Morrison Leanne G, Hargood Charlie, Pejovic Veljko, Geraghty Adam W A, Lloyd Scott, Goodman Natalie, Michaelides Danius T, Weston Anna, Musolesi Mirco, Weal Mark J, Yardley Lucy
Psychology, Faculty of Social, Human, and Mathematical Sciences, University of Southampton, Southampton, Hampshire, United Kingdom.
Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, Hampshire, United Kingdom.
PLoS One. 2017 Jan 3;12(1):e0169162. doi: 10.1371/journal.pone.0169162. eCollection 2017.
Push notifications offer a promising strategy for enhancing engagement with smartphone-based health interventions. Intelligent sensor-driven machine learning models may improve the timeliness of notifications by adapting delivery to a user's current context (e.g. location). This exploratory mixed-methods study examined the potential impact of timing and frequency on notification response and usage of Healthy Mind, a smartphone-based stress management intervention. 77 participants were randomised to use one of three versions of Healthy Mind that provided: intelligent notifications; daily notifications within pre-defined time frames; or occasional notifications within pre-defined time frames. Notification response and Healthy Mind usage were automatically recorded. Telephone interviews explored participants' experiences of using Healthy Mind. Participants in the intelligent and daily conditions viewed (d = .47, .44 respectively) and actioned (d = .50, .43 respectively) more notifications compared to the occasional group. Notification group had no meaningful effects on percentage of notifications viewed or usage of Healthy Mind. No meaningful differences were indicated between the intelligent and non-intelligent groups. Our findings suggest that frequent notifications may encourage greater exposure to intervention content without deterring engagement, but adaptive tailoring of notification timing does not always enhance their use. Hypotheses generated from this study require testing in future work.
ISRCTN67177737.
推送通知为增强基于智能手机的健康干预措施的参与度提供了一种很有前景的策略。智能传感器驱动的机器学习模型可以通过根据用户当前的情境（如位置）调整推送时间来提高通知的及时性。这项探索性的混合方法研究考察了时间安排和频率对基于智能手机的压力管理干预措施“健康心灵”的通知响应和使用情况的潜在影响。77名参与者被随机分配使用“健康心灵”的三个版本之一，这三个版本分别提供：智能通知；在预定义时间范围内的每日通知；或在预定义时间范围内的不定期通知。自动记录通知响应和“健康心灵”的使用情况。电话访谈探讨了参与者使用“健康心灵”的体验。与不定期通知组相比，智能通知组和每日通知组的参与者查看通知的比例（分别为d = 0.47和0.44）和采取行动的比例（分别为d = 0.50和0.43）更高。通知组对查看通知的百分比或“健康心灵”的使用情况没有显著影响。智能通知组和非智能通知组之间没有显著差异。我们的研究结果表明，频繁的通知可能会鼓励更多地接触干预内容而不会降低参与度，但通知时间的适应性调整并不总能增加其使用。本研究产生的假设需要在未来的工作中进行检验。
ISRCTN67177737。
