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

Advancements in Wearable Digital Health Technology: A Review of Epilepsy Management

This review explores recent advancements in wearable digital health technology specifically designed to manage epilepsy. Epilepsy presents unique challenges in monitoring and management due to the unpredictable nature of seizures. Wearable devices offer continuous monitoring and real-time data collection, providing insights into seizure patterns and trends. Wearable technology is important in epilepsy management because it enables early detection, prediction, and personalized intervention, empowering patients and healthcare providers. Key findings highlight the potential of wearable devices to improve seizure detection accuracy, enhance patient empowerment through real-time monitoring, and facilitate data-driven decision-making in clinical practice. However, further research is needed to validate the accuracy and reliability of these devices across diverse patient populations and clinical settings. Collaborative efforts between researchers, clinicians, technology developers, and patients are essential to drive innovation and advancements in wearable digital health technology for epilepsy management, ultimately improving outcomes and quality of life for individuals with this neurological condition.

Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease

BACKGROUND

Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression.

OBJECTIVES

The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale.

METHODS

Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time.

RESULTS

Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]).

CONCLUSIONS

Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The Importance of Digital Health Literacy in an Evolving Parkinson's Disease Care System

Digital health technologies are growing at a rapid pace and changing the healthcare landscape. Our current understanding of digital health literacy in Parkinson's disease (PD) is limited. In this review, we discuss the potential challenges of low digital health literacy in PD with particular attention to telehealth, deep brain stimulation, wearable sensors, and smartphone applications. We also highlight inequities in access to digital health technologies. Future research is needed to better understand digital health literacy among individuals with PD and to develop effective solutions. We must invest resources to evaluate, understand, and enhance digital health literacy for individuals with PD.

Barriers and facilitators for healthcare providers to implement family-centered care in Parkinson's disease: a scoping review

Aims

This study aimed to identify and synthesize the barriers and facilitators to the implementation of family-centered care in Parkinson's disease (PD) and to provide a reference for evidence-based Parkinson's disease nursing practice.

Methods

This scoping review follows the methodology framework proposed by Arksey and O'Malley. Four databases including PubMed, Web of Science, Embase, and Cochrane Library were searched. Barriers and facilitators were summarized based on the ecological family-centered model.

Results

Through a comprehensive literature search, 35 studies were found for this scoping review. Barriers and facilitators to implementing family-centered care in PD included physiological factors, environmental factors, culturally based conflicts, living arrangements, education or skills training, group experiences, and individual and family consultations.

Conclusion

Implementing family-centered care in Parkinson's disease is essential to providing comprehensive care that improves outcomes for both PD patients and their family members.

A smartphone-based tapping task as a marker of medication response in Parkinson's disease: a proof of concept study

Tapping tasks have the potential to distinguish between ON-OFF fluctuations in Parkinson's disease (PD) possibly aiding assessment of medication status in e-diaries and research. This proof of concept study aims to assess the feasibility and accuracy of a smartphone-based tapping task (developed as part of the cloudUPDRS-project) to discriminate between ON-OFF used in the home setting without supervision. 32 PD patients performed the task before their first medication intake, followed by two test sessions after 1 and 3 h. Testing was repeated for 7 days. Index finger tapping between two targets was performed as fast as possible with each hand. Self-reported ON-OFF status was also indicated. Reminders were sent for testing and medication intake. We studied task compliance, objective performance (frequency and inter-tap distance), classification accuracy and repeatability of tapping. Average compliance was 97.0% (± 3.3%), but 16 patients (50%) needed remote assistance. Self-reported ON-OFF scores and objective tapping were worse pre versus post medication intake (p < 0.0005). Repeated tests showed good to excellent test-retest reliability in ON (0.707 ≤ ICC ≤ 0.975). Although 7 days learning effects were apparent, ON-OFF differences remained. Discriminative accuracy for ON-OFF was particularly good for right-hand tapping (0.72 ≤ AUC ≤ 0.80). Medication dose was associated with ON-OFF tapping changes. Unsupervised tapping tests performed on a smartphone have the potential to classify ON-OFF fluctuations in the home setting, despite some learning and time effects. Replication of these results are needed in a wider sample of patients.