Abstract
Mobile health (mHealth) has emerged as a potential solution to providing valuable ecological information about the severity and burden of Parkinson’s disease (PD) symptoms in real-life conditions. Objective: The objective of our study was to explore the feasibility and usability of an mHealth system for continuous and objective real-life measures of patients’ health and functional mobility, in unsupervised settings. Methods: Patients with a clinical diagnosis of PD, who were able to walk unassisted, and had an Android smartphone were included. Patients were asked to answer a daily survey, to perform three weekly active tests, and to perform a monthly in-person clinical assessment. Feasibility and usability were explored as primary and secondary outcomes. An exploratory analysis was performed to investigate the correlation between data from the mKinetikos app and clinical assessments. Results: Seventeen participants (85%) completed the study. Sixteen participants (94.1%) showed a medium-to-high level of compliance with the mKinetikos system. A 6-point drop in the total score of the Post-Study System Usability Questionnaire was observed. Conclusions: Our results support the feasibility of the mKinetikos system for continuous and objective real-life measures of a patient’s health and functional mobility. The observed correlations of mKinetikos metrics with clinical data seem to suggest that this mHealth solution is a promising tool to support clinical decisions.
Highlights
Parkinson’s disease (PD) is a complex neurodegenerative disorder, with a multitude of fluctuating and heterogeneous motor and non-motor manifestations [1]
Exploratory Analysis To correlate the Mobile health (mHealth) system metrics, created based on the data collected during the study, with the recommended clinical tools for evaluating changes in patients’ health and functional mobility, to understand their ability to accurately evaluate these constructs and monitor changes over time
Twenty PD patients were assessed for eligibility and included in the study between November and December 2019
Summary
Parkinson’s disease (PD) is a complex neurodegenerative disorder, with a multitude of fluctuating and heterogeneous motor and non-motor manifestations [1]. The currently available therapeutic interventions drastically improve symptoms and quality of life of early stage PD [2]. The ability to provide optimized and personalized care is based on a clinical interview, diaries, and scales, performed during short in-person meetings, which take place at best every 3 or 6 months [3–5]. As Parkinson’s disease is a complex disease with symptoms that vary across the day and with the medication cycle, a patient’s condition during visits may not accurately reflect the degree and nature of their disability, limiting both the clinician’s ability to capture an accurate image of the patient’s health and manage the disease [3]. According to the published evidence, the patient’s performance differs substantially when comparing in-clinic (supervised) with real-life (unsupervised) assessments [6]
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