Objective: To systematically review current evidence on wearable sensor devices for the objective monitoring of mobility-related ADLs in patients with PD.

Background: Detecting Activities of Daily Living (ADL) in Parkinson’s disease (PD) is important for understanding patients’real-world functional status and dependency. However, conventional ADL assessments rely on patient recall and questionnaires, which are subjective and prone to recall bias, and lack continuous real-world monitoring. Digital biomarkers from wearable sensors offer a more objective approach to evaluating ADL, but current evidence remains limited.

Method: A comprehensive literature search was conducted in PubMed, Scopus, and IEEE Xplore, yielding 4,692 records. After removing duplicates, 3,231 unique records were screened. Studies were eligible if they included at least 2 patients with PD, used wearable sensor devices, and reported the detection accuracy for at least 1 mobility-related ADL task. Sixteen studies met the inclusion criteria. The review processes followed the PRISMA 2020 guidelines and was registered in PROSPERO(CRD420261278712).

Results: Five mobility-related ADL tasks were identified: walking, standing, sitting, lying, and stair climbing. Walking detection showed consistently acceptable to high performance using accelerometers alone, accelerometer–gyroscope combinations, and multi-sensor configurations (GradeB). Standing detection showed lower performance with accelerometers alone but improved with multi-sensor configurations (GradeB). Sitting detection could be detected as acceptable to high performance using either accelerometers alone or multi-sensor configurations (GradeB). Lying detection improved with accelerometer–gyroscope combinations (GradeC) and multi-sensor configurations (GradeB), but was inconsistent with accelerometers alone (GradeD). Stair-climbing detection was evaluated in only 2 studies, providing limited evidence despite high reported performance (GradeC).

Conclusion: Wearable inertial sensors show acceptable to high performance for detecting mobility-related ADLs in patients with PD, with the strongest evidence for walking detection. Detection of sitting, standing, and lying improves with multi-sensor configurations, while evidence for stair climbing remains limited. Future studies should prioritize larger cohorts, standardized methodologies, and real-world validation.

Figure 1: PRISMA flow diagram

Table 1: Characteristics and results

Table 2: Certainty of evidence

References: 1. Goubault E, Duval C, Martin C, Lebel K. Innovative detection and segmentation of mobility activities in patients living with Parkinson’s disease using a single ankle-positioned smartwatch. Sensors (Basel). 2024;24(17):5486.
2. Salarian A, Russmann H, Vingerhoets FJG, Burkhard PR, Aminian K. Ambulatory monitoring of physical activities in patients with Parkinson’s disease. IEEE Trans Biomed Eng. 2007;54(12):2296-2305.
3. Nguyen H, Lebel K, Bogard S, Goubault E, Boissy P, Duval C. Using inertial sensors to automatically detect and segment activities of daily living in people with Parkinson’s disease. IEEE Trans Neural Syst Rehabil Eng. 2018;26(1):197-204.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/advancing-mobility-related-activities-of-daily-living-monitoring-in-parkinsons-disease-a-systematic-review-of-wearable-sensor-technologies/