A novel digital biomarker of sarcopenia in frail elderly: New combination of gait parameters under dual-task walking

Abstract

BackgroundFrailty caused by deterioration in multiple physiological systems has led to a significant increase in adverse events such as falls, disability, and death in frail older people. Similar to frailty, sarcopenia, defined as loss of skeletal muscle mass and strength, is tightly related to mobility disorders, falls, and fractures. With population aging, co-occurrences of frailty and sarcopenia are increasingly common in the elderly, which are more deleterious for the health and independence of older adults. But the high similarity and overlap between the frailty and sarcopenia increase the difficulty of early recognition of frailty with sarcopenia. The purpose of this study is to use detailed gait assessment to determine the more convenient and sensitive digital biomarker of sarcopenia in the frail population.MethodsNinety-five frail elderly people (age = 86 ± 7 years old, BMI, and body mass index = 23.21 ± 3.40 kg/m2) were screened out by the evaluation of Fried criteria. Then, 41 participants (46%) were identified with sarcopenia, and 51 participants (54%) were identified without sarcopenia. Using a validated wearable platform, participants’ gait performance was evaluated under single-task and dual-task (DT). Participants walked back and forth on the 7-m-long trail for 2 min at a habitual speed. Gait parameters of interest include cadence, gait cycle duration, step duration, gait speed, variability of gait speed, stride length, turn duration, and steps in turn.ResultsOur results showed that compared with the frail elderly without sarcopenia, the gait performance of the sarcopenic group in single-task and dual-task walking was worse. Overall, the parameters with high performance were the gait speed (DT) (OR 0.914; 95% CI 0.868–0.962) and turn duration (DT) (OR 7.907; 95% CI 2.401–26.039) under dual-task conditions, and the AUC in distinguishing between frail older adults with and without sarcopenia was 0.688 and 0.736, respectively. Turn duration in dual-task testing had larger observed effect than gait speed to identify sarcopenia in the frail population, this result remained significant even after controlling for potential confounds. When gait speed (DT) and turn duration (DT) were combined in the model, AUC increased from 0.688 to 0.763.ConclusionThis study shows that gait speed and turn duration under dual-task are good predictors of sarcopenia in frail elderly, and turn duration (DT) has a better predictive ability. The gait speed (DT) combined with turn duration (DT) is a potential gait digital Biomarker of sarcopenia in the frail elderly. Dual-task gait assessment and detailed gait indexes provide important value for identification of sarcopenia in frail elderly people.

Effects of Multi-Task Mode on Cognition and Lower Limb Function in Frail Older Adults: A Systematic Search and Review

“…All included studies reported results for lower limb functional indicators, such as gait speed, turn duration, step length and step frequency; and two of the studies [24,29] presented gait indicator results by reporting dual-task costs (DTCs). The results of four studies [23,25,27,28] showed that DT testing significantly worsens gait performance in frail older adults; and both the studies by Piche et al and Zheng et al showed that the DTC of gait was significantly higher in the frail population [24,29]. Cognitive function outcomes were reported in two studies; and the indicators chosen were call accuracy and counting accuracy.…”

“…All studies [23][24][25][26][27][28][29] reporting MT testing in the frail elderly population used the DT mode; with seven studies using the cognitive-motor DT mode, including a walking task while performing a counting task, walking while talking or naming an animal, etc. ; and one study [29] additionally selecting the moto-motor DT mode (walking while holding a glass of water).…”

“…The seven DT test studies contained a total of 489 subjects, 105 males and 289 females (one study did not report the gender of the subjects [23]), with female subjects dominating the studies. Six studies involved participants over the age of 70, two of whom were over the age of 80 [27,28], and one involved participants under the age of 70 [29].…”

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

“…Kang et al [ 28 ] found sarcopenic elderly men walked significantly slower daily than non-sarcopenic peers. Dual-task assessments demonstrate greater slowing in individuals with sarcopenia versus single-task walking [ 29 ]. Greater dual-task costs are associated with increased fall risks and mobility impairment.…”

“…Kang et al [ 28 ] showed daily gait speed derived from wearables accurately reflected sarcopenia diagnosis. Machine learning models using combinations of gait features derived from wearables may enable sarcopenia detection and monitoring [ 29 ]. Compared to infrequent clinical gait tests, continuous monitoring could facilitate early identification of mobility decline.…”