Related Experiment Video
Updated: Jun 1, 2025
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
Published on: November 7, 2014
Detecting freezing of gait: A comprehensive toolkit for enhanced Parkinson's assessment
Warongporn Phuenpathom1, Onanong Phokaewvarangkul1, Roongroj Bhidayasiri2
1Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, 10330, Thailand.
Introduction:
Detecting Freezing of Gait (FOG) poses challenges, with the subjective 6-item FOG Questionnaire relying solely on patient perception. We aim to create a holistic FOG Detection Toolkit combining subjective and objective elements (descriptions, images, and videos) to improve FOG detection precision.
Methods:
Development of the FOG Detection Toolkit involved a detailed cover sheet on FOG and its triggers, along with video exemplars and a 4-item FOG-specific self-assessment questionnaire, all rigorously validated. The toolkit was administered to 100 eligible consecutive Parkinson's disease (PD) patients at a PD referral clinic in a major public university hospital in Thailand. The FOG Detection Toolkit results are based on the total score from a 4-item FOG-specific self-assessment questionnaire (range: 0-16). Freezers were identified by scores ≥6.
Results:
The cover sheet, images, and videos displayed robust content validity and inter-rater reliability. The 4-item questionnaire exhibited high sensitivity (98 %) and specificity (100 %), with a substantial Area Under the Curve (AUC) of 0.990 and satisfactory construct validity (r = 0.68; p = 0.01). Users reported positive pragmatic (1.75) and hedonic (1.34) experiences. Patients with FOG scored significantly higher on the Toolkit and demonstrated distinct gait parameters (p < 0.001).
Conclusion:
The FOG Detection Toolkit showcases strong diagnostic performance, adequate construct validity, and positive user experience, facilitating accurate FOG detection. Its utility extends outside clinical environments, promising broader applicability for FOG management.
