Objective: TolongitudinallyevaluatewearableIMU-derived gaitparameters across two clinical visits andcharacterizepharmacological responsephenotypes inParkinsondiseasepatientswith freezingof gait (FOG).

Background: FOG affects upto 80% of patientswith advanced PD and frequentlydemonstrates absent or partial responsiveness to dopaminergic therapy.Wearable IMU sensors enable objective quantification of gait dynamics beyond conventional rating scales.Longitudinal real-world characterization ofpharmacological FOGsubtypes using wearable sensor data remains limited.

Method: Twenty PD patients with clinician-confirmed FOG were evaluated at two visits (mean interval: four months);14 completed both sessions (mean age77.4 years;10 males,4 females). Parameters cadence, step variability (CV), FOG index,stride length were extracted from lower-limb IMU recordings synchronized with video. Assessments included MDS-UPDRS Part III, NFOG-Q, H&Y staging, and LEDD. Ethics:Istanbul MedipolUniversityNIREC (E-10840098-202.3.02-5327).

Results: Cadence improved from75.3±16.4 to 82.1±12.2 steps/min; CV from100.7±89.7% to 76.4±47.9%. NFOG-Q remained stable (V1: 5.9±4.4 vs V2: 6.1±3.9; Δ+0.1±3.5) despite a mean LEDD increase of +96 mg/day (382→478 mg).FOG index exceeded the pathological threshold (>1.0) in H1 and H8 at V2. Four pharmacological phenotypes were identified:

(1) Levodopa-responsive OFF-FOG: H1 (LEDD +150 mg, NFOG-Q −3) and H3 (rasagiline add-on, NFOG-Q −3) showed concordant clinical and sensor-derived improvement.

(2) Levodopa-refractory ON/OFF-FOG: H9 (LEDD +475 mg, NFOG-Q +5) and H11 (LEDD +200 mg; 11-year disease duration) exhibited non-dopaminergic, likely cholinergic-mediated FOG.(3) Drug-transition rebound FOG: H5 worsened markedly (NFOG-Q 0→10) after amantadine withdrawal and levodopa initiation.(4) Dopaminergic overdose ON-state FOG:H13 improved after LEDD reduction (−225 mg, NFOG-Q 10→5). CV correlated withNFOG-Q and detected festination-related instability not captured by MDS-UPDRS PartIII.

Conclusion: IMU-basedgaitanalysisenabledlongitudinalFOGphenotypingandidentified four pharmacological response profiles, including a previously underrecognized levodopa dose-reduction responder. CV emerged as a sensitive digital biomarker beyond conventional scales.These findings support wearable IMU systems for FOG phenotyping and as a platform for closed-loop vibrotactile cueing in Parkinsondisease.

References: Sun, Y. et al. (2024). Digital biomarkers for precision diagnosis and monitoring in Parkinson’s disease. npj Digital Medicine.
https://www.nature.com/articles/s41746-024-01217-2

Sapienza, S., Tsurkalenko, O., Giraitis, M., et al. (2024). Assessing the clinical utility of inertial sensors for home monitoring in Parkinson’s disease. npj Parkinson’s Disease.
https://www.nature.com/articles/s41531-024-00755-6

Polvorinos-Fernández, C., Sigcha, L., Borzì, L., et al. (2024). Evaluating motor symptoms in Parkinson’s disease through wearable sensors: a systematic review of digital biomarkers. Applied Sciences, 14.
https://www.mdpi.com/2076-3417/14/22/10189

Rábano-Suárez, P., Del Campo, N., Benatru, I., et al. (2025). Digital outcomes as biomarkers of disease progression in early Parkinson’s disease. Movement Disorders.
https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.30056

Mancini, M., McKay, J.L., Cockx, H., et al. (2025). Technology for measuring freezing of gait: Current state of the art and recommendations. Journal of Parkinson’s Disease.
https://journals.sagepub.com/doi/10.1177/1877718X241301065

Ali, F., Padilla, H., Blazek, A.M., et al. (2025). Gait analysis in neurologic disorders: methodology, applications, and clinical considerations. Neurology.
https://www.neurology.org/doi/10.1212/WNL.0000000000214154

Zampogna, A., Pietrosanti, L., Saggio, G., et al. (2025). L-Dopa comparably improves gait and limb movements in Parkinson’s disease: a wearable sensor analysis. Biomedicines.
https://www.mdpi.com/2227-9059/13/11/27

Qi, W., Shen, S., Dong, C., et al. (2025). Digital biomarkers for Parkinson disease: bibliometric analysis and scoping review of deep learning for freezing of gait. Journal of Medical Internet Research.
https://www.jmir.org/2025/1/e71560

Bougea, A. (2025). Application of wearable sensors in Parkinson’s disease: state of the art. Journal of Sensor and Actuator Networks.
https://www.mdpi.com/2224-2708/14/2/23

Di Biase, L., Pecoraro, P.M., Pecoraro, G., et al. (2024). Machine learning and wearable sensors for automated Parkinson’s disease diagnosis aid: a systematic review. Journal of Neurology.
https://link.springer.com/article/10.1007/s00415-024-12611-x

Wearable IMU systems provide objective digital biomarkers capable of capturing gait variability and motor fluctuations beyond traditional clinical scales (Sun et al., 2024; Sapienza et al., 2024).

Gait variability metrics such as stride-to-stride variability and cadence irregularity are among the most sensitive digital markers of freezing of gait (Polvorinos-Fernández et al., 2024).
Sensor-derived gait parameters can detect heterogeneous pharmacological responses, including levodopa-responsive and levodopa-resistant FOG phenotypes (Mancini et al., 2025; Zampogna et al., 2025).

Longitudinal wearable monitoring enables objective tracking of disease progression and treatment response in Parkinson disease (Rábano-Suárez et al., 2025).

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MDS Abstracts - https://www.mdsabstracts.org/abstract/wearable-imu-based-gait-analysis-reveals-pharmacological-freezing-of-gait-phenotypes-in-parkinson-disease-a-longitudinal-two-visit-study/