Predicting effects of a digital stress intervention for patients with depressive symptoms: Development and validation of meta-analytic prognostic models using individual participant data.

IF 4.5 1区心理学 Q1 PSYCHOLOGY, CLINICAL Journal of consulting and clinical psychology Pub Date : 2024-04-01 Epub Date: 2023-12-21 DOI:10.1037/ccp0000852

Mathias Harrer, Harald Baumeister, Pim Cuijpers, Elena Heber, Dirk Lehr, Ronald C Kessler, David Daniel Ebert

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Abstract

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).

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预测数字压力干预对抑郁症状患者的影响：利用个体参与者数据开发和验证元分析预后模型。

目的：数字压力干预作为一种 "间接 "治疗抑郁症的方法可能会有所帮助，但对于哪些人来说这是一种可行的选择仍不清楚。在这项研究中，我们建立了一些模型，预测数字压力干预在 6 个月随访时对抑郁症状的个体化益处：方法：从 k = 6 项随机试验（数字压力干预与等待名单）中收集了 N = 1,525 名抑郁症状患者（流行病学研究中心抑郁量表，CES-D ≥ 16）的数据。使用多层次最小绝对收缩和选择算子以及提升算法建立了预后模型，并使用引导偏差校正和内部外部交叉验证进行了验证。随后，根据临床得出的治疗分配切点，估算了有治疗建议和无治疗建议人群的预期效果：结果：结果表明，R²=21.0%-23.4%，在模型乐观度校正后（R²=17.0%-19.6%），结果表明模型乐观度略有下降。通过对抑郁症状进行中期评估，预测结果大大提高（乐观校正后的 R2 = 32.6%-35.6%）。以最小重要差异 d = -0.24 作为分配切点，约有 84.6%-93.3% 的患者可通过此类干预获得帮助，而其余 6.7%-15.4% 的患者的临床获益可忽略不计（δ^ = -0.02 至 -0.19）。以可靠的变化作为切点，较小的子集（39.3%-46.2%）具有可观的预期收益（δ^ = -0.68），可获得治疗建议：结论：应用于个体参与者数据的元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方法的不同益处。虽然大多数患者似乎都能从中获益，但所开发的模型可以作为一种筛选工具，帮助确定哪些患者需要接受更深入的抑郁症治疗。(PsycInfo Database Record (c) 2023 APA, 版权所有）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of consulting and clinical psychology PSYCHOLOGY, CLINICAL-

CiteScore

9.00

自引率

3.40%

发文量

期刊介绍： The Journal of Consulting and Clinical Psychology® (JCCP) publishes original contributions on the following topics: the development, validity, and use of techniques of diagnosis and treatment of disordered behaviorstudies of a variety of populations that have clinical interest, including but not limited to medical patients, ethnic minorities, persons with serious mental illness, and community samplesstudies that have a cross-cultural or demographic focus and are of interest for treating behavior disordersstudies of personality and of its assessment and development where these have a clear bearing on problems of clinical dysfunction and treatmentstudies of gender, ethnicity, or sexual orientation that have a clear bearing on diagnosis, assessment, and treatmentstudies of psychosocial aspects of health behaviors. Studies that focus on populations that fall anywhere within the lifespan are considered. JCCP welcomes submissions on treatment and prevention in all areas of clinical and clinical–health psychology and especially on topics that appeal to a broad clinical–scientist and practitioner audience. JCCP encourages the submission of theory–based interventions, studies that investigate mechanisms of change, and studies of the effectiveness of treatments in real-world settings. JCCP recommends that authors of clinical trials pre-register their studies with an appropriate clinical trial registry (e.g., ClinicalTrials.gov, ClinicalTrialsRegister.eu) though both registered and unregistered trials will continue to be considered at this time.