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Evaluating the Feasibility and Acceptability of an Artificial-Intelligence-Enabled and Speech-Based Distress Screening Mobile App for Adolescents and Young Adults Diagnosed with Cancer: A Study Protocol.
Zhang, Anao; Kamat, Aarti; Acquati, Chiara; Aratow, Michael; Kim, Johnny S; DuVall, Adam S; Walling, Emily.
Afiliación
  • Zhang A; School of Social Work, University of Michigan, Ann Arbor, MI 48109, USA.
  • Kamat A; Adolescent and Young Adult Oncology Program, University of Michigan Health, Ann Arbor, MI 48109, USA.
  • Acquati C; Adolescent and Young Adult Oncology Program, University of Michigan Health, Ann Arbor, MI 48109, USA.
  • Aratow M; Department of Pediatrics, University of Michigan Health, Ann Arbor, MI 48109, USA.
  • Kim JS; Graduate College of Social Work, University of Houston, Houston, TX 77204, USA.
  • DuVall AS; Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Walling E; Ellipsis Health, San Francisco, CA 94102, USA.
Cancers (Basel) ; 14(4)2022 Feb 12.
Article en En | MEDLINE | ID: mdl-35205663
Adolescents and young adults (AYAs) diagnosed with cancer are an age-defined population, with studies reporting up to 45% of the population experiencing psychological distress. Although it is essential to screen and monitor for psychological distress throughout AYAs' cancer journeys, many cancer centers fail to effectively implement distress screening protocols largely due to busy clinical workflow and survey fatigue. Recent advances in mobile technology and speech science have enabled flexible and engaging methods to monitor psychological distress. However, patient-centered research focusing on these methods' feasibility and acceptability remains lacking. Therefore, in this project, we aim to evaluate the feasibility and acceptability of an artificial intelligence (AI)-enabled and speech-based mobile application to monitor psychological distress among AYAs diagnosed with cancer. We use a single-arm prospective cohort design with a stratified sampling strategy. We aim to recruit 60 AYAs diagnosed with cancer and to monitor their psychological distress using an AI-enabled speech-based distress monitoring tool over a 6 month period. The primary feasibility endpoint of this study is defined by the number of participants completing four out of six monthly distress assessments, and the acceptability endpoint is defined both quantitatively using the acceptability of intervention measure and qualitatively using semi-structured interviews.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline / Qualitative_research / Screening_studies Idioma: En Revista: Cancers (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline / Qualitative_research / Screening_studies Idioma: En Revista: Cancers (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza