{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T18:52:04Z","timestamp":1792349524067,"version":"4.4.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T00:00:00Z","timestamp":1792281600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"National Library of Medicine, National Institutes of Health","award":["R01LM013038"],"award-info":[{"award-number":["R01LM013038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Ovarian cancer (OvCa) patients and caregivers face complex, evolving information needs, yet digital health platforms often lack tailoring. This study developed and validated computational personas to support a tailored recommender system (Health E-Librarian with Personalized Recommendations [HELPeR]) and address the cold-start problem, in which limited information about new users reduces the system\u2019s ability to generate relevant recommendations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We used a mixed-methods design integrating 4 data sources: (1) online OvCa forum posts (n\u2009=\u2009457; 60 users), (2) semi-structured interviews with patients and caregivers (n\u2009=\u200920), (3) clinical case notes from a caregiver support program, and (4) expert input from gynecologic oncology nurses. Forum data were manually annotated and analyzed using k-modes clustering to identify user subgroups. Qualitative data were analyzed using thematic analysis, and findings were integrated to construct and refine personas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Five distinct user clusters were identified from forum data. Integration of multi-source data yielded 15 personas (9 patients, 6 caregivers) representing trajectories from diagnosis to end-of-life care. Patient personas captured needs related to symptom management, treatment decision-making, financial concerns, and psychosocial support, while caregiver personas emphasized communication, emotional burden, self-care, and care coordination. Personas reflected dynamic, context-dependent needs and were validated for clinical realism through expert review.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Persona-based modeling provides an interpretable framework for tailored recommendation and approximates digital twin approaches, helping mitigate cold-start limitations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Multi-source persona development is a feasible strategy for enabling tailored digital health interventions and improving tailored information delivery in oncology care.<\/jats:p>\n                  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