Activity tracker issues: validity and standardization

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Yep, there are issues with activity trackers.  Just because a tracker may have a well-known name doesn’t mean it is accurate, or accurate for all activity measures. And just because a tracker says it measures variable “X” doesn’t mean it is measured accurately.  Very few activity trackers and fitness apps have been validated, and if they have, results may not match the assertions marketed by the product developers.

Most activity tracker data is not yet ready for entry into Electronic Health Records (EHRs).  First, products need to be validated, then their measures need to have standard codes (LOINC, SNOMED, UCUM) attached to them before the data should be accepted into EHRs or research databases.  For some unknown reason, exercise stakeholders lag far behind other key adopters of health IT codes (‘big” labs, all health-related federal agencies, care organizations, insurance companies, EHR vendors, and Health Information Exchanges; source: LOINC) in utilizing the codes for the variables they measure.

Regarding validation studies, I applaud FirstBeat for their extensive listing of studies on, or using, their products/technology (I receive no compensation nor am I an investor or currently an owner of a FirstBeat product).  It’s often difficult just to find out how a fitness app or activity tracker measures (estimates) calories – which equation is used, and equations are misused e.g. Resting Metabolic Rate (RMR) for Basal Metabolic Rate (BMR).  Simple errors like this can contribute 10-20% overestimation of calories and energy expenditure (1).

Furthermore, RMR can vary significantly with gender, age, or obesity (2,3,4,5).  However, most physical activity observational studies, which is what activity trackers are suited for, still use the ‘standard’ MET (Metabolic Equivalent) oxygen value of 3.5 ml/kg/min as RMR.  This RMR few adults have, most are significantly lower (2,3,4,5).  Since the ‘standard’ MET is widely used in observational studies, and most of those studies use questionnaires that have their own validity issues (6), accuracy can vary significantly from an MET that was actually measured in a lab.

Activity trackers have the potential to gather more accurate physical activity measures compared to the same measures gathered from patient reported questionnaires.  This can be a good thing for observational research.  However, if activity trackers also defer to the ‘standard’ MET, use metabolic equations incorrectly, are not validated, do not attach their measures to standard health IT codes, and importantly, if EHRs, researchers, and users do not demand that they do, then tapping into their research potential will be delayed.

References:

1. Dietary Reference Intakes: Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, Amino Acids.  IOM 2005; page 112.

2. Metabolic equivalent: one size does not fit all.  J Appl Physiol 99: 1112–1119, 2005.

3. Examining Variations of Resting Metabolic Rate of Adults: A Public Health Perspective.  Med. Sci. Sports Exerc., Vol. 46, No. 7, pp. 1352–1358, 2014.

4. Errors in MET Estimates of Physical Activities Using 3.5 ml·kg–1·min–1 as the Baseline Oxygen Consumption. Journal of Physical Activity and Health, 2010, 7, 508-516.

5. Correction factors for the calculation of metabolic equivalents (MET) in overweight to extremely obese subjects. International Journal of Obesity (2014) 38, 13831387.

6. A systematic review of reliability and objective criterion-related validity of physical activity questionnaires. International Journal of Behavioral Nutrition and Physical Activity 2012, 9:103 pgs 1-55.

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