Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May 29:13:e19519.
doi: 10.7717/peerj.19519. eCollection 2025.

Longitudinal analysis of step counts in Parkinson's disease patients: insights from a web-based application and generalized additive model

Yuan Gu  1 , Yishu Gong  2
Affiliations

Longitudinal analysis of step counts in Parkinson's disease patients: insights from a web-based application and generalized additive model

Yuan Gu et al. PeerJ. .

Abstract

Background: Parkinson's disease (PD) is a chronic neurological disorder that affects millions of people worldwide. A common motor symptom associated with PD is gait impairment, leading to reduced step count and mobility.

Methods: Monitoring and analyzing step count data can provide valuable insights into the progression of the disease and the effectiveness of various treatments. In our study, the generalized additive model (GAM) was used to identify statistically significant variables for step counts. Additionally, a web application was developed as an interactive visualization tool.

Results: The GAM model shows that the following variables are statistically significant for daily step counts: sex (p = 0.03), handedness (p = 0.015), PD status of father (p = 0.056), COVID-19 status (Yes vs. No, p = 0.008), cohort (PD vs. Healthy, p < 0.0001), the cubic regression spline with three basis functions of age by cohorts (p < 0.0001), and the random effect of individual age trajectories (p = 0.0001).

Conclusions: Based on the PPMI data, we find that sex, handedness, PD status of father, COVID-19 status, cohort, and the smoothing functions of age are all statistically significant for step counts. Additionally, a web application tailored specifically for step count analysis in PD patients was developed. This tool provides a user-friendly interface for patients, caregivers, and healthcare professionals to track and analyze step count data, facilitating personalized treatment plans and enhancing the management of PD.

Keywords: Longitudinal; Parkinson’s disease; Shiny; Step count; Web-application.

PubMed Disclaimer

Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Trajectories of daily steps over time.
Figure 2
Figure 2. Box plots of daily steps average over 3 months by different time (3 months intervals).
Figure 3
Figure 3. Standardized residuals vs. sample for GAM model diagnostics.
Figure 4
Figure 4. Q-Q plot of standardized residuals for GAM model diagnostics.
Figure 5
Figure 5. Daily steps comparison between healthy (blue) and PD groups over age curves: GAM fitted curve; connected lines: original daily steps.
See this image and copyright information in PMC

References

    1. Adams JL, Kangarloo T, Tracey B, O’Donnell P, Volfson D, Latzman RD, Zach N, Alexander R, Bergethon P, Cosman J, Anderson D, Best A, Severson J, Kostrzebski MA, Auinger P, Wilmot P, Pohlson Y, Waddell E, Jensen-Roberts S, Gong Y, Kilambi KP, Herrero TR, Ray Dorsey E, Parkinson Study Group Watch-PD Study Investigators and Collaborators Using a smartwatch and smartphone to assess early Parkinson’s disease in the WATCH-PD study. Npj Parkinson’s Disease. 2023;9(1):64. doi: 10.1038/s41531-023-00497-x. - DOI - PMC - PubMed
    1. Ahmadi MN, Rezende LFM, Ferrari G, del Pozo Cruz B, Lee IM, Stamatakis E. Do the associations of daily steps with mortality and incident cardiovascular disease differ by sedentary time levels? A device-based cohort study. British Journal of Sports Medicine. 2024;58(5):261–268. doi: 10.1136/bjsports-2023-107221. - DOI - PMC - PubMed
    1. Amara AW, Chahine L, Seedorff N, Caspell-Garcia CJ, Coffey C, Simuni T, The Parkinson’s Progression Markers Initiative Self-reported physical activity levels and clinical progression in early Parkinson’s disease. Parkinsonism and Related Disorders. 2019;61:118–125. doi: 10.1016/j.parkreldis.2018.11.006. - DOI - PubMed
    1. Atri R, Urban K, Marebwa B, Simuni T, Tanner C, Siderowf A, Frasier M, Haas M, Lancashire L. Deep learning for daily monitoring of Parkinson’s disease outside the clinic using wearable sensors. Sensors. 2022;22(18):6831. doi: 10.3390/s22186831. - DOI - PMC - PubMed
    1. Banach M, Lewek J, Surma S, Penson PE, Sahebkar A, Martin SS, Bajraktari G, Henein MY, Reiner Ž, da Browa AB, Bytyçi I. The association between daily step count and all-cause and cardiovascular mortality: a meta-analysis. European Journal of Preventive Cardiology. 2023;30(18):1975–1985. doi: 10.1093/eurjpc/zwad229. - DOI - PubMed

LinkOut - more resources