{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Saskia Scholten"
    },
    {
      "name": "Verena Eunike Hofmann"
    },
    {
      "name": "Felicitas Kininger"
    },
    {
      "name": "Julia Glombiewski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cpkswk82jd19z.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/edscr_v4",
  "fulltext_url": null,
  "id": "cpkswk82jd19z",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/edscr_v4/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Clinical Decision Making"
    ],
    [
      "bepress",
      "Clinical Psychology"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://osf.io/edscr/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cpkswk82jd19z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:34Z",
      "native_id": "edscr",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/edscr_v4/"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "bepress:Clinical Psychology",
      "subfield": "psychiatry"
    }
  ],
  "title": "From Data to Dialogue: Using Person-Specific Networks to Personalize Cognitive-Behavioral Treatment for Chronic Pain",
  "title_text": "From Data to Dialogue: Using Person-Specific Networks to Personalize Cognitive-Behavioral Treatment for Chronic Pain",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:34Z",
  "url": "https://research.draconitic.com/paper/cpkswk82jd19z",
  "venue": "PsyArXiv",
  "versions": []
}