Insights into Pharmacotherapy Management for Parkinson's Disease Patients Using Wearables Activity Data.

We investigate what supervised classification models using clinical and wearables data are best suited to address two important questions about the management of Parkinson's Disease (PD) patients: 1) does a PD patient require pharmacotherapy or not, and 2) whether therapies are having an effect. Currently, patient management is suboptimal due to using subjective patient reported episodes to answer these questions. METHODOLOGY: Clinical and real environment sensor data (memory, tapping, walking) was provided by the mPower study (6805 participants). From the data, we derived relevant clinical scenarios: S1) before vs. after initiating pharmacotherapy, and S2) before vs. after taking medication. For each scenario we designed and tested 6 methods of supervised classification. Precision, Accuracy and Area Under the Curve (AUC) were computed using 10-fold cross-validation. RESULTS: The best classification models were: S1) Decision Trees on Tapping activity data (AUC 0.95, 95% CI 0.05); and S2) K-Nearest Neighbours on Gait data (mean AUC 0.70, 95% CI 0.07, 46% participants with AUC > 0.70). CONCLUSIONS: Automatic patient classification based on sensor activity data can objectively inform PD medication management, with significant potential for improving patient care

Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson’s Disease Management:Perspectives of Patients and Neurologists

Objective: Early detection of Parkinson's Disease (PD) progression remains a challenge. As remote patient monitoring solutions (RMS) and artificial intelligence (AI) technologies emerge as potential aids for PD management, there's a gap in understanding how end users view these technologies. This research explores patient and neurologist perspectives on AI-assisted RMS. Methods: Qualitative interviews and focus-groups were conducted with 27 persons with PD (PwPD) and six neurologists from Finland and Italy. The discussions covered traditional disease progression detection and the prospects of integrating AI and RMS. Sessions were recorded, transcribed, and underwent thematic analysis. Results: The study involved five individual interviews (four Italian participants and one Finnish) and six focus-groups (four Finnish and two Italian) with PwPD. Additionally, six neurologists (three from each country) were interviewed. Both cohorts voiced frustration with current monitoring methods due to their limited real-time detection capabilities. However, there was enthusiasm for AI-assisted RMS, contingent upon its value addition, user-friendliness, and preservation of the doctor-patient bond. While some PwPD had privacy and trust concerns, the anticipated advantages in symptom regulation seemed to outweigh these apprehensions. Discussion: The study reveals a willingness among PwPD and neurologists to integrate RMS and AI into PD management. Widespread adoption requires these technologies to provide tangible clinical benefits, remain user-friendly, and uphold trust within the physician-patient relationship. Conclusion: This study offers insights into the potential drivers and barriers for adopting AI-assisted RMS in PD care. Recognizing these factors is pivotal for the successful integration of these digital health tools in PD management.</p

Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson’s Disease Management:Perspectives of Patients and Neurologists

Objective: Early detection of Parkinson's Disease (PD) progression remains a challenge. As remote patient monitoring solutions (RMS) and artificial intelligence (AI) technologies emerge as potential aids for PD management, there's a gap in understanding how end users view these technologies. This research explores patient and neurologist perspectives on AI-assisted RMS. Methods: Qualitative interviews and focus-groups were conducted with 27 persons with PD (PwPD) and six neurologists from Finland and Italy. The discussions covered traditional disease progression detection and the prospects of integrating AI and RMS. Sessions were recorded, transcribed, and underwent thematic analysis. Results: The study involved five individual interviews (four Italian participants and one Finnish) and six focus-groups (four Finnish and two Italian) with PwPD. Additionally, six neurologists (three from each country) were interviewed. Both cohorts voiced frustration with current monitoring methods due to their limited real-time detection capabilities. However, there was enthusiasm for AI-assisted RMS, contingent upon its value addition, user-friendliness, and preservation of the doctor-patient bond. While some PwPD had privacy and trust concerns, the anticipated advantages in symptom regulation seemed to outweigh these apprehensions. Discussion: The study reveals a willingness among PwPD and neurologists to integrate RMS and AI into PD management. Widespread adoption requires these technologies to provide tangible clinical benefits, remain user-friendly, and uphold trust within the physician-patient relationship. Conclusion: This study offers insights into the potential drivers and barriers for adopting AI-assisted RMS in PD care. Recognizing these factors is pivotal for the successful integration of these digital health tools in PD management.</p

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP). Methods: Thirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback. Results: Adherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score > 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS. Conclusion: This study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP). Methods: Thirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback. Results: Adherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score > 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS. Conclusion: This study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP

An innovative approach for health care delivery to obese patients: from health needs identification to service integration

In Europe, more than half of the population is overweight or obese, and effort to design, validate, and implement innovative approaches is required to address social and health unmet needs of obese patients in terms of health promotion, disease prevention, and integration of services. The challenge is improving the collaboration between the different health and care stakeholders involved in the lives of obese patients, changing the socio-cultural attitude towards food intake and other behaviours leading to a negative impact on their health-related quality of life. The digital transformation of health and care can support changes in healthcare systems, healthy policy, and approaches to patient care and better implementation of the different health promotion and disease prevention strategies between all the stakeholders and support obese patients. Based on the previously experience adopted by Blueprint Partners with the Blueprint persona and user scenario in the context of models of care and prevention, health policies and analysis of risk factors affecting health and quality of life of obese subjects, the study aimed to simulate an integrated care pathway, through a multidisciplinary approach, developing and applying solutions and good clinical practices addressing the social and health unmet needs of obese patients. A pilot study assessed the quality of life (QoL), adherence to the Mediterranean diet, efficacy and interoperability of a digital health platform, Paginemediche. it. A qualitative approach has been adopted to identify and specify key digital solutions and high-impact user scenarios in Active and Healthy Ageing (AHA). To achieve a successful result, an iterative and collaborative approach has been followed to develop a user-centred perspective to the identification of solutions addressing health needs with different complexity along the entire life-course. Four initial key topic areas were chosen and used to identify different digital solutions that may meet the needs of the population segments defined by both age and the complexity of their health status. All data, derived from the industry representatives in the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA), were collected via a survey to how digital solutions best met the needs of the various population segments represented by personas. Subsequently, innovative solutions were designed based on how a user from a target group interacts with technologies, developing "personas" belonging to specific "population segments" with different conditions and needs. Then, a high-impact user scenario, based on the correlation of personas' needs, good clinical practices and digital solutions available targeting needs which playing a role in the health and care delivery for the persona, has been developed. In the end, to evaluate how digital solutions and technologies can support obese patients during their weight loss or management of their related comorbidities in current service provision, ten obese patients were enrolled to evaluate a Digital Health platform, pagininemediche.it, developed. Matilde, the Blueprint persona developed, highlighted some of the main needs (social support, development of a health-friendly environment and educational program on healthy nutrition and physical activity) that may be addressed by integrating innovative solutions in the care of obese patients. Based on her profile, a high-impact user scenario diagram correlates health and social needs with digital solutions and can help key actors in the creation of a well-integrated care approach. Moreover, the evaluation of the digital platform, paginemediche.it, demonstrated how digital solutions can motivate and support obese patients in changing habits towards a healthy lifestyle, although no further statistical significance has been identified in the quality of life assessment because of the limited number of the patients, and short period of observation. Overweight or obese patients tend to be marginalized and the subject of a real social stigma. Digital solutions may be useful to overcome psychological factors that prevent obese patients from starting their journey for a lifestyle change. The suggested approach, which considers health needs, IT skills, socioeconomic context, interoperability, and integration gaps that may influence the adoption of innovative solutions tailored to improve health outcomes is person-centred, and identify what is important for obese patients. The implementation of a persona and user scenario approach may also be useful for the early involvement of end-users in solutions' design and adaptation, increasing adherence, and the effectiveness of digital solutions. Persona profiles, the user scenario, and the related digital solution also consider the potential benefits that can derive for both patients and health system in term of reduced emergency room admissions, waiting lists, and health related expenditures

The Phenomenology, Pathophysiology and Progression of the Core Features of Lewy Body Dementia

Lewy body dementias – Dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) - are disabling neurodegenerative conditions defined pathologically by the presence of intraneuronal α-synuclein rich aggregates (‘Lewy bodies’ and ‘Lewy neurites’). These disorders are characterized by a set of ‘core’ clinical features, namely cognitive fluctuations, visual hallucinations, motor parkinsonism, and most recently added, REM sleep behaviour disorder. These features are central to the diagnosis of Lewy bodies dementias (especially DLB) and discriminate them from other neurodegenerative disorders. Despite decades of research, the etiopathogenesis underlying Lewy body disorders is poorly understood. This accounts for the relative lack of objective biomarkers and both symptomatic and disease modifying therapies. The present thesis comprises a series of investigations that seeks to understand the phenomenology, pathophysiology, and clinical progression of Lewy body dementias through focus on each of the core clinical features. Systematic review and empiric studies are organized under the respective headings of cognitive fluctuations, visual hallucinations, REM sleep behaviour disorder, motor features, interrelationships, and clinical progression of the core features. Novel clinical and pathophysiological insights are obtained which have implications for the prediction and diagnosis of core features, the development of new objective biomarkers, and clinical endpoints of disease progression. From these studies, a shared pathophysiological basis for the core features is postulated and potential avenues for future directions are highlighted, focusing on replication and validation of new biomarkers and clinical measures, discovery of new biomarkers and mechanisms, and translation to prodromal and patient cohorts