AbstractThis narrative review examines the utility of gait digital biomarkers in Parkinson’s disease (PD) research and clinical trials across four contexts: disease susceptibility/risk, disease progression, response to exercise, and fall prediction. The review of the literature to date suggests that upper body characteristics of gait (e.g., arm swing, trunk motion) may indicate susceptibility/risk of PD, while pace aspects (e.g., gait speed, stride length) are informative for tracking disease progression, exercise response, and fall likelihood. Dynamic stability aspects (e.g., trunk regularity, double-support time) worsen with disease progression but can improve with exercise. Gait variability emerges as a sensitive biomarker across all 4 contexts but with low specificity. The lack of standardized gait testing protocols and the lack of a minimum set of quantified digital gait biomarkers limit data harmonization across studies. Future studies, using a commonly agreed upon protocol, could be used to demonstrate the utility of specific gait biomarkers for clinical practice.
IntroductionParkinson’s disease (PD) is one of the first diseases for which digital biomarkers of mobility have been explored1,2. Traditionally, biomarkers have been defined as characteristics (physiologic, pathologic, or anatomic) that can be measured as an indicator of a biological process2,3,4. More recently, digital biomarkers have been defined as markers that can be measured by validated technologies5, such as wearable sensors coupled with computational tools, either in the clinic or in daily life. Specific biomarker contexts of use have been established by the U.S. Food and Drug Administration and the National Institutes of Health as part of their joint Biomarkers, Endpoints, and other Tools (BEST) resource6,7. Preferred biomarkers for PD are linked to fundamental features of PD neuropathology3,4, may be sensitive to preclinical disease, are correlated to disease progression2, and are responsive to treatment3,8.Mobility limitations, specifically those related to gait impairments, are among the earliest and most debilitating signs of PD9,10. Gait disturbances in people with PD often impair functional independence and are a major cause of morbidity and mortality. Gait characteristics affected early in PD include9: loss of arm swing (asymmetrically), slow turns, and shuffling, as well as increased temporal variability11,12, see Fig. 1. In addition, as PD progresses, reduction in step length, problems with gait rhythmicity, and freezing of gait can occur9,13. Emerging evidence from studies of gait changes in prodromal PD9 (REM Sleep behavior disorders14,15 or genetic mutations16) demonstrate that gait changes may occur well before a diagnosis of PD. Gait disturbances in people with PD frequently become difficult to treat and are not typically improved through pharmacological or surgical treatment9.Fig. 1: Stereotypical gait pattern in PD and independent domains of gait with their associated digital gait metrics.RoM Range of Motion, CoV Coefficient of Variation, SD standard deviation.Full size imageGait is a complex, yet stereotyped, automatic movement pattern that consists of several different physiological characteristics. As it is comprised of multiple characteristics sensitive to survival, age and pathology, several conceptual gait models have been established in the literature9,13. These gait models are helpful in reducing redundancy and aid in the interpretation of different gait domains. The most frequently used gait domain model lists five domains of gait: pace/turning, rhythm, asymmetry, variability, and stability/upper body (Fig. 1)17,18. Gait domains have been shown to be relatively independent and likely controlled by independent neural circuitry (e.g.; pace, rhythm, variability, asymmetry, upper body, and dynamic stability)17,19.Clinicians agree that gait impairments are highly relevant because of the importance of mobility to quality of life20. Gait impairments are also important because they are among the earliest diagnostic signs of PD, reflect disease severity and progression, are variably responsive to treatment, and have potential to predict falls9,21. However, gait impairments are most often assessed with subjective clinical scales that are limited in the gait domains assessed, such as the postural instability/gait disturbance (PIGD) portion of the revised Movement Disorders Society (MDS) Unified Parkinson’s disease Rating Scale (MDS-UPDRS) Motor Score (Part III)22,23 which has only one item evaluating gait on a scale of 0 to 4 and is not able to measure subtle changes in gait nor predicts falls24. Adding a prescribed test of gait utilizing digital technology offers a wide range of potentially reliable and responsive outcomes that may provide a continuous range of measurement, and be sensitive to small changes in disease state. In addition, there are a wide variety of tools that can be used as gait biomarkers.Gait Biomarkers: Tools for quantitative analysisVarious tools, including differing types of task parameters (e.g., dual-tasking, courses with or without turns or obstacles) and equipment (e.g., ranging from simple stopwatches and tape measures to optical, pressure-sensitive mats, and wearable Inertial Measurement Units, IMUs), are used to quantify gait. The metrics derived from these tools reflect diverse gait domains and are emerging as potential biomarkers for PD. Here, we review the gait metrics shown to be useful in the literature, given the absence of clear guidelines regarding which specific gait characteristics should be assessed for the aforementioned four different contexts of use, or whether the same gait metrics can be used across all four contexts.Stopwatches are used for clinic-friendly walk tests, such as the 10-meter, 2- and 6-minute walk tests, that specify distance and/or duration criteria. These tests, which are valid, reliable, and strongly correlated with each other25, assess gait speed or distance, providing a measure of functional status and overall health20. However, gait speed, alone, is not the most specific or sensitive biomarker of PD26,27 and may not best reflect the underlying pathophysiology of PD. Furthermore, the evaluation of gait speed and/or distance walked does not capture important aspects of upper and lower limb spatiotemporal metrics that have potential to provide more PD-sensitive and PD-specific biomarkers of gait28. Capturing continuous kinematic measures during walking requires more advanced instrumentation and technology.Commercial technologies have emerged as feasible methods to objectively characterize gait29,30,31,32,33,34. Studies over the past twenty years on quantitative gait characteristics in PD have used various technologies, including footswitches, optical cameras, pressure mats, and IMUs. Optical camera systems, which have been the gold standard to measure gait, allow for upper and lower body measures of gait while walking with the limitation of the calibrated volume, usually contained within 5 to 7 m of walking space35,36,37. Although this technique is very accurate, it is also time-consuming and requires trained personnel35,36,37. More portable solutions include footswitches and pressure mats, but are limited to measuring only the lower body during gait and/or a limited walking space (for the pressure mat). Even better, wearable IMUs can quickly and accurately quantify numerous gait measures from both the upper and lower body30,38,39, even as people go about their daily lives. Passive monitoring of gait in daily life holds great promise to derive sensitive information about gait variability across the day or over multiple days which could translate into important gait biomarkers30,38,39.Although reduced arm swing amplitude and increased arm swing asymmetry have been recognized as early clinical signs of PD40 only a small percentage of gait studies have quantified upper body characteristics during gait19. Together with reduced arm swing range of motion, measures of trunk range of motion during walking in the transverse, coronal or sagittal plane41 as well as measures of trunk motion regularity and smoothness42, have been found to be informative in discriminating between people with early PD and healthy controls. Of the papers that report arm measurements in PD during gait, 70% were published after 2015. Asymmetries of arm swing and/or step during gait can be calculated in various ways but often are expressed as a percent difference between right and left parameters43,44. Trunk-related measures can be calculated from accelerometers placed on the waist at the lumbar level or gyroscope on the sternum42. Studies have included a variety of digital gait measures, so the summary below is based on those reported.The use of footswitches and IMUs provides reliable measures of gait across long bouts of walking and to quantify stride-to-stride variability of different gait outcomes, which may be a sensitive measure in PD45,46. Usually, a one-minute walk, or a minimum of 20 strides, is the shortest duration or number of strides recommended to capture gait variability45,46. Gait variability is usually quantified with the coefficient of variation or the standard deviation of the number of strides walked45,46. The most common cause of gait variability (temporal or spatial) in PD is poor dynamic postural control of the trunk while walking, which is associated with compensatory postural stepping responses to recover equilibrium that disrupts gait rhythm47. Increased gait variability (stride-time and step-length) in people with PD and elderly people without movement disorders has been associated with a greater risk of falls48,49,50,51, and has also been identified as a precursor to freezing of gait (FOG).Activities of daily living frequently require walking and performing simultaneous cognitive or motor tasks, such as talking with someone or carrying groceries. Another way to assess gait impairments in people with PD is therefore using concurrent dual-task conditions, walking while performing subtractions for example, or other concurrent cognitive tasks52 to exacerbate impairments, such as freezing of gait53,54. Over the years, dual-task walking research has expanded rapidly52,53 and has also been used in research settings to bring out subtle changes in gait, for example, in people with prodromal PD55 or to evaluate susceptibility/risk of future falls. Despite the wide use of dual-task paradigm in gait, it is hard to draw consistent conclusions because of the breadth of methodological variations in using it, such as instructions given on prioritization, type of task chosen, and speed of task52. Standardization has not been reached, although general guidelines in which dual-task should be preferred for PD have recently been reported52.Gait Biomarkers: Domains for identifying disease risk, progression, response, and fall riskDifferent gait domains may be useful for understanding different contexts of impairment in PD (e.g., contexts of use). For example, more gait domains are impaired in later stages of PD, compared to earlier stages, as they may be related to the degeneration of different circuit types (e.g., dopaminergic, cholinergic, etc.)56. In particular, levodopa has been shown to increase the pace of gait (e.g., speed, stride length, etc.), but not improve postural stability (e.g., double support time, trunk stability) while walking57. Our focus in this narrative review is to summarize current evidence of specific gait biomarkers for use in clinical trials in the following 4 contexts of use6,7 as indicated in Fig. 2: (1) indicating the potential for symptomatic and pre-symptomatic disease (susceptibility/risk), (2) monitoring disease progression (progression), (3) assessing response to exercise (response), and (4) identifying the likelihood of a fall (prognostic).Fig. 2Context of use for gait biomarkers for Parkinson’s disease summarized in this review.Full size image
1) Gait Biomarkers to Indicate Susceptibility/Risk for PDThe clinical diagnosis of PD relies on the manifestation of the classic motor signs of bradykinesia, tremor, and rigidity58. By the time of clinical diagnosis, 50-70% of the nigral dopaminergic neurons are lost59. Therefore, there is still the need to identify reliable, easily accessible and sensitive biomarkers to detect early pathological changes. Might gait biomarkers be used to indicate risk for PD?One way to address this question is to look at gait studies in people who carry genetic mutations linked to PD, but do not yet have manifest PD (e.g.; normal clinical scales), see Table 1A. Only seven such studies have been published thus far: five in non-manifesting carriers of LRRK2, one in heterozygous Parkin carriers, and one in PINK1 carriers. These studies report a number of significant gait differences between non-manifesting carriers and healthy controls. Among the five studies focused on LRRK2, three studies reported greater stride time variability in non-manifesting LRRK2 carriers relative to healthy controls during regular (comfortably paced)60, fast-paced55,60, and dual-task walking16,55,60. In contrast, the fourth study noted lower stride-time variability in non-manifesting LRRK2 carriers than control subjects during fast walking61. This same study noted that carriers had longer than normal step times on the non-dominant side during fast walking and that longer step times correlated with lower dopamine transporter levels61. In addition, the fifth study of gait during daily life in non-manifesting LRRK2 carriers noted greater variability in arm swing amplitude across different epochs of walking over 7 days, but didn’t measure lower limb variables as only wrist-mounted accelerometers were used62. The single study of gait in non-manifesting Parkin carriers reported the ability to discriminate between this heterozygous carrier group and healthy controls using multivariate analysis with machine learning algorithms and found that dual-task gait best discriminated Parkin carriers from controls63. Finally, a study in heterozygous PINK1 mutation carriers found reduced bilateral arm swing in non-manifest carriers compared to healthy controls64. To date, there are no gait studies in non-manifesting GBA carriers, but there are four studies of people with manifest PD who were GBA carriers. These studies report more asymmetric arm swing65, slower gait speed in single-task66,67,68 and dual-task conditions68, and increased gait variability67 in those with manifest PD with GBA mutations compared to those with PD without GBA mutations. Across all the studies of genetic mutations related to early PD that we found, the most consistent finding was increased gait variability, with the most pronounced differences generally seen during challenging gait tasks (e.g., dual-task conditions).Table 1 List of references found for the 4 different context of use: (A) Gait biomarkers to Indicate Susceptibility/Risk portion. (B) Gait biomarkers to monitor disease progression. (C) Gait Biomarkers for Response to Aerobic Exercise, and (D) Gait Biomarkers for Prognosis (Falls)Full size tableAnother way to determine if digital gait measures can detect early, pathological changes in PD is to examine people with idiopathic, rapid eye movement sleep (REM) behavior disorder (iRBD). IRBD is characterized by elevated muscle tone during REM sleep (termed REM sleep without atonia, or RSWA) along with vigorous, sometimes violent, movements during REM sleep. The presence of iRBD is a strong predictor for developing synucleinopathies, including PD, Lewy body dementia, and multiple system atrophy69,70,71. More than 80% of iRBD patients convert to PD or another α-synucleinopathy with a median overall prodromal phase of 8 years, and the estimated prevalence of RBD in PD is 42%70,71. We identified 8 studies that attempted to detect prodromal gait abnormalities with quantitative measures in patients with iRBD by comparing them to healthy controls (with some also including individuals with idiopathic PD).We identified seven studies that reported significant gait differences between people with iRBD and age-matched healthy controls and PD participants. One study in iRBD used a machine learning approach with 59 gait parameters collected during single-task gait using an inertial sensor system and found that arm and trunk range of motion, asymmetry of limb movement, and abnormal gait rhythm (cadence) best discriminated participants with iRBD from healthy controls and people with PD with 100% sensitivity and 91 to and 95% specificity15. Other studies show that gait velocity is lower in participants with iRBD compared to controls72,73. Individuals with PD and RSWA (with or without dream enactment symptoms), were found to have slower gait velocity and shorter step lengths, compared to individuals with PD without RSWA and healthy controls14. People with iRBD have also been shown to have significantly decreased trunk range of motion and peak trunk angular velocity, and longer step time before turning than healthy controls in single-task, dual-task, and fast-paced walking74. Greater step length asymmetry during fast-paced walking75 and increased step-width variability and step-time during dual-task walking76 has also been reported in iRBD compared to healthy controls.There are conflicting reports on gait variability in people with iRBD, with one study reporting reduced gait velocity variability72 and another reporting increased stride time, double-support time and swing time variability compared to controls73. However, these two reports have fundamental protocol differences. For example, the first study72 collected data during daily life and the second one73 used a research laboratory, this difference in gait protocols is known to impact gait variability46,77. Of course, studies of gait in people with iRBD are limited by not knowing who will eventually express a synucleinopathy and what type they might manifest, as well as how long this expression might take (years to decades)78, so long-term longitudinal studies are needed.In summary, the most promising gait biomarkers for susceptibility/risk of developing PD are aspects of gait asymmetry and variability, for both upper and lower limbs, reduced trunk range of motion especially while challenging gait at either faster than comfortable speed or by adding a concurrent, dual-task (see Table 2). The gait biomarkers linked to the trunk motion and arm swing during gait may be directly linked to early PD neurophysiology. In fact, recent work79,80 suggests that early dopaminergic denervation in Parkinson’s disease follows a somatotopically-related pattern, starting with the upper-limb representation in the putamen and progressing over a 2-year period to the less-affected hemisphere. These changes could be associated with the clinical presentation and evolution of motor features reported by clinicians, and specifically, be reflected in reduced and asymmetric arm swing range of motion while walking. Lastly, the finding that these subtle changes in gait in prodromal or early disease are more obvious when challenging gait with a concurrent, cognitive dual-task for example, may reflect intact compensatory mechanisms at this stage. In fact, gait control may become less automatic due to damage of the basal ganglia, cortical areas, such as the prefrontal cortex, and require additional attention to compensate as the disease progresses9,16,53. However, when attention is devoted to a cognitive task, this compensatory mechanism breaks down, revealing underlying deficits of automatic axial control of gait.Table 2 Summary of the emerging gait biomarkers for the different context of use consideredFull size tableGait biomarkers to monitor disease progressionA critical limitation in the development of disease-modifying interventions for PD is the lack of reliable, objective measures of progression, particularly early in the disease course81. The etiology of idiopathic PD is unidentified and there is no known cure. There is, however, increasing evidence for neuroprotective and neuromodulatory therapies that may delay disease progression82. Unfortunately, clinical trials that aim to prevent or slow the progress of mobility disability in PD are limited by the low sensitivity of gold-standard outcome measures that depend upon expert qualitative assessment9,83,84.Objective measures of gait may provide markers of disease progression that can be extremely useful for clinical trials. Table 1B lists potential markers of disease progression summarized from seven studies. A recent, two-year study in 266 individuals with early PD showed that people with PD had greater declines in walking speed (clinically measured with a stop-watch in a 10-meter walk test) compared to declines in fine motor function of the upper extremities or quality of life85. Of the seven studies that have examined longitudinal progression of gait biomarkers in PD, all but one of them included people with PD already on dopaminergic medication for PD. The one, small longitudinal study of people with newly diagnosed PD who had not yet started dopaminergic medication found that the duration of turning 180 deg while walking back and forth over 7 meters showed reliable worsening over 18 months, compared to stable turning duration in healthy controls86.The MODEP study (MODeling Epidemiological data to study Parkinson’s disease progression)87, found that natural-pace gait characteristics were more sensitive to changes over 5 years than fast-paced gait among people with early and mid-stage PD compared to healthy older adults. Those with early PD significantly reduced their natural paced gait speed compared to healthy controls over time, rate of 2.1 percent per year in early PD compared to less than 1 percent in healthy controls. Interestingly, healthy controls decreased their gait variability over time, whereas those with early PD significantly increased their gait variability over 5 years87. Two other reports from the MODEP study PD88,89 showed that harmonic ratios, calculated from vertical (VT), medio-lateral (ML), and anterior-posterior (AP) accelerations of the trunk, worsened over 5 years in people with early PD71,72. Stride time variability, and stride regularity from trunk VT and AP accelerations were identified as features that worsened over time in mid-stage PD88. No additional longitudinal effect was noticed during dual-task walking compared to single-task walking89.Another paper, part of the ICICLE-PD study (Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation-PD), examined gait over 18 months in 121 people with PD who were newly diagnosed, and already taking PD medications84. Significant reduction in step length and swing time were found over 18 months, compared to 184 healthy controls84. The ICICLE study also tested the same cohort of people with PD over 6 years to examine the relationship between gait changes and medication changes over time90. Gait deterioration associated with aging were accelerated by the presence of PD and 4 gait biomarkers significantly changed with disease progression only in people with PD: increased variability of lower-limb swing time, step time, step width, and reduced swing time asymmetry. These findings extend previous work in this cohort over shorter periods by revealing additional changes in gait asymmetry and variability that were not evident over 18 or 36 months84. Of interest, the increase in medication use over time was related to increase in step width variability, pointing to differences at least partially based on medication status90.Another report in 91 individuals with early PD, the Oxford Quantification in Parkinsonism (OxQUIP) study, found changes in gait within 15 months from baseline, although clinical rating was not able to capture signs of disease progression91. This study used a machine learning approach to identify the combination of gait measures that best predicted the MDS-UPDRS Part III score and showed such combinations of measures to be more sensitive than the MDS-UPDRS III in capturing changes over time. Among the individual measures sensitive to change over time were: the angle of the foot (at heel strike and toe-off) and stride length. A smaller value in the angle of the foot at heel strike or toe-off indicated more shuffling of steps, that may increase with disease severity91.It is not known why gait characteristics progress more rapidly in some people with PD than others. PD has been classified into one of three clinical phenotypes based on motor symptoms: Postural Instability and Gait Disability (PIGD), tremor dominant (TD) and Intermediate92,93,94 but it is unclear if gait progresses faster in one group than another. The distinction was operationalized by creating a tremor subscore and PIGD subscore based on items of the Unified Parkinson’s Disease Rating Scale (UPDRS) or the MDS-UPDRS. Overall, the PIGD phenotype has been associated with slower gait speed and reduced stride length compared to the TD phenotype95,96,97,98,99. In addition, the PIGD phenotype also showed higher variability in gait characteristics84, in particular variability of stride time95 and stride length99, compared to the TD phenotype. However, longitudinal studies of digital gait characteristics in PIGD and tremor phenotypes are lacking in the literature, with only one longitudinal study previously cited tracking gait quantitatively84.In summary, the most promising gait biomarkers to monitor disease progression, despite the paucity of longitudinal studies, appear to be declines in gait pace (stride velocity, stride length and angle of the foot at heel strike), gait variability (stride time), and trunk stability, detected when walking long distances at a comfortable speed, see Table 2. Interestingly, changes in gait are best detected when participants with PD walk at a comfortable speed during single-task gait, as the addition of a concurrent, cognitive dual-task or fast walking does not help in identifying changes due to disease progression, possibly because of variability to response to the secondary task or because instructions to move as fast as possible invoke compensatory mechanisms to ovecome the decline in the automated gait pattern. As the disease progresses, we think that the degeneration of the putamen may also progress and spread to greater bilateral involvement, resulting in clinically-apparent slowness of gait and increased overall variability of gait accompanied by a progressive decline in gait automaticity; however, large-sample longitudinal studies have not yet included measures of arm swing nor turning characteristics of gait, nor molecular imaging. In addition, discrepancies in findings may be associated with performing gait under different medication conditions. This may be important especially early in the disease, since, medications can mask progression of gait in early PD100.Gait biomarkers for response to gait specific aerobic exerciseAerobic exercise has emerged as an intervention to address gait impairments for people with various neurologic conditions, including PD. Over the past 10 years, research studies investigating PD have employed gait specific aerobic exercise in a myriad of ways, including walking overground101,102, walking on a traditional treadmill103,104,105,106,107, walking on treadmills that apply concurrent perturbations to the participant108,109,110,111, robot-assisted walking112, Nordic walking113,114, walking in a virtual-reality environment115, walking on a curved treadmill116,117, and walking on a treadmill while carrying an additional load118. While gait velocity and distance, captured with a stop-watch and tape measure, are the two gait biomarkers most commonly assessed, other spatial and temporal characteristics have only been occasionally measured. Table 1C summarizes the evidence on gait metric outcomes for aerobic exercise.Objective measures of gait are more sensitive to change with exercise than clinical measures in people with PD which has a direct impact on the sample size of exercise studies119. Walking velocity and walking distance consistently benefit from aerobic gait exercise in people with PD. In 11 studies that measured changes in gait velocity after aerobic gait exercise, 10 demonstrated a benefit from the intervention101,102,103,108,109,112,114,115,117,118, while only one did not104. Similarly, all 11 studies that measured the impact of aerobic gait exercise on walking distance demonstrated a benefit from the intervention102,103,104,106,107,108,109,112,113,115,120.The literature is promising, but less robust for other spatial and temporal gait characteristics, summary in Table 2. Only 4 studies measured the effects of aerobic gait exercise on step length or stride length, but all showed an improvement103,111,117,118. Both studies that measured step time cadence103,117, as well as the single studies that each measured stride-time variability111 or double-support time103, demonstrated improvement from the aerobic gait exercise intervention. Similarly, both studies assessing freezing of gait showed that freezing improved with aerobic exercise, as well112,117. However, the single study looking at step symmetry did not demonstrate a significant benefit from the gait aerobic exercise111. In addition, no gait-specific aerobic exercise studies have examined spatial and temporal aspects of turning during walking.To investigate the responsiveness of gait biomarkers to exercise, here we only considered studies using exercise with modes that involve gait and are designed to meet moderate or vigorous aerobic exercise intensity levels. Assuming they include interventions that meet the operational definition of gait exercise, studies employing technology (e.g., virtual reality, robotic assistance, etc.) were included. The overwhelming majority of studies considered a limited set of gait biomarkers, such as gait speed or distance, only assessed with a stopwatch, with only four studies using technology to measure gait spatio-temporal parameters and gait stride-time variability. Collectively, aerobic gait exercise improves aspects of gait pace (e.g., speed and distance), spatial and temporal gait parameters, gait variability, and gait stability.These positive changes in gait were reported for exercise durations ranging from 4 weeks to 6 months and included participants with a range of disease severities, highlighting the importance of exercise in improving mobility across disease progression. Exercise can modify brain function121 and reduce the symptoms of PD122, thus improving gait function as well. The mechanism by which exercise modifies brain function is not well understood but increased cortical vascularity123, increased brain-derived neurotrophic factor, BDNF, and reduced loss of dopamine121,124,125 are among possibilities.Gait Biomarkers for Prognosis (Falls)Gait disturbances lead to falls in PD, which can have a profound impact on independence and quality of life126. Given the importance of falls for quality of life, it is an urgent priority to be able to predict those who are at a higher risk of falling early in the course of the disease. Ultimately, this early predictability could enhance our understanding of the mechanisms underlying falls, while also providing opportunity to initiate evidence-based treatments to minimize falls.Recent studies have suggested that risk of falls might be predicted with the use of digital biomarkers, measured either in clinic or daily-living environments, see Table 1D127,128,129,130,131. For example, Greene et al. 131 used inertial sensors during a long timed-up and Go (TUG) course to predict fall frequency with moderate accuracy, pointing to multiple measures rather than a single measure. When considering walking speed as a marker, two studies suggested that slow backward, but not forward, walking speed had the potential to be a biomarker for future falls, particularly in de-novo PD130,132. Contributing to the debate about self-paced forward walking, Nemanich & colleagues demonstrated that the difference between self-paced walking and fast-paced walking may be more predictive of future falls than self-paced gait alone133. One small study suggested that increased step time variability and increased walking cadence in the Off-medication state can be important predictors of future falls127. Lastly, two recent studies investigated whether gait and turning biomarkers in daily life could predict falls in people with moderate PD128,129. One study showed that specific gait and turning measures (toe-out angle of the foot, pitch angle of the foot during mid-swing, and peak turn velocity) observed during daily life walking achieved an AUC of 0.94 in predicting future falls while the AUC of past falls as marker of future falls was 0.77128. The other study129 compared data aggregation approaches and machine learning models for the prospective prediction of fall risk using gait parameters derived either from continuous real-world recordings or from unsupervised gait tests. The highest balanced accuracy of 0.74 was achieved with a Random Forest Classifier applied to the real-world gait data when aggregating all walking bouts (short and long) and days of each participant.Collectively and as shown in Table 2, reductions in gait pace (stride length and overall gait velocity), particularly with backward walking134,135, in addition to increased gait variability, appear to be the strongest predictors of falling, although careful consideration of medication state is important, among other factors126. While previous fall history is a strong predictor of future falls136,137, specific gait parameters that may be modifiable, may also be important biomarkers to consider in interventional trials.Prediction of falls in PD is complex in nature and abnormal gait is only one of many intrinsic and extrinsic fall-risk factors. Larger and longer studies of the role of gait biomarkers in predicting fall risk for people with PD are needed and other factors, such as previous falls, medication, age, standing balance, cognition, and activity involvement need to be taken into consideration. Lastly, backward walking134,135 or mobility in daily life128 may offer more sensitive measures for fall risk, but larger studies are needed to confirm these preliminary findings.Highlights and conclusionThere is a critical, unmet need to provide practical, quantitative, easily obtained biomarkers of PD as outcomes for clinical trials. This narrative review focused on gait digital biomarkers as optimal candidates because mobility is affected early in PD138 and gait quality dramatically influences quality of life138.Table 2 summarizes the evidence for emerging gait digital biomarkers for the 4 specific contexts of use (disease susceptibility/risk, progression, exercise response, and fall risk/prognosis). Upper body aspects of gait, such as arm swing range of motion and asymmetry, trunk range of motion, and gait asymmetry seem to be unique biomarkers indicating the potential for developing a disease in individuals who do not currently have a clinically-apparent disease (susceptibility/risk). Pace aspects of gait, such as speed of gait and spatio-temporal characteristics such as stride length, may be most helpful in tracking disease progression, assessing response to exercise and indicating the likelihood of falls. Dynamic stability aspects of gait, such as trunk regularity (how smooth and consistent trunk movement is during gait)139,140 and double-support time, also worsen with disease progression, but can improve with exercise. Gait variability, specifically stride-time variability, seems to be the only gait biomarker sensitive across the 4 contexts of use, reflecting sensitivity but potentially poor specificity. However, our summary of the best gait measures for each context of use is limited to studies that do not include a wide array of measures as upper body and foot measures are often left out. The gait protocol used to collect data is very important. In fact, while single-task gait at a natural, comfortable speed is best for biomarkers for prognosis and disease progression, a more challenging protocol with a concurrent cognitive dual-task or asking to walk at fast speed seem to best indicate disease risk.Some contextual considerations need to be listed. The relative benefits of collecting digital biomarkers of gait while individuals are in dopaminergic medication on- versus off-state must be considered. The benefits of collecting digital biomarkers of gait while patients are in the on-medication state are both logistical and physiological. Logistically, it is easier to collect data in the on state because subjects are often more comfortable as they have less rigidity, bradykinesia, and tremor. This comfort level is especially important for long-term or home-based monitoring. Also, when collecting gait measures in the on-medication state, the treating team or clinical trial investigators do not have to wait for medication effects to “turn on” (e.g., waiting for the person with PD and the examiner to feel that levodopa has kicked in) as they would need to do for an on/off medication comparison evaluation. Physiologically, collecting digital biomarkers of gait while people with PD are taking their usual medications is more representative of real-life and may capture aspects of the gait exam that are: (1) not improved by dopaminergic medications (e.g., dynamic postural stability)141 or (2) actually worsened by dopaminergic medications (e.g., postural sway and dyskinesia)142. However, there are several benefits of collecting data in the Off dopaminergic medication state, including the ability to assess the natural physiology of PD early in the disease course, the ease of comparing treatment groups without the need to stratify or match for dopamine equivalent doses, and a greater ability to compare groups of people with PD to groups with atypical parkinsonian disorders (e.g., progressive supranuclear palsy, corticobasal syndrome, multiple system atrophy and vascular parkinsonism), if the goal is early disease identification.Similar to dopaminergic medications, one must consider the milieu of cholinergic and anticholinergic medications (such as donepezil and oxybutynin) as well as the interaction of deep brain stimulation (DBS) for impact on the results of digital biomarkers of gait. Evidence suggests that anticholinergic medications may worsen gait143,144. Since DBS is usually recommended mid to late in the disease course, testing gait when DBS is turned off may be more poorly tolerated and thus not feasible. Fortunately, there are a number of portable technologies to measure gait simultaneous with DBS stimulation without significant interference. Overall, testing digital gait markers off medication (and off DBS, when applicable) may be more helpful early in the disease course and for diagnostic discrimination, while testing on medication and on stimulation on may be more appropriate for advanced disease monitoring.Although this narrative review highlights the positive results of digital biomarkers of gait, it also shows the paucity of longitudinal trials including digital gait measures, and the lack of comparison of gait digital biomarkers with the MDS-UPDRS, which is the current clinical gold standard used in clinical trials and only used quantitative comparisons in one study. If digital gait biomarkers are more responsive to change than the MDS-UPDRS, it could result in smaller, more efficient clinical trials for PD. Gait biomarkers may also be helpful in Phase II and Phase III clinical trials as an endpoint aimed at slowing the progression or reducing the severity of PD. Lastly, work is needed to standardize gait assessments before more specific gait biomarkers can be recommended, as important factors vary greatly across studies, including distance/time walked, type of concurrent cognitive task, and inclusion of turns, which all potentially affect gait biomarkers.The advent of automatic, real-time analysis of gait based on body-worn, inertial sensors has made it feasible to quantify gait characteristics in people with PD in the clinic, as well as in clinical trials. Neurologists can use gait analysis to aid in early diagnosis, monitor response to medications and to track disease progression. Physical therapists would benefit from digital gait measures to measure response to exercise intervention as well as to predict risk of falls. Patients may also want to monitor their gait for themselves or for their physical therapists by wearing sensors during daily life. Digital health technologies to quantify gait are already available for clinicians and patients, but evidence on the usefulness of specific gait biomarkers for clinical practice needs to be accumulated. It should also be mentioned that other promising techniques to quickly characterize gait in the clinic are rapidly developing. In fact, advanced computer vision techniques, often combined with machine learning, are used to analyze gait from video recordings145,146,147. In the future, after careful validation, quantitative analysis of gait could be even more easily available in clinical practice148.
ReferencesDeng, K. et al. Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson’s disease. Commun. Biol. 5, 58, https://doi.org/10.1038/s42003-022-03002-x (2022).Article
PubMed
PubMed Central
Google Scholar
Stephenson, D., Badawy, R., Mathur, S., Tome, M. & Rochester, L. Digital progression biomarkers as novel endpoints in clinical trials: a multistakeholder perspective. J. Parkinsons Dis. 11, S103–S109, https://doi.org/10.3233/JPD-202428 (2021).Article
PubMed
PubMed Central
Google Scholar
Gerlach, M. et al. Biomarker candidates of neurodegeneration in Parkinson’s disease for the evaluation of disease-modifying therapeutics. J. Neural Transm. 119, 39–52, https://doi.org/10.1007/s00702-011-0682-x (2012).Article
CAS
PubMed
Google Scholar
Horak, F. B. & Mancini, M. Objective biomarkers of balance and gait for Parkinson’s disease using body-worn sensors. Mov. Disord. 28, 1544–1551, https://doi.org/10.1002/mds.25684 (2013).Article
PubMed
PubMed Central
Google Scholar
Coravos, A., Khozin, S. & Mandl, K. D. Developing and adopting safe and effective digital biomarkers to improve patient outcomes. npj Digit. Med. 2, 14, https://doi.org/10.1038/s41746-019-0090-4 (2019).Article
PubMed
PubMed Central
Google Scholar
Califf, R. M. Biomarker definitions and their applications. Exp. Biol. Med. 243, 213–221, https://doi.org/10.1177/1535370217750088 (2018).Article
CAS
Google Scholar
Cagney, D. N. et al. The FDA NIH biomarkers, EndpointS, and other Tools (BEST) resource in neuro-oncology. Neuro Oncol. 20, 1162–1172, https://doi.org/10.1093/neuonc/nox242 (2018).Article
CAS
PubMed
Google Scholar
Lang, A. E. Clinical trials of disease-modifying therapies for neurodegenerative diseases: the challenges and the future. Nat. Med. 16, 1223–1226, https://doi.org/10.1038/nm.2220 (2010).Article
CAS
PubMed
Google Scholar
Mirelman, A. et al. Gait impairments in Parkinson’s disease. Lancet Neurol. 18, 697–708, https://doi.org/10.1016/s1474-4422(19)30044-4 (2019).Article
PubMed
Google Scholar
Tan, D., Danoudis, M., McGinley, J. & Morris, M. E. Relationships between motor aspects of gait impairments and activity limitations in people with Parkinson’s disease: a systematic review. Parkinson. Relat. Disord. 18, 117–124, https://doi.org/10.1016/j.parkreldis.2011.07.014 (2012).Article
Google Scholar
Berardelli, A., Rothwell, J. C., Thompson, P. D. & Hallett, M. Pathophysiology of bradykinesia in Parkinson’s disease. Brain 124, 2131–2146, https://doi.org/10.1093/brain/124.11.2131 (2001).Article
CAS
PubMed
Google Scholar
Teshuva, I. et al. Using wearables to assess bradykinesia and rigidity in patients with Parkinson’s disease: a focused, narrative review of the literature. J. Neural Transm. 126, 699–710, https://doi.org/10.1007/s00702-019-02017-9 (2019).Article
PubMed
Google Scholar
Fasano, A. & Bloem, B. R. Gait disorders. Continuum 19, 1344–1382, https://doi.org/10.1212/01.Con.0000436159.33447.69 (2013).Amundsen-Huffmaster, S. L. et al. REM sleep without atonia and gait impairment in people with mild-to-moderate Parkinson’s disease. J. Parkinson Dis. 11, 767–778, https://doi.org/10.3233/jpd-202098 (2021).Article
CAS
Google Scholar
Cochen De Cock, V. et al. Classifying idiopathic rapid eye movement sleep behavior disorder, controls, and mild parkinson’s disease using gait parameters. Mov. Disord. 37, 842–846, https://doi.org/10.1002/mds.28894 (2022).Article
PubMed
Google Scholar
Mirelman, A. et al. Arm swing as a potential new prodromal marker of Parkinson’s disease. Mov. Disord. 31, 1527–1534, https://doi.org/10.1002/mds.26720 (2016).Article
CAS
PubMed
PubMed Central
Google Scholar
Lord, S. et al. Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. J. Gerontol. A Biol. Sci. Med. Sci., https://doi.org/10.1093/gerona/gls255 (2012).Morris, R. et al. A model of free-living gait: A factor analysis in Parkinson’s disease. Gait Posture 52, 68–71, https://doi.org/10.1016/j.gaitpost.2016.11.024 (2017).Article
PubMed
Google Scholar
Horak, F. B., Mancini, M., Carlson-Kuhta, P., Nutt, J. G. & Salarian, A. Balance and gait represent independent domains of mobility in Parkinson disease. Phys. Ther. 96, 1364–1371, https://doi.org/10.2522/ptj.20150580 (2016).Article
PubMed
PubMed Central
Google Scholar
Middleton, A., Fritz, S. L. & Lusardi, M. Walking speed: the functional vital sign. J. Aging Phys. Act. 23, 314–322, https://doi.org/10.1123/japa.2013-0236 (2015).Article
PubMed
Google Scholar
Speelman, A. D., van Nimwegen, M., Borm, G. F., Bloem, B. R. & Munneke, M. Monitoring of walking in Parkinson’s disease: validation of an ambulatory activity monitor. Parkinson Relat. Disord. 17, 402–404, https://doi.org/10.1016/j.parkreldis.2011.02.006 (2011).Article
CAS
Google Scholar
Goetz, C. G. et al. Teaching program for the movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale: (MDS-UPDRS). Mov. Disord. 25, 1190–1194, https://doi.org/10.1002/mds.23096 (2010).Article
PubMed
Google Scholar
Goetz, C. G. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23, 2129–2170, https://doi.org/10.1002/mds.22340 (2008).Article
PubMed
Google Scholar
Bloem, B. R. et al. Measurement instruments to assess posture, gait, and balance in Parkinson’s disease: Critique and recommendations. Mov. Disord. 31, 1342–1355, https://doi.org/10.1002/mds.26572 (2016).Article
PubMed
Google Scholar
Chan, W. L. S. & Pin, T. W. Reliability, validity and minimal detectable change of 2-minute walk test, 6-minute walk test and 10-meter walk test in frail older adults with dementia. Exp. Gerontol. 115, 9–18, https://doi.org/10.1016/j.exger.2018.11.001 (2019).Article
PubMed
Google Scholar
Shah, V. V. et al. Effect of Bout length on gait measures in people with and without Parkinson’s disease during daily life. Sensors 20, https://doi.org/10.3390/s20205769 (2020).Shah, V. V. et al. Digital biomarkers of mobility in Parkinson’s disease during daily living. J. Parkinsons Dis. https://doi.org/10.3233/jpd-201914 (2020).Article
PubMed
PubMed Central
Google Scholar
Zanardi, A. P. J. et al. Gait parameters of Parkinson’s disease compared with healthy controls: a systematic review and meta-analysis. Sci. Rep. 11, 752, https://doi.org/10.1038/s41598-020-80768-2 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Dorsey, E. R., Venuto, C., Venkataraman, V., Harris, D. A. & Kieburtz, K. Novel methods and technologies for 21st-century clinical trials: a review. JAMA Neurol. 72, 582–588,, https://doi.org/10.1001/jamaneurol.2014.4524 (2015).Article
PubMed
PubMed Central
Google Scholar
Dorsey, E. R., Papapetropoulos, S., Xiong, M. & Kieburtz, K. The first frontier: digital biomarkers for neurodegenerative disorders. Digit. Biomark. 1, 6–13, https://doi.org/10.1159/000477383 (2017).Article
PubMed
PubMed Central
Google Scholar
Erb, M. K. et al. mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson’s disease. NPJ Digit. Med. 3, 6, https://doi.org/10.1038/s41746-019-0214-x (2020).Article
PubMed
PubMed Central
Google Scholar
Sieberts, S. K. et al. Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge. NPJ Digit. Med. 4, 53, https://doi.org/10.1038/s41746-021-00414-7 (2021).Article
PubMed
PubMed Central
Google Scholar
Guo, C. C. et al. Digital devices for assessing motor functions in mobility-impaired and healthy populations: systematic literature review. J. Med. Internet Res. 24, e37683, https://doi.org/10.2196/37683 (2022).Article
PubMed
PubMed Central
Google Scholar
Viceconti, M. et al. On the use of wearable sensors as mobility biomarkers in the marketing authorization of new drugs: A regulatory perspective. Front. Med 9, 996903, https://doi.org/10.3389/fmed.2022.996903 (2022).Article
Google Scholar
Benedetti, M. G. et al. SIAMOC position paper on gait analysis in clinical practice: General requirements, methods and appropriateness. Results of an Italian consensus conference. Gait Posture 58, 252–260, https://doi.org/10.1016/j.gaitpost.2017.08.003 (2017).Article
PubMed
Google Scholar
Cappozzo, A. Gait analysis methodology. Hum. Mov. Sci. 3, 27–50, https://doi.org/10.1016/0167-9457(84)90004-6 (1984).Article
Google Scholar
Summan, R. et al. Spatial calibration of large volume photogrammetry based metrology systems. Measurement 68, 189–200, https://doi.org/10.1016/j.measurement.2015.02.054 (2015).Article
Google Scholar
Salchow-Hömmen, C. et al. Review-emerging portable technologies for gait analysis in neurological disorders. Front. Hum. Neurosci. 16, 768575, https://doi.org/10.3389/fnhum.2022.768575 (2022).Article
PubMed
PubMed Central
Google Scholar
Del Din, S., Kirk, C., Yarnall, A. J., Rochester, L. & Hausdorff, J. M. Body-Worn sensors for remote monitoring of Parkinson’s disease motor symptoms: vision, state of the art, and challenges ahead. J. Parkinsons Dis. 11, S35–s47, https://doi.org/10.3233/jpd-202471 (2021).Article
PubMed
PubMed Central
Google Scholar
Buchthal, F. & Fernandez-Ballesteros, M. L. Electromyographic study of the muscles of the upper arm and shoulder during walking in patients with Parkinson’s disease. Brain 88, 875–896, https://doi.org/10.1093/brain/88.5.875 (1965).Article
CAS
PubMed
Google Scholar
Zampieri, C. et al. The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 81, 171–176, https://doi.org/10.1136/jnnp.2009.173740 (2010).Article
PubMed
Google Scholar
Buckley, C., Galna, B., Rochester, L. & Mazzà, C. Upper body accelerations as a biomarker of gait impairment in the early stages of Parkinson’s disease. Gait Posture 71, 289–295, https://doi.org/10.1016/j.gaitpost.2018.06.166 (2019).Article
PubMed
Google Scholar
Sadeghi, H., Allard, P., Prince, F. & Labelle, H. Symmetry and limb dominance in able-bodied gait: a review. Gait Posture 12, 34–45, https://doi.org/10.1016/s0966-6362(00)00070-9 (2000).Article
CAS
PubMed
Google Scholar
Yogev, G., Plotnik, M., Peretz, C., Giladi, N. & Hausdorff, J. M. Gait asymmetry in patients with Parkinson’s disease and elderly fallers: when does the bilateral coordination of gait require attention? Exp. Brain Res. 177, 336–346, https://doi.org/10.1007/s00221-006-0676-3 (2007).Article
PubMed
Google Scholar
Galna, B., Lord, S. & Rochester, L. Is gait variability reliable in older adults and Parkinson’s disease? Towards an optimal testing protocol. Gait Posture 37, 580–585, https://doi.org/10.1016/j.gaitpost.2012.09.025 (2013).Article
PubMed
Google Scholar
Rennie, L. et al. The reliability of gait variability measures for individuals with Parkinson’s disease and healthy older adults - The effect of gait speed. Gait Posture 62, 505–509, https://doi.org/10.1016/j.gaitpost.2018.04.011 (2018).Article
PubMed
Google Scholar
Kuo, A. D. & Donelan, J. M. Dynamic principles of gait and their clinical implications. Phys. Ther. 90, 157–174, https://doi.org/10.2522/ptj.20090125 (2010).Article
PubMed
PubMed Central
Google Scholar
Ma, L. et al. Gait variability is sensitive to detect Parkinson’s disease patients at high fall risk. Int. J. Neurosci. 132, 888–893, https://doi.org/10.1080/00207454.2020.1849189 (2022).Article
PubMed
Google Scholar
Morrison, S. et al. The relation between falls risk and movement variability in Parkinson’s disease. Exp. Brain Res. 239, 2077–2087, https://doi.org/10.1007/s00221-021-06113-9 (2021).Article
PubMed
Google Scholar
Hausdorff, J. M. Stride variability: beyond length and frequency. Gait Posture 20, 304, https://doi.org/10.1016/j.gaitpost.2003.08.002 (2004).Article
CAS
PubMed
Google Scholar
Hausdorff, J. M. Gait variability: methods, modeling and meaning. J. Neuroeng. Rehabil. 2, 19, https://doi.org/10.1186/1743-0003-2-19 (2005).Article
PubMed
PubMed Central
Google Scholar
Raffegeau, T. E. et al. A meta-analysis: Parkinson’s disease and dual-task walking. Parkinson Relat. Disord. 62, 28–35, https://doi.org/10.1016/j.parkreldis.2018.12.012 (2019).Article
Google Scholar
Kelly, V. E., Eusterbrock, A. J. & Shumway-Cook, A. A review of dual-task walking deficits in people with Parkinson’s disease: motor and cognitive contributions, mechanisms, and clinical implications. Parkinsons Dis. 2012, 918719, https://doi.org/10.1155/2012/918719 (2012).Article
PubMed
Google Scholar
Vitorio, R. et al. Dual-task costs of quantitative gait parameters while walking and turning in people with Parkinson’s disease: beyond gait speed. J. Parkinsons Dis. 11, 653–664, https://doi.org/10.3233/jpd-202289 (2021).Article
PubMed
Google Scholar
Mirelman, A. et al. Gait alterations in healthy carriers of the LRRK2 G2019S mutation. Ann. Neurol. 69, 193–197, https://doi.org/10.1002/ana.22165 (2011).Article
PubMed
Google Scholar
Peterson, D. S. & Horak, F. B. Neural Control of Walking in People with Parkinsonism. Physiol. (Bethesda) 31, 95–107, https://doi.org/10.1152/physiol.00034.2015 (2016).Article
CAS
Google Scholar
Horak, F. B., Mancini, M., Carlson-Kuhta, P., Nutt, J. G. & Salarian, A. Balance and gait represent independent domains of mobility in Parkinson’s Disease. Phys. Ther. 96, 1364–1371, https://doi.org/10.2522/ptj.20150580 (2016).Article
PubMed
PubMed Central
Google Scholar
Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov. Disord. 30, 1591–1601, https://doi.org/10.1002/mds.26424 (2015).Article
PubMed
Google Scholar
FEARNLEY, J. M. & LEES, A. J. Ageing and Parkinson’s disease: substantia Nigra regional selectivity. Brain 114, 2283–2301, https://doi.org/10.1093/brain/114.5.2283 (1991).Article
PubMed
Google Scholar
Mirelman, A. et al. Fall risk and gait in Parkinson’s disease: the role of the LRRK2 G2019S mutation. Mov. Disord. 28, 1683–1690, https://doi.org/10.1002/mds.25587 (2013).Article
CAS
PubMed
Google Scholar
Sánchez-Rodríguez, A. et al. Sensor-based gait analysis in the premotor stage of LRRK2 G2019S-associated Parkinson’s disease. Parkinson. Relat. Disord. 98, 21–26, https://doi.org/10.1016/j.parkreldis.2022.03.020 (2022).Article
CAS
Google Scholar
van den Heuvel, L. et al. Actigraphy detects greater intra-individual variability during gait in non-manifesting LRRK2 mutation carriers. J. Parkinsons Dis. 8, 131–139, https://doi.org/10.3233/jpd-171151 (2018).Article
PubMed
Google Scholar
Prasuhn, J. et al. Task matters - challenging the motor system allows distinguishing unaffected Parkin mutation carriers from mutation-free controls. Parkinson. Relat. Disord. 86, 101–104, https://doi.org/10.1016/j.parkreldis.2021.03.028 (2021).Article
Google Scholar
Nürnberger, L. et al. Ultrasound-based motion analysis demonstrates bilateral arm hypokinesia during gait in heterozygous PINK1 mutation carriers. Mov. Disord. 30, 386–392, https://doi.org/10.1002/mds.26127 (2015).Article
CAS
PubMed
Google Scholar
Gera, A. et al. Gait asymmetry in glucocerebrosidase mutation carriers with Parkinson’s disease. PLoS One 15, e0226494, https://doi.org/10.1371/journal.pone.0226494 (2020).Article
CAS
PubMed
PubMed Central
Google Scholar
Martini, D. N. et al. Sensorimotor inhibition and mobility in genetic subgroups of Parkinson’s disease. Front. Neurol. 11, 893, https://doi.org/10.3389/fneur.2020.00893 (2020).Article
PubMed
PubMed Central
Google Scholar
Morris, R. et al. Cognition as a mediator for gait and balance impairments in GBA-related Parkinson’s disease. NPJ Parkinsons Dis. 8, 78, https://doi.org/10.1038/s41531-022-00344-5 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Srulijes, K. et al. Dual-task performance in GBA Parkinson’s disease. Parkinsons Dis. 2017, 8582740, https://doi.org/10.1155/2017/8582740 (2017).Article
CAS
PubMed
PubMed Central
Google Scholar
Iranzo, A. et al. Rapid-eye-movement sleep behaviour disorder as an early marker for a neurodegenerative disorder: a descriptive study. Lancet Neurol. 5, 572–577, https://doi.org/10.1016/s1474-4422(06)70476-8 (2006).Article
PubMed
Google Scholar
Postuma, R. B. et al. Quantifying the risk of neurodegenerative disease in idiopathic REM sleep behavior disorder. Neurology 72, 1296–1300, https://doi.org/10.1212/01.wnl.0000340980.19702.6e (2009).Article
CAS
PubMed
PubMed Central
Google Scholar
Postuma, R. B. et al. Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study. Brain 142, 744–759, https://doi.org/10.1093/brain/awz030 (2019).Article
PubMed
PubMed Central
Google Scholar
Del Din, S. et al. Continuous real-world gait monitoring in Idiopathic REM sleep behavior disorder. J. Parkinsons Dis. 10, 283–299, https://doi.org/10.3233/jpd-191773 (2020).Article
PubMed
Google Scholar
McDade, E. M. et al. Subtle gait changes in patients with REM sleep behavior disorder. Mov. Disord. 28, 1847–1853, https://doi.org/10.1002/mds.25653 (2013).Article
PubMed
PubMed Central
Google Scholar
Ma, L. et al. Detection of motor dysfunction with wearable sensors in patients with idiopathic rapid eye movement disorder. Front. Bioeng. Biotechnol. 9, 627481, https://doi.org/10.3389/fbioe.2021.627481 (2021).Article
PubMed
PubMed Central
Google Scholar
Ehgoetz Martens, K. A. et al. Subtle gait and balance impairments occur in idiopathic rapid eye movement sleep behavior disorder. Mov. Disord. 34, 1374–1380, https://doi.org/10.1002/mds.27780 (2019).Article
PubMed
Google Scholar
Ehgoetz Martens, K. A. et al. The neural signature of impaired dual-tasking in idiopathic rapid eye movement sleep behavior disorder patients. Mov. Disord. 35, 1596–1606, https://doi.org/10.1002/mds.28114 (2020).Article
CAS
PubMed
Google Scholar
Tamburini, P. et al. Moving from laboratory to real life conditions: Influence on the assessment of variability and stability of gait. Gait Posture 59, 248–252, https://doi.org/10.1016/j.gaitpost.2017.10.024 (2018).Article
PubMed
Google Scholar
Zhang, H. et al. Risk factors for phenoconversion in rapid eye movement sleep behavior disorder. Ann. Neurol. 91, 404–416, https://doi.org/10.1002/ana.26298 (2022).Article
CAS
PubMed
Google Scholar
Monje, M. H. G. et al. Motor onset Topography and progression in Parkinson’s disease: the upper limb is first. Mov. Disord. 36, 905–915, https://doi.org/10.1002/mds.28462 (2021).Article
PubMed
Google Scholar
Pineda-Pardo, J. A., Sánchez-Ferro, Á., Monje, M. H. G., Pavese, N. & Obeso, J. A. Onset pattern of nigrostriatal denervation in early Parkinson’s disease. Brain 145, 1018–1028, https://doi.org/10.1093/brain/awab378 (2022).Article
PubMed
PubMed Central
Google Scholar
Buckley, C. et al. The role of movement analysis in diagnosing and monitoring neurodegenerative conditions: insights from gait and postural control. Brain Sci. 9, 34, https://doi.org/10.3390/brainsci9020034 (2019).Article
PubMed
PubMed Central
Google Scholar
Sarkar, S., Raymick, J. & Imam, S. Neuroprotective and therapeutic strategies against Parkinson’s disease: recent perspectives. Int. J. Mol. Sci. 17, 904, https://doi.org/10.3390/ijms17060904 (2016).Article
CAS
PubMed
PubMed Central
Google Scholar
Evans, J. R. et al. The natural history of treated Parkinson’s disease in an incident, community based cohort. J. Neurol. Neurosurg. Psychiatry 82, 1112–1118, https://doi.org/10.1136/jnnp.2011.240366 (2011).Article
PubMed
Google Scholar
Galna, B., Lord, S., Burn, D. J. & Rochester, L. Progression of gait dysfunction in incident Parkinson’s disease: impact of medication and phenotype. Mov. Disord. 30, 359–367, https://doi.org/10.1002/mds.26110 (2015).Article
CAS
PubMed
Google Scholar
Ellis, T. D. et al. Identifying clinical measures that most accurately reflect the progression of disability in Parkinson disease. Parkinson. Relat. Disord. 25, 65–71, https://doi.org/10.1016/j.parkreldis.2016.02.006 (2016).Article
Google Scholar
Salarian, A. et al. Analyzing 180 degrees turns using an inertial system reveals early signs of progression of Parkinson’s disease. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 224–227, https://doi.org/10.1109/IEMBS.2009.5333970 (2009).Article
PubMed Central
Google Scholar
Hobert, M. A. et al. Progressive gait deficits in Parkinson’s disease: a wearable-based biannual 5-year prospective study. Front. Aging Neurosci. 11, 22, https://doi.org/10.3389/fnagi.2019.00022 (2019).Article
PubMed
PubMed Central
Google Scholar
Micó-Amigo, M. E. et al. Potential markers of progression in idiopathic Parkinson’s disease derived from assessment of circular gait with a single body-fixed-sensor: a 5 year longitudinal Study. Front. Hum. Neurosci. 13, 59, https://doi.org/10.3389/fnhum.2019.00059 (2019).Article
PubMed
PubMed Central
Google Scholar
Micó-Amigo, M. E. et al. Dual vs. single tasking during circular walking: what better reflects progression in Parkinson’s disease? Front. Neurol. 10, 372, https://doi.org/10.3389/fneur.2019.00372 (2019).Article
PubMed
PubMed Central
Google Scholar
Wilson, J. et al. Gait progression over 6 years in Parkinson’s disease: effects of age, medication, and pathology. Front. Aging Neurosci. 12, 577435, https://doi.org/10.3389/fnagi.2020.577435 (2020).Article
PubMed
PubMed Central
Google Scholar
Sotirakis, C. et al. Identification of motor progression in Parkinson’s disease using wearable sensors and machine learning. NPJ Parkinsons Dis. 9, 142, https://doi.org/10.1038/s41531-023-00581-2 (2023).Article
PubMed
PubMed Central
Google Scholar
Álvarez, I., Latorre, J., Aguilar, M., Pastor, P. & Llorens, R. Validity and sensitivity of instrumented postural and gait assessment using low-cost devices in Parkinson’s disease. J. Neuroeng. Rehabil. 17, 149, https://doi.org/10.1186/s12984-020-00770-7 (2020).Article
PubMed
PubMed Central
Google Scholar
Lee, S. H. et al. Parkinson’s disease subtyping using clinical features and biomarkers: literature review and preliminary study of subtype clustering. Diagnostics 12, 112, https://doi.org/10.3390/diagnostics12010112 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
von Coelln, R. & Shulman, L. M. Clinical subtypes and genetic heterogeneity: of lumping and splitting in Parkinson disease. Curr. Opin. Neurol. 29, 727–734, https://doi.org/10.1097/wco.0000000000000384 (2016).Article
Google Scholar
Herman, T., Weiss, A., Brozgol, M., Giladi, N. & Hausdorff, J. M. Identifying axial and cognitive correlates in patients with Parkinson’s disease motor subtype using the instrumented Timed Up and Go. Exp. Brain Res. 232, 713–721, https://doi.org/10.1007/s00221-013-3778-8 (2014).Article
PubMed
Google Scholar
Koh, S. B., Park, K. W., Lee, D. H., Kim, S. J. & Yoon, J. S. Gait analysis in patients with Parkinson’s disease: relationship to clinical features and freezing. J. Mov. Disord. 1, 59–64 (2008).
Google Scholar
Orcioli-Silva, D. et al. Objective measures of unobstructed walking and obstacle avoidance in Parkinson’s disease subtypes. Gait Posture 62, 405–408, https://doi.org/10.1016/j.gaitpost.2018.03.046 (2018).Article
PubMed
Google Scholar
Vervoort, G. et al. Distal motor deficit contributions to postural instability and gait disorder in Parkinson’s disease. Behav. Brain Res. 287, 1–7, https://doi.org/10.1016/j.bbr.2015.03.026 (2015).Article
PubMed
Google Scholar
Wu, Z. et al. Can quantitative gait analysis be used to guide treatment of patients with different subtypes of Parkinson’s disease? Neuropsychiatr. Dis. Treat. 16, 2335–2341, https://doi.org/10.2147/ndt.S266585 (2020).Article
CAS
PubMed
PubMed Central
Google Scholar
Brzezicki, M. A., Conway, N., Sotirakis, C., FitzGerald, J. J. & Antoniades, C. A. AntiParkinsonian medication masks motor signal progression in de novo patients. Heliyon 9, e16415, https://doi.org/10.1016/j.heliyon.2023.e16415 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Uc, E. Y. et al. Phase I/II randomized trial of aerobic exercise in Parkinson disease in a community setting. Neurology 83, 413–425, https://doi.org/10.1212/wnl.0000000000000644 (2014).Article
PubMed
PubMed Central
Google Scholar
Mak, M. K. Y. & Wong-Yu, I. S. K. Six-month community-based brisk walking and balance exercise alleviates motor symptoms and promotes functions in people with parkinson’s disease: a randomized controlled trial. J. Parkinsons Dis. 11, 1431–1441, https://doi.org/10.3233/jpd-202503 (2021).Article
CAS
PubMed
Google Scholar
Nadeau, A., Pourcher, E. & Corbeil, P. Effects of 24 wk of treadmill training on gait performance in Parkinson’s disease. Med. Sci. Sports Exerc. 46, 645–655, https://doi.org/10.1249/mss.0000000000000144 (2014).Article
PubMed
Google Scholar
Di Martino, S. et al. Aerobic rehabilitation program for improving muscle function in Parkinson’s disease. Restor. Neurol. Neurosci. 36, 13–20, https://doi.org/10.3233/rnn-170738 (2018).Article
PubMed
Google Scholar
Schenkman, M. et al. Effect of high-intensity treadmill exercise on motor symptoms in patients with de novo Parkinson disease: a phase 2 randomized clinical trial. JAMA Neurol. 75, 219–226, https://doi.org/10.1001/jamaneurol.2017.3517 (2018).Article
PubMed
Google Scholar
Arfa-Fatollahkhani, P. et al. Effects of treadmill training on the balance, functional capacity and quality of life in Parkinson’s disease: A randomized clinical trial. J. Complement. Integr. Med. 17, https://doi.org/10.1515/jcim-2018-0245 (2019).Soke, F., Guclu-Gunduz, A., Kocer, B., Fidan, I. & Keskinoglu, P. Task-oriented circuit training combined with aerobic training improves motor performance and balance in people with Parkinson’s Disease. Acta Neurol. Belg. 121, 535–543, https://doi.org/10.1007/s13760-019-01247-8 (2021).Article
PubMed
Google Scholar
Shulman, L. M. et al. Randomized clinical trial of 3 types of physical exercise for patients with Parkinson disease. JAMA Neurol. 70, 183–190,, https://doi.org/10.1001/jamaneurol.2013.646 (2013).Article
PubMed
PubMed Central
Google Scholar
Steib, S. et al. Perturbation during treadmill training improves dynamic balance and gait in Parkinson’s disease: a single-blind randomized controlled pilot trial. Neurorehabil. Neural Repair 31, 758–768, https://doi.org/10.1177/1545968317721976 (2017).Article
PubMed
Google Scholar
Gaßner, H. et al. Perturbation treadmill training improves clinical characteristics of gait and balance in Parkinson’s disease. J. Parkinsons Dis. 9, 413–426, https://doi.org/10.3233/jpd-181534 (2019).Article
PubMed
Google Scholar
Steib, S. et al. Exploring gait adaptations to perturbed and conventional treadmill training in Parkinson’s disease: Time-course, sustainability, and transfer. Hum. Mov. Sci. 64, 123–132, https://doi.org/10.1016/j.humov.2019.01.007 (2019).Article
PubMed
Google Scholar
Capecci, M. et al. Clinical effects of robot-assisted gait training and treadmill training for Parkinson’s disease. A randomized controlled trial. Ann. Phys. Rehabil. Med. 62, 303–312, https://doi.org/10.1016/j.rehab.2019.06.016 (2019).Article
PubMed
Google Scholar
Cugusi, L. et al. Effects of a Nordic Walking program on motor and non-motor symptoms, functional performance and body composition in patients with Parkinson’s disease. NeuroRehabilitation 37, 245–254, https://doi.org/10.3233/nre-151257 (2015).Article
PubMed
Google Scholar
Monteiro, E. P. et al. Effects of Nordic walking training on functional parameters in Parkinson’s disease: a randomized controlled clinical trial. Scand. J. Med. Sci. Sports 27, 351–358, https://doi.org/10.1111/sms.12652 (2017).Article
CAS
PubMed
Google Scholar
de Melo, G. E. L. et al. Effect of virtual reality training on walking distance and physical fitness in individuals with Parkinson’s disease. NeuroRehabilitation 42, 473–480, https://doi.org/10.3233/nre-172355 (2018).Article
PubMed
Google Scholar
Cheng, F. Y. et al. Positive effects of specific exercise and novel turning-based treadmill training on turning performance in individuals with Parkinson’s disease: a randomized controlled trial. Sci. Rep. 6, 33242, https://doi.org/10.1038/srep33242 (2016).Article
CAS
PubMed
PubMed Central
Google Scholar
Cheng, F. Y., Yang, Y. R., Wu, Y. R., Cheng, S. J. & Wang, R. Y. Effects of curved-walking training on curved-walking performance and freezing of gait in individuals with Parkinson’s disease: A randomized controlled trial. Parkinson. Relat. Disord. 43, 20–26, https://doi.org/10.1016/j.parkreldis.2017.06.021 (2017).Article
Google Scholar
Trigueiro, L. C. et al. Effects of treadmill training with load on gait in Parkinson disease: a randomized controlled clinical trial. Am. J. Phys. Med. Rehabil. 94, 830–837, https://doi.org/10.1097/phm.0000000000000249 (2015).Article
PubMed
Google Scholar
Hasegawa, N. et al. Responsiveness of objective versus clinical balance domain outcomes for exercise intervention in Parkinson’s disease. Front. Neurol. 11, 940, https://doi.org/10.3389/fneur.2020.00940 (2020).Article
PubMed
PubMed Central
Google Scholar
Rose, M. H., Løkkegaard, A., Sonne-Holm, S. & Jensen, B. R. Improved clinical status, quality of life, and walking capacity in Parkinson’s disease after body weight-supported high-intensity locomotor training. Arch. Phys. Med. Rehabil. 94, 687–692, https://doi.org/10.1016/j.apmr.2012.11.025 (2013).Article
PubMed
Google Scholar
Petzinger, G. M. et al. Enhancing neuroplasticity in the basal ganglia: the role of exercise in Parkinson’s disease. Mov. Disord. 25, S141–S145, https://doi.org/10.1002/mds.22782 (2010).Article
PubMed
PubMed Central
Google Scholar
Robichaud, J. A. & Corcos, D. M. Motor deficits, exercise, and Parkinson’s disease. Quest 57, 79–101, https://doi.org/10.1080/00336297.2005.10491844 (2005).Article
Google Scholar
Rhyu, I. J. et al. Effects of aerobic exercise training on cognitive function and cortical vascularity in monkeys. Neuroscience 167, 1239–1248, https://doi.org/10.1016/j.neuroscience.2010.03.003 (2010).Article
CAS
PubMed
Google Scholar
Fisher, B. E. et al. The effect of exercise training in improving motor performance and corticomotor excitability in people with early Parkinson’s disease. Arch. Phys. Med. Rehabil. 89, 1221–1229, https://doi.org/10.1016/j.apmr.2008.01.013 (2008).Article
PubMed
PubMed Central
Google Scholar
Vučković, M. G. et al. Exercise elevates dopamine D2 receptor in a mouse model of Parkinson’s disease: in vivo imaging with [18F]fallypride. Mov. Disord. 25, 2777–2784, https://doi.org/10.1002/mds.23407 (2010).Article
PubMed
PubMed Central
Google Scholar
Fasano, A., Canning, C. G., Hausdorff, J. M., Lord, S. & Rochester, L. Falls in Parkinson’s disease: A complex and evolving picture. Mov. Disord. 32, 1524–1536, https://doi.org/10.1002/mds.27195 (2017).Article
PubMed
Google Scholar
Hoskovcová, M. et al. Predicting falls in Parkinson disease: what is the value of instrumented testing in OFF medication state? PLoS One 10, e0139849, https://doi.org/10.1371/journal.pone.0139849 (2015).Article
CAS
PubMed
PubMed Central
Google Scholar
Shah, V. V. et al. Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson’s disease. Front. Neurol. 14, 1096401, https://doi.org/10.3389/fneur.2023.1096401 (2023).Article
PubMed
PubMed Central
Google Scholar
Ullrich, M. et al. Fall risk Prediction in Parkinson’s disease using real-world inertial sensor gait data. IEEE J. Biomed. Health Inf. 27, 319–328, https://doi.org/10.1109/jbhi.2022.3215921 (2023).Article
Google Scholar
Kwon, K. Y. et al. Association between baseline gait parameters and future fall risk in patients with De Novo Parkinson’s disease: forward versus backward gait. J. Clin. Neurol., https://doi.org/10.3988/jcn.2022.0299 (2024).Greene, B. R., Premoli, I., McManus, K., McGrath, D. & Caulfield, B. Predicting fall counts using wearable sensors: a novel digital biomarker for Parkinson’s Disease. Sensors 22, 54, https://doi.org/10.3390/s22010054 (2021).Article
PubMed
PubMed Central
Google Scholar
Duncan, R. P. & Earhart, G. M. Should one measure balance or gait to best predict falls among people with Parkinson disease? Parkinson’s. Dis. 2012, 923493, https://doi.org/10.1155/2012/923493 (2012).Article
Google Scholar
Nemanich, S. T. et al. Predictors of gait speeds and the relationship of gait speeds to falls in men and women with Parkinson disease. Parkinsons Dis. 2013, 141720, https://doi.org/10.1155/2013/141720 (2013).Article
PubMed
PubMed Central
Google Scholar
Hackney, M. E. & Earhart, G. M. Backward walking in Parkinson’s disease. Mov. Disord. 24, 218–223, https://doi.org/10.1002/mds.22330 (2009).Article
PubMed
PubMed Central
Google Scholar
Hackney, M. E. & Earhart, G. M. The effects of a secondary task on forward and backward walking in Parkinson’s disease. Neurorehabil. Neural Repair 24, 97–106, https://doi.org/10.1177/1545968309341061 (2010).Article
PubMed
PubMed Central
Google Scholar
Canning, C. G., Paul, S. S. & Nieuwboer, A. Prevention of falls in Parkinson’s disease: a review of fall risk factors and the role of physical interventions. Neurodegener. Dis. Manag. 4, 203–221, https://doi.org/10.2217/nmt.14.22 (2014).Article
PubMed
Google Scholar
Pickering, R. M. et al. A meta-analysis of six prospective studies of falling in Parkinson’s disease. Mov. Disord. 22, 1892–1900, https://doi.org/10.1002/mds.21598 (2007).Article
PubMed
Google Scholar
Mammen, J. R. et al. Relative Meaningfulness and Impacts of Symptoms in People with Early-Stage Parkinson’s Disease. J. Parkinsons Dis. 13, 619–632, https://doi.org/10.3233/jpd-225068 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Moe-Nilssen, R., Aaslund, M. K., Hodt-Billington, C. & Helbostad, J. L. Gait variability measures may represent different constructs. Gait Posture 32, 98–101, https://doi.org/10.1016/j.gaitpost.2010.03.019 (2010).Article
PubMed
Google Scholar
Yack, H. J. & Berger, R. C. Dynamic stability in the elderly: identifying a possible measure. J. Gerontol. 48, M225–230,, https://doi.org/10.1093/geronj/48.5.m225 (1993).Article
CAS
PubMed
Google Scholar
Jansen, J. A. F. et al. Exploring the levodopa-paradox of freezing of gait in dopaminergic medication-naïve Parkinson’s disease populations. NPJ Parkinsons Dis. 9, 130, https://doi.org/10.1038/s41531-023-00575-0 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Curtze, C., Nutt, J. G., Carlson-Kuhta, P., Mancini, M. & Horak, F. B. Levodopa Is a Double-Edged Sword for Balance and Gait in People With Parkinson’s Disease. Mov. Disord. 30, 1361–1370, https://doi.org/10.1002/mds.26269 (2015).Article
CAS
PubMed
PubMed Central
Google Scholar
Duvoisin, R. C. Cholinergic-anticholinergic antagonism in parkinsonism. Arch. Neurol. 17, 124–136, https://doi.org/10.1001/archneur.1967.00470260014002 (1967).Article
CAS
PubMed
Google Scholar
Bohnen, N. I. et al. Regional cerebral cholinergic nerve terminal integrity and cardinal motor features in Parkinson’s disease. Brain Commun. 3, fcab109, https://doi.org/10.1093/braincomms/fcab109 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Hu, K. et al. Vision-Based Freezing of Gait Detection With Anatomic Directed Graph Representation. IEEE J. Biomed. Health Inf. 24, 1215–1225, https://doi.org/10.1109/jbhi.2019.2923209 (2020).Article
Google Scholar
Sabo, A., Iaboni, A., Taati, B., Fasano, A. & Gorodetsky, C. Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease. Biomed. Eng. Online 22, 120, https://doi.org/10.1186/s12938-023-01175-y (2023).Article
PubMed
PubMed Central
Google Scholar
Stenum, J., Hsu, M. M., Pantelyat, A. Y. & Roemmich, R. T. Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change. PLOS Digit Health 3, e0000467, https://doi.org/10.1371/journal.pdig.0000467 (2024).Article
PubMed
PubMed Central
Google Scholar
Alberts, J. L. et al. The Parkinson’s disease waiting room of the future: measurements, not magazines. Front. Neurol. 14, 1212113, https://doi.org/10.3389/fneur.2023.1212113 (2023).Article
PubMed
PubMed Central
Google Scholar
Download referencesAcknowledgementsThe authors would like to thank the community of people affected by Parkinson’s disease, and specifically those who are exercising within the SPARX3 trial. Also, authors would like to thank Graham Harker, BS, MPH, for his help in editing the manuscript.Author informationAuthors and AffiliationsDepartment of Neurology, Oregon Health & Science University, Portland, OR, USAMartina Mancini, Marian L. Dale & Fay B. HorakDepartment of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, USAMitra AfshariCarespace Health & Wellness, Waterloo, ON, CanadaQuincy AlmeidaDepartment of Neurology, University of Minnesota, Minneapolis, MN, USASommer Amundsen-HuffmasterDepartment of Physical Medicine & Rehabilitation, University of Colorado, Aurora, CO, USAKatherine Balfany & Cory ChristiansenDepartment of Medicine (Neurology) and Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, CanadaRichard CamicioliDepartment of Physical Therapy & Athletic Training, University of Utah, Salt Lake, UT, USALeland E. Dibble, Leah Ling & Genevieve OlivierProgram in Physical Therapy, Washington University School of Medicine in St. Louis, St Louis, MO, USAGammon M. EarhartDepartment of Physical Therapy, Boston University, Boston, MA, USATerry D. EllisDepartment of Physical Therapy & Human Movement Sciences, Northwestern University, Chicago, IL, USAGarett J. Griffith & Daniel M. CorcosEmory University School of Medicine, Department of Medicine, Division of Geriatrics and Gerontology, Atlanta, GA, USAMadeleine E. HackneyVA Center for Visual and Neurocognitive Rehabilitation, Atlanta, GA, USAMadeleine E. HackneyDepartment of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA, USAJammie HopkinsDepartment of Medicine, Faculty of Kinesiology, Sport, & Recreation and Neuroscience and Mental Health Institute, University of Alberta, Alberta, AB, CanadaKelvin E. JonesDepartments of Anatomy & Cell Biology and Neurological Sciences, Rush University Medical Center, Chicago, IL, USAJoan A. O’KeefeDepartment of Rehabilitation & Regenerative Medicine (Programs in Physical Therapy), and GH Sergievsky Center, Columbia University, New York, NY, USAKimberly Kwei & Ashwini K. RaoDepartment of Physical Therapy at the School of Health Professions at UT Health San Antonio, San Antonio, TX, USAAnjali SivaramakrishnanAuthorsMartina ManciniView author publicationsYou can also search for this author inPubMed Google ScholarMitra AfshariView author publicationsYou can also search for this author inPubMed Google ScholarQuincy AlmeidaView author publicationsYou can also search for this author inPubMed Google ScholarSommer Amundsen-HuffmasterView author publicationsYou can also search for this author inPubMed Google ScholarKatherine BalfanyView author publicationsYou can also search for this author inPubMed Google ScholarRichard CamicioliView author publicationsYou can also search for this author inPubMed Google ScholarCory ChristiansenView author publicationsYou can also search for this author inPubMed Google ScholarMarian L. DaleView author publicationsYou can also search for this author inPubMed Google ScholarLeland E. DibbleView author publicationsYou can also search for this author inPubMed Google ScholarGammon M. EarhartView author publicationsYou can also search for this author inPubMed Google ScholarTerry D. EllisView author publicationsYou can also search for this author inPubMed Google ScholarGarett J. GriffithView author publicationsYou can also search for this author inPubMed Google ScholarMadeleine E. HackneyView author publicationsYou can also search for this author inPubMed Google ScholarJammie HopkinsView author publicationsYou can also search for this author inPubMed Google ScholarFay B. HorakView author publicationsYou can also search for this author inPubMed Google ScholarKelvin E. JonesView author publicationsYou can also search for this author inPubMed Google ScholarLeah LingView author publicationsYou can also search for this author inPubMed Google ScholarJoan A. O’KeefeView author publicationsYou can also search for this author inPubMed Google ScholarKimberly KweiView author publicationsYou can also search for this author inPubMed Google ScholarGenevieve OlivierView author publicationsYou can also search for this author inPubMed Google ScholarAshwini K. RaoView author publicationsYou can also search for this author inPubMed Google ScholarAnjali SivaramakrishnanView author publicationsYou can also search for this author inPubMed Google ScholarDaniel M. CorcosView author publicationsYou can also search for this author inPubMed Google ScholarContributionsDesign: MM, FH, CC, DMC Execution: MM, MA, QA, SA-H, LD, GME, TE, MEH, KEJ, LL, JAO, KK, GO, AKR Review and critique: MM, QA, SAH, KB, RC, CC, MLD, LD, GME, TE, GJG, MEH, JH, FBH, KEJ, JOK, AKR, AS, DMC. Writing of the first draft: MM, MA, QA, SA-H, LD, GME, TE, MEH, KEJ, LL, JAO, KK, GO, AKR.Corresponding authorCorrespondence to
Martina Mancini.Ethics declarations
Competing interests
Fay Horak is an employee of APDM Wearable Technologies—a Clario company that may have a commercial interest in the results of this research and technology. This potential conflict has been reviewed and managed by OHSU. All other authors declare no Competing Financial or Non-Financial Interests.
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Reprints and permissionsAbout this articleCite this articleMancini, M., Afshari, M., Almeida, Q. et al. Digital gait biomarkers in Parkinson’s disease: susceptibility/risk, progression, response to exercise, and prognosis.
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