OBJECTIVE Digital stress interventions could be helpful as an "indirect" treatment for depression, but it remains unclear for whom this is a viable option. In this study, we developed models predicting individualized benefits of a digital stress intervention on depressive symptoms at 6-month follow-up. METHOD Data of N = 1,525 patients with depressive symptoms (Center for Epidemiological Studies' Depression Scale, CES-D ≥ 16) from k = 6 randomized trials (digital stress intervention vs. waitlist) were collected. Prognostic models were developed using multilevel least absolute shrinkage and selection operator and boosting algorithms, and were validated using bootstrap bias correction and internal-external cross-validation. Subsequently, expected effects among those with and without a treatment recommendation were estimated based on clinically derived treatment assignment cut points. RESULTS Performances ranged from R² = 21.0%-23.4%, decreasing only slightly after model optimism correction (R² = 17.0%-19.6%). Predictions were greatly improved by including an interim assessment of depressive symptoms (optimism-corrected R2 = 32.6%-35.6%). Using a minimally important difference of d = -0.24 as assignment cut point, approximately 84.6%-93.3% of patients are helped by this type of intervention, while the remaining 6.7%-15.4% would experience clinically negligible benefits (δ^ = -0.02 to -0.19). Using reliable change as cut point, a smaller subset (39.3%-46.2%) with substantial expected benefits (δ^ = -0.68) receives a treatment recommendation. CONCLUSIONS Meta-analytic prognostic models applied to individual participant data can be used to predict differential benefits of a digital stress intervention as an indirect treatment for depression. While most patients seem to benefit, the developed models could be helpful as a screening tool to identify those for whom a more intensive depression treatment is needed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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预测数字压力干预对抑郁症状患者的效果：使用个体参与者数据开发和验证荟萃分析预后模型。


目的 数字压力干预可能有助于作为抑郁症的“间接”治疗方法，但目前尚不清楚这对谁来说是一个可行的选择。在这项研究中，我们开发了模型，预测数字压力干预在 6 个月的随访中对抑郁症状的个体化益处。方法 收集来自 k = 6 项随机试验（数字压力干预与候补名单）的 N = 1,525 名有抑郁症状的患者（流行病学研究中心抑郁量表，CES-D ≥ 16）的数据。使用多级最小绝对收缩和选择算子以及增强算法开发预后模型，并使用引导偏差校正和内部外部交叉验证进行验证。随后，根据临床得出的治疗分配切点来估计有或没有治疗建议的患者的预期效果。结果 性能范围为 R² = 21.0%-23.4%，模型乐观校正后仅略有下降（R² = 17.0%-19.6%）。通过纳入抑郁症状的中期评估，预测得到了极大的改善（乐观校正 R2 = 32.6%-35.6%）。使用 d = -0.24 的最小重要差异作为分配切点，大约 84.6%-93.3% 的患者通过此类干预得到帮助，而其余 6.7%-15.4% 的患者将获得临床上可忽略不计的益处 (δ^ = -0.02至-0.19）。使用可靠的变化作为切入点，具有显着预期效益 (δ^ = -0.68) 的较小子集 (39.3%-46.2%) 会收到治疗建议。结论应用于个体参与者数据的荟萃分析预后模型可用于预测数字压力干预作为抑郁症间接治疗的不同益处。 虽然大多数患者似乎受益，但开发的模型可能有助于作为筛选工具来识别那些需要更强化抑郁症治疗的患者。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。