Association of Sit-to-Stand Capacity and Free-Living Performance Using Thigh-Worn Accelerometers among 60- to 90-Yr-Old Adults

Löppönen, Antti; Delecluse, Christophe; Suorsa, Kristin; Karavirta, Laura; Leskinen, Tuija; Meulemans, Lien; Portegijs, Erja; Finni, Taija; Rantanen, Taina; Stenholm, Sari; Rantalainen, Timo; Roie, Evelien Van

doi:10.1249/mss.0000000000003178

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…Whilst for accurate activity intensity identification, individual physical and demographic characteristics should be included to allow for the development of tailored algorithms, which is not practical. As acceptable compromises, advances in the currently available commercial activity monitors would be to either utilise age-specific algorithms that incorporate the entire physical behaviour spectrum [2], or alternatively, universal algorithms to characterise and track specific activities such as sit-to-stand transitions [16,17].…”

Section: Introductionmentioning

confidence: 99%

Concurrent Validity of Four Activity Monitors in Older Adults

Wullems,

Verschueren,

Degens

et al. 2024

Sensors

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Sedentary behaviour (SB) and physical activity (PA) have been shown to be independent modulators of healthy ageing. We thus investigated the impact of activity monitor placement on the accuracy of detecting SB and PA in older adults, as well as a novel random forest algorithm trained on data from older persons. Four monitor types (ActiGraph wGT3X-BT, ActivPAL3c VT, GENEActiv Original, and DynaPort MM+) were simultaneously worn on five anatomical sites during ten different activities by a sample of twenty older adults (70.0 (12.0) years; 10 women). The results indicated that collecting metabolic equivalent (MET) data for 60 s provided the most representative results, minimising variability. In addition, thigh-worn monitors, including ActivPAL, Random Forest, and Sedentary Sphere—Thigh, exhibited superior performance in classifying SB, with balanced accuracies ≥ 94.2%. Other monitors, such as ActiGraph, DynaPort MM+, and GENEActiv Sedentary Sphere—Wrist, demonstrated lower performance. ActivPAL and GENEActiv Random Forest outperformed other monitors in participant-specific balanced accuracies for SB classification. Only thigh-worn monitors achieved acceptable overall balanced accuracies (≥80.0%) for SB, standing, and medium-to-vigorous PA classifications. In conclusion, it is advisable to position accelerometers on the thigh, collect MET data for ≥60 s, and ideally utilise population-specific trained algorithms.

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Section: Introductionmentioning

confidence: 99%

Concurrent Validity of Four Activity Monitors in Older Adults

Wullems,

Verschueren,

Degens

et al. 2024

Sensors

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Free-Living Sit-to-Stand Characteristics as Predictors of Lower Extremity Functional Decline among Older Adults

LÖPPÖNEN,

KARAVIRTA,

FINNI

et al. 2024

Medicine &Amp; Science in Sports &Amp; Exercise

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Purpose Habitual strength and power-demanding activities of daily life may support the maintenance of adequate lower-extremity functioning with ageing, but this has been sparingly explored. Hence, we examined whether the characteristics of free-living sit-to-stand (STS) transitions predict a decline in lower-extremity functioning over a 4-year follow-up. Methods 340 community-dwelling older adults (60% women, age 75, 80 or 85 years) participated in this prospective cohort study. At baseline, a thigh-worn accelerometer was used continuously (3-7 days) to monitor the number and intensity of free-living STS transitions. A decline in lower-extremity functioning was defined as a drop of ≥2 points in the Short Physical Performance Battery (SPPB) from baseline to follow-up. Maximal isometric knee-extension strength was measured in the laboratory. Results 85 participants (75% women) declined in SPPB over 4 years. After adjusting for age, sex, and baseline SPPB points, higher free-living peak STS angular velocity (odds ratio [OR] = 0.70; 95% confidence interval [CI] = 0.52-0.92, per 20 deg/s increase) protected against a future decline. When adjusting the model for maximal isometric knee-extension strength, the statistical significance was attenuated (OR = 0.72; 95% CI = 0.54-0.96, per 20 deg/s increase). Conclusions Performing STS transitions at higher velocities in the free-living environment can prevent a future decline in lower-extremity function. This indicates that changes in daily STS behavior may be useful in the early identification of functional loss. Free-living peak STS angular velocity may be a factor underlying the longitudinal association of lower-extremity strength and performance.

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The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

González-Castro,

Leirós-Rodríguez,

Prada-García

et al. 2024

J Med Internet Res

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Background Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. Objective The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. Methods A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. Results We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. Conclusions The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. Trial Registration PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv

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Association of Sit-to-Stand Capacity and Free-Living Performance Using Thigh-Worn Accelerometers among 60- to 90-Yr-Old Adults

Cited by 4 publications

References 42 publications

Concurrent Validity of Four Activity Monitors in Older Adults

Concurrent Validity of Four Activity Monitors in Older Adults

Free-Living Sit-to-Stand Characteristics as Predictors of Lower Extremity Functional Decline among Older Adults

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

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