TY - JOUR
T1 - Monitoring medication optimization in patients with Parkinson’s disease
AU - Moradi, Hamid
AU - Hannink, Julius
AU - Stallforth, Sabine
AU - Gladow, Till
AU - Ringbauer, Stefan
AU - Mayr, Martin
AU - Winkler, Jürgen
AU - Klucken, Jochen
AU - Eskofier, Bjoern M
N1 - *This work was supported by the Bavarian Ministry for Economy, Regional Development and Energy via the project DiGaitAppPD - Digital gait analysis as a health application for therapy monitoring in Parkinson’s patients (grand No. LSM-1910-0011/0012/0013/0014)
PY - 2023/7
Y1 - 2023/7
N2 - Medication optimization is a common component of the treatment strategy in patients with Parkinson’s disease. As the disease progresses, it is essential to compensate for the movement deterioration in patients. Conventionally, examining motor deterioration and prescribing medication requires the patient’s onsite presence in hospitals or practices. Home-monitoring technologies can remotely deliver essential information to physicians and help them devise a treatment decision according to the patient’s need. Additionally, they help to observe the patient’s response to these changes. In this regard, we conducted a longitudinal study to collect gait data of patients with Parkinson’s disease while they received medication changes. Using logistic regression classifier, we could detect the annotated motor deterioration during medication optimization with an accuracy of 92%. Moreover, an in-depth examination of the best features illustrated a decline in gait speed and swing phase duration in the deterioration phases due to suboptimal medication.
AB - Medication optimization is a common component of the treatment strategy in patients with Parkinson’s disease. As the disease progresses, it is essential to compensate for the movement deterioration in patients. Conventionally, examining motor deterioration and prescribing medication requires the patient’s onsite presence in hospitals or practices. Home-monitoring technologies can remotely deliver essential information to physicians and help them devise a treatment decision according to the patient’s need. Additionally, they help to observe the patient’s response to these changes. In this regard, we conducted a longitudinal study to collect gait data of patients with Parkinson’s disease while they received medication changes. Using logistic regression classifier, we could detect the annotated motor deterioration during medication optimization with an accuracy of 92%. Moreover, an in-depth examination of the best features illustrated a decline in gait speed and swing phase duration in the deterioration phases due to suboptimal medication.
M3 - Conference article
SN - 2509-2715
SP - 69
EP - 75
JO - GeroScience
JF - GeroScience
T2 - <br/>45th Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2023
Y2 - 24 July 2023 through 27 July 2023
ER -