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Development of an exercise therapy referral clinical support tool for patients with osteoporosis at risk for falls.

Authors :
Bullock, Garrett S.
Duncan, Pamela
Chandler, Alison M.
Aguilar, Aylin A.
Latham, Nancy
Storer, Tom
Alexander, Neil
McDonough, Christine M.
Source :
Journal of the American Geriatrics Society. Jun2024, Vol. 72 Issue 6, p1810-1816. 7p.
Publication Year :
2024

Abstract

Background: The purpose of this study was to develop a clinical support tool for osteoporosis clinic providers to support risk assessment and referrals for evidence‐based exercise therapy programs. Methods: A sequential Delphi method was used with a multidisciplinary group of national falls experts, to provide consensus on referral to exercise therapy for patients at risk for falls. The Delphi study included a primary research team, expert panel, and clinical partners to answer the questions: (1) "What patient characteristics are needed to develop a clinical support tool?"; (2) "What are the recommended exercise referrals for patients with osteoporosis at risk for falls?" The consensus process consisted of two rounds with 8 weeks between meetings. Two qualitative researchers analyzed the data using a modified version of a matrix analysis approach. Results: The following were the most important variables to include when determining exercise therapy referrals for patients with osteoporosis: Patient history and demographics, falls history over the last year, current physical function and balance, caregiver and transportation status, socioeconomic and insurance status, and patient preference. Potential exercise therapy referrals included one‐on‐one physical therapy, group physical therapy, home health, community‐based exercise programs, and not acceptable for exercise therapy. Conclusions: Patient characteristics including patient history, physical function and balance performance, socioeconomic and insurance status, and patient preference for exercise therapy are important to inform both the medical provider and patient with osteoporosis to choose the most appropriate exercise therapy referral. Adoption of the algorithmic suggestions may have a significant impact on uptake and adherence to exercise therapy, ultimately improving patient physical function and reducing falls risk. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00028614
Volume :
72
Issue :
6
Database :
Academic Search Index
Journal :
Journal of the American Geriatrics Society
Publication Type :
Academic Journal
Accession number :
177946615
Full Text :
https://doi.org/10.1111/jgs.18796