n = 1, type 3: n = 3) or combined different types of training paradigms within their intervention (type 1 and 2: n = 2; all types: n = 2). The meta-analyses revealed significant overall effects of WS training on static steady-state balance outcomes including mediolateral (eyes open: Hedges' g = 0.82, CI: 0.43-1.21; eyes closed: g = 0.57, CI: 0.14-0.99) and anterior-posterior sway (eyes open: g = 0.55, CI: 0.01-1.10; eyes closed: g = 0.44, CI: 0.02-0.86). No effects on habitual gait speed were found in the meta-analysis ( g = -0.19, CI: -0.68 to 0.29). Two RCTs reported significant improvements for selected gait variables including single support time, and fast gait speed. One study identified effects on proactive balance (Alternate Step Test), but no effects were found for the Timed Up and Go test and the Berg Balance Scale. Two studies reported positive results on feasibility and usability. Only one study was performed in an unsupervised setting. Conclusion: This review provides evidence for a positive effect of WS training on static steady-state balance in studies with usual care controls and studies with conventional balance training controls. Specific gait parameters and proactive balance measures may also be improved by WS training, yet limited evidence is available. Heterogeneous training paradigms, small sample sizes, and short intervention durations limit the validity of our findings. Larger studies are required for estimating the true potential of WS technology. © 2017 S. Karger AG, Basel KeywordsInertial measurement unit · Force sensor · Postural balance · Gait · Biofeedback · Exergame · Systematic review Abstract Background: Wearable sensors (WS) can accurately measure body motion and provide interactive feedback for supporting motor learning. Objective: This review aims to summarize current evidence for the effectiveness of WS training for improving balance, gait and functional performance. Methods: A systematic literature search was performed in PubMed, Cochrane, Web of Science, and CINAHL. Randomized controlled trials (RCTs) using a WS exercise program were included. Study quality was examined by the PEDro scale. Metaanalyses were conducted to estimate the effects of WS balance training on the most frequently reported outcome parameters. Results: Eight RCTs were included (Parkinson n = 2, stroke n = 1, Parkinson/stroke n = 1, peripheral neuropathy n = 2, frail older adults n = 1, healthy older adults n = 1). The sample size ranged from n = 20 to 40. Three types of training paradigms were used: (1) static steady-state balance training, (2) dynamic steady-state balance training, which includes gait training, and (3) proactive balance training. RCTs either used one type of training paradigm (type 2:

Background: Decreasing performance of the sensory systems’ for balance control, including the visual, somatosensory and vestibular system, is associated with increased fall risk in older adults. A smartphone-based version of the Timed Up-and-Go (mTUG) may allow screening sensory balance impairments through mTUG subphases. The association between mTUG subphases and sensory system performance is examined. Methods: Functional mobility of forty-one community-dwelling older adults (>55 years) was measured using a validated mTUG. Duration of mTUG and its subphases ‘sit-to-walk’, ‘walking’, ‘turning’, ‘turn-to-sit’ and ‘sit-down’ were extracted. Sensory systems’ performance was quantified by validated posturography during standing (30 s) under different conditions. Visual, somatosensory and vestibular control ratios (CR) were calculated from posturography and correlated with mTUG subphases. Results: Vestibular CR correlated with mTUG total time (r = 0.54; p < 0.01), subphases ‘walking’ (r = 0.56; p < 0.01), and ‘turning’ (r = 0.43; p = 0.01). Somatosensory CR correlated with mTUG total time (r = 0.52; p = 0.01), subphases ‘walking’ (r = 0.52; p < 0.01) and ‘turning’ (r = 0.44; p < 0.01). Conclusions: Supporting the proposed approach, results indicate an association between specific mTUG subphases and sensory system performance. mTUG subphases ‘walking’ and ‘turning’ may allow screening for sensory system deterioration. This is a first step towards an objective, detailed and expeditious balance control assessment, however needing validation in a larger study.

No abstract

Over the last decades, educational programs involving age simulation suits (ASS) emerged with the ambition to further the understanding of age-related loss experiences, enhance empathy and reduce negative attitudes toward older adults in healthcare settings and in younger age groups at large. However, the impact of such “instant aging” interventions on individuals’ personal views on aging have not been studied yet. The aim of the current study is to address possible effects of ASS interventions on multiple outcomes related to views on aging, i.e., aging-related cognitions (i.e., expectations regarding social losses), awareness of age-related change (AARC) and age stereotypes. Moreover, we explore effects on broader constructs with relevance to aging, i.e., perceived obsolescence, risk perceptions, as well as desired support through technology. In a within-subjects design, N = 40 participants (M = 61.4 years, SD = 6.16) went through a series of established geriatric assessments (i.e., Timed up and Go) with and without an ASS. Views on aging constructs were assessed in standardized questionnaires before and after the ASS intervention. Changes in aging-related cognitions were observed, with more negative expectations regarding social integration and continuous development after wearing the ASS. AARC and age stereotypes did not change from pre- to post-assessment, but participants reported an increased susceptibility to age-associated impairments and stronger feelings of obsolescence. Those participants who exhibited higher difficulties in geriatric assessments while wearing the suit reported higher openness to be supported by intelligent assistive devices or robots afterwards. We conclude that ASS interventions should only be combined with education on losses and gains during the aging process to prevent negative effects on individual views on aging. On the other hand, potentials regarding technology acceptance and formation of intentions to engage in prevention and health behaviors among middle-aged to young-old adults are discussed.

No abstract

The G‑CBM is a valid and reliable tool for measuring subtle balance deficits in older high-functioning adults. The absence of ceiling effects emphasizes the use of this scale in this cohort. The G‑CBM can now be utilized in clinical practice.

Age simulation suits (ASS) are widely used to simulate sensory and physical restrictions that typically occur as people age. This review has two objectives: first, we synthesize the current research on ASS in terms of the observed psychological and physical effects associated with ASS. Second, we analyze indicators able to estimate the validity of ASS in simulating “true” ageing processes. Following the PRISMA guidelines, eight electronic databases were searched (BASE, Cinhal, Cochrane, Google Scholar, ProQuest, PsychINFO, Pubmed, and Web of Science). Qualitative and quantitative studies addressing effects of ASS interventions regarding psychological outcomes (i.e., empathy, attitudes) or physical parameters (i.e., gait, balance) were included. The Mixed Methods Appraisal Tool was applied for quality assessment. Of 1890 identified citations, we included 94 for full-text screening and finally 26 studies were examined. Publication years ranged from 2001 to 2021. Study populations were predominantly based on students in health-related disciplines. Results suggest that ASS can initiate positive effects on attitudes toward (dweighted = 0.33) and empathy for older adults (dweighted = 0.54). Physical performance was significantly reduced; however, there is only little evidence of a realistic simulation of typical ageing processes. Although positive effects of ASS are supported to some extent, more diverse study populations and high-quality controlled designs are needed. Further, validation studies examining whether the simulation indeed reflects “real” ageing are needed and should build on reference data generated by standardized geriatric assessments or adequate comparison groups of older adults.Prospero registration: 232686.