Personal informatics (PI) has become an area of significant research over the past decade, maturing into a sub-field that seeks to support people from many backgrounds and life contexts in collecting and finding value in their personal data. PI research includes a focus on people with chronic conditions as a monolithic group, but currently fails to distinguish the needs of people with motor disabilities (MD). To understand how current PI literature addresses those needs, we conducted a mapping review on PI publications engaged with people with MD. We report results from 50 publications identified in the ACM DL, Pubmed, JMIR, SCOPUS, and IEEE Xplore. Our analysis shows significant incompatibilities between the needs of individuals with MD and the ways that PI literature supports them. We also found inconsistencies in the ways that disability levels are reported, that PI literature for MD excludes non-health-related data domains, and an insufficient focus on PI tools' accessibility and usability for some MD users. In contrast with Epstein et al.'s [36] recent PI review, behavior change and habit awareness were the most common motivation in these publications. Finally, many of the reviewed articles reported involvement by caregivers, trainers, healthcare providers, and researchers across the PI stages. In addition to these insights, we provide recommendations for designing PI technology through a user-centric lens that will broaden the scope of PI and include people regardless of their motor abilities.
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A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease
Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist’s subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018–2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community’s growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.
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- Award ID(s):
- 2114499
- PAR ID:
- 10423469
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 22
- Issue:
- 15
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 5491
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s22155491
