Successful weight loss maintainers use health-tracking smartphone applications more than a nationally representative sample: comparison of the National Weight Control Registry to Pew Tracking for Health

Identifying Behavior Change Techniques in an Artificial Intelligence-Based Fitness App: A Content Analysis

In the field of artificial intelligence-based fitness apps, the effective integration of behavior change techniques (BCTs) is critical for promoting physical activity and improving health outcomes. However, the specific BCTs employed by apps and their impact on user engagement and behavior change are not explored sufficiently. This study investigates the Freeletics fitness app through a mixed-methods approach to evaluate the use of BCTs. In the quantitative analysis, fifteen unique BCTs were identified based on the Behavior Change Technique Taxonomy (V1). In the qualitative analysis, user reviews (n=400) were examined to understand perspectives on the app's effectiveness in promoting behavior change. Goal setting, action planning, self-monitoring of behavior, and social support were among the most prevalent BCTs identified in the Freeletics app, and their effectiveness in enhancing user engagement and promoting behavior change was also highlighted by user reviews. Among the areas of improvement identified in the study were the need for simplifying personalization options and addressing user concerns regarding the specificity of feedback. The study underscores the importance of integrating BCTs effectively within AI-based fitness apps to drive user engagement and facilitate behavior change. It contributes valuable insights into the design and implementation of BCTs in fitness apps and offers recommendations for developers, emphasizing the significance of goal setting, feedback mechanisms, self-monitoring, and social support. By understanding the impact of specific BCTs on user behavior and addressing user concerns, developers can create more effective fitness apps, ultimately promoting healthier lifestyles and positive behavior change.

Inducing consumers to use calorie information: a multinational investigation

OBJECTIVE

We identify individuals who set daily intake budgets and examine if an intervention making people estimate their calorie intake up to a certain point in the day helps those setting daily budgets to regulate their calorie intake for the remainder of the day, after high prior consumption.

DESIGN

We conducted an online experiment in five countries: Australia, China, Germany, India, and the UK (n = 3,032) using a 2 (setting calorie budget: yes vs. no, measured) x 2 (intervention: intake reminder vs. control, manipulated) between-subjects design, with the amount of prior consumption measured. Participants were contacted in the afternoon. Those in the intervention condition were asked to estimate their prior calorie intake on that day.

MAIN OUTCOME MEASURES

We measured the individual characteristics of those who set daily calorie budgets and the intended calorie intake for the remainder of the day.

RESULTS

Among people who set daily calorie budgets, the intervention reduced intended calorie intake for the remainder of the day by 176 calories if they had already consumed a high amount of calories that day.

CONCLUSION

A timely intervention to estimate one's calorie intake can lower additional intended calorie intake among those who set daily calorie budget.

Engaging primary care patients with existing online tools for weight loss: A pilot trial

Objective

Free online tools show potential for promoting weight loss at a low cost, but there is limited evidence about how to effectively engage patients with them. To address this, a low-dose, flexible intervention was developed that aims to enhance weight-related discussions with primary care providers (PCPs) and engage patients with an organic (i.e., not researcher-created) weight loss-focused social media community and online self-monitoring tool. Feasibility and acceptability of the intervention was evaluated in a single-arm, 12-week pilot.

Methods

PCPs were recruited at two clinics, then PCP's patients with upcoming appointments were identified and recruited. Patients received an interactive online kickoff before their scheduled primary care appointment, then 8 follow-up messages over 12 weeks via email or their electronic health record patient portal. Patients completed assessments at baseline, post-appointment, and week 12. Primary care providers and patients completed semi-structured interviews.

Results

All PCPs approached enrolled (n = 6); patient recruitment was on track to meet the study goal prior to COVID-19 restrictions, and n = 27 patients enrolled. Patient satisfaction with the pre-appointment kickoff was high. Twenty-four patients reported discussing weight-related topics at their primary care appointment and all were satisfied with the discussion. Twenty-two patients completed 12-week assessments. Of these, 15 reported engaging with the self-monitoring tool and 9 with the social media community. Patient interviews revealed reasons for low social media community engagement, including perceived lack of fit. On average, patients with available data (n = 21) lost 2.4 ± 4.1% of baseline weight, and 28.6% of these patients lost ≥3% of baseline weight. Primary care providers reported high intervention satisfaction.

Conclusions

The intervention and trial design show potential, although additional strategies are needed to promote tool engagement.

The Quality of Indian Obesity-Related mHealth Apps: PRECEDE-PROCEED Model–Based Content Analysis

Background

The prevalence of obesity in India is increasing at an alarming rate. Obesity-related mHealth apps have proffered an exciting opportunity to remotely deliver obesity-related information. This opportunity raises the question of whether such apps are truly effective.

Objective

The aim of this study was to identify existing obesity-related mHealth apps in India and evaluate the potential of the apps’ contents to promote health behavior change. This study also aimed to discover the general quality of obesity-related mHealth apps.

Methods

A systematic search for obesity-related mHealth apps was conducted in both the Google Play Store and the Apple App Store. The features and quality of the sample apps were assessed using the Mobile Application Rating Scale (MARS) and the potential of the sample apps’ contents to promote health behavior change was assessed using the PRECEDE-PROCEED Model (PPM).

Results

A total of 13 apps (11 from the Google Play Store and 2 from the Apple App Store) were considered eligible for the study. The general quality of the 13 apps assessed using MARS resulted in mean scores ranging from 1.8 to 3.7. The bivariate Pearson correlation between the MARS rating and app user rating failed to establish statistically significant results. The multivariate regression analysis result indicated that the PPM factors are significant determinants of health behavior change (F3,9=63.186; P<.001) and 95.5% of the variance (R2=0.955; P<.001) in the dependent variable (health behavior change) can be explained by the independent variables (PPM factors).

Conclusions

In general, mHealth apps are found to be more effective when they are based on theory. The presence of PPM factors in an mHealth app can greatly influence the likelihood of health behavior change among users. So, we suggest mHealth app developers consider this to develop efficient apps. Also, mHealth app developers should consider providing health information from credible sources and indicating the sources of the information, which will increase the perceived credibility of the apps among the users. We strongly recommend health professionals and health organizations be involved in the development of mHealth apps. Future research should include mHealth app users to understand better the apps’ effectiveness in bringing about health behavior change.

Maintenance phase of a physical activity intervention in older kidney transplant recipients: A 12-month follow-up

Daily walking activities are associated with improving cardiovascular outcomes in older kidney transplant recipients. However, little is known regarding physical activity adherence outcomes in older kidney recipients. The purpose of this randomized controlled trial 12-month follow-up study was to evaluate the feasibility of the intervention (SystemCHANGE™ + activity tracker) during the maintenance period (7-12 months), compared to an attention-control group (activity tracker only) in older kidney recipients (age 60 and older). The sample included 60 participants (n = 30 IG; n = 30 ACG). Adherence rates for wearing the activity tracker daily were 96.5% in the IG and 80.8% in the ACG. The IG demonstrated within-group improvements for blood pressure at 12 months. Overall, there was a decrease in the average daily steps observed in both groups. These data suggest this intervention is feasible and additional boosters should be considered during the maintenance period to encourage physical activity.

Successful weight loss maintenance: A systematic review of weight control registries

Weight loss maintenance is a major challenge for obesity treatment. Weight control registries can be useful in identifying psychological and behavioural factors that could contribute to better long-term success. The objective of this study is to describe the existing weight control registries and their participants and identify correlates of weight loss maintenance. A comprehensive search of peer-reviewed articles published until November 2018 was conducted in PubMed, Web of Science, and Scopus. Studies that reported results from weight control registries were considered. Fifty-two articles, corresponding to five registries (the United States, Portugal, Germany, Finland, and Greece), were included. Registries differed in inclusion criteria and procedures. Of 51 identified weight loss and maintenance strategies, grouped in 14 domains of the Oxford Food and Activity Behaviors taxonomy, the following were the most frequently reported: having healthy foods available at home, regular breakfast intake, increasing vegetable consumption, decreasing sugary and fatty foods, limiting certain foods, and reducing fat in meals. Increased physical activity was the most consistent positive correlate of weight loss maintenance. To our knowledge, this is the first systematic review of information about successful weight loss maintenance obtained from weight control registries. Key common influential characteristics of success were identified, which can inform future prospective studies and weight management initiatives.

Remotely Delivered Interventions for Obesity Treatment

PURPOSE OF REVIEW

This review synthesizes recent research on remotely delivered interventions for obesity treatment, including summarizing outcomes and challenges to implementing these treatments as well as outlining recommendations for clinical implementation and future research.

RECENT FINDINGS

There are a wide range of technologies used for delivering obesity treatment remotely. Generally, these treatments appear to be acceptable and feasible, though weight loss outcomes are mixed. Engagement in these interventions, particularly in the long term, is a significant challenge. Newer technologies are rapidly developing and enable tailored and adaptable interventions, though research in this area is in its infancy. Further research is required to optimize potential benefits of remotely delivered interventions for obesity.

Changing from the inside out? Examining relationships between overweight identification, dieting behaviours, and body measurements over time

OBJECTIVE

To investigate whether changes in overweight identification were associated with dieting behaviours and body measurements over time.

DESIGN

Longitudinal study with assessments at three time points: before and twice during (i.e., baseline, 6 months, 12 months) a 1-year self-directed weight loss attempt.

METHOD

Eighty individuals with overweight or obesity (classified by BMI ≥ 25) reported their personal (i.e., I see myself as overweight), social (i.e., I identify/feel strong ties with other overweight people), and affective (i.e., I am pleased to be overweight) overweight identification; dieting behaviours (e.g., eat less, exercise, eat more fruit and vegetables); and had their body measurements taken (i.e., weight, height, body fat, waist circumference).

RESULTS

Linear mixed modelling was used to examine between-person differences and within-person changes in overweight identification on dieting behaviours and body measurements over time. Between-person differences mattered for measurements: Higher personal overweight identification was associated with higher BMI, body fat, and waist circumference over time. Higher social overweight identification was associated with higher BMI over time. Within-person changes mattered for behaviours over time: At 12 months, decreases in social overweight identification were associated with increases in a subset of 'Eat Less, Move More' dieting behaviours, but not a subset of 'Healthy' dieting behaviours. At 12 months, decreases in affective overweight identification were also associated with increases in 'Eat Less, Move More' dieting behaviours.

CONCLUSION

Addressing different aspects of overweight identification and how they change over time, may harness an important psychological pathway to support behavioural change and health irrespective of weight loss. Statement of contribution What is already known on this subject? Psychological factors, such as self-concept clarity and weight stigma, are associated with dieting behaviours and body measurements. Qualitative data suggest that identity change may be tied to dieting behaviours and weight loss. What does this study add? New insights into the nature of another psychological factor, overweight identification, among individuals with overweight and obesity attempting to lose weight. The first quantitative evidence that different aspects of overweight identification, and changes in these aspects of overweight identification over time, influence body measurements and dieting behaviours.

Food Cue Reactivity and the Brain-Heart Axis During Cognitive Stress Following Clinically Relevant Weight Loss

Successful weight loss maintainers are more vulnerable to stress induced eating. The aim of our study was to determine what effect an attention-demanding cognitive performance task had on brain-heart reactivity to visual food cues in women who maintained clinically relevant weight loss vs. women who had never weight cycled. A clinical weight loss group (CWL, n = 17) and a BMI-matched control group (CTL, n = 23) completed modified Stroop tasks that either included high calorie food pictures (Food Stroop) or excluded food cues (Office Stroop). ECG, breathing rate, and EEG were recorded. CWL participants: The Eating Restraint scores (Three Factor Eating Questionnaire) of the CWL participants correlated negatively with their heart rates recorded during the Food Stroop task (r = 0.62, p < 0.01). There was no such relationship in CTL participants. The P200 latencies in CWL participants evoked by the Stroop color-word cues at the C3 electrode were positively correlated to the log high frequency power in their cardiac spectrograms during the Food Stroop (r = 0.63, p < 0.02). There were no such relationships in the Office Stroop task nor in CTL participants. Combined Groups: Participants' heart rates were significantly lower (p < 0.05) and their RMSSD values and the log Total Power in their cardiac spectrograms were significantly greater during the Food Stroop vs. Office Stroop (p < 0.01, Bonferroni corrected). In conclusion Eating Restraint scores in CWL participants correlated with their Stroop heart rates, while the P200 latencies evoked by the Stroop cues correlated with the log high frequency power in their cardiac spectrograms (marker of cardiac vagal activation) during the Food Stroop task. This provides evidence that even 12 months after successful weight loss maintenance the cardiac ANS reactivity to food cues while completing a cognitive performance test was still different to that in individuals of normal weight who never weight cycled. Across all participants the cardiac ANS reactivity evoked by performing the Stroop task was lowered by food cues suggesting that the dampening effect of food cues on cardiac ANS reactivity may be one of the drivers of ‘stress induced' eating.

Dietary Management of Obesity: Cornerstones of Healthy Eating Patterns

Several dietary patterns, both macronutrient and food based, can lead to weight loss. A key strategy for weight management that can be applied across dietary patterns is to reduce energy density. Clinical trials show that reducing energy density is effective for weight loss and weight loss maintenance. A variety of practical strategies and tools can help facilitate successful weight management by reducing energy density, providing portion control, and improving diet quality. The flexibility of energy density gives patients options to tailor and personalize their dietary pattern to reduce energy intake for sustainable weight loss.

Successful weight loss maintainers use health-tracking smartphone applications more than a nationally representative sample: comparison of the National Weight Control Registry to Pew Tracking for Health

Objective

The objective of this paper is to evaluate successful weight loss maintainers' use of self‐monitoring technology.

Methods

National Weight Control Registry (NWCR) participants, who maintained a ≥13.6 kg weight loss for ≥1 year, completed an online survey about self‐monitoring technology use. The NWCR sample (n = 794) was compared with a demographically similar subsample of 833 individuals answering the same questions in the Pew Tracking for Health Survey.

Results

The NWCR had higher rates of tracking weight, diet or exercise using any modality (92.8% vs. 71.3%), on a regular basis (67.4% vs. 41.3%), and frequency of updating records, compared with Pew (ps < .01). Smartphone ownership was higher in NWCR participants (80.2% vs. 52.8%, p < .001), and NWCR smartphone owners had 23.1 times greater odds for using diet, food or calorie counter apps (58.9% vs. 5.9%) and 15.5 times greater odds for using weight monitoring apps (31.7% vs. 3.0%; all ps < .01). Pew respondents more often changed their behaviour based on their tracking data (ps < .01).

Conclusion

Use of self‐monitoring technology is common in weight loss maintainers: more so than in a nationally representative sample. However, the national sample more often changed their behaviour based on tracking data, perhaps suggesting that weight loss maintainers could derive additional benefit from technology they are already using.