JMIR Publications

ABSTRACT

Background:

The objective of this study is to review and summarize recent developments in wearable technology detailing the key enabling technologies (i.e., sensor components) and applications of wearable technology as they relate to lower extremity osteoarthritis.

Objective:

The objective of this study is to review and summarize recent developments in wearable technology detailing the key enabling technologies (i.e., sensor components) and applications of wearable technology as they relate to lower extremity osteoarthritis.

Methods:

A literature search was performed in March 2021 using the PubMed and EMBASE databases for publications on wearable movement technologies in lower-limb OA. Papers published within the previous 5 years were identified. The search was limited to original research studies published in English. Duplicate studies, systematic reviews, conference abstracts, and study protocols were removed. Sample keywords and their combinations included: (osteoarthritis OR TKA OR total knee arthroplasty OR total knee replacement) AND (wearable* OR sensor).

Results:

From the literature, 72 studies were determined relevant and subsequently included in this review. Wearable technology has successfully been implemented for gait assessment, movement pattern training using feedback, assessment of intervention outcomes, and physical activity monitoring. Additionally, some studies demonstrated algorithms or measurement systems that could be used for movement pattern training with feedback in future implementations. Study participants identified appearance and comfort during use as key aspects for the acceptance of wearable technology, and enjoyed seeing both quantitative sensor data as well as qualitative patient-reported outcomes.

Conclusions:

Advancements in wearable sensor technology allow for data collection and analysis in both accurate and unobtrusive ways. The technology can be used to passively collect data, implement exercise interventions, or actively retrain movement patterns. Future opportunities remain to have more efficient, smaller systems and provide biofeedback for new, previously unused metrics.