Background: Frailty affects over 50% of older adults in long-term care (LTC), and early detection is critical due to its potential reversibility. Wearable sensors enable continuous monitoring of gait and physical activity, and machine learning has shown promise in detecting frailty among community-dwelling older adults. However, its applicability in LTC remains underexplored. Furthermore, dynamic gait outcomes (eg, gait stability and symmetry) may offer more sensitive frailty indicators than traditional measures like gait speed, yet their potential remains largely untapped. Objective: This study aimed to evaluate whether frailty in LTC facilities could be effectively identified using machine learning models trained on gait and daily physical activity data derived from a single accelerometer. Methods: This study is a cross-sectional secondary analysis of baseline data from a 2-arm cluster randomized controlled trial. Of the 164 individuals initially enrolled, 51 participants (age: mean 85.0, SD 9.0 years; female: n=24, 47.1%) met the inclusion criteria of completing all assessments required for this study and were included in the final analysis. Frailty status was assessed using the fatigue, resistance, ambulation, incontinence, loss of weight, nutritional approach, and help with dressing (FRAIL-NH) scale. Participants completed a 5-meter walking task while wearing a 3D accelerometer. Following this task, the accelerometer was used to record daily physical activity over approximately 1 week. A total of 34 dynamic and spatial-temporal gait outcomes, 3 physical activity variables, and 6 demographic characteristics were extracted. Five conventional machine learning models were trained to classify frailty status using a leave-one-out cross-validation approach. Model performance was evaluated based on accuracy and the area under the receiver operating characteristic curve. To enhance model interpretability, explainable artificial intelligence techniques were used to identify the most influential predictive outcomes. Results: The extreme gradient boosting model demonstrated the optimal performance with an accuracy of 86.3% and an area under the curve of 0.92. Explainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, and a lower gait symmetry score. Conclusions: Our findings suggest that dynamic gait outcomes may serve as more sensitive indicators of frailty than spatial-temporal gait outcomes (eg, gait speed) in LTC settings, offering valuable insights for enhancing frailty detection and management.

Background: Frailty is a dynamic geriatric syndrome associated with adverse health outcomes, yet its progression can be mitigated through targeted interventions. Objective: This study aimed to investigate predictors of frailty transitions in Chinese older adults, focusing on physical activity (PA) and physical function. Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS), we examined transitions between frailty states (robust, prefrail, and frail) from 2011 (baseline) to 2013 (follow-up) among 1014 participants aged 65 years and older. The following outcomes were assessed, including frailty using the physical frailty phenotype, PA using a modified International Physical Activity Questionnaire, and physical function using the Short Physical Performance Battery (SPPB) and handgrip strength. Ordinal logistic regression models were used to examine the relationship between PA, physical function, and frailty transitions. Results: Results showed that higher PA levels and better physical function reduced the likelihood of worsening frailty or increased the probability of transitioning to robustness. Key findings from the subgroup include: among robust individuals, greater handgrip strength predicted maintained robustness (average marginal effects [AME]=1.12%; P =.02); in prefrail individuals, higher vigorous PA (AME=21.76%; P =.04) and handgrip strength (AME=0.64%; P =.003) increased transitions to robustness; for frail individuals, increased low-intensity PA (AME =22.48%; P =.04) and higher SPPB walking subscores (AME=27.73%; P =.02) promoted improvement to nonfrailty. Conclusions: These findings highlight the importance of tailored interventions based on baseline frailty status. Promoting PA and improving physical function, particularly muscle strength and mobility function, may help delay or reverse frailty progression.

Background: Plantar sensation and ankle proprioception occur in a stage-like variance across the life span. However, changes in adolescents, young adults, middle-aged adults, and older adults remain unclear. The aim of this study was to investigate the differences of plantar sensation and ankle proprioception in adolescents to older adults. Methods: A total of 212 participants were recruited in the study and were divided into four groups, including adolescents (n = 46), young adults (n = 55), middle-aged adults (n = 47), and older adults (n = 54). Plantar tactile sensitivity/tactile acuity/vibration threshold and ankle movement threshold/joint position sense/force sense were assessed in all groups. The Kruskal–Wallis H test was used to analyze the differences in Semmes–Weinstein monofilaments between different age groups in different plantar positions. One-way analysis of variance was used to determine differences in foot vibration threshold, two-point discrimination, and ankle proprioception between different age groups. Results: Significant differences were found in the Semmes–Weinstein monofilament test (p < .001), the two-point discrimination test (p < .05), and the vibration threshold test (p < .05) in the six tested plantar positions among adolescents, young adults, middle-aged adults, and older adults. For ankle proprioception, significant differences were found in movement thresholds in ankle plantar flexion (p = .01), ankle dorsiflexion (p < .001), ankle inversion (p < .001), and ankle eversion (p < .001), as well as relative absolute errors in the ankle force senses of ankle plantar flexion (p = .02) and ankle dorsiflexion (p = .02) across the four age groups. Conclusion: Plantar sensation and ankle proprioception were sensitive in adolescents and young adults than in middle-aged adults and older adults.

This study investigated lower extremity biomechanics when running on different surfaces among runners with different footstrike patterns. Thirty rearfoot strikers (RFSs) and non-rearfoot strikers (nRFSs) ran at 3.3 m/s on a specially designed indoor track covered with three surfaces: artificial grass, synthetic rubber, and concrete. A motion capture system with ten cameras combined a force plate was used to collect marker trajectory and ground reaction force (GRF) during the running stance phase. A two-way analysis of variance with statistical parametric mapping was employed to evaluate differences in the biomechanics of the lower extremities between footstrike patterns and among running surfaces. The nRFSs exhibited significantly greater ankle inversion angles and increased inversion and internal rotation moments at midstance compared to the RFSs. Conversely, the RFSs demonstrated significantly greater knee abduction moments in late stance. Running on stiffer surfaces was associated with greater vertical GRF in late stance, as well as increased knee and hip extension moments during midstance. Furthermore, running on stiffer surfaces was associated with increased knee abduction moments, hip abduction moments, and hip external rotation moments during late stance. These findings suggested that nRFSs endure more ankle loads, while RFSs face increased knee loads. However, regardless of the footstrike pattern, runners may benefit from selecting softer surfaces to reduce the risk of injury.

Background and Objectives: Falls are a major public health concern among older adults, often leading to injuries, impaired mobility, and loss of independence. Dual-task walking, where a secondary task is performed while walking, simulates real-life challenges and is linked to fall risk. This study aimed to investigate how dual-tasking affects stepping strategies, inter-joint coordination, and coordination variability during walking in older adults with and without a history of falls. Research Design and Methods: Twenty community-dwelling older adults (10 fallers, 10 non-fallers), aged 65 and older, completed a 2-min walking test under three conditions: single-task (ST) walking, motoric dual-task (MDT) walking (holding a glass of water), and cognitive dual-task (CDT) walking (serial subtractions). Gait data were collected using inertial measurement units. Stepping strategies were quantified by the changes in cadence and stride length, while inter-joint coordination was analyzed using vector coding. Two-way repeated measures ANOVA was used to assess task and group effects on variables. Results: Task-specific adaptations were observed: MDT prompted greater stride length adjustments, while CDT led to more balanced cadence and stride length adjustments (F = 8.346, p = .010, η2p = .317). Fallers exhibited more anti-phase coordination in hip flexion–knee flexion than non-fallers during dual-task conditions (p ≤ .042). In CDT walking, fallers showed a lower frequency of distal phase in hip flexion–knee flexion and a higher frequency of anti-phase in hip flexion–ankle dorsiflexion compared to ST (p ≤ .044). Coordination variability decreased during MDT for hip flexion–ankle dorsiflexion in both groups compared to ST (p ≤ .027). Discussion and Implications: This study provided better understanding on the differences of stepping strategies and phase-specific coordination patterns between older adult fallers and non-fallers, particularly under dual-task walking conditions. The conservative motor control strategies in fallers suggest a prioritization of stability over adaptability, potentially increasing fall risk during complex walking tasks.

Context: Somatosensory dysfunction in people with chronic ankle instability (CAI) has been reported to influence not only peripheral performance but also sensory reweighting of central nervous system. Sensory constraints, such as visual or somatosensory perturbations originating from sway surroundings or sway surface, continuously occur during body movements. Dynamically reweighting somatosensory, visual and vestibular cues are crucial for postural stability. However, due to contradictory evidence, it is unclear how sensory constraints affect sensorimotor functions and sensory reweighting ability in individuals with CAI. Objective: To examine the impact of sensory constraints on sensorimotor functions, sensory reweighting ability, and the sensory strategy selection of people with CAI during postural control. Data Sources: PubMed, Web of Science, EMBASE, Cochrane, SPORTDiscus, and Medline. Study Selection: Two authors independently screened article titles, abstracts, and full texts to select peer-reviewed studies exploring sensory constraints on sensorimotor functions in people with CAI and healthy controls. Study Design: Meta-analyses of descriptive epidemiological study. Level of Evidence: Level 4. Data Extraction: Center-of-pressure, center-of-gravity, and time-to-boundary measures that represent postural stability, and muscle activity amplitude and activation onset time were extracted. Results: A total of 43 articles were included. Compared with healthy controls, the CAI group exhibited static (Hedges’s g = 0.53) and dynamic (Hedges’s g = 1.05) deficits with visual constraint, as well as increased medial gastrocnemius activity (Hedges’s g = 1.02) during unilateral stance with visual constraint. The CAI group showed decreased sensory reweighting during unilateral stance with visual constraint (Hedges’s g = 0.26) and bilateral stance with visual and somatosensory constraints (Hedges’s g = 0.45). Conclusion: Visual constraint could alter postural control and muscle activation patterns in people with CAI. Sensory reweighting ability of those with CAI may fluctuate based on task and sensory constraints, and visual dominance strategy may help people with CAI modulate posture when visual information is reliable.