TY - JOUR

T1 - Predicting Effects of a Digital Stress Intervention for Patients With Depressive Symptoms

T2 - Development and Validation of Meta-Analytic Prognostic Models Using Individual Participant Data

AU - Harrer, Mathias

AU - Baumeister, Harald

AU - Cuijpers, Pim

AU - Heber, Elena

AU - Lehr, Dirk

AU - Kessler, Ronald C.

AU - Ebert, David Daniel

N1 - Funding Information: Mathias Harrer is supported by a fellowship of the Bavarian Research Institute for Digital Transformation, an institute of the Bavarian Academy of Sciences and Humanities (Grant Z.5-M7426.6.5/3/21). Publisher Copyright: © 2023 American Psychological Association

PY - 2024/4/1

Y1 - 2024/4/1

N2 - 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 (δˆ = −0.02 to −0.19). Using reliable change as cut point, a smaller subset (39.3%–46.2%) with substantial expected benefits (δˆ = −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.

AB - 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 (δˆ = −0.02 to −0.19). Using reliable change as cut point, a smaller subset (39.3%–46.2%) with substantial expected benefits (δˆ = −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.

KW - depression

KW - heterogeneity of treatment effects

KW - machine learning

KW - prognosis

KW - stress

KW - Psychology

UR - http://www.scopus.com/inward/record.url?scp=85183392771&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/d4feb648-ddc3-3bb9-afbb-f05b92821a8d/

U2 - 10.1037/ccp0000852

DO - 10.1037/ccp0000852

M3 - Journal articles

C2 - 38127574

AN - SCOPUS:85183392771

VL - 92

SP - 226

EP - 235

JO - Journal of Consulting and Clinical Psychology

JF - Journal of Consulting and Clinical Psychology

SN - 0022-006X

IS - 4

ER -