The Effectiveness of Combining Nonmobile Interventions With the Use of Smartphone Apps With Various Features for Weight Loss: Systematic Review and Meta-analysis

Exploring factors of adherence to weight loss interventions in population with overweight/obesity: an umbrella review

Adherence is key for achieving the optimal benefits from a weight loss intervention. Despite the number of studies on factors that promote adherence, their findings suggest inconsistent and fragmented evidence. The aim of this study was to review the existing factors of adherence to weight loss interventions and to find factors that facilitate the design of effective intervention programs. Six databases were searched for this umbrella review; after the screening process, 21 studies were included. A total of 47 factors were identified in six groups as relevant for adherence: (i) sociodemographic (n = 7), (ii) physical activity (n = 2), (iii) dietary (n = 8), (iv) behavioral (n = 4), (v) pharmacological (n = 3), and (vi) multi-intervention (n = 23). In addition, a map of adherence factors was created. The main findings are that with respect to demographic factors, the development of personalized intervention strategies based on the characteristics of specific populations is encouraged. Moreover, self-monitoring has been shown to be effective in behavioral, dietary, and multi-interventions, while technology has shown potential in dietary, behavioral, and multi-interventions. In addition, multi-interventions are adherence-promoting strategies, although more evidence is required on adherence to pharmacological interventions. Overall, the factor map can be controlled and modified by researchers and practitioners to improve adherence to weight loss interventions.

Exploring the benefits and costs of a growth mindset in a digital app weight management program

We explored the potential benefits and costs of believing one can change their weight (i.e. growth mindset) in the context of a digital weight management program. We investigated mechanisms by which growth mindsets relate to weight loss achievement and body shame. Among participants seeking to lose weight (N = 1626; 74.7% female; 77.9% White; Mage = 45.7), stronger growth mindsets indirectly predicted greater weight loss achievement through positive offset expectations and subsequent increased program engagement. Additionally, stronger growth mindsets predicted less body shame through positive offset expectations but predicted more body shame through increased onset responsibility, replicating the double-edged sword model of growth mindsets. We conclude with applications that leverage growth mindsets for optimal behavior change while mitigating costs such as body shame.

Digital therapeutics lead to clinically significant body weight loss in patients with metabolic dysfunction-associated steatotic liver disease: A systematic review and meta-analysis

BACKGROUND

Most patients with metabolic dysfunction-associated steatotic liver disease are unable to achieve clinically significant body weight loss with traditional in-person approaches. Digital therapeutic (DTx)-delivered interventions offer promise to remove barriers to weight loss success inherent to traditional resource-heavy in-person programs and at a population level, but their efficacy remains relatively unknown.

METHODS

Published studies were identified through May 2023 by searching the following electronic databases: PubMed and Embase (Ovid). DTx intervention was compared to standard of care. The primary outcome was a change in body weight. Secondary outcomes included clinically significant body weight loss (≥5%) and change in liver enzymes.

RESULTS

Eight studies comprising 1001 patients met inclusion criteria (mean age: 47 y; body mass index: 33.2 kg/m2). The overall rate of clinically significant body weight loss was 33%, with DTx lifestyle interventions ranging from 4 to 24 months in length. DTx lifestyle intervention achieved statistically significant body weight loss (absolute change -3.4 kg, 95% CI: -4.8 to -2.0 kg, p < 0.01, relative change -3.9%, 95% CI: -6.6 to -1.3, p < 0.01) as well as clinically significant body weight loss of ≥5% (risk ratio: 3.0, 95% CI: 1.7-5.5, p < 0.01) compared to standard of care. This was seen alongside improvement in liver enzymes.

CONCLUSIONS

DTx-delivered lifestyle intervention programs lead to greater amounts of body weight loss than traditional in-person lifestyle counseling. These results further support the role of DTx in delivering lifestyle intervention programs to patients with metabolic dysfunction-associated steatotic liver disease and suggest that this scalable intervention offers promise to benefit the billions of patients worldwide with this condition.

An Adaptive Behavioral Intervention for Weight Loss Management: A Randomized Clinical Trial

Importance

Lifestyle interventions for weight loss are difficult to implement in clinical practice. Self-managed mobile health implementations without or with added support after unsuccessful weight loss attempts could offer effective population-level obesity management.

Objective

To test whether a wireless feedback system (WFS) yields noninferior weight loss vs WFS plus telephone coaching and whether participants who do not respond to initial treatment achieve greater weight loss with more vs less vigorous step-up interventions.

Design, Setting, and Participants

In this noninferiority randomized trial, 400 adults aged 18 to 60 years with a body mass index of 27 to 45 were randomized in a 1:1 ratio to undergo 3 months of treatment initially with WFS or WFS plus coaching at a US academic medical center between June 2017 and March 2021. Participants attaining suboptimal weight loss were rerandomized to undergo modest or vigorous step-up intervention.

Interventions

The WFS included a Wi-Fi activity tracker and scale transmitting data to a smartphone app to provide daily feedback on progress in lifestyle change and weight loss, and WFS plus coaching added 12 weekly 10- to 15-minute supportive coaching calls delivered by bachelor's degree-level health promotionists viewing participants' self-monitoring data on a dashboard; step-up interventions included supportive messaging via mobile device screen notifications (app-based screen alerts) without or with coaching or powdered meal replacement. Participants and staff were unblinded and outcome assessors were blinded to treatment randomization.

Main Outcomes and Measures

The primary outcome was the between-group difference in 6-month weight change, with the noninferiority margin defined as a difference in weight change of -2.5 kg; secondary outcomes included between-group differences for all participants in weight change at 3 and 12 months and between-group 6-month weight change difference among nonresponders exposed to modest vs vigorous step-up interventions.

Results

Among 400 participants (mean [SD] age, 40.5 [11.2] years; 305 [76.3%] women; 81 participants were Black and 266 were White; mean [SD] body mass index, 34.4 [4.3]) randomized to undergo WFS (n = 199) vs WFS plus coaching (n = 201), outcome data were available for 342 participants (85.5%) at 6 months. Six-month weight loss was -2.8 kg (95% CI, -3.5 to -2.0) for the WFS group and -4.8 kg (95% CI, -5.5 to -4.1) for participants in the WFS plus coaching group (difference in weight change, -2.0 kg [90% CI, -2.9 to -1.1]; P < .001); the 90% CI included the noninferiority margin of -2.5 kg. Weight change differences were comparable at 3 and 12 months and, among nonresponders, at 6 months, with no difference by step-up therapy.

Conclusions and Relevance

A wireless feedback system (Wi-Fi activity tracker and scale with smartphone app to provide daily feedback) was not noninferior to the same system with added coaching. Continued efforts are needed to identify strategies for weight loss management and to accurately select interventions for different individuals to achieve weight loss goals.

Trial Registration

ClinicalTrials.gov Identifier: NCT02997943.

Scalable Telehealth Cancer Care: integrated healthy lifestyle program to live well after cancer treatment

Northwestern University's Center for Scalable Telehealth Cancer Care (STELLAR) is 1 of 4 Cancer Moonshot Telehealth Research Centers of Excellence programs funded by the National Cancer Institute to establish an evidence base for telehealth in cancer care. STELLAR is grounded in the Institute of Medicine's vision that quality cancer care includes not only disease treatment but also promotion of long-term health and quality of life (QOL). Cigarette smoking, insufficient physical activity, and overweight and obesity often co-occur and are associated with poorer treatment response, heightened recurrence risk, decreased longevity, diminished QOL, and increased treatment cost for many cancers. These risk behaviors are prevalent in cancer survivors, but their treatment is not routinely integrated into oncology care. STELLAR aims to foster patients' long-term health and QOL by designing, implementing, and sustaining a novel telehealth treatment program for multiple risk behaviors to be integrated into standard cancer care. Telehealth delivery is evidence-based for health behavior change treatment and is well suited to overcome access and workflow barriers that can otherwise impede treatment receipt. This paper describes STELLAR's 2-arm randomized parallel group pragmatic clinical trial comparing telehealth-delivered, coach-facilitated multiple risk behavior treatment vs self-guided usual care for the outcomes of reach, effectiveness, and cost among 3000 cancer survivors who have completed curative intent treatment. This paper also discusses several challenges encountered by the STELLAR investigative team and the adaptations developed to move the research forward.

Assessing the accessibility and quality of mobile health applications for the treatment of obesity in the German healthcare market

Introduction

Overweight and obesity are among the most prevalent health problems worldwide leading to various diseases and having a significant impact on the healthcare system. In Germany, the prevalence of obesity among adults is 19%. Mobile health applications offer a new approach to treatment and prevention and have been proven effective in previous studies. However, it is essential to investigate the availability and quality of these digital applications. The aim of this systematic assessment is to evaluate the accessibility and quality of digital health applications in German language designed to treat obesity.

Methods

In January 2024, a systematic search for mobile health applications was conducted on both the Google Play Store and Apple App Store. Just those apps available in German for both iOS and Android were considered acceptable. The German Mobile Application Rating Scale (MARS-G) was used to assess the quality of the apps. The content of mobile health applications was evaluated using the guideline from the German Obesity Society for the treatment of obesity. The characteristics of the apps were summarized and presented, and the results were analyzed using descriptive statistics and presented in tables.

Results

After screening, ten apps were included in the review. The apps varied in terms of calorie tracking, individual workout plans, educational aspects, nutritional plans, and exercises for behavioral change. On average, 6.4 out of 12 items of the German Obesity guideline recommendations were fulfilled. The MARS score (possible range from 1-5) reached a mean of 3.39 (SD = 0.39). The section "Engagement" had the lowest quality score with a mean of 3.14 (SD = 0.57), while the section "Aesthetics" achieved the highest mean of 3.57 (SD = 0.52).

Discussion

Most German mobile health applications for managing obesity meet some guideline recommendations. They demonstrate adequate to good quality according to the MARS score. Assessing the quality of mobile health applications can be challenging for patients, despite being easily accessible and low-threshold. However, such digital health applications, reimbursed by the German SHI, offer evidence-based information, even if access can be associated with higher hurdles.

App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World Data

BACKGROUND

Early weight loss is an established predictor for treatment outcomes in weight management interventions for people with obesity. However, there is a paucity of additional, reliable, and clinically actionable early predictors in weight management interventions. Novel blended-care weight management interventions combine coach and app support and afford new means of structured, continuous data collection, informing research on treatment adherence and outcome prediction.

OBJECTIVE

Against this backdrop, this study analyzes app engagement as a predictor for weight loss in large-scale, real-world, blended-care interventions. We hypothesize that patients who engage more frequently in app usage in blended-care treatment (eg, higher logging activity) lose more weight than patients who engage comparably less frequently at 3 and 6 months of intervention.

METHODS

Real-world data from 19,211 patients in obesity treatment were analyzed retrospectively. Patients were treated with 3 different blended-care weight management interventions, offered in Switzerland, the United Kingdom, and Germany by a digital behavior change provider. The principal component analysis identified an overarching metric for app engagement based on app usage. A median split informed a distinction in higher and lower engagers among the patients. Both groups were matched through optimal propensity score matching for relevant characteristics (eg, gender, age, and start weight). A linear regression model, combining patient characteristics and app-derived data, was applied to identify predictors for weight loss outcomes.

RESULTS

For the entire sample (N=19,211), mean weight loss was -3.24% (SD 4.58%) at 3 months and -5.22% (SD 6.29%) at 6 months. Across countries, higher app engagement yielded more weight loss than lower engagement after 3 but not after 6 months of intervention (P3 months<.001 and P6 months=.59). Early app engagement within the first 3 months predicted percentage weight loss in Switzerland and Germany, but not in the United Kingdom (PSwitzerland<.001, PUnited Kingdom=.12, and PGermany=.005). Higher age was associated with stronger weight loss in the 3-month period (PSwitzerland=.001, PUnited Kingdom=.002, and PGermany<.001) and, for Germany, also in the 6-month period (PSwitzerland=.09, PUnited Kingdom=.46, and PGermany=.03). In Switzerland, higher numbers of patients' messages to coaches were associated with higher weight loss (P3 months<.001 and P6 months<.001). Messages from coaches were not significantly associated with weight loss (all P>.05).

CONCLUSIONS

Early app engagement is a predictor of weight loss, with higher engagement yielding more weight loss than lower engagement in this analysis. This new predictor lends itself to automated monitoring and as a digital indicator for needed or adapted clinical action. Further research needs to establish the reliability of early app engagement as a predictor for treatment adherence and outcomes. In general, the obtained results testify to the potential of app-derived data to inform clinical monitoring practices and intervention design.

[Strategies for the prevention and non-pharmacological treatment of obesity. Models of care]

The prevalence of obesity has increased in recent years worldwide. In this context, strategies for management obesity in primary care are essential. The first step in the treatment of obesity are lifestyle intervention programs. The three pillars of these programs, ideally of high intensity (high frequency of visits), are dietary intervention, exercise and behavioral therapy. There is no universal model of care for patients with obesity, but it must take into account key aspects, such as facilitating the access and adherence of the patient and a multidisciplinary and coordinated care among professionals at different levels of healthcare. The components of the model of care and its format should be defined according to the resources available and the characteristics of the population to be treated.

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.

Clinical Service Incorporating Mobile Technology on Weight Loss in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease: A Translation From Research Trial

BACKGROUND

The prevalence and healthcare cost of metabolic dysfunction-associated steatotic liver disease (MASLD) has increased alongside the epidemic surge in obesity and Type 2 diabetes. Weight loss through lifestyle modification remains the primary effective therapy for MASLD. Incorporation of mobile technology in lifestyle interventions has been previously found to be efficacious and cost-effective in facilitating weight loss. However, there is a paucity of studies that have successfully translated lifestyle research into clinical service for weight loss to alleviate disease burden. Our study aimed to describe the process of translating a mobile technology-enabled trial into a tertiary hospital outpatient dietetics service for patients with MASLD.

METHODS

The Iowa Model of Evidence-Based Practice to Improve Quality Care was used as a framework for this paper to guide implementation at the organizational level.

RESULTS

Regular engagement of key operational staff and the hospital management team facilitated open discussions of the challenges faced and enabled rapid implementation of strategies that contributed to the smooth piloting of the service. A service adoption rate of 81% was achieved. Preliminary outcome evaluation found that the percentage of patients achieving ≥ 5% weight loss from baseline at 6 months was comparable at 54% and 52% for the service and trial groups, respectively.

CONCLUSIONS

Evaluation of the implementation process found that a hybrid model of care (in-person consultation supplemented with app coaching) preserved interpersonal connections while maximizing the convenience and scalability of mobile app-enabled service. Although high digital acceptance and adoption rates propelled by COVID-19-supported telehealth, it is prudent to assess patient's access to technology and digital literacy and offer resources to help them benefit from telehealth services.

Mobile health behaviour change support system as independent treatment tool for obesity: a randomized controlled trial

BACKGROUND/OBJECTIVES

Digital health interventions are increasingly utilized as an adjunct to face-to-face counselling in the treatment of obesity. However, previous studies have shown inconsistent efficacy when digital interventions are used as stand-alone treatment. The purpose of this study was to investigate whether a mobile health behaviour change support system (mHBCSS) is effective in weight reduction and weight loss maintenance without additional counselling. Furthermore, changes in cardiometabolic risk factors were investigated.

METHODS

In this randomized controlled trial, a mHBCSS intervention was conducted for 200 volunteers with obesity (BMI 30-40 kg/m² and age 18-65 years). The study participants were randomly assigned into two groups: immediate access to mHBCSS intervention or wait-list control with access to mHBCSS after 6 months. Anthropometric and metabolic traits were also measured. The primary outcome was weight loss from the baseline to the 6-month visit.

RESULTS

Among 200 participants (88.5% women), mean BMI (SD) was 34.3 kg/m² (2.8) and age 46.5 years (9.5). The retention rate was 98.5% and 89.0% at the 6- and 12-month visits, respectively. At the 6-month visit, those with immediate access to mHBCSS had significantly greater weight loss (-2.5%, 95% CI -3.4 to -1.6, p < 0.001) compared with the wait-list control group (0.2%, 95% CI -0.4 to 0.9, p = 0.466; between groups p < 0.001). Weight loss was maintained until the 12-month time point in the mHBCSS group (-2.1%, 95% CI -3.3 to -0.9, p = 0.001). The usage of mHBCSS had no significant effect on metabolic traits.

CONCLUSION

The mHBCSS as a stand-alone treatment of obesity results in weight reduction and weight loss maintenance with remarkable adherence rate. Further studies are needed to establish how to best implement the scalable and resource-efficient mHBCSS into the standard care of obesity to achieve optimal weight loss results.

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.

Mobile App Intervention of a Randomized Controlled Trial for Patients With Obesity and Those Who Are Overweight in General Practice: User Engagement Analysis Quantitative Study

BACKGROUND

The Health eLiteracy for Prevention in General Practice trial is a primary health care-based behavior change intervention for weight loss in Australians who are overweight and those with obesity from lower socioeconomic areas. Individuals from these areas are known to have low levels of health literacy and are particularly at risk for chronic conditions, including diabetes and cardiovascular disease. The intervention comprised health check visits with a practice nurse, a purpose-built patient-facing mobile app (mysnapp), and a referral to telephone coaching.

OBJECTIVE

This study aimed to assess mysnapp app use, its user profiles, the duration and frequency of use within the Health eLiteracy for Prevention in General Practice trial, its association with other intervention components, and its association with study outcomes (health literacy and diet) to determine whether they have significantly improved at 6 months.

METHODS

In 2018, a total of 22 general practices from 2 Australian states were recruited and randomized by cluster to the intervention or usual care. Patients who met the main eligibility criteria (ie, BMI>28 in the previous 12 months and aged 40-74 years) were identified through the clinical software. The practice staff then provided the patients with details about this study. The intervention consisted of a health check with a practice nurse and a lifestyle app, a telephone coaching program, or both depending on the participants' choice. Data were collected directly through the app and combined with data from the 6-week health check with the practice nurses, the telephone coaching, and the participants' questionnaires at baseline and 6-month follow-up. The analyses comprised descriptive and inferential statistics.

RESULTS

Of the 120 participants who received the intervention, 62 (52%) chose to use the app. The app and nonapp user groups did not differ significantly in demographics or prior recent hospital admissions. The median time between first and last app use was 52 (IQR 4-95) days, with a median of 5 (IQR 2-10) active days. App users were significantly more likely to attend the 6-week health check (2-sided Fisher exact test; P<.001) and participate in the telephone coaching (2-sided Fisher exact test; P=.007) than nonapp users. There was no association between app use and study outcomes shown to have significantly improved (health literacy and diet) at 6 months.

CONCLUSIONS

Recruitment and engagement were difficult for this study in disadvantaged populations with low health literacy. However, app users were more likely to attend the 6-week health check and participate in telephone coaching, suggesting that participants who opted for several intervention components felt more committed to this study.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12617001508369; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373505.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2018-023239.

Effectiveness of holistic mobile health interventions on diet, and physical, and mental health outcomes: a systematic review and meta-analysis

Background

Good physical and mental health are essential for healthy ageing. Holistic mobile health (mHealth) interventions-including at least three components: physical activity, diet, and mental health-could support both physical and mental health and be scaled to the population level. This review aims to describe the characteristics of holistic mHealth interventions and their effects on related behavioural and health outcomes among adults from the general population.

Methods

In this systematic review and meta-analysis, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, Scopus, China National Knowledge Infrastructure, and Google Scholar (first 200 records). The initial search covered January 1, 2011, to April 13, 2022, and an updated search extended from April 13, 2022 to August 30, 2023. Randomised controlled trials (RCTs) and non-randomised studies of interventions (NRSIs) were included if they (i) were delivered via mHealth technologies, (ii) included content on physical activity, diet, and mental health, and (iii) targeted adults (≥18 years old) from the general population or those at risk of non-communicable diseases (NCDs) or mental disorders. Studies were excluded if they targeted pregnant women (due to distinct physiological responses), individuals with pre-existing NCDs or mental disorders (to emphasise prevention), or primarily utilised web, email, or structured phone support (to focus on mobile technologies without exclusive human support). Data (summary data from published reports) extraction and risk-of-bias assessment were completed by two reviewers using a standard template and Cochrane risk-of-bias tools, respectively. Narrative syntheses were conducted for all studies, and random-effects models were used in the meta-analyses to estimate the pooled effect of interventions for outcomes with comparable data in the RCTs. The study was registered in PROSPERO, CRD42022315166.

Findings

After screening 5488 identified records, 34 studies (25 RCTs and 9 pre-post NRSIs) reported in 43 articles with 5691 participants (mean age 39 years, SD 12.5) were included. Most (91.2%, n = 31/34) were conducted in high-income countries. The median intervention duration was 3 months, and only 23.5% (n = 8/34) of studies reported follow-up data. Mobile applications, short-message services, and mobile device-compatible websites were the most common mHealth delivery modes; 47.1% (n = 16/34) studies used multiple mHealth delivery modes. Of 15 studies reporting on weight change, 9 showed significant reductions (6 targeted on individuals with overweight or obesity), and in 10 studies reporting perceived stress levels, 4 found significant reductions (all targeted on general adults). In the meta-analysis, holistic mHealth interventions were associated with significant weight loss (9 RCTs; mean difference -1.70 kg, 95% CI -2.45 to -0.95; I2 = 89.00%) and a significant reduction in perceived stress levels (6 RCTs; standardised mean difference [SMD] -0.32; 95% CI -0.52 to -0.12; I2 = 14.52%). There were no significant intervention effects on self-reported moderate-to-vigorous physical activity (5 RCTs; SMD 0.21; 95%CI -0.25 to 0.67; I2 = 74.28%) or diet quality scores (5 RCTs; SMD 0.21; 95%CI -0.47 to 0.65; I2 = 62.27%). All NRSIs were labelled as having a serious risk of bias overall; 56% (n = 14/25) of RCTs were classified as having some concerns, and the others as having a high risk of bias.

Interpretation

Findings from identified studies suggest that holistic mHealth interventions may aid reductions in weight and in perceived stress levels, with small to medium effect sizes. The observed effects on diet quality scores and self-reported moderate-to-vigorous physical activity were less clear and require more research. High-quality RCTs with longer follow-up durations are needed to provide more robust evidence. To promote population health, future research should focus on vulnerable populations and those in middle- and low-income countries. Optimal combinations of delivery modes and components to improve efficacy and sustain long-term effects should also be explored.

Funding

National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme and Physical Activity and Nutrition Determinants in Asia (PANDA) Research Programme.

Effective Behavior Change Techniques in Digital Health Interventions for the Prevention or Management of Noncommunicable Diseases: An Umbrella Review

BACKGROUND

Despite an abundance of digital health interventions (DHIs) targeting the prevention and management of noncommunicable diseases (NCDs), it is unclear what specific components make a DHI effective.

PURPOSE

This narrative umbrella review aimed to identify the most effective behavior change techniques (BCTs) in DHIs that address the prevention or management of NCDs.

METHODS

Five electronic databases were searched for articles published in English between January 2007 and December 2022. Studies were included if they were systematic reviews or meta-analyses of DHIs targeting the modification of one or more NCD-related risk factors in adults. BCTs were coded using the Behavior Change Technique Taxonomy v1. Study quality was assessed using AMSTAR 2.

RESULTS

Eighty-five articles, spanning 12 health domains and comprising over 865,000 individual participants, were included in the review. We found evidence that DHIs are effective in improving health outcomes for patients with cardiovascular disease, cancer, type 2 diabetes, and asthma, and health-related behaviors including physical activity, sedentary behavior, diet, weight management, medication adherence, and abstinence from substance use. There was strong evidence to suggest that credible source, social support, prompts and cues, graded tasks, goals and planning, feedback and monitoring, human coaching and personalization components increase the effectiveness of DHIs targeting the prevention and management of NCDs.

CONCLUSIONS

This review identifies the most common and effective BCTs used in DHIs, which warrant prioritization for integration into future interventions. These findings are critical for the future development and upscaling of DHIs and should inform best practice guidelines.

Engagement with a nationally-implemented digital behaviour change intervention: Usage patterns over the 9-month duration of the National Health Service Digital Diabetes Prevention Programme

Background

Digital behaviour change interventions may offer a scalable way to promote weight loss by increasing physical activity and improving diet. However, user engagement is necessary for such benefits to be achieved. There is a dearth of research that assesses engagement with nationally implemented digital programmes offered in routine practice. The National Health Service Digital Diabetes Prevention Programme (NHS-DDPP) is a nine-month digital behaviour change intervention delivered by independent providers for adults in England who are at high risk of developing type 2 diabetes. This study reports engagement with the NHS-DDPP for users enrolled onto the programme over the nine-month duration.

Methods

Anonymous usage data was obtained for a cohort of service users (n = 1826) enrolled on the NHS-DDPP with three independent providers, between December 2020 and June 2021. Usage data were obtained for time spent in app, and frequency of use of NHS-DDPP intervention features in the apps including self-monitoring, goal setting, receiving educational content (via articles) and social support (via health coaches and group forums), to allow patterns of usage of these key features to be quantified across the nine-month intervention. Median usage was calculated within nine 30-day engagement periods to allow a longitudinal analysis of the dose of usage for each feature.

Results

App usage declined from a median of 32 min (IQR 191) in month one to 0 min (IQR 14) in month nine. Users self-monitored their behaviours (e.g., physical activity and diet) a median of 117 times (IQR 451) in the apps over the nine-month programme. The open group discussion forums were utilised less regularly (accessed a median of 0 times at all time-points). There was higher engagement with some intervention features (e.g., goal setting) when support from a health coach was linked to those features.

Conclusions

App usage decreased over the nine-month programme, although the rate at which the decrease occurred varied substantially between individuals and providers. Health coach support may promote engagement with specific intervention features. Future research should assess whether engagement with particular features of digital diabetes prevention programmes is associated with outcomes such as reduced bodyweight and HbA1c levels.

The Effectiveness of eHealth Interventions for Weight Loss and Weight Loss Maintenance in Adults with Overweight or Obesity: A Systematic Review of Systematic Reviews

PURPOSE OF REVIEW: The purpose of this study is to evaluate the effectiveness of eHealth interventions for weight loss and weight loss maintenance among adults with overweight or obesity through a systematic review of systematic reviews. RECENT FINDINGS: This study included 26 systematic reviews, covering a total of 338 original studies, published between 2018 and 2023. The review indicates that eHealth interventions are more effective than control interventions or no care and comparable to face-to-face interventions. The effect sizes remain relatively small when comparing eHealth interventions to any control conditions, with mean differences of weight loss results from - 0.12 kg (95% CI - 0.64 to 0.41 kg) in a review comparing eHealth interventions to face-to-face care to - 4.32 kg (- 5.08 kg to - 3.57 kg) in a review comparing eHealth interventions to no care. The methodological quality of the included studies varies considerably. However, it can be concluded that interventions with human contact work better than those that are fully automated. In conclusion, this systematic review of systematic reviews provides an updated understanding of the development of digital interventions in recent years and their effectiveness for weight loss and weight loss maintenance among adults with overweight or obesity. The findings suggest that eHealth interventions can be a valuable tool for delivering obesity care to more patients economically. Further research is needed to determine which specific types of eHealth interventions are most effective and how to best integrate them into clinical practice.

Development of "LvL UP 1.0": a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders

Background

Non-communicable diseases (NCDs) and common mental disorders (CMDs) are the leading causes of death and disability worldwide. Lifestyle interventions via mobile apps and conversational agents present themselves as low-cost, scalable solutions to prevent these conditions. This paper describes the rationale for, and development of, "LvL UP 1.0″, a smartphone-based lifestyle intervention aimed at preventing NCDs and CMDs.

Materials and Methods

A multidisciplinary team led the intervention design process of LvL UP 1.0, involving four phases: (i) preliminary research (stakeholder consultations, systematic market reviews), (ii) selecting intervention components and developing the conceptual model, (iii) whiteboarding and prototype design, and (iv) testing and refinement. The Multiphase Optimization Strategy and the UK Medical Research Council framework for developing and evaluating complex interventions were used to guide the intervention development.

Results

Preliminary research highlighted the importance of targeting holistic wellbeing (i.e., both physical and mental health). Accordingly, the first version of LvL UP features a scalable, smartphone-based, and conversational agent-delivered holistic lifestyle intervention built around three pillars: Move More (physical activity), Eat Well (nutrition and healthy eating), and Stress Less (emotional regulation and wellbeing). Intervention components include health literacy and psychoeducational coaching sessions, daily "Life Hacks" (healthy activity suggestions), breathing exercises, and journaling. In addition to the intervention components, formative research also stressed the need to introduce engagement-specific components to maximise uptake and long-term use. LvL UP includes a motivational interviewing and storytelling approach to deliver the coaching sessions, as well as progress feedback and gamification. Offline materials are also offered to allow users access to essential intervention content without needing a mobile device.

Conclusions

The development process of LvL UP 1.0 led to an evidence-based and user-informed smartphone-based intervention aimed at preventing NCDs and CMDs. LvL UP is designed to be a scalable, engaging, prevention-oriented, holistic intervention for adults at risk of NCDs and CMDs. A feasibility study, and subsequent optimisation and randomised-controlled trials are planned to further refine the intervention and establish effectiveness. The development process described here may prove helpful to other intervention developers.

Mobile health lifestyle intervention program leads to clinically significant loss of body weight in patients with NASH

BACKGROUND AIMS

Lifestyle intervention remains the foundation of clinical care for patients with NASH; however, most patients are unsuccessful in enacting sustained behavioral change. There remains a clear unmet need to develop lifestyle intervention programs to support weight loss. Mobile health (mHealth) programs offer promise to address this need, yet their efficacy remains unexplored.

APPROACH RESULTS

We conducted a 16-week randomized controlled clinical trial involving adults with NASH. Patients were randomly assigned (1:1 ratio) to receive Noom Weight (NW), a mHealth lifestyle intervention program, or standard clinical care. The primary end point was a change in body weight. Secondary end points included feasibility (weekly app engagement), acceptability (>50% approached enrolled), and safety. Of 51 patients approached, 40 (78%) were randomly assigned (20 NW and 20 standard clinical care). NW significantly decreased body weight when compared to standard clinical care (-5.5 kg vs. -0.3 kg, p = 0.008; -5.4% vs. -0.4%, p = 0.004). More NW subjects achieved a clinically significant weight loss of ≥5% body weight (45% vs. 15%, p = 0.038). No adverse events occurred, and the majority (70%) of subjects in the NW arm met the feasibility criteria.

CONCLUSIONS

This clinical trial demonstrated that NW is not only feasible, acceptable, and safe but also highly efficacious because this mHealth lifestyle intervention program led to significantly greater body weight loss than standard clinical care. Future large-scale studies are required to validate these findings with more representative samples and to determine if mHealth lifestyle intervention programs can lead to sustained, long-term weight loss in patients with NASH.

Evaluation of overweight control applications with cognitive-behavioral therapy approach: A systematic review

Background and Aims

Overweight and obesity lead to the development of physical diseases. Cognitive factors play a vital role in controlling one's weight. Currently, cognitive-behavioral therapy (CBT) interventions are recognized as a subcategory of lifestyle modification programs that can be implemented to control weight and modify eating patterns as well as physical activity. Nowadays, smartphone-based applications are utilized to implement behavioral interventions. The main purpose of this study is to evaluate the quality of CBT-based smartphone applications available on Google Play and the App Store in the field of overweight control.

Methods

Smartphone-based utility applications available on Google Play and App Store were identified in March 2021. Weight control smartphone applications were obtained based on inclusion and exclusion criteria. The app name, platform, version, number of downloads, password protection, affiliations, and features of retrieved apps were tabulated. The Mobile Application Rating Scale was utilized to evaluate the quality of the identified apps.

Results

Seventeen CBT-based weight control smartphone apps were retrieved. The average engagement, functionality, aesthetics, and information quality scores were 3.65, 3.92, 3.80, and 3.91, respectively. Also, the average score in an aspect containing the usefulness of the app, frequency of using the application, cost, and user satisfaction was 3.5.

Conclusion

Future applications related to this field can be improved by providing a personalization program according to the needs of users and the possibility of online chatting with the therapist. Further improvements can be achieved by improving the areas of engagement, aesthetics, and subjective quality as well as having appropriate privacy policies.

Essential elements of weight loss apps for a multi-ethnic population with high BMI: a qualitative study with practical recommendations

Smartphone weight loss apps are constantly being developed but the essential elements needed by a multi-ethnic population with overweight and obesity remains unclear. Purpose: To explore the perceptions of an Asian multi-ethnic population with overweight and obesity on the essential elements of weight loss apps. Twenty two participants were purposively sampled from a specialist weight management clinic in Singapore from 13 April to 30 April 2021. Recorded interviews were conducted using face-to-face and videoconferencing modalities. Data saturation was reached at the 18th participant. Data analysis was performed using inductive content analysis with constant comparison between and within transcripts. Findings: Three themes and eight subthemes on the essential app components emerged-(a) comprehensive and flexible calorie counters; (b) holistic, gradual and individualized behavior change recommendations tailored for people with overweight and obesity, and (c) just-in-time reminders of future consequences. There was a need to incorporate flexible options for food logging; break down general recommendations into small steps towards sustainable changes; tailor app contents for people with overweight and obesity; and evoke one's considerations of future consequences. Future weight loss apps should be designed to meet the needs of those with overweight and obesity, the very population that needs assistance with weight loss. Future apps could consider leveraging the capacity of artificial intelligence to provide personalized weight management in terms of sustaining self-regulation behaviors, optimizing goal-setting and providing personalized and timely recommendations for weight loss.

A Digital Lifestyle Coach (E-Supporter 1.0) to Support People With Type 2 Diabetes: Participatory Development Study

BACKGROUND

A healthy lifestyle, including regular physical activity and a healthy diet, is becoming increasingly important in the treatment of chronic diseases. eHealth interventions that incorporate behavior change techniques (BCTs) and dynamic tailoring strategies could effectively support a healthy lifestyle. E-Supporter 1.0 is an eCoach designed to support physical activity and a healthy diet in people with type 2 diabetes (T2D).

OBJECTIVE

This paper aimed to describe the systematic development of E-Supporter 1.0.

METHODS

Our systematic design process consisted of 3 phases. The definition phase included the selection of the target group and formulation of intervention objectives, and the identification of behavioral determinants based on which BCTs were selected to apply in the intervention. In the development phase, intervention content was developed by specifying tailoring variables, intervention options, and decision rules. In the last phase, E-Supporter 1.0 integrated in the Diameter app was evaluated using a usability test in 9 people with T2D to assess intervention usage and acceptability.

RESULTS

The main intervention objectives were to stimulate light to moderate-vigorous physical activities or adherence to the Dutch dietary guidelines in people with T2D. The selection of behavioral determinants was informed by the health action process approach and theories explaining behavior maintenance. BCTs were included to address relevant behavioral determinants (eg, action control, self-efficacy, and coping planning). Development of the intervention resulted in 3 types of intervention options, consisting of motivational messages, behavioral feedback, and tailor-made supportive exercises. On the basis of IF-THEN rules, intervention options could be tailored to, among others, type of behavioral goal and (barriers to) goal achievement. Data on these variables could be collected using app data, activity tracker data, and daily ecological momentary assessments. Usability testing revealed that user experiences were predominantly positive, despite some problems in the fixed delivery of content.

CONCLUSIONS

The systematic development approach resulted in a theory-based and dynamically tailored eCoach. Future work should focus on expanding intervention content to other chronic diseases and lifestyle behaviors, enhancing the degree of tailoring and evaluating intervention effects on acceptability, use, and cost-effectiveness.

Effectiveness of dietary interventions delivered by digital health to adults with chronic conditions: Systematic review and meta-analysis

BACKGROUND

Digital health interventions may facilitate management of chronic conditions; however, no reviews have systematically assessed the effectiveness of dietary interventions delivered by digital health platforms for improving dietary intake and clinical outcomes for adults with diet-related chronic conditions.

METHODS

Databases CINAHL, CENTRAL, Embase and MEDLINE were searched from inception to April 2021 to identify controlled trials for dietary education delivered by digital health (mobile or electronic health) to adults with diet-related chronic conditions. Random effects analysis was performed for diet quality, food groups, nutrients and clinical outcomes. Screening, data extraction and quality checking were completed in duplicate.

RESULTS

Thirty-nine studies were included involving 7333 participants. Significant changes were found for Mediterranean diet adherence score (standardised mean difference [SMD] = 0.79; 95% confidence interval [CI] = 0.18 to 1.40), overall fruit and vegetable intake (mean difference [MD]: 0.63 serves/day; 95% CI = 0.27-0.98), fruit intake alone (MD = 0.58 serves/day; 95% CI = 0.39 to 0.77) and sodium intake (SMD = -0.22; 95% CI = -0.44 to -0.01). Improvements were also found for waist circumference [MD = -2.24 centimetres; 95% CI = -4.14 to -0.33], body weight (MD = -1.94 kg; 95% CI = -2.63 to -1.24) and haemoglobin A1c (MD = -0.17%; 95% CI = -0.29 to -0.04). Validity of digital assessment tools to measure dietary intake were not reported. The quality of evidence was considered to have low to moderate certainty.

CONCLUSIONS

Modest improvements in diet and clinical outcomes may result from intervention via digital health for those with diet-related chronic conditions. However, additional robust trials with better reporting of digital dietary assessment tools are needed to support implementation within clinical practice.

Sustainability of Weight Loss Through Smartphone Apps: Systematic Review and Meta-analysis on Anthropometric, Metabolic, and Dietary Outcomes

Background

Evidence on the long-term effects of weight management smartphone apps on various weight-related outcomes remains scarce.

Objective

In this review, we aimed to examine the effects of smartphone apps on anthropometric, metabolic, and dietary outcomes at various time points.

Methods

Articles published from database inception to March 10, 2022 were searched, from 7 databases (Embase, CINAHL, PubMed, PsycINFO, Cochrane Library, Scopus, and Web of Science) using forward and backward citation tracking. All randomized controlled trials that reported weight change as an outcome in adults with overweight and obesity were included. We performed separate meta-analyses using random effects models for weight, waist circumference, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, blood glucose level, blood pressure, and total energy intake per day. Methodological quality was assessed using the Cochrane Risk of Bias tool.

Results

Based on our meta-analyses, weight loss was sustained between 3 and 12 months, with a peak of 2.18 kg at 3 months that tapered down to 1.63 kg at 12 months. We did not find significant benefits of weight loss on the secondary outcomes examined, except for a slight improvement in systolic blood pressure at 3 months. Most of the included studies covered app-based interventions that comprised of components beyond food logging, such as real-time diet and exercise self-monitoring, personalized and remote progress tracking, timely feedback provision, smart devices that synchronized activity and weight data to smartphones, and libraries of diet and physical activity ideas.

Conclusions

Smartphone weight loss apps are effective in initiating and sustaining weight loss between 3 and 12 months, but their effects are minimal in their current states. Future studies could consider the various aspects of the socioecological model. Conversational and dialectic components that simulate health coaches could be useful to enhance user engagement and outcome effectiveness.

Trial Registration

International Prospective Register of Systematic Reviews (PROSPERO) CRD42022329197; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=329197

What Intervention Elements Drive Weight Loss in Blended-Care Behavior Change Interventions? A Real-World Data Analysis with 25,706 Patients

BACKGROUND

Blended-care behavior change interventions (BBCI) are a combination of digital care and coaching by health care professionals (HCP), which are proven effective for weight loss. However, it remains unclear what specific elements of BBCI drive weight loss.

OBJECTIVES

This study aims to identify the distinct impact of HCP-elements (coaching) and digital elements (self-monitoring, self-management, and education) for weight loss in BBCI.

METHODS

Long-term data from 25,706 patients treated at a digital behavior change provider were analyzed retrospectively using a ridge regression model to predict weight loss at 3, 6, and 12 months.

RESULTS

Overall relative weight loss was -1.63 kg at 1 month, -3.61 kg at 3 months, -5.28 kg at 6 months, and -6.55 kg at 12 months. The four factors of BBCI analyzed here (coaching, self-monitoring, self-management, and education) predict weight loss with varying accuracy and degree. Coaching, self-monitoring, and self-management are positively correlated with weight losses at 3 and 6 months. Learn time (i.e., self-guided education) is clearly associated with a higher degree of weight loss. Number of appointments outside of app coaching with a dietitian (coach) was negatively associated with weight loss.

CONCLUSIONS

The results testify to the efficacy of BBCI for weight loss-with particular positive associations per time point-and add to a growing body of research that characterizes the distinct impact of intervention elements in real-world settings, aiming to inform the design of future interventions for weight management.

The Potential Role of Digital Health in Obesity Care

Obesity is a complex, multi-factorial, chronic condition which increases the risk of a wide range of diseases including type 2 diabetes mellitus, cardiovascular disease and certain cancers. The prevalence of obesity continues to rise and this places a huge economic burden on the healthcare system. Existing approaches to obesity treatment tend to focus on individual responsibility and diet and exercise, failing to recognise the complexity of the condition and the need for a whole-system approach. A new approach is needed that recognises the complexity of obesity and provides patient-centred, multidisciplinary care which more closely meets the needs of each individual with obesity. This review will discuss the role that digital health could play in this new approach and the challenges of ensuring equitable access to digital health for obesity care. Existing technologies, such as telehealth and mobile health apps and wearable devices, offer emerging opportunities to improve access to obesity care and enhance the quality, efficiency and cost-effectiveness of weight management interventions and long-term patient support. Future application of machine learning and artificial intelligence to obesity care could see interventions become increasingly automated and personalised.