Original articleCalibration of Accelerometer Output for Ambulatory Adults With Multiple Sclerosis
Section snippets
Participants
We recruited a convenience sample of people with MS who resided in central Illinois and were participants from previous research studies in our laboratory. The sample of people without MS was recruited from the university community to be similar in age, sex, height, and weight compared with the sample of those who had MS. The method of recruitment involved a telephone call from a member of the research team who described the study and its procedures, answered all questions, and conducted a
Results
The sample consisted of 24 individuals (20 women) with a diagnosis of MS that was confirmed in writing by a neurologist, and 24 individuals without MS (20 women) who were similar in age, sex, height, and weight. The mean ± SD age, height, and weight for those with MS were 43.5±12.2 years, 167.5±11.6cm, and 76.7±19.2kg, respectively. The mean ± SD age, height, and weight for the control sample were 40.9±11.4 years, 170.1±8.0cm, and 72.5±15.0kg, respectively. The 2 groups did not differ in age (P
Discussion
The present study involved an examination of the association between the rates of ActiGraph accelerometer activity counts and energy expenditure during the dynamic activity of walking in persons with MS versus controls, and the generation of cut-points based on counts per minute that represent categories of light, moderate, and vigorous physical activity based on common MET categories in persons with MS. The primary findings were that (1) individuals with MS had a greater energy expenditure,
Conclusions
Our findings provide evidence for a strong linear relationship between activity counts and energy expenditure during walking in persons with MS and cut-points based on counts per minute for quantifying time spent in light, moderate, and vigorous physical activity using accelerometers in this population. This evidence supports the validity of quantifying physical activity using accelerometers and associated cut-points in ambulatory persons with MS.
References (20)
- et al.
Persistent pain and uncomfortable sensations in persons with multiple sclerosis
Pain
(2007) - et al.
Physical exercise in multiple sclerosis: supportive care or a putative disease-modifying treatment
Expert Rev Neurother
(2006) - et al.
Effect of exercise training on quality of life in multiple sclerosis: a meta-analysis
Mult Scler
(2008) - et al.
Effect of exercise training on walking mobility in multiple sclerosis: a meta-analysis
Neurorehabil Neural Repair
(2009) - et al.
Calibration of the Computer Science and Applications, Inc. accelerometer
Med Sci Sports Exerc
(1998) - et al.
An exploratory study of two measures of free-living physical activity for people with multiple sclerosis
Clin Rehabil
(2008) - et al.
Measuring activity patterns using actigraphy in multiple sclerosis
Chronobiol Int
(2007) - et al.
Validity of physical activity measures in ambulatory individuals with multiple sclerosis
Disabil Rehabil
(2006) - et al.
Physical activity and multiple sclerosis: validity of self-report and objective measures
Fam Community Health
(2007) - et al.
Quantification of lower physical activity in persons with multiple sclerosis
Med Sci Sports Exerc
(1997)
Cited by (49)
Calibration and validation of accelerometry to measure physical activity in adult clinical groups: A systematic review
2019, Preventive Medicine ReportsCitation Excerpt :The statistical approach adopted to translate activity counts and EE into cut-points could substantially impact the derived thresholds. For example, whilst linear regression has been most widely used (Aadland and Anderssen, 2012; Aadland and Steene-Johannessen, 2012; Motl et al., 2009; Sandroff et al., 2014a,b; Serra et al., 2017; Weikert et al., 2011), it assumes that the relationship between activity counts and metabolic data (i.e. VO2, METs) is linear. To address this issue, recent calibration studies have incorporated more flexible statistical methods, such as ROC analysis, hierarchical models, and machine learning (Bassett et al., 2000; Crouter et al., 2011; Freedson et al., 2005; Montoye et al., 2017).
Six-Minute Walk Test Performance in Persons With Multiple Sclerosis While Using Passive or Powered Ankle-Foot Orthoses
2018, Archives of Physical Medicine and RehabilitationIs physical behavior affected in fatigued persons with multiple sclerosis?
2015, Archives of Physical Medicine and RehabilitationCitation Excerpt :Physical behavior assessment was performed using 3-dimensional accelerometry (ActiGraph GT3X+ modela; 4.6×3.3×1.5cm; 19g). This device has demonstrated validity and reliability in measuring physical behavior in persons with MS and healthy adults.22,23 Participants wore the accelerometer on an elastic belt that was positioned at the waist.
Measurement of Physical Activity Using Accelerometry in Persons With Multiple Sclerosis
2023, Journal for the Measurement of Physical BehaviourWearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective
2023, Journal of Medical Internet ResearchAccelerometer Cut-Points for Physical Activity Assessment in Adults with Mild to Moderate Huntington’s Disease: A Cross-Sectional Multicentre Study
2022, International Journal of Environmental Research and Public Health
No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit on the authors or on any organization with which the authors are associated.