February 6, 2025

Cool Rabbits

Healthcare Enthusiast

Validity and reliability of the Apple Health app on iPhone for measuring gait parameters in children, adults, and seniors

Validity and reliability of the Apple Health app on iPhone for measuring gait parameters in children, adults, and seniors
  • van Schooten, K. S. et al. Ambulatory fall-risk assessment: Amount and quality of daily-life gait predict falls in older adults. J. Gerontol. A Biol. Sci. Med. Sci. 70, 608–615.

    Article 
    PubMed 

    Google Scholar
     

  • Savica, R. et al. Comparison of gait parameters for predicting cognitive decline: The Mayo Clinic Study of Aging. J. Alzheimers Dis. 55, 559–567 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Studenski, S. et al. Gait speed and survival in older adults. JAMA 305, 50–58 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Braun, T. et al. Association of clinical outcome assessments of mobility capacity and incident disability in community-dwelling older adults—A systematic review and meta-analysis. Ageing Res. Rev. 81, 101704 (2022).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Middleton, A., Fritz, S. L. & Lusardi, M. Walking speed: The functional vital sign. J. Aging Phys. Act. 23, 314–322 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Rasmussen, L. J. H. et al. Association of neurocognitive and physical function with gait speed in midlife. JAMA Netw. Open 2, e1913123. https://doi.org/10.1001/jamanetworkopen.2019.13123 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lord, S. et al. Independent domains of gait in older adults and associated motor and nonmotor attributes: Validation of a factor analysis approach. J. Gerontol. A Biol. Sci. Med. Sci. 68, 820–827 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Verghese, J., Holtzer, R., Lipton, R. B. & Wang, C. Quantitative gait markers and incident fall risk in older adults. J. Gerontol. A Biol. Sci. Med. Sci. 64, 896–901 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Woo, J., Ho, S. C. & Yu, A. L. Walking speed and stride length predicts 36 months dependency, mortality, and institutionalization in Chinese aged 70 and older. J. Am. Geriatr. Soc. 47, 1257–1260 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Doi, T. et al. Spatio-temporal gait variables predicted incident disability. J. Neuroeng. Rehabil. 17, 11. https://doi.org/10.1186/s12984-020-0643-4 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Simon, S. R. Quantification of human motion: Gait analysis-benefits and limitations to its application to clinical problems. J. Biomech. 37, 1869–1880 (2004).

    Article 
    PubMed 

    Google Scholar
     

  • Muro-de-la-Herran, A., Garcia-Zapirain, B. & Mendez-Zorrilla, A. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14, 3362–3394. https://doi.org/10.3390/s140203362 (2014).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hou, Y., Wang, S., Li, J., Komal, S. & Li, K. Reliability and validity of a wearable inertial sensor system for gait assessment in healthy young adults. In 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). 1–6. https://doi.org/10.1109/CISP-BMEI53629.2021.9624463 (2021).

  • Lanovaz, J. L., Oates, A. R., Treen, T. T., Unger, J. & Musselman, K. E. Validation of a commercial inertial sensor system for spatiotemporal gait measurements in children. Gait Posture 51, 14–19 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Morris, R. et al. Validity of Mobility Lab (version 2) for gait assessment in young adults, older adults and Parkinson’s disease. Physiol. Meas. 40, 095003 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kobsar, D. et al. Validity and reliability of wearable inertial sensors in healthy adult walking: A systematic review and meta-analysis. J. Neuroeng. Rehabil. 17, 62. https://doi.org/10.1186/s12984-020-00685-3 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Werner, C. et al. Concurrent validity, test–retest reliability, and sensitivity to change of a single body-fixed sensor for gait analysis during rollator-assisted walking in acute geriatric patients. Sensors 20, 4866. https://doi.org/10.3390/s20174866 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Picerno, P. et al. Wearable inertial sensors for human movement analysis: A five-year update. Expert Rev. Med. Devices 18, 79–94 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • World Health Organization. International Classification of Functioning, Disability and Health: ICF. http://apps.who.int/iris/bitstream/handle/10665/42407/9241545429.pdf?sequence=1 (2001).

  • Hillel, I. et al. Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring. Eur. Rev. Aging Phys. Act. 16, 6. https://doi.org/10.1186/s11556-019-0214-5 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Robles-García, V. et al. Spatiotemporal gait patterns during overt and covert evaluation in patients with Parkinson’s disease and healthy subjects: Is there a Hawthorne effect? J. Appl. Biomech. 31, 189–194 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Toosizadeh, N. et al. Motor performance assessment in Parkinson’s disease: Association between objective in-clinic, objective in-home, and subjective/semi-objective measures. PLoS ONE 10, e0124763. https://doi.org/10.1371/journal.pone.0124763 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Carcreff, L. et al. Walking speed of children and adolescents with cerebral palsy: Laboratory versus daily life. Front. Bioeng. Biotechnol. 8, 812 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Warmerdam, E. et al. Long-term unsupervised mobility assessment in movement disorders. Lancet Neurol. 19, 462–470 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Del Din, S., Godfrey, A., Mazzà, C., Lord, S. & Rochester, L. Free-living monitoring of Parkinson’s disease: Lessons from the field. Mov. Disord. 31, 1293–1313 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Rochester, L. et al. A roadmap to inform development, validation and approval of digital mobility outcomes: The Mobilise-D approach. Digit. Biomark. 4, 13–27 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Moore, S. A. et al. Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: A feasibility, validity and reliability study. J. Neuroeng. Rehabil. 14, 130. https://doi.org/10.1186/12984-017-0341-z (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bongartz, M. et al. Validity, reliability, and feasibility of the uSense activity monitor to register physical activity and gait performance in habitual settings of geriatric patients. Physiol. Meas. 40, 095005 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Statista Research Department. Number of smartphone subscriptions worldwide from 2016 to 2021, with forecasts from 2022 to 2027. https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ (2022).

  • Howell, D. R. et al. Reliability and minimal detectable change for a smartphone-based motor-cognitive assessment: Implications for concussion management. J. Appl. Biomech. 37, 380–387 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kelly, M. et al. A novel smartphone application is reliable for repeat administration and comparable to the Tekscan Strideway for spatiotemporal gait. Measurement (Lond.) 192, 110882 (2022).

    PubMed 

    Google Scholar
     

  • Shahar, R. T. & Agmon, M. Gait analysis using accelerometry data from a single smartphone: Agreement and consistency between a smartphone application and gold-standard gait analysis system. Sensors 21, 7497. https://doi.org/10.3390/s21227497 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Christensen, J. C. et al. The validity and reliability of the onestep smartphone application under various gait conditions in healthy adults with feasibility in clinical practice. J. Orthop. Surg. Res. 17, 417 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Manor, B. et al. Smartphone app-based assessment of gait during normal and dual-task walking: Demonstration of validity and reliability. JMIR Mhealth Uhealth 6, e36. https://doi.org/10.2196/mhealth.8815 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tchelet, K., Stark-Inbar, A. & Yekutieli, Z. Pilot study of the encephalog smartphone application for gait analysis. Sensors 19, 5179. https://doi.org/10.3390/s19235179 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rashid, U. et al. Validity and reliability of a smartphone app for gait and balance assessment. Sensors 22, 124. https://doi.org/10.3390/s22010124 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Howell, D. R., Lugade, V., Taksir, M. & Meehan, W. P. 3rd. Determining the utility of a smartphone-based gait evaluation for possible use in concussion management. Phys. Sportsmed. 48, 75–80 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Apple Inc. Measuring walking quality through iPhone mobility metrics. https://www.apple.com/de/healthcare/docs/site/Measuring_Walking_Quality_Through_iPhone_Mobility_Metrics.pdf (2022).

  • Clavijo-Buendía, S. et al. Construct validity and test-retest reliability of a free mobile application for spatio-temporal gait analysis in Parkinson’s disease patients. Gait Posture 79, 86–91 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Su, D. et al. Simple smartphone-based assessment of gait characteristics in Parkinson disease: Validation study. JMIR Mhealth Uhealth 9, e25451. https://doi.org/10.2196/25451 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shema-Shiratzky, S., Beer, Y., Mor, A. & Elbaz, A. Smartphone-based inertial sensors technology—Validation of a new application to measure spatiotemporal gait metrics. Gait Posture 93, 102–106 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Bourke, A. K., Scotland, A., Lipsmeier, F., Gossens, C. & Lindemann, M. Gait characteristics harvested during a smartphone-based self-administered 2-minute walk test in people with multiple sclerosis: Test–retest reliability and minimum detectable change. Sensors 20, 5906. https://doi.org/10.3390/s20205906 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Statista Research Department. Smartphone OS in 2019, by age group. https://www.statista.com/statistics/1133193/smartphone-os-by-age/ (2020).

  • Zou, G. Y. Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Stat. Med. 31, 3972–3981 (2012).

    Article 
    MathSciNet 
    CAS 
    PubMed 

    Google Scholar
     

  • WHO Consultation on Obesity & World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation. https://apps.who.int/iris/handle/10665/42330 (2000).

  • Katzman, R. et al. Validation of a short Orientation-Memory-Concentration Test of cognitive impairment. Am. J. Psychiatry 140, 734–739 (1983).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • EuroQol Group. EuroQol–a new facility for the measurement of health-related quality of life. Health Policy 16, 199–208 (1990).

    Article 

    Google Scholar
     

  • Ludwig, K., Graf von der Schulenburg, J. M. & Greiner, W. German value set for the EQ-5D-5L. Pharmacoeconomics 36, 663–674 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Roberts, H. C. et al. A review of the measurement of grip strength in clinical and epidemiological studies: Towards a standardised approach. Age Ageing 40, 423–429 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Ortega, F. B. et al. Physical fitness levels among European adolescents: The HELENA study. Br. J. Sports Med. 45, 20–29 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Steiber, N. Strong or weak handgrip? Normative reference values for the German population across the life course stratified by sex, age, and body height. PLoS ONE 11, e0163917. https://doi.org/10.1371/journal.pone.0163917 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Craig, C. L. et al. International physical activity questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 35, 1381–1395 (2003).

    Article 
    PubMed 

    Google Scholar
     

  • IPAQ group. Guidelines for the data processing and analysis of the International Physical Activity Questionnaire. https://sites.google.com/site/theipaq/scoring-protocol (2005).

  • Apple Inc. Apple Health. https://apps.apple.com/app/health/id1242545199 (2022).

  • Apple Inc. Share your data in Health on iPhone. https://support.apple.com/guide/iphone/share-your-health-data-iph5ede58c3d/15.0/ios/15.0 (2022).

  • Portney, L. & Watkins, M. P. Foundation of Clinical Research. Application to practice 3rd edn. (Pearson Education, London, 2009).


    Google Scholar
     

  • Bland, J. M. & Altman, D. G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1, 307–310 (1986).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Critchley, L. A. & Critchley, J. A. A meta-analysis of studies using bias and precision statistics to compare cardiac output measurement techniques. J. Clin. Monit. Comput. 15, 85–91 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Atkinson, G. & Nevill, A. M. Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports Med. 26, 217–238 (1998).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Soulard, J., Vaillant, J., Balaguier, R. & Vuillerme, N. Spatio-temporal gait parameters obtained from foot-worn inertial sensors are reliable in healthy adults in single- and dual-task conditions. Sci. Rep. 11, 10229. https://doi.org/10.1038/s41598-021-88794-4 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang, S. L. et al. Minimal detectable change of the timed “up & go” test and the dynamic gait index in people with Parkinson disease. Phys. Ther. 91, 114–121 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Solway, S., Brooks, D., Lacasse, Y. & Thomas, S. A qualitative systematic overview of the measurement properties of functional walk tests used in the cardiorespiratory domain. Chest 119, 256–270 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mueller, A. et al. Continuous digital monitoring of walking speed in frail elderly patients: Noninterventional validation study and longitudinal clinical trial. JMIR Mhealth Uhealth 7, e15191. https://doi.org/10.2196/15191 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rojer, A. G. M. et al. Robustness of in-laboratory and daily-life gait speed measures over one year in high functioning 61- to 70-year-old adults. Gerontology 67, 650–659 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Storm, F. A., Buckley, C. J. & Mazzà, C. Gait event detection in laboratory and real life settings: Accuracy of ankle and waist sensor based methods. Gait Posture 50, 42–46 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Pacini Panebianco, G., Bisi, M. C., Stagni, R. & Fantozzi, S. Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements. Gait Posture 66, 76–82 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Trojaniello, D., Cereatti, A. & Della Croce, U. Accuracy, sensitivity and robustness of five different methods for the estimation of gait temporal parameters using a single inertial sensor mounted on the lower trunk. Gait Posture 40, 487–492 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • De Ridder, R. et al. Concurrent validity of a commercial wireless trunk triaxial accelerometer system for gait analysis. J. Sport Rehabil. 28, 295–1 (2019).

    Article 

    Google Scholar
     

  • Bravi, M. et al. Concurrent validity and inter trial reliability of a single inertial measurement unit for spatial-temporal gait parameter analysis in patients with recent total hip or total knee arthroplasty. Gait Posture 76, 175–181 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Peraza, L. R. et al. An automatic gait analysis pipeline for wearable sensors: A pilot study in Parkinson’s disease. Sensors 21, 8286. https://doi.org/10.3390/s21248286 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Silsupadol, P., Teja, K. & Lugade, V. Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket. Gait Posture 58, 516–522 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Godfrey, A., Del Din, S., Barry, G., Mathers, J. C. & Rochester, L. Instrumenting gait with an accelerometer: A system and algorithm examination. Med. Eng. Phys. 37, 400–407 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Del Din, S., Godfrey, A. & Rochester, L. Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson’s disease: Toward clinical and at home use. IEEE J. Biomed. Health Inform. 20, 838–847 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Hartmann, A., Luzi, S., Murer, K., de Bie, R. A. & de Bruin, E. D. Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults. Gait Posture 29, 444–448 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Byun, S., Han, J. W., Kim, T. H. & Kim, K. W. Test-retest reliability and concurrent validity of a single tri-axial accelerometer-based gait analysis in older adults with normal cognition. PLoS ONE 11, e0158956. https://doi.org/10.1371/journal.pone.0158956 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Perera, S., Mody, S. H., Woodman, R. C. & Studenski, S. A. Meaningful change and responsiveness in common physical performance measures in older adults. J. Am. Geriatr. Soc. 54, 743–749 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Perera, S. et al. Are estimates of meaningful decline in mobility performance consistent among clinically important subgroups? (Health ABC study). J. Gerontol. A Biol. Sci. Med. Sci. 69, 1260–1268 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar