From Data to Dialogue: Using Person-Specific Networks to Personalize Cognitive-Behavioral Treatment for Chronic Pain
Abstract
Objective: Person-specific network models are increasingly proposed for identifying optimal treatment targets, but how they function in clinical decision-making remains largely unknown. This proof-of-concept study integrated ecological momentary assessment (EMA), person-specific networks, and modular evidence-based interventions in the treatment of chronic pain and examined feasibility, preliminary effects, and acceptability. Method: Twelve patients completed treatment with one of nine therapists. An intensive EMA phase (six prompts daily for three weeks) informed network-based module recommendations, which were compared with independent clinical judgment. Subsequent phases were monitored three times weekly and analyzed in a single-case experimental design with randomized baselines. Patients and therapists were interviewed after treatment. Results: Participants provided a mean of 298 observations; seven of 12 met the 80% compliance criterion in all phases. Network-based recommendations largely diverged from clinical judgment. Disability decreased on average (b = -0.45, p = .03), but individual effects were mixed, with no significant baseline-corrected Tau and two participants deteriorating. Patients and therapists primarily valued self-monitoring and structured, data-informed reflection, which increased awareness of everyday patterns and structured the therapeutic dialogue. Conclusions: The clinical value of data-informed personalization may lie less in algorithmic precision than in facilitating patient-centered, reflective clinical reasoning.
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BibTeX
@misc{scholten2026from,
title = {{From Data to Dialogue: Using Person-Specific Networks to Personalize Cognitive-Behavioral Treatment for Chronic Pain}},
author = {Saskia Scholten and Verena Eunike Hofmann and Felicitas Kininger and Julia Glombiewski},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/edscr_v4},
url = {https://osf.io/preprints/psyarxiv/edscr_v4/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - From Data to Dialogue: Using Person-Specific Networks to Personalize Cognitive-Behavioral Treatment for Chronic Pain
AU - Saskia Scholten
AU - Verena Eunike Hofmann
AU - Felicitas Kininger
AU - Julia Glombiewski
PY - 2026
DA - 2026/10/29
PB - PsyArXiv
DO - 10.31234/osf.io/edscr_v4
UR - https://osf.io/preprints/psyarxiv/edscr_v4/
AB - Objective: Person-specific network models are increasingly proposed for identifying optimal treatment targets, but how they function in clinical decision-making remains largely unknown. This proof-of-concept study integrated ecological momentary assessment (EMA), person-specific networks, and modular evidence-based interventions in the treatment of chronic pain and examined feasibility, preliminary effects, and acceptability. Method: Twelve patients completed treatment with one of nine therapists. An intensive EMA phase (six prompts daily for three weeks) informed network-based module recommendations, which were compared with independent clinical judgment. Subsequent phases were monitored three times weekly and analyzed in a single-case experimental design with randomized baselines. Patients and therapists were interviewed after treatment. Results: Participants provided a mean of 298 observations; seven of 12 met the 80% compliance criterion in all phases. Network-based recommendations largely diverged from clinical judgment. Disability decreased on average (b = -0.45, p = .03), but individual effects were mixed, with no significant baseline-corrected Tau and two participants deteriorating. Patients and therapists primarily valued self-monitoring and structured, data-informed reflection, which increased awareness of everyday patterns and structured the therapeutic dialogue. Conclusions: The clinical value of data-informed personalization may lie less in algorithmic precision than in facilitating patient-centered, reflective clinical reasoning.
ER -