Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer

Modest, Jacob M; Clair, Brian; DeMasi, Robin; Meulenaere, Stacy; Howley, Anthony; Aubin, Michelle E.; Jones, Martin

doi:10.1016/j.jht.2018.03.004

Cited by 25 publications

(19 citation statements)

References 3 publications

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“…While most of the studies reviewed in this manuscript involved healthy participants, some recruited patients with joint pain. These studies included groups of individuals with neck pain [30, 31], shoulder pathology [27, 32, 33], various upper limb injuries [34, 35] or knee pain [36, 37]. The intra-rater and inter-rater reliability of the apps in these clinical populations was typically adequate in these nine studies, with the exception of Pereira et al [37].…”

Section: Discussionmentioning

confidence: 99%

Reliability and validity of clinically accessible smartphone applications to measure joint range of motion: A systematic review

et al. 2019

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Measuring joint range of motion is an important skill for many allied health professionals. While the Universal Goniometer is the most commonly utilised clinical tool for measuring joint range of motion, the evolution of smartphone technology and applications (apps) provides the clinician with more measurement options. However, the reliability and validity of these smartphones and apps is still somewhat uncertain. The aim of this study was to systematically review the literature regarding the intra- and inter-rater reliability and validity of smartphones and apps to measure joint range of motion. Eligible studies were published in English peer-reviewed journals with full text available, involving the assessment of reliability and/or validity of a non-videographic smartphone app to measure joint range of motion in participants >18 years old. An electronic search using PubMed, Medline via Ovid, EMBASE, CINAHL, and SPORTSDiscus was performed. The risk of bias was assessed using a standardised appraisal tool. Twenty-three of the eligible 25 studies exceeded the minimum 60% score to be classified as a low risk of bias, although 3 of the 13 criteria were not achieved in >50% of the studies. Most of the studies demonstrated adequate intra-rater or inter-rater reliability and/or validity for >50% of the range of motion tests across all joints assessed. However, this level of evidence appeared weaker for absolute (e.g. mean difference ± limit of agreement, minimal detectable change) than relative (e.g. intraclass correlation, correlation) measures; and for spinal rotation than spinal extension, flexion and lateral flexion. Our results provide clinicians with sufficient evidence to support the use of smartphones and apps in place of goniometers to measure joint motion. Future research should address some methodological limitations of the literature, especially including the inclusion of absolute and not just relative reliability and validity statistics.

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Section: Discussionmentioning

confidence: 99%

Reliability and validity of clinically accessible smartphone applications to measure joint range of motion: A systematic review

et al. 2019

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“…Unlike walking, few studies focused on wrist ROM assessment. While this work relied on the computation of angles using information on the gravity vector orientation, other approaches essentially involved direct on-screen reading of angles from built-in iPhone apps [9,11] or third-party applications [10]. The choice of the gravity vector in the iPhone's reference frame as a representation of orientation in space was motivated by the access to the vertical direction while keeping the same axis convention, as opposed to other representations such as attitude quaternion, which is specified with respect to a laboratory reference frame with arbitrary directions for x → and y → .…”

Section: Pain/discomfortmentioning

confidence: 99%

“…Activity-and mobility-specific tasks have been successfully used in several studies to collect "real-world" data, providing insights into the effect of therapies on the daily lives of patients suffering from RA as well as other conditions. In particular, iPhone-embedded sensors have been used to collect range of motion (ROM) data for wrist joint [9][10][11] in healthy subjects. Likewise, several studies have explored the use of smartphone-based sensor data collection to analyze gait [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Developing Smartphone-Based Objective Assessments of Physical Function in Rheumatoid Arthritis Patients: The PARADE Study

et al. 2020

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Background: Digital biomarkers that measure physical activity and mobility are of great interest in the assessment of chronic diseases such as rheumatoid arthritis, as it provides insights on patients' quality of life that can be reliably compared across a whole population. Objective: To investigate the feasibility of analyzing iPhone sensor data collected remotely by means of a mobile software application in order to derive meaningful information on functional ability in rheumatoid arthritis patients. Methods: Two objective, active tasks were made available to the study participants: a wrist joint motion test and a walk test, both performed remotely and without any medical supervision. During these tasks, gyroscope and accelerometer time-series data were captured. Processing schemes were developed using machine learning techniques such as logistic regression as well as explicitly programmed algorithms to assess data quality in both tasks. Motion-specific features including wrist joint range of motion (ROM) in flexion-extension (for the wrist motion test) and gait parameters (for the walk test) were extracted from high quality data and compared with subjective pain and mobility parameters, separately captured via the application. Results: Out of 646 wrist joint motion samples collected, 289 (45%) were high quality. Data collected for the walk test included 2,583 samples (through 867 executions of the test) from which 651 (25%) were high quality. Further analysis of high-quality data highlighted links between reduced mobility and in-creased symptom severity. ANOVA testing showed statistically significant differences in wrist joint ROM between groups with light-moderate (220 participants) versus severe (36 participants) wrist pain (p < 0.001) as well as in average step times between groups with slight versus moderate problems walking about (p < 0.03). Conclusion: These findings demonstrate the potential to capture and quantify meaningful objective clinical information remotely using iPhone sensors and represent an early step towards the development of patient-centric digital endpoints for clinical trials in rheumatoid arthritis.

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“…Professional rehabilitation care is beneficial for pain management and reduction of the occurrence of adverse complications [ 36 ]. Intelligent monitoring APP helps professional medical staff and patients to establish harmonious relationship [ 10 ], provide timely and professional medical services, and reduce the occurrence of adverse complications. Moreover, it can provide remote consultation and home care.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Rehabilitation Assistance Tools for Distal Radius Fracture: A Systematic Review Based on Literatures and Mobile Application Stores

Chen

Lin

Han

et al. 2020

Computational and Mathematical Methods in Medicine

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Objective. To systematically analyze the existing intelligent rehabilitation mobile applications (APPs) related to distal radius fracture (DRF) and evaluate their features and characteristics, so as to help doctors and patients to make evidence-based choice for appropriate intelligent-assisted rehabilitation. Methods. Literatures which in regard to the intelligent rehabilitation tools of DRF were systematic retrieved from the PubMed, the Cochrane library, Wan Fang, and VIP Data. The effective APPs were systematically screened out through the APP markets of iOS and Android mobile platform, and the functional characteristics of different APPs were evaluated and analyzed. Results. A total of 8 literatures and 31 APPs were included, which were divided into four categories: intelligent intervention, angle measurement, intelligent monitoring, and auxiliary rehabilitation games. These APPs provide support for the patients’ home rehabilitation guidance and training and make up for the high cost and space limitations of traditional rehabilitation methods. The intelligent intervention category has the largest download ratio in the APP market. Angle measurement tools help DRF patients to measure the joint angle autonomously to judge the degree of rehabilitation, which is the most concentrated type of literature research. Some of the APPs and tools have obtained good clinical verification. However, due to the restrictions of cost, geographic authority, and applicable population, a large number of APPs still lack effective evidence to support popularization. Conclusion. Patients with DRF could draw support from different kinds of APPs in order to fulfill personal need and promote self-management. Intelligent rehabilitation APPs play a positive role in the rehabilitation of patients, but the acceptance of the utilization for intelligent rehabilitation APPs is relatively low, which might need follow-up research to address the conundrum.

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Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer

Cited by 25 publications

References 3 publications

Reliability and validity of clinically accessible smartphone applications to measure joint range of motion: A systematic review

Reliability and validity of clinically accessible smartphone applications to measure joint range of motion: A systematic review

Developing Smartphone-Based Objective Assessments of Physical Function in Rheumatoid Arthritis Patients: The PARADE Study

Intelligent Rehabilitation Assistance Tools for Distal Radius Fracture: A Systematic Review Based on Literatures and Mobile Application Stores

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