In people with Parkinson’s disease (pwPD), walking dysfunctions represent a very common and disabling feature which is typically expressed by a gait pattern characterized by short stride length, increased cadence, and reduced velocity [
Although physical activity (PA) has been found to be beneficial in improving mobility in pwPD [
While there is a certain consensus on the fact that PA contributes to improving gait and mobility [
To partly overcome such limits, this study aims firstly to describe the patterns of PA in a cohort of pwPD based on a 3-month monitoring. Then, during the same period, quantitative data on the quality of gait patterns, by means of spatiotemporal and kinematic parameters, were also collected and correlated with PA indicators. The hypothesis to verify is that individuals who exhibit better gait features are characterized by higher and more intense PA during their daily lives.
The study was performed in the period March–December 2017 and involved 18 outpatients with PD (10 females and 8 males) followed up at the Neurology Department of the G. Brotzu General Hospital (Cagliari, Italy) who were enrolled on a voluntary basis. Their demographic and clinical characteristics are shown in Table
Anthropometric and demographic aspects of participants.
Variable | Mean ± SD | Range (min–max) | |
---|---|---|---|
Age (years) | 68.0 ± 10.8 | 53–83 | |
Height (cm) | 165.6 ± 7.9 | 150–178 | |
Body mass (kg) | 69.2 ± 9.4 | 50–81 | |
PD duration (years) | 9.9 ± 6.0 | 4–27 | |
Hoehn and Yahr (H&Y) | 1.9 ± 0.4 | 1.5–2.5 | |
Unified Parkinsonʼs Disease Rating Scale (UPDRS III) | Overall score | 17.8 ± 9.6 | 5–32 |
Axial subscore (items 27–30) | 2.8 ± 1.5 | 1–5 |
Values are expressed as mean ± SD.
All participants met the following criteria: diagnosis of PD according to the UK Brain Bank criteria [
Data on PA were collected using a triaxial accelerometer (ActiGraph GT3X; Acticorp Co., Pensacola, FL, USA) previously employed in similar studies carried out on individuals with PD [
The use of 3 different processing procedures, although all based on the same device and the same physical variable (i.e., VM), was suggested by the fact that, to date, a validated set of cut-points for wrist placement of the accelerometer in individuals with PD is unavailable. Thus, the algorithm of Hildebrand et al. [
A 3D computerized gait analysis was performed at the beginning (T0) and at the end (T3) of the 3-month evaluation period to calculate both spatiotemporal and kinematic gait parameters using an optoelectronic system composed of 8 infrared cameras (Smart-D; BTS Bioengineering, Italy) set at a frequency of 120 Hz. After anthropometric data collection, 22 spherical retroreflective passive markers (14 mm in diameter) were placed on the skin of the individual’s lower limbs and trunk at specific landmarks, following the protocol described by Davis et al. [ Five spatiotemporal parameters (gait speed, cadence, stride length, stance, and swing-phase duration) Nine kinematic parameters, namely, pelvic tilt, rotation, and obliquity, hip flexion-extension, adduction-abduction, and rotation, knee flexion-extension, ankle dorsi-plantarflexion, and foot progression (i.e., the angle between the axis of the foot and the walking direction) Dynamic range of motion (ROM) for hip and knee flexion-extension and ankle dorsi-plantarflexion calculated during the whole gait cycle as the difference between the maximum and minimum values of each angle recorded during a trial
Kinematic data were summarized using the Gait Variable Score (GVS) and the Gait Profile Score (GPS), which are concise measures of gait quality proposed by Baker et al. [
The possible differences in PA levels associated with each time slot were assessed using the one-way analysis of variance for repeated measures (RM-ANOVA) considering as the independent variable the time slot and as dependent variables the PA parameters. The level of significance was set at
The hourly trends of step count and VM and the mean values of PA classified as a function of its intensity calculated on the 3 selected time slots are illustrated in Figures
Hourly trend (average value of 3 months) of step counts and vector magnitude counts.
Physical activity amount classified as a function of intensity for the 3 time slots. (MVPA = moderate‐to‐vigorous physical activity; VM = vector magnitude).
Physical activity patterns for the morning, afternoon, and evening time slots calculated as means of the 3-month monitoring period.
Physical activity patterns | ||||
---|---|---|---|---|
TS 1 (hours 6–12) | TS 2 (hours 12–18) | TS 3 (hours 18–24) | ||
Wallén et al. [ |
Sedentary behavior (%) | 33.41 ± 21.66 | 37.62 ± 18.70 | 42.78 ± 21.28a |
Low intensity (%) | 23.09 ± 8.55 | 26.01 ± 6.72 | 25.59 ± 6.91 | |
Moderate intensity (%) | 29.32 ± 13.85 | 26.21 ± 12.03 | 23.79 ± 13.05a | |
Vigorous intensity (%) | 13.93 ± 16.06 | 10.08 ± 12.21a | 7.66 ± 9.70a | |
MVPA |
43.25 ± 20.31 | 36.29 ± 18.50a | 31.45 ± 16.53a | |
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Nero et al. [ |
Speed ≤ 1.04 m/s (%) | 57.08 ± 26.65 | 62.99 ± 20.81 | 68.39 ± 19.62a |
Speed 1.05–1.30 m/s (%) | 26.53 ± 13.00 | 23.00 ± 10.86 | 21.38 ± 11.60a | |
Speed ≥ 1.31 m/s (%) | 14.65 ± 16.82 | 10.96 ±13.42 | 8.68 ± 10.39a | |
MVPA (%) | 41.18 ± 23.47 | 33.96 ± 21.10 | 30.07 ± 19.56a | |
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Hildebrand et al. [ |
Light intensity (%) | 59.05 ± 23.93 | 65.97 ± 20.41a | 70.87 ± 18.75a |
Moderate intensity (%) | 35.77 ± 19.10 | 30.58 ± 16.87 | 25.54 ±17.25a | |
Vigorous intensity (%) | 5.18 ± 8.91 | 3.44 ± 6.54 | 3.22 ± 9.86 | |
MVPA |
40.95 ± 23.93 | 34.03 ± 20.41 | 29.91 ± 19.59a | |
Steps counts (daily steps) | 4313 ± 1973 | 3437 ± 1719a | 2889 ± 1557a | |
Vector magnitude (counts per day) | 735639 ± 452680 | 610262 ± 372729a | 512835 ± 323037a,b |
Values are expressed as mean ± SD. MVPA: moderate-to-vigorous physical activity; TS: time slot; asignificant difference vs. TS 1; bsignificant difference vs. TS 2;
ANOVA revealed a significant main effect of the time slot for both step counts (
The results of the classification of PA intensity with the three algorithms employed show similar results. For the Hildebrand algorithm, the effect of time slots was significant for percentage of time spent in sedentary activity (
Spatiotemporal and kinematic parameters of gait did not vary significantly between the beginning and the end of the 3-month period, except for the GVS of pelvic tilt, as visible from data in Tables
Values of the spatiotemporal parameters of gait at the beginning and end of the 3-month observation period.
Spatiotemporal parameters of gait | |||
---|---|---|---|
T0 | T3 |
| |
Step length (m) | 0.59 ± 0.10 | 0.59 ± 0.01 | 0.824 |
Gait speed (m/s) | 1.18 ± 0.23 | 1.18 ± 0.19 | 0.980 |
Cadence (steps/min) | 120.39 ± 11.18 | 120.88 ± 9.09 | 0.819 |
Stance-phase duration (s) | 0.60 ± 0.07 | 0.60 ± 0.05 | 0.754 |
Swing-phase duration (s) | 0.40 ± 0.04 | 0.39 ± 0.03 | 0.522 |
Values are expressed as mean ± SD.
Values of the kinematic parameters of gait at the beginning and end of the 3-month observation period.
Kinematic parameters of gait | ||||
---|---|---|---|---|
T0 | T3 |
| ||
GPS (°) | 7.31 ± 1.61 | 7.84 ± 2.44 | 0.637 | |
GVS (°) | Pelvic tilt | 5.51 ± 3.90 | 7.64 ± 5.10 | 0.042 |
Pelvic rotation | 3.80 ± 1.30 | 4.29 ± 1.79 | 0.285 | |
Pelvic obliquity | 2.47 ± 1.18 | 2.67 ± 1.13 | 0.481 | |
Hip flexion-extension | 8.30 ± 4.09 | 10.41 ± 6.22 | 0.059 | |
Hip abduction-adduction | 3.96 ± 1.59 | 4.15 ± 1.50 | 0.564 | |
Hip rotation | 8.59 ± 2.70 | 8.14 ± 2.99 | 0.641 | |
Knee flexion-extension | 9.17 ± 3.64 | 9.37 ± 4.13 | 0.157 | |
Ankle dorsi-plantarflexion | 7.17 ± 2.48 | 6.22 ± 2.28 | 0.319 | |
Foot progression | 7.80 ± 3.77 | 7.89 ± 4.14 | 0.828 | |
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ROM (°) | Hip flexion-extension | 42.15 ± 7.45 | 43.27 ± 7.23 | 0.197 |
Knee flexion-extension | 57.39 ± 4.23 | 57.72 ± 5.28 | 0.479 | |
Ankle dorsi-plantarflexion | 25.22 ± 6.70 | 26.47 ± 6.82 | 0.129 |
Values are expressed as mean ± SD.
To conclude, Tables
Spearman’s correlation analysis between physical activity intensity and spatiotemporal parameters of gait.
Correlation between physical activity and spatiotemporal parameters of gait | ||||||
---|---|---|---|---|---|---|
Gait speed | Stride length | Cadence | Stance phase | Swing phase | ||
Wallén et al. [ |
Sedentary behavior (%) | −0.088 | −0.482 |
0.509 |
−0.430 | −0.675 |
Low intensity (%) | 0.060 | −0.049 | 0.309 | −0.153 | −0.361 | |
Moderate intensity (%) | −0.105 | 0.159 | −0.451 | 0.374 | 0.612 |
|
Vigorous intensity (%) | 0.067 | 0.423 | −0.531 |
0.427 | 0.674 |
|
MVPA |
0.007 | 0.378 | −0.575 |
0.457 | 0.717 |
|
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Nero et al. [ |
Speed ≤ 1.04 m/s (%) | −0.009 | −0.356 | 0.591 |
−0.503 |
−0.687 |
Speed 1.05–1.30 m/s (%) | −0.104 | 0.169 | −0.534 |
0.444 | 0.683 |
|
Speed ≥ 1.31 m/s (%) | 0.024 | 0.367 | −0.544 |
0.412 | 0.669 |
|
MVPA (%) | −0.025 | 0.325 | −0.591 |
0.495 |
0.690 |
|
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Hildebrand et al. [ |
Light intensity (%) | 0.007 | −0.358 | 0.575 |
−0.467 | −0.704 |
Moderate intensity (%) | −0.072 | 0.291 | −0.575 |
0.474 | 0.734 |
|
Vigorous intensity (%) | 0.206 | 0.514 |
−0.437 | 0.313 | 0.604 |
|
MVPA (%) | 0.007 | 0.378 | −0.575 |
0.456 | 0.717 |
|
Step count | 0.343 | 0.586 |
−0.375 | 0.239 | 0.588 |
|
Vector magnitude count | −0.001 | 0.360 | −0.577 |
0.469 |
0.704 |
Correlation analysis between physical activity intensity and kinematic parameters of gait.
Correlation between physical activity and kinematic parameters of gait | ||||||||
---|---|---|---|---|---|---|---|---|
GPS | GVS hip FE | GVS knee FE | GVS ankle DP | ROM hip | ROM knee | ROM ankle | ||
Wallén et al. [ |
Sedentary behavior (%) | 0.310 | 0.019 | 0.203 | −0.106 | −0.346 | −0.474 |
−0.424 |
Low intensity (%) | 0.123 | 0.239 | −0.181 | −0.465 | −0.038 | −0.007 | −0.267 | |
Moderate intensity (%) | −0.606 |
−0.380 | −0.536 |
0.63 | 0.143 | 0.276 | 0.246 | |
Vigorous intensity (%) | −0.334 | −0.099 | −0.168 | 0.205 | 0.315 | 0.397 | 0.329 | |
MVPA |
−0.336 | −0.164 | −0.135 | 0.276 | 0.286 | 0.373 | 0.341 | |
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Nero et al. [ |
Speed ≤ 1.04 m/s (%) | 0.326 | 0.216 | 0.143 | −0.244 | −0.315 | −0.381 | −0.307 |
Speed 1.05–1.30 m/s (%) | −0.576 |
−0.394 | −0.527 |
0.018 | 0.195 | 0.377 | 0.212 | |
Speed ≥ 1.31 m/s (%) | −0.275 | −0.107 | −0.100 | 0.265 | 0.282 | 0.383 | 0.304 | |
MVPA (%) | −0.306 | −0.217 | −0.115 | 0.272 | −0.275 | 0.370 | 0.298 | |
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Hildebrand et al. [ |
Light intensity (%) | 0.327 | 0.182 | 0.143 | −0.265 | −0.276 | −0.356 | −0.328 |
Moderate intensity (%) | −0.498 |
−0.279 | −0.307 | 0.216 | 0.207 | 0.313 | 0.266 | |
Vigorous intensity (%) | −0.308 | −0.094 | −0.156 | 0.112 | 0.424 | 0.490 |
0.402 | |
MVPA (%) | −0.336 | −0.164 | −0.135 | 0.276 | 0.286 | 0.373 | 0.341 | |
Step count | −0.184 | −0.106 | −0.001 | 0.108 | 0.503 |
0.575 |
0.336 | |
Vector magnitude count | −0.323 | −0.170 | −0.150 | 0.261 | 0.282 | 0.362 | 0.320 |
Duration of the swing phase and cadence were found to be the gait variables significantly correlated with a larger number of PA parameters regardless of the algorithm considered (11 to 14 significant correlations out of 15 possible). Stride length was found to be significantly correlated only with step counts (rho = 0.59) and percentage of sedentary activity (rho = −0.48) calculated according to Wallén, while no significant correlations were found for gait speed. As regards the kinematic variables, the GPS was found to be significantly correlated negatively with the percentage of moderate activity as calculated by the Wallén (rho = −0.61) and Hildebrand (rho = −0.50) algorithms. The GVS associated with knee flexion-extension was also found to be negatively correlated with the percentage of moderate activity according to Wallén (rho = −0.54) and with the percentage of time spent at walking speed between 1.05 and 1.30 m/s (Nero algorithm, rho = −0.53). Dynamic ROM of the knee was negatively correlated with sedentary activity (Wallén algorithm, rho = −0.47) and positively correlated with vigorous activity (Hildebrand algorithm, rho = 0.49). Finally, step count was found to be positively correlated with both dynamic ROMs of the hip and knee (rho = 0.50 and 0.57, respectively).
The aim of the present study was to perform long-term monitoring of PA in pwPD and investigate the existence of possible correlations between PA and gait parameters, with these being objectively assessed using the gold standard for quantitative analysis of human motion, namely, the motion capture system. Our results detected a pattern for PA of pwPD with low-mild disability. They clearly show the existence of two peaks of PA, one in the morning (approximately hour 10) and another in the evening located between 6 and 7 PM. Unfortunately, a direct comparison with previous studies is difficult because even though several of these continuously monitored PA, they mostly report only examples of the curves of variation of PA parameters (usually the number of steps) during the day [
One of the purposes of our study was to compare different algorithms previously validated for use in pwPD [
Hourly trends for lowest-intensity physical activity (<3 MET) as calculated using 3 different cut-point sets.
The most innovative aspect of the present study is represented by the search for possible correlations between PA and gait patterns, with the latter being investigated using 3D computerized gait analysis, which represents the gold standard for human movement analysis. The results show that cadence and swing-phase duration exhibit the highest number of significant correlations with amount and intensity of PA performed. Individuals who spent less time in sedentary behavior and more time in moderate-to-vigorous activity are likely to exhibit a gait pattern characterized by reduced cadence and increased swing phase. Instead, the relationship of PA intensity with both stride length and stance-phase duration appears to be less generalized.
The reduction in swing-phase duration, which is a physiologic sign of gait deterioration associated with aging [
Cadence has been recognized as one of the gait parameters most suitable for representing ambulatory activity in free living, and in young healthy individuals, it has been found to be strongly correlated with PA intensity [
Finally, the overall quality of the gait pattern, as expressed by GPS, appears to be moderately correlated with the percentage of time spent in moderate-intensity PA, consistent in all the tested approaches; in particular, the alterations at the knee joint level appear to be the most involved in this process. Previous studies highlighted the existence of alterations of knee flexion-extension during gait, especially in terms of inadequate extension in the stance phase [
Some limitations of the study are to be acknowledged. Firstly, the participants were all volunteers, as the particular nature of the study (i.e., long-term use of a wearable device 24 h/day) required high levels of compliance to achieve reliable results [
This study investigated the relationship between amount and intensity of PA performed by individuals affected by PD with low-mild disability (objectively assessed using wrist-worn triaxial accelerometers) and the kinematic features of their gait patterns provided by computerized 3D gait analysis. PA parameters were estimated using different sets of cut-points for the accelerometric counts previously validated on pwPD and healthy older adults. The results show a daily trend, described similarly by all the approaches tested, characterized by two distinct peaks of activity, located in the morning and early evening. The main hypothesis of the study, namely, the existence of a relationship between the quality of the gait pattern and amount/intensity of performed PA, was substantially confirmed by the results of the correlation analysis. In particular, higher and more intense activity appears to be related to swing-phase duration and cadence, while the percentage of time spent in moderate activity also appears to be associated with the overall quality of gait kinematics (expressed by means of the GPS summary index) and with the alteration of flexion-extension of the knee joint. Although further studies on larger cohorts are necessary to better elucidate the influence of the disability level, gender, and socioeconomic status, the findings of the present study suggest that the continuous monitoring of PA in pwPD may represent a useful tool in predicting possible changes in the gait pattern and verify the effectiveness of rehabilitative treatments and PA programs.
The authors have no conflicts of interest to report.
MPa, MM, CC, and RP planned the study. GC, CC, and RP recruited participants and performed neurologic and clinical assessments. MPa, MPo, and GP acquired and analyzed the data. MPa and MPo wrote and corrected the manuscript.
The authors wish to thank the Sardinian Association of Patients with Parkinson’s Disease (ASAMPA) and in particular the chairperson Prof. Carlo Anchisi, for their valuable support. This study was funded by the Autonomous Region of Sardinia (grant CRP-78543 L.R. 7/2007) and by the Fondazione di Sardegna.