School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
VTT Technical Research Centre of Finland Ltd, Espoo, Finland.
J Med Internet Res. 2022 Feb 24;24(2):e31530. doi: 10.2196/31530.
Digital health interventions may offer a scalable way to prevent type 2 diabetes (T2D) with minimal burden on health care systems by providing early support for healthy behaviors among adults at increased risk for T2D. However, ensuring continued engagement with digital solutions is a challenge impacting the expected effectiveness.
We aimed to investigate the longitudinal usage patterns of a digital healthy habit formation intervention, BitHabit, and the associations with changes in T2D risk factors.
This is a secondary analysis of the StopDia (Stop Diabetes) study, an unblinded parallel 1-year randomized controlled trial evaluating the effectiveness of the BitHabit app alone or together with face-to-face group coaching in comparison with routine care in Finland in 2017-2019 among community-dwelling adults (aged 18 to 74 years) at an increased risk of T2D. We used longitudinal data on usage from 1926 participants randomized to the digital intervention arms. Latent class growth models were applied to identify user engagement trajectories with the app during the study. Predictors for trajectory membership were examined with multinomial logistic regression models. Analysis of covariance was used to investigate the association between trajectories and 12-month changes in T2D risk factors.
More than half (1022/1926, 53.1%) of the participants continued to use the app throughout the 12-month intervention. The following 4 user engagement trajectories were identified: terminated usage (904/1926, 46.9%), weekly usage (731/1926, 38.0%), twice weekly usage (208/1926, 10.8%), and daily usage (83/1926, 4.3%). Active app use during the first month, higher net promoter score after the first 1 to 2 months of use, older age, and better quality of diet at baseline increased the odds of belonging to the continued usage trajectories. Compared with other trajectories, daily usage was associated with a higher increase in diet quality and a more pronounced decrease in BMI and waist circumference at 12 months.
Distinct long-term usage trajectories of the BitHabit app were identified, and individual predictors for belonging to different trajectory groups were found. These findings highlight the need for being able to identify individuals likely to disengage from interventions early on, and could be used to inform the development of future adaptive interventions.
ClinicalTrials.gov NCT03156478; https://clinicaltrials.gov/ct2/show/NCT03156478.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12889-019-6574-y.
数字健康干预措施通过为有患 2 型糖尿病（T2D）风险的成年人提供早期健康行为支持，可能为预防 T2D 提供一种可扩展的方法，同时对医疗系统的负担最小。然而，确保对数字解决方案的持续参与是一个挑战，影响着预期的效果。
我们旨在研究数字健康习惯养成干预措施 BitHabit 的纵向使用模式，并探讨其与 T2D 风险因素变化的关联。
这是 StopDia（停止糖尿病）研究的二次分析，这是一项在 2017-2019 年期间在芬兰进行的、为期 1 年的、非盲平行、随机对照试验，评估了 BitHabit 应用程序单独或与面对面小组辅导联合应用与常规护理相比，在有 T2D 风险的社区居住成年人（年龄在 18 至 74 岁之间）中的有效性。我们使用了来自随机分配到数字干预组的 1926 名参与者的纵向使用数据。应用潜在类别增长模型来识别研究期间应用程序的用户参与轨迹。使用多项逻辑回归模型来检查轨迹成员的预测因素。使用协方差分析来研究轨迹与 12 个月 T2D 风险因素变化之间的关联。
超过一半（1022/1926，53.1%）的参与者在 12 个月的干预期间继续使用该应用程序。确定了以下 4 种用户参与轨迹：终止使用（904/1926，46.9%）、每周使用（731/1926，38.0%）、每两周使用（208/1926，10.8%）和每日使用（83/1926，4.3%）。第一个月的积极使用、使用后 1 至 2 个月的净推荐值较高、年龄较大以及基线时饮食质量较好，增加了属于持续使用轨迹的可能性。与其他轨迹相比，每日使用与饮食质量的更高改善以及 BMI 和腰围在 12 个月时的更明显下降相关。
确定了 BitHabit 应用程序的不同长期使用轨迹，并发现了属于不同轨迹组的个体预测因素。这些发现强调了需要能够尽早识别可能退出干预的个体，并可以用来为未来的适应性干预提供信息。
ClinicalTrials.gov NCT03156478；https://clinicaltrials.gov/ct2/show/NCT03156478。
国际注册报告标识符（IRRID）：RR2-10.1186/s12889-019-6574-y。
