Objective Identification of Pharmacological Interference in Parkinsonian Gait Using Continuous Digital Phenotyping

Objective Identification of Pharmacological Interference in Parkinsonian Gait Using Continuous Digital Phenotyping | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Objective Identification of Pharmacological Interference in Parkinsonian Gait Using Continuous Digital Phenotyping Zhenghua Li, Kenichi Yamamura This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9521853/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Building upon our previous demonstration that Apple HealthKit enables longitudinal monitoring of Parkinsonian gait [1], this study evaluates its utility for short-term pharmacological assessment. Using a self-controlled longitudinal design, gait performance was quantified across four pharmacological phases: pre-medication, on-medication, withdrawal day, and post-withdrawal recovery. Objective gait parameters—including coefficient of variation (CV), walking speed, asymmetry, and step length—were derived from real-world walking data. Rotigotine administration resulted in marked deterioration in gait stability, characterized by a substantial increase in CV and worsening asymmetry, accompanied by reduced step length. These abnormalities were further exacerbated on the withdrawal day and were rapidly reversible following discontinuation, with CV decreasing to below baseline levels. These findings demonstrate that continuous digital phenotyping enables objective identification of pharmacological interference and supports data-driven, individualized treatment strategies in Parkinson’s disease. Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Figures Figure 1 Figure 2 Introduction Gait impairment is a major determinant of disability in Parkinson’s disease (PD), yet its evaluation remains largely dependent on subjective clinical assessment [2,3]. Conventional tools such as MDS-UPDRS provide only episodic and coarse evaluation, limiting their ability to capture temporal variability and real-world motor fluctuations [2,3]. Recent advances in wearable technology have enabled continuous and objective measurement of gait. In our previous study, we demonstrated that Apple HealthKit-derived metrics can quantify long-term disease progression [1]. However, whether this framework can be applied to short-term pharmacological evaluation remains unclear. The present study addresses this gap by introducing the concept of “pharmacological interference,” in which medication destabilizes motor control rather than improving it. Using a self-controlled longitudinal design, we evaluate gait changes across four pharmacological phases with emphasis on variability (CV) as a marker of motor noise. Case Presentation The subject was a 77-year-old male with Parkinson’s disease. Gait assessments were maily conducted on a standardized outdoor route (~800 m round trip), ensuring consistency and safety. The study consisted of four phases: (1) pre-medication, (2) on-medication, (3) withdrawal day, (4) post-withdrawal recovery. The experimental timeline and gait test schedule are summarized in Table 1 . Methods Study Design A longitudinal, single-subject design was employed, structured into four pharmacological phases (Table 1). Data Acquisition Gait data were collected using Apple Watch and iPhone-based applications. Walking speed curves were obtained via GPS tracking, and gait parameters were extracted from HealthKit. Outcome Measures Primary outcome: Coefficient of variation (CV) of walking speed Secondary outcomes: Walking speed Gait asymmetry Step length Statistical Analysis Comparisons across phases were performed using one-way ANOVA. Given the repeated-measures design, emphasis was placed on consistency across parameters and effect magnitude. Results Quantitative analysis (Table 2) Significant differences were observed across all gait parameters among the four phases (p < 0.001). During the on-medication phase (2), CV increased markedly compared to pre-medication (1), indicating substantial deterioration in gait stability. This was accompanied by increased asymmetry and reduced step length. On the withdrawal day (3), these abnormalities were further exacerbated, with CV reaching its highest value. In contrast, during the post-withdrawal recovery phase (4), all parameters improved significantly. Notably, CV decreased to below baseline levels, indicating restoration of motor stability. These results are summarized in Table 2 . Temporal gait dynamics (Figure 1) Representative walking speed curves are shown in Figure 1A–D . During pre-medication (A), walking speed remained relatively stable. In contrast, during on-medication (B) and withdrawal day (C), pronounced irregular fluctuations were observed throughout the walking period. Following drug discontinuation (D), speed profiles became markedly smoother, indicating recovery of stable gait rhythm. Spatial mapping of the walking route ( Figure 1E ) demonstrated that localized speed reductions corresponded to predictable environmental factors (e.g., intersections), whereas irregular fluctuations observed during phases (2) and (3) were distributed across the entire route, supporting intrinsic motor instability. Integrated parameter visualization (Figure 2) Quantitative changes in gait parameters are visualized in Figure 2A–D . CV (Figure 2A) showed the most prominent change, with a substantial increase during the on-medication phase and peak elevation on the withdrawal day, followed by marked reduction during recovery. Walking speed (Figure 2B) decreased during medication and withdrawal phases, while asymmetry (Figure 2C) increased in parallel with CV. Step length (Figure 2D) decreased during instability and recovered after discontinuation. Together, these results demonstrate a consistent pattern of motor deterioration during medication and recovery after discontinuation. Discussion This study demonstrates that continuous digital phenotyping enables objective identification of pharmacological interference in Parkinsonian gait. In this patient, administration of rotigotine resulted in a marked increase in gait variability, as reflected by CV, accompanied by increased asymmetry and reduced step length. These findings indicate disruption of motor stability rather than improvement. Importantly, these changes were reversible. Following discontinuation, gait parameters returned to baseline and further stabilized, supporting a causal relationship between medication and motor deterioration. The results suggest that, in certain PD phenotypes, the primary deficit lies not in reduced motor output but in instability of motor execution. In this context, dopaminergic stimulation may exacerbate motor noise, leading to impaired coordination. A major strength of this study is its real-world, continuous assessment framework. Unlike clinic-based evaluations, this approach enables gait assessment anytime and anywhere, independent of walking distance, allowing detection of clinically relevant phenomena that would otherwise remain unrecognized. This study extends our previous work by demonstrating that digital gait metrics can be used not only for disease monitoring but also for pharmacological evaluation [1]. Parkinson’s disease is highly heterogeneous, both in clinical presentation and pharmacological response [3]. The present findings support a paradigm shift toward objective, within-subject evaluation using continuous digital phenotyping. Future studies adopting similar approaches may enable truly personalized, data-driven treatment strategies. Conclusion Continuous digital phenotyping enables objective, real-world evaluation of pharmacological effects in Parkinsonian gait. This approach allows identification of both therapeutic benefit and drug-induced deterioration, supporting individualized treatment strategies. Declarations Author contributions : Wrote the manuscript and Conceptualization: K.Y. Data curation, Formal analysis: Z.L. Investigation: Z.L., K.Y. Writing – original draft: K.Y. Writing – review & editing: Z.L. All authors approved the final manuscript. Ethics statement This study is a self-observational single-subject study. No identifiable personal data are included. Consent The participant provided informed consent. Competing interests The authors declare no competing interests. Funding This study received no external funding. Data availability Data are available from the corresponding author upon reasonable request. References Li, Z., Shen, J., Ishida, H., Yamamura, K. (2026). Longitudinal quantification of Parkinsonian gait using Apple HealthKit: a single-subject digital phenotyping study. Research Square. Available at: https://doi.org/10.21203/rs.3.rs-9045339/v1 Hausdorff, J. M. (2009). Gait dynamics in Parkinson's disease: Common and distinct behavior among stride length, gait variability, and fractal-like scaling. Movement Disorders, 24(8), 1233–1241. Espay, A. J., et al. (2016). Biomarkers of Parkinson’s disease: Current and future perspectives. The Lancet Neurology, 15(4), 418–430. Li, Z., et al. (2026). "Longitudinal Quantification of Parkinsonian Gait Using Apple HealthKit." Research Square . Hausdorff, J. M. (2009). "Gait dynamics in Parkinson's disease: Common and distinct mechanisms." Movement Disorders . Espay, A. J., et al. (2016). "Biomarkers of Parkinson’s disease: tools for a diagnosis, a progression, or a prescription?" The Lancet Neurology . Del Din, S., Godfrey, A., Galna, B., Lord, S., Rochester, L. (2016). Free-living gait characteristics in ageing and Parkinson’s disease: impact of environment and ambulatory bout length. Journal of NeuroEngineering and Rehabilitation, 13, 46. Weiss, A., Herman, T., Giladi, N., Hausdorff, J. M. (2014). Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PLoS ONE, 9(5), e96675. Tables Table 1. Study timeline showing pharmacological status and walking test schedule Sun Mon Tue Wed Thur Fri Sat Date 260208 260209 260210 260211 260212 260213 260214 RTP 4.5 mg 0 mg Gait test Date 260215 260216 260217 260218 260219 260220 260221 RTP Gait test + + Date 260222 260223 260224 260225 260226 260227 260228 RTP Gait test + Date 260301 260302 260303 260304 260305 260306 260307 RTP 4.5 mg Gait test + Date 260308 260309 260310 260311 260312 260313 260314 RTP Gait test + + Date 260315 260316 260317 260318 260319 260320 260321 RTP 0 mg Gait test + + + + + Table 2. Quantitative comparison of gait parameters across pharmacological phases Phase N (days) CV (%) Walking speed (km/h) Asymmetry (%) Step Length (cm) (1) Pre-medication 3 8.8 ± 1.4 3.52 ± 0.12 0.8 ± 0.2 40.5 ± 1.4 (2) On-medication 4 28.6 ± 6.2* 2.84 ± 0.18 3.5 ± 0.6* 33.2 ± 1.9* (3) Withdrawal Day 1 35.9 2.45 4.2 31.5 (4) Post-withdrawal Recovery 3 6.1 ± 0.3† 3.98 ± 0.12† 0.3 ± 0.1† 44.5 ± 1.1† *Significant impairment compared to (1) (p < 0.01). †Significant improvement compared to (2) (p < 0.01). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9521853","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":630765471,"identity":"5420239b-8d02-4aaf-83f4-1f47b3d5b5a5","order_by":0,"name":"Zhenghua Li","email":"","orcid":"","institution":"Kumamoto University","correspondingAuthor":false,"prefix":"","firstName":"Zhenghua","middleName":"","lastName":"Li","suffix":""},{"id":630765474,"identity":"2ca8b7cd-4acc-4179-a7a5-62ecbb5f8b35","order_by":1,"name":"Kenichi Yamamura","email":"data:image/png;base64,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","orcid":"","institution":"Kumamoto University","correspondingAuthor":true,"prefix":"","firstName":"Kenichi","middleName":"","lastName":"Yamamura","suffix":""}],"badges":[],"createdAt":"2026-04-25 03:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9521853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9521853/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108070812,"identity":"e8443b74-27ec-43b3-a46e-7965ed7fc4c3","added_by":"auto","created_at":"2026-04-29 05:57:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1445299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal and spatial characteristics of walking speed across pharmacological phases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Pre-medication (260223). Walking distance: 0.42 km (14:55–15:05). (B) On-medication with rotigotine patch (260315). Walking distance: 0.42 km (14:48–14:59) and 0.50 km (14:14–14:28). (C) Withdrawal day (260316). (D) Post-withdrawal recovery (260321). Walking distance: 0.49 km (11:00–11:10). (E) Corresponding walking route map with color-coded speed. Red segments indicate localized reductions in speed at intersections, while other irregular slowdowns reflect hesitation during gait.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9521853/v1/78138bdc9943704ccf703a7f.png"},{"id":108070813,"identity":"7862c058-db2f-477e-95da-7bc0b8823367","added_by":"auto","created_at":"2026-04-29 05:57:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197558,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative comparison of gait parameters across pharmacological phases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar graphs show mean values (± SD) of gait parameters across four pharmacological conditions: (1) pre-medication, (2) on-medication, (3) withdrawal day, and (4) post-withdrawal recovery. (A) Coefficient of variation (CV), representing gait variability (motor noise) (B) Walking speed (C) Gait asymmetry (D) Step length. CV increased markedly during the on-medication phase and reached its highest value on the withdrawal day, indicating severe deterioration in gait stability. In contrast, all parameters improved after discontinuation, with CV decreasing to below baseline levels, demonstrating recovery of motor stability.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9521853/v1/5d90ce405eb80b15e937bcca.png"},{"id":108181679,"identity":"5a291302-243b-4aca-b59f-4ed79f09b4fa","added_by":"auto","created_at":"2026-04-30 08:58:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1682101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9521853/v1/75dc054f-566f-4047-8ff7-39bfd91bcd7a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Objective Identification of Pharmacological Interference in Parkinsonian Gait Using Continuous Digital Phenotyping","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGait impairment is a major determinant of disability in Parkinson’s disease (PD), yet its evaluation remains largely dependent on subjective clinical assessment [2,3]. Conventional tools such as MDS-UPDRS provide only episodic and coarse evaluation, limiting their ability to capture temporal variability and real-world motor fluctuations [2,3].\u003c/p\u003e\n\u003cp\u003eRecent advances in wearable technology have enabled continuous and objective measurement of gait. In our previous study, we demonstrated that Apple HealthKit-derived metrics can quantify long-term disease progression [1]. However, whether this framework can be applied to short-term pharmacological evaluation remains unclear.\u003c/p\u003e\n\u003cp\u003eThe present study addresses this gap by introducing the concept of “pharmacological interference,” in which medication destabilizes motor control rather than improving it. Using a self-controlled longitudinal design, we evaluate gait changes across four pharmacological phases with emphasis on variability (CV) as a marker of motor noise.\u003c/p\u003e"},{"header":"Case Presentation","content":"\u003cp\u003eThe subject was a 77-year-old male with Parkinson’s disease. Gait assessments were maily conducted on a standardized outdoor route (~800 m round trip), ensuring consistency and safety.\u003c/p\u003e\n\u003cp\u003eThe study consisted of four phases:\u2028(1) pre-medication,\u2028(2) on-medication,\u2028(3) withdrawal day,\u2028(4) post-withdrawal recovery.\u003c/p\u003e\n\u003cp\u003eThe experimental timeline and gait test schedule are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA longitudinal, single-subject design was employed, structured into four pharmacological phases (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGait data were collected using Apple Watch and iPhone-based applications. Walking speed curves were obtained via GPS tracking, and gait parameters were extracted from HealthKit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary outcome:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCoefficient of variation (CV) of walking speed\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSecondary outcomes:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eWalking speed\u003c/li\u003e\n \u003cli\u003eGait asymmetry\u003c/li\u003e\n \u003cli\u003eStep length\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparisons across phases were performed using one-way ANOVA. Given the repeated-measures design, emphasis was placed on consistency across parameters and effect magnitude.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eQuantitative analysis (Table 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences were observed across all gait parameters among the four phases (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eDuring the on-medication phase (2), CV increased markedly compared to pre-medication (1), indicating substantial deterioration in gait stability. This was accompanied by increased asymmetry and reduced step length.\u003c/p\u003e\n\u003cp\u003eOn the withdrawal day (3), these abnormalities were further exacerbated, with CV reaching its highest value.\u003c/p\u003e\n\u003cp\u003eIn contrast, during the post-withdrawal recovery phase (4), all parameters improved significantly. Notably, CV decreased to below baseline levels, indicating restoration of motor stability.\u003c/p\u003e\n\u003cp\u003eThese results are summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal gait dynamics (Figure 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative walking speed curves are shown in \u003cstrong\u003eFigure 1A–D\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eDuring pre-medication (A), walking speed remained relatively stable. In contrast, during on-medication (B) and withdrawal day (C), pronounced irregular fluctuations were observed throughout the walking period.\u003c/p\u003e\n\u003cp\u003eFollowing drug discontinuation (D), speed profiles became markedly smoother, indicating recovery of stable gait rhythm.\u003c/p\u003e\n\u003cp\u003eSpatial mapping of the walking route (\u003cstrong\u003eFigure 1E\u003c/strong\u003e) demonstrated that localized speed reductions corresponded to predictable environmental factors (e.g., intersections), whereas irregular fluctuations observed during phases (2) and (3) were distributed across the entire route, supporting intrinsic motor instability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated parameter visualization (Figure 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative changes in gait parameters are visualized in \u003cstrong\u003eFigure 2A–D\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eCV (Figure 2A) showed the most prominent change, with a substantial increase during the on-medication phase and peak elevation on the withdrawal day, followed by marked reduction during recovery.\u003c/p\u003e\n\u003cp\u003eWalking speed (Figure 2B) decreased during medication and withdrawal phases, while asymmetry (Figure 2C) increased in parallel with CV. Step length (Figure 2D) decreased during instability and recovered after discontinuation.\u003c/p\u003e\n\u003cp\u003eTogether, these results demonstrate a consistent pattern of motor deterioration during medication and recovery after discontinuation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that continuous digital phenotyping enables objective identification of pharmacological interference in Parkinsonian gait.\u003c/p\u003e\n\u003cp\u003eIn this patient, administration of rotigotine resulted in a marked increase in gait variability, as reflected by CV, accompanied by increased asymmetry and reduced step length. These findings indicate disruption of motor stability rather than improvement.\u003c/p\u003e\n\u003cp\u003eImportantly, these changes were reversible. Following discontinuation, gait parameters returned to baseline and further stabilized, supporting a causal relationship between medication and motor deterioration.\u003c/p\u003e\n\u003cp\u003eThe results suggest that, in certain PD phenotypes, the primary deficit lies not in reduced motor output but in instability of motor execution. In this context, dopaminergic stimulation may exacerbate motor noise, leading to impaired coordination.\u003c/p\u003e\n\u003cp\u003eA major strength of this study is its real-world, continuous assessment framework. Unlike clinic-based evaluations, this approach enables gait assessment anytime and anywhere, independent of walking distance, allowing detection of clinically relevant phenomena that would otherwise remain unrecognized.\u003c/p\u003e\n\u003cp\u003eThis study extends our previous work by demonstrating that digital gait metrics can be used not only for disease monitoring but also for pharmacological evaluation [1].\u003c/p\u003e\n\u003cp\u003eParkinson’s disease is highly heterogeneous, both in clinical presentation and pharmacological response [3]. The present findings support a paradigm shift toward objective, within-subject evaluation using continuous digital phenotyping. Future studies adopting similar approaches may enable truly personalized, data-driven treatment strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eContinuous digital phenotyping enables objective, real-world evaluation of pharmacological effects in Parkinsonian gait. This approach allows identification of both therapeutic benefit and drug-induced deterioration, supporting individualized treatment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e:\u2028\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWrote the manuscript and Conceptualization: K.Y.\u2028\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData curation, Formal analysis: Z.L.\u2028\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInvestigation: Z.L., K.Y.\u2028\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting – original draft: K.Y.\u2028\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting – review \u0026amp; editing: Z.L.\u2028\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a self-observational single-subject study. No identifiable personal data are included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participant provided informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi, Z., Shen, J., Ishida, H., Yamamura, K. (2026). Longitudinal quantification of Parkinsonian gait using Apple HealthKit: a single-subject digital phenotyping study. Research Square. Available at: https://doi.org/10.21203/rs.3.rs-9045339/v1\u003c/li\u003e\n\u003cli\u003eHausdorff, J. M. (2009). Gait dynamics in Parkinson's disease: Common and distinct behavior among stride length, gait variability, and fractal-like scaling. Movement Disorders, 24(8), 1233\u0026ndash;1241.\u003c/li\u003e\n\u003cli\u003eEspay, A. J., et al. (2016). Biomarkers of Parkinson\u0026rsquo;s disease: Current and future perspectives. The Lancet Neurology, 15(4), 418\u0026ndash;430.\u003c/li\u003e\n\u003cli\u003eLi, Z., et al. (2026). \"Longitudinal Quantification of Parkinsonian Gait Using Apple HealthKit.\" \u003cem\u003eResearch Square\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eHausdorff, J. M. (2009). \"Gait dynamics in Parkinson's disease: Common and distinct mechanisms.\" \u003cem\u003eMovement Disorders\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEspay, A. J., et al.\u003c/strong\u003e (2016). \"Biomarkers of Parkinson\u0026rsquo;s disease: tools for a diagnosis, a progression, or a prescription?\" \u003cem\u003eThe Lancet Neurology\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDel Din, S., Godfrey, A., Galna, B., Lord, S., Rochester, L. (2016). Free-living gait characteristics in ageing and Parkinson\u0026rsquo;s disease: impact of environment and ambulatory bout length. Journal of NeuroEngineering and Rehabilitation, 13, 46.\u003c/li\u003e\n\u003cli\u003eWeiss, A., Herman, T., Giladi, N., Hausdorff, J. M. (2014). Objective assessment of fall risk in Parkinson\u0026rsquo;s disease using a body-fixed sensor worn for 3 days. PLoS ONE, 9(5), e96675.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Study timeline showing pharmacological status and walking test schedule\u003c/p\u003e\n\u003ctable style=\"border: none;border-collapse: collapse;width: 569px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:58.15pt;border:solid windowtext 1.5pt;border-right: solid black 1.0pt;padding:.75pt .75pt 0in .75pt;height:9.35pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:56.25pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.5pt;border-right:solid black 1.0pt;padding:.75pt .75pt 0in .75pt;height:9.35pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eSun\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:50.6pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.5pt;border-right:solid black 1.0pt;padding:.75pt .75pt 0in .75pt;height:9.35pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eMon\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:52.5pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.5pt;border-right:solid black 1.0pt;padding:.75pt .75pt 0in .75pt;height:9.35pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eTue\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:50.6pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.5pt;border-right:solid black 1.0pt;padding:.75pt .75pt 0in .75pt;height:9.35pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eWed\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:50.6pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.5pt;border-right:solid black 1.0pt;padding:.75pt .75pt 0in .75pt;height:9.35pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eThur\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:52.5pt;border-top:solid windowtext 1.5pt;border-left:none;border-bottom:solid windowtext 1.5pt;border-right:solid black 1.0pt;padding:.75pt .75pt 0in .75pt;height:9.35pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eFri\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.25pt;border:solid windowtext 1.5pt;border-left: none;padding:.75pt .75pt 0in .75pt;height:9.35pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eSat\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58.15pt;border-width: medium 1pt 1pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black windowtext;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eDate\u003c/span\u003e\u003c/p\u003e\n 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\u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor windowtext black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58.15pt;border-width: medium 1pt 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black windowtext windowtext;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eGait test\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor windowtext windowtext currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58.15pt;border-width: medium 1pt 1pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black windowtext;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eDate\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan 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medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(251, 0, 7);padding: 0.75pt 0.75pt 0in;height: 8.65pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor windowtext black currentcolor;background: rgb(251, 0, 7);padding: 0.75pt 0.75pt 0in;height: 8.65pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58.15pt;border-width: medium 1pt 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black windowtext windowtext;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eGait test\u003c/span\u003e\u003c/p\u003e\n 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style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e260310\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e260311\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e260312\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e260313\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor windowtext black currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu 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1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(251, 0, 7);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(251, 0, 7);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(251, 0, 7);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(251, 0, 7);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(251, 0, 7);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor windowtext black currentcolor;background: rgb(251, 0, 7);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58.15pt;border-width: medium 1pt 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black windowtext windowtext;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp 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style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor windowtext windowtext currentcolor;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58.15pt;border-width: medium 1pt 1pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black windowtext;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp 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style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e260318\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e260319\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e260320\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor windowtext black currentcolor;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e260321\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58.15pt;border-width: medium 1pt 1pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black windowtext;background: rgb(242, 242, 242);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eRTP\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(251, 0, 7);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e0 mg\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor windowtext black currentcolor;background: rgb(225, 224, 224);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58.15pt;border-width: medium 1pt 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black windowtext windowtext;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003eGait test\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(130, 202, 63);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(130, 202, 63);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(130, 202, 63);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50.6pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52.5pt;border-width: medium 1pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black windowtext currentcolor;background: rgb(130, 202, 63);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor windowtext windowtext currentcolor;background: rgb(130, 202, 63);padding: 0.75pt 0.75pt 0in;height: 9.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Yu Mincho\",serif;text-align:center;'\u003e\u003cspan style='font-size:12px;font-family:\"Arial\",sans-serif;color:black;'\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003eTable 2. Quantitative comparison of gait parameters across pharmacological phases\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eN\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eCV\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eWalking speed\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(km/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eAsymmetry (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eStep Length (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e(1) Pre-medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e8.8 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.52 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e40.5 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e(2) On-medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e28.6 \u0026plusmn; 6.2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.84 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e33.2 \u0026plusmn; 1.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e(3) Withdrawal Day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e35.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e(4) Post-withdrawal Recovery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e6.1 \u0026plusmn; 0.3\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.98 \u0026plusmn; 0.12\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.3 \u0026plusmn; 0.1\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e44.5 \u0026plusmn; 1.1\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 662px;\"\u003e\n \u003cp\u003e*Significant impairment compared to (1) (p \u0026lt; 0.01).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026dagger;Significant improvement compared to (2) (p \u0026lt; 0.01).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9521853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9521853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Building upon our previous demonstration that Apple HealthKit enables longitudinal monitoring of Parkinsonian gait [1], this study evaluates its utility for short-term pharmacological assessment. Using a self-controlled longitudinal design, gait performance was quantified across four pharmacological phases: pre-medication, on-medication, withdrawal day, and post-withdrawal recovery. Objective gait parameters—including coefficient of variation (CV), walking speed, asymmetry, and step length—were derived from real-world walking data. Rotigotine administration resulted in marked deterioration in gait stability, characterized by a substantial increase in CV and worsening asymmetry, accompanied by reduced step length. These abnormalities were further exacerbated on the withdrawal day and were rapidly reversible following discontinuation, with CV decreasing to below baseline levels. These findings demonstrate that continuous digital phenotyping enables objective identification of pharmacological interference and supports data-driven, individualized treatment strategies in Parkinson’s disease.","manuscriptTitle":"Objective Identification of Pharmacological Interference in Parkinsonian Gait Using Continuous Digital Phenotyping","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 05:57:50","doi":"10.21203/rs.3.rs-9521853/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf90c5d4-723f-4420-abe6-02ddf3bfa7c5","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67135165,"name":"Health sciences/Diseases"},{"id":67135166,"name":"Health sciences/Medical research"},{"id":67135167,"name":"Health sciences/Neurology"},{"id":67135168,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-29T05:57:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 05:57:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9521853","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9521853","identity":"rs-9521853","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}