The Analytical and Clinical Validity of the pfSTEP Digital Biomarker of the Susceptibility/Risk of Declining Physical Function in Community-Dwelling Older Adults

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are fit for purpose. The aim of this study was to use the V3 framework to assess the analytical and clinical validity of a wrist-worn measurement system of stepping volume and rate, formed by the GENEActiv accelerometer and GENEAcount step counting algorithm. The analytical validity was assessed by measuring the level of agreement between the wrist-worn system and a thigh-worn system (activPAL), the reference measure. The clinical validity was assessed by establishing the prospective association between the changes in stepping volume and rate with changes in physical function (SPPB score). The agreement of the thigh-worn reference system and the wrist-worn system was excellent for total daily steps (CCC = 0.88, 95% CI 0.83–0.91) and moderate for walking steps and faster-paced walking steps (CCC = 0.61, 95% CI 0.53–0.68 and 0.55, 95% CI 0.46–0.64, respectively). A higher number of total steps and faster paced-walking steps was consistently associated with better physical function. After 24 months, an increase of 1000 daily faster-paced walking steps was associated with a clinically meaningful increase in physical function (0.53 SPPB score, 95% CI 0.32–0.74). We have validated a digital susceptibility/risk biomarker—pfSTEP—that identifies an associated risk of low physical function in community-dwelling older adults using a wrist-worn accelerometer and its accompanying open-source step counting algorithm.

[1]  W. Kraus,et al.  Prospective Association of Daily Steps With Cardiovascular Disease: A Harmonized Meta-Analysis , 2022, Circulation.

[2]  Matthew R. Patterson,et al.  Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm , 2022, Sensors.

[3]  F. Harrell,et al.  Association of step counts over time with the risk of chronic disease in the All of Us Research Program , 2022, Nature Medicine.

[4]  S. Lord,et al.  Development and large-scale validation of the Watch Walk wrist-worn digital gait biomarkers , 2022, Scientific Reports.

[5]  Matthew R. Patterson,et al.  Stepping up with GGIR: Validity of step cadence derived from wrist-worn research-grade accelerometers using the verisense step count algorithm , 2022, Journal of sports sciences.

[6]  E. Stamatakis,et al.  Prospective Associations of Daily Step Counts and Intensity With Cancer and Cardiovascular Disease Incidence and Mortality and All-Cause Mortality , 2022, JAMA internal medicine.

[7]  Scott W. Ducharme,et al.  A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-adults study , 2022, International Journal of Behavioral Nutrition and Physical Activity.

[8]  E. Stamatakis,et al.  Association of Daily Step Count and Intensity With Incident Dementia in 78 430 Adults Living in the UK , 2022, JAMA neurology.

[9]  Samantha R. Fox,et al.  How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway , 2022, Sensors.

[10]  R. Buendía,et al.  Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data , 2022, Sensors.

[11]  M. Llabre,et al.  Associations of steps per day and step intensity with the risk of diabetes: the Hispanic Community Health Study / Study of Latinos (HCHS/SOL) , 2022, International Journal of Behavioral Nutrition and Physical Activity.

[12]  H. Johansen-Berg,et al.  Effect of a physical activity and behaviour maintenance programme on functional mobility decline in older adults: the REACT (Retirement in Action) randomised controlled trial , 2022, The Lancet. Public health.

[13]  L. Ferrucci,et al.  Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts , 2022, The Lancet. Public health.

[14]  D. Dunstan,et al.  Associations of Daily Steps and Step Intensity With Incident Diabetes in a Prospective Cohort Study of Older Women: The OPACH Study , 2022 .

[15]  Ryan J. Frayne,et al.  Comparison of habitual stepping cadence analysis methods: Relationship with step counts. , 2021, Gait & posture.

[16]  M. Wilhelm,et al.  Validation of open-source step-counting algorithms for wrist-worn tri-axial accelerometers in cardiovascular patients. , 2021, Gait & posture.

[17]  C. Lewis,et al.  Steps per Day and All-Cause Mortality in Middle-aged Adults in the Coronary Artery Risk Development in Young Adults Study , 2021, JAMA network open.

[18]  U. Ekelund,et al.  Association of accelerometer-derived step volume and intensity with hospitalizations and mortality in older adults: A prospective cohort study , 2021, Journal of sport and health science.

[19]  William N. Scott,et al.  Validity of a Novel Research-Grade Physical Activity and Sleep Monitor for Continuous Remote Patient Monitoring , 2021, Sensors.

[20]  Kaigang Li,et al.  Comparison of Energy Expenditure and Step Count Measured by ActiGraph Accelerometers Among Dominant and Nondominant Wrist and Hip Sites , 2020 .

[21]  M. Izquierdo,et al.  Performance of the Short Physical Performance Battery in Identifying the Frailty Phenotype and Predicting Geriatric Syndromes in Community-Dwelling Elderly , 2020, The journal of nutrition, health & aging.

[22]  D. Cook,et al.  The effects of step-count monitoring interventions on physical activity: systematic review and meta-analysis of community-based randomised controlled trials in adults , 2020, International Journal of Behavioral Nutrition and Physical Activity.

[23]  W. Kraus,et al.  Systematic review of the prospective association of daily step counts with risk of mortality, cardiovascular disease, and dysglycemia , 2020, International Journal of Behavioral Nutrition and Physical Activity.

[24]  Anisoara Paraschiv-Ionescu,et al.  Real-World Gait Bout Detection Using a Wrist Sensor: An Unsupervised Real-Life Validation , 2020, IEEE Access.

[25]  Ariel V. Dowling,et al.  Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs) , 2020, npj Digital Medicine.

[26]  C. Matthews,et al.  Association of Daily Step Count and Step Intensity With Mortality Among US Adults. , 2020, JAMA.

[27]  J. Guralnik,et al.  The Tribulations of Trials: Lessons Learnt Recruiting 777 Older Adults Into REtirement in ACTion (REACT), a Trial of a Community, Group-Based Active Aging Intervention Targeting Mobility Disability , 2020, The journals of gerontology. Series A, Biological sciences and medical sciences.

[28]  Abolfazl Soltani,et al.  Real-World Gait Speed Estimation Using Wrist Sensor: A Personalized Approach , 2020, IEEE Journal of Biomedical and Health Informatics.

[29]  T. Barreira,et al.  Volume and Intensity of Stepping Activity and Cardiometabolic Risk Factors in a Multi-ethnic Asian Population , 2020, International journal of environmental research and public health.

[30]  Laura Frey-Law,et al.  Choice of Processing Method for Wrist-Worn Accelerometers Influences Interpretation of Free-Living Physical Activity Data in a Clinical Sample , 2019 .

[31]  N. Vuillerme,et al.  Comparison of Step Count Assessed Using Wrist- and Hip-Worn Actigraph GT3X in Free-Living Conditions in Young and Older Adults , 2019, Front. Med..

[32]  J. Schrack,et al.  Age-Related Bias in Total Step Count Recorded by Wearable Devices. , 2019, JAMA internal medicine.

[33]  J. Guralnik,et al.  CLINICALLY MEANINGFUL CHANGE FOR PHYSICAL PERFORMANCE: PERSPECTIVES OF THE ICFSR TASK FORCE , 2019, The Journal of Frailty & Aging.

[34]  Alex V. Rowlands,et al.  GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data , 2019, Journal for the Measurement of Physical Behaviour.

[35]  J. Buring,et al.  Association of Step Volume and Intensity With All-Cause Mortality in Older Women. , 2019, JAMA internal medicine.

[36]  Lindsay P. Toth,et al.  Dominant vs. Non-Dominant Wrist Placement of Activity Monitors: Impact on Steps per Day , 2019, Journal for the Measurement of Physical Behaviour.

[37]  A. Bourke,et al.  Validation of the activPAL3 in Free-Living and Laboratory Scenarios for the Measurement of Physical Activity, Stepping, and Transitions in Older Adults , 2019, Journal for the Measurement of Physical Behaviour.

[38]  W. Kraus,et al.  Daily Step Counts for Measuring Physical Activity Exposure and Its Relation to Health , 2019, Medicine and science in sports and exercise.

[39]  Jeffrey M. Hausdorff,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 , 2019, European Review of Aging and Physical Activity.

[40]  H. Shimada,et al.  Relationship between Daily and In-laboratory Gait Speed among Healthy Community-dwelling Older Adults , 2019, Scientific Reports.

[41]  Lindsay P. Toth,et al.  Effects of Brief Intermittent Walking Bouts on Step Count Accuracy of Wearable Devices , 2019, Journal for the Measurement of Physical Behaviour.

[42]  H. Johansen-Berg,et al.  A community-based physical activity intervention to prevent mobility-related disability for retired older people (REtirement in ACTion (REACT)): study protocol for a randomised controlled trial , 2018, Trials.

[43]  Rosie Arthur,et al.  Wear compliance, sedentary behaviour and activity in free-living children from hip-and wrist-mounted ActiGraph GT3X+ accelerometers , 2018, Journal of sports sciences.

[44]  Dinesh John,et al.  “What Is a Step?” Differences in How a Step Is Detected among Three Popular Activity Monitors That Have Impacted Physical Activity Research , 2018, Sensors.

[45]  Kaberi Dasgupta,et al.  The impact of accelerometer wear location on the relationship between step counts and arterial stiffness in adults treated for hypertension and diabetes. , 2017, Journal of science and medicine in sport.

[46]  R. Plotnikoff,et al.  Comparability and feasibility of wrist- and hip-worn accelerometers in free-living adolescents. , 2017, Journal of science and medicine in sport.

[47]  Pierre-André Farine,et al.  A wrist sensor and algorithm to determine instantaneous walking cadence and speed in daily life walking , 2017, Medical & Biological Engineering & Computing.

[48]  L. Ferrucci,et al.  Short Physical Performance Battery and all-cause mortality: systematic review and meta-analysis , 2016, BMC Medicine.

[49]  Shaghayegh Zihajehzadeh,et al.  Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor , 2016, PloS one.

[50]  P. Kearney,et al.  Number of Days Required to Estimate Habitual Activity Using Wrist-Worn GENEActiv Accelerometer: A Cross-Sectional Study , 2016, PloS one.

[51]  G. Schofield,et al.  Adolescent physical activity levels: discrepancies with accelerometer data analysis , 2016, Journal of sports sciences.

[52]  Tamara B Harris,et al.  Comparison of physical activity assessed using hip- and wrist-worn accelerometers. , 2016, Gait & posture.

[53]  P. Dall,et al.  Validity and reliability of the activPAL3 for measuring posture and stepping in adults and young people. , 2016, Gait & posture.

[54]  Ben Stansfield,et al.  Quantifying the cadence of free-living walking using event-based analysis. , 2015, Gait & posture.

[55]  Catrine Tudor-Locke,et al.  Comparison of step outputs for waist and wrist accelerometer attachment sites. , 2015, Medicine and science in sports and exercise.

[56]  M. Granat,et al.  True cadence and step accumulation are not equivalent: the effect of intermittent claudication on free-living cadence. , 2015, Gait & posture.

[57]  Ben Stansfield,et al.  Characteristics of very slow stepping in healthy adults and validity of the activPAL3™ activity monitor in detecting these steps. , 2015, Medical engineering & physics.

[58]  David R Bassett,et al.  Evaluation of ActiGraph's low-frequency filter in laboratory and free-living environments. , 2015, Medicine and science in sports and exercise.

[59]  Joss Langford,et al.  Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents , 2014, Journal of applied physiology.

[60]  Richard P Troiano,et al.  Evolution of accelerometer methods for physical activity research , 2014, British Journal of Sports Medicine.

[61]  M. Granat,et al.  Step accumulation per minute epoch is not the same as cadence for free-living adults. , 2013, Medicine and science in sports and exercise.

[62]  S. Intille,et al.  Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. , 2013, Medicine and science in sports and exercise.

[63]  E. Cerin,et al.  Comparison of three models of actigraph accelerometers during free living and controlled laboratory conditions , 2013, European journal of sport science.

[64]  Malcolm H Granat,et al.  Event-based analysis of free-living behaviour , 2012, Physiological measurement.

[65]  Minsoo Kang,et al.  Impact of accelerometer wear time on physical activity data: a NHANES semisimulation data approach , 2012, British Journal of Sports Medicine.

[66]  C. Cook,et al.  The short physical performance battery as a predictor for long term disability or institutionalization in the community dwelling population aged 65 years old or older , 2012 .

[67]  Gregory J Welk,et al.  Protocols for evaluating equivalency of accelerometry-based activity monitors. , 2012, Medicine and science in sports and exercise.

[68]  Catrine Tudor-Locke,et al.  Patterns of adult stepping cadence in the 2005-2006 NHANES. , 2011, Preventive medicine.

[69]  Alberto G. Bonomi,et al.  Identifying Types of Physical Activity With a Single Accelerometer: Evaluating Laboratory-trained Algorithms in Daily Life , 2011, IEEE Transactions on Biomedical Engineering.

[70]  Michael Catt,et al.  Validation of the GENEA Accelerometer. , 2011, Medicine and science in sports and exercise.

[71]  S. Studenski,et al.  What is a meaningful change in physical performance? Findings from a clinical trial in older adults (the LIFE-P study) , 2009, The journal of nutrition, health & aging.

[72]  Vernon M Chinchilli,et al.  The Concordance Correlation Coefficient for Repeated Measures Estimated by Variance Components , 2009, Journal of biopharmaceutical statistics.

[73]  Ilkka Korhonen,et al.  Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.

[74]  P. Bossuyt,et al.  Evaluation of diagnostic tests when there is no gold standard. A review of methods. , 2007, Health technology assessment.

[75]  M. Granat,et al.  The validity and reliability of a novel activity monitor as a measure of walking , 2006, British Journal of Sports Medicine.

[76]  S. Studenski,et al.  Meaningful Change and Responsiveness in Common Physical Performance Measures in Older Adults , 2006, Journal of the American Geriatrics Society.

[77]  L. Ferrucci,et al.  A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. , 1994, Journal of gerontology.

[78]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.