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Background When older adults fall below the thresholds of functional geriatric assessment (FGA), they may already be at risk of mobility impairment. A reduction in (jumping) power could be an indication of functional decline, one of the main risk factors for falls. Objective This paper explores whether six-month delta (∆) values of muscle power can predict 24-month follow-up FGA in older adults. Methods This observational study of independent, healthy, high-performing community-dwelling adults aged 70 + years involved FGA (mobility, balance, and endurance tests) at baseline (t0), after 6 months (t1), and after 24 months (t2); maximum jumping power (max JP) was determined at t0 and t1. A predictive linear model was developed in which the percentage change of Δmax JP0,1 was transferred to all FGA (t0) values. The results were compared with measured FGA values at t2 via sensitivity and specificity in terms of the clinically meaningful change (CMC) or the minimal detectable change (MDC). Results In 176 individuals (60% female, mean age 75.3 years) the mean percentage (SD) between predicted and measured FGA ranged between 0.4 (51.3) and 18.11 (51.9). Sensitivity to identify the CMC or MDC of predicted FGA tests at t2 ranged between 17.6% (Timed up and go) and 75.0% (5-times-chair-rise) in a test-to-test comparison and increased to 97.6% considering clinically conspicuousness on global FGA. Conclusion The potential of jumping power to predict single tests of FGA was low regarding sensitivity and specificity of CMC (or MDC). 6 months Δmax JP seem to be suitable for predicting physical function, if the measured and predicted tests were not compared at the test level, but globally, in the target group in the long term.

Health-enabling technologies (HET) have high potential in rehabilitation to support patients performing their home exercises. The modeling of human movements as well as the modelling of quality criteria of an exercise performance remains challenging when implementing HETs. A combination of data-driven approaches and knowledge-based methods may deliver new insights. This requires structured quality assessments of concrete exercise performances from a therapists' point of view. Therefore, a structured, easy to use questionnaire to assess home exercise performances is developed and implemented. The questionnaire consists of eight items in three categories: (1-4) overall assessment of quality and quantity, (5-7) need for correction, and (8) correction hints. The collected data will be the basis for mathematical modeling of home exercise performance assessment as foundation for the development of patient supporting HETs.

Health-enabling technologies (HET) have high potential in rehabilitation to support patients performing their home exercises. The modeling of human movements as well as the modelling of quality criteria of an exercise performance remains challenging when implementing HETs. A combination of data-driven approaches and knowledge-based methods may deliver new insights. This requires structured quality assessments of concrete exercise performances from a therapists’ point of view. Therefore, a structured, easy to use questionnaire to assess home exercise performances is developed and implemented. The questionnaire consists of eight items in three categories: (1–4) overall assessment of quality and quantity, (5–7) need for correction, and (8) correction hints. The collected data will be the basis for mathematical modeling of home exercise performance assessment as foundation for the development of patient supporting HETs.

Designing health-enabling technologies (HETs) to support individualized physiotherapeutic exercises requires comprehensive knowledge of bio-psycho-social factors to be considered. Therefore, this review identified factors for individualization of therapeutic exercises in patients with musculoskeletal shoulder disorders in peer-reviewed articles searched in MEDLINE. The final full-text analysis included 16 of 335 search results and extracted nineteen main categories of individualization factors. The most frequently identified main categories include progression of exercises, exercise framework, and assessment. An iterative approach with constant reassessments represents the key principle for the process of individualization. Categories that are difficult to standardize were rarely mentioned, but should also be considered. The identified factors can improve the design-process of HETs by sensitizing developers, enable further formal modelling, and support communication between developers, physiotherapists, and patients.

Designing health-enabling technologies (HETs) to support individualized physiotherapeutic exercises requires comprehensive knowledge of bio-psycho-social factors to be considered. Therefore, this review identified factors for individualization of therapeutic exercises in patients with musculoskeletal shoulder disorders in peer-reviewed articles searched in MEDLINE. The final full-text analysis included 16 of 335 search results and extracted nineteen main categories of individualization factors. The most frequently identified main categories include progression of exercises, exercise framework, and assessment. An iterative approach with constant reassessments represents the key principle for the process of individualization. Categories that are difficult to standardize were rarely mentioned, but should also be considered. The identified factors can improve the design-process of HETs by sensitizing developers, enable further formal modelling, and support communication between developers, physiotherapists, and patients.