The ageing of the population is growing significantly and will challenge healthcare systems. Chronic diseases in the older population require a change in service delivery, and new technologies can be a key element in ensuring the viability and sustainability of these systems. However, the generation gap and the physical and cognitive decline commonly associated with the older generation are barriers to the transition to these models of care. Despite this, there has been a trend towards digital healthcare, which has many potential benefits for the older population. Numerous studies have assessed the acceptability of new technologies for older people in healthcare. These studies highlight the importance of perceived usefulness, compatibility, ease of use and personalisation of the technology. Personalisation is necessary to ensure that the system is useful for users, and different characteristics such as country of origin, gender, age, or comfort with the technology should be taken into account. A person-centred approach in the development of new health technology systems is essential to ensure that applications can be better tailored to the needs of different ageing populations. Many organisations have dedicated time and resources to ensure a person-centred approach in the development of new health technology systems, and putting the individual first is the best way forward in digital health. This article presents the work carried out in this regard in the framework of the European TeNDER project together with an analysis of the results obtained in terms of satisfaction, usefulness, and usability from end-users. The dynamic and continuous process carried out throughout the TeNDER project translates the needs reported by users, as far as personalisation of interactions is concerned. All end-users held a positive opinion about the usability and usefulness of the system.

The ageing of the population is growing significantly and will challenge healthcare systems. Chronic diseases in the older population require a change in service delivery, and new technologies can be a key element in ensuring the viability and sustainability of these systems. However, the generation gap and the physical and cognitive decline commonly associated with the older generation are barriers to the transition to these models of care. Despite this, there has been a trend towards digital healthcare, which has many potential benefits for the older population. Numerous studies have assessed the acceptability of new technologies for older people in health care. These studies highlight the importance of perceived usefulness, compatibility, ease of use and personalisation of the technology. Personalisation is necessary to ensure that the system is useful for users, and different characteristics such as country of origin, gender, age or comfort with the technology should be taken into account. A person-centred approach in the development of new health technology systems is essential to ensure that applications can be better tailored to the needs of different ageing populations. Many organisations have dedicated time and resources to ensure a person-centred approach in the development of new health technology systems, and putting the individual first is the best way forward in digital health. This article presents the work carried out in this regard in the framework of the European TeNDER project together with an analysis of the results obtained in terms of satisfaction, usefulness and usability by end users.

Background: Monitoring motor symptoms in patients with Parkinson disease (PD) presents significant challenges due to the complex nature of symptom progression, variations in medication responses, and the fluctuations that can occur throughout the day. Traditional neurological visits provide only a limited perspective of a patient’s overall condition, with challenges in achieving accurate and objective assessments of symptoms. To bridge this gap, extended monitoring in nonclinical settings could play a critical role in personalizing treatments and improving their efficacy. Wearable devices have emerged as potential tools for assessing PD symptom severity; however, studies integrating both in-clinic and free-living conditions, as well as multiday monitoring, remain scarce. Defining digital biomarkers that provide valuable insights into motor symptoms could enable comprehensive monitoring and tracking of PD in various contexts, facilitating more precise medication adjustments and the implementation of advanced therapeutic strategies. Objective: This study aims to collect a dataset to support the proposal and definition of digital biomarkers of PD motor symptoms using wearable devices. Data will be collected both in a supervised setting and continuously in a remote, free-living context during participants’ normal daily activities; the study will include patients with PD and healthy controls. The goal is to identify reliable digital biomarkers that can effectively distinguish patients with PD from healthy controls and classify disease severity in both supervised and unsupervised free-living environments. Methods: This paper outlines a protocol for an observational case-control study aimed at assessing motor symptoms in patients with PD using a smartwatch. The smartwatch will record accelerometer, gyroscope, and physical activity data. Participants will be instructed to perform a series of exercises guided via a smartphone. Measurements will be collected in 2 settings: a supervised clinical environment, with motor symptoms assessments conducted at the beginning and end of the study, and in an unsupervised free-living context for 1 week. In both settings, participants will be required to wear the smartwatch while performing the same set of exercises. In their daily routine, participants will be required to wear the smartwatch continuously throughout the day, removing it only at night for charging. Results: Participant recruitment and data collection started in December 2024 and will continue until spring 2025. The study aims to enroll 20 participants with PD and 20 healthy controls. Conclusions: It is anticipated that the generation of a dataset of accelerometer and gyroscope signal data recorded from patients with PD at various stages of the disease, alongside data from a control group, will enable robust comparative and impactful analyses. In addition, the study seeks to develop analytical techniques capable of tracking PD symptoms in real-life scenarios, both in everyday settings and clinical environments. Trial Registration: ClinicalTrials.gov NCT06817772; https://clinicaltrials.gov/study/NCT06817772 International Registered Report Identifier (IRRID): DERR1-10.2196/72820

Assessing motor symptoms in Parkinson’s disease (PD) is challenging due to the progressive evolution of the condition and the variability of symptoms, which are not fully captured by periodic clinical visits. In this context, wearable sensors and machine learning (ML) have emerged as a viable path toward objective and continuous monitoring, although achieving robust generalization to free-living conditions remains a challenge. This work explores the egg-beating task, a simple everyday activity, as a digital approach for PD motor assessment using smartwatch-based inertial measurements and ML techniques. Twenty-two individuals with PD and sixteen healthy controls (HC) completed a one-minute egg-beating task while wearing a smartwatch equipped with tri-axial accelerometer and gyroscope sensors. Data were recorded both under supervised clinical conditions and during unsupervised home sessions. Time- and frequency-domain features were extracted from the inertial signals, and models trained exclusively on supervised recordings were then tested on supervised, unsupervised, and combined data. PD participants showed systematically lower movement amplitude, slower oscillation frequency, and a progressive drop in signal energy over the course of the task, all of which align with the characteristic features of bradykinesia. The support vector machine achieved the best overall performance, reaching 90% accuracy in distinguishing PD from healthy controls under supervised conditions, with a reduction of less than 4% when applied to unsupervised data. These results support the egg-beating task as a practical and ecologically valid method for real-world motor assessment, with potential for future use in remote monitoring and longitudinal assessment.

BACKGROUND Monitoring motor symptoms in patients with Parkinson disease (PD) presents significant challenges due to the complex nature of symptom progression, variations in medication responses, and the fluctuations that can occur throughout the day. Traditional neurological visits provide only a limited perspective of a patient’s overall condition, with challenges in achieving accurate and objective assessments of symptoms. To bridge this gap, extended monitoring in nonclinical settings could play a critical role in personalizing treatments and improving their efficacy. Wearable devices have emerged as potential tools for assessing PD symptom severity; however, studies integrating both in-clinic and free-living conditions, as well as multiday monitoring, remain scarce. Defining digital biomarkers that provide valuable insights into motor symptoms could enable comprehensive monitoring and tracking of PD in various contexts, facilitating more precise medication adjustments and the implementation of advanced therapeutic strategies. OBJECTIVE This study aims to collect a dataset to support the proposal and definition of digital biomarkers of PD motor symptoms using wearable devices. Data will be collected both in a supervised setting and continuously in a remote, free-living context during participants’ normal daily activities; the study will include patients with PD and healthy controls. The goal is to identify reliable digital biomarkers that can effectively distinguish patients with PD from healthy controls and classify disease severity in both supervised and unsupervised free-living environments. METHODS This paper outlines a protocol for an observational case-control study aimed at assessing motor symptoms in patients with PD using a smartwatch. The smartwatch will record accelerometer, gyroscope, and physical activity data. Participants will be instructed to perform a series of exercises guided via a smartphone. Measurements will be collected in 2 settings: a supervised clinical environment, with motor symptoms assessments conducted at the beginning and end of the study, and in an unsupervised free-living context for 1 week. In both settings, participants will be required to wear the smartwatch while performing the same set of exercises. In their daily routine, participants will be required to wear the smartwatch continuously throughout the day, removing it only at night for charging. RESULTS Participant recruitment and data collection started in December 2024 and will continue until spring 2025. The study aims to enroll 20 participants with PD and 20 healthy controls. CONCLUSIONS It is anticipated that the generation of a dataset of accelerometer and gyroscope signal data recorded from patients with PD at various stages of the disease, alongside data from a control group, will enable robust comparative and impactful analyses. In addition, the study seeks to develop analytical techniques capable of tracking PD symptoms in real-life scenarios, both in everyday settings and clinical environments. CLINICALTRIAL ClinicalTrials.gov NCT06817772; https://clinicaltrials.gov/study/NCT06817772 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/72820