Noom Inc, Seoul, Republic of Korea.
Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
JMIR Mhealth Uhealth. 2020 Jan 21;8(1):e13273. doi: 10.2196/13273.
Weight loss interventions using mobile phone apps have recently shown promising results.
This study aimed to analyze the short-term weight loss effect of a mobile coaching intervention when it is integrated with a local public health care center and a regional hospital's antiobesity clinic as a multidisciplinary model.
A total of 150 overweight or obese adults signed up to complete an 8-week antiobesity intervention program with human coaching through a mobile platform. Paired t tests and multiple linear regression analysis were used to identify the intervention factors related to weight change.
Among the 150 participants enrolled in this study, 112 completed the 8-week weight loss intervention. Weight (baseline: mean 77.5 kg, SD 12.9; after intervention: mean 74.8 kg, SD 12.6; mean difference -2.73 kg), body mass index, waist circumference, fat mass (baseline: mean 28.3 kg, SD 6.6; after intervention: mean 25.7 kg, SD 6.3; mean difference -2.65 kg), and fat percentage all showed a statistically significant decrease, and metabolic equivalent of task (MET) showed a statistically significant increase after intervention. In multiple linear regression analysis, age (beta=.07; P=.06), △MET (beta=-.0009; P=.10), number of articles read (beta=-.01; P=.04), and frequency of weight records (beta=-.05; P=.10; R=0.4843) were identified as significant factors of weight change. Moreover, age (beta=.06; P=.03), sex (female; beta=1.16; P=.08), △MET (beta=-.0009; P<.001), and number of articles read (beta=-.02; P<.001; R=0.3728) were identified as significant variables of fat mass change.
The multidisciplinary approach, combining a mobile health (mHealth) care app by health care providers, was effective for short-term weight loss. Additional studies are needed to evaluate the efficacy of mHealth care apps in obesity treatment.
使用手机应用程序进行减肥干预最近显示出有希望的结果。
本研究旨在分析将移动教练干预与当地公共医疗保健中心和地区医院的减肥诊所整合为多学科模式时的短期减肥效果。
共有 150 名超重或肥胖成年人报名参加了一项为期 8 周的减肥干预计划，通过移动平台接受人类教练的指导。采用配对 t 检验和多元线性回归分析来确定与体重变化相关的干预因素。
在这项研究中，共有 150 名参与者报名参加，其中 112 名完成了 8 周的减肥干预。体重（基线：均值 77.5 千克，标准差 12.9；干预后：均值 74.8 千克，标准差 12.6；平均差值 -2.73 千克）、体重指数、腰围、脂肪量（基线：均值 28.3 千克，标准差 6.6；干预后：均值 25.7 千克，标准差 6.3；平均差值 -2.65 千克）和脂肪百分比均显示出统计学上的显著降低，而代谢当量（MET）在干预后显示出统计学上的显著增加。在多元线性回归分析中，年龄（β=0.07；P=0.06）、△MET（β=-0.0009；P=0.10）、阅读文章数量（β=-0.01；P=0.04）和体重记录频率（β=-0.05；P=0.10；R=0.4843）被确定为体重变化的显著因素。此外，年龄（β=0.06；P=0.03）、性别（女性；β=1.16；P=0.08）、△MET（β=-0.0009；P<.001）和阅读文章数量（β=-0.02；P<.001；R=0.3728）被确定为脂肪量变化的显著变量。
医疗保健提供者结合移动健康（mHealth）应用程序的多学科方法对短期减肥有效。需要进一步研究来评估 mHealth 应用程序在肥胖治疗中的疗效。
