Use of Mobile Phone App Interventions to Promote Weight Loss: Meta-Analysis

Improving Acceptability of mHealth Apps-The Use of the Technology Acceptance Model to Assess the Acceptability of mHealth Apps: Systematic Review

BACKGROUND

Mobile health apps (MHAs) are increasingly used in modern health care provision. The technology acceptance model (TAM) is the most widely used framework for predicting health care technology acceptance. Since the advent of this model in 1989, technology has made generational advancements, and extensions of this model have been implemented.

OBJECTIVE

This systematic review aimed to re-examine TAM models to establish their validity for predicting the acceptance of modern MHAs, reviewing relevant core and extended constructs, and the relationships between them.

METHODS

In this systematic review, MEDLINE, Embase, Global Health, APA PsycINFO, CINAHL, and Scopus databases were searched on March 8, 2024, with no time constraints, for studies assessing the use of TAM-based frameworks for MHA acceptance. Studies eligible for data extraction were required to be peer-reviewed, English-language, primary research articles evaluating MHAs with health-related utility, using TAM as the primary technology acceptance evaluation framework, and reporting app use data. Data were extracted and grouped into 5 extended TAM construct themes. Quality assessment was conducted using the Joanna Briggs Institute (JBI) tools. For cross-sectional methodologies (9/14, 64%), the JBI checklist for analytical cross-sectional studies was used. For non-cross-sectional studies (5/14, 36%), the JBI checklist most relevant to the specific study design was used. For mixed methods studies (1/14, 7%), the JBI checklist for qualitative studies was applied, in addition to the JBI checklist most suited to the quantitative design. A subsequent narrative synthesis was conducted in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology.

RESULTS

A total of 2790 records were identified, and 14 were included. Furthermore, 10 studies validated the efficacy of TAM and its extensions for the assessment of MHAs. Relationships between core TAM constructs (perceived usefulness, perceived ease of use, and behavioral intention) were validated. Extended TAM constructs were grouped into 5 themes: health risk, application factors, social factors, digital literacy, and trust. Digital literacy, trust, and application factor extended construct themes had significant predictive capacity. Application factors had the strongest MHA acceptance predictive capabilities. Perceived usefulness and extended constructs related to social factors, design aesthetics, and personalization were more influential for those from deprived socioeconomic backgrounds.

CONCLUSIONS

TAM is an effective framework for evaluating MHA acceptance. While original TAM constructs wield significant predictive capacity, the incorporation of social and clinical context-specific extended TAM constructs can enhance the model's predictive capabilities. This review's findings can be applied to optimize MHAs' user engagement and minimize health care inequalities. Our findings also underscore the necessity of adapting TAM and other acceptability frameworks as the technological and social landscape evolves.

TRIAL REGISTRATION

PROSPERO CRD42024532974; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024532974.

Role of Technology in Promoting Heart Healthy Behavior Change to Increase Equity in Optimal Cardiovascular Health: A Scientific Statement From the American Heart Association

Populations most affected by cardiovascular health disparities, including underrepresented populations with lower socioeconomic status, people with disabilities, and those living in underserved rural communities, are disproportionately exposed to adverse social determinants of health. Specifically, economic instability and suboptimal living conditions within the neighborhood and built environment directly determine access to resources and opportunities for healthful behaviors. In this scientific statement, we examined the technology-enabled interventions that address cardiovascular health behaviors from adolescence to adulthood in populations most affected by health disparities. We used a broad definition of technology, including wearables, applications, and telehealth, for behavior tracking. Aligning with Life's Essential 8, we focused on interventions targeting behavior change related to physical activity, sedentary time, dietary intake, tobacco cessation, and sleep health to improve cardiovascular health. The digital determinants of health are important adjuncts to the social determinants and operate at the individual, interpersonal, community, and societal levels. The digital determinants of health include the impact of digital technologies (eg, wearables, telemedicine) across health outcomes. Evidence of effective interventions using technology to improve cardiovascular health through positive behavior change is critical for preventing cardiovascular disease events. Stronger evidence is needed to inform and implement effective approaches that are scalable and cost-effective across communities and health care institutions to advance digital equity in cardiovascular health. Dissemination of digital solutions to improve cardiovascular health in communities or across health care systems must ensure effective, feasible, available, and affordable solutions for populations most in need.

The Role of Mobile Apps in Obesity Management: Systematic Review and Meta-Analysis

BACKGROUND

Obesity is a growing public health concern worldwide, significantly contributing to premature mortality and noncommunicable diseases. Weight reduction through lifestyle interventions, including diet and physical activity, is the primary approach to combating obesity, with studies showing that a 5% to 10% reduction in body weight can notably reduce obesity-related complications. Recently, smartphone apps have emerged as popular tools to aid in weight loss. However, the effectiveness of smartphone-only apps for weight management in people with overweight or obesity without comorbidities remains unclear.

OBJECTIVE

This meta-analysis aims to evaluate the efficacy of these apps in supporting weight loss and improving body composition in such populations.

METHODS

A systematic review and meta-analysis were conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, with a search across databases including PubMed, Scopus, Cochrane Library, and others. The inclusion criteria were randomized controlled trials involving adults (aged ≥18 years) with overweight or obesity (BMI≥25 kg/m2) and assessing the use of smartphone-only apps for weight loss. Studies using additional devices or involving participants with comorbidities were excluded. Data extraction focused on weight loss, BMI, waist circumference, and body fat percentage, and the risk of bias was assessed using the Revised Cochrane Risk-of-Bias tool.

RESULTS

A total of 11 randomized controlled trials with 1717 participants were included in the meta-analysis. The interventions, lasting between 60 days and 12 months, involved diet and exercise monitoring via smartphone apps. At 4-6 months of follow-up, app-based interventions significantly reduced body weight (standardized mean difference -0.33, 95% CI -0.48 to -0.17; P<.001; I2=49%) and BMI (mean difference [MD] -0.76, 95% CI -1.42 to -0.10; P=.02). Reductions in body fat percentage were also observed at 3 months (MD -0.79, 95% CI -1.38 to -0.20; P=.009) and between 4 and 6 months (MD -0.46, 95% CI -0.71 to -0.20; P<.001). However, no significant effects on waist circumference were noted (P=.07).

CONCLUSIONS

Smartphone apps demonstrate a modest but statistically significant effect on weight loss and BMI reduction over a 4- to 6-month period in individuals with overweight or obesity. The effectiveness of these interventions appears limited beyond 6 months, with a tendency for weight regain. Many apps lack the personalized support necessary to sustain long-term weight loss, contributing to high dropout rates. Future development of weight loss apps should focus on enhanced customization to improve user adherence and long-term outcomes.

TRIAL REGISTRATION

PROSPERO CRD42024570999; https://tinyurl.com/2xw6j4fy.

Predicting Post-Fracture Recovery with Smartphone Mobility Data: A Proof-of-Concept Study

BACKGROUND

After a lower-extremity fracture, the patient's priority is to regain function. To date, our ability to measure function has been limited. However, high-fidelity sensors in smartphones continuously measure mobility, providing an expansive pre- and post-injury gait history. We assessed whether pre-injury mobility data, combined with demographic and injury data, reliably predicted post-fracture mobility.

METHODS

We enrolled 107 adult patients (mean age, 45 years; 43% female, 62% White, 36% Black, 1% Asian, 1% more than one race) ≥6 months after the surgical treatment of a lower-extremity fracture. Consenting patients exported their Apple iPhone mobility metrics, including step count, walking speed, step length, walking asymmetry, and double-support time. We integrated these mobility measures with demographic and injury data. Using nonlinear modeling, we assessed whether pre-injury mobility metrics combined with baseline data predicted post-fracture mobility.

RESULTS

All models were well calibrated and had model fits ranging from an adjusted R2 of 0.18 (walking asymmetry) to 0.61 (double-support time). Pre-injury function strongly predicted post-injury mobility in all models. After the injury, the average daily step count increased by 65 steps each week (95% confidence interval [CI], 56 to 75). Weekly gains were significantly greater within 6 weeks after the injury (92 daily steps per week; 95% CI, 58 to 127) than 20 to 26 weeks post-injury (19 daily steps per week; 95% CI, 11 to 27; p < 0.001). Greater pre-injury steps were associated with increased post-injury mobility (301 daily steps post-injury per 1,000 steps pre-injury; 95% CI, 235 to 367). Mean walking speed declined by 0.200 m/s (95% CI, -0.257 to -0.143) from injury to 8 weeks post-injury. From 12 to 26 weeks post-injury, the average walking speed increased by 0.071 m/s (95% CI, 0.044 to 0.097).

CONCLUSIONS

These proof-of-concept findings highlight the value of high-fidelity pre-injury mobility data in predicting recovery. Individualized recovery projections can provide patient-friendly counseling tools and useful clinical insight for surgeons.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Association of Digital Health Interventions With Maternal and Neonatal Outcomes: Systematic Review and Meta-Analysis

BACKGROUND

Gestational weight gain (GWG) is crucial to maternal and neonatal health, yet many women fail to meet recommended guidelines, increasing the risk of complications. Digital health interventions offer promising solutions, but their effectiveness remains uncertain. This study evaluates the impact of such interventions on GWG and other maternal and neonatal outcomes.

OBJECTIVE

This study aimed to investigate the effect of digital health interventions among pregnant women and newborns.

METHODS

A total of 2 independent researchers performed electronic literature searches in the PubMed, Embase, Web of Science, and Cochrane Library databases to identify eligible studies published from their inception until February 2024; an updated search was conducted in August 2024. The studies included randomized controlled trials (RCTs) related to maternal and neonatal clinical outcomes. The Revised Cochrane risk-of-bias tool for randomized trials was used to examine the risk of publication bias. Stata (version 15.1; StataCorp) was used to analyze the data.

RESULTS

We incorporated 42 pertinent RCTs involving 148,866 participants. In comparison to the routine care group, GWG was markedly reduced in the intervention group (standardized mean difference-0.19, 95% CI -0.25 to -0.13; P<.001). A significant reduction was observed in the proportion of women with excessive weight gain (odds ratio [OR] 0.79, 95% CI 0.69-0.91; P=.001), along with an increase in the proportion of women with adequate weight gain (OR 1.33, 95% CI 1.10-1.64; P=.003). Although no significant difference was reported for the proportion of individuals below standardized weight gain, there is a significant reduction in the risk of miscarriage (OR 0.66, 95% CI 0.46-0.95; P=.03), preterm birth (OR 0.8, 95% CI 0.75-0.86; P<.001), as well as complex neonatal outcomes (OR 0.93, 95% CI 0.87-0.99; P=.02). Other maternal and fetal outcomes were not significantly different between the 2 groups (all P>.05).

CONCLUSIONS

The findings corroborate our hypothesis that digitally facilitated health care can enhance certain facets of maternal and neonatal outcomes, particularly by mitigating excessive weight and maintaining individuals within a reasonable weight gain range. Therefore, encouraging women to join the digital health team sounds feasible and helpful.

TRIAL REGISTRATION

PROSPERO CRD42024564331; https://tinyurl.com/5n6bshjt.

The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap with leading comorbidities such as heart disease, innovative solutions are necessary to improve risk prediction and management strategies. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in healthcare, offering novel approaches to chronic disease prevention. This narrative review explores the role of AI/ML in obesity risk prediction and management, with a special focus on childhood obesity. We begin by examining the multifactorial nature of obesity, including genetic, behavioral, and environmental factors, and the limitations of traditional approaches to predict and treat morbidity associated obesity. Next, we analyze AI/ML techniques commonly used to predict obesity risk, particularly in minimizing childhood obesity risk. We shift to the application of AI/ML in obesity management, comparing perspectives from healthcare providers versus patients. From the provider's perspective, AI/ML tools offer real-time data from electronic medical records, wearables, and health apps to stratify patient risk, customize treatment plans, and enhance clinical decision making. From the patient's perspective, AI/ML-driven interventions offer personalized coaching and improve long-term engagement in health management. Finally, we address key limitations and challenges, such as the role of social determinants of health, in embracing the role of AI/ML in obesity management, while offering our recommendations based on our literature review.

The past, present, and future of behavioral obesity treatment

Over the last century, hundreds of evaluations have been conducted to examine weight-management interventions related to diet, physical activity, and behavior therapy. These investigations have contributed to a growing body of knowledge that has consistently advanced the field of obesity treatment, while also revealing some persistent challenges. This narrative review summarizes key findings from randomized controlled trials conducted in adults that have combined diet, physical activity, and behavior therapy, an approach variously referred to as behavioral treatment, comprehensive lifestyle modification, or intensive lifestyle intervention. The review shows that current behavioral approaches induce average reductions in baseline body weight of 5 to 10% at 6 to 12 months. Such losses have proven effective in reducing the risk of type 2 diabetes in persons with impaired glucose tolerance and in improving other obesity-related complications. These benefits have also been associated with reductions in healthcare costs. Despite these advances, behavioral treatment is challenged by the need for larger losses to achieve optimal improvements in health, by difficulties associated with maintaining weight loss, and by barriers limiting access to treatment. New anti-obesity medications, when combined with behavioral obesity treatment, hold promise of addressing the first two issues.

Evaluation of Intermittent Restricted Eating Using the Interval Weight Loss Online Platform in an Everyday Setting

Background/Objectives: Obesity remains a global health challenge. Many commercial online weight loss programs are available, and they have advantages in terms of scalability and access. Few of these programs have been evaluated for effectiveness in a real-world context. This study reports on the weight loss achieved, platform engagement, and characteristics of successful weight loss predictions in subscribers to the Interval Weight Loss (IWL) program. The Interval Weight Loss program promotes intermittent restricted eating in addition to lifestyle changes in diet composition, exercise, and sleep. Methods: Data for 1705 adults subscribing to the program for >30 days between 2019 and 2024 were included in the analysis. A linear mixed model with polynomial terms was used to model weight loss over time with interaction terms for gender and age. Survival analysis was used to model the proportions and time frame of those meeting 2%, 5%, and 10% weight loss targets and the proportion meeting their goal weight. The focus of the analysis was on the effect at 365 days. Descriptive data from a subset of participants (n = 205) who completed a questionnaire about change in lifestyle habits and mood are also presented. Results: Of those who stayed in the program for at least 365 days, 25.4% achieved their goal weight, 17.6% achieved a 10% weight loss, and 62% achieved a 5% weight loss. By 49 days, 50% had lost 2% of their weight. Significant interactions indicated that males and females in their 60s and 70s were the most responsive to the program. Conclusions: The online commercial Interval Weight Loss platform based on intermittent restricted eating resulted in significant weight loss in a cohort of subscribers in a real-world setting.

Family-based mobile application needs in childhood obesity management in South Korea: a qualitative study

PURPOSE

It is essential to investigate the users' needs in developing user-centered, family-based applications (apps) for managing childhood obesity.

METHODS

This study conducted focus group interviews with ten parents, ten children, and four app developers to investigate the need to develop a family-based mobile app for childhood obesity. The interview period was from July 17 to August 30, 2023. The data were analyzed using thematic analysis.

RESULTS

The results highlighted 31 key themes critical to family-based app development, including the need for comprehensive content promoting healthy lifestyle habits, tailored expert support, and motivational factors for app engagement, which were categorized as intrinsic, extrinsic, and social motivations and technological features. Participants also highlighted potential challenges, such as app usability issues and the need for a user-friendly interface. The pivotal role of parents in guiding and supporting their children's health behaviors was emphasized, particularly in creating a supportive environment that encourages sustained engagement with the app.

CONCLUSION

These findings provide a comprehensive understanding of the needs and preferences of parents, children, and app developers in managing childhood obesity. Emphasis on integrating healthy lifestyle content, providing expert support, and incorporating motivational strategies is crucial for the app's success. Additionally, addressing potential difficulties and recognizing the pivotal role of parents are essential considerations. These insights will inform the design and development of a user-centered mobile app that supports families in managing childhood obesity.

Effectiveness of an mHealth App That Uses Financial Incentives and Gamification to Promote Health Behavior Change in Adolescents and Caregivers: Protocol for a Clinic-Based Randomized Controlled Trial

BACKGROUND

Adolescent and adult obesity continues to be a public health epidemic in the United States. Despite the popularity of mHealth apps with gamification among adolescents, there are insufficient studies to evaluate the efficacy of gamified mHealth apps and financial incentives to motivate sustained health behavior change in adolescents or their adult caregivers.

OBJECTIVE

This study aims to evaluate the effectiveness of gamification techniques and financial incentives used in the novel "CommitFit" mHealth app to motivate health behavior change and improve various mental and physical health metrics in adolescents and their caregivers.

METHODS

This study is a 3-month randomized controlled trial (RCT) with 30 adolescents (aged 13-15 years) and their adult caregivers (N=60). It evaluates "CommitFit," which uses gamification including points and leaderboards to motivate logging and achievement of self-selected health behavior goals (eg, more water, sleep, physical activity, fruits, or vegetables or fewer sugary beverages). The RCT had three arms, each with 10 dyads: (1) CommitFit-only users; (2) CommitFit$, where adolescents were paid US $0.05 for each point they earned; and (3) waitlist control. Intervention dyads used the app for 3 months and had the option to use it for the fourth month without prompts or extra financial incentives. User analytic software was used to evaluate the frequency of user logs and goal achievement. Monthly surveys evaluated self-reported change in the 5 CommitFit health behaviors. Changes in BMI and blood pressure were evaluated for all participants at 3 clinical visits. Mental health, gamification, and behavior economics surveys were completed during the clinical visits.

RESULTS

Recruitment began in August 2023 and was completed in 10 weeks. The research team successfully recruited and enrolled 30 dyads. Researchers emailed and called 89 caregivers on a physician-approved adolescent patient list, a 33% recruitment rate. Data collection and analysis will be conducted in the spring and summer of 2024. The results of this study are anticipated to be published between late 2024 and early 2025.

CONCLUSIONS

This RCT will expand knowledge of the effectiveness of gamification techniques, financial incentives, and mHealth apps to motivate sustained health behavior change among adolescents and caregivers. These results may offer new opportunities to caregivers, health insurers, health care systems, and clinicians to motivate health behavior change in adolescents and caregivers, with the ultimate goal of preventing or reducing obesity and obesity-related diseases. Additional gamification, mental health surveys, and app user analytics included in the study may provide further insight into the characteristics of adolescents or caregivers who would benefit the most from using a gamified mHealth app like CommitFit.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/63505.

Designing sustainable mobile weight management applications: information technology (IT) experts perspectives

Background

Weight management mobile applications have become increasingly popular as a tool for individuals to achieve their weight loss goals. However, key challenges with mobile weight management applications are sustaining user engagement and promoting long-term behavior change. This study examines the key functions and features of weight management mobile applications from the viewpoint of information technology (IT) experts. We have developed a framework that outlines the optimal design strategies for mobile apps to support sustainable weight management.

Methods

We conducted face-to-face interviews with five experts to evaluate and give input on the proposed framework.

Results

Eight specialized functionalities have been categorized and defined based on initial framework system features and recommended features based on experts' feedback: (I) customization; (II) usability; (III) integration; (IV) online support; (V) tracking; (VI) nutrition database; (VII) gamification; and (VIII) notification. In addition, common functionalities were identified as (I) registration/sign in; (II) data privacy; (III) security; and (IV) user feedback. An online discussion indicates the system functionality in four themes: (I) configuration; (II) data analytics; (III) support/performance; and (IV) platform: iOS, Android, Native.

Conclusions

These functionalities and features will be incorporated into a framework to design mobile applications for sustainable weight management. Also, to be part of user interface development for the user experience experiments.

Evaluation of a mobile behavior change program for weight loss in breast cancer survivors

Post-diagnosis weight gain is common in early-stage breast cancer and is associated with increased risk of recurrence and mortality. Intentional weight loss is difficult to maintain, and digital lifestyle interventions may provide a scalable approach to address this challenge. In this prospective single-arm study (ClinicalTrials.gov NCT04753268; February 15, 2021), key eligibility criteria included: stage I-III breast cancer, body mass index (BMI) ≥ 27.5 kg/m2, and completion of cancer treatment ≥6 months before study enrollment. Participants were provided with a behavioral change mobile application (Noom®). The primary endpoint was a change in self-reported weight from baseline to 26 weeks. Secondary endpoints included engagement, changes in physical activity, dietary patterns, and patient-reported outcomes (PRO). In total, 31 patients were enrolled (mean age 56.8 ± 9.9, mean baseline BMI 33.5 kg/m2 ± 6.5). The mean weight change was -4.8 kg ( ± 4.4, P < 0.001), mean percent weight change was -5.6% ( ± 5.0%); 11/31 patients (35.5%) lost ≥5% of their initial weight. Metrics of digital application engagement associated with weight loss ≥5% included articles read (P = 0.012), weights logged (P = 0.006), food records logged (P = 0.001), messages sent (P = 0.001), and application open count (P = 0.014). Significant increases were seen in mean daily step count (P = 0.004), GPAQ scores (P = 0.002), and Body Image Scale scores (P < 0.001). Mean energy intake remained consistently in a calorie-restricted range of 1300-1400 kcal/day. In this study, breast cancer survivors were highly engaged with a behavioral change smartphone application which led to clinically significant weight loss, increased physical activity, maintenance of an energy-restricted diet, and improvements in body image.

Proof-of-concept testing of a mobile application-delivered mindfulness exercise for emotional eaters: RAIN delivered as a step-by-step image sequence

Background

Over fifty percent of individuals with overweight and obesity are emotional eaters. Emotional eating can be theorized as a conditioned response to eat for reasons that are not associated with physiological hunger. We conducted this proof-of-concept study to gather evidence that a mobile app that delivers a common non-meditative mindfulness exercise called RAIN, in a step-by-step image sequence can improve emotional eating and other outcomes over a 3-week period.

Methods

Forty-nine Canadian adults who self reported as emotional eaters (mean age =30.7 years) were recruited through social media and participated in a workshop in which RAIN and its use on the app were introduced. Participants were asked to use the app every time that they experienced a non-homeostatic craving to eat for three weeks. Emotional eating, reactivity to food cravings, perceived loss of control around food, distress tolerance, and eating-specific mindfulness were assessed pre- and post-intervention.

Results

Improvements on all outcomes were found (r-range, -0.58 to -0.28). The feasibility of the mobile application was demonstrated by a low attrition rate (8%), high user satisfaction, and strong app engagement metrics.

Conclusions

The data provide proof-of-concept evidence that a mobile app that delivers a mindfulness exercise in a step-by-step image sequence has potential to be effective and thus identifies a new approach that may reduce emotional eating in an accessible and affordable manner.

Smartphone Applications to Prevent Type 2 Diabetes: A Systematic Review and Meta-Analysis

INTRODUCTION

Evidence supporting the use of apps for lifestyle behavior change and diabetes prevention in people at high risk of diabetes is lacking. The aim of this systematic review was to determine the acceptability and effectiveness of smartphone applications (apps) for the prevention of type 2 diabetes.

METHODS

PubMed, Embase, CINAHL and PsychInfo were searched from 2008 to 2023. Included studies involved adults at high risk of developing diabetes evaluating an app intervention with the aim of preventing type 2 diabetes. Random-effects meta-analyses were conducted for weight loss, body mass index (BMI), glycated hemoglobin, and waist circumference. Narrative synthesis was conducted for all studies, including qualitative studies exploring user perspectives.

RESULTS

Twenty-four studies (n=2,378) were included in this systematic review, including 9 randomized controlled trials (RCTs) with an average duration of 6 months, 10 quasi-experimental and 7 qualitative studies. Socially disadvantaged groups were poorly represented. Six RCTs were combined in meta-analyses. Apps were effective at promoting weight loss [mean difference (MD) -1.85; 95% CI -2.90 to -0.80] and decreasing BMI [MD -0.90, 95% CI -1.53 to -0.27], with no effect on glycated hemoglobin and waist circumference. No studies reported on diabetes incidence. Qualitative studies highlighted the need for app personalization.

DISCUSSION

Smartphone apps have a promising effect on preventing type 2 diabetes by supporting weight loss. Future robust trials should include diverse populations in co-design and evaluation of apps and explore the role of artificial intelligence in further personalizing interventions for higher engagement and effectiveness.

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.

Do digital health interventions hold promise for stroke prevention and care in Black and Latinx populations in the United States? A scoping review

BACKGROUND

Black and Latinx populations are disproportionately affected by stroke and are likely to experience gaps in health care. Within fragmented care systems, remote digital solutions hold promise in reversing this pattern. However, there is a digital divide that follows historical disparities in health. Without deliberate attempts to address this digital divide, rapid advances in digital health will only perpetuate systemic biases. This study aimed to characterize the range of digital health interventions for stroke care, summarize their efficacy, and examine the inclusion of Black and Latinx populations in the evidence base.

METHODS

We searched PubMed, the Web of Science, and EMBASE for publications between 2015 and 2021. Inclusion criteria include peer-reviewed systematic reviews or meta-analyses of experimental studies focusing on the impact of digital health interventions on stroke risk factors and outcomes in adults. Detailed information was extracted on intervention modality and functionality, clinical/behavioral outcome, study location, sample demographics, and intervention results.

RESULTS

Thirty-eight systematic reviews met inclusion criteria and yielded 519 individual studies. We identified six functional categories and eight digital health modalities. Case management (63%) and health monitoring (50%) were the most common intervention functionalities. Mobile apps and web-based interventions were the two most commonly studied modalities. Evidence of efficacy was strongest for web-based, text-messaging, and phone-based approaches. Although mobile applications have been widely studied, the evidence on efficacy is mixed. Blood pressure and medication adherence were the most commonly studied outcomes. However, evidence on the efficacy of the various intervention modalities on these outcomes was variable. Among all individual studies, only 38.0% were conducted in the United States (n = 197). Of these U.S. studies, 54.8% adequately reported racial or ethnic group distribution. On average, samples were 27.0% Black, 17.1% Latinx, and 63.4% White.

CONCLUSION

While evidence of the efficacy of selected digital health interventions, particularly those designed to improve blood pressure management and medication adherence, show promise, evidence of how these interventions can be generalized to historically underrepresented groups is insufficient. Including these underrepresented populations in both digital health experimental and feasibility studies is critical to advancing digital health science and achieving health equity.

Associations between behaviour change technique clusters and weight loss outcomes of automated digital interventions: a systematic review and meta-regression

Automated digital interventions for weight loss represent a highly scalable and potentially cost-effective approach to treat obesity. However, current understanding of the active components of automated digital interventions is limited, hindering efforts to improve efficacy. Thus, the current systematic review and meta-analysis (preregistration: PROSPERO 2021-CRD42021238878) examined relationships between utilisation of behaviour change techniques (BCTs) and the efficacy of automated digital interventions for producing weight loss. Electronic database searches (December 2020 to March 2021) were used to identify trials of automated digital interventions reporting weight loss as an outcome. BCT clusters were coded using Michie's 93-item BCT taxonomy. Mixed-effects meta-regression was used to examine moderating effects of BCT clusters and techniques on both within-group and between-group measures of weight change. One hundred and eight conditions across sixty-six trials met inclusion criteria (13,672 participants). Random-effects meta-analysis revealed a small mean post-intervention weight loss of -1.37 kg (95% CI, -1.75 to -1.00) relative to control groups. Interventions utilised a median of five BCT clusters, with goal-setting, feedback and providing instruction on behaviour being most common. Use of Reward and Threat techniques, and specifically social incentive/reward BCTs, was associated with a higher between-group difference in efficacy, although results were not robust to sensitivity analyses.

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.

Development and Promotion of an mHealth App for Adolescents Based on the European Code Against Cancer: Retrospective Cohort Study

BACKGROUND

Mobile health technologies, underpinned by scientific evidence and ethical standards, exhibit considerable promise and potential in actively engaging consumers and patients while also assisting health care providers in delivering cancer prevention and care services. The WASABY mobile app was conceived as an innovative, evidence-based mobile health tool aimed at disseminating age-appropriate messages from the European Code Against Cancer (ECAC) to adolescents across Europe.

OBJECTIVE

This study aims to assess the outcomes of the design, development, and promotion of the WASABY app through a 3-pronged evaluation framework that encompasses data on social media promotion, app store traffic, and user engagement.

METHODS

The WASABY app's content, cocreated with cancer-focused civil society organizations across 6 European countries, drew upon scientific evidence from the ECAC. The app's 10 modules were designed using the health belief model and a gamification conceptual framework characterized by spaced repetition learning techniques, refined through 2 rounds of testing. To evaluate the effectiveness of the app, we conducted a retrospective cohort study using the WASABY app's user database registered from February 4 to June 30, 2021, using a 3-pronged assessment framework: social media promotion, app store traffic, and user engagement. Descriptive statistics and association analyses explored the relationship between sociodemographic variables and user performance analytics.

RESULTS

After extensive promotion on various social media platforms and subsequent traffic to the Apple App and Google Play stores, a sample of 748 users aged between 14 and 19 years was included in the study cohort. The selected sample exhibited a mean age of 16.08 (SD 1.28) years and was characterized by a predominant representation of female users (499/748, 66.7%). Most app users identified themselves as nonsmokers (689/748, 92.1%), reported either no or infrequent alcohol consumption (432/748, 57.8% and 250/748, 33.4%, respectively), and indicated being physically active for 1 to 5 hours per week (505/748, 67.5%). In aggregate, the app's content garnered substantial interest, as evidenced by 40.8% (305/748) of users visiting each of the 10 individual modules. Notably, sex and smoking habits emerged as predictors of app completion rates; specifically, male and smoking users demonstrated a decreased likelihood of successfully completing the app's content (odds ratio 0.878, 95% CI 0.809-0.954 and odds ratio 0.835, 95% CI 0.735-0.949, respectively).

CONCLUSIONS

The development and promotion of the WASABY app presents a valuable case study, illustrating the effective dissemination of evidence-based recommendations on cancer prevention within the ECAC through an innovative mobile app aimed at European adolescents. The data derived from this study provide insightful findings for the implementation of Europe's Beating Cancer Plan, particularly the creation of the EU Mobile App for Cancer Prevention.

The Efficacy of an mHealth App in Facilitating Weight Loss Among Japanese Fitness Center Members: Regression Analysis Study

BACKGROUND

Self-tracking smartphone apps have emerged as promising tools to encourage healthy behaviors. In this longitudinal study, we used gym use data from members of a major fitness club that operates gyms throughout Japan from January 2014 to December 2019.

OBJECTIVE

Our objective was to assess the extent to which a health and fitness self-tracking mobile app introduced to gym members on January 1, 2018, contributed to their weight loss. The app allows users to input information regarding diet, sleep, weight, and gym exercise so that they can receive personalized feedback from an artificial intelligence chatbot to improve their health behaviors.

METHODS

We used linear regression to quantify the association between app use and weight loss. The primary outcome of the study was the weight loss achieved by each gym user, which was calculated as the difference between their initial and final weights in kilograms, as recorded in the app. Individuals who did not attend the gym or failed to use the mobile app at least twice during the study period were excluded from the analysis. The model accounted for age, gender, distance between the gym and the member's residence, average weekly number of times a member used the gym, user's gym membership length in weeks, average weekly number of times a member input information into the app, and the number of weeks that the app was used at least once.

RESULTS

Data from 26,589 participants were analyzed. Statistically significant associations were detected between weight loss and 2 metrics related to app use: the average weekly frequency of use and the total number of weeks in which the app was used at least once. One input per week was found to be associated with a loss of 62.1 (95% CI 53.8-70.5) g, and 1 week of app use was associated with 21.7 (95% CI 20.5-22.9) g of weight loss from the day of the first input to that of the final input to the app. Furthermore, the average number of times that a member used the gym weekly was also shown to be statistically significantly associated with weight loss: 1 use per week was associated with 255.5 (95% CI 228.5-282.6) g of weight loss.

CONCLUSIONS

This empirical study demonstrated a significant association between weight loss among gym members and not only the frequency of weekly gym use but also the use of a health and fitness self-tracking app. However, further work is needed to examine the mechanisms through which mobile apps affect health behaviors and to identify the specific app features that are most effective in promoting weight loss.

An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study

BACKGROUND

Predicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatment. However, a lack of understanding and trust in these ML models impacts adoption among weight management experts. Recent advances in the field of explainable artificial intelligence enable the interpretation of ML models, yet it is unknown whether they enhance model understanding, trust, and adoption among weight management experts.

OBJECTIVE

This study aimed to build and evaluate an ML model that can predict 6-month weight loss success (ie, ≥7% weight loss) from 5 engagement and diet-related features collected over the initial 2 weeks of an intervention, to assess whether providing ML-based explanations increases weight management experts' agreement with ML model predictions, and to inform factors that influence the understanding and trust of ML models to advance explainability in early prediction of weight loss among weight management experts.

METHODS

We trained an ML model using the random forest (RF) algorithm and data from a 6-month weight loss intervention (N=419). We leveraged findings from existing explainability metrics to develop Prime Implicant Maintenance of Outcome (PRIMO), an interactive tool to understand predictions made by the RF model. We asked 14 weight management experts to predict hypothetical participants' weight loss success before and after using PRIMO. We compared PRIMO with 2 other explainability methods, one based on feature ranking and the other based on conditional probability. We used generalized linear mixed-effects models to evaluate participants' agreement with ML predictions and conducted likelihood ratio tests to examine the relationship between explainability methods and outcomes for nested models. We conducted guided interviews and thematic analysis to study the impact of our tool on experts' understanding and trust in the model.

RESULTS

Our RF model had 81% accuracy in the early prediction of weight loss success. Weight management experts were significantly more likely to agree with the model when using PRIMO (χ2=7.9; P=.02) compared with the other 2 methods with odds ratios of 2.52 (95% CI 0.91-7.69) and 3.95 (95% CI 1.50-11.76). From our study, we inferred that our software not only influenced experts' understanding and trust but also impacted decision-making. Several themes were identified through interviews: preference for multiple explanation types, need to visualize uncertainty in explanations provided by PRIMO, and need for model performance metrics on similar participant test instances.

CONCLUSIONS

Our results show the potential for weight management experts to agree with the ML-based early prediction of success in weight loss treatment programs, enabling timely and dynamic modification of intervention components to enhance intervention effectiveness. Our findings provide methods for advancing the understandability and trust of ML models among weight management experts.

V-care: An application to support lifestyle improvement in children with obesity

BACKGROUND

Obesity is increasing in the pediatric population, and it represents an important risk factor for the life-long development of several diseases. Although health promotion represents the mainstay of obesity prevention and treatment, lifestyle modification programs are often unsuccessful.

OBJECTIVES

The purpose of this article is to introduce the V-care app, a mobile health platform specifically developed to offer effective interaction and support young people in a long-term obesity treatment, combining different strategies to maximize the results of the lifestyle modification program and minimize the possibility of dropouts.

METHODS

The V-care app is based on a conventional client-server architecture, but novelties of our approach are the involvement of families in the lifestyle modification program, and the design inspired to psychological/behavioral change theories, with the aim of raising the chance of patients' compliance to the program. V-care implements a goal-based behavioral intervention, providing specific feedbacks according to the patient's performance. A pilot usability and acceptability study was performed on a sample of thirteen children aged 6-12 years, using a questionnaire with a 5-points Likert scale to evaluate eight system features, identified as essential requirements based on the analysis of strengths and weaknesses of similar systems in literature.

RESULTS

The pilot study highlighted very high rate of overall friendliness and perceived utility evaluation, while some critical issues emerged especially for the chatbot section, which may be due to the novelty of the technology. The positive evaluation of the design choices is confirmed by the average score greater than 3 for all the questions.

CONCLUSIONS

The V-care app represents a digital innovation in the pediatric healthcare, and it could be introduced in children's primary healthcare nationwide, with the aim to offer an intervention program for controlling and preventing childhood obesity.

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.

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

The use of artificial intelligence (AI) and big data in medicine has increased in recent years. Indeed, the use of AI in mobile health (mHealth) apps could considerably assist both individuals and health care professionals in the prevention and management of chronic diseases, in a person-centered manner. Nonetheless, there are several challenges that must be overcome to provide high-quality, usable, and effective mHealth apps. Here, we review the rationale and guidelines for the implementation of mHealth apps and the challenges regarding quality, usability, and user engagement and behavior change, with a special focus on the prevention and management of noncommunicable diseases. We suggest that a cocreation-based framework is the best method to address these challenges. Finally, we describe the current and future roles of AI in improving personalized medicine and provide recommendations for developing AI-based mHealth apps. We conclude that the implementation of AI and mHealth apps for routine clinical practice and remote health care will not be feasible until we overcome the main challenges regarding data privacy and security, quality assessment, and the reproducibility and uncertainty of AI results. Moreover, there is a lack of both standardized methods to measure the clinical outcomes of mHealth apps and techniques to encourage user engagement and behavior changes in the long term. We expect that in the near future, these obstacles will be overcome and that the ongoing European project, Watching the risk factors (WARIFA), will provide considerable advances in the implementation of AI-based mHealth apps for disease prevention and health promotion.

Effect of a novel telehealth device for dietary cognitive behavioral intervention in overweight or obesity care

Obesity has become a major public health issue which relate to numerous physical problems and highly comorbid with depression and anxiety. Recently, some studies of technology-based interventions for weight reduction emerged to overcome the barriers from time, cost and distance. Mood component and eating behavior related to obesity are less discussed so far with technology-based intervention though. This pilot study was aimed to investigate the effect of telehealth assisted intervention on weight reduction, mood status, and eating behavior change under a smartphone application (app) with novel 3D food picture recognition and incorporated with cognitive behavioral training programs. Adult aged 30-60 years old with overweight were recruited and randomly assigned to control-first group and intervention-first group. In period 1, control-first group had regular life and intervention-first group underwent app intervention; in period 2, two groups went crossover. Body composition and psychological/behavioral questionnaires were collected at baseline, end of period 1, and end of period 2. Nonparametric statistics was performed for data analyzing. A total of 20 participants were enrolled. In control-first group, there were statistically significant reduction in body weight (- 0.55 kg, p = 0.02) and change of body weight percentage (- 0.6%, p = 0.02) after App use. In intervention-first group, the fat percentage decreased by 0.4% after App use in period 1, and increased by 0.05% in period 2. The integrated crossover data revealed that subjects of App group had significant improvements in mindful eating behavior. This pilot study showed the effectiveness in using CogniNU app for weight control and eating behavior. The difference of short-term and long-term effectiveness of technology-based weight control intervention deserves more investigation in the future.Clinical Trial Registration: ISRCTN16082909.

The Efficacy of Mobile Applications for Weight Loss

Purpose of Review

A variety of mobile-based applications aimed at weight loss have become popular in recent years. This review describes the features and effectiveness of mobile weight loss apps.

Recent Findings

Overall, mobile apps can help patients lose weight either as well as or better than traditional paper-and-pencil weight loss interventions and often better than minimal intervention control groups. Mobile apps promote multiple strategies, including self-monitoring of diet, exercise, and weight, as well as social support and educational content. Significant variation exists in app types, which makes it difficult to conclude which features drive program effectiveness. Intervention success varies based on patients’ level of engagement with the app. There is a deficit of apps and app-based studies of older, less tech-savvy adults, ethnic/racial minorities, and low-income individuals, as well as longer-term studies.

Summary

Mobile apps can successfully help patients lose weight and represent a cost-effective, accessible alternative to intensive in-person weight loss programs. More research is needed into their long-term potential, especially for hard-to-reach populations.

Evidence Review for Preventing Osteoarthritis After an Anterior Cruciate Ligament Injury: An Osteoarthritis Action Alliance Consensus Statement

CONTEXT

The Osteoarthritis Action Alliance formed a secondary prevention task group to develop a consensus on secondary prevention recommendations to reduce the risk of osteoarthritis after a knee injury.

OBJECTIVE

Our goal was to provide clinicians with secondary prevention recommendations that are intended to reduce the risk of osteoarthritis after a person has sustained an anterior cruciate ligament injury. Specifically, this manuscript describes our methods, literature reviews, and dissenting opinions to elaborate on the rationale for our recommendations and to identify critical gaps.

DESIGN

Consensus process.

SETTING

Virtual video conference calls and online voting.

PATIENTS OR OTHER PARTICIPANTS

The Secondary Prevention Task Group consisted of 29 members from various clinical backgrounds.

MAIN OUTCOME MEASURE(S)

The group initially convened online in August 2020 to discuss the target population, goals, and key topics. After a second call, the task group divided into 9 subgroups to draft the recommendations and supportive text for crucial content areas. Twenty-one members completed 2 rounds of voting and revising the recommendations and supportive text between February and April 2021. A virtual meeting was held to review the wording of the recommendations and obtain final votes. We defined consensus as >80% of voting members supporting a proposed recommendation.

RESULTS

The group achieved consensus on 15 of 16 recommendations. The recommendations address patient education, exercise and rehabilitation, psychological skills training, graded-exposure therapy, cognitive-behavioral counseling (lacked consensus), outcomes to monitor, secondary injury prevention, system-level social support, leveraging technology, and coordinated care models.

CONCLUSIONS

This consensus statement reflects information synthesized from an interdisciplinary group of experts based on the best available evidence from the literature or personal experience. We hope this document raises awareness among clinicians and researchers to take steps to mitigate the risk of osteoarthritis after an anterior cruciate ligament injury.

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.

An mHealth Intervention to Reduce Gestational Obesity (mami-educ): Protocol for a Randomized Controlled Trial

BACKGROUND

The World Federation of Obesity warns that the main health problem of the next decade will be childhood obesity. It is known that factors such as gestational obesity produce profound effects on fetal programming and are strong predictors of overweight and obesity in children. Therefore, establishing healthy eating behaviors during pregnancy is the key to the primary prevention of the intergenerational transmission of obesity. Mobile health (mHealth) programs are potentially more effective than face-to-face interventions, especially during a public health emergency such as the COVID-19 outbreak.

OBJECTIVE

This study aims to evaluate the effectiveness of an mHealth intervention to reduce excessive weight gain in pregnant women who attend family health care centers.

METHODS

The design of the intervention corresponds to a classic randomized clinical trial. The participants are pregnant women in the first trimester of pregnancy who live in urban and semiurban areas. Before starting the intervention, a survey will be applied to identify the barriers and facilitators perceived by pregnant women to adopt healthy eating behaviors. The dietary intake will be estimated in the same way. The intervention will last for 12 weeks and consists of sending messages through a multimedia messaging service with food education, addressing the 3 domains of learning (cognitive, affective, and psychomotor). Descriptive statistics will be used to analyze the demographic, socioeconomic, and obstetric characteristics of the respondents. The analysis strategy follows the intention-to-treat principle. Logistic regression analysis will be used to compare the intervention with routine care on maternal pregnancy outcome and perinatal outcome.

RESULTS

The recruitment of study participants began in May 2022 and will end in May 2023. Results include the effectiveness of the intervention in reducing the incidence of excessive gestational weight gain. We also will examine the maternal-fetal outcome as well as the barriers and facilitators that influence the weight gain of pregnant women.

CONCLUSIONS

Data from this effectiveness trial will determine whether mami-educ successfully reduces rates of excessive weight gain during pregnancy. If successful, the findings of this study will generate knowledge to design and implement personalized prevention strategies for gestational obesity that can be included in routine primary care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05114174; https://clinicaltrials.gov/ct2/show/NCT05114174.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/44456.

Self-Monitoring Physical Activity, Diet, and Weight Among Adults Who Are Legally Blind: Exploratory Investigation

BACKGROUND

Obesity is a global pandemic. Lifestyle approaches have been shown effective for weight loss and weight loss maintenance. Central to these evidence-based approaches are increased physical activity, decreased caloric intake, regular self-weighing, and the tracking of these behaviors.

OBJECTIVE

This exploratory descriptive study surveyed adults who are legally blind to identify strategies related to tracking physical activity, diet, and weight. These health behaviors are essential components to evidence-based weight loss programs. We also identified areas where we can better support adults who are legally blind in their independent efforts to change these behaviors and improve their health.

METHODS

Participants (≥18 years of age) who self-identified as being legally blind were recruited using email announcements in low vision advocacy groups. They completed an interviewer-administered survey on the telephone and an in-person visit for standardized assessment of height and weight.

RESULTS

The participants (N=18) had an average age of 31.2 (SD 13.4) years; 50% (9/18) had normal weight (BMI 18.5 to <25); 44% (8/18) were female; 44% (8/18) were Black; and 39% (7/18) were Non-Hispanic White. Most participants (16/18, 89%) used their smartphone to access the internet daily, and 67% (12/18) had at least 150 mins of exercise per week. Although 78% (14/18) of the participants indicated tracking their weight, only 61% (11/18) could indicate how they tracked their weight, and 22% (4/18) indicated they tracked it mentally. Providing individuals with a talking scale was the most consistent recommendation (12/18, 67%) to facilitate independence in managing weight through lifestyle changes. Even though 50% (9/18) of the participants indicated using an app or electronic notes to track some portion of their diet, participants reported challenges with determining portion size and corresponding calorie counts. Most participants (17/18, 94%) reported using apps, electronic notes, smartphones, or wearable devices to track their physical activity. Although strategies such as using wearables and smartphones could provide measurements (eg, step counts) as well as recording data, they also pose financial and technology literacy barriers.

CONCLUSIONS

Technology-based solutions were identified for tracking weight, diet, and physical activity for weight management. These strategies have financial and technology literacy barriers. A range of strategies for adopting and tracking health behaviors will be needed to assist individuals with varying skills and life experiences.

Optimizing Internet Use during Adolescence: eHealth Solutions

Internet offers an immense opportunity as a resource for education, training, and health promotion. This narrative review summarizes the opportunities for health promotion among adolescents through use of the Internet and technology (defined as eHealth). The details of technology and use of Internet for adolescent-health related topics such as nutrition, fitness, sexual health, adventure, and violence were searched through PubMed. The review reports few digital solutions to address key challenges during adolescence like promotion of nutrition and sexual reproductive health, prevention of noncommunicable diseases, substance abuse, and mental health issues. eHealth was concluded as a potential solution for preventive and promotional health practices during adolescence. However, concerns of Internet addiction, safety, privacy, mental health disorders, and misinformation need to be addressed and monitored during adolescence.

Applications of digital Medicine in oncology: Prospects and challenges

The current state of oncology medical services is not encouraging and is unable to fully meet the needs of patients with cancer. In recent years, rapidly developing artificial intelligence technology and gradual advancements in mobile phones, sensors, and wearable devices, which have made these more compact, affordable, and popular, have greatly expanded the development of digital medicine. Digital medicine refers to clinical evidence-based technology and products with a direct impact on disease management and research. Integrating digital medicine into clinical practice has the advantages of broader applicability, greater cost-effectiveness, better accessibility, and improved diagnostic and therapeutic performance. Digital medicine has emerged in different clinical application scenarios, including cancer prevention, screening, diagnosis, and treatment, as well as clinical trials. Additionally, big data generated from digital medicine can be used to improve levels of clinical diagnosis and treatment. However, digital medicine also faces many challenges, including security regulation and privacy protection, product usability, data management, and optimization of algorithms. In summary, the application and development of digital medicine in the field of cancer face numerous opportunities and challenges.

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.

Prevention of type 2 diabetes through remotely-administered lifestyle programs: A systematic review

BACKGROUND

Although effective in reducing the risk of type 2 diabetes (T2D), in-person lifestyle modification programs have been underutilized. To minimize barriers related to convenience and accessibility, recent research has focused on developing virtual or remote interventions that may have comparable or greater efficacy in promoting lifestyle changes and reducing the risk of T2D. We conducted a systematic review to evaluate the efficacy of remotely-administered lifestyle interventions on preventing T2D.

METHODS

We searched an electronic database by using keywords, and a web-based software platform was used to screen articles and extract data in duplicate. We included articles with adolescent/adult participant populations who had prediabetes or were at elevated risk of diabetes, remotely-administered lifestyle interventions on T2D prevention, and changes in glycemic traits. We excluded trials of patients with prevalent T2D, interventions that included medications, and those not relevant to our outcomes of interest.

RESULTS

A total of 8 publications were included in this systematic review. Six papers indicated significant reductions in weight and/or glycemic biomarkers such as HbA1c and fasting glucose. All studies reported that remotely-administered interventions were convenient for participants, and one publication reported that participation of the online DPP group was significantly higher than the in-person DPP.

CONCLUSION

These findings suggest that remotely-administered lifestyle interventions for T2D prevention could be a promising participant-friendly alternative to in-person programs, with efficacy for reducing weight and glycemic biomarkers.

mHealth Research for Weight Loss, Physical Activity, and Sedentary Behavior: Bibliometric Analysis

BACKGROUND

Research into mobile health (mHealth) technologies on weight loss, physical activity, and sedentary behavior has increased substantially over the last decade; however, no research has been published showing the research trend in this field.

OBJECTIVE

The purpose of this study was to provide a dynamic and longitudinal bibliometric analysis of recent trends of mHealth research for weight loss, physical activity, and sedentary behavior.

METHODS

A comprehensive search was conducted through Web of Science to retrieve all existing relevant documents published in English between January 1, 2010, and November 1, 2021. We developed appropriate research questions; based on the proven bibliometric approaches, a search strategy was formulated to screen the title for eligibility. Finally, we conducted bibliometric analyses to explore the growth rate of publications; publication patterns; and the most productive authors, institutions, and countries, and visualized the trends in the field using a keyword co-occurrence network.

RESULTS

The initial search identified 8739 articles, of which 1035 were included in the analyses. Our findings show an exponential growth trend in the number of annual publications of mHealth technology research in these fields. JMIR mHealth and uHealth (n=214, 20.67%), Journal of Medical Internet Research (n=71, 6.86%), and BMC Public Health (n=36, 3.47%) were the top 3 journals, publishing higher numbers of articles. The United States remained the leading contributor in these areas (n=405, 39.13%), followed by Australia (n=154, 14.87%) and England (n=125, 12.07%). Among the universities, the University of Sydney (n=36, 3.47%) contributed the most mHealth technology research in these areas; however, Deakin University (n=25, 2.41%) and the National University of Singapore (n=23, 2.22%) were in the second and third positions, respectively.

CONCLUSIONS

Although the number of papers published on mobile technologies for weight loss, physical activity, and sedentary behavior was initially low, there has been an overall increase in these areas in recent years. The findings of the study indicate that mobile apps and technologies have substantial potential to reduce weight, increase physical activity, and change sedentary behavior. Indeed, this study provides a useful overview of the publication trends and valuable guidance on future research directions and perspectives in this rapidly developing field.

Effectiveness of mHealth/eHealth interventions on obesity treatment: a protocol for umbrella review of meta-analyses

INTRODUCTION

Mobile health (mHealth)/electronic health (eHealth) have the effect of facilitating weight loss in overweight and obese populations. However, studies have shown varied results and relatively high heterogeneity in the efficacy of mHealth/eHealth interventions. The aim of the paper was to systematically summarise published studies about the weight loss efficacy of mHealth/eHealth.

METHODS AND ANALYSIS

A comprehensive review of PubMed, Embase and Cochrane Library databases published from inception to 21 March 2021 will be conducted. The selected articles are meta-analyses that integrated the studies, which evaluated efficacy of mHealth/eHealth. Two people will select eligible articles and extract data independently. Any disputes will be resolved by discussion or the arbitration of a third person. The methodological quality of the included meta-analyses will be assessed with AMSTAR V.2 and the quality of evidence will be obtained with Grade of Recommendations Assessment, Development and Evaluation (GRADE). The study selection process will be presented using a flowchart. We will reanalyse each outcome with random effect methods. If possible, we will use funnel plot and Egger's test to evaluate if publication bias existed.

ETHICS AND DISSEMINATION

Ethical approval is not required for the study, as we collected data only from available published materials. This umbrella review will also be submitted to a peer-reviewed journal for publication after completion.

PROSPERO REGISTRATION NUMBER

CRD42021247006.

Long term Weight Loss in a Primary Care-Anchored eHealth Lifestyle Coaching Program in Denmark: Randomized Controlled Trial (Preprint)

Background

Long-term weight loss in people living with obesity can reduce the risk and progression of noncommunicable diseases. Observational studies suggest that digital coaching can lead to long-term weight loss.

Objective

We investigated whether an eHealth lifestyle coaching program for people living with obesity with or without type 2 diabetes led to significant, long-term (12-month) weight loss compared to usual care.

Methods

In a randomized controlled trial that took place in 50 municipalities in Denmark, 340 people living with obesity with or without type 2 diabetes were enrolled from April 16, 2018, to April 1, 2019, and randomized via an automated computer algorithm to an intervention (n=200) or a control (n=140) group. Patients were recruited via their general practitioners, the Danish diabetes organization, and social media. The digital coaching intervention consisted of an initial 1-hour face-to-face motivational interview followed by digital coaching using behavioral change techniques enabled by individual live monitoring. The primary outcome was change in body weight from baseline to 12 months.

Results

Data were assessed for 200 participants, including 127 from the intervention group and 73 from the control group, who completed 12 months of follow-up. After 12 months, mean body weight and BMI were significantly reduced in both groups but significantly more so in the intervention group than the control group (–4.5 kg, 95% CI –5.6 to –3.4 vs –1.5 kg, 95% CI –2.7 to –0.2, respectively; P<.001; and –1.5 kg/m², 95% CI –1.9 to –1.2 vs –0.5 kg/m², 95% CI –0.9 to –0.1, respectively; P<.001). Hemoglobin A_1c was significantly reduced in both the intervention (–6.0 mmol/mol, 95% CI –7.7 to –4.3) and control (–4.9 mmol/mol, 95% CI –7.4 to –2.4) groups, without a significant group difference (all P>.46).

Conclusions

Compared to usual care, digital lifestyle coaching can induce significant weight loss for people living with obesity, both with and without type 2 diabetes, after 12 months.

Trial Registration

ClinicalTrials.gov NCT03788915; https://clinicaltrials.gov/ct2/show/NCT03788915

mHealth prompts within diabetes prevention programs: a scoping review

Background

Mobile health (mHealth) prompts (e.g., text messaging, push notifications) are a commonly used technique within behaviour change interventions to prompt or cue a specific behaviour. Such prompts are being increasingly integrated into diabetes prevention programs (DPPs). While mHealth prompts provide a convenient and cost-effective way to reinforce behaviour change, no reviews to date have examined mHealth prompt use within DPPs. This scoping review aims to: (I) understand how mHealth prompts are being used within behaviour change interventions for individuals at risk for developing type 2 diabetes (T2D); and (II) provide recommendations for future mHealth prompt research, design, and application.

Methods

The scoping review methodology outlined by Arksey and O'Malley were followed. Medline, CINAHL, PsycInfo, Web of Science, and SportDiscus were searched. The search strategy combined keywords relating to T2D risk and mHealth prompts in conjunction with database-controlled vocabulary when available (e.g., MeSH for Medline).

Results

Of the 4,325 publications screened, 44 publications (based on 33 studies) met the inclusion criteria and were included for data extraction. Text messaging was the most widely used mHealth prompt (73%) followed by push notifications (21%). Only 30% of studies discussed the theoretical basis for prompt content and time of day messages were sent, and only 27% provided justification for prompt timing and frequency. Fourteen studies assessed participant satisfaction with mHealth prompts of which only two reported dissatisfaction due to either prompting frequency (hourly) or message content (solely focused on weight). Nine studies assessed behavioural outcomes including weight loss, physical activity, and diabetes incidence, and found mixed effects overall.

Conclusions

While mHealth prompts were well-received by participants, there are mixed effects on the influence of mHealth prompts on behavioural outcomes and diabetes incidence. More thorough reporting of prompt content development and delivery is needed, and more experimental research is needed to identify optimal content, delivery characteristics, and impact on behavioural and clinical outcomes.

The Cost-effectiveness of a Mass Media Campaign to Promote Smartphone Apps for Weight Loss: Updated Modeling Study

BACKGROUND

Evidence suggests that smartphone apps can be effective in the self-management of weight. Given the low cost, broad reach, and apparent effectiveness of weight loss apps, governments may seek to encourage their uptake as a tool to reduce excess weight in the population. Mass media campaigns are 1 mechanism for promoting app use. However, the cost and potential cost-effectiveness are important considerations.

OBJECTIVE

The aim of our study was to use modeling to assess the health impacts, health system costs, cost-effectiveness, and health equity of a mass media campaign to promote high-quality smartphone apps for weight loss in New Zealand.

METHODS

We used an established proportional multistate life table model that simulates the 2011 New Zealand adult population over the lifetime, subgrouped by age, sex, and ethnicity (Māori [Indigenous] or non-Māori). The risk factor was BMI. The model compared business as usual to a one-off mass media campaign intervention, which included the pooled effect size from a recent meta-analysis of smartphone weight loss apps. The resulting impact on BMI and BMI-related diseases was captured through changes in health gain (quality-adjusted life years) and in health system costs. The difference in total health system costs was the net sum of intervention costs and downstream cost offsets because of altered disease rates. An annual discount rate of 3% was applied to health gains and health system costs. Multiple scenarios and sensitivity analyses were conducted, including an equity adjustment.

RESULTS

Across the remaining lifetime of the modeled 2011 New Zealand population, the mass media campaign to promote weight loss app use had an estimated overall health gain of 181 (95% uncertainty interval 113-270) quality-adjusted life years and health care costs of -NZ $606,000 (-US $408,000; 95% uncertainty interval -NZ $2,540,000 [-US $1,709,000] to NZ $907,000 [US $610,000]). The mean health care costs were negative, representing overall savings to the health system. Across the outcomes examined in this study, the modeled mass media campaign to promote weight loss apps among the general population would be expected to provide higher per capita health gain for Māori and hence reduce health inequities arising from high BMI, assuming that the intervention would be as effective for Māori as it is for non-Māori.

CONCLUSIONS

A modeled mass media campaign to encourage the adoption of smartphone apps to promote weight loss among the New Zealand adult population is expected to yield an overall gain in health and to be cost-saving to the health system. Although other interventions in the nutrition and physical activity space are even more beneficial to health and produce larger cost savings (eg, fiscal policies and food reformulation), governments may choose to include strategies to promote health app use as complementary measures.

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

Background

The effectiveness of smartphone apps for weight loss is limited by the diversity of interventions that accompany such apps. This research extends the scope of previous systematic reviews by including 2 subgroup analyses based on nonmobile interventions that accompanied smartphone use and human-based versus passive behavioral interventions.

Objective

The primary objective of this study is to systematically review and perform a meta-analysis of studies that evaluated the effectiveness of smartphone apps on weight loss in the context of other interventions combined with app use. The secondary objective is to measure the impact of different mobile app features on weight loss and mobile app adherence.

Methods

We conducted a systematic review and meta-analysis of relevant studies after an extensive search of the PubMed, MEDLINE, and EBSCO databases from inception to January 31, 2022. Gray literature, such as abstracts and conference proceedings, was included. Working independently, 2 investigators extracted the data from the articles, resolving disagreements by consensus. All randomized controlled trials that used smartphone apps in at least 1 arm for weight loss were included. The weight loss outcome was the change in weight from baseline to the 3- and 6-month periods for each arm. Net change estimates were pooled across the studies using random-effects models to compare the intervention group with the control group. The risk of bias was assessed independently by 2 authors using the Cochrane Collaboration tool for assessing the risk of bias in randomized trials.

Results

Overall, 34 studies were included that evaluated the use of a smartphone app in at least 1 arm. Compared with controls, the use of a smartphone app–based intervention showed a significant weight loss of –1.99 kg (95% CI –2.19 to –1.79 kg; I²=81%) at 3 months and –2.80 kg (95% CI –3.03 to –2.56 kg; I²=91%) at 6 months. In the subgroup analysis, based on the various intervention components that were added to the mobile app, the combination of the mobile app, tracker, and behavioral interventions showed a statistically significant weight loss of –2.09 kg (95% CI –2.32 to –1.86 kg; I²=91%) and –3.77 kg (95% CI –4.05 to –3.49 kg; I²=90%) at 3 and 6 months, respectively. When a behavioral intervention was present, only the combination of the mobile app with intensive behavior coaching or feedback by a human coach showed a statistically significant weight loss of –2.03 kg (95% CI –2.80 to –1.26 kg; I²=83%) and –2.63 kg (95% CI –2.97 to –2.29 kg; I²=91%) at 3 and 6 months, respectively. Neither the type nor the number of mobile app features was associated with weight loss.

Conclusions

Smartphone apps have a role in weight loss management. Nevertheless, the human-based behavioral component remained key to higher weight loss results.

National Veterans Health Administration MOVE! Weight Management Program Participation During the COVID-19 Pandemic

Introduction

In response to COVID-19, the Veterans Health Administration (VHA) converted appropriate outpatient visits to virtual care, including MOVE! Weight Management Program for Veterans (MOVE!) visits. Before the pandemic, most veterans participated in MOVE! in person, with several telehealth modalities available. We sought to describe national trends in MOVE! participation during the pandemic (March 2020–January 2021) overall and by modality and to compare participation to prepandemic levels.

Methods

We conducted a national retrospective cohort study of veterans who participated in MOVE! from January 2018 through January 2021. We examined MOVE! participation across VHA aggregated at the national level by month, including the number of visits, participants, and new participants in person and via telehealth, including telephone, clinic-to-clinic synchronous video, anywhere-to-anywhere (eg, provider home to patient home) synchronous video, and remote education and monitoring. We also determined the percentage of all MOVE! visits attributable to each modality and the monthly percentage change in participation during the pandemic compared with monthly averages in prior years.

Results

Before March 2020, 20% to 30% of MOVE! was delivered via telehealth, which increased to 90% by April 2020. Early in the pandemic, telephone-delivered MOVE! was the most common modality, but anywhere-to-anywhere synchronous video participation increased over time. Compared with the same months in prior years, total monthly MOVE! participation remained 20% to 40% lower at the end of 2020 and into January 2021.

Conclusion

The VHA MOVE! program rapidly shifted to telehealth delivery of weight management services in response to the pandemic. However, a gap remained in the number of veterans receiving these services compared with prior years, suggesting potential unmet needs for weight management.

A pilot randomized trial of simplified versus standard calorie dietary self-monitoring in a mobile weight loss intervention

OBJECTIVE

This study tested the efficacy of a lower-burden, simplified dietary self-monitoring approach compared with a standard calorie monitoring approach for self-monitoring adherence and weight loss in a mobile-delivered behavioral weight loss intervention.

METHODS

Participants (n = 72) with overweight or obesity who had children 2 to 12 years of age living in the home were randomly assigned to a group that used simplified dietary self-monitoring of high-calorie foods (Simplified) or a group that tracked calories (Standard). Both groups received a wireless scale, Fitbit trackers, and a 6-month intervention delivered via a smartphone application with lessons, text messages, and weekly personalized feedback messages.

RESULTS

Percentage weight loss at 6 months was 5.7% (95% CI: -8.3% to -3.2%) in the Standard group and 4.0% (95% CI: -5.7% to -2.3%) in the Simplified group, which was not significantly different. Similar proportions reached 5% weight loss at 6 months (43.2% in Standard and 42.9% in Simplified). There were no differences in number of dietary tracking days or change in average daily caloric intake between groups.

CONCLUSIONS

Two mobile-delivered weight loss interventions produced clinically meaningful levels of weight loss at 6 months, with no differences in dietary tracking adherence or dietary intake. The results suggest that simplified monitoring of high-calorie foods could be a promising alternative to calorie monitoring.

An Evaluation of Evidence Underpinning Management Recommendations in Tobacco Use Disorder Clinical Practice Guidelines

INTRODUCTION

Clinical practice guidelines(CPGs) are important tools for medical decision-making. Given the high prevalence and financial burden associated with tobacco use disorder(TUD), it is critical that recommendations within CPGs are based on robust evidence. Systematic reviews(SRs) are considered the highest level of evidence, thus, we evaluated the quality of SRs underpinning CPG recommendations for TUD.

METHODS

We used PubMed to search for CPGs relating to TUD published between January 1, 2010 and May 21, 2021. SRs were extracted from CPG references and evaluated using Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) and A MeaSurement Tool to Assess Systematic Reviews(AMSTAR-2) tools. We then compared SRs conducted by the Cochrane Collaboration with non-Cochrane SRs using a Mann-Whitney U test and determined associations between PRISMA and AMSTAR-2 extracted characteristics using multiple regression.

RESULTS

Our search generated 10 CPGs with 98 SRs extracted. Mean PRISMA completion was 74.7%(SD = 16.7) and mean AMSTAR-2 completion was 53.8%(SD = 22.0) across all guidelines. Cochrane SRs were more complete than non-Cochrane studies in the PRISMA and AMSTAR-2 assessments. The regression model showed a statistically significant association between PRISMA completion and AMSTAR-2 rating, with those classified as "low" or "moderate" quality having higher PRISMA completion than those with "critically low" ratings.

CONCLUSION

We found substandard adherence to PRISMA and AMSTAR-2 checklists across SRs cited in TUD CPGs. A lack of recent SRs in CPGs could lead to outdated recommendations. Therefore, frequent guideline updates with recently published evidence may ensure more accurate clinical recommendations and improve patient care.

IMPLICATIONS

Systematic reviews used to underpin clinical practice guideline recommendations influence treatment decisions and, ultimately, patient outcomes. We found that many systematic reviews underpinning tobacco use disorder guideline recommendations were out of date and unsatisfactory in reporting and quality. Thus, including newer systematic reviews containing more recently conducted trials and better reporting could alter recommendations and improve the rate of successful tobacco cessation attempts.

Promoting Physical Activity and Weight Loss With mHealth Interventions Among Workers: Systematic Review and Meta-analysis of Randomized Controlled Trials

BACKGROUND

Physical activity (PA) is a vital factor in promoting health in the workforce. Mobile health (mHealth) interventions have recently emerged in workplace health promotion as an effective strategy for inducing changes in health behaviors among workers; however, the effectiveness of mHealth interventions in promoting PA and weight loss for workers is unclear.

OBJECTIVE

This study aims to provide a comprehensive analysis of current evidence on the effectiveness of mHealth interventions in promoting PA and weight loss among workers.

METHODS

We searched relevant databases, including PubMed, Embase, CINAHL Complete, and the Cochrane Library, for publications on mHealth interventions in the English or Korean language from inception to December 2020. Randomized controlled trials that evaluated the effectiveness of mHealth in improving PA and weight loss were retrieved. A meta-analysis with a random effects model and subgroup analyses was performed on PA types and mHealth intervention characteristics.

RESULTS

A total of 8 studies were included in this analysis. More than half of the studies (5/8, 63%) were identified as having a high risk of bias. The mHealth intervention group showed a significant improvement in PA (standardized mean difference [SMD] 0.22, 95% CI 0.03-0.41; P<.001; I2=78%). No significant difference in weight loss was observed when comparing the intervention group with the control groups (SMD 0.02, 95% CI -0.07 to 0.10; P=.48; I2=0%). A subgroup analysis was also performed; walking activity (SMD 0.70, 95% CI 0.21-1.19; P<.001; I2=83.3%), a multicomponent program (SMD 0.19, 95% CI 0.05-0.33; P=.03; I2=57.4%), objective measurement (SMD 0.58, 95% CI 0.05-1.10; P<.001; I2=87.3%), and 2 or more delivery modes (SMD 0.44, 95% CI 0.01-0.87; P<.001; I2=85.1%) were significantly associated with an enhancement in PA.

CONCLUSIONS

This study suggests that mHealth interventions are effective for improving PA among workers. Future studies that assess long-term efficacy with a larger population are recommended.

The usefulness of the nutrition apps in self-control of diabetes mellitus - the review of literature and own experience

Intensive technology development as well as the COVID-19 pandemic has contributed to the increased interest in the telemedicine and mHealth sector. Increasing availability and use of mobile devices as well as the constantly growing number of nutritional mobile applications, resulted in creating new tools for food and meals nutrients calculation which can be used by patients with diabetes. Variety of mobile applications and multiple functions included in them enable finding applications focused on individual patients' needs. The scientific data coming from research conducted so far suggest that the regular use of mobile nutritional applications contributes to improving metabolic control of diabetes and the reduction of the value of glycated hemoglobin in patients with diabetes. Innovative solutions bring hope also for a significant improvement in the quality of life of patients. The aim of this article was to summarize the EBM knowledge about the use of mHealth in self-control and diet of patients with diabetes, especially type 1 and to present and assess the nutrition mobile applications available in polish language according to their functionality in diabetic self-control.

Weight Reduction Interventions Using Digital Health for Employees with Obesity: A Systematic Review

PURPOSE

This study aimed to systematically review the literature on randomized controlled trials on weight reduction interventions using digital health for employees with obesity.

METHODS

All relevant articles published until September 2021 were systematically identified from six electronic databases: MEDLINE, EMBASE, CINAHL, PsycINFO, RISS, and KISS. Data selection and extraction were independently performed by three researchers. Methodological quality was assessed using the JBI Critical Appraisal Checklist for Randomized Controlled Trials. The results were narratively synthesized.

RESULTS

Eleven studies were included in the systematic review. All studies had a low risk of bias. The settings and sample sizes of the included studies were different. The contents of the interventions included nutrition, physical activity, behavioral change, incentives, and motivation. Four studies were based on social cognitive theory. A total of ten studies delivered web-based intervention, while the other used tele-monitoring device. A wide range of intervention strategies was used including providing online resources, tele-counseling, and patient-tailored advice. As a result of the intervention, a total of seven studies showed a significant weight reduction in both the intervention and comparison groups, with significant differences between groups.

CONCLUSION

Until now, use of digital health in weight reduction interventions for employees with obesity has been conducted on a web-based. Various contents such as nutrition, physical activity and theories were explored. Further study is required using more diverse delivery methods such as mobile application, use of wearable devices.

A Theory- and Evidence-Based Digital Intervention Tool for Weight Loss Maintenance (NoHoW Toolkit): Systematic Development and Refinement Study

Background

Many weight loss programs show short-term effectiveness, but subsequent weight loss maintenance is difficult to achieve. Digital technologies offer a promising means of delivering behavior change approaches at low costs and on a wide scale. The Navigating to a Healthy Weight (NoHoW) project, which was funded by the European Union’s Horizon 2020 research and innovation program, aimed to develop, test, and evaluate a digital toolkit designed to promote successful long-term weight management. The toolkit was tested in an 18-month, large-scale, international, 2×2 factorial (motivation and self-regulation vs emotion regulation) randomized controlled trial that was conducted on adults with overweight or obesity who lost ≥5% of their body weight in the preceding 12 months before enrollment into the intervention.

Objective

This paper aims to describe the development of the NoHoW Toolkit, focusing on the logic models, content, and specifications, as well as the results from user testing.

Methods

The toolkit was developed by using a systematic approach, which included the development of the theory-based logic models, the selection of behavior change techniques, the translation of these techniques into a web-based app (NoHoW Toolkit components), technical development, and the user evaluation and refinement of the toolkit.

Results

The toolkit included a set of web-based tools and inputs from digital tracking devices (smart scales and activity trackers) and modules that targeted weight, physical activity, and dietary behaviors. The final toolkit comprised 34 sessions that were distributed through 15 modules and provided active content over a 4-month period. The motivation and self-regulation arm consisted of 8 modules (17 sessions), the emotion regulation arm was presented with 7 modules (17 sessions), and the combined arm received the full toolkit (15 modules; 34 sessions). The sessions included a range of implementations, such as videos, testimonies, and questionnaires. Furthermore, the toolkit contained 5 specific data tiles for monitoring weight, steps, healthy eating, mood, and sleep.

Conclusions

A systematic approach to the development of digital solutions based on theory, evidence, and user testing may significantly contribute to the advancement of the science of behavior change and improve current solutions for sustained weight management. Testing the toolkit by using a 2×2 design provided a unique opportunity to examine the effect of motivation and self-regulation and emotion regulation separately, as well as the effect of their interaction in weight loss maintenance.