Measurement of Physical Activity Using Accelerometry in Persons With Multiple Sclerosis

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Robert W. Motl Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA

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The consequences of multiple sclerosis (MS), particularly gait and walking dysfunction, may obfuscate (i.e., make unclear in meaning) the measurement of physical activity using body-worn motion sensors, notably accelerometers. This paper is based on an invited keynote lecture given at the 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement, June 2022, and provides an overview of studies applying accelerometers for the measurement of physical activity behavior in MS. The overview includes initial research uncovering a conundrum with the interpretation of activity counts from accelerometers as a measure of physical activity. It then reviews research on calibration of accelerometer output based on its association with energy expenditure in yielding a biologically based metric for studying physical activity in MS. The paper concludes with other applications and lessons learned for guiding future research on physical activity measurement using accelerometry in MS and other populations with neurological diseases and conditions.

Multiple sclerosis (MS) is an immune-mediated, neurogenerative disease of the central nervous system with a prevalence approximating one million adults in the United States (Wallin et al., 2019). This disease results in demyelination and transection of axons and subsequent loss of neurons throughout the brain, brain stem, and spinal cord. The extent and location of central nervous system damage manifest in the consequences of MS, and gait and walking dysfunction are common and debilitating consequences of this disease (Sasaki et al., 2017). Other consequences of MS include depression, fatigue, pain, cognitive dysfunction, and compromised quality of life and participation. This is noteworthy as the consequences of MS, particularly gait and walking dysfunction, can obfuscate (i.e., make unclear in meaning) the measurement of physical activity using body-worn devices such as motion sensors, including pedometers and accelerometers. This same issue exists in other diseases and conditions wherein gait and walking dysfunction are common such as Parkinson’s disease, aging, and Down syndrome.

This paper is based on an invited keynote lecture given at the 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM), June 2022, and provides an overview of studies applying motion sensors, particularly body-worn accelerometers, for the measurement of physical activity behavior in MS. The paper reviews research on the first application of accelerometry in MS and then reviews subsequent research on the meaning of output from accelerometry in MS. The paper further reviews research on calibration of accelerometer output based on its association with energy expenditure for yielding a biologically based metric for studying physical activity in MS. The paper concludes with additional applications and lessons learned for guiding future research on physical activity measurement using accelerometry in MS and other populations with neurological diseases and conditions.

The First Application of Accelerometry for Measuring Physical Activity in MS

The first application of accelerometry in MS involved a study examining the hypothesis that physical activity is lower in MS than healthy controls (Ng & Kent-Braun, 1997). The researchers recruited three samples consisting of (a) healthy persons with MS (i.e., no cardiovascular, metabolic, or immunologic disorders); (b) healthy sedentary controls (one or fewer formal exercise sessions of 20+ min/session for 3+ months); and (c) active healthy controls (vigorous dynamic exercise 3+ days/week for 20+ min/session for 3+ months). The participants completed a seven-day physical activity recall (7dPAR) and wore a triaxial accelerometer (TriTrac-R3D, Hemokinetics) around the waist during the waking hours of the day for 7 days. The researchers compared the three samples on levels of “physical activity” using the accelerometer and 7dPAR. There were no differences in physical activity based on 7dPAR between the MS sample and sample of sedentary controls, but both groups reported less physical activity than the physically active control sample. By comparison, there were differences in accelerometer measured physical activity (i.e., counts/day) between the MS sample and sample of sedentary controls, and both groups had less physical activity than the physically active control sample. The researchers interpreted the results as indicating that accelerometry provided a more sensitive measure for detecting differences in physical activity than self-report/recall in MS. Such an interpretation has a notable caveat. The lack of difference in physical activity levels based on 7dPAR between MS and sedentary controls actually is consistent with the operational definitions of the two samples, and would seemingly support the construct validity of the self-report instrument as a measure of physical activity. By comparison, the difference in physical activity levels based on accelerometry between MS and sedentary controls is inconsistent with the operational definitions of the two samples, and would seemingly suggest other contributors to score meaning beyond physical activity. One possibility is walking dysfunction that is prevalent even early in MS, as this could influence the vertical displacement of the center of mass during ambulation and result in lower accelerometer counts in the MS than sedentary controls. If correct, this might imply that accelerometer output reflects contributions of both physical activity and walking dysfunction in MS, as reported in the next section and summarized in Table 1.

Table 1

The Conundrum With Accelerometer Output in MS: Measurement of Physical Activity, Walking Dysfunction, or Both?

Research questionStudy number (reference)Key findings/resultsConclusion
Does accelerometer output of vertical counts/day measure physical activity in MS?Study 1 (Motl et al., 2006)Strong correlations between (a) counts/day from accelerometer and steps/day from pedometer (r = .93) and (b) weaker, but still moderate-to-strong correlations between counts/day from accelerometer and scores from two self-report measures of physical activity (r = .52 and r = .75) in 30 persons with MS.The research supports the construct validity of counts/day from accelerometers as a measure of physical activity among persons with MS.
Study 2 (Gosney et al., 2007)Strong correlations between (a) counts/day from accelerometer and steps/day from pedometer (r = .82) and (b) weaker, but still moderate-to-strong correlations between counts/day from accelerometer and scores from two self-report measures of physical activity (r = .53 and r = .36) in 196 persons with MS. 
Does accelerometer output of vertical counts/day measure walking dysfunction in MS?Study 1 (Weikert et al., 2012)Strong correlations between accelerometer output (counts/day) and walking endurance (r = .78) and walking agility (r = −.68) performance tests in 33 persons with MS.The research indicates that accelerometer output of counts/day provides a measure of walking mobility in MS.
Study 2 (Motl et al., 2013)Strong and consistent correlations between accelerometer output (counts/day) and neurological disability (r = −.52), self-reported mobility disability (r = −.55) and walking dysfunction (r = −.62) scores, and walking speed (r = −.60) and endurance (r = .63) performance tests in 256 persons with MS. 
Does accelerometer output of vertical counts/day measure both physical activity and walking dysfunction in MS?Study 1 (Weikert et al., 2010)Moderate correlations between accelerometer counts/day and scores from self-report questionnaires of physical activity (r = .36 and r = .34) and walking mobility (r = −.38 and r = −.40) in 269 persons with MS.

Accelerometer counts cross-loaded on both physical activity and walking mobility latent variables in confirmatory factor analysis of data from 269 persons with MS.
The research indicates that accelerometer output in counts/day provides a measure of both physical activity and walking mobility in persons with MS, whereas self-report instruments are measuring either physical activity or walking mobility in this population.

Note. MS = multiple sclerosis.

The Conundrum With Accelerometer Output in MS: Measurement of Physical Activity, Walking Dysfunction, or Both?

An initial study examined the construct validity of physical activity measures in MS based on administration of multiple physical activity measures (i.e., two self-report surveys, a pedometer, and an accelerometer) in conjunction with a nomological net (i.e., conceptual framework that identifies and specifies interrelationships among constructs; Motl et al., 2006). Participants were 30 persons with an established, definite diagnosis of MS who wore a pedometer (Yamax SW-200) and a single-axis accelerometer (ActiGraph Model 7164) during the waking hours of a 7-day period. After the 7-day period, the participants completed a self-administered physical activity questionnaire (Godin Leisure-Time Exercise Questionnaire [GLTEQ]) and then underwent a 7dPAR. There were strong correlations (a) between scores from GLTEQ and 7dPAR (r = .84); (b) between counts/day and steps/day from the accelerometer and pedometer (r = .93), respectively; and (c) weaker, but still moderate to strong correlations between scores from the self-report and objective measures (range of r = .44–.75). The results supported the preliminary validity of accelerometer output as a measure of physical activity in MS.

Those results were then replicated in a larger study examining the construct validity of physical activity measures in MS again using multiple measurement methods in conjunction with a nomological net (Gosney et al., 2007). Participants (N = 196) completed two self-report measures of physical activity (GLTEQ and abbreviated International Physical Activity Questionnaire [IPAQ]) and wore a pedometer (Yamax SW-200) and an accelerometer (ActiGraph Model 7164) during the waking hours of a 7-day period. There was a large correlation between counts/day and steps/day from the objective devices (r = .82) and a moderate correlation between scores from the self-report surveys (r = .37). The correlations across measurement methods were moderate to large in magnitude (range of r = .32–.53). Such results replicated previous research on the construct validity of counts/day from accelerometers as a measure of physical activity among persons with MS.

The contrasting notion that accelerometer output might reflect contributions of walking dysfunction rather than physical activity was supported in a study examining correlations among measures of walking mobility and physical activity in MS and controls (Weikert et al., 2012). The sample of 66 participants (33 MS and 33 matched controls) completed a battery of questionnaires, performed the 6-min walk (6MW) and timed up and go (TUG), and wore an accelerometer (ActiGraph Model 7164) for a 7-day period. Participants completed the GLTEQ and abbreviated IPAQ after the 7-day period. Accelerometer output (counts/day) was significantly correlated with only mobility (6MW, r = .78; TUG, r = −.68) measures in MS, whereas it correlated with both mobility (6MW, r = .58; TUG, r = −.49) and physical activity (GLTEQ, r = .56; IPAQ, r = .53) measures in controls. These results supported the early notion that counts/day from accelerometers might measure walking mobility, rather than physical activity in persons with MS.

Those results were replicated in a larger study examining accelerometry as a possible ecologically valid and objective measure of community ambulation in MS (Motl et al., 2013). The sample of 256 persons with MS completed the Patient Determined Disease Steps (PDDS) and Multiple Sclerosis Walking Scale-12 (MSWS-12), underwent a neurological examination for the generation of an Expanded Disability Status Scale score, undertook the timed 25-foot walk and 6MW, and wore an ActiGraph accelerometer (ActiGraph Model 7164) during the waking hours of a 7-day period. The accelerometer output (counts/day) was significantly correlated with Expanded Disability Status Scale (r = −.52), PDDS (r = −.55), and MSWS-12 (r = −.62) scores, and timed 25-foot walk (r = −.60) and 6MW (r = .63) performance. Such results provided additional evidence from a large sample of persons with MS that accelerometry provides a measure of walking mobility in MS.

One final study adopted a novel approach of decomposing shared variance in physical activity, walking impairment, and accelerometry by using confirmatory factor analysis with physical activity and walking mobility modeled as latent variables (Weikert et al., 2010). The sample of 269 persons with a definite diagnosis of relapsing-remitting MS completed the GLTEQ, abbreviated IPAQ, MSWS-12, PDDS, and then wore an ActiGraph accelerometer (ActiGraph Model 7164) for 7 days. The data were analyzed using bivariate correlation and confirmatory factor analysis. The results indicated that (a) the GLTEQ and IPAQ scores were strongly correlated and loaded significantly on a physical activity latent variable, (b) the MSWS-12 and PDDS scores were strongly correlated and loaded significantly on a walking mobility latent variable, and (c) accelerometer counts/day correlated similarly with the scores from the four self-report questionnaires and cross-loaded on both physical activity and walking mobility latent variables. This final study indicated that accelerometer output in counts/day provides a measure of both physical activity and walking mobility in persons with MS, whereas self-report instruments are measuring either physical activity or walking mobility in this population.

Calibration of Accelerometer Output Based on Its Association With Energy Expenditure in MS: The Solution for the Conundrum?

One possible approach for resolving the conundrum in measurement involves adopting a calibration approach for interpreting accelerometer output based on its association with energy expenditure during physical activity (i.e., moderate-to-vigorous physical activity [MVPA]). This approach was initially developed for healthy adults and involved establishing count ranges (i.e., cut points) for processing accelerometer output that align with common metabolic equivalents of task (MET) categories for physical activity intensity (Freedson et al., 1998). This provided researchers with cut points for operationally defining and processing accelerometer output into time spent in selected intensity categories such as MVPA. The sample of 50 adults wore a uniaxial accelerometer (ActiGraph Model 7164) on a belt positioned on the right hip, and expired gases were collected and analyzed using open circuit spirometry during three 6-min bouts of exercising on a motorized treadmill (slow walking, fast walking, and jogging) with 5 min of rest between bouts. Activity counts from the accelerometer and steady-state oxygen consumption were strongly correlated across the three conditions (r = .88). This permitted estimation of activity count cutoffs corresponding with light (<3 METS, <1,952 counts/min), moderate (3–5.99 METS, 1,952–5,725 counts/min), hard (6–8.99 METS, 5,725–9,498 counts/min), and very hard (9+ METS, >9,498 counts/min) levels of intensity. Such results provided the first biologically based accelerometer count ranges for establishing time periods of engaging in different intensities of physical activity over the course of a day and across multiple days.

The first study calibrating the output of an accelerometer as a measure of MVPA in MS involved a sample of 24 persons with MS and 24 controls who were similar in age, sex, height, and weight (Motl et al., 2009). The participants undertook three 6-min periods of walking at 3.2, 4.8, and 6.4 km/hr on a motor-driven treadmill, and activity counts and energy expenditure were measured with an accelerometer (ActiGraph Model 7164) worn on the right hip and open-circuit spirometry, respectively. The results indicated that (a) persons with MS had greater energy expenditure, but not activity counts, during all treadmill walking speeds than did controls; (b) there was a strong linear relationship between activity counts and energy expenditure during treadmill walking, but the slope of the relationship was steeper in persons with MS than in controls; and (c) the cut points for light, moderate, and vigorous physical activity were lower in persons with MS than in controls. These results provided evidence for a strong linear relationship between activity counts and energy expenditure during walking in persons with MS and yielded preliminary cut points based on counts/minute for quantifying time spent in light, moderate, and vigorous physical activity using accelerometers in MS.

Another study examined the association between rates of activity counts and energy expenditure during walking by using two models of accelerometers and generated cut points representing MVPA in persons with MS (Sandroff, Motl, et al., 2012). The sample of 43 persons with MS and 43 matched controls undertook 5 min of seated rest and five 6-min periods of walking at 54, 67, 80, 94, and 107 m/min on a motor-driven treadmill (slow through fast walking). Participants wore two models of accelerometers (ActiGraph Models 7164 and GT3X) and a mouthpiece in-line with an open-circuit spirometry system for measuring energy expenditure. Strong linear associations were observed between accelerometer activity counts and energy expenditure, and the magnitude of association did not significantly differ between MS and controls for either model of accelerometer. The slope of the linear relationships was significantly steeper in persons with MS than controls, and this resulted in distinct cut points for MVPA based on accelerometer counts for persons with MS (e.g., 1,723 counts/min for Model 7164 and 1,584 counts/min for Model GT3X) and controls (e.g., 2,017 counts/min for Model 7164 and 1,950 counts/min for Model GT3X). The strong linear relationship between activity counts and energy expenditure and resulting cut points for quantifying time spent in MVPA should allow for better understanding of health-promoting physical activity and its predictors and consequences when using accelerometers in MS.

There is seemingly benefit of calibrating accelerometer output and creation of MS-specific cut points for MVPA in MS. For example, one study compared physical activity levels between samples of 77 persons with MS and 77 matched controls using the GLTEQ, abbreviated IPAQ, and waist-worn accelerometer (ActiGraph model 7164). The accelerometer data were processed and scored using MS- and control-specific cut points for MVPA (Sandroff, Dlugonski, et al., 2012). The results indicated statistically significant (p < .01) differences between groups in accelerometer activity counts (d = 0.66), accelerometer step counts (d = 0.71), accelerometer-based time spent in MVPA (d = 0.40), GLTEQ scores (d = 0.62), and IPAQ scores (d = 0.56). Interestingly, the magnitude of group differences in physical activity was smallest for MVPA measured using MS- and control-specific cut points for processing accelerometer data. Such results suggested that the calibrated accelerometer data might provide a more realistic estimate of differences in physical activity based on correcting for influences of gait and walking dysfunction into the raw signal and O2 cost of walking. This collectively indicates that there is benefit in the resolution and interpretation of accelerometer data in MS through application of a calibration approach that addresses the underlying dissociation in the relationship between energy expenditure and accelerometer counts during ambulatory physical activity in MS compared with controls.

Other Applications

The approach for calibrating accelerometer output based on a biologically based substrate of physical activity (i.e., energy expenditure) has extended beyond waist-worn applications, activity counts, and samples of MS. For example, the calibration approach has yielded MVPA cut points for processing accelerometer data in breast cancer survivors (Trinh et al., 2019), Parkinson’s disease (Jeng et al., 2020a), Down syndrome (Agiovlasitis et al., 2011), and even wheelchair users (Learmonth et al., 2016). The calibration approach has been applied for generating MVPA cut points for processing step data in MS (Agiovlasitis & Motl, 2014; Agiovlasitis et al., 2016), Down syndrome (Agiovlasitis, Beets, et al., 2012), and Parkinson’s disease (Jeng et al., 2020b). There have been additional applications for generating cut points for processing wrist-based accelerometer data in Parkinson’s disease (Jeng et al., 2022) and Down’s syndrome (Agiovlasitis, Motl, et al., 2012). This suggests a broad-evidence base for accurately quantifying MVPA using accelerometers in a range of diseases and conditions.

Lesson Learned

One primary lesson learned through previous research is that accelerometers and other motion sensors might yield data applicable for measurement of more than physical activity in MS and other chronic diseases and conditions. There may be an opportunity for calibrating accelerometers and other motions sensors against gait and balance systems for generating unique outcomes for capturing movement dysfunction in MS and other conditions and diseases. This might involve calibration of accelerometer output with laboratory systems, such as motion capture and/or force plates or portable mobility lab systems (Mancini & Horak, 2016), for developing unique features and metrics of accelerometer data for measuring gait, walking dysfunction, balance, and falls (Hua et al., 2018). Such an effort is important as clinicians and researchers are interested in measurement tools for capturing the effects of clinical and experimental interventions on gait, mobility, and balance metrics in free-living conditions. There might even be value in monitoring patient status in free-living conditions using an integrative smart system that relies on accelerometer data.

Limitations of the Paper

There are notable limitations of the research included in this paper as it did not derive from a systematic review process or include a review of pedometers and commercially available, consumer-grade motion sensors. This paper did not discuss issues regarding accuracy of output from accelerometers, albeit there are published data supporting accuracy of accelerometer outcomes in MS and beyond (Motl et al., 2010). The paper did not review measurement of sedentary behavior using accelerometers and other body-worn sensors nor did it resolve issues with the validity of self-reports (i.e., cognitive impairment and score meaning). The research in this paper nearly exclusively focused on one brand of accelerometer (ActiGraph) and the content may or may not apply broadly for other models of ActiGraph accelerometers (wherein technology has presumable improved over time along with the development of cut points, or other brands of accelerometers and motion sensors).

Concluding Caveat

There is a noteworthy caveat of research calibrating accelerometer data. As in Alice in Wonderland, how far down the rabbit hole do we go? This caveat centers on the question of the extent and number of calibration studies that must be performed before we have developed sufficient cut points for processing accelerometer data that can be generalizable across populations. Do we need cut points for MVPA for every population with chronic diseases and conditions that influence gait, walking, and energy expenditure during movement? Or, is there an approach for generating “enough” or “sufficient numbers” of calibration efforts across a range of populations for creating generalizable cut points that can be applied without substantial population specific calibration? This paper does not resolve such a caveat, but rather raises it as a point of discussion that the field must resolve through dialogue and systematic collaboration.

Acknowledgment

This paper is based on a keynote presentation provided at the 8th ICAMPAM in Keystone, CO.

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Address author correspondence to robmotl@uic.edu, https://orcid.org/0000-0003-0112-2803.

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  • Agiovlasitis, S., Beets, M.W., Motl, R.W., & Fernhall, B. (2012). Step-rate thresholds for moderate and vigorous activity in persons with Down syndrome. Journal of Science and Medicine in Sport, 15(5), 425430. https://doi.org/10.1016/j.jsams.2012.03.001

    • Search Google Scholar
    • Export Citation
  • Agiovlasitis, S., & Motl, R.W. (2014). Step-rate thresholds for physical activity intensity in persons with multiple sclerosis. Adapted Physical Activity Quarterly, 31(1), 418. https://doi.org/10.1123/apaq.2013-0008

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