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Quality control and data reduction procedures for accelerometry-derived measures of physical activity
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by Rachel Colley, Sarah Connor Gorber and Mark S. Tremblay
Abstract
Keywords
Findings
Authors
What is already known on this subject?
What does this study add?
Abstract
Background
This article describes four key quality control and data reduction issues that researchers should consider when using accelerometry to measure physical activity: monitor reliability, spurious data, monitor wear time, and number of valid days required for analysis.
Data source and methods
Exploratory analyses were conducted on an unweighted subsample (n=987) of the accelerometry data from the Canadian Health Measures Survey. Participants were asked to wear an accelerometer for 7 consecutive days. Calibration, reliability, biological plausibility and compliance issues were explored using descriptive statistics.
Results
Ongoing calibration is an effective method for identifying malfunctioning accelerometers. The percentage of files deemed viable for analysis depends on participant compliance, the allowable interruption period chosen and the minimum wear-time-per-day criterion. A 60-minute allowable interruption period and 10-hours-per-day wear time criteria resulted in 95% of the subsample having at least 1 valid day, and 84% having at least 4 valid days.
Interpretation
Before the derivation of physical activity outcomes, accelerometry data should undergo standardized quality control and data reduction procedures to prevent mis-representation of the results. Incomplete accelerometry data should be handled carefully, and strategies to improve compliance in the field are warranted.
Keywords
ambulation, data quality, error, health measurement, quality control
Findings
Considerable evidence indicates that sedentary behaviour is a major risk factor for obesity and several other chronic conditions. Population-level surveillance of physical activity has historically relied on questionnaires, a method of assessing lifestyle behaviours that can be affected by measurement bias. Objective measurement devices, notably accelerometers, have the potential to overcome many problems associated with self-reports, and they provide robust and detailed information about physical activity. However, because small inconsistencies can have a substantial impact on outcome variables, stringent quality control and data reduction procedures are necessary. [Full text]
Authors
Rachel Colley (1-613-737-7600x4118; Rachel.Colley@statcan.gc.ca) and Mark Tremblay are with the Healthy Active Living and Obesity Research Group, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario; Rachel Colley and Sarah Connor Gorber are with the Health Analysis Division at Statistics Canada, Ottawa, Ontario.
What is already known on this subject?
- Accelerometry-derived measures of physical activity continue to be published in the research literature. However, the implementation and reporting of data reduction and analytical methods is inconsistent.
- Given the potential impact that data reduction procedures can have on physical activity outcomes, consensus is needed among researchers using these devices.
- Publication of recommendations about processing accelerometry data from the National Health and Nutritional Examination Survey facilitated the establishment of consistent procedures for the Canadian Health Measures Survey.
What does this study add?
- One of the primary challenges in using accelerometers to derive information about physical activity is low compliance with wearing the devices. The resultant incomplete data create interpretation issues and require consistent quality control and data reduction procedures.
- Four important quality control and data reduction steps are presented that help address incomplete accelerometry data and should be considered before deriving physical activity information: intra- and inter-monitor reliability, spurious data thresholds, derivation of wear time, and number of valid days required for analysis.
- The information is particularly relevant for researchers who work with or compare their results to Canadian Health Measures Survey accelerometry data.
