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BeWell24:一款智能手机“应用程序”的开发与过程评估,该程序旨在改善代谢风险增加的美国退伍军人的睡眠、久坐及活动行为。

BeWell24: development and process evaluation of a smartphone "app" to improve sleep, sedentary, and active behaviors in US Veterans with increased metabolic risk.

作者信息

Buman Matthew P, Epstein Dana R, Gutierrez Monica, Herb Christine, Hollingshead Kevin, Huberty Jennifer L, Hekler Eric B, Vega-López Sonia, Ohri-Vachaspati Punam, Hekler Andrea C, Baldwin Carol M

机构信息

School of Nutrition and Health Promotion, Arizona State University, 500 N. 3rd Street, Mail Code 9020, Phoenix, AZ, 85004-2135, USA.

Phoenix Veterans Affairs Health Care System, Phoenix, AZ, 85012, USA.

出版信息

Transl Behav Med. 2016 Sep;6(3):438-48. doi: 10.1007/s13142-015-0359-3.

Abstract

Lifestyle behaviors across the 24-h spectrum (i.e., sleep, sedentary, and active behaviors) drive metabolic risk. We describe the development and process evaluation of BeWell24, a multicomponent smartphone application (or "app") that targets behavior change in these interdependent behaviors. A community-embedded iterative design framework was used to develop the app. An 8-week multiphase optimization strategy design study was used to test the initial efficacy of the sleep, sedentary, and exercise components of the app. Process evaluation outcomes included objectively measured app usage statistics (e.g., minutes of usage, self-monitoring patterns), user experience interviews, and satisfaction ratings. Participants (N = 26) logged approximately 60 % of their sleep, sedentary, and exercise behaviors, which took 3-4 min/day to complete. Usage of the sleep and sedentary components peaked at week 2 and remained high throughout the intervention. Exercise component use was low. User experiences were mixed, and overall satisfaction was modest.

摘要

24小时范围内的生活方式行为(即睡眠、久坐和活动行为)会引发代谢风险。我们描述了BeWell24的开发和过程评估,这是一款针对这些相互关联行为中的行为改变的多组件智能手机应用程序(或“应用”)。采用了社区嵌入式迭代设计框架来开发该应用。一项为期8周的多阶段优化策略设计研究用于测试该应用的睡眠、久坐和运动组件的初始功效。过程评估结果包括客观测量的应用使用统计数据(例如,使用分钟数、自我监测模式)、用户体验访谈和满意度评分。参与者(N = 26)记录了大约60%的睡眠、久坐和运动行为,完成这些行为每天需要3 - 4分钟。睡眠和久坐组件的使用在第2周达到峰值,并在整个干预过程中保持较高水平。运动组件的使用较少。用户体验好坏参半,总体满意度一般。

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