In their own words: Topic analysis of the motivations and strategies of over 6,000 long-term weight-loss maintainers

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.

Behavioral, psychological, and environmental predictors of weight regain in a group of successful weight losers in a widely available weight-management program

OBJECTIVE

The aim of this study was to identify predictors of weight regain and continued weight maintenance among individuals already successful at long-term weight loss in a widely available weight-management program.

METHODS

Participants were 2843 weight-loss maintainers in WeightWatchers who had maintained weight loss ≥9.1 kg for ≥1 year (average 25.5 kg for 3.5 years; BMI = 26.7 kg/m2 ). Validated behavioral, psychosocial, and home environmental questionnaires were administered at study entry and 1 year later. Discriminant analysis identified variables that discriminated gainers (≥2.3-kg gain) from maintainers (±2.3-kg change).

RESULTS

Over the 1 year of follow-up, 43% were gainers (mean [SD], 7.2 [5.4] kg), and 57% were maintainers (0.4 [1.2] kg). Compared with maintainers, gainers were younger and had higher initial weight, more recent weight losses, and larger initial weight losses. Standardized canonical coefficients indicated that the 1-year changes that most discriminated gainers from maintainers were greater decreases in the ability to accept uncomfortable food cravings, urges, and desires to overeat (0.232); self-monitoring (0.166); body image (0.363); and body satisfaction (0.194) and greater increases in disinhibition (0.309) and bodily pain (0.147). The canonical correlation was 0.505 (p < 0.001).

CONCLUSIONS

Future interventions to prevent regain should consider targeting overeating in response to internal and external food cues and declines in self-monitoring and body image.

Best (but oft forgotten) statistical practices: Measuring real-world intervention effectiveness using electronic health data

The evidence base supporting the use of most interventions consists primarily of data from randomized controlled trials (RCTs), but how and to whom interventions are delivered in clinical practice may differ substantially from these foundational RCTs. With the increasing availability of electronic health data, it is now feasible to study the "real-world" effectiveness of a wide range of interventions. However, real-world intervention effectiveness studies using electronic health data face many challenges including data quality, selection bias, confounding by indication, and lack of generalizability. In this article, we describe the key barriers to generating high-quality evidence from real-world intervention effectiveness studies and suggest statistical best practices for addressing them.

Uncovering Barriers and Facilitators of Weight Loss and Weight Loss Maintenance: Insights from Qualitative Research

Long-term weight loss maintenance is often difficult to achieve. This review analysed qualitative data on self-perceived barriers and facilitators of weight loss and weight loss maintenance among weight loss intervention participants. A literature search was conducted using electronic databases. Qualitative studies written in English and published between 2011-2021 were eligible for inclusion if they explored the perspectives and experiences of individuals who received standardised dietary and behavioural support for weight loss. Studies were excluded if weight loss was achieved through self-directed methods, only increasing physical activity, or surgical or pharmacological interventions. Fourteen studies were included, totaling 501 participants from six countries. Thematic analysis was used to identify four aggregate themes: internal factors (i.e., motivation and self-efficacy), programme-specific factors (i.e., the intervention diet), social factors (i.e., supporters and saboteurs), and environmental factors (i.e., an obesogenic environment). Our findings demonstrate that internal, social, and environmental factors all influence weight loss success, as well as the acceptability of the weight loss intervention. Future interventions may be more successful if they prioritise participant acceptability and engagement by, for example, providing tailored interventions, a structured relapse management plan, strategies to enhance autonomous motivation and emotional self-regulation, and extended contact during weight loss maintenance.

Recovery from weight regain among long-term weight loss maintainers in WW

OBJECTIVE

This study sought to understand the process and strategies to recover from regain among weight loss maintainers.

METHODS

Participants in WeightWatchers (WW; n = 2457) had lost ≥9 kg for ≥1 year and were grouped based on self-reported weight change after maximum loss: sustained maintenance ("Stable"), ups and downs ("Gain-Lose"), and regain ("Gain"). The groups were compared on weight control strategies, and the Gain-Lose and Gain groups reported on attempts to reverse weight regain.

RESULTS

Mean weight loss was 28.5 kg and duration of ≥9 kg loss was 3.5 years. During this time, 48% reported weight stability, and the remaining reported some regain (Gain-Lose, 29% or Gain, 23%). Among Gain and Gain-Lose, action to lose regained weight occurred after gaining >4 kg. Compared with Gain, Gain-Lose sustained reengagement efforts longer (16 vs. 10 weeks) and had better dietary choices (3.4 vs. 3.2), self-monitoring (2.9 vs. 2.7), and psychological coping (2.5 vs. 2.4) scores. Among Gain-Lose, the most successful (< 2.3 kg vs. >2.3 kg regain) initiated weight loss efforts after less regain (2.3 vs. 4.5 kg).

CONCLUSIONS

Reengaging with weight loss after regains may be most successful if focused on diet, self-monitoring, and psychological coping and initiated with less regain.

Behavioral Interventions to Attenuate Driven Overeating and Weight Regain After Bariatric Surgery

Weight regain after bariatric surgery is associated with problematic eating behaviors that have either recurred after a period of improvement or are new-onset behaviors. Problematic eating behaviors after bariatric surgery have been conceptualized in different ways in the literature, such as having a food addiction and experiencing a loss of control of eating. The intersection of these constructs appears to be driven overeating defined as patients' experiences of reduced control of their eating which results in overeating behavior. The purpose of this review is to define patient experiences of driven overeating through the behavioral expression of emotion-based eating, reward-based eating, and executive functioning deficits-namely impulsivity-which is associated with weight regain after having bariatric surgery. Delineating concepts in this way and determining treatment strategies accordingly may reduce distress related to the inevitable return of increased hunger, cravings, portion sizes, and tolerance for highly palatable foods after surgery. Along with standard behavioral weight maintenance strategies, topics including acceptance, motivation, emotion-based eating, reward-based/impulsive eating, physical activity, and self-compassion are discussed. These concepts have been adapted for patients experiencing weight regain after having bariatric surgery and may be particularly helpful in attenuating driven overeating and weight regain.