@Article{信息:doi 10.2196 / /移动医疗。4730，作者=“Goyal, Shivani和Morita, Plinio P和Picton, Peter和Seto, Emily和Zbib, Ahmad和Cafazzo, Joseph A”，标题=“采用以消费者为中心的移动健康应用评估和预防心脏病:<30天研究”，期刊=“JMIR移动健康uHealth”，年=“2016”，月=“3”，日=“24”，卷=“4”，数=“1”，页=“e32”，关键词=“健康行为;生活方式;心血管疾病;预防;减少风险;移动应用;移动电话;背景:生活方式行为的改变可以降低心血管疾病的风险，这是世界范围内死亡的主要原因之一，可降低高达80{\%}。我们假设一个动态风险评估和行为改变工具作为一个移动应用程序交付，由一个著名的非营利组织主办，将促进社区成员的吸收。 We also predicted that the uptake would be influenced by incentives offered for downloading the mobile app. Objective: The primary objective of our study was to evaluate the engagement levels of participants using the novel risk management app. The secondary aim was to assess the effect of incentives on the overall uptake and usage behaviors. Methods: We publicly launched the app through the iTunes App Store and collected usage data over 5 months. Aggregate information included population-level data on download rates, use, risk factors, and user demographics. We used descriptive statistics to identify usage patterns, t tests, and analysis of variance to compare group means. Correlation and regression analyses determined the relationship between usage and demographic variables. Results: We captured detailed mobile usage data from 69,952 users over a 5-month period, of whom 23,727 (33.92{\%}) were registered during a 1-month AIR MILES promotion. Of those who completed the risk assessment, 73.92{\%} (42,380/57,330) were female, and 59.38{\%} (34,042/57,330) were <30 years old. While the older demographic had significantly lower uptake than the younger demographic, with only 8.97{\%} of users aged ≥51 years old downloading the app, the older demographic completed more challenges than their younger counterparts (F8, 52,422 = 55.10, P<.001). In terms of engagement levels, 84.94{\%} (44,537/52,431) of users completed 1--14 challenges over a 30-day period, and 10.03{\%} (5,259/52,431) of users completed >22 challenges. On average, users in the incentives group completed slightly more challenges during the first 30 days of the intervention (mean 7.9, SD 0.13) than those in the nonincentives group (mean 6.1, SD 0.06, t28870=--12.293, P<.001, d=0.12, 95{\%} CI --2.02 to --1.47). The regression analysis suggested that sex, age group, ethnicity, having 5 of the risk factors (all but alcohol), incentives, and the number of family histories were predictors of the number of challenges completed by a user (F14, 56,538 = 86.644, P<.001, adjusted R2 = .021). Conclusion: While the younger population downloaded the app the most, the older population demonstrated greater sustained engagement. Behavior change apps have the potential to reach a targeted population previously thought to be uninterested in or unable to use mobile apps. The development of such apps should assume that older adults will in fact engage if the behavior change elements are suitably designed, integrated into daily routines, and tailored. Incentives may be the stepping-stone that is needed to guide the general population toward preventative tools and promote sustained behavior change. ", issn="2291-5222", doi="10.2196/mhealth.4730", url="http://mhealth.www.mybigtv.com/2016/1/e32/", url="https://doi.org/10.2196/mhealth.4730", url="http://www.ncbi.nlm.nih.gov/pubmed/27012937" }