Body-Worn Sensors for Parkinson’s disease: A qualitative approach with patients and healthcare professionals

The experience and perception of wearable devices in Parkinson's disease patients: a systematic review and meta-synthesis of qualitative studies

BACKGROUND

Parkinson's disease (PD) is the fastest-growing neurodegenerative disorder, affecting over 8.5 million people worldwide, with symptoms that severely impact patients' quality of life. While treatments like levodopa and deep brain stimulation help manage symptoms, they require frequent adjustments and have limitations. Wearable devices offer real-time monitoring of motor and non-motor symptoms, enabling personalized treatment, but challenges related to comfort, usability, and patient adherence hinder their widespread adoption. Many individuals with PD experience discomfort, emotional distress, or interface difficulties, reducing long-term adherence. This study synthesizes qualitative research on patients' experiences with wearables to identify key usability barriers and improve device design for better clinical integration.

METHODS

Following the Joanna Briggs Institute methodology for qualitative systematic reviews, we searched PubMed, Web of Science, Embase, Cochrane Library, CINAHL, CNKI, WanFang, and VIP databases up to March 3, 2025. Additional gray literature and reference lists were examined manually. Included qualitative studies underwent comprehensive assessment, integration, and analysis.

RESULTS

Nine studies were included, identifying four main themes and eleven sub-themes. The four primary themes were physiological experience, psychological Responses, social Interaction, and Expectation.

CONCLUSION

This meta-synthesis reveals the dual role of wearable devices in managing Parkinson's disease, improving patient autonomy and disease control while presenting challenges in comfort, reliability, and emotional well-being. The findings emphasize the need for personalized, context-sensitive devices that adapt to fluctuating PD symptoms, address privacy concerns, and seamlessly integrate into clinical practice to improve clinical outcomes and patient adherence.

Diverse preferences, different solutions: Exploring remote monitoring preferences in Parkinson's disease through a discrete choice experiment

BackgroundRemote monitoring solutions (RMS) have the potential to improve Parkinson's disease (PD) management by enabling continuous symptom tracking and personalized care. Understanding patient preferences for RMS features is essential for successful implementation.ObjectiveThis study aimed to investigate the preferences of people with Parkinson's disease (PwP) for RMS features and identify preference heterogeneity across distinct patient subgroups.MethodsFrom November 2023 to February 2024, a discrete choice experiment (DCE) was conducted among PwP in Finland and Italy to elicit preferences for RMS attributes, including monitoring frequency, time spent filling questionnaires, home video recordings, and clinical benefits (delay in advanced symptom onset). Latent class analysis (LCA) was used to identify subgroups with distinct preference patterns, and adoption probabilities under varying RMS scenarios were estimated.ResultsA total of 411 PwP participated, revealing significant heterogeneity in RMS preferences. While clinical benefits, particularly delaying advanced symptom onset, were the most valued attribute overall, preferences diverged across subgroups. Some participants strongly preferred home video recordings, whereas others expressed aversion to this feature. A smaller subgroup exhibited reluctance toward RMS adoption, regardless of its benefits.ConclusionsPwP generally view RMS favorably, but preferences for specific features vary substantially across subgroups. Clinical benefits are a key driver of adoption, while home video recordings elicit both strong preference and aversion, highlighting the impracticality of a one-size-fits-all approach. Tailoring RMS to diverse patient needs, addressing concerns, and enhancing usability through customization are essential for successful implementation and widespread acceptance in PD management.

How can patients shape digital medicine? A rapid review of patient and public involvement and engagement in the development of digital health technologies for neurological conditions

INTRODUCTION

Patient and Public Involvement and Engagement (PPIE) involves working 'with' or 'by' patients and the public, rather than 'to,' 'about,' or 'for' them, and is integral to neurological and digital health research. This rapid review examined PPIE integration in the development and implementation of digital health technologies for neurological conditions.

METHODS

Key terms were input into six databases. Included articles were qualitative studies or PPIE activities involving patient perspectives in shaping digital health technologies for neurological conditions. Bias was evaluated using the NICE qualitative checklist, with reporting following PRISMA guidelines.

RESULTS

2,140 articles were identified, with 28 included. Of these, 25 were qualitative studies, and only three were focused PPIE activities. Patient involvement was mostly limited to one-off consultations during development.There was little evidence of PPIE during implementation, and minimal reporting on its impact.

CONCLUSIONS

PPIE has been inconsistently reported in this research area, highlighting the need for more guidance and best-practice examples This review used a UK-based definition of PPIE, which may have excluded relevant activities from other countries. Future reviews should broaden terminology to capture PPIE integration globally.

Patient perspectives on the use of digital medical devices and health data for AI-driven personalised medicine in Parkinson's Disease

Introduction

Parkinson's Disease (PD) affects around 8.5 million people currently with numbers expected to rise to 12 million by 2040. PD is characterized by fluctuating motor and non-motor symptoms demanding accurate monitoring. Recent advancements in digital medical devices (DMDs) like wearables and AI offer promise in addressing these needs. However, the successful implementation of DMDs in healthcare relies on patients' willingness to adopt and engage with these digital tools.

Methods

To understand patient perspectives in individuals with PD, a cross-sectional study was conducted as part of the EU-wide DIGIPD project across France, Spain, and Germany. Multidisciplinary teams including neurodegenerative clinics and patient organizations conducted surveys focusing on (i) sociodemographic information, (ii) use of DMDs (iii) acceptance of using health data (iv) preferences for the DMDs use. We used descriptive statistics to understand the use of DMDs and patient preferences and logistic regression models to identify predictors of willingness to use DMDs and to share health data through DMDs.

Results

In total 333 individuals with PD participated in the study. Findings revealed a high willingness to use DMDs (90.3%) and share personal health data (97.4%,) however this differed across sociodemographic groups and was more notable among older age groups (under 65 = 17.9% vs. over 75 = 39.29%, p = 0.001) and those with higher education levels less willing to accept such use of data (university level = 78.6% vs. 21.43% with secondary level, p = 0.025). Providing instruction on the use of DMDs and receiving feedback on the results of the data collection significantly increased the willingness to use DMDs (OR = 3.57, 95% CI = 1.44-8.89) and (OR = 3.77, 95% CI = 1.01-14.12), respectively.

Conclusion

The study emphasizes the importance of considering patient perspectives for the effective deployment of digital technologies, especially for older and more advanced disease-stage patients who stand to benefit the most.

Human Factors, Human-Centered Design, and Usability of Sensor-Based Digital Health Technologies: Scoping Review

BACKGROUND

Increasing adoption of sensor-based digital health technologies (sDHTs) in recent years has cast light on the many challenges in implementing these tools into clinical trials and patient care at scale across diverse patient populations; however, the methodological approaches taken toward sDHT usability evaluation have varied markedly.

OBJECTIVE

This review aims to explore the current landscape of studies reporting data related to sDHT human factors, human-centered design, and usability, to inform our concurrent work on developing an evaluation framework for sDHT usability.

METHODS

We conducted a scoping review of studies published between 2013 and 2023 and indexed in PubMed, in which data related to sDHT human factors, human-centered design, and usability were reported. Following a systematic screening process, we extracted the study design, participant sample, the sDHT or sDHTs used, the methods of data capture, and the types of usability-related data captured.

RESULTS

Our literature search returned 442 papers, of which 85 papers were found to be eligible and 83 papers were available for data extraction and not under embargo. In total, 164 sDHTs were evaluated; 141 (86%) sDHTs were wearable tools while the remaining 23 (14%) sDHTs were ambient tools. The majority of studies (55/83, 66%) reported summative evaluations of final-design sDHTs. Almost all studies (82/83, 99%) captured data from targeted end users, but only 18 (22%) out of 83 studies captured data from additional users such as care partners or clinicians. User satisfaction and ease of use were evaluated for 83% (136/164) and 91% (150/164) of sDHTs, respectively; however, learnability, efficiency, and memorability were reported for only 11 (7%), 4 (2%), and 2 (1%) out of 164 sDHTs, respectively. A total of 14 (9%) out of 164 sDHTs were evaluated according to the extent to which users were able to understand the clinical data or other information presented to them (understandability) or the actions or tasks they should complete in response (actionability). Notable gaps in reporting included the absence of a sample size rationale (reported for 21/83, 25% of all studies and 17/55, 31% of summative studies) and incomplete sociodemographic descriptive data (complete age, sex/gender, and race/ethnicity reported for 14/83, 17% of studies).

CONCLUSIONS

Based on our findings, we suggest four actionable recommendations for future studies that will help to advance the implementation of sDHTs: (1) consider an in-depth assessment of technology usability beyond user satisfaction and ease of use, (2) expand recruitment to include important user groups such as clinicians and care partners, (3) report the rationale for key study design considerations including the sample size, and (4) provide rich descriptive statistics regarding the study sample to allow a complete understanding of generalizability to other patient populations and contexts of use.

Patient Experience and Feasibility of a Remote Monitoring System in Parkinson's Disease

BACKGROUND

Remote monitoring systems have the potential to measure symptoms and treatment effects in people with Parkinson's disease (PwP) in the home environment. However, information about user experience and long-term compliance of such systems in a large group of PwP with relatively severe PD symptoms is lacking.

OBJECTIVE

The aim was to gain insight into user experience and long-term compliance of a smartwatch (to be worn 24/7) and an online dashboard to report falls and receive feedback of data.

METHODS

We analyzed the data of the "Bringing Parkinson Care Back Home" study, a 1-year observational cohort study in 200 PwP with a fall history. User experience, compliance, and reasons for noncompliance were described. Multiple Cox regression models were used to identify determinants of 1-year compliance.

RESULTS

We included 200 PwP (mean age: 69 years, 37% women), of whom 116 (58%) completed the 1-year study. The main reasons for dropping out of the study were technical problems (61 of 118 reasons). Median wear time of the smartwatch was 17.5 h/day. The online dashboard was used by 77% of participants to report falls. Smartphone possession, shorter disease duration, more severe motor symptoms, and less-severe freezing and balance problems, but not age and gender, were associated with a higher likelihood of 1-year compliance.

CONCLUSIONS

The 1-year compliance with this specific smartwatch was moderate, and the user experience was generally good, except battery life and data transfer. Future studies can build on these findings by incorporating a smartwatch that is less prone to technical issues.

Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device

OBJECTIVE

Freezing of Gait (FOG) is prevalent in people with Parkinson's disease (PD) and severely disrupts mobility. Detecting the exact boundaries of FOG episodes may facilitate new technologies in "breaking" FOG in real-time. This study investigates the performance of automatic device-based FOG detection.

METHODS

Eight machine-learning classifiers (including Neural Networks, Ensemble methods, and Support Vector Machines) were developed using (i) accelerometer and (ii) combined accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed mobility tasks designed to elicit FOG. Two clinicians independently annotated exact FOG episodes using synchronized video and a flowchart algorithm based on international guidelines. Device-detected FOG episodes were compared to annotated episodes using 10-fold cross-validation and Interclass Correlation Coefficients (ICC) for agreement.

RESULTS

Development used 50,962 windows of data and annotated activities (>10 hours). Strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity, and ICC1,1 = 0.97 for total FOG duration). Device performance varied by method, complexity, and cost matrix. The Neural Network using 67 accelerometer features achieved high sensitivity to FOG (89% sensitivity, 81% specificity, and ICC1,1 = 0.83) and stability (validation loss 5%).

CONCLUSION

The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The strong clinician agreement indicates room for improvement in future device-based FOG detection.

SIGNIFICANCE

This study may enhance PD care by reducing reliance on visual FOG inspection, demonstrating that high sensitivity in automatic FOG detection is achievable.

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.

The most used questionnaires for evaluating the usability of robots and smart wearables: A scoping review

Background

As the field of robotics and smart wearables continues to advance rapidly, the evaluation of their usability becomes paramount. Researchers may encounter difficulty in finding a suitable questionnaire for evaluating the usability of robotics and smart wearables. Therefore, the aim of this study is to identify the most commonly utilized questionnaires for assessing the usability of robots and smart wearables.

Methods

A comprehensive search of databases, including PubMed, Web of Science, and Scopus, was conducted for this scoping review. Two authors performed the selection of articles and data extraction using a 10-field data extraction form. In cases of disagreements, a third author was consulted to reach a consensus. The inclusions were English-language original research articles that utilized validated questionnaires to assess the usability of healthcare robots and smart wearables. The exclusions comprised review articles, non-English publications, studies not focused on usability, those assessing clinical outcomes, articles lacking questionnaire details, and those using non-validated or researcher-made questionnaires. Descriptive statistics methods (frequency and percentage), were employed to analyze the data.

Results

A total of 314 articles were obtained, and after eliminating irrelevant and duplicate articles, a final selection of 50 articles was included in this review. A total of 17 questionnaires were identified to evaluate the usability of robots and smart wearables, with 10 questionnaires specifically for wearables and 7 questionnaires for robots. The System Usability Scale (50%) and Post-Study System Usability Questionnaire (19.44%) were the predominant questionnaires utilized to assess the usability of smart wearables. Moreover, the most commonly used questionnaires for evaluating the usability of robots were the System Usability Scale (56.66%), User Experience Questionnaire (16.66%), and Quebec User Evaluation of Satisfaction with Assistive Technology (10%).

Conclusion

Commonly employed questionnaires serve as valuable tools in assessing the usability of robots and smart wearables, aiding in the refinement and optimization of these technologies for enhanced user experiences. By incorporating user feedback and insights, designers can strive towards creating more intuitive and effective robotic and wearable solutions.

A scoping review of the patient experience with wearable technology

Objective

This scoping review explores patients' experience with wearable technology. Its aims are to: (a) examine studies that contain empirical findings related to patients' experience with wearables; (b) compare these findings within and across studies; and (c) identify areas in need of greater understanding.

Methods

A Preferred Reporting Items for Scoping Review (PRISMA) guided approach was followed. Four databases of peer-reviewed articles (CINAHL, EMBASE, PubMed, and Web of Science) were searched for empirical articles involving patients' experience of using wearable technology. A standardized data abstraction form recorded relevant information on the articles identified. Data analysis included frequency counts for all abstracted categories; and itemized (by study) findings related to patients' wearable experience including satisfaction.

Results

Forty-six studies comprised the final review sample. The research literature examining patients' wearable experience is characterized by variety in terms of sample sizes, medical situations and wearable devices examined, research settings, and geographic location. This literature supports a positive patient experience with wearables in terms of satisfaction and usability, although the evidence is mixed when it comes to comfort. The moderate to higher satisfaction, usability, and comfort findings across studies do not suggest any sort of pattern with respect to the type of wearable, medical situation, or location.

Conclusions

The review findings suggest that health care organizations should view wearable technology as a viable complement to traditional aspects of patient care. However, from a patient experience standpoint, there is still much to know and validate in this regard, especially as the technology continues to advance.

Overview on wearable sensors for the management of Parkinson's disease

Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.

Mapping Relevance of Digital Measures to Meaningful Symptoms and Impacts in Early Parkinson's Disease

BACKGROUND

Adoption of new digital measures for clinical trials and practice has been hindered by lack of actionable qualitative data demonstrating relevance of these metrics to people with Parkinson's disease.

OBJECTIVE

This study evaluated of relevance of WATCH-PD digital measures to meaningful symptoms and impacts of early Parkinson's disease from the patient perspective.

METHODS

Participants with early Parkinson's disease (N = 40) completed surveys and 1:1 online-interviews. Interviews combined: 1) symptom mapping to delineate meaningful symptoms/impacts of disease, 2) cognitive interviewing to assess content validity of digital measures, and 3) mapping of digital measures back to personal symptoms to assess relevance from the patient perspective. Content analysis and descriptive techniques were used to analyze data.

RESULTS

Participants perceived mapping as deeply engaging, with 39/40 reporting improved ability to communicate important symptoms and relevance of measures. Most measures (9/10) were rated relevant by both cognitive interviewing (70-92.5%) and mapping (80-100%). Two measures related to actively bothersome symptoms for more than 80% of participants (Tremor, Shape rotation). Tasks were generally deemed relevant if they met three participant context criteria: 1) understanding what the task measured, 2) believing it targeted an important symptom of PD (past, present, or future), and 3) believing the task was a good test of that important symptom. Participants did not require that a task relate to active symptoms or "real" life to be relevant.

CONCLUSION

Digital measures of tremor and hand dexterity were rated most relevant in early PD. Use of mapping enabled precise quantification of qualitative data for more rigorous evaluation of new measures.

Successful implementation of technology in the management of Parkinson's disease: Barriers and facilitators

Background

Parkinson's disease (PD) is a progressive neurodegenerative disease with a fast increasing prevalence. Several pharmacological and non-pharmacological interventions are available to alleviate symptoms. Technology can be used to improve the efficiency, accessibility and feasibility of these treatments. Although many technologies are available, only few are actually implemented in daily clinical practice.

Aim

Here, we study the barriers and facilitators, as experienced by patients, caregivers and/or healthcare providers, to successful implement technology for PD management.

Methods

We performed a systematic literature search in the PubMed and Embase databases until June 2022. Two independent raters screened the titles, abstracts and full texts on: 1) people with PD; 2) using technology for disease management; 3) qualitative research methods providing patients', caregivers and/or healthcare providers' perspective, and; 4) full text available in English or Dutch. Case studies, reviews and conference abstracts were excluded.

Results

We found 5420 unique articles of which 34 were included in this study. Five categories were made: cueing (n = 3), exergaming (n = 3), remote monitoring using wearable sensors (n = 10), telerehabilitation (n = 8) and remote consultation (n = 10). The main barriers reported across categories were unfamiliarity with technology, high costs, technical issues and (motor) symptoms hampering the use of some technologies. Facilitators included good usability, experiencing beneficial effects and feeling safe whilst using the technology.

Conclusion

Although only few articles presented a qualitative evaluation of technologies, we found some important barriers and facilitators that may help to bridge the gap between the fast developing technological world and actual implementation in day-to-day living with PD.