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Smartphone-Based Hand Function Assessment:Systematic Review
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DOI：10.2196/51564.png)
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Abstract
En 中文
Background: Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity andreliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems furtheraggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in,high-efficiency sensors in smartphones can be used as effective tools for hand function assessment. Objective: This review aims to evaluate existing studies on hand function evaluation using smartphones. Methods: An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1)smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studiesin the English language and excluded conference proceedings and trial register records. Two reviewers independently screenedall studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the MixedMethods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for handfunction assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The datawere synthesized and tabulated based on each of the research questions. Results: In total, 46 studies were included. Overall, 11 types of hand dysfunction-related problems were identified, such asParkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal rangeof motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to handarthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Moststudies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severityevaluation, disease prediction, and feature aggregation.Conclusions: This systematic review highlights the potential of smartphone-based hand function assessment. The reviewsuggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of handdysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) takeadvantage of smartphones'multiple built-in sensors to assess hand function comprehensively, focus on developing ML methodsfor processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of theresearch are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature,affecting the evidence's completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions.Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-qualityreference studies and meta-analyses.(J Med Internet Res 2024;26:e51564) doi: 10.2196/51564
Keywords:
hand function assessment
smartphone-based sensing
rehabilitation
digital health
mobile health
mHealth
mobile phone
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