Parkinson Disease
Conditions
Keywords
Freezing of Gait
Brief summary
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.
Interventions
OTHERWalk under 3 conditions (normal, physical tasks, verbal tasks)
Each patient will have 2 visits : * First visit in the ON state phase, i.e. when their oral treatment allows the maximum improvement of dopamine-responsive parkinsonian symptoms. * A second visit will be scheduled 15 +/- 7 days from the first. Patients will then be assessed in the OFF phase after having stopped taking their antiparkinsonian medications for at least 12 hours before the start of the visit, in order to promote episodes of FOG For each visit, the patient will be asked to walk at a comfortable speed under the following 3 conditions: * Normal condition without addition of additional physical and verbal tasks * Condition with added physical tasks: The physical task of holding a ball in the center of a tray * Condition with added verbal tasks: The verbal task of saying as many words as possible starting with a specific letter. Conditions of passage are randomized per patient. Each subject will complete the course a maximum of 18 times in blocks of 3 conditions.
Sponsors
Rennes University Hospital
Study design
Allocation
NA
Intervention model
SEQUENTIAL
Primary purpose
DIAGNOSTIC
Masking
NONE
Intervention model description
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.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Patient over 18 years old * With Parkinson's disease according to the United Kingdom Brain Bank criteria * Presenting episodes of freezing of gait assessed on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS II - question 13 with a score between 1 and 3 in order to have a patient walking without technical assistance) produced by the neurologist * Able to walk 30 meters independently * Affiliated to a social security scheme or beneficiary of such a scheme * Having signed a free and informed consent in writing
Exclusion criteria
* Montreal Cognitive Assessment (MOCA) \< 20/30 * Other neurological or orthopedic history that interferes with walking * Pregnant, parturient or breastfeeding women * Adults subject to legal protection (safeguard of justice, curatorship, guardianship), persons deprived of liberty * Persons undergoing psychiatric care, persons admitted to a health or social establishment for purposes other than research * Minors * Persons unable to express their consent * Simultaneous participation in another research related to balance and/or walking
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Precision | Through study completion, an average of 15+/-7 days | Every second of the course will be analyzed to define : * the true positive time: FOG detected by the algorithm and the two experts * True negative time: FOG detected by neither the algorithm nor the two experts * False positive time: FOG detected by the algorithm but not by the two experts * False negative time: FOG not detected by the algorithm but detected by both experts For each course completed, accuracy will then be calculated as the ratio between the sum of the time spent in true positive and true negative divided by the time taken to complete the course. 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | Through study completion, an average of 15+/-7 days | 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. |
| Time difference | Through study completion, an average of 15+/-7 days | The time difference between the start of the episode detected by the experts and that detected with the |
| Specificity | Through study completion, an average of 15+/-7 days | 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. |
| Positive predictive value (PPV) | Through study completion, an average of 15+/-7 days | 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. |
| Negative predictive value (NPV) | Through study completion, an average of 15+/-7 days | 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. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Algorithm performance according to pathway | Through study completion, an average of 15+/-7 days | 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). |
| Algorithm performance according to medical conditions (ON/OFF) | Through study completion, an average of 15+/-7 days | 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). |
| Ability to generate FOG episodes according to medical conditions (ON/OFF) | Through study completion, an average of 15+/-7 days | 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). |
| Ability to generate FOG episodes between different pathway modalities | Through study completion, an average of 15+/-7 days | 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). |
Countries
France
Outcome results
None listed