This paper is in the following e-collection/theme issue:

NIH funded proposals with peer-review reports (USA) (112) mHealth for Data Collection and Research (1196) Neurology and Neurosciences (286) Falls and Emergency Detection with Sensors and other Technologies (57) Falls Prevention in the Elderly (101) Telehealth and Telemonitoring (2086) Wearable Devices and Sensors (1133) Digital Biomarkers and Digital Phenotyping (284) Dementia and Cognitive Decline (696)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71955, first published .
Improving Prediction of Falls and Cognitive Impairment in Parkinson Disease: Protocol for a Decentralized Observational Study

Improving Prediction of Falls and Cognitive Impairment in Parkinson Disease: Protocol for a Decentralized Observational Study

Improving Prediction of Falls and Cognitive Impairment in Parkinson Disease: Protocol for a Decentralized Observational Study

Authors of this article:

Audrey Hoyt1 Author Orcid Image ;   Casey Dorney1 Author Orcid Image ;   Peggy Auinger1 Author Orcid Image ;   Kathryn Murphy1 Author Orcid Image ;   Michelle Porto1 Author Orcid Image ;   Katrina Schmier1 Author Orcid Image ;   Renee Wilson1 Author Orcid Image ;   James C Beck2 Author Orcid Image ;   Stephanie Benvengo3 Author Orcid Image ;   Kevin Biglan1, 4 Author Orcid Image ;   E Ray Dorsey1 Author Orcid Image ;   Alberto J Espay5 Author Orcid Image ;   Eric A Macklin6 Author Orcid Image ;   Mariana HG Monje7 Author Orcid Image ;   Dan Novak2 Author Orcid Image ;   David Oakes1 Author Orcid Image ;   Larsson Omberg8 Author Orcid Image ;   Michael A Schwarzschild6 Author Orcid Image ;   Solveig K Sieberts9 Author Orcid Image ;   Tanya Simuni7 Author Orcid Image ;   Caroline M Tanner10 Author Orcid Image ;   Daniel Weintraub11 Author Orcid Image ;   Ruth B Schneider1 Author Orcid Image
Article Authors Cited by Tweetations (1) Metrics

    Protocol

    1University of Rochester Medical Center, Rochester, United States

    2Parkinson's Foundation, New York, NY, United States

    3Michael J. Fox Foundation, New York, United States

    4Eli Lilly (United States), Indianapolis, United States

    5University of Cincinnati Medical Center, Cincinnati, United States

    6Massachusetts General Hospital, Boston, MA, United States

    7Northwestern University, Chicago, IL, United States

    8Koneksa Health, New York, NY, United States

    9Sage Bionetworks, Seattle, United States

    10University of California, San Francisco, San Francisco, United States

    11University of Pennsylvania, Philadelphia, United States

    Corresponding Author:

    Ruth B Schneider, MD

    University of Rochester Medical Center

    265 Crittenden Blvd, Box MIND

    Rochester, 14642

    United States

    Phone: 1 585 273 1856

    Email: ruth_schneider@urmc.rochester.edu


    Background: Falls and cognitive impairment are major sources of disability in Parkinson disease (PD). The ability to accurately identify individuals with PD at high risk for falls and cognitive impairment would provide an opportunity for intervention and potentially improve long-term outcomes. In a previous study, Assessing Telehealth Outcomes in Multiyear Extensions of Parkinson Disease Trials (AT-HOME PD), we remotely characterized participants with early PD who had participated in 1 of 2 PD clinical trials over 2 years of follow-up. These participants with advancing disease provide a unique opportunity to examine whether the capture of objective in-home measures via digital tools and bothersome symptoms via direct participant report improves the prediction of disease milestones.

    Objective: Assessing Telehealth Outcomes in Multiyear Extensions of Parkinson Disease Trials–2 (AT-HOME PD2) aims to examine whether digital tools and remote participant reporting can improve the prediction of falls and cognitive impairment, quantify changes in physical activity over time, and explore the relationship between physical activity and clinical progression over time.

    Methods: This is a decentralized observational study of up to 200 individuals with PD, with clinical and digital phenotyping for up to 3 years of follow-up. Participants are those who took part in the STEADY-PD III (NCT02168842), Study of Urate Elevation in Parkinson’s Disease, Phase 3 (SURE-PD3; NCT02642393), AT-HOME PD (NCT03538262), or PD GENEration (NCT04057794) studies. All participants complete 2 video visits per year, wear 2 wrist-worn sensors (Fitbit Charge 5 and ActiGraph CentrePoint Insight Watch) for 1 week each month, complete smartphone-based motor tasks (using the mPower 2.0 app) for 10 days each quarter, and complete online surveys (within the companion Fox Insight study) each quarter. Falls are assessed via a weekly automated telephone call. A cognitive diagnosis is determined by a consensus committee that considers scores on a global cognitive measure, detailed neuropsychological tests, a cognitive-related disability measure, and clinical information. Prediction models will be constructed, and prediction accuracy will be compared across the models.

    Results: Recruitment for the study was initiated in September 2023. Enrollment is ongoing, with 142 participants enrolled as of January 2025. Within the cohort, the average age is 69.2 (SD 8.7) years; 85 (59.9%) participants are male, 137 (96.5%) are White, and 2 (1.4%) are Hispanic or Latino; and the average disease duration is 8.9 (SD 1.3) years.

    Conclusions: AT-HOME PD2 is remotely clinically and digitally phenotyping participants with midstage PD to predict falls and cognitive impairment and to provide insights into long-term progression.

    International Registered Report Identifier (IRRID): DERR1-10.2196/71955

    JMIR Res Protoc 2025;14:e71955

    doi:10.2196/71955

    Keywords

    Parkinson disease; falls; decentralized; digital health technologies 


    Parkinson disease (PD) is a progressive, neurodegenerative disorder that is increasing in prevalence [1]. Falls and cognitive impairment are major sources of disability in PD, associated with loss of independence, hospitalization, long-term care facility placement, and mortality [2-7], contributing to the estimated US $52 billion annual cost for PD care in the United States [8]. The early identification of individuals at risk for falls and cognitive decline is critical to enable appropriate intervention, which may take the form of home modifications, medication changes, or other adjustments, and to improve long-term outcomes. While many PD-specific and nonspecific risk factors for falls have been identified, the single best predictor for falls remains a prior history of falls, and new, more accurate models for fall prediction are needed [9-11]. Similarly, while many PD-specific and nonspecific risk factors for dementia have been identified [12-17], few studies have examined predictors of the new onset of PD-related mild cognitive impairment [18-20].

    Digital tools, which enable frequent and objective in-home assessment of motor [21-24] and nonmotor function [25-29], stand to improve the management of PD and may improve the prediction of falls and cognitive impairment. However, they are not routinely used in clinical practice to aid in the prediction of falls or cognitive impairment. Many fall prediction models incorporate performance-based tests, which are typically conducted in a traditional clinic setting. However, relevant metrics, such as gait speed, cadence, and stride time variability, can be measured with digital tools in the home [11,30,31]. Studies have demonstrated the potential to use digital tools to predict falls and time to a first fall [32,33]. Digital tools have also been used to demonstrate that slower gait speed, lower levels of physical activity, less time spent outside of the home, and fewer trips outside the home are associated with cognitive impairment in older adults [34,35]. Novel methods of capturing the most bothersome symptoms and their functional impact [36] can generate a participant-reported natural history of PD [37] that may contextualize and inform understanding of data obtained from objective in-home assessment. Studies are needed to determine whether in-home digital metrics and remote participant reporting can improve the prediction of important PD-related clinical milestones.

    In a previous study, AT-HOME PD (Assessing Telehealth Outcomes in Multiyear Extensions of Parkinson Disease Trials), we remotely characterized motor and nonmotor progression of participants with early PD using annual video visits, quarterly smartphone-based motor assessments, and quarterly online surveys over 2 years of follow-up [38]. AT-HOME PD was designed to assess a novel framework for the remote follow-up of clinical trial participants, to compare digital and traditional outcomes, and to identify novel digital disease measures. AT-HOME PD enrolled 226 participants (mean age 64.7, SD 8.8 y; 40% female; mean time since diagnosis 3.8, SD 1.2 y) from 42 US states and 1 Canadian province, who participated in either STEADY-PD III (NCT02168842) or Study of Urate Elevation in Parkinson’s Disease, Phase 3 (SURE-PD3; NCT02642393)—both multicenter, randomized, controlled, phase III studies of potential disease-slowing agents [38-40].

    In this study, we will leverage this well-characterized cohort and describe the protocol for AT-HOME PD2 (Assessing Telehealth Outcomes in Multiyear Extensions of Parkinson Disease Trials–2), a decentralized observational study with clinical and digital phenotyping that will: (1) evaluate the extent to which digital tools and remote participant reporting can improve the prediction of falls and cognitive impairment compared with traditional models; (2) quantify change in physical activity and gait over time; and (3) explore the relationship between physical activity, development of falls or cognitive impairment, and symptom progression over time. We hypothesize that digital assessment of gait, sleep, and physical activity in the real world will improve the prediction of falls and cognitive impairment compared with a model composed of traditional measures.


    AT-HOME PD2 is a decentralized, observational PD study with up to 3 years of follow-up. All visits and assessments are conducted remotely in the participant’s natural environment. Participants complete 2 video visits per year, wear 2 sensors (Fitbit Charge 5 and ActiGraph CentrePoint Insight Watch) for 1 week each month, complete smartphone-based motor tasks (using the mPower 2.0 app) each quarter, and complete online surveys (via Fox Insight) each quarter (Figure 1).

    ‎
    Figure 1. Overview of AT-HOME PD2 (Assessing Telehealth Outcomes in Multiyear Extensions of Parkinson Disease Trials–2) study design.

    Study Setting

    Visits are conducted by video at the University of Rochester using a cloud-based, HIPAA (Health Insurance Portability and Accountability Act)–compliant platform.

    Ethical Considerations

    This study was approved by the University of Rochester Institutional Review Board (STUDY00008433). All participants provide informed consent and may choose to opt out of the study. Participants receive US $50 for each video visit, for up to a total of US $350, and keep the Fitbit upon completion of the study. The study team makes every effort to maintain data confidentiality and protect participants’ personal information. This includes assigning each participant a globally unique identifier, which is used to label collected information; restricting access to the study databases; transferring study data securely; and deidentifying study data before sharing it with qualified external researchers.

    Study Participants

    Eligible participants were originally defined to be those who participated in AT-HOME PD and those who participated in STEADY-PD III (NCT02168842) or SURE-PD3 (NCT02642393) and provided consent to future research contact but did not participate in AT-HOME PD. Participants must have access to an internet-enabled device that supports video visits. Participants are provided with a webcam, Bluetooth adapter, smartphone stand, tablet stand, or an Android study phone with a data plan, when needed, to enable participation. There are no other technological requirements. A history of prior falls or cognitive impairment does not exclude individuals from participating.

    In December 2024, because of concerns regarding our ability to meet the enrollment target, recruitment was opened to individuals who participated in PD GENEration (NCT04057794), a PD genetic testing study led by the Parkinson’s Foundation [41,42]. Specifically, AT-HOME PD2 participation was opened to individuals who participated in the first 2 phases of PD GENEration, which included clinical assessments [42]. The eligibility criteria are the same, with 2 exceptions. Participants from PD GENEration must have been diagnosed with PD 6 to 12 years ago and possess an iPhone. The disease duration requirement was added to ensure that newly recruited PD GENEration participants had a similar disease duration to those already participating in AT-HOME PD2. The iPhone stipulation was added, given the plans to incorporate the use of a smartphone app that is available only on iOS systems.

    Study Procedures

    Enrollment of the STEADY-PD III, SURE-PD3, and AT-HOME PD Participants

    Outreach to potential participants is conducted via telephone, email, and mail. Interested potential participants are sent a link to the electronic informed consent (eConsent) and scheduled for a screening visit, which may be performed by telephone or video. During the screening visit, informed consent is obtained, eligibility is confirmed, demographics are collected, and the need for equipment is assessed. The Fitbit Charge 5, ActiGraph CentrePoint Insight Watch, and any other needed equipment are shipped to the participant after the screening visit. Enrollment occurs on a rolling basis, with the enrollment visit scheduled within 60 days of the screening visit, and participants from the original AT-HOME PD study joining between month 48 and month 72, relative to their baseline AT-HOME PD visit. During the enrollment visit, participants receive instructions on mPower 2.0, Fitbit Charge 5, and ActiGraph CentrePoint Insight Watch use and, if required, assistance in enrolling in Fox Insight. All other assessments are as described subsequently and listed in Table 1.

    Table 1. The assessments conducted during annual video visits.
    Assessment domainInstrument or measure
    Motor assessments
    • Movement Disorder Society–Unified Parkinson’s Disease Rating Scale parts II, III, and IV
    • Clinician falls and freezing assessment
    • Patient falls assessment
    • mPower 2.0 motor tasks
    Cognitive assessments
    • Montreal Cognitive Assessment
    • CANTABa connect: Rapid Visual Information Processing
    • CANTAB connect: Paired Associates Learning
    • CANTAB connect: Pattern Recognition Memory
    • CANTAB connect: Spatial Working Memory
    • CANTAB connect: One Touch Stockings of Cambridge
    Other nonmotor assessments
    • Movement Disorder Society–Non-Motor Scale
    • Movement Disorder Society–Unified Parkinson’s Disease Rating Scale part I
    Global assessments
    • Schwab and England Activities of Daily Living
    • Clinical Global Impression (Severity and Change)b
    • Patient Global Impression (Severity and Change)b
    Study assessments
    • System Usability Scale
    • Perceived Research Burden Assessment

    aCANTAB: Cambridge Neuropsychological Test Automated Battery.

    bClinical Global Impression–Change and Patient Global Impression–Change are only completed during the follow-up visits.

    Enrollment of PD GENEration Participants

    The Parkinson’s Foundation sends PD GENEration participants an email invitation to participate in AT-HOME PD2. Interested potential participants who provide their consent to be contacted by the AT-HOME PD2 study team will then complete a screening form. Outreach to potential participants is conducted via telephone, email, or mail. The remainder of the procedures is as described earlier.

    Video Visits

    Participants are followed for up to 3 years and complete annual video visits with a comprehensive clinical assessment and interim video visits to support adherence to study activities. Patient-reported outcomes are completed within Research Electronic Data Capture (REDCap) outside of the scheduled clinical assessment visit. Some outcomes—falls assessment, Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part Ib and II [43], and Patient Global Impression-Severity and Change [44]—are completed before the video visit, while others—System Usability Scale [45] and Perceived Research Burden Assessment [46]—are completed after the video visit. The remainder of the assessments (Table 1) and any incomplete pre–video visit patient-reported outcomes are completed during the video visit. Participants also complete up to 3 interim research coordinator–led video visits. These are brief visits during which the mPower 2.0 active tasks are completed and adherence with the digital health technologies is reviewed.

    Remote Monitoring With Digital Health Technologies

    Within the mPower 2.0 smartphone app, participants complete 3 active motor tasks (ie, resting tremor, gait and balance, and finger tapping) each day for 10 days every 3 months. To minimize variability in performance, participants are instructed to perform the tasks during their self-identified PD medication ON state and to complete them at approximately the same time each day. Participants are also asked to complete 3 cognitive tasks (ie, dimensional card sort, digit symbol substitution test, and spatial working memory) once every 3 months. Participants may optionally consent to passive monitoring of movement and motor activity patterns.

    Participants wear 2 sensors, the Fitbit Charge 5 and the ActiGraph CentrePoint Insight Watch, on their nondominant wrist for 7 days each month. Specifically, participants are instructed to wear the sensors during waking hours for 6 days and one complete 24-hour period on the seventh day. Participants are randomly assigned to wear either the Fitbit Charge 5 or the ActiGraph CentrePoint Insight Watch in the most distal position. Participants use the Fitbit app and the CentrePoint Connect app to sync their data to the Fitabase and CentrePoint platforms, respectively, throughout their wear week. The Fitbit Charge 5 is equipped with an accelerometer and photoplethysmography sensor. It captures wear time, motor parameters (eg, steps taken, distance, active zone min), and nonmotor parameters (eg, heart rate, heart rate variability, breathing rate, skin temperature, oxygen saturation, sleep stages, and sleep interruptions). The ActiGraph CentrePoint Insight Watch is equipped with an accelerometer and a near-body temperature sensor. It captures wear time, motor parameters (eg, steps taken, nonsedentary time, and physical activity), and nonmotor parameters (eg, sleep time, sleep onset latency, and sleep efficiency).

    Adherence to digital health technologies is assessed during each visit. Participants receive a monthly calendar that indicates expected sensor wear and mPower 2.0 use. During the review of the calendar with the participant at the enrollment visit, the coordinator encourages them to inform the team if they need to adjust or reschedule a month’s sensor wear week to accommodate their personal schedule. Outside of the scheduled visits, the study team sends a reminder to the participant shortly before their next sensor wear week. The study team conducts monthly monitoring of the CentrePoint and Fitabase dashboards to monitor sensor battery levels, ensure consistent data uploads are completed by the participant, and identify any errors that require troubleshooting. If the battery level of a sensor falls below 20% or a data upload error occurs for a participant, the study team contacts the participant to assist with troubleshooting.

    Fox Insight

    Fox Insight is an online, observational research study sponsored by the Michael J Fox Foundation for Parkinson’s Research [47]. Every 3 months, participants complete a set of questionnaires, including the Patient Global Impression–Change, PD Patient Report of Problems [36], PD questionnaire-8 [48], and the Non-Motor Symptoms Questionnaire [49]. The Penn Parkinson’s Daily Activities Questionnaire [50], EQ-5D [51], and MDS-UPDRS part II are completed on a biannual basis. The Geriatric Depression Scale-15 [52], REM Sleep Behavior Disorder Single-Question Screen [53], and Physical Activity Scale for the Elderly [54] are completed annually.

    Outcomes: Falls Assessment

    Participants are provided with the following definition of a fall, based on an established expert consensus definition [55]: “a fall is an unexpected event in which you come to rest on the ground, floor, or a lower level.” To limit the possibility of recall bias, participants receive a weekly automated telephone call during which they are asked to enter the number of falls they have had (if any) in the past week [56]. Participants who are unable to answer the automated telephone calls (eg, those on Canadian phone plans who cannot receive calls from the United States) receive a weekly email from the study team instead. Participants who report a fall receive follow-up from a study coordinator to gather additional details on the fall circumstances and outcomes.

    Outcomes: Cognitive Assessment

    A consensus committee comprised of movement disorder and cognitive experts will use cognitive, functional, and clinical data to assign a cognitive diagnosis (ie, normal cognition, mild cognitive impairment, or dementia) for each participant at each annual visit. The committee will consider scores—including change from baseline—on a global cognitive measure (Montreal Cognitive Assessment) [57,58]; on specific neuropsychological tests (ie, Paired Associates Learning, Pattern Recognition Memory, Spatial Working Memory, One Touch Stockings of Cambridge; and Rapid Visual Information Processing from the Cambridge Neuropsychological Test Automated Battery) [59], and on the 15-item Penn Daily Activities Questionnaire, a measure of cognition-related functional abilities. The committee will also consider clinical information, such as the use of cholinesterase inhibitors.

    Safety Monitoring

    We capture information on events of interest (eg, hospitalization, participation in a clinical trial, and neurological events) as spontaneously reported by the participant or observed by a member of the study team. To reduce the risk of falls during study assessments, participants who typically use an assistive device or have the assistance of another person to walk are instructed to complete walking tasks under their usual conditions. Participants and/or any member of the study team may opt not to complete walking tasks because of a perceived safety concern. We have a medical safety escalation plan that guides the response of the study team to medical or safety issues that may arise during a video visit. For example, in the event of an urgent medical concern that requires immediate attention, the participant or care partner is instructed to contact emergency services. If they are unable to do so, a member of the study team will contact emergency services on their behalf and remain in contact with the participant until emergency services arrive.

    Data Sharing

    All study data from the video visits, mPower 2.0, Fitbit, ActiGraph, Fox Insight, and falls assessments will converge in Synapse (Sage Bionetworks), a cloud-based data management platform. Within Synapse, deidentified clinical and digital data will be available to qualified researchers. Clinical data will be transferred to the Parkinson’s Disease Biomarkers Program Data Management Resource, merged with data from the STEADY-PD III, SURE-PD3, AT-HOME PD, and PD GENEration parent studies, and made available to qualified researchers.

    Statistical Analysis

    Statistical analysis will focus on (1) determining the extent to which digital tools and remote participant reporting improve the prediction of falls and cognitive impairment; (2) quantifying change in physical activity and gait metrics over time; and (3) exploring the relationship between physical activity, falls, cognitive impairment, and progression.

    Prediction accuracy for time to first fall after baseline (AT-HOME PD2 enrollment), time to a recurrent fall (2 falls within 1 year), time to first-ever fall, and development of mild cognitive impairment and dementia at 1, 2, and 3 years after baseline will be compared among (1) a model using traditional measures; (2) an augmented model that also includes measures captured by remote assessment of motor and cognitive function; and (3) an augmented model that further includes participant-reported bothersome symptoms. Predictors included in the traditional model will comprise age; sex; disease duration (ie, years since diagnosis); measures of disease disability (Schwab and England Activities of Daily Living and Hoehn and Yahr); prior history of falls; prior history of freezing of gait; cognitive performance (Montreal Cognitive Assessment); medication use; measures of disease severity (MDS-UPDRS, MDS-Non-Motor Scale); and, for prediction of cognitive impairment, markers of socioeconomic status (ie, education and income level). The augmented digital model will also include digital measures of gait, physical activity, steps taken, heart rate, sleep parameters, motor function, and cognition. The augmented participant report model will also include curated descriptions of postural instability and cognition. Prediction models will be constructed using both traditional Cox proportional hazards regression and logistic regression, as well as survival or random forest classification methods. Prediction accuracy will be compared using the relative concordance index (for falls) or the area under the receiver operating characteristic curve (AUC; for mild cognitive impairment and dementia). We will evaluate the incremental value of the augmented models based on an improvement in the concordance index or AUC that is significantly greater than 0.05 at a 2-tailed significance level of P<.05.

    We will calculate the mean steps per day and the mean time spent in moderate-to-vigorous physical activity for the Fitbit (using proprietary algorithms) and for the ActiGraph (using PD-specific cut points [60]) for each 7-day monitoring period. Gait dynamics (eg, step regularity and freezing of gait) will be extracted from active motor task performance and passively collected data within the mPower 2.0 app. We will visualize the interdevice reliability between the Fitbit and ActiGraph for step count and moderate-to-vigorous physical activity using Bland-Altman plots and quantify it using intraclass correlation coefficients.

    Linear mixed effects models will be used to analyze the trajectories over time in physical activity, steps taken, and gait measures collected passively with the Fitbit, ActiGraph, and mPower 2.0 app over the 3-year follow-up period. Pearson or Spearman correlations will be used to assess the relationships between changes in these passively collected measures and changes in patient-reported outcome measures at specific follow-up time points.

    Cox proportional hazards regression models will be used to examine the association between physical activity (ie, moderate-to-vigorous physical activity during the initial 7-day monitoring period) and the development of clinically relevant milestones (ie, the time to a first-ever fall and a fall after baseline, the time to a recurrent fall, the time to mild cognitive impairment, and the time to dementia). Associations between the trajectories over time in physical activity and traditionally assessed motor and cognitive progression will be examined by jointly modeling physical activity and symptom responses using mixed models that account for the correlation between these responses [61].

    Sample Size

    In 2 midstage PD cohorts, 30% to 32% of participants experienced at least 1 fall over a year [62,63]. Assuming a sample size of 160 participants, a fall rate of 30%, and an AUC of 0.70 using traditional predictors, the study will have 80% power to declare that prediction using remote assessment measures is significantly better than an AUC of 0.75 if the true AUC for this model is at least 0.85. The study will have 80% power to declare that prediction using these measures in combination with reports of participant experience is significantly better than an AUC of 0.85 if the true AUC for this model is at least 0.94. A sample size of 200 participants will account for a 20% dropout rate.

    Study Oversight

    The steering committee is composed of the principal investigator (RS), the study biostatistician (PA), and individuals with wide-ranging expertise (AA, JB, SB, KB, RD, AE, EM, DN, DO, SS, MS, TS, CT, and DW). The steering committee meets quarterly and provides oversight of the study, including the review of safety data and study progress, as well as oversight of analysis and publication plans.


    A notice of award from the National Institutes of Health was received on April 3, 2023 (Multimedia Appendix 1). The study was approved by the University of Rochester Institutional Review Board in August 2023. Study recruitment was initiated on September 15, 2023, and is ongoing. The original pool of potential participants included 215 individuals from the AT-HOME PD study and 256 individuals from the STEADY-PD III or SURE-PD3 studies who had provided consent for future contact but did not participate in the AT-HOME PD study. Participation was opened to individuals from the PD GENEration study on November 18, 2024. As of January 15, 2025, 142 participants have enrolled. Of these, the average age is 69.2 (SD 8.7) years, 85 (59.9%) are male, 137 (96.5%) are Caucasian, 2 (1.4%) are Hispanic or Latino, and participants have a mean disease duration of 8.9 (SD 1.3) years. Among enrolled participants, 110 (77%) participated in the AT-HOME PD study, 9 (6%) in STEADY-PD III only, 8 (6%) in SURE-PD3 only, and 15 (11%) were recruited from the PD GENEration study. Among enrolled participants, 135 reside in 34 US states and the District of Columbia, and 7 reside in 1 Canadian province. Enrollment is anticipated to be completed by April 2025.


    AT-HOME PD2 has enrolled 71% of the target number of participants from 3 research cohorts and is expected to generate new insights into the prediction of falls and cognitive impairment, inform the understanding of the role of physical activity in the management of midstage PD, and extend the longitudinal characterization of early PD clinical trial participants to up to 10 years through a participant-friendly, decentralized framework. Remote clinical and digital phenotyping in AT-HOME PD2 will complement in-person observational cohort studies, such as the Parkinson’s Progression Markers Initiative [64] and the Oxford Parkinson’s Disease Centre Discovery cohort [65], and long-term follow-up studies of other clinical trial cohorts, such as Levodopa in Early PD [66], and will provide insights into PD progression in the current treatment era. The inclusion of participants from PD GENEration will enable comparison of progression between clinical trial and observational research cohorts.

    In AT-HOME PD2, traditional clinical assessments are complemented by digital tools, including a smartphone-based app, a commercial wrist-worn sensor, and a research-grade wrist-worn sensor. Many larger-scale digital phenotyping efforts have focused on populations with early-stage, untreated PD [67-69]; however, digital tools also have the potential to support the development of new treatments for and the care of individuals with later-stage PD. In AT-HOME PD2, we focus on a population with later-stage disease and use a combination of active motor assessments and passive data collection to generate insights into PD motor impairment, gait, physical activity, steps taken, and sleep. Our primary aim is centered on improving the prediction of falls and cognitive impairment—2 patient-centered disease milestones that are highly relevant to individuals with later-stage disease. To this end, we will use rigorous methods to capture the occurrence of falls and cognitive impairment. Falls are captured via a weekly automated telephone call, which will limit the potential for recall bias. Cognitive diagnosis is determined by a consensus cognitive committee comprised of experienced physicians who will use cognitive, functional, and clinical data to determine cognitive status [70].

    Nonetheless, we recognize that this approach has limitations; for example, we anticipate that adherence to weekly fall reporting will decline over the study period. In addition, we recognize that some participants will have already experienced a fall or developed cognitive impairment at the time of study entry. While we will primarily focus on the prediction of time to first fall and development of mild cognitive impairment and dementia after study entry, secondary analyses will evaluate the prediction of fall frequency, time to first fall requiring hospitalization, time to first fall resulting in injury, time to first-ever fall, time to a recurrent fall, time to development of mild cognitive impairment, and time to development of dementia. In addition, we acknowledge that we do not capture every potential risk factor—for example, objective assessment of autonomic function is limited to the capture of heart rate, and neither of the wrist-worn sensors captures blood pressure. We also acknowledge the potential for differential attrition, with participants with greater physical or cognitive impairment more likely to be lost to follow-up. To partially address this, we collect contact information for a secondary informant at baseline.

    Beyond the prediction of clinically meaningful events, this study will yield 3-year longitudinal data on change in motor and nonmotor digital measures, which will inform understanding of progression in this understudied stage of PD. Increased physical activity in PD is likely associated with slower decline [71,72] and a lower risk of mortality [73]; yet, studies to date on longitudinal change in physical activity have largely been limited to early PD or relied on self-report [68,74]. We will use active motor tasks and passive monitoring to objectively measure physical activity, steps taken, and gait parameters in a midstage PD sample. In turn, we will identify measures that capture progression over up to 3 years and examine the relationship with change in traditional clinical measures (eg, the MDS-UPDRS), and with the occurrence of clinically meaningful events. An improved understanding of the impact of physical activity on clinical outcomes will inform the counseling of individuals with midstage PD.

    In addition, we will compare the performance of the consumer-oriented Fitbit with that of the research-grade ActiGraph device. Digital devices and algorithms must be validated in the appropriate context of use, which in this case is the monitoring of midstage PD in the home environment. While commercial devices are widely available, easily accessible, and may be acceptable for at-home assessment of physical activity in PD, they require validation before integration into clinical care or clinical trials. In this study, participants will wear the Fitbit and ActiGraph simultaneously on the same wrist, which will enable comparisons between devices. We will compare the feasibility of remote wrist-worn sensor assessment through an evaluation of wear-time adherence. We will also examine interdevice reliability through a comparison of physical activity, step count, and sleep parameters. The identification of a consumer device that accurately and reliably captures activity and sleep in PD would provide a potentially scalable method for monitoring these variables as part of clinical care.

    In conclusion, in AT-HOME PD2, we will remotely clinically and digitally phenotype 200 participants with midstage PD, with the goal of improving the prediction of falls and cognitive impairment, characterizing long-term changes in physical activity, and examining the relationship between physical activity and clinical progression.

    Acknowledgments

    This study is funded by the National Institute of Neurological Disorders and Stroke under award number . The authors acknowledge the important contributions of the late Dr Ira Shoulson to this work. The Fox Insight study is funded by the Michael J Fox Foundation for Parkinson’s Research. The authors thank the Parkinson’s community for participating in this study and making this research possible.

    Data Availability

    The datasets generated or analyzed during this study will be available to qualified researchers in the Sage Bionetworks Synapse repository [75]. The clinical dataset generated or analyzed during this study will be available to qualified researchers in the Parkinson’s Disease Biomarkers Program Data Management Resource [76].

    Authors' Contributions

    AH contributed to data collection, data analysis, and drafting of the manuscript. CD was responsible for data collection and critical review of the manuscript. PA contributed to study design, data analysis, and critical review of the manuscript. KM, MP, KS, and RW were responsible for data collection and critical review of the manuscript. AA, JB, SB, KB, ERD, AE, EAM, MHGM, DN, DO, LO, MS, SS, TS, CT, and DW contributed to study design and critical review of the manuscript. RBS was responsible for study conception, study design, and drafting of the manuscript.

    Conflicts of Interest

    AH, CD, PA, KM, MP, KS, RW, DN, DO, LO, and MAS report no financial conflicts. AJE has received grant support from the National Institutes of Health and the Michael J Fox Foundation; personal compensation as a consultant or scientific advisory board member for Mitsubishi Tanabe Pharma America (formerly Neuroderm); Amneal; Acadia; Avion; Acorda; Bial; Kyowa Kirin; Supernus (formerly USWorldMeds); NeuroDiagnostics, Inc (SYNAPS Dx); Intrance Medical Systems, Inc; Merz; Praxis Precision Medicines; Citrus Health; and Herantis Pharma. AJE serves as chair of the Data Safety Monitoring Board of AskBio and has received publishing royalties from Lippincott Williams & Wilkins, Cambridge University Press, and Springer. He cofounded REGAIN Therapeutics and is a coinventor of the patent “Compositions and Methods for Treatment and/or Prophylaxis of Proteinopathies.” MHMG has received speaker honoraria from Bial. In the last 12 months, TS has served as a consultant for AskBio; BlueRock Therapeutics; Critical Path for Parkinson’s Consortium; Denali; General Electric, Inc; Kyowa; the Michael J Fox Foundation; Neuroderm/MTPA; Prevail/Lilly; Roche/Genentech; Sanofi; Sinopia; Takeda; TrueBinding; and Vanqua Bio. TS has served on advisory boards for AskBio; Biohaven; Denali; GAIN; General Electric, Inc; Kyowa; Neuron23; the Parkinson Study Group; Prevail/Lilly; and Roche/Genentech. TS has also served as a member of the scientific advisory boards for Koneksa, Neuroderm/MTPA, Sanofi, and UCB, and received research funding from Neuroderm, the National Institute of Neurological Disorders and Stroke, the Michael J Fox Foundation, the Parkinson’s Foundation, Prevail, Roche, and UCB. In the past 12 months, CMT has served as a consultant for Neurocrine, Praxis (advisory board via the International Parkinson’s and Movement Disorder Society), Jazz/Cavion, Roche/Genentech, and Bial. CMT also reports grant support to her institution from the Michael J Fox Foundation, the National Institutes of Health, Gateway LLC, the Department of Defense, Roche Genentech, and the Parkinson’s Foundation. In the past year, DN has received research funding or support from the Michael J Fox Foundation for Parkinson’s Research, the International Parkinson and Movement Disorder Society, the National Institutes of Health, the Parkinson’s Foundation, and the US Department of Veterans Affairs; honoraria for consultancy from AbbVie, Boehringer Ingelheim, Cerevel Therapeutics, CHDI Foundation, Citrus Health Group, Medscape, Modality.AI, the Parkinson Study Group, Roche, Sage Therapeutics, Scion NeuroStim, Signant Health, and Vanqua Bio; and license fee payments from the University of Pennsylvania for the Questionnaire for Impulsive-Compulsive Disorder in Parkinson’s Disease and the Questionnaire for Impulsive-Compulsive Disorder in Parkinson’s Disease Rating Scale. KB is a full-time employee and minor stockholder of Eli Lilly. ERD has received honoraria from Adivo Associates France, the American Neurological Association, Dartmouth, Elsevier, HMP Education, International Parkinson and Movement Disorder Society, Massachusetts Medical Society, the Michael J Fox Foundation, the National Institutes of Health, the National Multiple Sclerosis Society, Northwestern University, the Patient-Centered Outcomes Research Institute, the Philadelphia Neurological Society, Stanford University, Stony Brook, Sutter Health, the Texas Neurological Society, and the University of Toronto. ERD has served as a consultant for Abbott; AbbVie; Acadia; Acorda Therapeutics; Advarra; Aspen Neuroscience, Inc; Biogen; Biohaven Pharmaceuticals; BioSensics; Boehringer Ingelheim; Caraway Therapeutics; Cerevance; CuraSen; DConsult2; Denali Therapeutics; Eli Lilly; Genentech; HanAll BioPharma; Health & Wellness Partners; HMP Education; Kairos Cinema, LLC (PBS); Karger; KOL groups; Life Sciences Consultant; Mediflix; MedRhythms; Merck; Mitsubishi Tanabe Pharma America, Inc; MJH Holdings; NACCME; Novartis; Otsuka; Praxis Medicine; Sanofi; Seelos Therapeutics; Spark Therapeutics; Springer Healthcare; Theravance Biopharmaceuticals; VivoSense; and WebMD. ERD has received grant support from Averitas Pharma, Biogen, the Burroughs Wellcome Fund, the Michael J Fox Foundation, the National Institutes of Health, Pfizer, Photopharmics, Roche, and the Safra Foundation, and holds ownership interests in Included Health, Mediflix, SemCap, and Synapticure. SS is an employee of Sage Bionetworks. RBS receives research funding from the National Institutes of Health, the Michael J Fox Foundation for Parkinson’s Research, Biohaven Pharmaceuticals, Acadia Pharmaceuticals, and Bial, and receives monetary support from Clintrex Corporation for participation on a Data and Safety Monitoring Board.

    Multimedia Appendix 1

    Peer review report from CIDH - Clinical Informatics and Digital Health Study Section, Healthcare Delivery and Methodologies Integrated Review Group (National Institutes of Health, USA).

    PDF File (Adobe PDF File), 88 KB
    1. Dorsey ER, Sherer T, Okun MS, Bloem BR. The emerging evidence of the Parkinson pandemic. J Parkinsons Dis. Dec 18, 2018;8(s1):S3-S8. [CrossRef]
    2. Aarsland D, Larsen JP, Tandberg E, Laake K. Predictors of nursing home placement in Parkinson's disease: a population-based, prospective study. J Am Geriatr Soc. Aug 2000;48(8):938-942. [CrossRef] [Medline]
    3. Tison F, Dartigues JF, Auriacombe S, Letenneur L, Boller F, Alpérovitch A. Dementia in Parkinson's disease: a population-based study in ambulatory and institutionalized individuals. Neurology. Apr 1995;45(4):705-708. [CrossRef] [Medline]
    4. Xu J, Gong DD, Man CF, Fan Y. Parkinson's disease and risk of mortality: meta-analysis and systematic review. Acta Neurol Scand. Feb 2014;129(2):71-79. [CrossRef] [Medline]
    5. Santos García D, de Deus Fonticoba T, Cores C, Suárez Castro E, Hernández Vara J, Jesús S, et al. Falls predict acute hospitalization in Parkinson's disease. J Parkinsons Dis. 2023;13(1):105-124. [FREE Full text] [CrossRef] [Medline]
    6. Harris-Hayes M, Willis AW, Klein SE, Czuppon S, Crowner B, Racette BA. Relative mortality in U.S. Medicare beneficiaries with Parkinson disease and hip and pelvic fractures. J Bone Joint Surg Am. Feb 19, 2014;96(4):e27. [FREE Full text] [CrossRef] [Medline]
    7. Paul G, Paul BS, Gautam PL, Singh G, Kaushal S. Parkinson's disease in intensive care unit: an observational study of frequencies, causes, and outcomes. Ann Indian Acad Neurol. 2019;22(1):79-83. [FREE Full text] [CrossRef] [Medline]
    8. Yang W, Hamilton JL, Kopil C, Beck JC, Tanner CM, Albin RL, et al. Current and projected future economic burden of Parkinson's disease in the U.S. NPJ Parkinsons Dis. Jul 09, 2020;6:15. [FREE Full text] [CrossRef] [Medline]
    9. van der Marck MA, Klok MP, Okun MS, Giladi N, Munneke M, Bloem BR. Consensus-based clinical practice recommendations for the examination and management of falls in patients with Parkinson's disease. Parkinsonism Relat Disord. Apr 2014;20(4):360-369. [CrossRef] [Medline]
    10. Pickering RM, Grimbergen YA, Rigney U, Ashburn A, Mazibrada G, Wood B, et al. A meta-analysis of six prospective studies of falling in Parkinson's disease. Mov Disord. Oct 15, 2007;22(13):1892-1900. [CrossRef] [Medline]
    11. Fasano A, Canning CG, Hausdorff JM, Lord S, Rochester L. Falls in Parkinson's disease: a complex and evolving picture. Mov Disord. Nov 2017;32(11):1524-1536. [CrossRef] [Medline]
    12. Phongpreecha T, Cholerton B, Mata IF, Zabetian CP, Poston KL, Aghaeepour N, et al. Multivariate prediction of dementia in Parkinson's disease. NPJ Parkinsons Dis. Aug 25, 2020;6:20. [FREE Full text] [CrossRef] [Medline]
    13. De Roy J, Postuma RB, Brillon-Corbeil M, Montplaisir J, Génier Marchand D, Escudier F, et al. Detecting the cognitive prodrome of dementia in Parkinson's disease. J Parkinsons Dis. 2020;10(3):1033-1046. [FREE Full text] [CrossRef] [Medline]
    14. Leta V, Urso D, Batzu L, Weintraub D, Titova N, Aarsland D, et al. Constipation is associated with development of cognitive impairment in de novo Parkinson's disease: a longitudinal analysis of two international cohorts. J Parkinsons Dis. 2021;11(3):1209-1219. [FREE Full text] [CrossRef] [Medline]
    15. Anang JB, Gagnon JF, Bertrand JA, Romenets SR, Latreille V, Panisset M, et al. Predictors of dementia in Parkinson disease: a prospective cohort study. Neurology. Sep 30, 2014;83(14):1253-1260. [FREE Full text] [CrossRef] [Medline]
    16. Guo Y, Liu FT, Hou XH, Li JQ, Cao XP, Tan L, et al. Predictors of cognitive impairment in Parkinson's disease: a systematic review and meta-analysis of prospective cohort studies. J Neurol. Aug 2021;268(8):2713-2722. [CrossRef] [Medline]
    17. Liu G, Locascio JJ, Corvol JC, Boot B, Liao Z, Page K, et al. Prediction of cognition in Parkinson's disease with a clinical-genetic score: a longitudinal analysis of nine cohorts. Lancet Neurol. Aug 2017;16(8):620-629. [CrossRef] [Medline]
    18. Hu MT, Szewczyk-Królikowski K, Tomlinson P, Nithi K, Rolinski M, Murray C, et al. Predictors of cognitive impairment in an early stage Parkinson's disease cohort. Mov Disord. Mar 2014;29(3):351-359. [FREE Full text] [CrossRef] [Medline]
    19. Hogue O, Fernandez HH, Floden DP. Predicting early cognitive decline in newly-diagnosed Parkinson's patients: a practical model. Parkinsonism Relat Disord. Nov 2018;56:70-75. [CrossRef] [Medline]
    20. Schrag A, Siddiqui UF, Anastasiou Z, Weintraub D, Schott JM. Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson's disease: a cohort study. Lancet Neurol. Jan 2017;16(1):66-75. [FREE Full text] [CrossRef] [Medline]
    21. Adams JL, Lizarraga KJ, Waddell EM, Myers TL, Jensen-Roberts S, Modica JS, et al. Digital technology in movement disorders: updates, applications, and challenges. Curr Neurol Neurosci Rep. Mar 03, 2021;21(4):16. [FREE Full text] [CrossRef] [Medline]
    22. Ellis RJ, Ng YS, Zhu S, Tan DM, Anderson B, Schlaug G, et al. A validated smartphone-based assessment of gait and gait variability in Parkinson's disease. PLoS One. Oct 30, 2015;10(10):e0141694. [CrossRef] [Medline]
    23. Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson’s disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef]
    24. López-Blanco R, Velasco MA, Méndez-Guerrero A, Romero JP, Del Castillo MD, Serrano JI, et al. Smartwatch for the analysis of rest tremor in patients with Parkinson's disease. J Neurol Sci. Jun 15, 2019;401:37-42. [CrossRef] [Medline]
    25. Full KM, Kerr J, Grandner MA, Malhotra A, Moran K, Godoble S, et al. Validation of a physical activity accelerometer device worn on the hip and wrist against polysomnography. Sleep Health. Apr 2018;4(2):209-216. [FREE Full text] [CrossRef] [Medline]
    26. Quante M, Kaplan ER, Cailler M, Rueschman M, Wang R, Weng J, et al. Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms. Nat Sci Sleep. Jan 18, 2018;10:13-20. [FREE Full text] [CrossRef] [Medline]
    27. Slater JA, Botsis T, Walsh J, King S, Straker LM, Eastwood PR. Assessing sleep using hip and wrist actigraphy. Sleep Biol Rhythms. Jan 19, 2015;13(2):172-180. [CrossRef]
    28. Mirelman A, Hillel I, Rochester L, Del Din S, Bloem BR, Avanzino L, et al. Tossing and turning in bed: nocturnal movements in Parkinson's disease. Mov Disord. Jun 2020;35(6):959-968. [CrossRef] [Medline]
    29. Janssen Daalen JM, van den Bergh R, Prins EM, Moghadam MS, van den Heuvel R, Veen J, et al. Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art. NPJ Digit Med. Jul 11, 2024;7(1):186. [FREE Full text] [CrossRef] [Medline]
    30. Hoskovcová M, Dušek P, Sieger T, Brožová H, Zárubová K, Bezdíček O, et al. Predicting falls in Parkinson disease: what is the value of instrumented testing in OFF medication state? PLoS One. Oct 7, 2015;10(10):e0139849. [FREE Full text] [CrossRef] [Medline]
    31. Lindholm B, Nilsson MH, Hansson O, Hagell P. External validation of a 3-step falls prediction model in mild Parkinson's disease. J Neurol. Dec 2016;263(12):2462-2469. [FREE Full text] [CrossRef] [Medline]
    32. Lo C, Arora S, Baig F, Lawton MA, El Mouden C, Barber TR, et al. Predicting motor, cognitive and functional impairment in Parkinson's. Ann Clin Transl Neurol. Aug 2019;6(8):1498-1509. [FREE Full text] [CrossRef] [Medline]
    33. Weiss A, Herman T, Giladi N, Hausdorff JM. Objective assessment of fall risk in Parkinson's disease using a body-fixed sensor worn for 3 days. PLoS One. May 6, 2014;9(5):e96675. [FREE Full text] [CrossRef] [Medline]
    34. Piau A, Wild K, Mattek N, Kaye J. Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res. Aug 30, 2019;21(8):e12785. [FREE Full text] [CrossRef] [Medline]
    35. Taraldsen K, Helbostad JL, Follestad T, Bergh S, Selbæk G, Saltvedt I. Gait, physical function, and physical activity in three groups of home-dwelling older adults with different severity of cognitive impairment - a cross-sectional study. BMC Geriatr. Dec 01, 2021;21(1):670. [FREE Full text] [CrossRef] [Medline]
    36. Javidnia M, Arbatti L, Hosamath A, Eberly SW, Oakes D, Shoulson I. Predictive value of verbatim Parkinson's disease patient-reported symptoms of postural instability and falling. J Parkinsons Dis. 2021;11(4):1957-1964. [FREE Full text] [CrossRef] [Medline]
    37. Shoulson I, Arbatti L, Hosamath A, Eberly SW, Oakes D. Longitudinal cohort study of verbatim-reported postural instability symptoms as outcomes for online Parkinson's disease trials. J Parkinsons Dis. 2022;12(6):1969-1978. [FREE Full text] [CrossRef] [Medline]
    38. Schneider RB, Omberg L, Macklin EA, Daeschler M, Bataille L, Anthwal S, et al. Design of a virtual longitudinal observational study in Parkinson's disease (AT-HOME PD). Ann Clin Transl Neurol. Feb 2021;8(2):308-320. [FREE Full text] [CrossRef] [Medline]
    39. Parkinson Study Group SURE-PD3 Investigators, Schwarzschild MA, Ascherio A, Casaceli C, Curhan GC, Fitzgerald R, et al. Effect of urate-elevating inosine on early Parkinson disease progression: the SURE-PD3 randomized clinical trial. JAMA. Sep 14, 2021;326(10):926-939. [FREE Full text] [CrossRef] [Medline]
    40. Parkinson Study Group STEADY-PD III Investigators. Isradipine versus placebo in early Parkinson disease: a randomized trial. Ann Intern Med. May 05, 2020;172(9):591-598. [FREE Full text] [CrossRef] [Medline]
    41. Cook L, Verbrugge J, Schwantes-An TH, Schulze J, Beck JC, Naito A, et al. Providing genetic testing and genetic counseling for Parkinson's disease to the community. Genet Med. Oct 2023;25(10):100907. [FREE Full text] [CrossRef] [Medline]
    42. Cook L, Verbrugge J, Schwantes-An TH, Schulze J, Foroud T, Hall A, et al. Parkinson's disease variant detection and disclosure: PD GENEration, a North American study. Brain. Aug 01, 2024;147(8):2668-2679. [FREE Full text] [CrossRef] [Medline]
    43. Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, 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. Nov 15, 2008;23(15):2129-2170. [CrossRef] [Medline]
    44. Guy W, National Institute of Mental Health, Psychopharmacology Research Branch, Early Clinical Drug Evaluation Program. ECDEU Assessment Manual for Psychopharmacology. Rockville, MD. U. S. Deptartment of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute of Mental Health, Psychopharmacology Research Branch, Division of Extramural Research Programs; 1976.
    45. Brooke J. SUS: a 'quick and dirty' usability scale. In: Usability Evaluation In Industry. Boca Raton, FL. CRC Press; 1996.
    46. Lingler JH, Schmidt KL, Gentry AL, Hu L, Terhorst LA. A new measure of research participant burden: brief report. J Empir Res Hum Res Ethics. Oct 2014;9(4):46-49. [FREE Full text] [CrossRef] [Medline]
    47. Smolensky L, Amondikar N, Crawford K, Neu S, Kopil CM, Daeschler M, 23andMe Research Team, et al. Fox Insight collects online, longitudinal patient-reported outcomes and genetic data on Parkinson's disease. Sci Data. Feb 24, 2020;7(1):67. [FREE Full text] [CrossRef] [Medline]
    48. Luo N, Tan LC, Zhao Y, Lau PN, Au WL, Li SC. Determination of the longitudinal validity and minimally important difference of the 8-item Parkinson's Disease Questionnaire (PDQ-8). Mov Disord. Jan 30, 2009;24(2):183-187. [CrossRef] [Medline]
    49. Romenets SR, Wolfson C, Galatas C, Pelletier A, Altman R, Wadup L, et al. Validation of the non-motor symptoms questionnaire (NMS-Quest). Parkinsonism Relat Disord. Jan 2012;18(1):54-58. [CrossRef] [Medline]
    50. Brennan L, Siderowf A, Rubright JD, Rick J, Dahodwala N, Duda JE, et al. The Penn Parkinson's Daily Activities Questionnaire-15: psychometric properties of a brief assessment of cognitive instrumental activities of daily living in Parkinson's disease. Parkinsonism Relat Disord. Apr 2016;25:21-26. [FREE Full text] [CrossRef] [Medline]
    51. Martinez-Martin P, Jeukens-Visser M, Lyons KE, Rodriguez-Blazquez C, Selai C, Siderowf A, et al. Health-related quality-of-life scales in Parkinson's disease: critique and recommendations. Mov Disord. Nov 2011;26(13):2371-2380. [CrossRef] [Medline]
    52. Weintraub D, Oehlberg KA, Katz IR, Stern MB. Test characteristics of the 15-Item Geriatric Depression Scale and Hamilton Depression Rating Scale in Parkinson Disease. Am J Geriatr Psychiatry. Feb 2006;14(2):169-175. [CrossRef]
    53. Postuma RB, Arnulf I, Hogl B, Iranzo A, Miyamoto T, Dauvilliers Y, et al. A single-question screen for rapid eye movement sleep behavior disorder: a multicenter validation study. Mov Disord. Jun 2012;27(7):913-916. [FREE Full text] [CrossRef] [Medline]
    54. Washburn RA, McAuley E, Katula J, Mihalko SL, Boileau RA. The physical activity scale for the elderly (PASE): evidence for validity. J Clin Epidemiol. Jul 1999;52(7):643-651. [CrossRef] [Medline]
    55. Lamb SE, Jørstad-Stein EC, Hauer K, Becker C, Prevention of Falls Network Europe and Outcomes Consensus Group. Development of a common outcome data set for fall injury prevention trials: the prevention of Falls Network Europe consensus. J Am Geriatr Soc. Sep 2005;53(9):1618-1622. [CrossRef] [Medline]
    56. van der Marck MA, Overeem S, Klok PC, Bloem BR, Munneke M. Evaluation of the falls telephone: an automated system for enduring assessment of falls. J Am Geriatr Soc. Mar 2011;59(2):340-344. [CrossRef] [Medline]
    57. Tarolli CG, Andrzejewski K, Zimmerman GA, Bull M, Goldenthal S, Auinger P, et al. Feasibility, reliability, and value of remote video-based trial visits in Parkinson's disease. J Parkinsons Dis. Oct 27, 2020;10(4):1779-1786. [FREE Full text] [CrossRef] [Medline]
    58. Gill DJ, Freshman A, Blender JA, Ravina B. The Montreal cognitive assessment as a screening tool for cognitive impairment in Parkinson's disease. Mov Disord. May 15, 2008;23(7):1043-1046. [CrossRef] [Medline]
    59. McAllister C, Williams-Gray C, Mason S, Foltynie T, Abbott R, Nathan P, et al. Characterisation of cognitive decline over 5 years in an incident Parkinson’s disease cohort: results from the CamPaIGN Study (P6.061). Neurology. Apr 06, 2015;84(14_supplement):1. [CrossRef]
    60. Jeng B, Cederberg KL, Lai B, Sasaki JE, Bamman MM, Motl RW. Wrist-based accelerometer cut-points for quantifying moderate-to-vigorous intensity physical activity in Parkinson's disease. Gait Posture. Jan 2022;91:235-239. [CrossRef] [Medline]
    61. Fieuws S, Verbeke G. Joint modelling of multivariate longitudinal profiles: pitfalls of the random-effects approach. Stat Med. Oct 30, 2004;23(20):3093-3104. [CrossRef] [Medline]
    62. Duncan RP, Leddy AL, Cavanaugh JT, Dibble LE, Ellis TD, Ford MP, et al. Accuracy of fall prediction in Parkinson disease: six-month and 12-month prospective analyses. Parkinsons Dis. 2012;2012:237673. [FREE Full text] [CrossRef] [Medline]
    63. Custodio N, Lira D, Herrera-Perez E, Montesinos R, Castro-Suarez S, Cuenca-Alfaro J, et al. Predictive model for falling in Parkinson disease patients. eNeurologicalSci. Dec 2016;5:20-24. [FREE Full text] [CrossRef] [Medline]
    64. Parkinson Progression Marker Initiative. The Parkinson Progression Marker Initiative (PPMI). Prog Neurobiol. Dec 2011;95(4):629-635. [FREE Full text] [CrossRef] [Medline]
    65. Szewczyk-Krolikowski K, Tomlinson P, Nithi K, Wade-Martins R, Talbot K, Ben-Shlomo Y, et al. The influence of age and gender on motor and non-motor features of early Parkinson's disease: initial findings from the Oxford Parkinson Disease Center (OPDC) discovery cohort. Parkinsonism Relat Disord. Jan 2014;20(1):99-105. [CrossRef] [Medline]
    66. Frequin HL, Verschuur CV, Suwijn SR, Boel JA, Post B, Bloem BR, et al. Long-term follow-up of the LEAP study: early versus delayed levodopa in early Parkinson's disease. Mov Disord. Jul 2024;39(6):975-982. [CrossRef] [Medline]
    67. Burq M, Rainaldi E, Ho KC, Chen C, Bloem BR, Evers LJ, et al. Virtual exam for Parkinson's disease enables frequent and reliable remote measurements of motor function. NPJ Digit Med. May 23, 2022;5(1):65. [FREE Full text] [CrossRef] [Medline]
    68. Adams JL, Kangarloo T, Tracey B, O'Donnell P, Volfson D, Latzman RD, et al. Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study. NPJ Parkinsons Dis. May 17, 2023;9(1):64. [FREE Full text] [CrossRef] [Medline]
    69. Lipsmeier F, Taylor KI, Postuma RB, Volkova-Volkmar E, Kilchenmann T, Mollenhauer B, et al. Reliability and validity of the Roche PD mobile application for remote monitoring of early Parkinson's disease. Sci Rep. Jul 15, 2022;12(1):12081. [FREE Full text] [CrossRef] [Medline]
    70. Pigott K, Rick J, Xie SX, Hurtig H, Chen-Plotkin A, Duda JE, et al. Longitudinal study of normal cognition in Parkinson disease. Neurology. Oct 13, 2015;85(15):1276-1282. [FREE Full text] [CrossRef] [Medline]
    71. Tsukita K, Sakamaki-Tsukita H, Takahashi R. Long-term effect of regular physical activity and exercise habits in patients with early Parkinson disease. Neurology. Mar 22, 2022;98(8):e859-e871. [FREE Full text] [CrossRef] [Medline]
    72. Amara AW, Chahine L, Seedorff N, Caspell-Garcia CJ, Coffey C, Simuni T. Self-reported physical activity levels and clinical progression in early Parkinson's disease. Parkinsonism Relat Disord. May 2019;61:118-125. [FREE Full text] [CrossRef] [Medline]
    73. Yoon SY, Suh JH, Yang SN, Han K, Kim YW. Association of physical activity, including amount and maintenance, with all-cause mortality in Parkinson disease. JAMA Neurol. Dec 01, 2021;78(12):1446-1453. [FREE Full text] [CrossRef] [Medline]
    74. Cavanaugh JT, Ellis TD, Earhart GM, Ford MP, Foreman KB, Dibble LE. Capturing ambulatory activity decline in Parkinson's disease. J Neurol Phys Ther. Jul 2012;36(2):51-57. [FREE Full text] [CrossRef] [Medline]
    75. Synapse. URL: https://www.synapse.org/ [accessed 2025-08-14]
    76. PDBP. National Institute of Neurological Disorders and Stroke. URL: https://pdbp.ninds.nih.gov/ [accessed 2025-08-14]

    ‎
    AT-HOME PD: Assessing Telehealth Outcomes in Multiyear Extensions of Parkinson Disease Trials
    AT-HOME PD2: Assessing Telehealth Outcomes in Multiyear Extensions of Parkinson Disease Trials–2
    AUC: area under the receiver operating characteristic curve
    HIPAA: Health Insurance Portability and Accountability Act
    MDS-UPDRS: Movement Disorder Society–Unified Parkinson’s Disease Rating Scale
    PD: Parkinson disease
    REDCap: Research Electronic Data Capture
    SURE-PD3: Study of Urate Elevation in Parkinson’s Disease, Phase 3

    Edited by A Schwartz; The proposal for this study was peer-reviewed by: CIDH - Clinical Informatics and Digital Health Study Section, Healthcare Delivery and Methodologies Integrated Review Group (National Institutes of Health, USA). See the Multimedia Appendix for the peer-review report; submitted 03.02.25; accepted 07.07.25; published 27.08.25.

    Copyright

    ©Audrey Hoyt, Casey Dorney, Peggy Auinger, Kathryn Murphy, Michelle Porto, Katrina Schmier, Renee Wilson, James C Beck, Stephanie Benvengo, Kevin Biglan, E Ray Dorsey, Alberto J Espay, Eric A Macklin, Mariana HG Monje, Dan Novak, David Oakes, Larsson Omberg, Michael A Schwarzschild, Solveig K Sieberts, Tanya Simuni, Caroline M Tanner, Daniel Weintraub, Ruth B Schneider. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 27.08.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.