Home‐Based Monitoring of Falls Using Wearable Sensors in Parkinson's Disease

Abstract

Introduction Falling is among the most serious clinical problems in Parkinson's disease (PD). We used body‐worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life. Methods We matched all 2063 elderly individuals with self‐reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5‐year window, with an average follow‐up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1‐year cumulative incidence of falling. Results The incidence rate of any fall was higher among self‐reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self‐reported PD patients than controls (95% confidence interval, 1.6–2.0). Conclusion Having PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime “falling disease.” The results also point to the feasibility of using body‐worn sensors to monitor falls in daily life. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial

“…Moreover, our findings are consistent with the Movement Disorder Society Task Force on Technology roadmap [ 20 ] as well as with patient attitudes on technology use [ 21 ]. Our mHealth platform, as relevant studies suggest, can be an effective tool for the passive, unobtrusive monitoring and evaluation of symptoms [ 22 ], defining new phenotypical biomarkers [ 23 ], detection of serious events such as falls [ 24 ], detection of worsening in the overall health status of the patients, and the provision of better disease management and improved care [ 25 ], the latter being already extensively studied in ongoing clinical trials (eg, NCT03741920 and NCT02657655). mHealth may also help rehabilitation [ 26 , 27 ] and facilitate telemedicine since it enables home-based [ 28 ], multidisciplinary [ 29 ] approaches for the management of PD.…”

Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson’s Disease in the Home or a Home-like Environment

“…The choice of the home as a testing location to focus on for this review derives from perceiving the opportunities that this setting may bring to patients, clinicians and researchers. For people with PD, being tested at home could enable better appreciation of some activities of daily living (ADLs) which occur more naturally away from a clinic or lab environment [20], rare events such as falls [21], activities which impact upon wellbeing and quality of life [22] and outcomes such as sleep quality which are costly and logistically difficult to measure longitudinally in the clinic/lab. Technology deployed to the home could provide measurements to the clinician/researcher which would otherwise have required clinician time to obtain [23], have scalability to large numbers of people with PD remotely [24], and reduce the cost of clinic visit/clinical trial contacts [25].…”

Protocol for PD SENSORS: Parkinson’s Disease Symptom Evaluation in a Naturalistic Setting producing Outcome measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson’s disease

“…The testing location is often an artificial environment like a clinic or laboratory. This can increase costs 9 and impair the ability of studies to appraise important activities of daily living (ADLs) such as engagement in social activities, 10 rare events such as falls 11 and other metrics which affect well-being and quality of life such as sleep. 12 Development of technologies for the measurement of various aspects of PD has been evolving over the past 15 years.…”