Background: The sit-to-stand (STS) test has been deployed as surrogate measures of strength or physical performance in sarcopenia diagnosis. This study examines the relationship of two common STS variants – Five Times Sit-to-Stand Test (5TSTS) and 30 s Chair Stand Test (30CST) – with grip strength, muscle mass and functional measures, and their impact on sarcopenia prevalence in community-dwelling older adults.Methods: This is a cross-sectional analysis of 887 community-dwelling adults aged ≥50 years. Participants completed a battery of physical fitness tests - 5TSTS, 30CST, grip strength, gait speed, Timed-Up-and-Go (TUG) for dynamic balance and six-minute walk test (6MWT) for cardiorespiratory endurance. Muscle mass was measured using multi-frequency segmental bioelectrical impedance analysis (BIA). We performed correlation analysis between STS performance and other fitness measures and muscle mass, followed by multiple linear regression for the independent determinants of STS performance.Results: Mean participant age was 67.3±7 years, with female predominance (72.9%). STS tests exhibited weak correlations with grip strength (30CST,r= 0.290; 5TSTS,r= − 0.242; bothp< 0.01), and stronger correlations with gait speed (30CST,r= 0.517; 5TSTS,r= − 0.533; both p< 0.01), endurance (30CST,r= 0.558; 5TSTS,r= − 0.531; bothp< 0.01) and dynamic balance (30CST,r= − 0.501; 5TSTS,r= 0.646; both p< 0.01). Muscle mass correlated with grip strength but not STS. In multiple regression analysis, all fitness measures were independently associated with 30CST performance. Performance in both STS tests remained independent of muscle mass. There was no significant difference in prevalence of possible sarcopenia diagnosis using grip strength or STS (30CST, 25.0%; 5TSTS, 22.1%; grip strength, 22.3%;p= 0.276). When both measures are used, prevalence is significantly higher (42.0%;p= 0.276). Prevalence of confirmed sarcopenia with inclusion of muscle mass was significantly lower using STS compared with grip strength (30CST, 4.6%; 5TSTS, 4.1% vs. grip strength, 7.1%;p< 0.05).Conclusion: In the sarcopenia construct, STS tests better represents muscle physical performance rather than muscle strength. Different subsets of population with possible sarcopenia are identified depending on the test used. The lack of association of STS performance with muscle mass results in a lower prevalence of confirmed sarcopenia compared with grip strength, but may better reflect changes in muscle quality.

Background and Purpose— We aim to compare demographics and functional outcomes of patients with stroke in a variety of vascular territories who underwent inpatient rehabilitation. Such comparative data are important in functional prognostication, rehabilitation, and healthcare planning, but literature is scarce and isolated. Methods— Using data collected prospectively over a 9-year period, we studied 2213 individuals who sustained first-ever ischemic strokes and were admitted to an inpatient stroke rehabilitation program. Strokes were divided into anterior cerebral artery, middle cerebral artery (MCA), posterior cerebral artery, brain stem, cerebellar, small-vessel strokes, and strokes occurring in more than one vascular territory. The main functional outcome measure was the Functional Independence Measure (FIM). Repeated-measures analysis of covariance with post hoc analyses was used to compare functional outcomes of the stroke groups. Results— The most common stroke groups were MCA stroke (50.8%) and small-vessel stroke (12.8%). After adjustments for age, gender, risk factors, and admission year, the stroke groups can be arranged from most to least severe disability on admission: strokes in more than one vascular territory, MCA, anterior cerebral artery, posterior cerebral artery, brain stem, cerebellar, and small-vessel strokes. The sequence was similar on discharge, except cerebellar strokes had the least disability rather than small-vessel strokes. Hemispheric (more than one vascular territory, MCA, anterior cerebral artery, posterior cerebral artery) strokes collectively have significantly lower admission and discharge total and cognitive FIM scores compared with the other stroke groups. MCA stroke had the lowest FIM efficiency and cerebellar stroke the highest. Regardless, patients with stroke made significant ( P <0.001) and approximately equal ( P =0.535) functional gains in all groups. Higher admission motor and cognitive FIM scores, longer rehabilitation stay, younger patients, lower number of medical complications, and a year of admission after 2000 were associated with higher discharge total FIM scores on multiple regression analysis. Conclusions— Patients with stroke made significant functional gains and should be offered rehabilitation regardless of stroke vascular territory. The initial functional status at admission, rather than the stroke subgroup, better predicts discharge functional outcomes postrehabilitation.

A well-established connection exists between increased gait variability and greater fall likelihood in Parkinson’s disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhythmic auditory cueing continues to receive attention as a promising gait therapy for PD, its widespread delivery remains bottlenecked. The present paper describes a smartphone-based mobile application (“SmartMOVE”) to address both needs. The accuracy of smartphone-based gait analysis (utilizing the smartphone’s built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact–based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously. Four outcome measures of gait and gait variability were calculated. Mixed-factorial analysis of variance revealed several instances in which between-group differences (e.g., increased gait variability in PD patients relative to healthy controls) yielded medium-to-large effect sizes (eta-squared values), and cueing-mediated changes (e.g., decreased gait variability when PD patients walked with auditory cues) yielded small-to-medium effect sizes—while at the same time, device-related measurement error yielded small-to-negligible effect sizes. These findings highlight specific opportunities for smartphone-based gait analysis to serve as an alternative to conventional gait analysis methods (e.g., footswitch systems or sensor-embedded walkways), particularly when those methods are cost-prohibitive, cumbersome, or inconvenient.