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Characterizing observed and effective behavioral engagement with smartphone cognitive behavioral therapy for body dysmorphic disorder: A methods roadmap and use case.
Weingarden, Hilary; Garriga Calleja, Roger; Greenberg, Jennifer L; Snorrason, Ivar; Matic, Aleksandar; Quist, Rachel; Harrison, Oliver; Hoeppner, Susanne S; Wilhelm, Sabine.
Afiliação
  • Weingarden H; Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
  • Garriga Calleja R; Koa Health, London, UK.
  • Greenberg JL; Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
  • Snorrason I; Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
  • Matic A; Koa Health, London, UK.
  • Quist R; Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
  • Harrison O; Koa Health, London, UK.
  • Hoeppner SS; Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
  • Wilhelm S; Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
Internet Interv ; 32: 100615, 2023 Apr.
Article em En | MEDLINE | ID: mdl-36969390
ABSTRACT
Smartphone psychotherapies are growing in popularity, yet little is understood about (1) how people prefer to engage with psychotherapy apps, or (2) which engagement patterns constitute effective engagement. The present study uses secondary data from a 12-week randomized waitlist-controlled trial of smartphone-delivered cognitive behavioral therapy (CBT) for body dysmorphic disorder (BDD) (N = 77) to address these aims. Additionally, using the present study as a use-case, we seek to provide a roadmap for how researchers may improve upon methodological limitations of existing smartphone psychotherapy engagement research. We measured behavioral engagement via 19 objective variables derived from phone analytics data, which we reduced via factor analysis into two factors 1) use volume and frequency, and 2) session duration. Cluster analysis based on engagement factors yielded three engager types, which mapped onto "deep" users, "samplers," and "light" users. The clusters did not differ significantly in improvement in BDD severity across treatment, although deep users improved more than light users at a marginally significant level. Results suggest that varying patterns of preferred engagement may be efficacious. Moreover, the study's methods provide an example of how researchers can measure and study behavioral engagement comprehensively and objectively. Trial Registration ClinicalTrials.gov Identifier NCT04034693.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Prognostic_studies Idioma: En Revista: Internet Interv Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Prognostic_studies Idioma: En Revista: Internet Interv Ano de publicação: 2023 Tipo de documento: Article