Maaike Goris ¹ Pieter Ginis ¹ Clint Hansen ² Christian Schlenstedt ^2,3 Jeffrey M Hausdorff ^4,5,6 Nicholas D'Cruz ¹ Wim Vandenberghe ⁷ Walter Maetzler ² Alice Nieuwboer ¹ Moran Gilat ^1*

• ¹ KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
• ² Christian-Albrechts-University Kiel, University Hospital Schleswig-Holstein, Department of Neurology, Kiel, Germany
• ³ Institute of Interdisciplinary Exercise Science and Sports Medicine, Medical School Hamburg,, Hamburg, Germany
• ⁴ Department of Physical Therapy, Faculty of Medical & Health Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
• ⁵ Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
• ⁶ Rush Alzheimer’s Disease Center, Rush University Medical Center and Department of Orthopaedic Surgery, Rush Medical College, Chigaco, United States
• ⁷ Department of Neurology, University Hospitals Leuven, Leuven, Brussels, Belgium

Freezing of gait (FOG) is a disabling symptom for people with Parkinson’s disease (PwPD). Turning on the spot for one minute in alternating directions (360 turn) while performing a cognitive dual- task (DT) is a fast and sensitive way to provoke FOG. The FOG-index is a widely used wearable sensor-based algorithm to quantify FOG severity during turning. Despite that, the FOG index’s FOG-index’s classification performance and criterion validity is not tested against the gold standard (i.e. video-rated time spent freezing). Therefore, this study aimed to evaluate the FOG-index’s classification performance and criterion validity to assess FOG severity during 360 turn. Additionally, we investigated the FOG index’s FOG-index’s optimal cutoff values to differentiate between PwPD with and without FOG. 164 PwPD self-reported the presence of FOG on the New Freezing of Gait Questionnaire (NFOGQ) and performed the DT 360 turn in the ON medication state while being videoed and wearing five wearable sensors. Two independent clinical experts rated FOG on video. ROC-AUC values assessed the FOG-index’s classification accuracy against self-reported FOG and expert ratings. Spearman-rho was used to evaluate the correlation between expert and FOG-index ratings of FOG severity. Twenty-eight patients self-reported FOG, while 104 were classified as a freezer by the experts. The FOG-index had limited classification agreement with the NFOGQ (AUC=0.60, p=0.115, sensitivity 46.4%, specificity 72.8%,) and the experts (AUC=0.65, p<0.001, sensitivity 68.3%, specificity 61.7%). Only weak correlations were found between the algorithm outputs and expert ratings for FOG severity (rho=0.13-0.38). A surprisingly large discrepancy was found between self-reported and expert-rated FOG during the 360 turning task, indicating PwPD do not always notice FOG in daily life. The FOG-index achieved suboptimal classification performance and poor criterion validity to assess FOG severity. Regardless, 360 turning proved a sensitive task to elicit FOG . Further development of the FOG-index is warranted, and long-term follow-up studies are needed to assess the predictive value of the 360 turning task for classifying FOG conversion.