Luc Evers

Scopus Publications

Interpretable and granular video-based quantification of motor characteristics from the finger-tapping test in Parkinson’s disease
Tahereh Zarrat Ehsan, Michael Tangermann, Yağmur Güçlütürk, SooYoon Shin, King Chung Ho, et al.
Npj Parkinson S Disease, 2026
Accurately quantifying motor characteristics in Parkinson’s disease is crucial for monitoring disease progression and optimizing treatment strategies. The finger-tapping test is a standard motor assessment. Clinicians visually evaluate a patient’s tapping performance and assign an overall severity score based on tapping amplitude, speed, and irregularity. Simultaneous video recording during the standard test enables a more objective, continuous quantification of detailed motor characteristics, thereby reducing the subjectivity and inter-rater variability inherent in clinical evaluations. This paper introduces a computer vision-based method for quantifying granular PD motor characteristics from video recordings. Four sets of clinically relevant features are proposed to characterize hypokinesia , bradykinesia , sequence effect , and hesitation-halts . We evaluate our approach on video recordings and clinical evaluations of 446 people with PD from the Personalized Parkinson Project. Using principal component analysis with varimax rotation, we show that the extracted features largely align with the four clinically defined motor deficits, while additionally revealing finer-grained substructures within the sequence effect and hesitation-halts domains. In addition, we have used these features to train machine learning classifiers to estimate the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) finger-tapping severity score. Compared to state-of-the-art approaches, our method achieves a higher accuracy in MDS-UPDRS score prediction, while still providing an interpretable quantification of individual finger-tapping motor characteristics. In addition, we present the first large-scale dataset of finger-tapping, comprising 4073 video recordings. In summary, the proposed framework provides a practical solution for the objective assessment of PD motor characteristics, that can potentially be applied in both clinical and remote settings. Future work is needed to assess its responsiveness to symptomatic treatment and disease progression.
Daily-Life, Sensor-Derived Tremor Measures Are Sensitive to Progression in Early Parkinson's Disease
Nienke A. Timmermans, Ioan Gabriel Bucur, Diogo C. Soriano, Erik Post, Hayriye Cagnan, et al.
Annals of Neurology, 2026
Objective Sensitive outcome measures are critical for evaluating the efficacy of novel treatments for Parkinson's disease (PD). In this study, we assess the sensitivity to change of sensor‐derived daily‐life tremor measures over 2 years in unmedicated and medicated persons with early PD. Methods We used 2‐year continuous wrist sensor data (median wear time: 22 hours/day) from the Personalized Parkinson Project (n = 462 medicated; n = 78 unmedicated at baseline), in combination with annual clinical evaluations of tremor severity. From the gyroscope data, we derived previously validated weekly measures for tremor time and power, which were smoothed over time using piecewise linear trend estimation. One‐ and 2‐year standardized response means (SRMs) were computed to compare the sensitivity to change between the sensor‐derived tremor measures and clinical tremor scores. Results In unmedicated participants with tremor, sensor‐derived tremor measures demonstrated a high sensitivity to progression (2‐year SRMs ranged from 0.67 to 1.09), which was significantly larger than clinical tremor scores (2‐year SRMs ranged from 0.21 to 0.41). In medicated participants, sensor‐derived tremor time decreased (2‐year SRM of −0.18), which was associated with both an increase in dopaminergic medication dose and higher disease duration. In contrast, the sensor‐derived tremor power measures and clinical rest tremor scores (measured in the OFF state) increased slightly (2‐year SRMs ranging from 0.11 to 0.27). Interpretation Before initiation of symptomatic treatment, sensor‐derived daily‐life tremor measures are substantially more sensitive to progression than clinical tremor scores, making them a promising tool to evaluate the efficacy of disease‐modifying treatments in early PD. ANN NEUROL 2026
Quantifying arm swing in Parkinson’s disease: a method accounting for arm activities during free-living gait
Erik Post, Twan van Laarhoven, Yordan P. Raykov, Max A. Little, Jorik Nonnekes, et al.
Journal of Neuroengineering and Rehabilitation, 2025
Background Accurately measuring hypokinetic arm swing during free-living gait in Parkinson’s disease (PD) is challenging due to other concurrent arm activities. We developed a method to isolate gait segments without these arm activities. Methods Wrist accelerometer and gyroscope data were collected from 25 individuals with PD and 25 age-matched controls while performing unscripted activities in their home environment. This was done after overnight withdrawal of dopaminergic medication (‘pre-medication’) and approximately one hour after intake (‘post-medication’). Using video annotations as ground truth, we trained and evaluated two classifiers: one for detecting gait and one for detecting gait segments without other arm activities. Based on the filtered gait segments, arm swing was quantified using the median and 95th percentile range of motion (RoM). These arm swing parameters were evaluated in three ways: (1) the agreement between predicted and video-annotated gait segments without other arm activities, (2) the sensitivity to differences between PD and controls, and (3) the sensitivity to the effects of dopaminergic medication. Results On the most affected side, the mean (SD) balanced accuracy for detecting gait without other arm activities was 0.84 (0.10) pre-medication and 0.88 (0.09) post-medication. The agreement between arm swing parameters of predicted and video-annotated gait segments without other arm activities was high irrespective of medication state (intra-class correlation coefficients: median RoM: 0.99; 95th percentile RoM: 0.97). Both the median and 95th percentile RoM were smaller in PD pre-medication compared to controls (median: $$\\Delta = -18.80^{\\circ }$$ Δ = - 18 . 80 ∘ , 95% CI [ $$-$$ - 30.63, $$-$$ - 10.60], p < 0.001; 95th percentile: $$\\Delta = -28.34^{\\circ }$$ Δ = - 28 . 34 ∘ , 95% CI [ $$-$$ - 38.26, $$-$$ - 18.18], p < 0.001), and smaller in pre- compared to post-medication (median: $$\\Delta = -12.31 ^{\\circ }$$ Δ = - 12 . 31 ∘ , 95% CI [ $$-$$ - 21.35, $$-$$ - 5.59], p < 0.001; 95th percentile: $$\\Delta = -19.04 ^{\\circ }$$ Δ = - 19 . 04 ∘ , 95% CI [ $$-$$ - 28.48, $$-$$ - 11.14], p < 0.001). The differences in RoM between pre- and post-medication were larger after filtering gait for the median (p < 0.01) and 95th percentile RoM (p = 0.01). Conclusions Filtering out gait segments with other concurrent arm activities is feasible and increases the change in arm swing parameters following dopaminergic medication in free-living conditions. This approach may be used to monitor treatment effect and disease progression in daily life.
A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s disease
Nienke A. Timmermans, Roberta Terranova, Diogo C. Soriano, Hayriye Cagnan, Yordan P. Raykov, et al.
Npj Parkinson S Disease, 2025
Wearable sensors can objectively and continuously monitor daily-life tremor in Parkinson’s Disease (PD). We developed an open-source algorithm for real-life monitoring of PD tremor which achieves generalizable performance across different wrist-worn devices. We achieved this using a unique combination of two independent, complementary datasets. The first was a small, but extensively video-labeled gyroscope dataset collected during unscripted activities at home (n = 24 PD; n = 24 controls). We used this to train and validate a logistic regression tremor detector based on cepstral coefficients. The second was a large, unsupervised dataset (n = 517 PD; n = 50 controls, data collected for 2 weeks with a different device), used to externally validate the algorithm. Results show that our algorithm can reliably quantify real-life PD tremor (sensitivity of 0.61 (0.20) and specificity of 0.97 (0.05)). Weekly aggregated tremor time and power showed excellent test-retest reliability and moderate correlation to MDS-UPDRS rest tremor scores. This opens possibilities to support clinical trials and individual tremor management with wearable technology.
Cardiorespiratory Response to Exercise in Parkinson's Disease: Associations with Autonomic Dysfunction and Physical Activity
Kars I. Veldkamp, Sabine Schootemeijer, Hilde Joosten, Bastiaan R. Bloem, Luc J.W. Evers, et al.
Movement Disorders Clinical Practice, 2025
BackgroundBoth autonomic dysfunction and low levels of physical activity could contribute to reduced cardiorespiratory fitness in people with Parkinson's disease (PD). However, the interrelationship between these concepts is not well understood.ObjectivesOur aim was to gain more insight into autonomic dysfunction and physical activity in relation to cardiorespiratory fitness in PD.MethodsWe included 59 individuals with PD (37% women, mean age 65.1 years; mean disease duration 4.5 years; Hoehn and Yahr stage 1–3). All participants completed a cardiopulmonary exercise test (CPET). We examined the association between parameters assessed during a CPET (peak oxygen consumption [VO2peak], maximum heart rate [HRmax], heart rate recovery at 1 and 3 mins [HRrec1, HRrec3]), autonomic dysfunction (Scales for Outcomes in Parkinson's disease‐Autonomic dysfunction, SCOPA‐AUT), and physical activity in daily life (step counts, measured using a smartphone for a period of 4 weeks). Using multivariable regression, we adjusted for age, sex, and beta blocker use.ResultsHigher SCOPA‐AUT was associated with lower HRmax (β = −0.72, 95% CI = [−1.40, −0.04], P = 0.040). Higher step counts were associated with higher HRrec3 (β = 3.21, 95% CI = [0.56, 5.86], P = 0.019), and with higher VO2peak (β = 1.16, 95% CI = [0.10, 2.22], P = 0.032). No statistically significant associations were found between both SCOPA‐AUT and step counts with HRrec1.ConclusionsBoth autonomic dysfunction and physical activity are associated with a reduced cardiorespiratory response to exercise in PD, but affected different CPET parameters. Future studies should determine the responsiveness of these CPET parameters to change, in order to implement such parameters as endpoints in clinical trials.
Heart Rate Profiles During Exercise and Incident Parkinson's Disease
Stefan van Duijvenboden, Julia Ramírez, Job Scheurink, Sirwan K. L. Darweesh, Michele Orini, et al.
Annals of Neurology, 2025
ObjectiveTo determine whether established heart rate parameters of exercise, related to cardiac autonomic function, are associated with incident Parkinson's disease, independent of both clinical and autonomic prodromal features.MethodsA study of UK Biobank participants who performed a standardized bicycle exercise test (2009–2013), followed until November 2022, and analyzed in January 2024, was carried out. Heart rate increase from rest to exercise, and heart rate decrease from peak exercise to recovery were associated with incident Parkinson's disease. Multivariable adjustment was performed both for clinical characteristics and for prodromal non‐cardiac autonomic features.ResultsA total of 69,288 eligible participants (men 48%, mean age 56.8 years [SD 8.2 years]) were followed for 12.5 years: among the 319 (0.5%) who developed Parkinson's disease, recognized prodromal markers (constipation, bladder dysfunction) were more common at baseline. The median lag time to diagnosis was 9.3 years (interquartile range 4.4). Both heart rate increase (37.5 [SD 11.5] vs 40.8 [SD 12.4] b.p.m., p < 0.001) and recovery (23.4 [SD 8.8] vs. 27.8 [SD 10.3] b.p.m., p < 0.001) were significantly lower in incident cases compared with controls. Heart rate recovery was independently associated with incident Parkinson's disease, whereas heart rate increase was not. Specifically, a blunted heart rate lowering during recovery was associated with a 30% higher risk of incident Parkinson's disease (HR 1.3; 95% CI 1.1–1.4; p < 0.001 per 10 beats less recovery).InterpretationCollectively, this suggests that cardiac autonomic involvement precedes clinically manifest Parkinson's disease, and that heart rate recovery might serve as a quantitative prodromal marker. ANN NEUROL 2025
Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach
Luc J. W. Evers, Yordan P. Raykov, Tom M. Heskes, Jesse H. Krijthe, Bastiaan R. Bloem, et al.
Sensors, 2025
Objective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of tremor in the context of limited amounts of labeled training data, we propose to use prototypical networks, which can embed domain expertise about the heterogeneous tremor and non-tremor sub-classes. We evaluated our approach using data from the Parkinson@Home Validation study, including 8 PD patients with tremor, 16 PD patients without tremor, and 24 age-matched controls. We used wrist accelerometer data and synchronous expert video annotations for the presence of tremor, captured during unscripted daily life activities in and around the participants’ own homes. Based on leave-one-subject-out cross-validation, we demonstrate the ability of prototypical networks to capture free-living tremor episodes. Specifically, we demonstrate that prototypical networks can be used to enforce robust performance across domain-informed sub-classes, including different tremor phenotypes and daily life activities.
Conversational Agents Supporting Self-Management in People With a Chronic Disease: Systematic Review
Tessa F Peerbolte, Rozanne JA van Diggelen, Pieter van den Haak, Kim Geurts, Luc JW Evers, et al.
Journal of Medical Internet Research, 2025
Background Conversational agents (CAs) are increasingly used as a promising tool for scalable, accessible, and personalized self-management support of people with a chronic disease. Studies of CAs for self-management of chronic disease operate within a multidisciplinary domain: self-management originates from (behavioral) psychology and CAs stem from intervention technology, while diseases are typically studied within the biomedical context. To ensure their effectiveness, structured evaluations and descriptions of the interventions, integrating biomedical, behavioral, and technological perspectives, are essential. Objective We aimed to examine the design and evaluation of CAs for self-management support of chronic diseases, focusing on their characteristics, integration of behavioral change techniques, and evaluation methods. The findings will guide future research and inform intervention design. Methods We conducted a systematic search in the PubMed and Embase databases to identify studies that investigated CAs for chronic disease self-management, published from January 1, 2018, to April 15, 2024. Full-text journal articles, published in English, studying the efficacy or effectiveness of a CA in the context of self-management for chronic diseases in adults were included. Data extraction was guided by conceptual frameworks to ensure comprehensive reporting of intervention and methodologies: the behavioral intervention technology model and the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist. Risk of bias was assessed using the Risk of Bias 2 tool and the Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) tool (version 2). Results In total, 25 studies were included, primarily focusing on text-based, rule-based CAs delivered via a mobile apps. The chronic diseases predominantly targeted were diabetes and cancer. Commonly identified clusters of behavior change techniques were “shaping knowledge,” “feedback and monitoring,” “natural consequences,” and “associations.” However, reporting of behavior change techniques and their delivery was lacking, and intervention descriptions were limited. Studies were mostly in the early phase, with a great variety in intervention descriptions, study methods, and outcome measures. Conclusions Advancing the field of CA-based interventions requires transparent intervention descriptions, rigorous methodologies, consistent use of validated scales, standardized taxonomy, and reporting aligned with standardized frameworks. Enhanced integration of artificial intelligence–driven personalization and a focus on implementation in health care settings are critical for future research.
Advancing the Integration of Digital Health Technologies in the Drug Development Ecosystem
Sakshi Sardar, Cheryl D Coon, Scottie Kern, Huong Huynh, Diane Stephenson, et al.
Journal of Medical Internet Research, 2025
Optimized frameworks for efficient and scalable deployment of digital health technologies (DHT) are needed to address existing bottlenecks and unlock the opportunities for remote monitoring and operationalizing decentralized trials. DHTs offer immense potential opportunities for transformation in drug development by providing remote, high frequency, longitudinal insights into physiological processes, and how participants feel and function. Currently, DHT-based drug development tool–related efforts have yielded valuable insights into effective practices and areas that need improvement. However, the development of the required infrastructure is a resource-intensive task, and its efficiency can be greatly enhanced by systematically identifying the required components and aligning them in ways that will avoid trial-and-error approaches by various stakeholders. In this perspective paper, we aim to highlight these crucial aspects required for supporting the rapid and large-scale deployment of DHTs. We propose the development of various standardized consensus frameworks to clearly lay out processes for various stakeholders and facilitate the seamless integration of the next generation of health care–sensing technologies into drug development.
Digital biomarkers for non-motor symptoms in Parkinson’s disease: the state of the art
Jules M. Janssen Daalen, Robin van den Bergh, Eva M. Prins, Mahshid Sadat Chenarani Moghadam, Rudie van den Heuvel, et al.
Npj Digital Medicine, 2024
Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson’s disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant’s own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.
Developing better digital health measures of Parkinson’s disease using free living data and a crowdsourced data analysis challenge
Solveig K. Sieberts, Henryk Borzymowski, Yuanfang Guan, Yidi Huang, Ayala Matzner, et al.
Plos Digital Health, 2023
Need for personalized monitoring of Parkinson’s disease: the perspectives of patients and specialized healthcare providers
Luc J. W. Evers, José M. Peeters, Bastiaan R. Bloem, Marjan J. Meinders
Frontiers in Neurology, 2023
Usability and utility of a remote monitoring system to support physiotherapy for people with Parkinson's disease
Robin van den Bergh, Luc J. W. Evers, Nienke M. de Vries, Ana L. Silva de Lima, Bastiaan R. Bloem, et al.
Frontiers in Neurology, 2023
The effect of cardiovascular risk on disease progression in de novo Parkinson's disease patients: An observational analysis
Max J. Oosterwegel, Jesse H. Krijthe, Melina G. H. E. den Brok, Lieneke van den Heuvel, Edo Richard, et al.
Frontiers in Neurology, 2023
Time trends in demographic characteristics of participants and outcome measures in Parkinson's disease research: A 19-year single-center experience
Bart R. Maas, Bastiaan R. Bloem, Yoav Ben-Shlomo, Luc J.W. Evers, Rick C. Helmich, et al.
Clinical Parkinsonism and Related Disorders, 2023
Author Correction: Virtual exam for Parkinson’s disease enables frequent and reliable remote measurements of motor function (npj Digital Medicine, (2022), 5, 1, (65), 10.1038/s41746-022-00607-8)
Maximilien Burq, Erin Rainaldi, King Chung Ho, Chen Chen, Bastiaan R. Bloem, et al.
Npj Digital Medicine, 2022
Virtual exam for Parkinson’s disease enables frequent and reliable remote measurements of motor function
Maximilien Burq, Erin Rainaldi, King Chung Ho, Chen Chen, Bastiaan R. Bloem, et al.
Npj Digital Medicine, 2022
Identifying and characterising sources of variability in digital outcome measures in Parkinson’s disease
George Roussos, Teresa Ruiz Herrero, Derek L. Hill, Ariel V. Dowling, Martijn L. T. M. Müller, et al.
Npj Digital Medicine, 2022
Rapid dynamic naturalistic monitoring of bradykinesia in parkinson’s disease using a wrist-worn accelerometer
Jeroen G. V. Habets, Christian Herff, Pieter L. Kubben, Mark L. Kuijf, Yasin Temel, et al.
Sensors, 2021
The state of telemedicine for persons with Parkinson's disease
Robin van den Bergh, Bastiaan R. Bloem, Marjan J. Meinders, Luc J.W. Evers
Current Opinion in Neurology, 2021
Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life
Yordan P. Raykov, Luc J. W. Evers, Reham Badawy, Bastiaan R. Bloem, Tom M. Heskes, et al.
IEEE Journal of Biomedical and Health Informatics, 2021
Estimating the Effect of Early Treatment Initiation in Parkinson's Disease Using Observational Data
Lieneke van den Heuvel, Luc J.W. Evers, Marjan J. Meinders, Bart Post, Anne M. Stiggelbout, et al.
Movement Disorders, 2021
Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science
Diane Stephenson, Robert Alexander, Varun Aggarwal, Reham Badawy, Lisa Bain, et al.
Digital Biomarkers, 2020
Real-life gait performance as a digital biomarker for motor fluctuations: The Parkinson@Home validation study
Luc JW Evers, Yordan P Raykov, Jesse H Krijthe, Ana Lígia Silva de Lima, Reham Badawy, et al.
Journal of Medical Internet Research, 2020
Long-term unsupervised mobility assessment in movement disorders
Elke Warmerdam, Jeffrey M Hausdorff, Arash Atrsaei, Yuhan Zhou, Anat Mirelman, et al.
Lancet Neurology, 2020
Measuring Parkinson's disease over time: The real-world within-subject reliability of the MDS-UPDRS
Luc J.W. Evers, Jesse H. Krijthe, Marjan J. Meinders, Bastiaan R. Bloem, Tom M. Heskes
Movement Disorders, 2019
The Personalized Parkinson Project: Examining disease progression through broad biomarkers in early Parkinson's disease
B. R. Bloem, W. J. Marks, A. L. Silva de Lima, M. L. Kuijf, T. van Laar, et al.
BMC Neurology, 2019
Impact of motor fluctuations on real-life gait in Parkinson's patients
Ana Lígia Silva de Lima, Luc J.W. Evers, Tim Hahn, Nienke M. de Vries, Margaret Daeschler, et al.
Gait and Posture, 2018
Automated quality control for sensor based symptom measurement performed outside the lab
Reham Badawy, Yordan P. Raykov, Luc J. W. Evers, Bastiaan R. Bloem, Marjan J. Faber, et al.
Sensors Switzerland, 2018
Feasibility of large-scale deployment of multiple wearable sensors in Parkinson’s disease
Ana Lígia Silva de Lima, Tim Hahn, Luc J. W. Evers, Nienke M. de Vries, Eli Cohen, et al.
Plos One, 2017
Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review
Ana Lígia Silva de Lima, Luc J. W. Evers, Tim Hahn, Lauren Bataille, Jamie L. Hamilton, et al.
Journal of Neurology, 2017

Luc Evers

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