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Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD.
We previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD.
Fifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system.
High correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (
A smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD.
Parkinson disease (PD) is a common neurodegenerative disease associated with numerous movement disorders and symptoms. One of the most significant disorders of PD is abnormal gait [
Considerable effort has focused on the measurement of gait and mobility. Clinical rating scales [
With progress in smartphone technology, the inertial measurement unit (IMU) of smartphones—which consists of a 3D accelerometer, 3D gyroscope, and digital compass—has been utilized to capture movement associated with gait. Several studies have shown that the IMU of smartphones can accurately and reliably measure motion of the body in younger and older adults, as well as in those with PD [
Our team recently created a smartphone-based app enabling automatically guided assessment of gait when walking normally (ie, single task) and while performing a serial-subtraction cognitive task simultaneously (ie, dual task). The app was designed to provide standardized multimedia instructions to the user throughout the test to minimize the need for trained study personnel to administer the tests. Moreover, the assessment is completed with the phone placed in the user’s pants pocket, thereby removing the need for additional equipment to secure the phone to the body with a predetermined orientation. We previously demonstrated the validity and reliability of this app-based approach to measure gait characteristics in healthy adults [
Fifty-two patients diagnosed with idiopathic PD by clinicians of the Department of Movement Disorders at Beijing Tiantan Hospital (Beijing, China) completed this study. All participants provided written informed consent as approved by the Beijing Tiantan Hospital Institutional Review Board. The inclusion criteria were: (1) aged between 25 and 80 years, (2) clinically diagnosed with PD using the 2015 Movement Disorder Society diagnosis criteria [
The app was designed for use on the iPhone iOS platform. The goal of the app was to recreate standard laboratory-based assessments of standing and walking for use in the laboratory, clinics, and other nonlaboratory remote environments. Here, we focused on the functionality of the app to measure gait in participants with PD. The full description of the app, as well as the validity and reliability of the gait assessment in healthy adults, has been previously reported [
Screenshots of the smartphone gait and balance assessment app. The app, consisting of automatic animated, text, and voice instructions to users can measure the standing balance, 6-minute walking, and 45-second single- and dual-task walking performance (A). Before the walking test starts, users must first watch a comprehensive animation to introduce the testing procedure (B). Then, users must read several screens of text instruction (C). After reading the instruction, they press “begin” (D) and place the phone into the pocket of their pants to initiate the test. They then complete the walking trials following voice cues (including the message shown in panel E and the random 3-digit number provided in the dual-task condition). After pressing “Done” (F), the motion data of walking captured by the smartphone are automatically uploaded to a cloud-based server for storage and offline analysis.
All assessments were completed in the neurological clinics of Tiantan Hospital. All participants completed the tests in “medication-off” state, as defined by withholding their levodopa medication for at least 8 hours. The functional tests and clinical rating scales were completed before the walking assessment for all participants. Participants were given sufficient rest (at least 10 minutes) between each test to eliminate the effects of fatigue on test performance.
The walking assessment was completed along a straight 10-meter hallway of the hospital. Participants were instructed to wear comfortable shoes and pants or shorts with pockets. Each participant completed the app assessment twice, with each assessment entailing one trial of single-task walking and one trial of dual-task walking at the participant’s preferred speed. During each trial, participants were asked to walk down the 10-meter hallway, turn 180 degrees, and walk back to the start
Example of raw (A) and filtered smartphone- (black) and Mobility Lab (red)–recorded acceleration signals (B) along the Earth coordinate system vertical axis during straight walking for 5 seconds.
The Unified Parkinson Disease Rating Scale III (UPDRS III) was completed to assess PD severity. The UPDRS III assesses multiple aspects of function, including mental and mood function, motor control, activities of daily living, and complications of therapy. The total score of UPDRS III, ranging from 0 to 28, was used in the analysis, with greater scores reflecting more severe PD.
The Montreal Cognitive Assessment (MoCA) was used to examine global cognitive function, including visuospatial, executive function, attention/working memory, episodic memory, and language. The MoCA total score, which ranges from 0 to 30, was used for analysis, with lower scores reflecting worse cognitive function.
Participants completed the Hamilton Anxiety (HAM-A) [
The pipeline of signal processing and data analysis was described in our previous paper [
Finally, stride time and stride time variability were calculated for each trial. Stride time is typically defined as the time between two consecutive heel strikes of one foot, and thus relates to one complete gait cycle. Stride time variability is defined using the ratio of the SD of the stride times to the mean stride time within each walking trial. Longer stride time and greater stride time variability within a given walking condition (ie, single or dual task) have each been associated with aging [
Similar gait metrics were derived from the Mobility Lab data using the software platform provided with the system.
Statistical analyses were performed using JMP Pro 14 software (SAS Institute, Cary, NC). The significance level was set to
We examined the validity of app-derived stride time and stride time variability by assessing the correlation between app-derived metrics and the corresponding metrics derived from the Mobility Lab system using Pearson correlation analysis. The validity in single- and dual-task walking conditions was examined in separate models. We also calculated the absolute difference between the metrics measured by the app and by Mobility Lab. In addition, we examined the effects of task condition on stride time and stride time variability in separate two-way analysis of variance (ANOVA) models. The model factor was the task condition (ie, single and dual task), and the dependent variables were the stride time and stride time variability in each model, respectively. Each of these ANOVA models was adjusted for age, sex, and years of formal education.
Linear regression models were then used to examine the relationships between gait metrics measured by the app and scores of the UPDRS III, MoCA, HAM-A, and HAM-D scales. Age, sex, and years of formal education were included as covariates in each model.
Participants ranged in age from 40 to 83 years. No adverse events or safety issues were reported during the study. Each participant successfully completed the walking trials following the instructions provided by the app with no difficulty.
Participants characteristics (N=52).
Demographics and gait metrics | Value | ||
Female, n (%) | 19 (37) | ||
Age (years), mean (SD) | 63 (10) | ||
Education (years), mean (SD) | 11 (3) | ||
Height (m), mean (SD) | 1.7 (0.9) | ||
Body mass (kg), mean (SD) | 70 (21) | ||
Duration of Parkinson disease (years), mean (SD) | 6.4 (3.8) | ||
UPDRS IIIa | 38.3 (15.1) | ||
MoCAb, mean (SD) | 22 (4) | ||
HAM-Ac, mean (SD) | 11.8 (4) | ||
HAM-Dd, mean (SD) | 11.1 (5.1) | ||
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single task (s) | 1.09 (0.08) | |
|
dual task (s) | 1.17 (0.12) | |
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DTCe (%) | 2.7 (6) | |
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single task (%) | 40 (22) | |
|
dual task (%) | 63 (19) | |
|
DTC (%) | 5.1 (39.4) |
aUPDRS III: Unified Parkinson Disease Rating Scale III.
bMoCA: Montreal Cognitive Assessment.
cHAM-A: Hamilton Anxiety Scale.
dHAM-D: Hamilton Depression Scale.
eDTC: dual task cost.
fRatio of the SD to the mean of stride time.
The average number of identified strides during the 20-meter straight-walking trials was 9.5 (SD 1.5). The absolute difference between the app and Mobility Lab measures was quite small (
Correlation between stride time (A) and stride time variability (B) as measured by the app (x-axis) and the Mobility Lab system (y-axis) for each participant.
Absolute difference between gait metrics derived from the app and Mobility Lab data.
Condition | Stride time (seconds), mean (SD) | Stride time variation (seconds), mean (SD) |
Single task | 0.01 (0.008) | 0.003 (0.003) |
Dual task | 0.01 (0.009) | 0.002 (0.002) |
Linear regression models adjusted for age, sex, and duration of formal education demonstrated that the stride time variability of both single-task (β=.39,
Association between single- (A) and dual-task (B) stride time variability and total score of Unified Parkinson Disease Rating Scale III (UPDRS III).
Linear regression models revealed that the stride time variability of dual-task walking (β=.48,
Stride time variability of both single- and dual-task walking was significantly correlated with HAM-A (single task: β=.49,
Association between single- and dual- task stride time variability and the score of the Hamilton Anxiety (HAM-A) (A) and Hamilton Depression (HAM-D) (B) scales. Greater scores reflect a worse mood status.
This study provides a proof of concept that patients with PD can use a smartphone app by themselves to accurately assess gait during both single-task and cognitive dual-task walking conditions, with the phone placed in the front pocket of the pants or shorts. Moreover, gait metrics derived from app data, particularly under the dual-task condition, were associated with several important functional and clinical rating scales in PD. Such information may thus be used to help assess PD severity, its functional impact, and potentially the effectiveness of medication or other clinical interventions within this vulnerable population.
Typically, gait assessments are performed with low frequency (ie, only during in-person clinical visits, or only before and after a study intervention). However, recent studies have demonstrated that even within an individual, the characteristics of walking vary considerably within and between days. Such variance in function has been associated with multiple important outcomes [
Walking in everyday life often requires simultaneous performance of additional cognitive tasks such as speaking to others, thinking of questions, or reading signs in the environment. This dual tasking disrupts the performance of one or both tasks. Consistent with previous studies [
This study demonstrated the feasibility of using a smartphone app for gait assessment in PD, as well as the validity of app-derived gait metrics within this population. Several participants in this study had mild-to-moderate cognitive impairment as assessed by the MoCA (the mean score was 22) and all of the participants successfully completed the test without any reported difficulties. Future development and testing is nevertheless needed to optimize the design of the app for use in patients with PD that have more severe cognitive impairment, as well as for those with other comorbidities (eg, diabetes mellitus, cardiovascular disease, chronic pain), and those with limited vision, hearing loss, tremor, or other issues that may hinder the ability to interact with the smartphone. In this study, the association between gait metrics and functional characteristics was assessed cross-sectionally, and gait metrics were only assessed in person within the clinical setting. Future work is thus warranted to establish the usefulness of regular, smartphone-based gait assessments captured from remote settings (ie, patient homes). Work focused on remote assessment is of particular importance during the current COVID-19 pandemic as it promises to help maintain quality of care while reducing the spread of infectious disease. The usefulness of the app is also likely to be further optimized by validating other clinically meaningful metrics of gait (eg, gait speed and the asymmetry of gait); characterizing gait during turning [
analysis of variance
freezing of gait
Hamilton Anxiety Scale
Hamilton Depression Scale
inertial measurement unit
Montreal Cognitive Assessment
Parkinson disease
Unified Parkinson Disease Rating Scale III
ZL and TF are supported by grants from the Natural Science Foundation of China (81571226, 81771367, 81901833). XJ is supported by the Basic Research Project of Shenzhen Natural Science Foundation, Shenzhen Science and Technology Planning Project (JCYJ20170818111012390, JCYJ20190807145209306), Sanming Project of Medicine in Shenzhen (SZSM201812039), and Shenzhen Key Medical Discipline Construction Fund (SZXK012). JZ and BM are supported by a Hebrew SeniorLife Marcus Applebaum grant.
None declared.