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Effectiveness of Mobile Health Applications for 5% Body Weight Reduction in Obese and Overweight Adults
BACKGROUND: World Health Organization reports that over 1.9 billion adults are obese. Studies have found that people who reduce their body weight by 5% experience considerable health benefits. Currently, mobile health (mHealth) applications (apps) show effectiveness in body weight reduction. The pre...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society for the Study of Obesity
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8735823/ https://www.ncbi.nlm.nih.gov/pubmed/34853189 http://dx.doi.org/10.7570/jomes21014 |
Sumario: | BACKGROUND: World Health Organization reports that over 1.9 billion adults are obese. Studies have found that people who reduce their body weight by 5% experience considerable health benefits. Currently, mobile health (mHealth) applications (apps) show effectiveness in body weight reduction. The present study aimed to explore the effectiveness of a popular mHealth app in 5% body weight reduction and to identify factors that affect 5% body weight reduction in obese adults. We investigated the time it took users to achieve 5% body weight reduction according to usage characteristics and factors influencing this period of time. METHODS: This study was a secondary data analysis using data from 23,682 commercial mHealth app users. For analysis, logistic regressions, Kaplan-Meier estimators, log-rank tests, and Cox regressions were used. RESULTS: Variables in user characteristics including age (odds ratio [OR], 0.976; P<0.001), male (OR, 1.226; P<0.001), initial body mass index (OR, 1.009; P<0.001), frequency of data entry for body weight (OR, 1.004; P<0.001), frequency of exercise (OR, 1.002; P<0.001), and dinner intake (OR, 1.004; P<0.001) made significant contributions in predicting 5% weight reduction in the study cohort. Users who were obese and who more frequently entered their body weight, exercise, and dietary intake data reduced 5% body weight much sooner than other users. Data entry regarding initial body weight (exponentiation of the B coefficient [Exp(B)], 1.002; P<0.001), frequency in body weight entry (Exp(B), 1,001; P<0.001), dinner intake (Exp(B), 1.003; P<0.001), and evening snack intake (Exp(B), 1.001; P<0.001) significantly contributed to predicting the time needed to achieve a 5% body weight reduction in users. CONCLUSION: For 5% body weight reduction, mHealth apps are promising tools. Users who frequently monitor their health-related behaviors can expect a 5% reduction in body weight in a short period of time. |
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