{
  "abstract": "Intensive longitudinal interventions (ILIs), which deliver repeated interventions in people’s daily lives while intensively tracking their psychological states, offer unique opportunities for ecologically valid, real-time, and personalized support. However, there is a striking mismatch between the richness of the data these studies generate and the simplicity of the methods used to evaluate them: evaluations still rely largely on pre-post mean comparisons that collapse dozens or even hundreds of observations per person into a few summary statistics. To resolve this mismatch, we introduce a comprehensive effect size framework based on Bayesian multilevel time-varying autoregressive (TV-AR) models within the generalized additive mixed model (GAMM) approach. The framework organizes effect sizes along four dimensions: subject level (population-average, individual-specific, and between-group), parameter type (mean level, autoregressive effect, and variance), temporal scale (integrated and time-specific), and study phase (active intervention and post-intervention decay). These metrics move researchers beyond whether an intervention works to when effects emerge, plateau, or fade, how the intervention reshapes the dynamics and stability of a process, and for whom it is effective. We demonstrate the framework with data from a mindfulness-based ILI targeting negative affect, in which an intervention and a control group were assessed before and during the intervention, and provide openly available annotated R code and a tutorial. Overall, the framework provides a practical and flexible toolkit for evaluating ILIs, supporting the broader shift toward a more dynamic, process-oriented, and personalized intervention science.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Xiaohui Luo"
    },
    {
      "name": "Yue Liu"
    },
    {
      "name": "Hongyun Liu"
    },
    {
      "name": "Laura Bringmann"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ft9gq1pn6068y.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/tm6k8_v1",
  "fulltext_url": null,
  "id": "ft9gq1pn6068y",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/tm6k8_v1/",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Clinical Psychology"
    ],
    [
      "bepress",
      "Health Psychology"
    ],
    [
      "bepress",
      "Intervention Research"
    ],
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Quantitative Psychology"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "openalex",
      "3205|Mental Health Research Topics"
    ]
  ],
  "openalex": "W7213425350",
  "pdf_url": "https://osf.io/tm6k8/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ft9gq1pn6068y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:33Z",
      "native_id": "tm6k8",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/tm6k8_v1/"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "bepress:Clinical Psychology",
      "subfield": "psychiatry"
    }
  ],
  "title": "Beyond Pre-Post Comparisons: A Comprehensive Effect Size Framework for Intensive Longitudinal Interventions via Time-Varying Modeling",
  "title_text": "Beyond Pre-Post Comparisons: A Comprehensive Effect Size Framework for Intensive Longitudinal Interventions via Time-Varying Modeling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:33Z",
  "url": "https://research.draconitic.com/paper/ft9gq1pn6068y",
  "venue": "PsyArXiv",
  "versions": []
}