Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone

Gaio, Eduardo; Pauchard, Nicolas; Guilloteau, Adrien

doi:10.1016/j.hansur.2017.06.005

Cited by 25 publications

(13 citation statements)

References 9 publications

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“…However, this classification of poor relative intra-rater reliability for Tousignant-Laflamme et al [45] was primarily due to the results of one examiner using an iPhone 3, compared to the other examiner who used an iPhone 4. If we were to consider all the studies that assessed relative intra-rater reliability with an iPhone 4, all six studies demonstrated that smartphone apps had adequate relative intra-rater reliability [1, 34, 37, 42, 48, 64]. Thirteen of 17 studies reported good absolute intra-rater reliability as defined by a SEM or MDC < 5 o or LOA < ± 9.8 o for more than 50% of the joint movements they examined, with only four studies not satisfying this threshold [40, 44, 51, 59].…”

Section: Resultsmentioning

confidence: 99%

“…Twenty of 25 studies observed excellent/substantial relative validity as defined by ICC > 0.75, r > 0.9 or CCC > 0.95 for more than 50% of the joint movements examined, with five studies not meeting this threshold for this criteria [28, 30, 36, 45, 54]. Seventeen of 23 studies observed excellent/substantial absolute validity as defined by SEM or MDC < 5 o or LOA < ± 9.8 o for more than 50% of the joint movements they examined, with six studies not meeting this threshold of absolute validity [27, 34, 36, 42, 53, 57].…”

Section: Resultsmentioning

confidence: 99%

“…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%

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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…Validated tools to evaluate wrist ROM are lacking, and there is no reference standard comparator available. Previous work, however, has demonstrated the reliability of smartphone apps for the goniometric evaluation of joint ROM [ 29 , 30 ]. In our study, we saw a link between participants’ subjective assessment of severe wrist pain and functional assessment of wrist ROM, suggesting that combining the use of questionnaires and sensor-based recordings from the smartphone may provide a valuable combination to monitor and quantify patient symptoms and disease impacts.…”

Section: Discussionmentioning

confidence: 99%

Using a ResearchKit Smartphone App to Collect Rheumatoid Arthritis Symptoms From Real-World Participants: Feasibility Study

Crouthamel

Quattrocchi

Watts

et al. 2018

JMIR Mhealth Uhealth

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BackgroundUsing smartphones to enroll, obtain consent, and gather self-reported data from patients has the potential to enhance our understanding of disease burden and quantify physiological impact in the real world. It may also be possible to harness integral smartphone sensors to facilitate remote collection of clinically relevant data.ObjectiveWe conducted the Patient Rheumatoid Arthritis Data From the Real World (PARADE) observational study using a customized ResearchKit app with a bring-your-own-device approach. Our objective was to assess the feasibility of using an entirely digital approach (social media and smartphone app) to conduct a real-world observational study of patients with rheumatoid arthritis.MethodsWe conducted this observational study using a customized ResearchKit app with a bring-your-own-device approach. To recruit patients, the PARADE app, designed to guide patients through a series of tasks, was publicized via social media platforms and made available for patients in the United States to download from the Apple App Store. We collected patient-reported data, such as medical history, rheumatoid arthritis-related medications (past and present), and a range of patient-reported outcome measures. We included in the assessment a joint-pain map and a novel objective assessment of wrist range of movement, measured by the smartphone-embedded gyroscope and accelerometer.ResultsWithin 1 month of recruitment via social media campaigns, 399 participants self-enrolled, self-consented, and provided complete demographic data. Joint pain was the most frequently reported rheumatoid arthritis symptom to bother study participants (344/393, 87.5%). Severe patient-reported wrist pain appeared to be inversely linked with the range of wrist movement measured objectively by the app. At study entry, 292 of 399 participants (73.2%) indicated a preference for participating in a mobile app–based study. The number of participants in the study declined to 45 of 399 (11.3%) at week 12.ConclusionsDespite the declining number of participants over time, the combination of social media and smartphone app with sensor integration was a feasible and cost-effective approach for the collection of patient-reported data in rheumatoid arthritis. Integral sensors within smartphones can be harnessed to provide novel end points, and the novel wrist range of movement test warrants further clinical validation.

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“…The same is true for the Timed Up and Go test, in which a patient is asked to stand up from a chair without using their arms, walk a set distance, return, and sit down again 179,180 . Range of motion in the wrist 181 and ankle 182 can also be captured reliably via smartphone. In addition, falls can be distinguished from daily actions with an accuracy of 90% using a smartphone‐based accelerometer and gyroscope 183,184 .…”

Section: Digital Phenotyping: Mobile Technology To Collect Pghdmentioning

confidence: 99%

Innovations in research and clinical care using patient‐generated health data

Jim

Hoogland

Brownstein

et al. 2020

CA A Cancer J Clinicians

109

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Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

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Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone

Cited by 25 publications

References 9 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

Using a ResearchKit Smartphone App to Collect Rheumatoid Arthritis Symptoms From Real-World Participants: Feasibility Study

Innovations in research and clinical care using patient‐generated health data

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