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Abstract

Background

Research over the past twenty years has shown that gait outcomes have a high sensitivity for diagnosing Parkinson's disease (PD), for detecting the effects of interventions, and for monitoring disease progression, even in early disease. However, the lack of standardization in protocols and reported gait measures is impeding data aggregation across study sites and contributes to heterogeneity in the results, thus limiting the adoption of gait outcomes in clinical trials.

Objective

To provide recommendations for a minimum set of gait measures to be adopted in projects evaluating people with PD to enhance standardization across the field.

Methods

The Gait Advisors Leading Outcomes for Parkinson's (GALOP) committee is an advisory committee for the MJFF. Based on a five-step approach, GALOP generated recommendations for standardizing protocols that assess quantitative gait measures, following expert consensus on best practices.

Results

Built on the literature and consensus amongst experts, we recommend a minimum set of meta-data to accompany gait protocols and a minimum gait assessment protocol to be performed at a comfortable speed. Suggestions on challenging testing are provided.

Conclusions

To support and empower the scientific community, we have generated recommendations to collect and share gait data gathered from people with PD using an open data repository. Standardizing gait protocols and outcomes in PD has the potential of accelerating research and clinical trials, harmonizing protocols across study sites, fostering collaborations, and in the long run, improving patient care and quality of life.

Keywords

gait standardization Parkinson's disease consensus technology

Introduction

Specific gait impairments are one of the earliest diagnostic signs of Parkinson's disease (PD) and also reflect severity and progression of the disease.^1,2 Yet, in the gold standard clinical motor assessment, the Movement Disorders Revised Unified Parkinson's disease Rating Scale (MDS-UPDRS) Motor Score (Part III),³ has only one item evaluating “Gait” and one item evaluating “Freezing of Gait (FoG)”, both on a Likert scale of 0 to 4. “Problems with walking” are reported as increasingly important in people with PD as the disease progresses;^4,5 however, this is not yet reflected in a widely used, quantitative, and sensitive assessment.

Unfortunately, clinical trials that aim to prevent or slow the progression of immobility in PD may face limitations given the low sensitivity of gold-standard outcome measures, which depend on subjectively rated scales that are based on expert opinions.^2,6,7 Sensitive and objective markers of disease progression are also needed for earlier, more precise detection and characterization of subtle motor deficits in early phenotypic PD as well as in high-risk, pre-symptomatic populations.^8–10

Moreover, certain gait issues are often levodopa-resistant.¹¹ Objective, quantitative measures can address gaps in the advancement of disease modifying therapies by evaluating meaningful effects that are not masked by currently approved symptomatic dopaminergic treatments.

Quantitative gait characteristics, as measured in gait laboratories, are abnormal at the time of diagnosis and inevitably decline with PD progression.^7,12,13 Apart from gait impairments that are visible during routine clinical observation, such as the average reduction in gait speed, stride length and arm swing, there are a number of aspects of gait that are not easily observed visually but have shown to be highly sensitive and specific to PD¹⁴ when quantitatively measured using reliable and valid methods. For example, increased gait asymmetry and stride-to-stride variability (i.e. unsteadiness or inconsistency and arrhythmicity of stepping) can be measured throughout the disease.¹⁴ These metrics have been identified even in individuals who are considered at risk of developing PD,^10,15,16 as well as in those recently diagnosed and who are still medication free.¹⁷ The magnitude of variability also tends to increase with disease severity.^2,14,18 Gait variability is closely associated with falls in older adults^19–24 and represents a measure sensitive to a number of other neurological diseases.^22,25–31 However, despite its potential value, gait variability is often not reliably obtained or reported in PD studies.^32–34 In fact, due to poor standardization of gait protocols, there are reports calculating gait variability from intermittent, short walks, e.g. only 5 meters, and others from long walks, over minutes. This broad range in the testing condition can have a large impact on the results^32,33,35 and in discriminating people with PD from healthy controls.³⁶ Similarly, some studies include the entire walking trial including steps of acceleration and deceleration, for example before and after a turn to change direction,^36,37 while other studies only consider steady-state gait.^38,39

Inconsistency in protocol treatment of the confounding variable of turning is problematic for interpreting data. Turning (i.e. duration, velocity, and number of steps to make a turn) is often impaired in people with PD compared to healthy individuals of similar age even when gait speed is similar.^40,41 Moreover, turning is a key contributor to FoG in people with PD^42,43 and may present distinct features, when compared to basic straight-line walking.

Some gait protocols include straight line comfortable speed walking trials while others also include challenging dual tasks^44–46 to increase neural overload, also known as ‘cognitive-motor’ interference.⁴⁷ Variations also exist in the types of walking evaluated, with some studies using short walks while others evaluating endurance walking, and in the environments where gait is analyzed, such as clinical settings versus real world conditions. Additionally, the methodologies of data acquisition and processing differ dramatically making it challenging to draw consistent conclusions.

This wide variety means that even if the actual data can be harmonized, the differences in the protocols are a barrier to interpretation. Consistency is critical to demonstrate the efficacy of gait variability, the role of turns, and the impact of steady-state walking as measurable clinical outcomes. In the existing literature, there are no guidelines on a minimum protocol to obtain valid gait measures in people with PD, nor which protocol and gait measures would be more sensitive to use in the early versus late stage of the disease. Protocols and algorithms to assess gait quantitatively are extremely varied, received little to no attention on standardization, and their designs depend on the available instrumentation (shorter walks with motion analysis and pressure mat versus wearable technology), the environmental constraints (longer spaces in laboratory versus clinic, and shorter distances in daily life), and the original hypotheses. Although several papers point to the need for standardizing gait testing protocol and sensing systems in PD,^33,34,36,48 no guidelines have been published. To the best of our knowledge, the only document providing guidelines on a common data set for gait metrics in PD was recently put together by the National Institute of Neurological Disorders and Stroke (NINDS). However, these guidelines targeted solely the use of wearable sensors for research and clinical trials in PD, were not specific to gait, and did not include recommendations on a specific gait protocol, which is crucial for validity of gait measures. There is, therefore, a great need for standardization of the field, to enable harmonization between studies, allow for aggregation of information, and inform a better understanding of the value of gait measures for diagnosis, monitoring, prediction and responsiveness to interventions.

Here, our objective was to recommend a common protocol for multicenter, natural history, and intervention studies to support the inclusion of specific gait measures for clinical trials in PD. Recommendations are provided based on aggregated information from the literature and expert panel consensus.

Methods

The guidelines for a unifying protocol and a set of gait measures have been developed in a step-wise, tiered manner by first reviewing the existing literature and then having discussions (first subgroup and then plenary) to reach a consensus among an international and diverse group of experts on gait and PD.

The group of experts is part of an advisory committee for the Michael J. Fox Foundation for Parkinson's Research (year 2023). The group includes senior and junior experts in the field of gait, from academia to clinical care, who aim to progress research and treatment of gait impairments in PD. Specifically, the group was formed of ten members (MM, JH, PB, EP, RC, TE, JK, LG, AF, AN) and a chair (AM). Out of the 11 members, 4 are PT PhDs, 3 are MD movement disorders specialists, 3 are bioengineers, and one is an individual with PD living with gait issues.

The flow describing how the guidelines were developed is represented in Figure 1.

Figure 1.

Overview of the flow used to develop guidelines and minimum protocol for gait standardization in people with PD.

The data collection process was partly in line with the ACcurate COnsensus Reporting Document (ACCORD) procedures to achieve consensus.⁴⁹ Briefly, it included 5 stages. First, in Stage 1, a sub-committee (MM, JH, PB, EP, and AM) was formed to carry out the proposed work, in which the scope and timelines were defined during a virtual meeting. In Stage 2, the sub-committee discussed a narrative review of the recent literature, including reviews on gait in PD and the recently developed NIH guidelines on the reporting of digital outcomes for PD (part of the NINDS Common Data Elements initiative for PD, https://www.commondataelements.ninds.nih.gov/Parkinson%27s%20Disease). Then, a first outline was drafted (MM) and reviewed by the sub-committee (AM, JH, PB, EP). In Stage 3, the first draft of the guidelines was presented to the full committee during a virtual meeting and feedback was obtained. In Stage 4, gait standardization items were finalized, a guide, a data dictionary, and examples on how to report data were created (MM and AM), circulated to the sub-committee and full committee and agreed upon by all members. Lastly, Stage 5 consists of: a) consultation with patients and patient representatives to receive feedback, see Supplemental Material, and b) dissemination through article publication and presentations.

During the virtual meeting, 100% agreement was reached on the minimum set of items to report in the gait standardization document and final approval from the full committee was obtained at each stage.

Results

Figure 2 summarizes the recommended demographic and clinical measures, as well as the protocol guidelines and gait measures to report while investigating gait in people with PD. The following demographics should be reported, in reference to the time of testing: age, sexual orientation and gender identify (SOGI), height, weight, years of education, disease duration, race and ethnicity.

Figure 2.

Diagram to visualize summary data to report. Visit Type cross-sectional for a one-time visit. For a study with two or more visits (within a few weeks to one month), enter visit1, visit2 and so forth. For longitudinal studies over years, enter baseline, 6months, 12months, 18months, and so forth. ^PDQ-39 summary score. ^^To report the levodopa equivalent dose (mg), please refer to Jost et al.⁵⁰

The committee deemed the reporting of the demographics and clinical measures of importance, in that: 1) they characterize the participant and allow integration data from similar projects, and 2) some may influence expected or actual gait performance. For some of those demographics and clinical measures, the effects on gait have been previously investigated (age, disease duration,^2,18,51 DBS,⁵² cognition,^53,54), while for others, their influence on gait is not yet clear but warrants further consideration and may be important for future trials (SOGI^55,56). Regardless, all the SOGI questions have an option of “prefer not to answer”.

For the clinical measures, the MDS-UPDRS and Hoen & Yahr stage should each be reported; we recommend reporting all items as well as the total MDS-UPDRS score, as well as the commonly calculated sub-scores (i.e. PIGD, Tremor scores).³ Freezing of Gait (FOG) status and past falls (recollection of 6 months prior) should be collected. Information on whether participants have a deep brain stimulation (DBS) implanted, the anatomical target of DBS (including unilateral vs. bilateral implantation), or whether they have an infusion pump should be collected. We recommend the Montreal Cognitive Assessment (MoCA)⁵⁷ and the Parkinson Disease Questionnaire (PDQ-39)⁵⁸ to characterize overall cognition and patient-reported assessment of quality of life, respectively.

Levodopa equivalent dose should be calculated following recent recommendations.⁵⁰ As the guidelines are aimed at addressing a wide population of people with PD, walking aids are permitted if strictly necessary. However, information on whether the individual is using a walking aid (and the type) should be reported. It is important for the validity and interpretation of the gait measures that the individual can walk unassisted (although an examiner guarding gait to ensure safety is allowed, as long as there is no interference to the walking and turning of the individual).

To help standardize the heterogeneity across gait protocols (i.e. short versus long walks, technology, simple versus challenging tasks), the committee recommended a minimum protocol, see Figure 3, summarized in the Location-Specific Measures of Figure 2. Specifically, if the protocol includes collecting gait in prescribed conditions (with an examiner present explaining the protocol) in a laboratory, clinic or home, the committee recommends, at minimum, that the protocol include: 1) one continuous gait task of at least 1 min in duration, 2) over a path of 10-meters in length (allowing 1 m at each end for a safe turn), and 3) with participants performing 180 degrees turns at each end (ideally passing a line and turning back to the opposite end). The rationale for the choice of a continuous walk of at least 1 min over 10 meters was agreed upon to ensure the validity and reliability of the gait measures, in fact, variability measures need at least 30 to 50 steps.^32,33,36 The duration of 1-min was thought to be a good compromise in case multiple gait testing conditions can be assessed without making the testing excessively long and while still ensuring that an appropriate number of steps and strides are recorded. The length of 10-meters was proposed to ensure reaching steady-state gait before decelerating prior to a turn and accelerating after a turn to change direction.⁵⁹ If a 10-m path is not available, investigators should report the specific distance used and they should be aware that the reliability and validity of certain gait measures may be compromised. We acknowledge that many clinics may carry out the gait testing directly in the clinic space or hospital corridors and that this situation will expose the tested participant to interference of other individuals possibly passing by. For this reason, we recommend trying to minimize the interferences with possibly a wait sign in the area and instructing participants to carry out their test without acknowledging people passing by.

Figure 3.

Proposed basic protocol and set of measures for gait assessment in ambulatory people with Parkinson's disease. Tasks and measures in darker color indicate a minim set, tasks and measures in lighter color indicate suggested items. Participants should not use any walking aid or assistive device if possible.

Gait should be collected at least during comfortable walking speed and the medication status at the time of testing should be reported (e.g. ON, OFF, time since the last intake of PD medication). Specific measures are recommended to collect data on walking duration, length, turning strategy, walking speed, number of strides or steps used for the analysis, and the number of FOG episodes, if observed. The committee agrees on the importance of including turning metrics when reporting gait. Difficulties turning in PD occur early in the disease⁴¹ and are associated with clinical outcomes, such as falls, fear of falling, disease severity, and FoG.^60–63

Additional suggested protocol elements and measures are also provided, beyond the minimum recommendations of the consensus committee, as shown under “suggested” in Figure 3. Specifically, more challenging gait protocols are advised, if possible, such as a dual-task walking, using one of the 3 cognitive challenges, and/or a fast walk. If using a dual-task walking condition, we also suggest collecting seated, baseline cognitive task performance. The committee agrees on the importance of challenging gait. It is known that people with PD have difficulties performing a second task while walking, such as walking and talking.⁴⁶ As a consequence of performing two tasks at the same time, gait and/or their performance on the secondary task at hand deteriorates.^46,64,65 These difficulties in dual task walking often lead to increased disability, increased FoG and fall risk, and reduced quality of life.^46,64,66 The commission is aware of the possibility of emerging new biomarkers that arise to detect and predict freezing of gait in early disease.^12,67,68 Therefore, updating the current guidelines is indicated in the near future.

The technology and software used should be reported and provide validation details for use in PD.^{33,38,69–71} Specifically, as detailed in the Supplemental Material, the following should be stated regarding the used technology: 1) type of technology (e.g. gait mat, motion analysis, IMUs); 2) manufacturer, make and model; 3) sampling frequency; and 4) references on the validity of the specific technology in measuring gait. For the used software, the following should be specified: 1) ad-hoc or commercial algorithms; 2) definition used to identify bouts of gait; 3) references on the validity of the algorithm in people with PD, and 4) explanation on how the gait measures are calculated.

The specific selection of gait measures was partly in agreement with the recent recommendation of the NIH common data elements and demonstrated criterion validity and reliability, according to the COSMIN framework.⁷² As gait is multifactorial, several studies in the past have used conceptual gait models to identify independent gait domains.^73–75 Based on this information, we recommend as shown in Figure 3 to report, at a minimum, measures related to domains of: 1) pace, with gait speed, step or stride length; 2) rhythm, step or stride time, cadence; 3) asymmetry, swing or step time asymmetry using a normalized/logarithmic formula described in Supplemental Material; 4) variability, step or stride standard deviation; and 5) turning, turning velocity and number of steps/turn. These domains were chosen based on their consistent use in literature, although other domains or slightly different subdivisions have been proposed.^74,75 Additionally, if the technology allows and measures of upper-body/stability are available, then the angle of the foot at heel-strike,^39,76,77 arm swing range of motion and its asymmetry^41,78 should be reported, as well as the trunk coronal range of motion while walking.⁴¹

All the variables presented in Figure 2 should be made available in summary/derived form in a .csv file or a similar and de-identified version of data, meeting ethical and regulatory requirements, and should be deposited in an open access platform of choice. The Supplemental Material provides an example given to investigators on MJFF's supported studies on how to report results and definitions consistent with a data dictionary.

Discussion

In this paper, the Gait Advisors Leading Outcomes for Parkinson’s (GALOP) committee presents guidelines designed to provide recommendations for a minimum protocol to quantitatively assess gait in PD for non-expert and expert researchers, clinicians and health specialists interested in measuring gait. We intended to facilitate standardization of gait to enable harmonization of gait data from different studies in the PD research community in order to advance quantified gait as a clinical outcome measure and to move from small studies based on data collected locally or in a specific project to analyses based on large datasets, like those that have been done with respect to ageing.^79,80

A minimum set of data to accompany gait protocols (including basic demographics and clinical data), and a minimum gait protocol of at least 1-min of walking in a straight-line path of 10 m, including 180 degrees turns to be performed at a comfortable speed, have been agreed upon. This recommendation is similar to recent guidelines from a consensus on digital measures of gait in people with spinocerebellar ataxia.⁸¹ In addition, this committee also recommends adding additional challenging conditions such as fast-paced walking or dual-task to enable a comprehensive assessment of mobility. To note, prescribed, gait assessment in the laboratory has been extensively studied and is reliable for persons with PD.^37,38,48,82 The drawback is that such assessments may not reflect mobility during daily life.^83,84 The gait of people with PD is often paradoxically better when they know that they are being observed.⁸⁵ Unobtrusive community monitoring has the untapped potential to provide quantitative mobility measures that may be even more sensitive markers of subtle changes, compared to measures in the laboratory in people with PD.⁸⁴ Gait measured in the home and community during normal daily activities reflects real-world mobility affected by complex environments, multitasking, and daily motor fluctuations.^83–88 Nowadays the technology allows for measuring gait in these patient-relevant, ecological conditions; however, there are several challenges regarding the best protocol to use, data processing and data storage. With the current state of the literature,^{83,84,86–90} at least 3 days, best if 4 or more, of continuous monitoring of gait in daily life are recommended, for at least 8 h a day, best if 24/7.⁹¹

In the existing literature, there are also no guidelines on which protocol and gait measures would be more sensitive to use in the early versus late stage of the disease. Clearly, different metrics should be assessed at different stages of the disease^2,18 and the spectrum of gait conditions should be chosen accordingly. With this in mind, the proposed minimum set is seen as a foundation on which additional gait tasks (i.e. turning in place or other tasks to elicit FoG), additional gait measures (i.e. EMG, mobile imaging, etc.), additional measures of balance capacity (i.e. MiniBEST⁹²), as well as additional demographics (i.e. genetic status, co-existing medical issues) can be laid. We acknowledge the potential limitations of research and clinical settings regarding time, effort, cohort and research questions, thus opted for a minimum set of data to allow inclusivity towards standardization and harmonization of data.

With the current review of the literature, we do strongly suggest incorporating challenging walking conditions, such as dual-tasking and/or fast walking, which may be useful in the prodromal state, for example.¹⁰ Further, we intentionally did not specify outcomes to be gathered but only the common minimal set. However, we encourage a broad approach with transparency in reporting, to encourage the harmonization between studies. For example, studies show that gait measures of asymmetry (either lower or upper body) and variability, as well as turning, only when assessed during challenging conditions (fast or dual-task), are altered in individuals considered at risk of developing PD¹⁶ (either for genetic mutations^10,93 or RBD⁹⁴) compared to healthy controls. Instead, when gait is used to monitor disease progression, few studies show a decline of stride length,^76,95,96 gait speed,,^95–97 angle of the foot at heel-strike and gait variability⁷⁶ when measured during single-task gait at comfortable speed.^76,95,96,98 Investigators should specify the approach(es) / technology proposed to collect quantitative gait outcomes and provide a detailed explanation for all data collected and the technical and clinical validation of proposed assessments.⁹⁹ Of course, the specific hypothesis and context of use should also be delineated.

In addition to sharing all summary measures, we want to stress the importance of making raw data available to the extent possible, as well as computer code/algorithms used for pre-processing. There are many open-source data and software platforms, such as Zenodo, Physionet, and Github, that can be used for these purposes. The authors do not yet recommend a specific platform and file structure to be used, although some examples are starting to come out in the literature, including the example Supplemental Material presented here.^70,99,100

Strengths of the proposed recommendations include the large, international, multidisciplinary group of experts in PD and gait, the use of a collaborative process with open discussions, and active engagement of one individual with first-hand personal experience as a member of the committee. Our overall approach was based on a collaborative process with group participation and feedback. As such, the group members’ identities were known to each other, and the recommendations were written with group contributions and iterative feedback. Moreover, given the vast background and experience of the committee with multidisciplinary collaboration and teamwork, this type of group and teamwork flowed naturally to allow all members to participate openly. We acknowledge that the lack of a formal Delphi process may be a methodological limitation, though we also recognize that the literature supports various guidelines and statements without a formal Delphi method^81,101 (examples are NINDS guidelines and MDS Task Force^102,103).

The proposed set of guidelines is a first step in the direction of proposing a standardized gait assessment for a wide population of people with PD with the vision of being able to integrate datasets in the future and reach appropriate sample sizes for clinical trials. In the future, we envision that this protocol will be updated based on new knowledge and recommendations. As gait is crucial for moving independently in daily life, is one of the symptoms most important to people with PD,⁴ and is impacted in other movement disorders, this framework could later be implemented for other neurological diseases to advance the understanding of those diseases and enable the identification of common and distinct gait alterations across different diseases.

Supplemental Material

sj-csv-1-pkn-10.1177_1877718X241305626 - Supplemental material for A framework to standardize gait study protocols in Parkinson's disease

Supplemental material, sj-csv-1-pkn-10.1177_1877718X241305626 for A framework to standardize gait study protocols in Parkinson's disease by Martina Mancini, Jeffrey M Hausdorff, Elisa Pelosin, Paolo Bonato, Richard Camicioli, Terry D Ellis, Jochen Klucken, Larry Gifford, Alfonso Fasano, Alice Nieuwboer, Catherine Kopil, Katharina Klapper, Leslie Kirsch, David T Dexter, Rosie Fuest, Vicki Miller, Angelica Asis, Martijn LTM Müller, Diane Stephenson and Anat Mirelman in Journal of Parkinson's Disease

Supplemental Material

sj-csv-2-pkn-10.1177_1877718X241305626 - Supplemental material for A framework to standardize gait study protocols in Parkinson's disease

Supplemental material, sj-csv-2-pkn-10.1177_1877718X241305626 for A framework to standardize gait study protocols in Parkinson's disease by Martina Mancini, Jeffrey M Hausdorff, Elisa Pelosin, Paolo Bonato, Richard Camicioli, Terry D Ellis, Jochen Klucken, Larry Gifford, Alfonso Fasano, Alice Nieuwboer, Catherine Kopil, Katharina Klapper, Leslie Kirsch, David T Dexter, Rosie Fuest, Vicki Miller, Angelica Asis, Martijn LTM Müller, Diane Stephenson and Anat Mirelman in Journal of Parkinson's Disease

Supplemental Material

sj-csv-3-pkn-10.1177_1877718X241305626 - Supplemental material for A framework to standardize gait study protocols in Parkinson's disease

Supplemental material, sj-csv-3-pkn-10.1177_1877718X241305626 for A framework to standardize gait study protocols in Parkinson's disease by Martina Mancini, Jeffrey M Hausdorff, Elisa Pelosin, Paolo Bonato, Richard Camicioli, Terry D Ellis, Jochen Klucken, Larry Gifford, Alfonso Fasano, Alice Nieuwboer, Catherine Kopil, Katharina Klapper, Leslie Kirsch, David T Dexter, Rosie Fuest, Vicki Miller, Angelica Asis, Martijn LTM Müller, Diane Stephenson and Anat Mirelman in Journal of Parkinson's Disease

Supplemental Material

sj-docx-5-pkn-10.1177_1877718X241305626 - Supplemental material for A framework to standardize gait study protocols in Parkinson's disease

Supplemental material, sj-docx-5-pkn-10.1177_1877718X241305626 for A framework to standardize gait study protocols in Parkinson's disease by Martina Mancini, Jeffrey M Hausdorff, Elisa Pelosin, Paolo Bonato, Richard Camicioli, Terry D Ellis, Jochen Klucken, Larry Gifford, Alfonso Fasano, Alice Nieuwboer, Catherine Kopil, Katharina Klapper, Leslie Kirsch, David T Dexter, Rosie Fuest, Vicki Miller, Angelica Asis, Martijn LTM Müller, Diane Stephenson and Anat Mirelman in Journal of Parkinson's Disease

Footnotes

Acknowledgments

We are grateful for the time and perspectives shared from Michèle Bartlett and Jodie Forbes to our .

ORCID iDs

Martina Mancini

Jeffrey M Hausdorff

Elisa Pelosin

Paolo Bonato

Richard Camicioli

Jochen Klucken

Larry Gifford

Alice Nieuwboer

Catherine Kopil

Katharina Klapper

Leslie Kirsch

David T Dexter

Vicki Miller

Martijn LTM. Müller

Anat Mirelman

Funding

MJFF-024749 and The Michael J. Fox Foundation for Parkinson's Research.

Declaration of conflicting interests

Drs. Alice Nieuwboer and Anat Mirelman are Editorial Board Members of this journal but were not involved in the peer-review process of this article nor had access to any information regarding its peer-review. The remaining authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during this study.

Supplemental material

Supplemental material for this article is available online.

References

Speelman

van Nimwegen

Borm

, et al. Monitoring of walking in Parkinson's disease: validation of an ambulatory activity monitor. Parkinsonism Relat Disord 2011; 17: 402–404.

Mirelman

Bonato

Camicioli

, et al. Gait impairments in Parkinson's disease. Lancet Neurol 2019; 18: 697–708.

Goetz

Tilley

Shaftman

, 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 2008; 23: 2129–2170.

Port

Rumsby

Brown

, et al.

People with Parkinson's disease: what symptoms do they most want to improve and how does this change with disease duration?

J Parkinsons Dis 2021; 11: 715–724.

Mammen

Speck

Stebbins

, et al. Relative meaningfulness and impacts of symptoms in people with early-stage Parkinson's disease. J Parkinsons Dis 2023; 13: 619–632.

Evans

Mason

Williams-Gray

, et al. The natural history of treated Parkinson's disease in an incident, community based cohort. J Neurol Neurosurg Psychiatry 2011; 82: 1112–1118.

Galna

Lord

Burn

, et al. Progression of gait dysfunction in incident Parkinson's disease: impact of medication and phenotype. Mov Disord 2015; 30: 359–367.

Del Din

Elshehabi

Galna

, et al. Gait analysis with wearables predicts conversion to Parkinson disease. Ann Neurol 2019; 86: 357–367.

Maetzler

Del Din

Elshehabi

, et al. Reply to “quantitative motor functioning in prodromal Parkinson disease”. Ann Neurol 2019; 86: 981–982.

10.

Mirelman

Gurevich

Giladi

, et al. Gait alterations in healthy carriers of the LRRK2 G2019S mutation. Ann Neurol 2011; 69: 193–197.

11.

Smulders

Dale

Carlson-Kuhta

, et al. Pharmacological treatment in Parkinson's disease: effects on gait. Parkinsonism Relat Disord 2016; 31: 3–13.

12.

Mancini

Weiss

Herman

, et al. Turn around freezing: community-living turning behavior in people with Parkinson’s disease. Front Neurol 2018; 9: 18.

13.

Wilson

Alcock

Yarnall

, et al. Gait progression over 6 years in Parkinson's disease: effects of age, medication, and pathology. Front Aging Neurosci 2020; 12: 577435.

14.

Hausdorff

. Gait dynamics in Parkinson's disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos 2009; 19: 026113.

15.

McDade

Boot

Christianson

, et al. Subtle gait changes in patients with REM sleep behavior disorder. Mov Disord 2013; 28: 1847–1853.

16.

Maetzler

Mirelman

Pilotto

, et al.

Identifying subtle motor deficits before Parkinson's disease is diagnosed: what to look for?

J Parkinsons Dis 2024; 14: S287–S296.

17.

Baltadjieva

Giladi

Gruendlinger

, et al. Marked alterations in the gait timing and rhythmicity of patients with de novo Parkinson's disease. Eur J Neurosci 2006; 24: 1815–1820.

18.

Mirelman

Volkov

Salomon

, et al. Digital mobility measures: a window into real-world severity and progression of Parkinson's disease. Mov Disord 2024; 39: 328–338.

19.

Hausdorff

Rios

Edelberg

. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil 2001; 82: 1050–1056.

20.

Ayoubi

Launay

Kabeshova

, et al. The influence of fear of falling on gait variability: results from a large elderly population-based cross-sectional study. J Neuroeng Rehabil 2014; 11: 128.

21.

Maki

. Gait changes in older adults: predictors of falls or indicators of fear. J Am Geriatr Soc 1997; 45: 313–320.

22.

Montero-Odasso

Verghese

Beauchet

, et al. Gait and cognition: a complementary approach to understanding brain function and the risk of falling. J Am Geriatr Soc 2012; 60: 2127–2136.

23.

Beauchet

Allali

Annweiler

, et al. Gait variability among healthy adults: low and high stride-to-stride variability are both a reflection of gait stability. Gerontology 2009; 55: 702–706.

24.

Herman

Giladi

Gurevich

, et al.

Gait instability and fractal dynamics of older adults with a “cautious” gait: why do certain older adults walk fearfully?

Gait Posture 2005; 21: 178–185.

25.

Niechwiej-Szwedo

Inness

Howe

, et al. Changes in gait variability during different challenges to mobility in patients with traumatic brain injury. Gait Posture 2007; 25: 70–77.

26.

Kaipust

Huisinga

Filipi

, et al. Gait variability measures reveal differences between multiple sclerosis patients and healthy controls. Motor Control 2012; 16: 229–244.

27.

Socie

Sosnoff

. Gait variability and multiple sclerosis. Mult Scler Int 2013; 2013: 645197.

28.

Mc Ardle

Galna

Donaghy

, et al.

Do Alzheimer's and Lewy body disease have discrete pathological signatures of gait?

Alzheimers Dement 2019; 15: 1367–1377.

29.

Martini

Parrington

Stuart

, et al. Gait performance in people with symptomatic, chronic mild traumatic brain injury. J Neurotrauma 2021; 38: 218–224.

30.

Pieruccini-Faria

Black

Masellis

, et al. Gait variability across neurodegenerative and cognitive disorders: results from the Canadian Consortium of Neurodegeneration in Aging (CCNA) and the gait and brain study. Alzheimers Dement 2021; 17: 1317–1328.

31.

Shah

Rodriguez-Labrada

Horak

, et al. Gait variability in spinocerebellar ataxia assessed using wearable inertial sensors. Mov Disord 2021; 36: 2922–2931.

32.

Lord

Howe

Greenland

, et al. Gait variability in older adults: a structured review of testing protocol and clinimetric properties. Gait Posture 2011; 34: 443–450.

33.

Galna

Lord

Rochester

. Is gait variability reliable in older adults and Parkinson's disease? Towards an optimal testing protocol. Gait Posture 2013; 37: 580–585.

34.

Rennie

Löfgren

Moe-Nilssen

, 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 2018; 62: 505–509.

35.

Paterson

Lythgo

Hill

. Gait variability in younger and older adult women is altered by overground walking protocol. Age Ageing 2009; 38: 745–748.

36.

Rehman

RZU

Del Din

Shi

, et al. Comparison of walking protocols and gait assessment systems for machine learning-based classification of Parkinson's disease. Sensors (Basel) 2019; 19: 5363.

37.

Del Din

Godfrey

Rochester

. Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson's disease: toward clinical and at home use. IEEE J Biomed Health Inform 2016; 20: 838–847.

38.

Morris

Stuart

McBarron

, et al. Validity of mobility lab (version 2) for gait assessment in young adults, older adults and Parkinson's disease. Physiol Measure 2019; 40: 095003.

39.

Hasegawa

Shah

Carlson-Kuhta

, et al. How to select balance measures sensitive to Parkinson's disease from body-worn inertial sensors-separating the trees from the forest. Sensors 2019; 19: 3320.

40.

Crenna

Carpinella

Rabuffetti

, et al. The association between impaired turning and normal straight walking in Parkinson's disease. Gait Posture 2007; 26: 172–178.

41.

Zampieri

Salarian

Carlson-Kuhta

, et al. The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson's disease. J Neurol Neurosurg Psychiatry 2010; 81: 171–176.

42.

Mancini

Smulders

Cohen

, et al. The clinical significance of freezing while turning in Parkinson's disease. Neuroscience 2017; 343: 222–228.

43.

Zach

Janssen

Snijders

, et al. Identifying freezing of gait in Parkinson's disease during freezing provoking tasks using waist-mounted accelerometry. Parkinsonism Relat Disord 2015; 21: 1362–1366.

44.

Plotnik

Dagan

Gurevich

, et al. Effects of cognitive function on gait and dual tasking abilities in patients with Parkinson's disease suffering from motor response fluctuations. Exp Brain Res 2011; 208: 169–179.

45.

Raffegeau

Krehbiel

Kang

, et al. A meta-analysis: Parkinson's disease and dual-task walking. Parkinsonism Relat Disord 2019; 62: 28–35.

46.

Kelly

Eusterbrock

Shumway-Cook

. A review of dual-task walking deficits in people with Parkinson's disease: motor and cognitive contributions, mechanisms, and clinical implications. Parkinsons Dis 2012; 2012: 918719.

47.

Woollacott

Shumway-Cook

. Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture 2002; 16: 1–14.

48.

Rudisch

Jöllenbeck

Vogt

, et al. Agreement and consistency of five different clinical gait analysis systems in the assessment of spatiotemporal gait parameters. Gait Posture 2021; 85: 55–64.

49.

Gattrell

Hungin

Price

, et al. ACCORD Guideline for reporting consensus-based methods in biomedical research and clinical practice: a study protocol. Res Integr Peer Rev 2022; 7: 3.

50.

Jost

Kaldenbach

M-A

Antonini

, et al. Levodopa dose equivalency in Parkinson's disease: updated systematic review and proposals. Mov Disord 2023; 38: 1236–1252.

51.

Vila

Pérez

Mollinedo

, et al. Analysis of gait for disease stage in patients with Parkinson’s disease. Int J Env Res Public Health 2021; 18: 720.

52.

Schlenstedt

Shalash

Muthuraman

, et al. Effect of high-frequency subthalamic neurostimulation on gait and freezing of gait in Parkinson's disease: a systematic review and meta-analysis. Eur J Neurol 2017; 24: 18–26.

53.

Morris

Martini

Ramsey

, et al. Cognition as a mediator for gait and balance impairments in GBA-related Parkinson’s disease. NPJ Parkinsons Dis 2022; 8: 78.

54.

Lord

Galna

Coleman

, et al. Cognition and gait show a selective pattern of association dominated by phenotype in incident Parkinson's disease. Front Aging Neurosci 2014; 6: 249.

55.

Adams

Kumar

. The effect of estrogen in a man with Parkinson's disease and a review of its therapeutic potential. Int J Neurosci 2013; 123: 741–742.

56.

Lin

Rosendale

Deeb

. Expanding sexual and gender minority research in movement disorders: more than awareness and acceptance. Parkinsonism Relat Disord 2021; 87: 162–165.

57.

Nasreddine

Phillips

Bedirian

, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53: 695–699.

58.

Jenkinson

Fitzpatrick

Peto

, et al. The Parkinson's Disease Questionnaire (PDQ-39): development and validation of a Parkinson's disease summary index score. Age Ageing 1997; 26: 353–357.

59.

Lindemann

Najafi

Zijlstra

, et al. Distance to achieve steady state walking speed in frail elderly persons. Gait Posture 2008; 27: 91–96.

60.

Amar

Stack

Fitton

, et al. Fall frequency, predicting falls and participating in falls research: similarities among people with Parkinson's disease with and without cognitive impairment. Parkinsonism Relat Disord 2015; 21: 55–60.

61.

Bloem

Grimbergen

Cramer

, et al. Prospective assessment of falls in Parkinson's disease. J Neurol 2001; 248: 950–958.

62.

Bertoli

Croce

Cereatti

, et al. Objective measures to investigate turning impairments and freezing of gait in people with Parkinson's disease. Gait Posture 2019; 74: 187–193.

63.

Haertner

Elshehabi

Zaunbrecher

, et al. Effect of fear of falling on turning performance in Parkinson's disease in the lab and at home. Front Aging Neurosci 2018; 10: 78.

64.

Yogev

Giladi

Peretz

, et al.

Dual tasking, gait rhythmicity, and Parkinson's disease: which aspects of gait are attention demanding?

Eur J Neurosci 2005; 22: 1248–1256.

65.

Stegemöller

Wilson

Hazamy

, et al. Associations between cognitive and gait performance during single- and dual-task walking in people with Parkinson disease. Phys Ther 2014; 94: 757–766.

66.

Monaghan

Ragothaman

Harker

, et al. Freezing of gait in Parkinson's disease: implications for dual-task walking. J Parkinsons Dis 2023; 13: 1035–1046.

67.

D'Cruz

Seuthe

De Somer

, et al. Dual task turning in place: a reliable, valid, and responsive outcome measure of freezing of gait. Mov Disord 2022; 37: 269–278.

68.

Mancini

Hasegawa

Peterson

, et al. Digital measures of freezing of gait across the spectrum of normal, non-freezers, possible freezers and definite freezers. J Neurol 2023; 270: 4309–4317.

69.

Morris

Hickey

Del Din

, et al. A model of free-living gait: a factor analysis in Parkinson's disease. Gait Posture 2017; 52: 68–71.

70.

Kirk

Küderle

Micó-Amigo

, et al. Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device. Sci Rep 2024; 14: 1754.

71.

Del Din

Galna

Godfrey

, et al. Analysis of free-living gait in older adults with and without Parkinson's disease and with and without a history of falls: identifying generic and disease-specific characteristics. J Gerontol A Biol Sci Med Sci 2019; 74: 500–506.

72.

Terwee

Prinsen

CAC

Chiarotto

, et al. COSMIN Methodology for evaluating the content validity of patient-reported outcome measures: a Delphi study. Qual Life Res 2018; 27: 1159–1170.

73.

Lord

Galna

Verghese

, et al. Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. Gerontol A Biol Sci Med Sci 2013; 68: 820–827.

74.

Horak

Mancini

Carlson-Kuhta

, et al. Balance and gait represent independent domains of mobility in Parkinson disease. Phys Ther 2016; 96: 1364–1371.

75.

Arcolin

Corna

Giardini

, et al. Proposal of a new conceptual gait model for patients with Parkinson's disease based on factor analysis. Biomed Eng Online 2019; 18: 70.

76.

Sotirakis

Brzezicki

, et al. Identification of motor progression in Parkinson's disease using wearable sensors and machine learning. NPJ Parkinsons Dis 2023; 9: 142.

77.

Shah

McNames

Mancini

, et al. Quantity and quality of gait and turning in people with multiple sclerosis, Parkinson's disease and matched controls during daily living. J Neurol 2020; 267: 1188–1196.

78.

Mirelman

Bernad-Elazari

Thaler

, et al. Arm swing as a potential new prodromal marker of Parkinson's disease. Mov Disord 2016; 31: 1527–1534.

79.

Sprague

Zhu

Rosso

, et al. Correlates of gait speed among older adults from 6 countries: findings from the COSMIC collaboration. J Gerontol A Biol Sci Med Sci 2023; 78: 2396–2406.

80.

Studenski

Perera

Patel

, et al. Gait speed and survival in older adults. JAMA 2011; 305: 50–58.

81.

Ilg

Milne

Schmitz-Hübsch

, et al. Quantitative gait and balance outcomes for ataxia trials: consensus recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers. Cerebellum 2024; 23: 1566–1592.

82.

Godinho

Domingos

Cunha

, et al. A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson's disease. J Neuroeng Rehabil 2016; 13: 24.

83.

Polhemus

Bergquist

Bosch de Basea

, et al. Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review. BMJ Open 2020; 10: e038704.

84.

Maetzler

Domingos

Srulijes

, et al. Quantitative wearable sensors for objective assessment of Parkinson's disease. Mov Disord 2013; 28: 1628–1637.

85.

Hillel

Gazit

Nieuwboer

, et al. Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring. Eur Rev Aging Phys Act 2019; 16: 6.

86.

Del Din

Godfrey

Galna

, et al. Free-living gait characteristics in ageing and Parkinson's disease: impact of environment and ambulatory bout length. J Neuroeng Rehabil 2016; 13: 46.

87.

Del Din

Godfrey

Mazza

, et al. Free-living monitoring of Parkinson's disease: lessons from the field. Mov Disord 2016; 31: 1293–1313.

88.

Del Din

Kirk

Yarnall

, et al. Body-worn sensors for remote monitoring of Parkinson's disease motor symptoms: vision, state of the art, and challenges ahead. J Parkinsons Dis 2021; 11: S35–S47.

89.

Bianchini

Galli

Alborghetti

, et al. Four days are enough to provide a reliable daily step count in mild to moderate Parkinson's disease through a commercial smartwatch. Sensors 2023; 23: 8971.

90.

Paul

Ellis

Dibble

, et al. Obtaining reliable estimates of ambulatory physical activity in people with Parkinson's disease. J Parkinsons Dis 2016; 6: 301–305.

91.

Buekers

Chernova

Marchena

, et al. Reliability of real-world walking activity and gait assessment in people with Parkinson's disease - how many hours and days are needed? [abstract]. Mov Disord 2024; 39: Abstract #1226.

92.

Franchignoni

Horak

Godi

, et al. Using psychometric techniques to improve the Balance Evaluation Systems Test: the mini-BESTest. J Rehabil Med 2010; 42: 323–331.

93.

Mirelman

Heman

Yasinovsky

, et al. Fall risk and gait in Parkinson's disease: the role of the LRRK2 G2019S mutation. Mov Disord 2013; 28: 1683–1690.

94.

Ehgoetz Martens

Matar

Shine

, et al. The neural signature of impaired dual-tasking in idiopathic rapid eye movement sleep behavior disorder patients. Mov Disord 2020; 35: 1596–1606.

95.

Micó-Amigo

Kingma

Heinzel

, 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 2019; 13: 59.

96.

Hobert

Nussbaum

Heger

, et al. Progressive gait deficits in Parkinson's disease: a wearable-based biannual 5-year prospective study. Front Aging Neurosci 2019; 11: 22.

97.

Ellis

Cavanaugh

Earhart

, et al. Identifying clinical measures that most accurately reflect the progression of disability in Parkinson disease. Parkinsonism Relat Disord 2016; 25: 65–71.

98.

Micó-Amigo

Kingma

Heinzel

, et al.

Dual vs. Single tasking during circular walking: what better reflects progression in Parkinson's disease?

Front Neurol 2019; 10: 372.

99.

Micó-Amigo

Bonci

Paraschiv-Ionescu

, et al. Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium. J Neuroeng Rehabil 2023; 20: 78.

100.

Palmerini

Reggi

Bonci

, et al. Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization. Sci Data 2023; 10: 38.

101.

Goldman

Volpe

Ellis

, et al. Delivering multidisciplinary rehabilitation care in Parkinson's disease: an international consensus statement. J Parkinsons Dis 2024; 14: 135–166.

102.

Espay

Bonato

Nahab

, et al. Technology in Parkinson's disease: challenges and opportunities. Mov Disord 2016; 31: 1272–1282.

103.

Espay

Hausdorff

Sanchez-Ferro

, et al. A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies. Mov Disord 2019; 34: 657–663.

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