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. Author manuscript; available in PMC: 2021 Aug 1.
Published in final edited form as: Acta Neurol Scand. 2020 Apr 22;142(2):145–150. doi: 10.1111/ane.13250

Step-rate threshold for physical activity intensity in Parkinson’s disease

Brenda Jeng 1, Katie L Cederberg 1, Byron Lai 1, Jeffer E Sasaki 2, Marcas M Bamman 3,4,5, Robert W Motl 1,3
PMCID: PMC7357721  NIHMSID: NIHMS1590543  PMID: 32255504

Abstract

Objectives

To examine the relationship between step-rate and energy expenditure during treadmill walking in persons with PD and then further develop a step-rate cut-point for moderate-to-vigorous physical activity (MVPA) for persons with PD.

Materials & Methods

The sample consisted of 30 persons with mild-to-moderate PD and 30 controls matched by age and sex. Participants performed a 6-minute bout of over-ground walking at comfortable speed, and then completed three, 6-minute bouts of treadmill walking at 13.4 meters·min−1 slower, comfortable, and 13.4 meters·min−1 faster than comfortable speeds. The three treadmill speeds were based on the initial over-ground walking speed. The total number of steps per treadmill walking bout was recorded using a hand-tally counter, and energy expenditure was measured using a portable, indirect spirometry system.

Results

The results indicated a strong association between step-rate and energy expenditure for persons with PD (R2=0.92) and controls (R2=0.92). The analyses further indicated a steeper slope of the association for persons with PD compared with controls (t(58)=–1.87, p<0.05), resulting in a lower step-rate threshold (t(58)=2.19, p<0.05) for persons with PD (~80 steps·min−1) than controls (~93 steps·min−1).

Conclusion

Collectively, these results support the application of this disease-specific step-rate threshold for MVPA among persons with PD. This has important implications for physical activity promotion, prescription, and monitoring using accelerometers and pedometers for persons with PD to manage health and symptoms of PD.

Keywords: Parkinson disease, exercise, energy metabolism, oxygen consumption, walking

Parkinson’s disease (PD) is a neurodegenerative disorder with an estimated prevalence of nearly 1 million adults in the United States.1 The manifestations of PD include motor and non-motor symptoms, such as bradykinesia, tremors, rigidity, gait dysfunction, cognitive decline, depression, sleep disturbances, and sensory impairment.2 The disease manifestations further may be associated with physical activity, defined as any bodily movement produced by skeletal muscle contraction that increases energy expenditure above resting values (one’s resting metabolic rate considered as one metabolic equivalent of task, or MET). There is evidence that participation in moderate-to-vigorous physical activity (MVPA) has substantial benefits for health and disease-specific symptoms in PD,3 yet persons with PD are one-third less physically active than older adults from the general population.4

The veracity and interpretability of research on physical activity in PD depends on the accuracy and meaningfulness of physical activity measurement.5 There are multiple methods for measuring physical activity in PD, and one direct, gold-standard method of capturing physical activity and its intensity during ambulatory physical activity may be indirect measurement of energy expenditure using calorimetry. This method of measurement, however, is expensive and cannot be readily administered in the free-living environment over prolonged periods of time without disrupting behavior. Accordingly, an alternative and indirect method of capturing physical activity and its intensity involves measuring the rate of step counts per unit time and interpreting those data based on a cut-point for MVPA. Such an opportunity can be accomplished by examining the relationship between rate of step counts from motion sensors and energy expenditure from calorimetry.

There is existing research that has established step-rate cut-points for interpreting the output from motion sensors for quantifying MVPA in adults from the general population and other clinical populations with mobility disability such as multiple sclerosis. For example, one study examined the relationship between step-rate and energy expenditure during three bouts of treadmill walking among persons with multiple sclerosis and controls, and reported a steeper association between change in step-rate and energy expenditure in persons with multiple sclerosis than controls, and this resulted in a lower, disease-specific step-rate threshold for MVPA.6 Another study examined this relationship in persons with MS who had varying levels of ambulatory disability during over-ground walking at comfortable, slower, and faster speeds, and reported different step-rate thresholds for MVPA based on disability and height.7 Overall, those step-rate thresholds have been quite valuable for quantifying and prescribing physical activity in multiple sclerosis,8 and there might be similar value in PD.

Importantly, the step-rate cut-points for MVPA developed in controls and other populations with neurological conditions (i.e., multiple sclerosis) may not be entirely appropriate or applicable for persons with PD. Persons with PD exhibit gait dysfunction, even in the early stages of the disease, as well as other PD-specific symptoms such as bradykinesia, tremors, and rigidity;2 those features of PD may result in higher energy expenditure during walking at various speeds compared with controls and persons with multiple sclerosis. Importantly, persons with multiple sclerosis may display many of the same features as persons with PD, but PD is more prevalent in persons who are of older age, and age can result in a change in association between step-rate and energy expenditure. Those observations underscore the importance of developing PD-specific step-rate thresholds for MVPA. Of note, we are aware of one study that has generated cut-points for quantifying MVPA from the rate of activity counts (i.e., arbitrary unit of measurement for quantifying the intensity of bodily acceleration), rather than step counts, using research-grade accelerometer data in PD;9 however, it is not feasible for the general population to use these accelerometers and interpret the output. Data from research-grade accelerometers are not readily accessible without downloading the data via proprietary software, and such software might not provide accurate data based on existing algorithms in PD.10,11

Of note, the development of step-rate cut-points for MVPA in mild-to-moderate PD is further supported by evidence for the accuracy and precision of commercially available motion sensors. For example, one study examined the accuracy and precision of three commercially available motion sensors for step counts during 6-minute walking bouts in persons with PD and reported that a waist-worn motion sensor was the most accurate and precise in measuring step counts compared to wrist-worn motion sensors during both over-ground and treadmill walking.10 Another study examined the accuracy of two wrist-worn motion sensors during 2-minute bouts of over-ground walking in measuring steps and physical activity intensity in persons with PD and reported that a wrist-worn motion sensor was accurate in measuring steps but not physical activity intensity.12 Collectively, commercially available motion sensors may be accurate and precise in measuring steps in PD, and this supports the interest in developing step-rate thresholds for quantifying MVPA that can be applied for interpreting the output from such devices for prescribing, monitoring, and tracking physical activity in persons with PD.

The current study examined the relationship between step-rate and energy expenditure during treadmill walking in persons with PD and then further developed a step-rate cut-point for quantifying time spent in MVPA for persons with PD. We hypothesized that there would be a strong association between step-rate and energy expenditure in persons with PD and controls matched by age and sex. We further expected a steeper association in persons with PD and that this difference would result in a significantly lower step-rate threshold in persons with PD than controls. If the step-rate threshold for persons with PD is lower than controls, this threshold may possibly be used as an approach to identify persons who are at risk of developing PD-related motor symptoms and screen these individuals for preclinical diagnosis.13 The development of a PD-specific step-rate threshold may further facilitate the promotion, prescription, and monitoring of health-promoting physical activity using motion sensors among persons with PD.

MATERIALS & METHODS

Participants

Persons with PD were recruited through support groups, clinics, and community events in the Birmingham area. The inclusion criteria were the following: (a) diagnosis of PD, (b) age of 50–74 years, (c) Hoehn and Yahr stage 2 or 3, and (d) ability to walk independently without the use of an assistive device. The exclusion criteria for persons with PD were (a) unresponsiveness to dopaminergic medications or (b) motor impairments from neuroleptic medication or multiple strokes. Controls were recruited from community events and word-of-mouth. Controls were matched on age (within 5 years of a person with PD) and sex, and included in the study if they were (a) 50–74 years old, (b) apparently healthy characterized by the absence of major cardiovascular, neuromuscular, and/or pulmonary disease, and (c) ability to walk independently without assistive devices. Persons were excluded based on the presence of any medication (e.g., metformin) or diagnosis of a condition (e.g., diabetes) that significantly affects metabolism. The sample consisted of 30 persons with PD and 30 controls matched by age and sex who completed the study protocol.

Protocol

This protocol has been reported in a recent publication,10 and was administered in the study for multiple purposes including the assessment of motion sensory accuracy and precision and the development of a step-rate cut-point for quantifying time spent in MVPA for persons with PD. The study protocol was approved by the University’s Institutional Review Board, and all participants provided written informed consent. Participants came to the laboratory for a single session of data collection. Participants completed demographic and clinical information as well as the Physical Activity Readiness Questionnaire to identify contraindications for engaging in exercise.14 Height and weight were measured using a calibrated scale and stadiometer. One member of the research team assessed the disease status of the participants with PD using the Hoehn and Yahr rating and the Movement Disorder Society version of the Motor Examination from the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS ME). Participants were fitted with the portable metabolic system. Resting energy expenditure was measured during a 5-minute quiet rest in a seated position. Participants performed a 6-minute bout of over-ground walking and three, 6-minute walking bouts on a motor-driven treadmill (Trackmaster TMX428, Fullvision). Participants performed the initial over-ground walking bout at normal comfortable speed, and this was used to determine the participant’s three treadmill walking speeds for a manipulation of step-rate and energy expenditure. Participants walked at 13.4 meters·min−1 slower, comfortable walking speed, and 13.4 meters·min−1 faster than comfortable walking speed on the treadmill; this would presumably yield a linear manipulation of step-rate and energy expenditure for persons with PD and the controls. Participants rested for 5 minutes between each of the four walking bouts, and the order of treadmill speeds was not randomized or counter-balanced.

Measures

Step-rate

One member of the research team counted total steps taken during each of the three, 6-minute treadmill walking bouts using a hand-tally counter. Step-rate per walking bout was calculated by dividing total number of steps taken by the 6 minutes and expressed as steps·min−1. This method of manually counting steps has previously been undertaken in PD and other populations.6,10 and provides an accurate measurement of actual steps taken while walking.

Energy Expenditure

The rate of oxygen consumption (VO2), or energy expenditure, was measured using breath-by-breath analysis with a portable, open-circuit spirometry system (OXYCON Mobile, Vyaire). The O2 and CO2 gas analyzers and flow-meter were calibrated before each participant’s visit using a 3-L syringe and verified concentrations of gases based on the manufacturer’s recommendations. Participants were fitted with a face mask and a chest harness that held the portable metabolic system. We measured VO2 on 15-second intervals during the three, 6-minute bouts of treadmill walking. The outcome of interest was steady-state VO2, and this value was calculated by averaging the VO2 over the last three minutes of each 6-minute walking bout and expressed as mL·kg−1·min−1.

Data Processing and Analysis

The individual-level step-rate and metabolic data across rest and the three walking bouts were imported into Microsoft Excel for processing. This allowed the estimation of the squared multiple correlation coefficient (R2), intercept, slope, and step-rate cut-point for 3 METs (i.e., MVPA) per individual based on the linear association between step-rate and energy expenditure across the three speeds. The estimates were then used for subsequent analyses.

Data were analyzed using SPSS ver. 25. The step-rates and metabolic data from the three treadmill walking bouts were examined using two-way, mixed-factor analyses of variance (ANOVAs) with group (PD and control) as a between-subject factor and speed (slower, comfortable, faster) as a within-subject factor. The effect sizes for the ANOVAs were expressed as partial eta-squared (ηp2) with values of .01, .06, and .14 interpreted as small, moderate, and large effects, respectively.15 The R2, intercept, slope, and step-rate cut-point were analyzed between groups (PD and control) using independent samples t-tests. We interpreted the significance of all analysis using a one-sided p-value of 0.05.

RESULTS

The sample consisted of 30 persons with mild-to moderate PD and 30 controls matched by age and sex. The mean (SD) age of persons with PD and controls were 64.4(6.4) and 63.5(6.9) years, respectively. The male:female ratio was 19:11 in both groups. Persons with PD had a mean height of 170.6(8.5) cm and weight of 79.2(16.3) kg, and controls had a mean height of 174.2(9.1) cm and weight of 81.1(15.3) kg. There were no differences in height [t(58)=1.58, p>0.05] and weight [t(58)=0.46, p>0.05] between the two groups. The disease duration of our sample with PD was 6.6(5.2) years, and the median (interquartile range) score of the MDS-UPDRS ME was 21(28); this value is considered as mild-to-moderate disability severity based on the MDS-UPDRS.16 The slower, comfortable, and faster treadmill speeds for persons with PD were 48.3, 61.7, and 75.1 meters·min−1, whereas the speeds for controls were 53.6, 67.1, and 80.5 meters·min−1, respectively.

The step-rate data are presented in Table 1. The two-way ANOVA did not identify a significant group by speed interaction on step-rate (F=2.29, p>0.05, ηp2=0.04). There further was not a significant main effect of group on step-rate (F=0.24, p>0.05, ηp2=0.00), but there was a significant main effect of speed on step-rate (F=78.46, p<0.01, ηp2=0.58). This indicated that step-rate increased with faster treadmill speeds similarly across both groups.

Table 1.

Step-rate and metabolic (rate of oxygen consumption [VO2]) data for rest and three treadmill walking speeds in persons with Parkinson’s disease (PD) and controls.

Group Variable Rest Speed
Slower Comfortable Faster
PD (n=30) Step-rate 0(0) 101.6(11.6) 109.5(12.1) 116.5(11.8)
VO2 3.5(0.6) 11.5(2.8) 13.1(2.8) 15.6(3.4)
Controls (n=30) Step-rate 0(0) 102.8(15.1) 106.9(8.9) 113.9(9.6)
VO2 3.6(0.9) 10.5(2.1) 11.7(2.0) 13.7(2.5)
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Note: Values are reported as mean (SD). Step-rate data are expressed as steps·min−1 and VO2 expressed as mL·kg−1·min−1.

The metabolic data are presented in Table 1. The ANOVA identified a significant group by speed interaction on energy expenditure (F=4.21, p<0.05, ηp2=0.07). The interaction indicated that the increase in energy expenditure was greater with the increase in speed among those with PD compared with controls. There further was a significant main effect of group (F=4.99, p<0.05, ηp2=0.08) and a significant main effect of speed (F=285.17, p<0.01, ηp2=0.83) on energy expenditure. The main effects indicated that persons with PD expended more energy compared with controls across the three speeds, and that energy expenditure was higher with faster speeds across both groups.

There was a strong linear relationship between step-rate and energy expenditure across the three speeds in the overall sample based on the average (SD) R2 value of 0.92(0.08). The R2, slope, intercept, and step-rate cut-points of persons with PD and controls are provided in Table 2. The values for R2 (t(58)=0.03, p>0.05) and intercept (t(58)=–0.93, p>0.05) did not differ between persons with PD and controls. However, the value for slope (t(58)=–1.87, p<0.05) was significantly different between persons with PD and controls. The independent samples t-test further indicated a significant difference between groups in step-rate cut-points (t(58)=2.19, p<0.05).

Table 2.

Values of R2, slope, intercept, and step-rate cut-points data for persons with Parkinson’s disease (PD) and controls.

Variable PD (n=30) Controls (n=30) p-value
R2 0.92(0.09) 0.92(0.08) 0.49
Slope 0.0916(0.0232) 0.0815(0.0184) 0.04
Intercept 3.5432(1.0354) 3.3316(0.7004) 0.18
Step-rate cut-point 79.8(18.9) 92.7(26.2) 0.02
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Note: All values are presented as mean (SD).

DISCUSSION

This study examined the relationship between step-rate and energy expenditure in persons with mild-to-moderate PD and controls matched by age and sex. We further presented a step-rate threshold for MVPA among persons with PD. The results indicated a strong association between step-rate and energy expenditure for persons with PD and controls. The analyses further indicated a steeper slope of the association for persons with PD compared against controls; this resulted in a lower step-rate threshold for MVPA among persons with PD. Collectively, these results support the application of this PD-specific step-rate threshold for MVPA, and this has important implications for preclinical diagnosis,13 physical activity promotion, prescription, and monitoring in PD using motion sensors such as accelerometers and pedometers.

Our study generated the first step-rate cut-points for physical activity intensity, specifically MVPA, among persons with PD and examined if the cut-point may differ from controls matched by age and sex. Our results indicated a step-rate cut-point of ~80 steps·min−1 and ~93 steps·min−1 for levels of MVPA in persons with PD and controls, respectively; the cut-point for persons with PD was significantly lower than for controls. This finding is consistent with previous studies involving multiple sclerosis, such that persons with multiple sclerosis had a lower step-rate threshold than adults of the general population.6 Of note, it is not surprising that persons with PD had a lower step-rate threshold, as persons with PD may have motor symptoms (i.e., bradykinesia, rigidity, postural instability) resulting in gait abnormalities (i.e., increased double support time, reduced stride length, insufficient heel strike).17,18 However, a driving factor of this difference in the thresholds may be from energy expenditure. Persons with PD demonstrated greater energy expenditure compared with controls across the three speeds. Overall, the step-rate threshold for MVPA in persons with PD is lower than controls, and this supports application of these PD-specific cut-points in future research involving physical activity in PD.

This disease-specific step-rate threshold may have important implications for physical activity promotion, prescription, and monitoring for persons with PD. Clinicians and exercise specialists may promote walking at moderate intensity as a form of physical activity for management of health and symptoms of PD. With this disease-specific step-rate threshold, clinicians and exercise specialists may provide persons with PD with a prescription of accumulating, for example, 2,400 steps within 30 minutes three days a week, as this would meet the recommended physical activity guidelines.19 This can be used in conjunction with motion sensors that capture exercise intensity to confirm that persons with mild-to-moderate PD reach the moderate-intensity physical activity threshold. Moreover, there has been a substantial rise in wearable technology in persons with PD; however, with these motion sensors, the proprietary algorithms may not be applicable to persons with PD.5 Previous research has suggested a step-rate threshold for MVPA of ~100 steps·min−1 for adults of the general population, but this cut-point is ~20 steps·min−1 higher than for persons with PD and would lead to the misclassification of MVPA as well as the erroneous prescription of physical activity for PD. This highlights the importance of using disease-specific step-rate threshold, which can be easily measured using a pedometer. For instance, if a person with PD accumulates close to 400 steps in five minutes, this corresponds with the moderate-intensity threshold of ~80 steps·min−1; if not, the person can speed up to reach the moderate-intensity threshold or speed up to achieve a higher intensity. Physical activity can be monitored and tracked by both the person with PD and their clinician or exercise specialist to develop and implement a program that is appropriate and tailored for the individual with PD based on goals and physical abilities and re-evaluate, if necessary.

Our study is not without limitations. One limitation is that we recruited persons with mild-to-moderate PD who did not need assistive devices to walk; this limited the generalizability of our results amongst the larger population with PD, including persons with more severe PD who use mobility devices. Future studies should consider examining the generalizability of these cut-points to over-ground walking, as the protocol we used to generate cut-points involved treadmill walking at various speeds. This is important, as walking and its mechanics may differ between treadmill and over-ground walking in PD, and this possibility could influence the application of cut-points derived from a treadmill for over-ground walking. Another limitation is that we did not generate cut-points from research-grade accelerometer data, but rather from manually counted step counts. Of note, one study applied Actigraph accelerometers and generated cut-points for activity counts, rather than steps counts, from 3, three-minute bouts of walking across various speeds for older adults with PD;9 however, Borg’s Rate of Perceived Exertion, rather than indirect calorimetry, was used to generate the physical activity intensity cut-point. Those generated cut-points from research-grade accelerometer activity counts should be confirmed with metabolic data during onset of steady-state kinetics, and compared with step-rate threshold for flexibility in physical activity promotion, prescription, and monitoring for persons with PD.

Overall, there was a strong linear association between rates of step counts and energy expenditure across three speeds of treadmill walking in persons with PD and controls, and, importantly, the association was steeper in PD than controls. This resulted in a step-rate threshold for MVPA that was lower among persons with PD when compared with controls. This disease-specific threshold may have implications for clinicians and exercise specialists to better promote, prescribe, and monitor health-promoting physical activity to manage symptoms of PD and improve quality of life.

Acknowledgments

Funding

This work was supported by the National Institutes of Health (NIH) National Rehabilitation Research Resource to Enhance Clinical Trials (REACT, NIH P2CHD086851), a mentor-based post-doctoral fellowship from the National Multiple Sclerosis Society (MB 0011), and a pre-doctoral fellowship from the NIH National Heart, Lung, and Blood Institute (NIH T32HL105349). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Conflicts of Interest

The authors have no conflicts of interest to declare.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

†

Institution where work was conducted

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