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Reducing muscle weakness in nursing home residents: a study quantifying acceptance and feasibility of a formal training algorithm, and reliability of endpoint measures

Year: 2026
Type: article
Study design: Clinical Trial
Abstract: BACKGROUND: Sarcopenia is a growing problem, especially in nursing care. It is therefore mandatory to integrate measures such as resistance training to maintain muscle strength into nursing care. AIMS: The aim of this study was to investigate the acceptance and feasibility of a novel training algorithm in a nursing home environment. Furthermore, the reliability of measurements for the diagnosis of sarcopenia was tested in the nursing home setting. METHODS: Twenty-eight nursing home residents took part in the study, which encompassed two pre- and two post-examinations and a four-week training intervention. The training sessions were documented with regard to acceptance and feasibility as well as training motivation and intensity. RESULTS: A combined acceptance and feasibility of at least 54% was shown, quantifying adherence of the residents to the training. The operational feasibility was 91% and the exercise performance feasibility of the residents was between 88% and 94.2%. All intraclass correlation coefficients showed at least a good reliability (all ≥ 0.84). Training motivation was higher when participants trained in a group (p = 0.007), but training intensity was greater when they trained individually (p < 0.001). DISCUSSION: The main influencing factors for acceptance and feasibility were illness in general and a lack of motivation by the residents. Against the assumption, training was also possible during the weekends. CONCLUSIONS: In conclusion, the study shows that our proposed training algorithm is acceptable and feasible in a nursing home environment. In future, the efficacy of the training needs to be shown. TRIAL REGISTRATION NUMBER: DRKS00030211; Date of registration: 2022-09-12.
Language: English
Subfield: Physiology
Field: Medicine
Open Access status: hybrid
Estimated APC paid: $4,490
OpenAlex ID: W7124539443
FWCI:
Cites: 47
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