Department of Industrial Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands.
JMIR Mhealth Uhealth. 2022 Mar 31;10(3):e28801. doi: 10.2196/28801.
Although the health benefits of physical activity are well established, it remains challenging for people to adopt a more active lifestyle. Mobile health (mHealth) interventions can be effective tools to promote physical activity and reduce sedentary behavior. Promising results have been obtained by using gamification techniques as behavior change strategies, especially when they were tailored toward an individual's preferences and goals; yet, it remains unclear how goals could be personalized to effectively promote health behaviors.
In this study, we aim to evaluate the impact of personalized goal setting in the context of gamified mHealth interventions. We hypothesize that interventions suggesting health goals that are tailored based on end users' (self-reported) current and desired capabilities will be more engaging than interventions with generic goals.
The study was designed as a 2-arm randomized intervention trial. Participants were recruited among staff members of 7 governmental organizations. They participated in an 8-week digital health promotion campaign that was especially designed to promote walks, bike rides, and sports sessions. Using an mHealth app, participants could track their performance on two social leaderboards: a leaderboard displaying the individual scores of participants and a leaderboard displaying the average scores per organizational department. The mHealth app also provided a news feed that showed when other participants had scored points. Points could be collected by performing any of the 6 assigned tasks (eg, walk for at least 2000 m). The level of complexity of 3 of these 6 tasks was updated every 2 weeks by changing either the suggested task intensity or the suggested frequency of the task. The 2 intervention arms-with participants randomly assigned-consisted of a personalized treatment that tailored the complexity parameters based on participants' self-reported capabilities and goals and a control treatment where the complexity parameters were set generically based on national guidelines. Measures were collected from the mHealth app as well as from intake and posttest surveys and analyzed using hierarchical linear models.
The results indicated that engagement with the program inevitably dropped over time. However, engagement was higher for participants who had set themselves a goal in the intake survey. The impact of personalization was especially observed for frequency parameters because the personalization of sports session frequency did foster higher engagement levels, especially when participants set a goal to improve their capabilities. In addition, the personalization of suggested ride duration had a positive effect on self-perceived biking performance.
Personalization seems particularly promising for promoting the frequency of physical activity (eg, promoting the number of suggested sports sessions per week), as opposed to the intensity of the physical activity (eg, distance or duration). Replications and variations of our study setup are critical for consolidating and explaining (or refuting) these effects.
ClinicalTrials.gov NCT05264155; https://clinicaltrials.gov/ct2/show/NCT05264155.
尽管身体活动带来的健康益处已得到充分证实，但人们仍然难以采取更积极的生活方式。移动健康（mHealth）干预措施可以成为促进身体活动和减少久坐行为的有效工具。通过使用游戏化技术作为行为改变策略，已经取得了有希望的结果，尤其是当这些策略针对个人的偏好和目标进行定制时；然而，如何针对个人定制目标以有效促进健康行为仍不清楚。
在本研究中，我们旨在评估在游戏化 mHealth 干预背景下个性化目标设定的影响。我们假设，与具有通用目标的干预措施相比，根据终末用户（自我报告）当前和期望能力定制健康目标的干预措施将更具吸引力。
该研究设计为 2 臂随机干预试验。参与者是从 7 个政府组织的工作人员中招募的。他们参加了一个为期 8 周的数字健康促进活动，专门用于促进散步、骑自行车和运动。参与者可以使用 mHealth 应用程序在两个社交排行榜上跟踪自己的表现：一个显示参与者个人得分的排行榜和一个显示每个组织部门平均得分的排行榜。mHealth 应用程序还提供了一个新闻源，显示其他参与者何时获得了积分。通过完成 6 项指定任务之一（例如，步行至少 2000 米）可以获得积分。这 6 项任务中的 3 项任务的复杂性水平每两周更新一次，通过改变建议任务的强度或建议任务的频率来改变。这两个干预组是根据参与者的自我报告能力和目标随机分配的，一个是个性化治疗组，根据参与者的自我报告能力和目标定制复杂性参数，另一个是控制组，其中复杂性参数是根据国家指南通用设置的。从 mHealth 应用程序以及入组和测试后调查中收集了措施，并使用分层线性模型进行了分析。
结果表明，随着时间的推移，参与者对该计划的参与度不可避免地下降。然而，在入组调查中为自己设定目标的参与者的参与度更高。个性化的影响尤其体现在运动频率参数上，因为个性化的运动频率确实促进了更高的参与度，尤其是当参与者设定目标以提高自己的能力时。此外，个性化建议的骑行时间对自我感知的骑行表现有积极影响。
个性化似乎特别有利于促进身体活动的频率（例如，每周建议的运动次数），而不是身体活动的强度（例如，距离或持续时间）。我们研究方案的复制和变化对于巩固和解释（或反驳）这些效果至关重要。
ClinicalTrials.gov NCT05264155；https://clinicaltrials.gov/ct2/show/NCT05264155。
