2014
DOI: 10.1016/j.pmrj.2014.05.003
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Mobile Smartphone Applications for Body Position Measurement in Rehabilitation: A Review of Goniometric Tools

Abstract: This review highlights the validated goniometer apps that physiatrists and other health care practitioners can use with confidence in research and clinical practice. We found 12 apps corresponding to these criteria, but there is a need for validation studies on available or new apps focused on goniometric measurement in dynamic conditions, eg, during gait or when performing therapeutic exercises.

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Cited by 124 publications
(90 citation statements)
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“…For apps designed to recognize inappropriate exercise performance, validation of their ability to accurately and precisely track a user's movement is a vital prerequisite to their clinical implementation. However, in a recent systematic review, Milani et al 28 determined that, to date, no movement tracking smartphone app has been properly validated in dynamic conditions (e.g., validating measurements during active rotations, rather than at static positions), as was done in this study. Additionally, the authors identified a paucity of validation studies on apps measuring angular movement during therapeutic exercise.…”
Section: Discussionmentioning
confidence: 97%
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“…For apps designed to recognize inappropriate exercise performance, validation of their ability to accurately and precisely track a user's movement is a vital prerequisite to their clinical implementation. However, in a recent systematic review, Milani et al 28 determined that, to date, no movement tracking smartphone app has been properly validated in dynamic conditions (e.g., validating measurements during active rotations, rather than at static positions), as was done in this study. Additionally, the authors identified a paucity of validation studies on apps measuring angular movement during therapeutic exercise.…”
Section: Discussionmentioning
confidence: 97%
“…Additionally, the authors identified a paucity of validation studies on apps measuring angular movement during therapeutic exercise. 28 Pernek et al 29 did investigate the reliability of smartphone hardware when measuring exercise data. Specifically, they found that after 3,598 repetitions, the repetition miscount rate was approximately 1%, with a temporal detection error of 11%-indicating that a smartphone could detect correct repetition start and end times based on their time-warping algorithm.…”
Section: Discussionmentioning
confidence: 99%
“…This demonstrates the necessity for establishing techniques that enable accurate measurement of proprioception through JPS for clinicians to identify individuals at risk of sustaining injury through proprioceptive deficit, objectively monitor pathological decline and to enable creation of specific rehabilitation programmes that both maintain and enhance proprioception in pathological and non-pathological populations [3,10,21]. Therefore, overall further evidence is clearly warranted to determine the most reliable and accurate method of JPS assessment; following recent research development, emerging techniques such as smartphone applications could offer innovative and easily applicable approaches for clinical practice [25,26].…”
Section: Discussionmentioning
confidence: 99%
“…Although 2D IMC analysis may have associated initial costs and timing restraints, as highlighted by Smith et al [4], this method has demonstrated strong reliability for JPS assessment and although further more recent research into its reliability is warranted, this could potentially offer a more evidence-based alternative for clinical practice [19,35]. Currently, emerging evidence in relation to measurement of knee joint angles through smartphone applications could offer a cost-effective and easily clinically applicable alternative method of JPS measurement; however, further research is required to ascertain its reliability [25,26]. Such technology may be used in addition to audio bio-feedback, particularly at end of range measurements.…”
Section: Discussionmentioning
confidence: 99%
“…to measure physiological parameters such as limb movement [1]. This technology is gaining increasing ground in clinical practice, as it constitutes a fast, accurate and easy-to-interpret tool [11,12,13,14,15]. Moreover, these applications can be used by patients themselves outside the clinical environment [16].…”
Section: Introductionmentioning
confidence: 99%