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| ID | Type | Description | Link |
|---|---|---|---|
| 2023-A00561-44 | Other Identifier | ID RCB | |
| 23.01067.000298 | Other Identifier | SI RIPH2G |
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Parkinson's disease is the second most common neurodegenerative disease in the world. One of these manifestations is the freezing of gait (FOG) which affects 50 to 80% of Parkinsonian patients. It is defined as a brief and episodic absence or marked reduction in the forward progression of the feet despite the intention to walk. FOG is one of the most disabling symptoms causing a greater risk of falling and a loss of autonomy for these patients. This symptom is little or not dopamine-sensitive and little improved by surgery (deep brain stimulation).
Although this symptom is common and debilitating, it is difficult to assess clinically. The objective assessment of the presence and severity of FOG episodes can be done with tests such as the New-Freezing of Gait Questionnaire (N-FOGQ) with however limitations. Indeed, this filmed examination is scored a posteriori and the accumulation of the administration times which makes it difficult to use in routine clinical practice. To overcome these limitations, the use of a diary completed by the patient himself is a simple alternative to assess this symptom, but studies show that patients abandon this practice in the long term and that it is not used by patients with cognitive impairment.
Recent advances in miniaturization have made it possible to create light and compact sensors to assess these events objectively. Inertial measurement units have been widely used in the literature to detect FOG episodes. The choice of the detection algorithms are a major issue in the scientific community. To date, due to the heterogeneity of the protocols, no method is currently required as a reference.
The objective is to evaluate the accuracy of a new algorithm to detect the number of FOG episodes in Parkinsonian patients. This evaluation will be done on the freeze-inducing walking path.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Freezing of Gait | Experimental | Each patient will have 2 visits :
For each visit, the patient will be asked to walk at a comfortable speed under the following 3 conditions:
Conditions of passage are randomized per patient. Each subject will complete the course a maximum of 18 times in blocks of 3 conditions (normal, double physical task and double verbal task). A rest period will be observed. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Walk under 3 conditions (normal, physical tasks, verbal tasks) | Other | Each patient will have 2 visits :
For each visit, the patient will be asked to walk at a comfortable speed under the following 3 conditions:
Conditions of passage are randomized per patient. Each subject will complete the course a maximum of 18 times in blocks of 3 conditions. |
| Measure | Description | Time Frame |
|---|---|---|
| Precision | Every second of the course will be analyzed to define :
The average of these ratios is then calculated to estimate the accuracy of all runs performed, i.e. taking into account all repetitions performed by patients, regardless of run type and ON/OFF status. | Through study completion, an average of 15+/-7 days |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity | Sensitivity will be calculated as the ratio of time spent in "true positive" divided by the sum of time spent in "true positive" and "false negative". These ratios will then be averaged to estimate accuracy over all the runs performed, i.e. taking into account all the repetitions performed by patients, regardless of run type and ON/OFF status. | Through study completion, an average of 15+/-7 days |
| Measure | Description | Time Frame |
|---|---|---|
| Ability to generate FOG episodes between different pathway modalities | The ability to generate FOG episodes between different pathway modalities will be analyzed by comparing, for each pathway type, the percentage of time in FOG assessed by the experts using a mixed model (fixed effect on patients and random effect on pathway type). | Through study completion, an average of 15+/-7 days |
Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Rennes University Hospital | Recruiting | Rennes | Brittany Region | 35000 | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39913915 | Derived | Cordillet S, Drapier S, Leh F, Dumont A, Bidet F, Bonan I, Jamal K. Detecting Freezing of Gait in Parkinson Disease Using Multiple Wearable Sensors Sets During Various Walking Tasks Relative to Medication Conditions (DetectFoG): Protocol for a Prospective Cohort Study. JMIR Res Protoc. 2025 Feb 6;14:e58612. doi: 10.2196/58612. |
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| ID | Term |
|---|---|
| D010300 | Parkinson Disease |
| ID | Term |
|---|---|
| D020734 | Parkinsonian Disorders |
| D001480 | Basal Ganglia Diseases |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
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Patients will be evaluated in the "ON" state phase and in the "OFF" state phase.
For each visit, the patient will be asked to walk at a comfortable speed under the following 3 conditions: motor task, verbal, normal.
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| Specificity | Specificity will be calculated as the ratio of time spent in "true negative" divided by the sum of time spent in "true negative" and "false positive". These ratios will then be averaged to estimate accuracy over all the runs performed, i.e. taking into account all the repetitions performed by patients, regardless of run type and ON/OFF status. | Through study completion, an average of 15+/-7 days |
| Positive predictive value (PPV) | The positive predictive value will be calculated as the ratio of time spent in "true positive" divided by the sum of time spent in "true positive" and "false positive". The average of these ratios will then be calculated to estimate the accuracy over all the runs performed, i.e. taking into account all the repetitions performed by patients, regardless of run type and ON/OFF status. | Through study completion, an average of 15+/-7 days |
| Negative predictive value (NPV) | The negative predictive value will be calculated as the ratio of time spent in "true negative" divided by the sum of time spent in "true negative" and "false negative". The average of these ratios will then be calculated to estimate the accuracy over all the runs performed, i.e. taking into account all the repetitions performed by patients, regardless of run type and ON/OFF status. | Through study completion, an average of 15+/-7 days |
| Time difference | The time difference between the start of the episode detected by the experts and that detected with the | Through study completion, an average of 15+/-7 days |
| Ability to generate FOG episodes according to medical conditions (ON/OFF) | The ability to generate FOG episodes according to medical conditions (ON/OFF) will be analyzed by comparing for each type of pathway the percentage of time in FOG assessed by experts using a mixed model (fixed effect on patients and random effect on ON/OFF status). | Through study completion, an average of 15+/-7 days |
| Algorithm performance according to pathway | Algorithm performance (accuracy, sensitivity, specificity, PPV, NPV) according to pathway type will be analyzed using a mixed model (fixed effect on patients and random effect on pathway type). | Through study completion, an average of 15+/-7 days |
| Algorithm performance according to medical conditions (ON/OFF) | Algorithm performance (accuracy, sensitivity, specificity, PPV, NPV) according to medical conditions (ON/OFF) will be analyzed using a mixed model (fixed effect on patients and random effect on ON/OFF status). | Through study completion, an average of 15+/-7 days |
| D009422 | Nervous System Diseases |
| D009069 | Movement Disorders |
| D000080874 | Synucleinopathies |
| D019636 | Neurodegenerative Diseases |