A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors

Background Commercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions. This study aimed to evaluate methods to address missingness in data collected from commercial activity monitors. Methods This study utilised 1526 days of near complete data from 109 adults participating in a European weight loss maintenance study (NoHoW). We conducted simulation experiments to test a novel scaling methodology (NoHoW method) and alternative imputation strategies (overall/individual mean imputation, overall/individual multiple imputation, Kalman imputation and random forest imputation). Methods were compared for hourly, daily and 14-day physical activity estimates for steps, total daily energy expenditure (TDEE) and time in physical activity categories. In a second simulation study, individual multiple imputation, Kalman imputation and the NoHoW method were tested at different positions and quantities of missingness. Equivalence testing and root mean squared error (RMSE) were used to evaluate the ability of each of the strategies relative to the true data. Results The NoHoW method, Kalman imputation and multiple imputation methods remained statistically equivalent (p<0.05) for all physical activity metrics at the 14-day level. In the second simulation study, RMSE tended to increase with increased missingness. Multiple imputation showed the smallest RMSE for Steps and TDEE at lower levels of missingness (<19%) and the Kalman and NoHoW methods were generally superior for imputing time in physical activity categories. Conclusion Individual centred imputation approaches (NoHoW method, Kalman imputation and individual Multiple imputation) offer an effective means to reduce the biases associated with missing data from activity monitors and maximise data retention.

[1]  Daniel S. Laferriere,et al.  Procedures used to standardize data collected by RT3 triaxial accelerometers in a large-scale weight-loss trial. , 2009, Journal of physical activity & health.

[2]  N. Ridgers,et al.  Assessing free-living physical activity using accelerometry: Practical issues for researchers and practitioners , 2011 .

[3]  Greet Cardon,et al.  The effect of a cluster randomised control trial on objectively measured sedentary time and parental reports of time spent in sedentary activities in Belgian preschoolers: the ToyBox-study , 2016, International Journal of Behavioral Nutrition and Physical Activity.

[4]  L B Sardinha,et al.  Accuracy of a combined heart rate and motion sensor for assessing energy expenditure in free-living adults during a double-blind crossover caffeine trial using doubly labeled water as the reference method , 2014, European Journal of Clinical Nutrition.

[5]  P. Loprinzi,et al.  Differences in demographic, behavioral, and biological variables between those with valid and invalid accelerometry data: implications for generalizability. , 2013, Journal of physical activity & health.

[6]  T. Harris,et al.  Physical Activity Patterns and Mortality: The Weekend Warrior and Activity Bouts , 2019, Medicine and science in sports and exercise.

[7]  Mark Hopkins,et al.  Improving energy expenditure estimates from wearable devices: A machine learning approach , 2020, Jurnal sport science.

[8]  P S Freedson,et al.  Field evaluation of the Computer Science and Applications, Inc. physical activity monitor. , 2000, Medicine and science in sports and exercise.

[9]  Ruairi O'Driscoll,et al.  How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies , 2020, British Journal of Sports Medicine.

[10]  Johannes le Coutre Grand challenges in nutrition. , 2014 .

[11]  N. Wareham,et al.  Estimating energy expenditure by heart-rate monitoring without individual calibration. , 2001, Medicine and science in sports and exercise.

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  G Plasqui,et al.  Daily physical activity assessment with accelerometers: new insights and validation studies , 2013, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[14]  Nathaniel Schenker,et al.  Multiple imputation of completely missing repeated measures data within person from a complex sample: application to accelerometer data in the National Health and Nutrition Examination Survey , 2016, Statistics in medicine.

[15]  Jonathan Krakoff,et al.  Four‐Year Weight Losses in the Look AHEAD Study: Factors Associated With Long‐Term Success , 2011, Obesity.

[16]  Mark Hopkins,et al.  The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours , 2019, Health and Technology.

[17]  P. Teixeira,et al.  The NoHoW protocol: a multicentre 2×2 factorial randomised controlled trial investigating an evidence-based digital toolkit for weight loss maintenance in European adults , 2019, BMJ Open.

[18]  Virginia Pensabene,et al.  Assessment of the Fitbit Charge 2 for monitoring heart rate , 2018, PloS one.

[19]  Hirofumi Tanaka,et al.  Age-predicted maximal heart rate revisited. , 2001, Journal of the American College of Cardiology.

[20]  Mark Hopkins,et al.  Impact of physical activity level and dietary fat content on passive overconsumption of energy in non-obese adults , 2017, International Journal of Behavioral Nutrition and Physical Activity.

[21]  Clayon B Hamilton,et al.  Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data , 2018, JMIR mHealth and uHealth.

[22]  Nazeem Muhajarine,et al.  Towards uniform accelerometry analysis: a standardization methodology to minimize measurement bias due to systematic accelerometer wear-time variation. , 2014, Journal of sports science & medicine.

[23]  Nuala M. Byrne,et al.  Assessment of Physical Activity and Energy Expenditure: An Overview of Objective Measures , 2014, Front. Nutr..

[24]  L. Mâsse,et al.  Physical activity in the United States measured by accelerometer. , 2008, Medicine and science in sports and exercise.

[25]  C. Earnest,et al.  The Effects of Exercise and Physical Activity on Weight Loss and Maintenance. , 2018, Progress in cardiovascular diseases.

[26]  C. Drenowatz,et al.  The Role of Energy Flux in Weight Management , 2017 .

[27]  Ulf Ekelund,et al.  A systematic review of reliability and objective criterion-related validity of physical activity questionnaires , 2012, International Journal of Behavioral Nutrition and Physical Activity.

[28]  Minsoo Kang,et al.  How many hours are enough? Accelerometer wear time may provide bias in daily activity estimates. , 2013, Journal of physical activity & health.

[29]  Kong Y. Chen,et al.  Increased physical activity was associated with less weight regain six years after “The Biggest Loser” competition , 2017, Obesity.

[30]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

[31]  J. Banks,et al.  What they say and what they do: comparing physical activity across the USA, England and the Netherlands , 2018, Journal of Epidemiology & Community Health.

[32]  M. Kenward,et al.  Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls , 2009, BMJ : British Medical Journal.

[33]  Anna Wirz-Justice,et al.  Circadian Clues to Sleep Onset Mechanisms , 2001, Neuropsychopharmacology.

[34]  Leena Choi,et al.  Validation of accelerometer wear and nonwear time classification algorithm. , 2011, Medicine and science in sports and exercise.

[35]  Jacqueline Kerr,et al.  Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity , 2018, Statistical methods in medical research.

[36]  Jeff Goldsmith,et al.  Validation of the Fitbit One® for physical activity measurement at an upper torso attachment site , 2016, BMC Research Notes.

[37]  J. Beyene,et al.  Strategies for Dealing with Missing Accelerometer Data. , 2018, Rheumatic diseases clinics of North America.

[38]  William Speier,et al.  A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data , 2020, IEEE Journal of Biomedical and Health Informatics.

[39]  Nils Y. Hammerla,et al.  Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study , 2017, PloS one.

[40]  Graham W Horgan,et al.  An evaluation of the IDEEA™ activity monitor for estimating energy expenditure , 2012, British Journal of Nutrition.

[41]  L. Cadmus-Bertram,et al.  Accelerometer-derived physical activity and sedentary time by cancer type in the United States , 2017, PloS one.

[42]  John Staudenmayer,et al.  Statistical considerations in the analysis of accelerometry-based activity monitor data. , 2012, Medicine and science in sports and exercise.

[43]  Jeff Gill,et al.  Missing value imputation for physical activity data measured by accelerometer , 2018, Statistical methods in medical research.

[44]  David R Bassett,et al.  Calibration and validation of wearable monitors. , 2012, Medicine and science in sports and exercise.

[45]  Catrine Tudor-Locke,et al.  A Catalog of Rules, Variables, and Definitions Applied to Accelerometer Data in the National Health and Nutrition Examination Survey, 2003–2006 , 2012, Preventing chronic disease.

[46]  B. Ainsworth,et al.  Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. , 2000, Medicine and science in sports and exercise.

[47]  D A Schoeller,et al.  How much physical activity is needed to minimize weight gain in previously obese women? , 1997, The American journal of clinical nutrition.

[48]  Paul H Lee Data imputation for accelerometer-measured physical activity: the combined approach. , 2013, The American journal of clinical nutrition.

[49]  N. J. Wareham,et al.  The descriptive epidemiology of accelerometer-measured physical activity in older adults , 2016, International Journal of Behavioral Nutrition and Physical Activity.

[50]  A. Carriquiry,et al.  Energy Intake Derived from an Energy Balance Equation, Validated Activity Monitors, and Dual X-Ray Absorptiometry Can Provide Acceptable Caloric Intake Data among Young Adults. , 2018, The Journal of nutrition.

[51]  S. Studenski,et al.  Using Heart Rate and Accelerometry to Define Quantity and Intensity of Physical Activity in Older Adults , 2018, The journals of gerontology. Series A, Biological sciences and medical sciences.