Parkinson Disease
Conditions
Brief summary
The first goal of the study is to investigate whether an algorithm can reliably detect Freezing of Gait (FOG) in Parkinson patients based on participant gait data generated by a pressure insole. The second goal is to investigate whether Auditive Cueing (AC) based on such a detection reduces the frequency and length of FOG episodes in those participants. The study will be conducted per Good Clinical Practice principles.
Interventions
DEVICEAuditive cueing
Based on gait measurement, auditive cueing is generated automatically to check its impact on patients with Freezing Of Gait.
Sponsors
Ziekenhuis Oost Limburg (ZOL) Hospital Genk, Belgium
Ceriter Nederland BV
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SUPPORTIVE_CARE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No
Inclusion criteria
* must have more than 1 FOG episode/day * must be patient in Ziekenhuis Oost Limburg (ZOL)
Exclusion criteria
* not able to speak Dutch * cannot give informed consent (mental health)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The F1-score of FOG detection from the measured and classified (normal walk vs FOG) gait data compared to manually scored recorded video. | 8 walks are performed in 1 hospital visit within 4 weeks of enrollment. Total assessment time estimate is 2x 30 minutes. | The participants perform 4 walks without AC on standardized tracks while being video-recorded. During the walks, gait data is recorded and then scored (for each time point) by the algorithm into normal walk or FOG. The video recording is scored manually according to the criteria as described in reference Gilat. M. and represents the true state of walking. The F1-score is calculated from the algorithm scoring vs the manual scoring: a True Positive is true freezing which is classified by the algorithm as freezing. A True Negative is a true normal walk which is classified as a normal walk. Similarly, a False Positive is a true normal walk which is classified as FOG and a False Negative is a true FOG which is classified as a normal walk. The same 4 walks are performed both in OFF and in ON. OFF measurement is only performed when the PI has given permission to do so. ON measurement is performed 1 hour after taking their standard medication. |
| The change in the number of FOG-episodes with and without AC | 1 week after the first hospital visit (for Outcome 1). | The participants again perform 4 walks in OFF and in ON but now with AC. The average number of freezing episodes is calculated for each participant in OFF and in ON with and without AC. The change between the average without AC and the average with AC is calculated as well as its level of significance. These changes are computed for each walk, for each individual participant across all walks and across all participants. |
| The change in the total duration of FOG-episodes with and without AC | within 1 to 3 weeks after the first hospital visit | The participants again perform 4 walks in OFF and in ON but now with AC. The average duration of freezing episodes is calculated in the same way as their number per Outcome 2. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The sensitivity of FOG detection from the measured and classified (normal walk vs FOG) gait data compared to manually scored recorded video. | Within 4 weeks of enrollment | Same data collection as for outcome 1. Sensitivity is calculated from the algorithm scoring vs the manual scoring. |
| The specificity of FOG detection from the measured and classified (normal walk vs FOG) gait data compared to manually scored recorded video. | within 1 to 3 weeks after the first hospital visit | Same data collection as for outcome 1. Specificity is calculated from the algorithm scoring vs the manual scoring. |
Countries
Belgium
Outcome results
None listed