Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61–70 Years Old

Assessment of physical performance by standard clinical tests such as the 30-s Chair Stand (30CST) and the Timed Up and Go (TUG) may allow early detection of functional decline, even in high-functioning populations, and facilitate preventive interventions. Inertial sensors are emerging to obtain instrumented measures that can provide subtle details regarding the quality of the movement while performing such tests. We compared standard clinical with instrumented measures of physical performance in their ability to distinguish between high and very high functional status, stratified by the Late-Life Function and Disability Instrument (LLFDI). We assessed 160 participants from the PreventIT study (66.3 ± 2.4 years, 87 females, median LLFDI 72.31, range: 44.33–100) performing the 30CST and TUG while a smartphone was attached to their lower back. The number of 30CST repetitions and the stopwatch-based TUG duration were recorded. Instrumented features were computed from the smartphone embedded inertial sensors. Four logistic regression models were fitted and the Areas Under the Receiver Operating Curve (AUC) were calculated and compared using the DeLong test. Standard clinical and instrumented measures of 30CST both showed equal moderate discriminative ability of 0.68 (95%CI 0.60–0.76), p = 0.97. Similarly, for TUG: AUC was 0.68 (95%CI 0.60–0.77) and 0.65 (95%CI 0.56–0.73), respectively, p = 0.26. In conclusion, both clinical and instrumented measures, recorded through a smartphone, can discriminate early functional decline in healthy adults aged 61–70 years.

[1]  Lorenzo Chiari,et al.  Validity of a Smartphone-based instrumented Timed Up and Go. , 2012, Gait & posture.

[2]  Luigi Ferrucci,et al.  Assessing the building blocks of function: utilizing measures of functional limitation. , 2003, American journal of preventive medicine.

[3]  K. Hauer,et al.  Test–retest reliability and minimal detectable change of repeated sit-to-stand analysis using one body fixed sensor in geriatric patients , 2012, Physiological measurement.

[4]  E. Steyerberg,et al.  [Regression modeling strategies]. , 2011, Revista espanola de cardiologia.

[5]  Peter J. Beek,et al.  Older Adults with Weaker Muscle Strength Stand up from a Sitting Position with More Dynamic Trunk Use , 2018, Sensors.

[6]  Jeffrey M. Hausdorff,et al.  Can an accelerometer enhance the utility of the Timed Up & Go Test when evaluating patients with Parkinson's disease? , 2010, Medical engineering & physics.

[7]  Richard W. Bohannon,et al.  Reference values for adult grip strength measured with a Jamar dynamometer: a descriptive meta-analysis , 2006 .

[8]  Aslam Muhammad,et al.  mctest: An R Package for Detection of Collinearity among Regressors , 2016, R J..

[9]  W. Beam,et al.  A 30-s chair-stand test as a measure of lower body strength in community-residing older adults. , 1999, Research quarterly for exercise and sport.

[10]  Alan M Jette,et al.  Psychometric properties of the Late-Life Function and Disability Instrument: a systematic review , 2014, BMC Geriatrics.

[11]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[12]  M. Mancini,et al.  Sit-stand and stand-sit transitions in older adults and patients with Parkinson’s disease: event detection based on motion sensors versus force plates , 2012, Journal of NeuroEngineering and Rehabilitation.

[13]  C. Becker,et al.  Smartphone-based solutions for fall detection and prevention: the FARSEEING approach , 2012, Zeitschrift für Gerontologie und Geriatrie.

[14]  Mary Ann Schroeder,et al.  Diagnosing and Dealing with Multicollinearity , 1990, Western journal of nursing research.

[15]  Diane Podsiadlo,et al.  The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons , 1991, Journal of the American Geriatrics Society.

[16]  Michelle Shardell,et al.  Age-Related Change in Mobility: Perspectives From Life Course Epidemiology and Geroscience. , 2016, The journals of gerontology. Series A, Biological sciences and medical sciences.

[17]  Sabato Mellone Movement Analysis by means of inertial sensors: from bench to bedside , 2013 .

[18]  Kamiar Aminian,et al.  Mobile Health Applications to Promote Active and Healthy Ageing , 2017, Sensors.

[19]  Alan M Jette,et al.  Late Life Function and Disability Instrument: II. Development and evaluation of the function component. , 2002, The journals of gerontology. Series A, Biological sciences and medical sciences.

[20]  Jaap H. van Dieën,et al.  The Instrumented Sit-to-Stand Test (iSTS) Has Greater Clinical Relevance than the Manually Recorded Sit-to-Stand Test in Older Adults , 2016, PloS one.

[21]  Lee Bowman,et al.  Refining the categorization of physical functional status: the added value of combining self-reported and performance-based measures. , 2004, The journals of gerontology. Series A, Biological sciences and medical sciences.

[22]  Sean Pearson,et al.  Continuous Monitoring of Turning in Patients with Movement Disability , 2013, Sensors.

[23]  Wiebren Zijlstra,et al.  Assessment of spatio-temporal parameters during unconstrained walking , 2004, European Journal of Applied Physiology.

[24]  Jeffrey M. Hausdorff,et al.  An instrumented timed up and go: the added value of an accelerometer for identifying fall risk in idiopathic fallers , 2011, Physiological measurement.

[25]  J. Cummings,et al.  The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment , 2005, Journal of the American Geriatrics Society.

[26]  Jorunn L Helbostad,et al.  Estimation of gait cycle characteristics by trunk accelerometry. , 2004, Journal of biomechanics.

[27]  F. Horak,et al.  iTUG, a Sensitive and Reliable Measure of Mobility , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Bernd Grimm,et al.  Evaluating physical function and activity in the elderly patient using wearable motion sensors , 2016, EFORT open reviews.

[29]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[30]  Nir Giladi,et al.  Association Between Performance on Timed Up and Go Subtasks and Mild Cognitive Impairment: Further Insights into the Links Between Cognitive and Motor Function , 2014, Journal of the American Geriatrics Society.

[31]  Jeffrey M. Hausdorff,et al.  Using a Body-Fixed Sensor to Identify Subclinical Gait Difficulties in Older Adults with IADL Disability: Maximizing the Output of the Timed Up and Go , 2013, PloS one.

[32]  S M Rispens,et al.  Effect of calendar age on physical performance: A comparison of standard clinical measures with instrumented measures in middle-aged to older adults. , 2016, Gait & posture.

[33]  L. Chiari,et al.  Quantification of Motor Impairment in Parkinson's Disease Using an Instrumented Timed Up and Go Test , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  Jeffrey M. Hausdorff,et al.  Properties of the ‘Timed Up and Go’ Test: More than Meets the Eye , 2010, Gerontology.