Division of Public Health Sciences, Fred Hutch Cancer Center, Seattle, WA, United States.
Department of Psychology, University of Washington, Seattle, WA, United States.
J Med Internet Res. 2022 Aug 18;24(8):e39208. doi: 10.2196/39208.
Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes.
In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership.
Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app.
For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app.
Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.
人们对智能手机应用干预的参与方式以及这种参与是否能预测健康结果知之甚少。
在一项比较两种用于戒烟的智能手机应用的随机试验的背景下,本研究旨在确定 6 个月期间智能手机应用登录轨迹的不同群组,以及它们与 12 个月时戒烟结果的关联,以及预测基于数据的轨迹组群成员资格的基线用户特征。
对来自大型(N=2415)随机试验的两种用于戒烟的智能手机应用(iCanQuit 和 QuitGuide)的 182 天平滑登录数据进行功能聚类,以识别不同的轨迹组。使用逻辑回归确定组群成员资格与主要结局(12 个月时 30 天点吸烟戒断的患病率)之间的关联。最后,使用逻辑回归和多项逻辑回归检查与组群成员资格相关的基线特征。分别对每个应用程序进行分析。
对于 iCanQuit,参与者被分为 3 组:“1 周使用者”(1069 人中有 610 人,占 57.06%)、“4 周使用者”(1069 人中有 303 人,占 28.34%)和“26 周使用者”(1069 人中有 156 人,占 14.59%)。对于 12 个月随访时的戒烟率,与 1 周使用者相比,4 周使用者的戒烟几率高 50%(30%比 23%;比值比[OR]1.50,95%置信区间[CI]1.05-2.14;P=.03),而 26 周使用者的戒烟几率高 397%(56%比 23%;OR 4.97,95% CI 3.31-7.52;P<.001)。对于 QuitGuide,参与者被分为 2 组:“1 周使用者”(1064 人中有 695 人,占 65.32%)和“3 周使用者”(1064 人中有 369 人,占 34.68%)。3 周使用者与 1 周使用者相比,12 个月时的戒烟几率差异较小(23%比 21%;OR 1.16,95% CI 0.84-1.62;P=.37)。不同的基线特征预测了每个应用程序的轨迹组群成员资格。
1 周、3 周和 4 周的智能手机应用戒烟使用模式可能是人们参与数字健康干预的常见方式。4 周使用者和尤其是 iCanQuit 应用的 26 周使用者戒烟的几率明显更高。为了改善研究结果,可以针对那些早期退出这些干预措施(1 周使用者)的用户制定策略,并主动为他们提供更强化的干预措施,这可能是富有成效的。