Comprehensive Summary: Mobile and Web Apps for Weight Management in Overweight and Obese Adults: An Updated Umbrella Review and Meta-Analysis
Published on: 21 July 2025
Introduction and Background
Obesity is a pervasive global epidemic, with 2.2 billion adults affected in 2020 and projected to rise to 3.3 billion by 2030. It significantly increases the risk of noncommunicable diseases such as cardiovascular disease, type 2 diabetes, cancers, and psychological disorders including depression and anxiety. The health burden translates into substantial economic costs, with the World Obesity Federation estimating an annual impact of USD 4.32 trillion by 2035, equating to nearly 3% of the global GDP.
In response, digital health technologies have emerged as scalable, promising tools for weight management. Mobile health (mHealth), via smartphones and wearable devices, and web-based interventions offer self-monitoring, behavioral change support, and personalized feedback. However, evidence regarding their effectiveness compared to conventional or no intervention remains inconsistent, especially following the accelerated adoption of digital health during the COVID-19 pandemic.
Objectives and Methodology
This umbrella review synthesizes evidence from 11 systematic reviews (261 primary studies, 62,407 participants) analyzing the effectiveness of mobile applications and web-based interventions for weight loss in overweight and obese adults, compared with conventional treatments or no intervention. The study adhered to PRISMA guidelines and was registered in PROSPERO (CRD42025644218).
Eligibility criteria focused on adults (≥18 years) with BMI ≥25, interventions involving mobile or web-based apps designed for weight loss, and outcomes including weight, BMI, waist circumference, or body fat percentage. Systematic reviews with meta-analyses published up to February 2025 were included, with exclusion of studies using telemedicine not involving apps, AI or chatbots, and wearable devices alone.
Data extraction involved quality assessment via AMSTAR 2 and careful selection of representative effect sizes to avoid duplication. Meta-analyses were conducted using random-effects models, stratified by intervention modality (mobile app vs. web-based), human contact presence, and intervention duration (short-term ≤6 months, long-term ≥12 months). Overlap among primary studies was minimal (2.3% CCA), and publication bias was assessed where available.
Key Findings
Effectiveness of Interventions
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Mobile App Interventions: Demonstrated a modest but statistically significant pooled weight reduction of −1.32 kg (95% CI: −2.54 to −0.11; I²=86.1%) versus usual care or minimal intervention. This reflects consistent benefits of smartphone-delivered apps, particularly those emphasizing lifestyle self-monitoring and behavioral components.
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Web-Based Interventions:
- With human contact (e.g., coaching/support), weight loss favored intervention groups (−1.30 kg) but did not reach statistical significance; heterogeneity was low (I²=0%).
- Without human contact, no significant effect was observed (−1.15 kg; 95% CI: −2.78 to 0.49), with very high heterogeneity (I²=96.5%), indicating inconsistent outcomes.
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Overall eHealth Interventions: Showed significant reductions in body weight (−1.43 kg), BMI (−0.58 kg/m²), and waist circumference (−1.53 cm), with variable heterogeneity levels. Long-term interventions (≥12 months) maintained significant weight loss (−1.13 kg), while short-term (≤6 months) effects were not statistically significant.
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Body Fat Percentage: Data were limited but suggested a trend toward reduction (−1.40%), derived from a single review.
Moderators and Influencing Factors
Moderator analyses revealed that intervention type (smartphone apps), comparator group (usual care), intervention duration (short-term more effective for mHealth, long-term effective overall), and human support presence significantly influenced outcomes. Behavioral components such as goal-setting, feedback, and self-monitoring were key to enhanced adherence and effectiveness. Demographic factors like age and baseline BMI generally did not moderate effects significantly.
Qualitative Insights
Mobile apps, especially those promoting self-monitoring, enhance autonomy and user engagement, reducing reliance on frequent in-person visits. However, long-term engagement often wanes without adequate human interaction or support. Web-based programs benefit greatly from structured behavioral support and personalized feedback to maintain motivation and adherence.
Contextual and Equity Considerations
Most evidence stems from high-income countries (93.3%), with scant data from low- and middle-income regions, notably Latin America and Africa. Cultural tailoring, digital literacy, and infrastructure challenges pose barriers to equitable access. Digital exclusion risks exacerbating health disparities unless addressed through inclusive design, training, and policy support. Integration within primary healthcare (PHC) settings and vulnerable populations remains underexplored but crucial.
Emerging Role of Artificial Intelligence (AI)
While AI-based interventions (adaptive feedback, chatbots, predictive analytics) show promise for enhancing personalization and adherence, robust evidence is currently limited due to their novelty. Future research must prioritize rigorous, transparent methodologies to address challenges such as algorithmic bias and reproducibility, building on lessons from existing eHealth studies.
Implications for Practice and Policy
Digital health tools, especially mobile apps integrating behavior change techniques and long-term monitoring, hold potential as adjuncts to traditional weight management in PHC and public health. Implementing these tools sustainably requires investment in digital infrastructure, workforce training, governance, and cost-effectiveness evaluations. Successful examples include Brazil’s “Peso Saudável” platform and the NHS Digital Weight Management Programme in England, though challenges like digital divides persist.
Limitations and Future Directions
High heterogeneity, predominance of short-term studies, and underreporting of pharmacological components in comparators limit conclusions. There is an urgent need for culturally adapted, methodologically rigorous trials in diverse and vulnerable populations that assess long-term sustainability, cost-effectiveness, and equity impacts. Emerging technologies like AI require dedicated evaluation to maximize benefits and minimize risks.
Conclusion
Mobile app and long-term eHealth interventions yield modest but significant improvements in weight, BMI, and waist circumference in overweight and obese adults. Evidence for web-based interventions remains inconclusive. Future research and policy must focus on inclusivity, sustainability, and integration of emerging technologies to fully harness digital health’s potential in combating the global obesity epidemic.
SOURCE/READ THE FULL PAPER: https://www.mdpi.com/1660-4601/22/7/1152
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