Heightened clinical utility of smartphone versus body-worn inertial system for shoulder function B-B score

Pichonnaz, Claude; Aminian, Kamiar; Ancey, Céline; Jaccard, Hervé; Lécureux, Estelle; Duc, Cyntia; Farron, Alain; Jolles, Brigitte M.; Gleeson, Nigel

doi:10.1371/journal.pone.0174365

Cited by 11 publications

(12 citation statements)

References 49 publications

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“…Recently, in the orthopedic rehabilitation field, novel approaches have been proposed with smartphone application, 12–16 and since smartphones are equipped with Inertia Measurement Units, joint movements can be detected and measured. 5,17 Smartphone usage for home-based rehabilitation and exercise is an attractive option because the penetration rate of smartphones is extraordinarily high, 18–20 and given the focus on cost control and expected reductions in visits to clinics and numbers of meetings with physical therapists.…”

Section: Discussionmentioning

confidence: 99%

Effect of smartphone application-supported self-rehabilitation for frozen shoulder: a prospective randomized control study

et al. 2018

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Objectives: To evaluate the clinical efficacy of smartphone-assisted self-rehabilitation in patients with frozen shoulder. Design: A single-center, randomized controlled trial. Setting: Orthopedic department of a university hospital. Subjects: A total of 84 patients with frozen shoulder were recruited. Intervention: Patients were randomly divided into two groups: a smartphone-assisted exercise group (n = 42) and a conventional self-exercise group (n = 42). The study was performed over three months, during which each group performed home-based rehabilitation. Main measures: Visual analogue scale for pain and passive shoulder range of motion were assessed at baseline and after 4, 8, and 12 weeks of treatment. Technology Acceptance Model–2 and Usefulness, Satisfaction, and Ease of Use scores were evaluated in the smartphone group. Results: Initial visual analogue scale for pain of the smartphone group was 6.0 ± 2.2 and ended up with 1.8 ± 2.5 after 12 weeks, whereas the self-exercise group showed 5.8 ± 2.3 for the baseline visual analogue scale for pain and 2.2 ± 1.7 at the end. Significant time-dependent improvements in all measured values were observed in both groups (all Ps < 0.001), but no significant intergroup difference was observed after 4, 8, or 12 weeks of treatment. In the smartphone group, Technology Acceptance Model–2 and Usefulness, Satisfaction, and Ease of Use scores showed high patient satisfaction with smartphone-assisted exercise. Conclusion: There was no difference between home-based exercise using a smartphone application and a conventional self-exercise program for the treatment of frozen shoulder in terms of visual analogue scale for pain and range of motions.

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Section: Discussionmentioning

confidence: 99%

Effect of smartphone application-supported self-rehabilitation for frozen shoulder: a prospective randomized control study

et al. 2018

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“…Machine learning models have been successfully applied to automatic movement identification and recognition models to analyze lower limb movements in other clinical applications [ 16 , 17 , 18 , 19 , 20 ]. However, most IMU-based shoulder function assessment systems still rely on manual operation [ 10 , 21 , 22 , 23 , 24 ]. Our results demonstrate the feasibility and effectiveness of the ML-based functional shoulder task identification for supporting clinical assessment and proof of concept.…”

Section: Discussionmentioning

confidence: 99%

“…Each subject is asked to perform five shoulder tasks once, including cleaning head, cleaning upper back and shoulder, cleaning lower back, placing an object on a high shelf, and putting/removing an object from the back pocket. These shoulder tasks have been widely adopted for shoulder function assessment and evaluation in previous works [ 21 , 22 ]. The performed shoulder tasks and the corresponding three sub-tasks are listed in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

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Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Chang

Hsieh²,

Huang³

et al. 2020

Sensors

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Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment.

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“…Other studies have analyzed the reliability of mobile devices as clinical tools compared with an inertial sensor, in a similar way [22,28]. Pichonnaz et al validated the use of a smartphone (iPod, Apple) with an ICC of alpha=.97 compared with a Physilog reference system (Gait Up) using the B-B score test (hand to the Back and hand upwards as to change a Bulb) [29].…”

Section: Discussionmentioning

confidence: 99%

Reliability of a Smartphone Compared With an Inertial Sensor to Measure Shoulder Mobility: Cross-Sectional Study

Roldán-Jiménez¹,

Martín-Martín²,

Cuesta‐Vargas³

2019

JMIR Mhealth Uhealth

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BackgroundThe shoulder is one of the joints with the greatest mobility within the human body and its evaluation is complex. An assessment can be conducted using questionnaires or functional tests, and goniometry can complement the information obtained in this assessment. However, there are now validated devices that can provide more information on the realization of movement, such as inertial sensors. The cost of these devices is usually high and they are not available to all clinicians, but there are also inertial sensors that are implemented in mobile phones which are cheaper and widely available. Results from the inertial sensors integrated into mobile devices can have the same reliability as those from dedicated sensors.ObjectiveThis study aimed to validate the use of the Nexus 4 smartphone as a measuring tool for the mobility of the humerus during shoulder movement compared with a dedicated InertiaCube3 (Intersense) sensor.MethodsA total of 43 subjects, 27 affected by shoulder pathologies and 16 asymptomatic, participated in the study. Shoulder flexion, abduction, and scaption were measured using an InertiaCube3 and a Nexus 4 smartphone, which were attached to the participants to record the results simultaneously. The interclass correlation coefficient (ICC) was calculated based on the 3 movements performed.ResultsThe smartphone reliably recorded the velocity values and simultaneously recorded them alongside the inertial sensor. The ICCs of the 3 gestures and for each of the axes of movement were analyzed with a 95% CI. In the abduction movement, the devices demonstrated excellent interclass reliability for the abduction humeral movement axis (Cronbach alpha=.98). The axis of abduction of the humeral showed excellent reliability for the movements of flexion (Cronbach alpha=.93) and scaption (Cronbach alpha=.98).ConclusionsCompared with the InertiaCube3, the Nexus 4 smartphone is a reliable and valid tool for recording the velocity produced in the shoulder.

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Heightened clinical utility of smartphone versus body-worn inertial system for shoulder function B-B score

Cited by 11 publications

References 49 publications

Effect of smartphone application-supported self-rehabilitation for frozen shoulder: a prospective randomized control study

Effect of smartphone application-supported self-rehabilitation for frozen shoulder: a prospective randomized control study

Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Reliability of a Smartphone Compared With an Inertial Sensor to Measure Shoulder Mobility: Cross-Sectional Study

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