The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis
Abstract
:1. Introduction
- What is the intra- and inter-rater reliability of using the Microsoft Kinect, inertial sensors, smartphone applications, and digital inclinometers to calculate a joint angle in the shoulder?
- What are the types of inertial sensors, smartphone applications, and digital inclinometers currently used to calculate a joint angle in the shoulder?
- What clinical populations are utilising motion-tracking technology to calculate the joint angle in the shoulder?
- Which anatomical landmarks are used to assist the calculation of joint angle in the shoulder?
2. Materials and Methods
2.1. Inclusion and Exclusion Criteria
2.2. Data Extraction
2.3. Evaluation of Reliability Results
2.4. Evaluation of the Methodological Quality of the Studies
2.5. Data Analysis
3. Results
3.1. Flow of Studies
3.2. Description of Studies
3.3. Intra and Inter-Rater Reliability
3.3.1. The Microsoft Kinect
3.3.2. Inertial Sensors
3.3.3. Smartphone/Mobile Applications
3.3.4. Digital Inclinometer/Goniometer
3.4. Methodological Evaluation of the Measurement Properties
3.5. Synthesis of Results (Meta-Analysis)
3.6. Anatomical Landmarks
4. Discussion
4.1. Quality of Evidence
4.2. Clinical Implications
4.3. Limitations
Future Directions
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author | Sample Size (n) | Age (yr) Mean (SD) | Males (%) | Inclusion Criteria | Rater (n, Profession) | Movement Assessed | Position | Device | Sessions (n) | Time Interval |
---|---|---|---|---|---|---|---|---|---|---|
Awan et al., 2002 [91] | 56 | Not reported | 57.1 | No history of neurologic disease, arthritis, connective tissue disorder, or shoulder/neck injury or surgery | 2 PT MP | Passive • IR • ER | Supine | Digital inclinometer | 2 | 90–120 min |
Beshara et al., 2016 [92] | 9 | 36.6 (±13.3) | 33.3 | No history of neurologic disease, arthritis, connective tissue disorder, or shoulder/neck injury or surgery | 1 PT | Active • F • Abd | Standing | Microsoft Kinect (V.2) and Inertial sensors | 2 | 7 days |
Bonnechère et al., 2014 [93] | 48 | 26 (±8) | 62.5 | Healthy adults | 1 | Active • Abd | Standing | Microsoft Kinect (V.1.5) | 2 | 7 days |
Cai et al., 2019 [94] | 10 | 24.6 (±2.8) | 100 | No upper limb injuries or medication use that would have influenced their upper limb functions | 1 | Active • F • E • Abd • Add • IR • ER | Standing | Microsoft Kinect (V.2) | 2 | 7 days |
Chan et al., 2010 [95] | 1 | Not reported | 100 | Healthy, no pathology | 2 | Active • F • E • RER in 90° Abd | Standing Supine | iPod touch | 2 | Same day |
Chen et al., 2020 [96] | 10 | Not reported | Not reported | Healthy, aged 20–70 yrs, no discomfort or limited ROM of shoulder in the last year | 2 -1 PT -1 MP | Active • F • E • Abd • IR • ER | Standing | Inertial sensor (BoostFix) | 1 | Same day |
Cools et al., 2014 [97] | 30 | 22.1 (1.4) | 50 | No history of shoulder or neck pain or current participation in overhead sports on a competition level | 2 | Passive • ER • ER in 90° • Abd • IR in 90° • Abd • IR in forward • F | Sitting Supine | Digital inclinometer | 2 | 10 s |
Correll et al., 2018 [98] | 42 | 32.3 (2.1) | 71.4 | Healthy, 18–75 yrs old, able to easily move between standing and supine positions, able to actively move at least one shoulder into 90° of glenohumeral abduction | 2 PT student | Active • F • Abd • ER • IR | Supine | Digital inclinometer (HALO) | 2 | Same day |
Çubukçu et al., 2020 [99] | 40 | 22.1 (±3.1) | 55 | Healthy volunteers | 1 PT | Active • F • E • Abd • ER • IR | Standing | Microsoft Kinect (V.2) | 3 | 3 days |
Cuesta-Vargas et al., 2016 [100] | 37 | 56.1 (Healthy) 52.8 (Pathologic) | 40.5 | Healthy: no shoulder pain, negative NEER/Hawkin’s testPathologic: 18–75 yrs old, BMI 18–42 | 2 PT | Active • Abd | Standing | Inertial sensors (Inertia-Cube 3)- Sampling frequency 1000 Hz Smartphone (Nexus 4) 1280 × 768p resolution | 3 | 2 days |
Da Cunha Neto et al., 2018 [101] | 10 | Notreported | Not reported | Healthy | 2 | Active • F • E • Abd • Add | Standing | Microsoft Kinect (V.2) | 2 | Same day |
De Baets et al., 2020 [102] | 10 | 54 (±6) | 57.1 | Diagnosis of adhesive capsulitis in the past 6 months based on criterial described by the American Physical Therapy Association | 2 | Active • F • E • Abd • Add • IR • ER | Standing Seated | Inertial sensor (MCN Awinda motion capture system)-Sampling frequency 60 Hz | 2 | 2–5 days |
de Winter et al., 2004 [103] | 155 | 47 | 35.5 | Shoulder pain, 18–75 yrs, ability to co-operate (no dementia), sufficient knowledge of Dutch language | 2 PT | Passive • Abd • ER | Seated Supine | Digital inclinometer (Cybex EDI 320) | 1 | 1 h |
Dougherty et al., 2015 [104] | 90 | 23.5 (8.9) | 40 | 18 yrs +, pain free shoulder movement, no history of shoulder pain in preceding 12 months | 1 PT | Passive • FGH • F • Abd • GH • Abd • ER in neutral • Abd • ER in 90° • Abd • IR in 90° Abd | Seated Supine | Digital inclinometer | 2 | 7 days |
Hawi et al., 2014 [105] | 7 | Not Reported | Not Reported | Age 18+, free ROM without deficits | 1 | Active • FE • Abd • Add | Standing | Microsoft Kinect | 2 | Same day |
Huber et al., 2015 [106] | 10 | 22.1 (±0.9) | 60 | No shoulder pathology, pain-free | 1 | Active • F to 90° F to max • Abd to 90° • Sagittal F to 90° • Sagittal F to max • ER to max | Standing | Microsoft Kinect | 1 | Same day |
Hwang et al., 2017 [107] | 8 | 36.5 (±13.7) | Not Reported | Using a wheelchair for 1 yr, able to sit upright for at least 4 h/day, over 18 yrs old, use a wheelchair over 40 h/week | 1 | Active • F • E • Abd • Add | Seated | Microsoft Kinect (V.2) | 2 | Same day |
Kolber et al., 2011 [108] | 30 | 25.9 (3.1) | 40 | Asymptomatic adults | 2 PT | Active • F • Abd • IR • ER | Seated Supine Prone | Digital inclinometer (Acumar) | 2 | 2 days |
Kolber et al., 2012 [109] | 30 | 26 (4.2) | 30 | No cervical spine or upper extremity pain or recent shoulder surgery on dominant arm | 2 PT student | Active • Scaption | Seated | Digital inclinometer (Acumar) | 2 | 1 day |
Lim et al., 2015 [110] | 47 | 24.9 (±3.5) | 59.6 | No shoulder injuries or history of musculoskeletal and nervous system damage that could affect ROM, no pain around shoulder no performance of specialized shoulder muscle stretch or exercises or stretching in preceding 6 months | 2 PT | Passive • Abd | Supine Side-lying | Smartphone (iPhone 5) | 2 | 2 days |
Mejia-Hernandez et al., 2018 [111] | 75 | 46 | 72 | Older than 18 yrs, documented current shoulder diseases | 2 MP | Active & Passive • Forward F • Abd • GH • Abd • IR • ER | Seated Supine | Smartphone (iPhone 5) | 2 | Same day |
Milgrom et al., 2016 [112] | 5 | Not reported | 80 | Possess ability to self-propel a manual wheelchair, uses a wheelchair for at least 75% of daily activities, ≥18 yrs of age | 3 Kinect sensors “individual rater” | Active • F • Abd | Seated | Microsoft Kinect (V.1.8) | 2 | Same day |
Mitchell et al., 2014 [113] | 94 | Not reported | 0 | No shoulder pathology | 5 -2 PT -3 PT students | Active • ER | Supine | Smartphone (iPhone 4) | 2 | At least 15 min (<30 min) |
Picerno et al., 2015 [114] | 45 | M: 27 (±8) F: 22 (±3) | 55.6 | No previous or current shoulder impairment, no involvement in competitive sports at a professional level | 1 | Active • Abd | Seated | Inertial sensor (FreeSense)-Sampling frequency 200 Hz | 2 | Same day |
Poser et al., 2015 [115] | 23 | 44 | 39.1 | Asymptomatic people who are attending a Pilates gym | 3 PT | Active • ER • IR • Abd • Hor • Add | Supine Seated Side-lying | Digital Inclinometer (J-Tech) | 2 | Days (unspecific) |
Ramos et al., 2019 [116] | 54 | 26.3 (6) Healthy 25 (6) Shoulder pain | 25.9 | Healthy: Not reported Shoulder pain: Symptoms for at least 6 months and positive clinical tests for shoulder impingement | 1 | Active • F • Scaption | Seated | Mobile application (iPod) | 2 | 7 days |
Rigoni et al., 2019 [117] | 30 | 32.8 | 40 | Healthy volunteers | 2 | Active • F • Abd • ER • IR | Standing | Inertial Sensor (Biokin) | 1 | Same day |
Schiefer et al., 2015 [118] | 20 | 37.4 (±9.9) | 70 | Healthy subjects without or with known functional deficits, free of musculoskeletal complaints for at least one week before examination | 3 MP | Passive • ER • IR | Not reported | Inertial Sensor (CUELA system) | 1 | 1 day |
Scibek et al., 2013 [119] | 11 | 21.4 (±1.4) | 55.6 | Healthy, reporting no history of neck, upper extremity pathology in the last six months | Not reported | Active • F • GH • F • Abd | Seated | Digital inclinometer (Pro 360, Baseline) | 2 | 12–48 h |
Shin et al., 2012 [120] | 41 | 52.7 (±17.5) | 48.8 | Unilateral symptomatic shoulders | 3 MP | Active & Passive • Forward F • Abd • ER • ER at 90° • Abd • IR at 90° • Abd | StandingSupine | Smartphone (Galaxy S) | 2 | Same day |
Walker et al., 2016 [121] | 17 | 17 (±3) | 47 | Healthy, competitive swimmers, at least five swim sessions per week | 2 PT | Active • EL • EI • RER • Abd in IR | SupineStanding | Digital inclinometer (Dualer, J-Tech) | 2 | 30 min |
Werner et al., 2014 [122] | 24 | Not reported | 37.5 | Healthy and symptomatic shoulders, college students | 5 -4 MP -1 Medical student | Active • Forward F • Abd • ER at 0° • ER at 90° • IR at 90° • Abd | SupineStanding | Smartphone (iPhone) | 2 | Same day |
Device | Author | Intra-Rater Reliability | Inter-Rater Reliability | Level of Reliability |
---|---|---|---|---|
Microsoft Kinect | ||||
Shoulder | ||||
Flexion | Da Cuncha Neto et al. (2018) Hawi et al. (2014) Huber et al. (2015) Hwang et al. (2017) Milgrom et al. (2016) Çubukçu et al. (2020) Cai et al. (2019) | ICC 0.97 ICC 0.99 ICC 0.37, 0.85, 0.84, 0.95 ICC 0.96 (0.83–0.98), 0.92 (0.89–0.95) ICC 0.85 ICC 0.93, 0.99, 0.97. 0.96 | ICC 0.91 ICC 0.97 (0.84–1.00) | Good Good Poor–Good Good Good Moderate Good |
Extension | Da Cuncha Neto et al. (2018) Hawi et al. (2014) Hwang et al. (2017) Çubukçu et al. (2020) Cai et al. (2019) | ICC 0.97 ICC 0.99 ICC 0.96 (0.83–0.98), 0.92 (0.89–0.95) ICC 0.62 ICC 0.93, 0.99, 0.97, 0.96 | ICC 0.97 | Good Good Good Poor Good |
Abduction | Bonnechère et al. (2014) Hawi et al. (2014) Huber et al. (2015) Hwang et al. (2017) Milgrom et al. (2016) Cai et al. (2019) | ICC 0.73 ICC 0.96 ICC 0.76 ICC 0.92 (0.89–0.93), 0.96 (0.86–0.96) ICC 0.70, 0.75, 0.84, 0.82 | ICC 0.94 (0.72–0.99) | Moderate Good Moderate Good Good Moderate |
Adduction | Hawi et al. (2014) Hwang et al. (2017) Cai et al. (2019) | ICC 0.99 ICC 0.92 (0.89–0.93), 0.96 (0.86–0.96) ICC 0.70, 0.75, 0.84, 0.82 | Good Good Moderate | |
External rotation | Huber et al. (2015) Çubukçu et al. (2020) Cai et al. (2019) | ICC 0.98 ICC 0.87 ICC 0.93, 0.75, 0.90, 0.60 | Good Good Moderate–Good | |
Internal rotation | Çubukçu et al. (2020) Cai et al. (2019) | ICC 0.97 ICC 0.93, 0.75, 0.90, 0.60 | Good Moderate–Good | |
Microsoft Kinect & Inertial Sensor | ||||
Shoulder | ||||
Flexion | Beshara et al. (2016) | ICC 0.84 (0.45–0.96), 0.93 (0.72–0.98) | Moderate–Good | |
Abduction | Beshara et al. (2016) | ICC 0.52 (-0.17–0.87, 0.85 (0.47–0.96) | Poor–Moderate | |
Inertial Sensor | ||||
Shoulder | ||||
Flexion | Rigoni et al. (2019) Chen et al. (2020) De Baets et al. (2020) | ICC 0.68, 0.87, 0.91 | ICC 0.88 (0.80–0.92) ICC 0.90 (0.83–0.94), 0.95 (0.92–0.97) ICC 0.74, 0.83, 0.84 | Good Good Moderate–Good (Intra-rater) Moderate (Inter-rater) |
Extension | Chen et al. (2020) De Baets et al. (2020) | ICC 0.68, 0.87, 0.91 | ICC 0.77 (0.64–0.87), 0.80 (0.68–0.89) ICC 0.74, 0.83, 0.84 | Moderate Moderate–Good (Intra-rater) Moderate (Inter-rater) |
Abduction | Cuesta-Vargas et al. (2016) Picerno et al. (2015) Rigoni et al. (2019) Chen et al. (2020) De Baets et al. (2020) | ICC 0.78 (0.40–0.93), 0.98 (0.94–0.99) 0.99 (0.98–0.99), 0.96 (0.93–0.98) ICC 0.96 (0.93–0.98) ICC 0.73, 0.95 | ICC 0.49 (0.08–0.82), 0.99 (0.98–1.00), 0.99 (0.99–1.00) ICC 0.88 (0.81–0.93) ICC 0.97 (0.95–0.98), 0.98 (0.96–0.99) ICC 0.74, 0.80, 0.93 | Moderate–Good (Intra-rater) Poor–Good (Inter-rater) Good Good Good Moderate–Good |
Adduction | De Baets et al. (2020) | ICC 0.73, 0.95 | ICC 0.74, 0.80, 0.93 | Moderate–Good |
External rotation | Schiefer et al. (2015) Rigoni et al. (2019) Chen et al. (2020) De Baets et al. (2020) | ICC 0.85, 0.87, 0.89, 0.90 | ICC 0.71, 0.76, 0.81, 0.86 ICC 0.84 (0.74–0.90) ICC 0.95 (0.92–0.97), 0.96 (0.93–0.98) ICC 0.38, 0.84, 0.73, 0.87 | Moderate–Good Good Good Moderate–Good (Intra-rater) Poor–Good (Inter-rater) |
Internal rotation | Schiefer et al. (2015) Rigoni et al. (2019) Chen et al. (2020) De Baets et al. (2020) | ICC 0.85, 0.87, 0.89, 0.90 | ICC 0.68, 0.78, 0.87, 0.98 ICC 0.71 (0.56–0.82) ICC 0.91 (0.86–0.95), 0.97 (0.94–0.98) ICC 0.38, 0.84, 0.73, 0.87 | Moderate–Good Moderate Good Moderate–Good (Intra-rater) Poor–Good (Inter-rater) |
Smartphone/Mobile App | ||||
Shoulder | ||||
Flexion | Chan et al. (2010) Shin et al. (2012) Werner et al. (2014) Mejia-Hernandez et al. (2018) Ramos et al. (2019) | ICC 0.99 ICC 0.97 (0.95–0.99), 0.96 (0.92–0.98) 0.99 (0.98–0.99), 0.99 (0.99–1.00) ICC −0.21, −0.19, 0.01, 0.16, 0.27, 0.40 0.47, 0.50, 0.53, 0.56, 0.60, 0.71, 0.76, 0.82 | ICC 0.99 ICC 0.73 (0.59–0.83), 0.74 (0.61–0.84), 0.83 (0.73–0.90), 0.84 (0.74–0.90) ICC 0.75 (0.61–0.84), 0.97 (0.90–0.99) ICC 0.99 (0.98–0.99) ICC 0.06, 0.18, 0.19, 0.22, 0.25, 0.27, 0.30, 0.36, 0.40, 0.44, 0.47, 0.49, 0.68, 0.69 | Good Good (intra-rater) Moderate–Good (inter-rater) Moderate–Good Good Poor–Moderate |
Abduction | Lim et al. (2015) Shin et al. (2012) Werner et al. (2014) Mejia-Hernandez et al. (2018) | ICC 0.72, 0.89, 0.95, 0.97 ICC 0.96, 0.97, 0.99 | ICC 0.79, 0.94 ICC 0.70, 0.72, 0.78, 0.79 ICC 0.72 (0.58–0.83), 0.91 (0.68–0.97) ICC 0.99 (0.99–1.00) | Moderate–Good Good (intra-rater) Moderate (inter-rater) Moderate–Good Good |
Glenohumeral abduction | Mejia-Hernandez et al. (2018) | ICC 0.98 (0.97–0.99), 0.97 (0.95–0.99) | Good | |
External rotation | Chan et al. (2010) Mitchell et al. (2014) Shin et al. (2012) Werner et al. (2014) Mejia-Hernandez et al. (2018) | ICC 0.94, 0.96 ICC 0.79 (0.70–0.86) ICC 0.95, 0.97, 0.98 | ICC 0.88, 0.98 ICC 0.94 (0.87–0.98) ICC 0.76, 0.77, 0.78, 0.89, 0.90 ICC 0.85 (0.57–0.95), 0.86 (0.79–0.92), 0.88 (0.66–0.96) ICC 0.99 (0.98–0.99) | Good Moderate (intra-rater) Good (inter-rater) Good (intra-rater) Moderate–Good (inter-rater) Good Good |
Internal rotation | Shin et al. (2012) Werner et al. (2014) Mejia-Hernandez et al. (2018) | ICC 0.79, 0.97, 0.90, 0.93 0.99 | ICC 0.63, 0.66, 0.67, 0.68 ICC 0.81 (0.70–0.88), 0.86 (0.48–0.93) ICC 0.98 (0.97–0.99), 0.98 (0.96–0.98) | Moderate–Good Good Good |
Scaption | Ramos et al. (2019) | ICC −0.04, 0.10, 0.12, 0.31, 0.32, 0.39, 0.40, 0.45, 0.47, 0.52, 0.57, 0.63 | ICC −0.17, −0.06, 0.03, 0.07, 0.23, 0.26, 0.27, 0.28, 0.36, 0.45, 0.54, 0.73, 0.75, 0.81 | Poor–Moderate (intra-rater) Poor–Good (inter-rater) |
Digital Inclinometer/Goniometer | ||||
Shoulder | ||||
Flexion | Dougherty et al. (2015) Kolber et al. (2011) Scibek et al. (2013) Correll et al. (2018) | ICC 0.77, 0.82 ICC 0.83 ICC 0.67, 0.80, 0.90, 0.92, 0.96 ICC 0.86, 0.88 | ICC 0.58 ICC 0.18, 0.33, 0.50, 0.62, 0.68, 0.72, 0.76, 0.78, 0.85 ICC 0.89 | Moderate Moderate (intra-rater) Poor (inter-rater) Moderate–Good (intra-rater) Poor–Moderate (inter-rater) Good |
Elevation | Walker et al. (2016) | ICC 0.91, 0.92, 0.93, 0.95 | Good | |
Glenohumeral flexion | Dougherty et al. (2015) Scibek et al. (2013) | ICC 0.75, 0.77 ICC 0.75, 0.92, 0.94, 0.96, 0.99 | ICC 0.14, 0.35, 0.43, 0.63, 0.65, 0.69, 0.72, 0.83 | Moderate Moderate–Good (intra-rater) Poor–Good (inter-rater) |
Abduction | deWinter et al. (2004) Kolber et al. (2011) Poser et al. (2015) Dougherty et al. (2015) Scibek et al. (2013) Walker et al. (2016) Correll et al. (2018) | ICC 0.91 ICC 0.83, 0.92, 0.93, 0.96 ICC 0.73, 0.76 ICC 0.91, 0.94, 0.95, 0.96, 0.97, 0.99 ICC 0.89, 0.90, 0.91, 0.94 ICC 0.86, 0.91 | ICC 0.28, 0.78, 0.83 ICC 0.95 ICC 0.27, 0.32, 0.40, 0.60, 0.63, 0.64 ICC 0.48, 0.56, 0.58, 0.62, 0.65, 0.68, 0.70, 0.80, 0.83 ICC 0.93 | Poor–Good Moderate Moderate–Good (intra-rater) Poor–Moderate (inter-rater) Moderate Good (intra-rater) Poor–Good (inter-rater) Good Good |
Glenohumeral abduction | Dougherty et al. (2015) | ICC 0.60, 0.75 | Moderate | |
Horizontal abduction | Poser et al. (2015) | ICC 0.66, 0.81, 0.91, 0.94, 0.96 | ICC 0.17, 0.18, 0.24, 0.28, 0.31 | Moderate–Good (intra-rater) Poor (inter-rater) |
Digital Inclinometer/Goniometer External rotation | Awan et al. (2002) Cools et al. (2014) deWinter et al. (2004) Kolber et al. (2011) Poser et al. (2015) Dougherty et al. (2015) Walker et al. (2016) Correll et al. (2018) | ICC 0.58, 0.67 ICC 0.98, 0.95, 0.98 ICC 0.94 ICC 0.93, 0.94, 0.96, 0.97 ICC 0.28, 0.61, 0.66, 0.64, 0.68, 0.71 ICC 0.90, 0.94, 0.95 ICC 0.89, 0.90 | ICC 0.41, 0.51 ICC 0.98 ICC 0.56, 0.88, 0.90 ICC 0.88 ICC 0.70, 0.71, 0.72, 0.73, 0.76, 0.77 ICC 0.98 | Poor–Moderate (intra-rater) Poor (inter-rater) Good Poor–Good Good Good (intra-rater) Moderate (inter-rater) Poor–Moderate Good Good |
Internal rotation | Awan et al. (2002) Cools et al. (2014) Kolber et al. (2011) Poser et al. (2015) Dougherty et al. (2015) Walker et al. (2016) Correll et al. (2018) | ICC 0.64, 0.65, 0.72 ICC 0.89, 0.98, 0.99 ICC 0.87 ICC 0.91, 0.92, 0.94, 0.96, 0.97 ICC 0.64, 0.68 ICC 0.85, 0.90, 0.93, 0.96 ICC 0.82, 0.85 | ICC 0.50, 0.52, 0.62, 0.66 ICC 0.96, 0.98 ICC 0.93 ICC 0.63, 0.66, 0.73, 0.76, 0.78 ICC 0.96 | Poor–Moderate Good Good Good (intra-rater) Moderate (inter-rater) Poor–Moderate Moderate–Good Moderate–Good |
Scaption | Kolber et al. (2012) | ICC 0.88 | ICC 0.89 | Good |
Items | First Author and Year | ||||||||||
Awan (2002) | Beshara (2016) | Bonnechère (2014) | Cai (2019) | Chan (2010) | Chen (2020) | Cools (2014) | Correll (2018) | Çubukçu (2020) | Cuesta-Vargas (2016) | Da Cunha Neto (2018) | |
1. Were patients stable in the time between repeated measurements on the construct to be measured? | VG | VG | VG | VG | VG | VG | VG | VG | VG | VG | A |
2. Was the time interval between the repeated measurements appropriate? | A | VG | VG | VG | A | A | A | A | VG | VG | A |
3. Were the measurement conditions similar for the repeated measurements–except for the condition being evaluated as a source of variation? | VG | VG | VG | VG | A | VG | VG | VG | VG | VG | A |
4. Did the professional(s) administer the measurement without knowledge of scores or values of other repeated measurement(s) in the same patients? | VG | VG | VG | A | VG | VG | VG | VG | A | A | A |
5. Did the professionals(s) assign scores or determine values without knowledge of scores or values of other repeated measurements(s) in the same patients? | VG | VG | VG | A | VG | VG | VG | VG | A | A | A |
6. Were there any other important flaws in the design or statistical methods of the study? | D | D | VG | A | I | A | VG | VG | VG | VG | A |
7. For continuous scores: was an intraclass correlation (ICC) calculated? | A | VG | A | VG | A | VG | VG | VG | VG | VG | A |
8. For ordinal scores: was a (weighted) kappa calculated? | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
9. For dichotomous/nominal scores: was Kappa calculated for each category against the other categories combined? | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
Overall Score | D | D | A | A | I | A | A | A | A | A | A |
Items | First Author and Year | ||||||||||
De Baets (2020) | deWinter (2004) | Dougherty (2015) | Hawi (2014) | Huber (2015) | Hwang (2017) | Kolber (2011) | Kolber (2012) | Lim (2015) | Mejia-Hernandez (2018) | Milgrom (2016) | |
1 Were patients stable in the time between repeated measurements on the construct to be measured? | VG | VG | A | VG | VG | VG | VG | VG | VG | VG | VG |
2. Was the time interval between the repeated measurements appropriate? | A | A | VG | A | A | A | A | VG | VG | A | A |
3. Were the measurement conditions similar for the repeated measurements–except for the condition being evaluated as a source of variation? | VG | VG | VG | VG | VG | VG | VG | VG | VG | VG | VG |
4. Did the professional(s) administer the measurement without knowledge of scores or values of other repeated measurement(s) in the same patients? | A | A | VG | A | VG | A | VG | VG | VG | VG | A |
5. Did the professionals(s) assign scores or determine values without knowledge of scores or values of other repeated measurements(s) in the same patients? | A | A | VG | A | VG | A | VG | VG | VG | VG | A |
6. Were there any other important flaws in the design or statistical methods of the study? | A | VG | VG | D | A | D | VG | VG | VG | VG | D |
7. For continuous scores: was an intraclass correlation (ICC) calculated? | VG | A | A | VG | VG | A | VG | VG | VG | A | A |
8. For ordinal scores: was a (weighted) kappa calculated? | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
9. For dichotomous/nominal scores: was Kappa calculated for each category against the other categories combined? | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
Overall Score | A | A | A | D | A | D | A | VG | VG | A | D |
Items | First Author and Year | ||||||||||
Mitchell (2014) | Picerno (2015) | Poser (2015) | Ramos (2019) | Rigoni (2019) | Schiefer (2015) | Scibek (2013) | Shin (2012) | Walker (2016) | Werner (2014) | ||
1. Were patients stable in the time between repeated measurements on the construct to be measured? | VG | VG | VG | VG | VG | VG | VG | VG | VG | VG | |
2. Was the time interval between the repeated measurements appropriate? | A | A | VG | VG | A | A | A | A | A | A | |
3. Were the measurement conditions similar for the repeated measurements–except for the condition being evaluated as a source of variation? | VG | VG | VG | VG | VG | VG | VG | VG | VG | VG | |
4. Did the professional(s) administer the measurement without knowledge of scores or values of other repeated measurement(s) in the same patients? | VG | A | A | A | VG | VG | A | VG | VG | VG | |
5. Did the professionals(s) assign scores or determine values without knowledge of scores or values of other repeated measurements(s) in the same patients? | VG | A | A | A | VG | VG | A | VG | VG | VG | |
6. Were there any other important flaws in the design or statistical methods of the study? | VG | VG | A | VG | VG | A | A | VG | A | A | |
7. For continuous scores: was an intraclass correlation (ICC) calculated? | VG | VG | VG | A | VG | VG | VG | VG | VG | VG | |
8. For ordinal scores: was a (weighted) kappa calculated? | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
9. For dichotomous/nominal scores: was Kappa calculated for each category against the other categories combined? | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
Overall Score | A | A | A | A | A | A | A | A | A | A |
Items | First Author and Year | ||||||||||
Awan (2002) | Beshara (2016) | Bonnechère (2014) | Cai (2019) | Chan (2010) | Chen (2020) | Cools (2014) | Correll (2018) | Çubukçu (2020) | Cuesta-Vargas (2016) | Da Cunha Neto (2018) | |
1 Were patients stable in the time between repeated measurements on the construct to be measured? | VG | VG | VG | VG | VG | VG | VG | VG | VG | VG | A |
2. Was the time interval between the repeated measurements appropriate? | A | VG | A | VG | A | A | A | A | VG | VG | D |
3. Were the measurement conditions similar for the repeated measurements–except for the condition being evaluated as a source of variation? | VG | VG | VG | VG | A | VG | VG | VG | VG | VG | A |
4. Did the professional(s) administer the measurement without knowledge of scores or values of other repeated measurement(s) in the same patients? | VG | VG | VG | A | VG | VG | VG | VG | A | A | A |
5. Did the professionals(s) assign scores or determine values without knowledge of scores or values of other repeated measurements(s) in the same patients? | VG | VG | VG | A | VG | VG | VG | VG | A | A | A |
6 Were there any other important flaws in the design or statistical methods of the study? | D | D | VG | D | I | VG | VG | VG | VG | VG | VG |
7. For continuous scores: was the Standard Error of Measurement (SEM), Smallest Detectable Change (SDC), Limits of Agreement (LoA) or Coefficient of Variation (CV) calculated? | I | VG | VG | I | I | VG | VG | VG | VG | I | I |
8. For dichotomous/nominal/ordinal scores: was the percentage specific (e.g., positive and negative) agreement calculated? | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
Overall Score | I | D | A | I | I | A | A | A | A | I | I |
Items | First Author and Year | ||||||||||
De Baets (2020) | deWinter (2004) | Dougherty (2015) | Hawi (2014) | Huber (2015) | Hwang (2017) | Kolber (2011) | Kolber (2012) | Lim (2015) | Mejia-Hernandez (2018) | Milgrom (2016) | |
1. Were patients stable in the time between repeated measurements on the construct to be measured? | VG | VG | A | VG | VG | VG | VG | VG | VG | VG | VG |
2. Was the time interval between the repeated measurements appropriate? | A | A | VG | A | A | A | A | VG | VG | A | A |
3. Were the measurement conditions similar for the repeated measurements–except for the condition being evaluated as a source of variation? | VG | VG | VG | A | VG | VG | VG | VG | VG | VG | VG |
4. Did the professional(s) administer the measurement without knowledge of scores or values of other repeated measurement(s) in the same patients? | A | A | VG | A | VG | A | VG | VG | VG | VG | A |
5. Did the professionals(s) assign scores or determine values without knowledge of scores or values of other repeated measurements(s) in the same patients? | A | A | VG | A | VG | A | VG | VG | VG | VG | A |
6. Were there any other important flaws in the design or statistical methods of the study? | VG | VG | VG | D | VG | D | VG | VG | D | VG | D |
7. For continuous scores: was the Standard Error of Measurement (SEM), Smallest Detectable Change (SDC), Limits of Agreement (LoA) or Coefficient of Variation (CV) calculated? | VG | N/A | VG | I | VG | VG | VG | VG | I | VG | I |
8. For dichotomous/nominal/ordinal scores: was the percentage specific (e.g., positive and negative) agreement calculated? | N/A | VG | A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
Overall Score | A | A | A | I | A | D | A | VG | I | A | I |
Items | First Author and Year | ||||||||||
Mitchell (2014) | Picerno (2015) | Poser (2015) | Ramos (2019) | Rigoni (2019) | Schiefer (2015) | Scibek (2013) | Shin (2012) | Walker (2016) | Werner (2014) | ||
1. Were patients stable in the time between repeated measurements on the construct to be measured? | VG | VG | VG | VG | VG | VG | VG | VG | VG | VG | |
2. Was the time interval between the repeated measurements appropriate? | A | A | VG | VG | A | A | A | A | A | A | |
3. Were the measurement conditions similar for the repeated measurements–except for the condition being evaluated as a source of variation? | VG | VG | VG | VG | VG | VG | VG | VG | VG | VG | |
4. Did the professional(s) administer the measurement without knowledge of scores or values of other repeated measurement(s) in the same patients? | VG | A | A | A | VG | VG | A | VG | VG | VG | |
5. Did the professionals(s) assign scores or determine values without knowledge of scores or values of other repeated measurements(s) in the same patients? | VG | A | A | A | VG | VG | A | VG | VG | VG | |
6. Were there any other important flaws in the design or statistical methods of the study? | VG | VG | VG | VG | VG | VG | D | VG | VG | VG | |
7. For continuous scores: was the Standard Error of Measurement (SEM), Smallest Detectable Change (SDC), Limits of Agreement (LoA) or Coefficient of Variation (CV) calculated? | I | I | VG | VG | VG | VG | I | VG | VG | VG | |
8. For dichotomous/nominal/ordinal scores: was the percentage specific (e.g., positive and negative) agreement calculated? | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
Overall Score | I | I | A | A | A | A | I | A | A | A |
Device | Author | Anatomical Landmarks |
---|---|---|
Microsoft Kinect | Bonnechère et al. (2014) | Shoulder girdle centre, elbow, wrist, hand |
Hawi et al. (2014) | Shoulder centre and elbow | |
Huber et al. (2015) | Positions of shoulder and elbow joints relative to the trunk for flexion and abduction. Position of elbow and hand relative to trunk for external rotation | |
Milgrom et al. (2016) | Angle between the humerus vector (shoulder to elbow) and the torso vector (neck to shoulder midpoint) | |
Cai et al. (2019) | X = Unit vector perpendicular to the Y-axis and the Z-axis pointing anteriorly, Y: Unit vector going from the elbow joint center to the shoulder joint center, Z: Unit vector perpendicular to the plane formed by the Y-axis of the upper arm and the long axis vector of the forearm. | |
Microsoft Kinect & Inertial Sensor | Beshara et al. (2016) | 2 3D vectors, a vector from shoulder joint centre (below the acromion process) to the elbow centre (between the medical and lateral epicondyles). A vector from shoulder joint centre defined as a point on the 6th rib along the midaxillary line of the trunk. |
Inertial Sensor | Cuesta-Vargas et al. (2016) | Middle third of the humerus slightly posterior and in the flat part of the sternum |
Picerno et al. (2015) | Arbitrary point of the upper arm | |
Schiefer et al. (2015) | Laterally on the upper arms and on the forearms close to the wrist, on the dorsum of the hand. Sensors were placed in the middle of the segments. | |
Rigoni et al. (2019) | 10 cm distal to the lateral epicondyle | |
De Baets et al. (2020) | Sternal sensor: positioned on flat central part of the sternum, the scapular sensor halfway between the trigonum and the acromial angle, in alignment with the upper edge of the scapular spine. Humeral sensor: at the central third of the humerus, slightly posterior, at the level of the deltoid insertion. Lower arm sensor: positioned on the dorsal side, just proximal of the line between the radial and ulnar styloid process. | |
Smartphone/mobile app | Chan et al. (2010) | Acromion, humeral axis. |
Lim et al. (2015) | Front centre of humerus. | |
Mitchell et al. (2014) | Superior border of the mid-ulna. | |
Ramos et al. (2019) | Attached below the deltoid muscle origin | |
Mejia-Hernandez et al. (2018) | Distal portion of the humerus for seated movements. Wrist for supine movements. | |
Shin et al. (2012) | Ventral side of the patient’s forearm at the wrist level. | |
Digital Inclinometer | Dougherty et al. (2015) | Shoulder flexion: the anterior aspect of the arm, aligned parallel to the humerus. Shoulder abduction: The lateral aspect of the arm, aligned parallel to the humerus. External Rotation: The anterior aspect of the participant’s forearm. Internal Rotation: The posterior aspect of the participant’s forearm. |
Kolber et al. (2011) | Flexion: Distal arm proximal to the elbow. Abduction: Distal arm proximal to the elbow. External rotation: Distal forearm just proximal to the wrist. Internal rotation: Distal forearm just proximal to the wrist. | |
Kolber et al. (2012) | Scaption: Superior portion of the humeral shaft proximal to the elbow. | |
Poser et al. (2015) | Abduction: lateral and distal face of the humerus, with the inferior edge set at the beginning of the medial epicondyle. Horizontal adduction: spine of scapular and posterior face of the humerus, touching the olecranon. | |
Scibek et al. (2013) | Flexion and Abduction: Shaft of the humerus | |
Walker et al. (2016) | Shoulder internal/external rotation: 5 cm distal to the olecranon process of the elbow. Combined elevation: Just below the deltoid insertion with the face of the inclinometer in the coronal plane of movement. Shoulder abduction in internal rotation: Just below the deltoid insertion with the face of the inclinometer in the coronal plane of movement. | |
Correll (2018) | Shoulder flexion: the lateral aspect of the greater tubercle, the midaxillary line of the thorax and the lateral midline of the humerus, lateral epicondyle of the humerus or the olecranon process. Abduction: anterior aspect of the acromial process, midline of the anterior aspect of the sternum and the anterior midline of the humerus. External/Internal rotation: the olecranon process, the ulna and ulnar styloid. |
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Beshara, P.; Anderson, D.B.; Pelletier, M.; Walsh, W.R. The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis. Sensors 2021, 21, 8186. https://doi.org/10.3390/s21248186
Beshara P, Anderson DB, Pelletier M, Walsh WR. The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis. Sensors. 2021; 21(24):8186. https://doi.org/10.3390/s21248186
Chicago/Turabian StyleBeshara, Peter, David B. Anderson, Matthew Pelletier, and William R. Walsh. 2021. "The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis" Sensors 21, no. 24: 8186. https://doi.org/10.3390/s21248186
APA StyleBeshara, P., Anderson, D. B., Pelletier, M., & Walsh, W. R. (2021). The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis. Sensors, 21(24), 8186. https://doi.org/10.3390/s21248186