Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson’s disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the “ground-truth” for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.

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BackgroundEpidemiological studies show disparities in the provision of physical health-care for people with severe mental illness. This observation includes countries with universal health insurance. However, there is limited in-depth data regarding the barriers preventing equality of physical health-care provision for this population. This study applied the capabilities approach to examine the interface between general practitioners and patients with severe mental illness. The capabilities approach provides a framework for health status which conceptualizes the internal and external factors relating to the available options (capabilities) and subsequent health outcomes (functioning).MethodsSemi-structured in-depth interviews were conducted with 10 general practitioners and 15 patients with severe mental illness, and then thematically analyzed.Results: We identified factors manifesting across three levels: personal, relational-societal, and organizational. At the personal level, the utilization of physical health services was impaired by the exacerbation of psychiatric symptoms. At the relational level, both patients and physicians described the importance of a long-term and trusting relationship, and provided examples demonstrating the implications of relational ruptures. Finally, two structural-level impediments were described by the physicians: the absence of continuous monitoring of patients with severe mental illness, and the shortfall in psychosocial interventions.ConclusionThe capability approach facilitated the identification of barriers preventing equitable health-care provision for patients with severe mental illness. Based on our findings, we propose a number of practical suggestions to improve physical health-care for this population: 1. A proactive approach in monitoring patients’ health status and utilization of services. 2. Acknowledgment of people with severe mental illness as a vulnerable population at risk, that need increased time for physician-patient consultations. 3. Training and support for general practitioners. 4. Increase collaboration between general practitioners and mental-health professionals. 5. Educational programs for health professionals to reduce prejudice against people with severe mental illness.