Funkhouser Carter J, Weiner Lauren S, Crowley Ryann N, Davis Jon F, Koegler Frank H, Allen Nicholas B, Auerbach Randy P
Department of Psychiatry, Columbia University, New York, NY, USA; Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA.
Ksana Health, Inc., Eugene, OR, USA.
Behav Res Ther. 2025 Jul 1;193:104815. doi: 10.1016/j.brat.2025.104815.
Digital behavioral activation (BA) is scalable, accessible, and efficacious for depression. However, some individuals do not improve during digital BA, and identifying non-responders early is critical for facilitating adaptive intervention approaches (e.g., stepped care). To explore whether passive sensing data might serve as early predictors of symptom change, we tested whether early changes in passively sensed behavioral targets of BA predicted depression symptom changes during app-based BA. Young adults (N = 47) with elevated depressive symptoms completed a 12-week trial of an app-based BA intervention, Vira. The Vira app provided BA psychoeducation, assessed self-reported daily mood, and used smartphone sensors to passively assess time spent at home (i.e., homestay), walking, stationary time, time in bed, bedtime, and waketime each day. We quantified early behavioral changes by fitting a multilevel growth model for each behavior over the first 2 weeks of the intervention. Models included a random slope reflecting each participant's average day-to-day change in that behavior. We extracted these slope estimates and tested whether they predicted depressive symptom (PHQ-8) change from pre-to post-intervention. We hypothesized that individuals with greater early changes in intervention-targeted behaviors would experience greater reductions in depressive symptoms. As hypothesized, individuals with greater early decreases in passively sensed homestay (i.e., reduced behavioral withdrawal) experienced a greater reduction in depressive symptoms by the end of treatment (b = 0.94, p = .025). Early changes in other behaviors did not significantly predict depressive symptom change (ps > .158). Passively monitoring early changes in homestay during app-based BA may support the early identification of individuals at risk of symptom persistence, thus providing earlier opportunities to adjust treatment.
数字行为激活（BA）对于抑郁症具有可扩展性、可及性且效果显著。然而，一些个体在数字BA过程中并无改善，尽早识别无反应者对于促进适应性干预方法（如阶梯式护理）至关重要。为了探究被动感知数据是否可作为症状变化的早期预测指标，我们测试了在基于应用程序的BA过程中，BA被动感知行为目标的早期变化是否能预测抑郁症状的变化。患有抑郁症状的年轻成年人（N = 47）完成了一项为期12周的基于应用程序的BA干预试验，即Vira。Vira应用程序提供BA心理教育，评估自我报告的每日情绪，并使用智能手机传感器被动评估每天在家的时间（即居家时间）、步行时间、静止时间、卧床时间、就寝时间和起床时间。我们通过为干预的前两周内的每种行为拟合一个多层次增长模型来量化早期行为变化。模型包括一个反映每个参与者在该行为上平均每日变化的随机斜率。我们提取了这些斜率估计值，并测试它们是否能预测干预前后抑郁症状（PHQ - 8）的变化。我们假设在干预目标行为上早期变化较大的个体在抑郁症状上的减轻幅度会更大。正如假设的那样，在被动感知的居家时间上早期减少幅度较大（即行为退缩减少）的个体在治疗结束时抑郁症状的减轻幅度更大（b = 0.94，p = 0. 025）。其他行为的早期变化并未显著预测抑郁症状的变化（p值> 0.158）。在基于应用程序的BA过程中被动监测居家时间的早期变化可能有助于早期识别有症状持续风险的个体，从而提供更早的机会来调整治疗。
