{
  "abstract": "Objective: To determine whether machine-learning models using baseline self-report data could personalize selection between two behavioral strategies embedded within cognitive-behavioral therapy (CBT) for procrastination.\n\nMethods: Participants were 256 Polish-speaking university students aged 18–26 years (M=22.4; 73.0% women) who completed active treatment in two preregistered randomized trials (development sample, N=173; external validation sample, N=83). Participants received five weekly group CBT sessions with identical psychoeducation and cognitive restructuring but different behavioral modules: Realistic Planning and Timely Beginning (RPT) or Working Time Restriction (WTR). Separate partial least-squares regression models used 483 baseline demographic and self-report items to predict posttreatment Aitken Procrastination Inventory–Revised (API-R) scores under each strategy. Predicted between-strategy differences and Youden-index cutoffs generated recommendations: RPT, WTR, Either, or Neither. Because treatment assignment was randomized, outcomes could be compared between participants whose assigned treatment matched versus mismatched their model-derived recommendation.\n\nResults: In the development trial, matched participants had lower posttreatment API-R scores than mismatched participants (p=5.6×10−24; d=1.90) and higher likely-response rates (p=1.8×10−15; RR=4.6). In the validation trial, each treatment-specific model explained 9% of outcome variance. Matched participants again had lower API-R scores (p=.009; d=.53) and higher response rates (p=.04; RR=1.3). Differences remained at six months (p=.033; d=.40).\n\nConclusion: Together, these findings indicate that personalization of procrastination treatment can be achieved by modeling differential response to specific behavioral components rather than entire treatment packages, supporting precision intervention in procrastination and beyond. Further, explorative examination of predictors suggests that individuals may benefit from a mechanism-informed, but not strictly mechanism-deterministic, precision framework.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Devon LoParo"
    },
    {
      "name": "Magdalena Pietruch"
    },
    {
      "name": "Weronika Maria Browarczyk"
    },
    {
      "name": "Joachim Kowalski"
    },
    {
      "name": "Jarosław M. Michałowski"
    },
    {
      "name": "Marek Wypych"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ajqp9mvcyrdjc.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/7t6j9_v2",
  "fulltext_url": null,
  "id": "ajqp9mvcyrdjc",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/7t6j9_v2/",
  "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 Psychology"
    ],
    [
      "bepress",
      "Psychotherapy"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "openalex",
      "3203|Perfectionism, Procrastination, Anxiety Studies"
    ]
  ],
  "openalex": "W7204870541",
  "pdf_url": "https://osf.io/7t6j9/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ajqp9mvcyrdjc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:23Z",
      "native_id": "7t6j9",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/7t6j9_v2/"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "bepress:Clinical Psychology",
      "subfield": "psychiatry"
    }
  ],
  "title": "Optimizing Behavioral Strategy Selection during Cognitive Behavior Therapy for Procrastination using Machine Learning",
  "title_text": "Optimizing Behavioral Strategy Selection during Cognitive Behavior Therapy for Procrastination using Machine Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:23Z",
  "url": "https://research.draconitic.com/paper/ajqp9mvcyrdjc",
  "venue": "PsyArXiv",
  "versions": []
}