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Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75667, first published .
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How Well Do Older Adult Fitness Technologies Match User Needs and Preferences? Scoping Review of 2014-2024 Literature

How Well Do Older Adult Fitness Technologies Match User Needs and Preferences? Scoping Review of 2014-2024 Literature

Journals

  1. Om Sudhakar Powar , Harsh Bharat Pol , Suyash Dinesh Dabholkar , Purva Anant Manglekar , Siddharth Amol Gurav , Nurain Anis Sayyad . Nutrifit-The Fitness App. Vidhyayana 2026;11(6) View
  2. Choudhry A, Mshvelidze J, Mullen S, Kletenik D, Adler R. Motivations, Barriers, and the Potential of AI in Physical Activity Apps for US Older Adults: Interview Study. JMIR Aging 2026;9:e96114 View
  3. Li M, Shu J, Chang F, Peng T, Chen X, Wu Y, Tao T. Role-Contingent Visual Decision Support for Age-Friendly Furniture in Conversational Recommendation Interfaces: Appraisal Pathways to Trust and Decision Confidence. International Journal of Human–Computer Interaction 2026:1 View
  4. Tacca C, Gomer R, Thompson I, Vazquez Galvez A, Freeman C, Warner M, Bincalar A, Ng A, schraefel m. Design Framework for Older Adult Health and Fitness Technologies: A Proof of Concept Case Study (Preprint). JMIR Formative Research 2026 View