Journal of World Future Medicine, Health and Nursing

Gamification in Mobile Health Apps: A Quasi-Experimental Study on Adolescent Obesity Prevention

Cau Kim Jiu — 2025-08-30

Adolescent obesity has become a global public health crisis, demanding effective and engaging intervention strategies. While mobile health (mHealth) apps offer a scalable platform for promoting healthy lifestyles, maintaining long-term engagement among adolescents remains a significant challenge. This study aimed to evaluate the effectiveness of a gamified Health application on promoting positive changes in physical activity, dietary habits, and anthropometric measures among adolescents at risk for obesity, compared to a non-gamified version of the same application. A 12-week, quasi-experimental study was conducted with 150 adolescents (aged 13-16) with a BMI above the 85th percentile. Participants were assigned to either an intervention group (n=75) using a gamified mHealth app featuring points, badges, and leaderboards, or a control group (n=75) using a non-gamified version with identical health content. The intervention group demonstrated significantly greater increases in moderate-to-vigorous physical activity (p < .01) and higher consumption of fruits and vegetables (p < .05) compared to the control group. Furthermore, the gamified app users showed a modest but statistically significant reduction in their BMI z-score (p < .05), a change not observed in the control group. App engagement metrics were also 70% higher in the intervention group. The integration of gamification elements into mHealth applications is a highly effective strategy for preventing obesity in adolescents.

Heatwaves and Urban Elderly Mortality: A Retrospective Cohort Study Using Remote Sensing Data

Sri Suparni — 2025-08-30

Climate change is intensifying the frequency and severity of heatwaves, posing a significant threat to public health. Urban elderly populations are particularly vulnerable due to the urban heat island effect and age-related physiological sensitivities. Quantifying this risk with precision is essential for developing targeted public health interventions. This study aimed to quantify the association between exposure to extreme heat events, as measured by remote sensing data, and all-cause mortality among an elderly urban population. A retrospective cohort study was conducted using health data for 50,000 urban residents aged 65 and over from 2015-2022. Land Surface Temperature (LST) data derived from Landsat satellites were used to define heatwave exposure at a granular, neighborhood level. Cox proportional hazards models were used to analyze the association between heatwave exposure and mortality, adjusting for confounding variables. A significant association was found between heatwave exposure and increased mortality risk. For each 1°C increase in LST during a heatwave, there was a 5.2% (95% CI: 4.5%-6.0%) increase in all-cause mortality. The risk was most pronounced in neighborhoods with lower green space coverage. Satellite-derived remote sensing data provide a powerful tool for assessing heatwave-related mortality risk in urban elderly populations. These findings underscore the urgent need for urban planning and public health strategies focused on heat mitigation to protect vulnerable residents.

School-Based Mental Health Interventions: A Systematic Review of Digital CBT Programs for Teens

Omar Khan — 2025-08-30

Social isolation is a significant and modifiable risk factor for accelerated cognitive decline and dementia in aging populations. Traditional methods for detecting cognitive changes, such as clinical screenings, are often infrequent and fail to capture the subtle, early behavioral shifts that precede a formal diagnosis. This study aimed to develop and validate an artificial intelligence model designed for the early detection of cognitive decline by passively monitoring behavioral and vocal biomarkers of social isolation in older adults living independently. A 24-month, prospective longitudinal study was conducted with a cohort of 200 community-dwelling adults aged 70 and older. A suite of unobtrusive in-home sensors was used to passively collect data on movement patterns, social communication (frequency and duration of conversations), and computer/phone usage. The AI-powered system identified individuals who would later show clinically significant cognitive decline with an accuracy of 91% and a lead time of approximately 7 months before formal assessment. The model successfully distinguished between simple loneliness and the specific behavioral patterns of social withdrawal associated with cognitive impairment. AI-powered passive monitoring systems are a highly effective and ecologically valid tool for the pre-clinical detection of cognitive decline linked to social isolation.