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Gait Analysis for Identifying Normal Cognition, Subjective Cognitive Decline, and Mild Cognitive Impairment in Parkinson Disease: Diagnostic Study.

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Τίτλος: Gait Analysis for Identifying Normal Cognition, Subjective Cognitive Decline, and Mild Cognitive Impairment in Parkinson Disease: Diagnostic Study.
Συγγραφείς: Huang J; Department of Neurology, The First affiliated Hospital of Nanhua University, Hengyang, China., Wu L; GYENNO Science Co., Ltd., Shenzhen, China.; HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.; Guangdong Provincial Engineering Technology Research Center for Medical Artificial Intelligence in Neurological Disease, Shenzhen, Guangdong, China., Wang H; Department of Neurology, Jiangxi Medical College, Nanchang University, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Chen L; Department of Neurology, Jiangxi Medical College, Nanchang University, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Hu B; Department of Neurology, Jiangxi Medical College, Nanchang University, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China., Zhang F; GYENNO Science Co., Ltd., Shenzhen, China.; HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.; Guangdong Provincial Engineering Technology Research Center for Medical Artificial Intelligence in Neurological Disease, Shenzhen, Guangdong, China., Ren K; GYENNO Science Co., Ltd., Shenzhen, China.; HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.; Guangdong Provincial Engineering Technology Research Center for Medical Artificial Intelligence in Neurological Disease, Shenzhen, Guangdong, China., Ling Y; GYENNO Science Co., Ltd., Shenzhen, China.; HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.; Guangdong Provincial Engineering Technology Research Center for Medical Artificial Intelligence in Neurological Disease, Shenzhen, Guangdong, China., Chen Z; GYENNO Science Co., Ltd., Shenzhen, China.; HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.; Guangdong Provincial Engineering Technology Research Center for Medical Artificial Intelligence in Neurological Disease, Shenzhen, Guangdong, China., Huang W; Department of Neurology, Jiangxi Medical College, Nanchang University, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Πηγή: JMIR mHealth and uHealth [JMIR Mhealth Uhealth] 2026 Jun 24; Vol. 14, pp. e69273. Date of Electronic Publication: 2026 Jun 24.
Τύπος έκδοσης: Journal Article; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: JMIR Publications Inc Country of Publication: Canada NLM ID: 101624439 Publication Model: Electronic Cited Medium: Internet ISSN: 2291-5222 (Electronic) Linking ISSN: 22915222 NLM ISO Abbreviation: JMIR Mhealth Uhealth Subsets: MEDLINE
Imprint Name(s): Original Publication: Toronto: JMIR Publications Inc., [2013]-
Ιατρικοί όροι (MeSH): Parkinson Disease*/complications , Parkinson Disease*/psychology , Parkinson Disease*/physiopathology , Parkinson Disease*/diagnosis , Cognitive Dysfunction*/diagnosis , Cognitive Dysfunction*/psychology , Cognitive Dysfunction*/classification , Cognitive Dysfunction*/physiopathology , Gait Analysis*/methods , Gait Analysis*/instrumentation , Gait Analysis*/statistics & numerical data , Gait Analysis*/standards , Cognition*/physiology , Cognition*/classification, Humans ; Female ; Male ; Aged ; Middle Aged ; Machine Learning ; Dual-Task Tests
Περίληψη: Background: Patients with Parkinson disease (PD) along with subjective cognitive decline (PD-SCD) are considered an intermediate status between those with normal cognition (PD-NC) and those with mild cognitive impairment (PD-MCI). Wearable digital monitoring technologies and machine learning models offer significant potential for assessing cognitive impairment in patients with PD.
Objective: We aimed to evaluate the utility of wearable technology and machine learning for identifying ordinal cognitive stages (OCS) in PD based on timed up and go (TUG) tests (including single-task TUG [TUGst] and dual-task TUG [TUGdt]).
Methods: Patients with PD along with SCD, MCI, and NC were recruited in a movement disorder clinic. Patients performed TUGst and TUGdt gait trials wearing a motor function and motor symptom quantitative assessment system. In total, 209 kinematic parameters were synthesized for individual TUG to illustrate patients' motion profiles. We constructed dual-task cost parameters (DTC), describing the magnitude of the effect of the cognitive challenge on motion performance. Covariate-adjusted ordered logistic regression was used to compare parameter differences among 3 groups. Multiple machine learning models were used to classify the participants into 3 cognitive impairment levels, with features being selected based on P values from intergroup statistical tests. The total population was randomly divided into a training set and an independent validation set in a 7:3 ratio, and 10-fold cross-validation was used in the training set. Furthermore, this study used permutation importance and Shapley Additive Explanations (SHAP) analysis (including summary plots, bar plots, and waterfall plots) to explain the feature importance of the final model.
Results: The study included 65 age-matched patients (PD-NC: PD-SCD: PD-MCI= 14:21:30). Forty-five kinematic parameters were significantly different (P<.05) among the 3 groups, distributed across TUGst (n=25), TUGdt (n=12), and DTC (n=8) paradigms. Gait phase analysis revealed 35 parameters from walking phases, 9 from stand-to-sit transitions, and 1 from sit-to-stand transitions. Feature type distribution demonstrated predominance of variability features (n=20), followed by pace (n=12) and axial (n=8) characteristics. TUGdt paradigm analysis revealed pronounced movement differences between PD-MCI and both PD-NC and PD-SCD groups, particularly in variability, amplitude, pace, and axial domains. Cross-paradigm analysis identified consistent significant differences in specific features. These findings provide objective kinematic biomarkers for early cognitive state identification in Parkinson disease, with TUGdt parameters demonstrating superior discriminative capacity.
Conclusions: This suggests patients with PD-SCD could have early kinetic signs of cognitive impairment, positioning them between PD-NC and PD-MCI, and our multiclass support vector machine classification model with kinematic parameters achieved a recall rate above 0.70 in both training and validation datasets. The feature importance analysis revealed that DTC_Trunk-Right Rotation Max, DTC_Trunk-Max Transverse Angular Velocity, and dTUG_Lumbar-Right Sway Max Std were the most critical features for distinguishing cognitive states, providing scientific evidence for cognitive function screening based on kinematic parameters.
(©Juan Huang, Lingyu Wu, Hui Wang, Lin Chen, Binbin Hu, Fei Zhang, Kang Ren, Yun Ling, Zhonglue Chen, Wei Huang. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 24.06.2026.)
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Contributed Indexing: Keywords: Parkinson disease; kinematic parameters; machine learning; mild cognitive impairment; subjective cognitive decline; wearable sensor
Entry Date(s): Date Created: 20260624 Date Completed: 20260624 Latest Revision: 20260911
Update Code: 20260911
PubMed Central ID: PMC13347079
DOI: 10.2196/69273
PMID: 42341295
Βάση Δεδομένων: MEDLINE
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  Data: Background: Patients with Parkinson disease (PD) along with subjective cognitive decline (PD-SCD) are considered an intermediate status between those with normal cognition (PD-NC) and those with mild cognitive impairment (PD-MCI). Wearable digital monitoring technologies and machine learning models offer significant potential for assessing cognitive impairment in patients with PD.&lt;br /&gt;Objective: We aimed to evaluate the utility of wearable technology and machine learning for identifying ordinal cognitive stages (OCS) in PD based on timed up and go (TUG) tests (including single-task TUG [TUGst] and dual-task TUG [TUGdt]).&lt;br /&gt;Methods: Patients with PD along with SCD, MCI, and NC were recruited in a movement disorder clinic. Patients performed TUGst and TUGdt gait trials wearing&#160;a&#160;motor&#160;function&#160;and&#160;motor&#160;symptom&#160;quantitative&#160;assessment&#160;system. In total, 209 kinematic parameters were synthesized for individual TUG to illustrate patients&#39; motion profiles. We constructed dual-task cost parameters (DTC), describing the magnitude of the effect of the cognitive challenge on motion performance. Covariate-adjusted ordered logistic regression was used to compare parameter differences among 3 groups. Multiple machine learning models were used to classify the participants into 3 cognitive impairment levels, with features being selected based on P values from intergroup statistical tests. The total population was randomly divided into a training set and an independent validation set in a 7:3 ratio, and 10-fold cross-validation was used in the training set. Furthermore, this study used permutation importance and Shapley Additive Explanations (SHAP) analysis (including summary plots, bar plots, and waterfall plots) to explain the feature importance of the final model.&lt;br /&gt;Results: The study included 65 age-matched patients (PD-NC: PD-SCD: PD-MCI= 14:21:30). Forty-five kinematic parameters were significantly different (P&amp;lt;.05) among the 3 groups, distributed across TUGst (n=25), TUGdt (n=12), and DTC (n=8) paradigms. Gait phase analysis revealed 35 parameters from walking phases, 9 from stand-to-sit transitions, and 1 from sit-to-stand transitions. Feature type distribution demonstrated predominance of variability features (n=20), followed by pace (n=12) and axial (n=8) characteristics. TUGdt paradigm analysis revealed pronounced movement differences between PD-MCI and both PD-NC and PD-SCD groups, particularly in variability, amplitude, pace, and axial domains. Cross-paradigm analysis identified consistent significant differences in specific features. These findings provide objective kinematic biomarkers for early cognitive state identification in Parkinson disease, with TUGdt parameters demonstrating superior discriminative capacity.&lt;br /&gt;Conclusions: This suggests patients with PD-SCD could have early kinetic signs of cognitive impairment, positioning them between PD-NC and PD-MCI, and our multiclass support vector machine&#160;classification model with kinematic parameters achieved a recall rate above 0.70 in both training and validation datasets. The feature importance analysis revealed that DTC&amp;#95;Trunk-Right Rotation Max, DTC&amp;#95;Trunk-Max Transverse Angular Velocity, and dTUG&amp;#95;Lumbar-Right Sway Max Std were the most critical features for distinguishing cognitive states, providing scientific evidence for cognitive function screening based on kinematic parameters.&lt;br /&gt; (&#169;Juan Huang, Lingyu Wu, Hui Wang, Lin Chen, Binbin Hu, Fei Zhang, Kang Ren, Yun Ling, Zhonglue Chen, Wei Huang. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 24.06.2026.)
– Name: Ref
  Label: References
  Group: RefInfo
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– Name: SubjectMinor
  Label: Contributed Indexing
  Group:
  Data: &lt;i&gt;Keywords: &lt;/i&gt;Parkinson disease; kinematic parameters; machine learning; mild cognitive impairment; subjective cognitive decline; wearable sensor
– Name: DateEntry
  Label: Entry Date(s)
  Group: Date
  Data: &lt;i&gt;Date Created: &lt;/i&gt;20260624 &lt;i&gt;Date Completed: &lt;/i&gt;20260624 &lt;i&gt;Latest Revision: &lt;/i&gt;20260911
– Name: DateUpdate
  Label: Update Code
  Group: Date
  Data: 20260911
– Name: PubmedCentralID
  Label: PubMed Central ID
  Group: ID
  Data: PMC13347079
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.2196/69273
– Name: AN
  Label: PMID
  Group: ID
  Data: 42341295
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42341295
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.2196/69273
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: e69273
    Subjects:
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Female
        Type: general
      – SubjectFull: Male
        Type: general
      – SubjectFull: Aged
        Type: general
      – SubjectFull: Middle Aged
        Type: general
      – SubjectFull: Machine Learning
        Type: general
      – SubjectFull: Dual-Task Tests
        Type: general
      – SubjectFull: Parkinson Disease complications
        Type: general
      – SubjectFull: Parkinson Disease psychology
        Type: general
      – SubjectFull: Parkinson Disease physiopathology
        Type: general
      – SubjectFull: Parkinson Disease diagnosis
        Type: general
      – SubjectFull: Cognitive Dysfunction diagnosis
        Type: general
      – SubjectFull: Cognitive Dysfunction psychology
        Type: general
      – SubjectFull: Cognitive Dysfunction classification
        Type: general
      – SubjectFull: Cognitive Dysfunction physiopathology
        Type: general
      – SubjectFull: Gait Analysis methods
        Type: general
      – SubjectFull: Gait Analysis instrumentation
        Type: general
      – SubjectFull: Gait Analysis statistics & numerical data
        Type: general
      – SubjectFull: Gait Analysis standards
        Type: general
      – SubjectFull: Cognition physiology
        Type: general
      – SubjectFull: Cognition classification
        Type: general
    Titles:
      – TitleFull: Gait Analysis for Identifying Normal Cognition, Subjective Cognitive Decline, and Mild Cognitive Impairment in Parkinson Disease: Diagnostic Study.
        Type: main
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      – PersonEntity:
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            NameFull: Huang J
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            NameFull: Wu L
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            NameFull: Wang H
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            NameFull: Chen L
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            NameFull: Hu B
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            NameFull: Zhang F
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            NameFull: Ren K
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            NameFull: Ling Y
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            NameFull: Chen Z
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          Dates:
            – D: 24
              M: 06
              Text: 2026 Jun 24
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 2291-5222
          Numbering:
            – Type: volume
              Value: 14
          Titles:
            – TitleFull: JMIR mHealth and uHealth
              Type: main
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