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Objective: Freezing of Gait (FOG) is prevalent in people with Parkinson's disease (PD) and severely disrupts mobility. Detecting the exact boundaries of FOG episodes may facilitate new technologies in "breaking" FOG in real-time. This study investigates the performance of automatic device-based FOG detection., Methods: Eight machine-learning classifiers (including Neural Networks, Ensemble methods, and Support Vector Machines) were developed using (i) accelerometer and (ii) combined accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed mobility tasks designed to elicit FOG. Two clinicians independently annotated exact FOG episodes using synchronized video and a flowchart algorithm based on international guidelines. Device-detected FOG episodes were compared to annotated episodes using 10-fold cross-validation and Interclass Correlation Coefficients (ICC) for agreement., Results: Development used 50,962 windows of data and annotated activities (>10 hours). Strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity, and ICC1,1 = 0.97 for total FOG duration). Device performance varied by method, complexity, and cost matrix. The Neural Network using 67 accelerometer features achieved high sensitivity to FOG (89% sensitivity, 81% specificity, and ICC1,1 = 0.83) and stability (validation loss 5%)., Conclusion: The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The strong clinician agreement indicates room for improvement in future device-based FOG detection., Significance: This study may enhance PD care by reducing reliance on visual FOG inspection, demonstrating that high sensitivity in automatic FOG detection is achievable.

Objectives: Digital gait biomarkers collected from body-worn devices can remotely and continuously collect movement types, quantity, and quality in real life. This study assessed whether digital gait biomarkers from a wrist-worn device could identify people with frailty in a large sample of middle-aged and older adults., Design: Cross-sectional study., Setting and Participants: A total of 5822 middle-aged (43-64 years) and 4344 older adults (65-81 years) who participated in the UK Biobank study., Measures: Frailty was assessed using a modified Fried's frailty assessment and was defined as having ≥3 of the 5 frailty criteria (weakness, low activity levels, slowness, exhaustion, and weight loss). Fourteen digital gait biomarkers were extracted from accelerometry data collected from wrist-worn sensors worn continuously by participants for up to 7 days., Results: A total of 238 (4.1%) of the middle-aged group and 196 (4.5%) of the older group were categorized as frail. Multivariable logistic regression analysis revealed that less daily walking (as assessed by step counts), slower maximum walking speed, and increased step time variability best-identified people with frailty in the middle-aged group [area under the curve (95% CI): 0.70 (0.66-0.73)]. Less daily walking, slower maximum walking speed, increased step time variability, and a lower proportion of walks undertaken with a manual task best-identified people with frailty in the older group [0.73 (0.69-0.76)]., Conclusions and Implications: Our findings indicate that measures obtained from wrist-worn wearable devices worn in everyday life can identify individuals with frailty in both middle-aged and older people. These digital gait biomarkers may facilitate screening programs and the timely implementation of frailty-prevention interventions., Competing Interests: Disclosure The authors declare no conflicts of interest., (Copyright © 2024 Post-Acute and Long-Term Care Medical Association. Published by Elsevier Inc. All rights reserved.)

Walking is crucial for independence and quality of life. This study leverages wrist-worn sensor data from UK Biobank participants to establish normative daily-life walking data, stratified by age and sex, to provide benchmarks for research and clinical practice. The Watch Walk digital biomarkers were developed, validated, and applied to 92,022 participants aged 45-79 who wore a wrist sensor for at least three days. Normative data were collected for daily-life walking speed, step-time variability, step count, and 17 other gait and sleep biomarkers. Test-retest reliability was calculated, and associations with sex, age, self-reported walking pace, and mobility problems were examined. Population mean maximal and usual walking speeds were 1.49 and 1.15 m/s, respectively. The daily step count was 7749 steps, and step regularity was 65%. Women walked more regularly but slower than men. Walking speed, step count, longest walk duration, and step regularity decreased with age. Walking speed is associated with sex, age, self-reported pace, and mobility problems. Test-retest reliability was good to excellent (ICC ≥ 0.80). This study provides large-scale normative data and benchmarks for wrist-sensor-derived digital gait and sleep biomarkers from real-world data for future research and clinical applications.

Objectives: Physical decline can be associated with the onset of depressive symptoms in later life. This study aimed to identify physical and lifestyle risk factors for depressive symptom trajectories in community-dwelling older adults., Methods: Participants were 553 people aged 70-90 years who underwent baseline physical, psychological and lifestyle assessments. Group-based trajectory analysis was used to identify patterns of depressive symptom development over 6 years of follow-up. Strengths of associations between baseline functional test performances and depressive symptom trajectories were evaluated with univariable ordinal models. Subsequently, the adjusted cumulative odds ratio for the association between identified risk factors, demographic factors and baseline anti-depressant use were measured using multivariable ordinal logistic regression., Results: Three distinct depressive symptom trajectories were identified: a low-and-stable course (10% of participants), a low-and-increasing course (81%) and a moderate-and-increasing course (9%). Timed Up and Go test time was the strongest risk factor of depressive symptom trajectory, followed by Five Times Sit-to-Stand test performance, planned physical activity levels, and knee extension strength (adjusted standardised ORs 1.65, 95% CI 1.34-2.04; 1.44, 95% CI 1.16-1.77; 1.44, 95% CI 1.17-1.76 and 1.41, 95% CI 1.15-1.73 respectively). After adjusting for age, sex, body mass index and baseline anti-depressant use, Timed Up and Go test performance and knee extension strength were independently and significantly associated with depressive trajectories., Conclusions: Timed Up and Go test times, Five Times Sit-to-Stand test performance, planned physical activity levels and knee extension strength are associated with three discrete depressive symptom trajectories. These clinical tests may help identify older adults aged 70-90 years at risk of developing depressive symptoms and help guide subsequent strength and mobility interventions., (© 2024 AJA Inc’.)