A decision support system for Parkinson disease management: expert models for suggesting medication change

Bohanec, Marko; Miljković, Dragana; Valmarska, Anita; Boshkoska, Biljana Mileva; Gasparoli, Elisabetta; Gentile, Giovanni; Koutsikos, Konstantinos; Marcante, Andrea; Antonini, Angelo; Gatsios, Dimitrios; Rigas, George; Fotiadis, Dimitrios I.; Tsiouris, Κostas Μ.; Konitsiotis, Spiros

doi:10.1080/12460125.2018.1469320

Cited by 18 publications

(16 citation statements)

References 7 publications

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“…For HCP interfaces, the importance of workflow integration to support data-driven consultations has been emphasized [ 44 ]. A European Union–funded project has reported on the design of a clinical decision support system for PD that takes a holistic approach, which is in line with the functionalities suggested in this study [ 45 - 47 ]. The researchers emphasize the importance of enabling shared decision making [ 46 ].…”

Section: Discussionsupporting

confidence: 64%

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How People with Parkinson's Disease and Health Care Professionals Wish to Partner in Care Using eHealth: Co-Design Study

Wannheden¹,

Revenäs²

2020

J Med Internet Res

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Background Worldwide, the number of people with Parkinson’s disease (PD) is predicted to double between the years 2005 and 2030. Chronic care management requires active collaboration and knowledge exchange between patients and health care professionals (HCPs) for best possible health outcomes, which we describe as co-care. eHealth services have the potential to support the realization of co-care between people with PD (PwP) and HCPs. Objective This study aimed to explore how co-care could be operationalized in PD care, supported by eHealth. More specifically, this study explores PwP's and HCPs' expectations and desired eHealth functionalities to achieve co-care. Methods Principles of participatory design were used to enable the identification of co-care needs and design ideas, in a series of 4 half-day co-design workshops. The sample included 7 (4 women) PwP and 9 (4 women) HCPs, including 4 neurologists, 3 nurses, and 2 physiotherapists. The co-design process resulted in a functional prototype that was evaluated by the co-design participants in the last workshop. Data were collected through note cards produced by the participants during the first 3 workshops and focus group discussions during the 3rd and 4th workshops. The data were analyzed using qualitative thematic analysis. After the workshop series, the prototype was demonstrated at a Mini Fair for ongoing PD research and evaluated using a self-developed questionnaire with 37 respondents: 31 PwP (14 women) and 6 informal caregivers (3 women). Descriptive statistics are reported. Results The qualitative analysis of data resulted in 2 main themes. The first theme, core eHealth functionalities and their expected values, describes 6 desired eHealth functionalities for supporting PD co-care between PwP and HCPs: (1) self-tracking, (2) previsit forms, (3) graphical visualization, (4) clinical decision support, (5) self-care recommendations, and (6) asynchronous communication. The second theme, individual and organizational constraints, describes constraints that need to be addressed to succeed with an eHealth service for co-care. Individual constraints include eHealth literacy and acceptance; organizational constraints include teamwork and administrative workload. The majority of the questionnaire respondents (31/37, 84%) perceived that they would benefit from an eHealth service similar to the demonstrated prototype. All prototype functionalities were rated as very important or important by the majority of respondents (ranging from 86% to 97% per functionality). Conclusions This study adds to our knowledge on how PD co-care could be operationalized. Co-care implies a shift from episodic routine-driven care to more flexible care management that is driven by the mutual needs of patients and HCPs and supported by active information exchange between them, as well as automated information processing to generate patient-specific advice. More research is needed to further explore the concept of co-care in chronic care management and what it means for self-care and health care. International Registered Report Identifier (IRRID) RR2-10.2196/11278

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Section: Discussionsupporting

confidence: 64%

“…The researchers emphasize the importance of enabling shared decision making [ 46 ]. Machine learning techniques and medical knowledge are used to generate alerts and suggest appropriate actions to patients, informal caregivers, and health care professionals [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

How People with Parkinson's Disease and Health Care Professionals Wish to Partner in Care Using eHealth: Co-Design Study

Wannheden¹,

Revenäs²

2020

J Med Internet Res

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“…The clinicians had a dedicated mobile app (see Figure 3 ) that enabled them to check the demographic and clinical information, assess the overall status of the patient, evaluate symptoms monitored during the pilot period, and get decision support functionalities [ 18 ] on patient mobility.…”

Section: Methodsmentioning

confidence: 99%

Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial

Gatsios¹,

Antonini²,

Gentile³

et al. 2020

JMIR Mhealth Uhealth

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Background Mobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely. Objective We explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities. Methods PD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson’s Disease Rating Scale estimations by clinicians were explored. Results Of the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers’ burden. Motor Aspects of Experiences of Daily Living and patients’ self-rated health status also influence the system’s use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. Conclusions We found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care. Trial Registration ISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879 International Registered Report Identifier (IRRID) RR2-10.1186/s13063-018-2767-4

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“…Being also based on COP measurement, the novel system detects FoG episodes also characterized by trembling without effective steps, for example occurring at gait initiation, overcoming the limitations observed when accelerometers are used [ 20 , 21 ]. Finally, insights and information coming from continuous and specific sensor-based monitoring of these features over time combined with patient reported outcome and symptoms’ clinical assessment, may support clinical decisions in managing PD symptoms [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease

Marcante¹,

Marco

Gentile

et al. 2020

Sensors

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Freezing of Gait (FoG) is a common symptom in Parkinson’s Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

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A decision support system for Parkinson disease management: expert models for suggesting medication change

Cited by 18 publications

References 7 publications

How People with Parkinson's Disease and Health Care Professionals Wish to Partner in Care Using eHealth: Co-Design Study

How People with Parkinson's Disease and Health Care Professionals Wish to Partner in Care Using eHealth: Co-Design Study

Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial

Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease

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