scholarly journals Improving clinical data collection in the patients’ home in Parkinson’s disease: the SleepFit app (Preprint)

2019 ◽  
Author(s):  
Alessandro Mascheroni ◽  
Eun Kyoung Choe ◽  
Yuhan Luo ◽  
Michele Marazza ◽  
Clara Ferlito ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Collection ◽  
Clinical Data ◽  
New Technologies ◽  
Sleep Diary ◽  
Time Data ◽  
User Friendliness ◽  
Subjective Data ◽  
Home Based

BACKGROUND Home-based systems for ecological momentary assessment of clinically-relevant information in Parkinson’s Disease (PD) are helpful tools to improve patients’ care. Nevertheless, new technologies are not always easy-to-use for these patients. OBJECTIVE We developed a tablet-based application, SleepFit, specifically designed for patients with PD, to collect objective and subjective data at their home. SleepFit is presented with the improvements made from the prototype to the latest v2.0 version, aimed to enhance user-friendliness and the quality of the collected data. METHODS The core structure of SleepFit consists of: a) an electronic finger-tapping test; b) motor, sleepiness, and emotional subjective scales; c) a sleep diary. SleepFit v2.0 features enhanced ergonomics and graphics; automated flows that guide the patients in performing tasks throughout the 24 hours; secured real-time data collection and consultation; the possibility to easily integrate new tasks and features. Fifty-six patients with PD were asked to perform multiple home-assessments four times a day for two weeks. Patients’ compliance to SleepFit was calculated as the proportion of completed tasks out of the total number of expected tasks; satisfaction was evaluated as a potential willingness to use SleepFit again after the end of the study. RESULTS Fifty-two patients were included in the analyses. Overall compliance (all versions) was 88.9%. SleepFit was progressively enhanced and compliance increased from 87.9% to 89.9%. Among the patients who used the final version, 96.2% declared they would use SleepFit again. CONCLUSIONS SleepFit is an easy-to-use tablet-application to prospectively collect objective and subjective clinical data and to increase compliance in home-based studies in PD

scholarly journals Present and Future of Parkinson’s Disease in Spain: PARKINSON-2030 Delphi Project

2021 ◽  
Vol 11 (8) ◽  
pp. 1027
Author(s):  
Diego Santos García ◽  
Marta Blázquez-Estrada ◽  
Matilde Calopa ◽  
Francisco Escamilla-Sevilla ◽  
Eric Freire ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
New Technologies ◽  
Management Strategies ◽  
Multidisciplinary Teams ◽  
Modified Delphi ◽  
Advanced Stages ◽  
Diagnostic Capacity ◽  
Socio Economic Impact

Parkinson’s disease (PD) is a chronic progressive and irreversible disease and the second most common neurodegenerative disease worldwide. In Spain, it affects around 120.000–150.000 individuals, and its prevalence is estimated to increase in the future. PD has a great impact on patients’ and caregivers’ lives and also entails a substantial socioeconomic burden. The aim of the present study was to examine the current situation and the 10-year PD forecast for Spain in order to optimize and design future management strategies. This study was performed using the modified Delphi method to try to obtain a consensus among a panel of movement disorders experts. According to the panel, future PD management will improve diagnostic capacity and follow-up, it will include multidisciplinary teams, and innovative treatments will be developed. The expansion of new technologies and studies on biomarkers will have an impact on future PD management, leading to more accurate diagnoses, prognoses, and individualized therapies. However, the socio-economic impact of the disease will continue to be significant by 2030, especially for patients in advanced stages. This study highlighted the unmet needs in diagnosis and treatment and how crucial it is to establish recommendations for future diagnostic and therapeutic management of PD.

Home-Based Vigorous Tele-Exercise in People with Parkinson’s Disease: Feasibility Beyond Complexity

2021 ◽  
pp. 1-2
Author(s):  
Miguel Ángel Rodríguez ◽  
Irene Crespo ◽  
Miguel del Valle ◽  
Hugo Olmedillas
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Home Based

scholarly journals Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3553
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh A. Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Time Series ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Treatment Planning ◽  
Time Series Data ◽  
Classification Model ◽  
Series Data ◽  
Demographic Information ◽  
Subjective Data

Parkinson’s disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician’s initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson’s medication changes—clinically assessed by the MDS-Unified Parkinson’s Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

scholarly journals Predicting Parkinson’s Disease Medication Regimen Using Sensor Technology

2021 ◽  
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Classification Model ◽  
Sensor Data ◽  
Sensor Technology ◽  
Medication Regimen ◽  
Levodopa Dose ◽  
Subjective Data ◽  
Random Forest Classification

Abstract Parkinson’s disease (PD) medication treatment planning is generally based on subjective data through in-office, physicianpatient interactions. The Personal KinetiGraphTM (PKG) has shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to subtype patients based on levodopa regimens and response. We apply k-means clustering to a dataset of with-in-subject Parkinson’s medication changes—clinically assessed by the PKG and Hoehn & Yahr (H&Y) staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective PKG data and demographic information. Clinically relevant clusters were developed based on longitudinal dopaminergic regimens—partitioned by levodopa dose, administration frequency, and total levodopa equivalent daily dose—with the PKG increasing cluster granularity compared to the H&Y staging. A random forest classifier was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 87:9 ±1:3

scholarly journals The PIT: SToPP Trial—A Feasibility Randomised Controlled Trial of Home-Based Physiotherapy for People with Parkinson's Disease Using Video-Based Measures to Preserve Assessor Blinding

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Emma Stack ◽  
Helen Roberts ◽  
Ann Ashburn
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Outcome Measurement ◽  
Controlled Trial ◽  
Video Data ◽  
Video Production ◽  
Control Group ◽  
Group Participant ◽  
Home Based ◽  
Randomised Controlled

Purpose. To trial four-week's physiotherapy targeting chair transfers for people with Parkinson's disease (PwPD) and explore the feasibility of reliance on remote outcome measurement to preserve blinding.Scope. We recruited 47 PwPD and randomised 24 to a focused home physiotherapy programme (exercise, movement strategies, and cueing) and 23 to a control group. We evaluated transfers (plus mobility, balance, posture, and quality of life) before and after treatment and at followup (weeks 0, 4, 8, and 12) from video produced by, and questionnaires distributed by, treating physiotherapists. Participants fed back via end-of-study questionnaires. Thirty-five participants (74%) completed the trial. Excluding dropouts, 20% of questionnaire data and 9% of video data were missing or unusable; we had to evaluate balancein situ. We noted trends to improvement in transfers, mobility, and balance in the physiotherapy group not noted in the control group. Participant feedback was largely positive and assessor blinding was maintained in every case.Conclusions. Intense, focused physiotherapy at home appears acceptable and likely to bring positive change in those who can participate. Remote outcome measurement was successful; questionnaire followup and further training in video production would reduce missing data. We advocate a fully powered trial, designed to minimise dropouts and preserve assessor blinding, to evaluate this intervention.

Home-based dance exercise for adults with moderate Parkinson’s disease: Protocol with a pilot study for a non-randomized clinical trial using video-dances in a DVD format (Preprint)

2022 ◽  
Author(s):  
Manoela de Paula Ferreira ◽  
Adriano Zanardi da Silva ◽  
Bruna Yamaguchi ◽  
Sunita Mathur ◽  
Taina Ribas Melo ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Parkinson’S Disease ◽  
Clinical Trial ◽  
Parkinson's Disease ◽  
Cognitive Function ◽  
Pilot Study ◽  
Functional Mobility ◽  
Contemporary Dance ◽  
Home Based

BACKGROUND Many people with Parkinson’s disease (PD) have never received rehabilitation care due to lack of accessibility and transportation and high therapy costs for in-person rehabilitation. Home-based dance exercise is an innovative, low-cost therapy that may reduce accessibility barriers to exercise. Especially since the COVID-19 pandemic, home-exercise programs are a highly relevant, alternative approach for people with PD OBJECTIVE This clinical trial protocol aims to explore the effects of a Home-Based contemporary dance exercise program for people with moderate Parkinson’s Disease (PD), focusing on balance, functional mobility, quality of life (QOL), cognitive function, and depression. METHODS This protocol is for a non-randomized clinical trial for adults with moderate PD divided into control group (CG) and Experimental Group (EG). Participants from the EG will perform video-dances of the contemporary dance, delivered in a DVD format. The video-dances will be executed 16 weeks, three times per week, 30 minutes each day at home, with exercise intensity controlled by the BORG scale. Participants from the CG will not receive any new exercise therapy. As primary outcomes, the signs and symptoms of the PD assessed by the Unified Parkinson’s Disease Rating Scale – UPDRS II and III, Hoehn and Yahr for the PD severity, and health-related quality of life (HRQL), measured by the Parkinson’s Disease Questionnaire – PDQ-39) will be tested. Secondary outcomes include cognitive function by the Montreal Cognitive Assessment – MoCA, balance by the Mini-BESTest, functional mobility by the Timed “Up and Go” test – TUG and depression by the Geriatric Depression Scale – GDS. All outcomes will be assessed in an in-person evaluation by a blinded assessor before and after the 16 weeks of the program. RESULTS This protocol has a pilot study that included 10 participants (5 in each group). It was observed positive results favoring the EG over cognitive function (p = 0.034). In addition, HRQL, balance, and depression were improved after the pilot program in the EG, however, without significant difference. CONCLUSIONS This clinical trial has the potential to be a safe alternative exercise approach under COVID restrictions and travel-free therapy with effects on PD symptoms. CLINICALTRIAL RBR-58T68W (Brazilian Clinical Trials Registry)

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