessed the quality of studies using the Mixed Methods Appraisal Tool. We synthesised findings using a narrative approach.

RESULTS

17 process evaluations were included. Five interventions focused on reducing sedentary behaviour or sitting time, 12 aimed to increase physical activity or promote healthier lifestyles. Process evaluations indicated changes in sedentary behaviour outcomes were shaped by numerous factors including: barriers (eg, staffing difficulties and scheduling problems) and facilitators (eg, allowing for flexibility) to intervention delivery; contextual factors (eg, usual lifestyle and religious events) and individual factors (eg, pain, tiredness, illness, age and individual preferences).

DISCUSSION

Intervention requires careful consideration of different factors that could influence changes in sedentary behaviour outcomes to ensure that interventions can be tailored to suit different individuals and groups.

PROSPERO REGISTRATION NUMBER

CRD42018087403.

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

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.

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.

Development and cross-cultural validation of the Goal Content for Weight Maintenance Scale (GCWMS)

PURPOSE

Long-term weight management requires sustained engagement with energy-balance-related behaviours. According to self-determination theory, behaviour goals can support or undermine motivation depending on the quality of their content (i.e., extrinsic and intrinsic motivation). This study aimed to develop and validate the goal content for weight loss maintenance scale (GCWMS).

METHODS

The GCWMS was administered to 1511 participants who had achieved clinically significant weight losses and were taking part in a large weight loss maintenance study: the NoHoW Trial (ISRCTN88405328). The scale derived from two well-established questionnaires regarding exercise goals. Construct validity was examined for 4 theory-driven domains: Health Management, Challenge, Image, and Social Recognition. Split-sample confirmatory factor analysis was conducted to test the factorial validity and multi-group measurement invariance (configural, metric, scalar, and residual invariance). The reliability estimates were also assessed, and discriminant validity was evaluated using 2 conceptually related questionnaires.

RESULTS

The first analysis showed a poor fit of the original factorial structure. Subsequent investigation with a new specified model indicated close fit to the data after removal of 3 items χ²(58) = 599.982; p < .001; χ²/df = 10.345; CFI = 0.940; GFI = 0.941; SRMR = 0.063; RMSEA = 0.079 (LL = 0.073; UL = .084). Good internal consistency was achieved in all subscales (α > .775), convergent and divergent validity were verified through associations with other theoretical related constructs. Findings from multi-group invariance test demonstrated that the specified model of GCWMS achieved full measurement invariance for gender but did not support residual invariance across countries.

CONCLUSION

Findings support the hypothesised four-dimension structure of the GCWMS, confirming reliability and multi-group invariance in factor structure. Analysis also supports valid group means comparisons on latent factors at gender and at cross-cultural level. Ways to improve the quality of the scale are discussed.

LEVEL OF EVIDENCE

Level V, cross-sectional descriptive.

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.

Keep on running - a randomized controlled trial to test a digital evidence-based intervention for sustained adoption of recreational running: rationale, design and pilot feasibility study

BACKGROUND

This paper describes the rationale, intervention development, study design and results from the pilot feasibility study of the Keep On Running (KOR) trial. KOR aims to test a web-based brief theory-based intervention, targeting maintenance of recreational running behavior over time (i.e. relapse preventing).

METHODS

Intervention development was based both on Self-Determination Theory and on Self-Regulation Theory. As part of it, a pilot study was implemented (n=18) to measure intervention adherence and participant satisfaction in order to establish the feasibility and acceptability of the intervention toolkit. Furthermore, this pilot study was also used to test the feasibility and acceptability of the questionnaires selected to be part of the later RCT.

RESULTS

Pilot intervention acceptability was good, but overall adherence was low. Features such as feedback and social sharing should be added to the toolkit. The main trial should lessen questionnaire length and include data from usual monitoring gadgets and apps (APIs). The protocol of the RCT was adjusted to test the efficacy of the refined final version of the intervention, and the RCT that will test it, contributing to the understanding of recreational running sustainability, allowing the optimization of future interventions aimed at physical activity promotion.

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

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

CLINICAL TRIAL REGISTRATION

ISRCTN registry, identifier ISRCTN88405328.

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.

The impact of early body-weight variability on long-term weight maintenance: exploratory results from the NoHoW weight-loss maintenance intervention

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/m², 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 AdjR² = 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 (∆AdjR² = 1.2%) and 18 months (∆AdjR² = 1.3%) and by linear methods at 18 months (∆AdjR² = 1.1%). Greater non-linear weight variability measured over 12 weeks was associated with increased weight at 12 (∆AdjR² = 1.4%) and 18 (∆AdjR² = 2.2%) months; and 12-week linear variability was associated with increased weight at 12 (∆AdjR² = 2.1%) and 18 (∆AdjR² = 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.

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.

Body weight variability is not associated with changes in risk factors for cardiometabolic disease

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 (R2 = 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.

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).

A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors

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.

Estimating physical activity and sedentary behaviour in a free-living environment: A comparative study between Fitbit Charge 2 and Actigraph GT3X

BACKGROUND

Activity trackers such as the Fitbit Charge 2 enable users and researchers to monitor physical activity in daily life, which could be beneficial for changing behaviour. However, the accuracy of the Fitbit Charge 2 in a free-living environment is largely unknown.

OBJECTIVE

To investigate the agreement between Fitbit Charge 2 and ActiGraph GT3X for the estimation of steps, energy expenditure, time in sedentary behaviour, and light and moderate-to-vigorous physical activity under free-living conditions, and further examine to what extent placing the ActiGraph on the wrist as opposed to the hip would affect the findings.

METHODS

41 adults (n = 10 males, n = 31 females) were asked to wear a Fitbit Charge 2 device and two ActiGraph GT3X devices (one on the hip and one on the wrist) for seven consecutive days and fill out a log of wear times. Agreement was assessed through Bland-Altman plots combined with multilevel analysis.

RESULTS

The Fitbit measured 1,492 steps/day more than the hip-worn ActiGraph (limits of agreement [LoA] = -2,250; 5,234), while for sedentary time, it measured 25 min/day less (LoA = -137; 87). Both Bland-Altman plots showed fixed bias. For time in light physical activity, the Fitbit measured 59 min/day more (LoA = -52;169). For time in moderate-to-vigorous physical activity, the Fitbit measured 31 min/day less (LoA = -132; 71) and for activity energy expenditure it measured 408 kcal/day more than the hip-worn ActiGraph (LoA = -385; 1,200). For the two latter outputs, the plots indicated proportional bias. Similar or more pronounced discrepancies, mostly in opposite direction, appeared when comparing to the wrist-worn ActiGraph.

CONCLUSION

Moderate to substantial differences between devices were found for most outputs, which could be due to differences in algorithms. Caution should be taken if replacing one device with another and when comparing results.

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.

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.