The Assessment of Supportive Accountability in Adults Seeking Obesity Treatment: Psychometric Validation Study

Weight Loss Programs: Why Do They Fail? A Multidimensional Approach for Obesity Management

PURPOSE OF REVIEW

Despite the prevalence of weight loss programs, their success rates remain discouraging, with around half of individuals regaining lost weight within two years. The primary objective of this review is to explore the factors contributing to the failure of weight loss programs and to provide insights into effective weight management strategies.

RECENT FINDINGS

Factors contributing to the failure of weight loss programs include the impracticality of restrictive diets, potential metabolic impacts, limited focus on lifestyle changes, genetic predispositions, psychological influences, socioeconomic status, and medical conditions. A holistic approach considering these factors is crucial for safe and sustainable weight loss. Key findings indicate the importance of holistic approaches to weight management, including lifestyle modifications, medical interventions, and behavioral and psychological strategies. Effective weight loss strategies emphasize low-calorie, nutrient-rich diets, regular physical activity, and interventions tailored to individual needs. Combining multiple approaches offers the best chance of successful weight management and improved health outcomes. This review provides insights into the complexities of obesity management and the factors contributing to the failure of weight loss programs. It highlights the necessity of adopting a holistic approach that addresses dietary habits, physical activity, genetic factors, psychological well-being, and socioeconomic influences. Recommendations include implementing lifestyle modifications, medical interventions when necessary, and integrating behavioral and psychological support to achieve sustainable weight loss and mitigate the global health challenge posed by obesity.

Weight Loss with an AI-Powered Digital Platform for Lifestyle Intervention

BACKGROUND

Lifestyle intervention remains the cornerstone of weight loss programs in addition to pharmacological or surgical therapies. Artificial intelligence (AI) and other digital technologies can offer individualized approaches to lifestyle intervention to enable people with obesity to reach successful weight loss.

METHODS

SureMediks, a digital lifestyle intervention platform using AI, was tested by 391 participants (58% women) with a broad range of BMI (20-78 kg/m2), with the aim of losing weight over 24 weeks in a multinational field trial. SureMediks consists of a mobile app, an Internet-connected scale, and a discipline of artificial intelligence called Expert system to provide individualized guidance and weight-loss management.

RESULTS

All participants lost body weight (average 14%, range 4-22%). Almost all (98.7%) participants lost at least 5% of body weight, 75% lost at least 10%, 43% at least 15%, and 9% at least 20%, suggesting that this AI-powered lifestyle intervention was also effective in reducing the burden of obesity co-morbidities. Weight loss was partially positively correlated with female sex, accountability circle size, and participation in challenges, while it was negatively correlated with sub-goal reassignment. The latter three variables are specific features of the SureMediks weight loss program.

CONCLUSION

An AI-assisted lifestyle intervention allowed people with different body sizes to lose 14% body weight on average, with 99% of them losing more than 5%, over 24 weeks. These results show that digital technologies and AI might provide a successful means to lose weight, before, during, and after pharmacological or surgical therapies.

A low-burden, self-weighing intervention to prevent weight gain in adults with obesity who do not enroll in comprehensive treatment

Background

For individuals who are eligible but unlikely to join comprehensive weight loss programs, a low burden self-weighing intervention may be a more acceptable approach to weight management.

Methods

This was a single-arm feasibility trial of a 12-month self-weighing intervention. Participants were healthcare patients with a BMI ≥25 kg/m2 with a weight-related comorbidity or a BMI >30 kg/m2 who reported lack of interest in joining a comprehensive weight loss program, or did not enroll in a comprehensive program after being provided program information. In the self-weighing intervention, participants were asked to weigh themselves daily on a cellular connected scale and were sent text messages every other week with tailored weight change feedback, including messages encouraging use of comprehensive programs if weight gain occurred.

Results

Of 86 eligible patients, 39 enrolled (45.3%) in the self-weighing intervention. Self-weighing occurred on average 4.6 days/week (SD = 1.4). At 12 months, 12 participants (30.8%) lost ≥3% baseline weight, 11 (28.2%) experienced weight stability (±3% baseline), 6 (15.4%) gained ≥3% of baseline weight, and 10 (25.6%) did not have available weight data to evaluate. Three participants reported joining a weight loss program during the intervention (7.7%). Participants reported high intervention satisfaction in quantitative ratings (4.1 of 5), and qualitative interviews identified areas of satisfaction (e.g., timing and content of text messages) and areas for improvement (e.g., increasing personalization of text messages).

Conclusion

A low-burden self-weighing intervention can reach adults with overweight/obesity who would be unlikely to engage in comprehensive weight loss programs; the efficacy of this intervention for preventing weight gain should be further evaluated in a randomized trial.

Impact of feedback generation and presentation on self-monitoring behaviors, dietary intake, physical activity, and weight: a systematic review and meta-analysis

Self-monitoring of dietary intake, physical activity, and weight is a key strategy in behavioral interventions, and some interventions provide self-monitoring feedback to facilitate goal setting and promote engagement. This systematic review aimed to evaluate whether feedback increases intervention effectiveness, and which forms of feedback presentation (e.g., personalized vs. not personalized) and generation (i.e., human vs. algorithm-generated) are most effective. To achieve this aim, 5 electronic databases (PubMed/MEDLINE, Web of Science, CINAHL, PsycINFO, and Google Scholar) were searched in April 2022 and yielded 694 unique records, out of which 24 articles reporting on 19 studies were included (with a total of 3261 participants). Two reviewers independently screened titles and abstracts and then full texts and categorized articles as eligible or excluded according to the pre-registered criteria (i.e., availability of full text, peer reviewed manuscript in English; adult participants in a randomized controlled trial that included both self-monitoring and feedback; comparisons of different forms of feedback or comparisons of feedback vs. no feedback; primary outcomes of diet, physical activity, self-monitoring behavior, and/or weight). All included studies were assessed for methodological quality independently by two reviewers using the revised Cochrane risk-of-bias tool for randomized studies (version 2). Ten studies compared feedback to no feedback, 5 compared human- vs. algorithm-generated feedback, and the remaining 4 studies compared formats of feedback presentation (e.g., frequency, richness). A random effects meta-analysis indicated that physical activity interventions with feedback provision were more effective than physical activity interventions without feedback (d = 0.73, 95% CI [0.09;1.37]). No meta-analysis could be conducted for other comparisons due to heterogeneity of study designs and outcomes. There were mixed results regarding which form of feedback generation and presentation is superior. Limitations of the evidence included in this review were: lack of details about feedback provided, the brevity of most interventions, the exclusion of studies that did not isolate feedback when testing intervention packages, and the high risk of bias in many studies. This systematic review underlines the importance of including feedback in behavioral interventions; however, more research is needed to identify most effective forms of feedback generation and presentation to maximize intervention effectiveness.Trial registration (PROSPERO)CRD42022316206.

Sharing digital self-monitoring data with others to enhance long-term weight loss: A randomized controlled trial

BACKGROUND

Participants in behavioral weight loss (BWL) programs increasingly use digital tools to self-monitor weight, physical activity, and dietary intake. Data collected with these tools can be systematically shared with other parties in ways that might support behavior change.

METHODS

Adults age 18 to 70 with overweight/obesity (BMI 27-50 kg/m2) will enroll in a remotely delivered, 24-month BWL program designed to produce and maintain a 10% weight loss. Participants will be asked to use a wireless body weight scale, wearable activity sensor, and dietary intake app daily. All participants will receive individual and group counseling, engage in text messaging with members of their group, and appoint a friend or family member to serve in a support role. A 2x2x2 factorial design will test the effects of three types of data sharing partnerships: 1) Coach Share: The behavioral coach will regularly view digital self-monitoring data and address data observations. 2) Group Share: Participants will view each other's self-monitoring data in small-group text messages. 3) Friend/Family Share: A friend or family member will view the participant's data via automated message. The primary outcome is weight loss at 24 months. Mediators and moderators of intervention effects will be tested.

CONCLUSION

This study will provide a clear indication of whether data sharing can improve long-term weight loss. This study will be the first to discern the mechanisms of action through which each type of data sharing may be beneficial, and elucidate conditions under which the benefits of data sharing may be maximized.

"I Was Having My Midlife Fat Crisis": Exploring the Experiences and Preferences of Home-Based Exercise Programmes for Adults Living with Overweight and Obesity

The involvement of people with lived experience in the design of exercise programmes is more likely to lead to a more needs-sensitive and population-specific intervention. There is limited evidence of the integration of people with lived experience, particularly regarding home-based exercise programmes for adults living with overweight and obesity, despite this being a population that would significantly benefit from a suitably tailored programme. Semi-structured interviews were virtually conducted to explore 20 participants' experiences of exercising at home and their preferences for the design of future home-based exercise programmes. Codes were generated through thematic analysis, highlighting considerations such as comfort within a home-based environment, a desire for social connection, and the integration of technology. Four corresponding themes were generated, encapsulating participants' choice reasoning for home-based exercise, difficulties of engaging in home-based exercise, undertaking and adhering to home-based exercise, and factors that constitute the perfect programme. Although the involvement of people with lived experience in research can be time-consuming, this process is fundamental to the design of an effective and efficacious programme. These findings will inform the design and development of a home-based exercise programme for adults living with overweight and obesity.

Randomized controlled trial of Sunnyside: Individual versus group-based online interventions to prevent postpartum depression

BACKGROUND

Postpartum depression (PPD) is a serious mental health problem that has a prevalence rate of nearly 20% in the first three months after delivery. The purpose of this study was to evaluate the benefit of Sunnyside, an internet-based cognitive-behavioral intervention, delivered in a group format compared to the same intervention delivered individually for the prevention of PPD.

METHOD

210 people between 20- and 28-weeks gestation and who scored between 5 and 14 on the PHQ-8 and who did not meet criteria for major depression were recruited online. The Inventory of Depression and Anxiety Symptoms (IDAS), the Hamilton Rating Scale for Depression (HAMD), and the depression and anxiety modules of the MINI were obtained at baseline, post-treatment, and 12-weeks postpartum. Intervention adherence was measured by site usage.

RESULTS

Across self-report and interview measures of depression there were no significant differences in outcome between the group and the individual versions of the program. Rates of major depression and generalized anxiety disorder in the postpartum period were low and adherence to the conditions was similarly high. Participants in the individual condition were significantly more satisfied than participants in the group condition (p < 0.05).

LIMITATIONS

The sample was predominantly white (85%) and recruited online, which may limit generalizability.

CONCLUSIONS

The group intervention was not more effective than the individual intervention. However, ignoring groups, many measures improved over time. The results of this study provide evidence that mood symptoms improve when participating in an online preventive intervention for postpartum depression.

Using self-monitoring technology for nutritional counseling and weight management

Self-monitoring of weight, dietary intake, and physical activity is a key strategy for weight management in adults with obesity. Despite research suggesting consistent associations between more frequent self-monitoring and greater success with weight regulation, adherence is often suboptimal and tends to decrease over time. New technologies such as smartphone applications, e-scales, and wearable devices can help eliminate some of the barriers individuals experience with traditional self-monitoring tools, and research has demonstrated that these tools may improve self-monitoring adherence. To improve the integration of these tools in clinical practice, the current narrative review introduces the various types of self-monitoring technologies, presents current evidence regarding their use for nutrition support and weight management, and provides guidance for optimal implementation. The review ends with a discussion of barriers to the implementation of these technologies and the role that they should optimally play in nutritional counseling and weight management. Although newer self-monitoring technologies may help improve adherence to self-monitoring, these tools should not be viewed as an intervention in and of themselves and are most efficacious when implemented with ongoing clinical support.

The Supportive Accountability Inventory: Psychometric properties of a measure of supportive accountability in coached digital interventions

BACKGROUND

One of the most widely used coaching models is Supportive Accountability (SA) which aims to provide intervention users with clear expectations for intervention use, regular monitoring, and a sense that coaches are trustworthy, benevolent, and have domain expertise. However, few measures exist to study the role of the SA model on coached digital interventions. We developed the Supportive Accountability Inventory (SAI) and evaluated the underlying factor structure and psychometric properties of this brief self-report measure.

METHOD

Using data from a two-arm randomized trial of a remote intervention for major depressive disorder (telephone CBT [tCBT] or a stepped care model of web-based CBT [iCBT] and tCBT), we conducted an Exploratory Factor Analysis on the SAI item pool and explored the final SAI's relationship to iCBT engagement as well as to depression outcomes. Participants in our analyses (n = 52) included those randomized to a receive iCBT, but were not stepped up to tCBT due to insufficient response to iCBT, had not remitted prior to the 10-week assessment point, and completed the pool of 8 potential SAI items.

RESULTS

The best fitting EFA model included only 6 items from the original pool of 8 and contained two factors: Monitoring and Expectation. Final model fit was mixed, but acceptable (χ ² (4) = 5.24, p = 0.26; RMSR = 0.03; RMSEA = 0.091; TLI = 0.967). Internal consistency was acceptable at α = 0.68. The SAI demonstrated good convergent and divergent validity. The SAI at the 10-week/mid-treatment mark was significantly associated with the number of days of iCBT use (r = 0.29, p = .037), but, contrary to expectations, was not predictive of either PHQ-9 scores (F(2,46) = 0.14, p = .89) or QIDS-C scores (F(2,46) = 0.84, p = .44) at post-treatment.

CONCLUSION

The SAI is a brief measure of the SA framework constructs. Continued development to improve the SAI and expand the constructs it assesses is necessary, but the SAI represents the first step towards a measure of a coaching protocol that can support both coached digital mental health intervention adherence and improved outcomes.

Counselor Surveillance of Digital Self-Monitoring Data: A Pilot Randomized Controlled Trial

OBJECTIVE

This pilot study tested counselor access to participants' digital self-monitoring (SM) data as a means of improving long-term lifestyle modification (LM) outcomes.

METHODS

After 12 weeks of weight-loss treatment, participants (N = 77) were randomized to LM or LM+SHARE for weeks 13 to 52. All participants received monthly phone calls and weekly text messages from weeks 13 to 52 and were instructed to engage in daily digital SM of weight, eating, and exercise. In LM+SHARE, but not LM, counselors had access to SM device data. Assessments were conducted as weeks 0, 13, 26, and 52.

RESULTS

Retention, engagement, and treatment satisfaction were excellent. LM+SHARE participants, compared with LM, had more frequent SM of weight and eating. Weight loss continued at a similar rate in both conditions from weeks 13 to 26. From weeks 26 to 52, those in LM regained approximately 2 kg, whereas those in LM+SHARE maintained weight loss, a significant difference. Nonetheless, total weight loss did not significantly differ by condition. Engagement in dietary SM mediated the effect of condition on weight.

CONCLUSIONS

Counselor access to SM data is feasible and acceptable. Additional research is warranted to determine whether it can meaningfully improve outcomes.