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Digital endpoints & wearable monitoring for Parkinson’s and Movement Disorder Trials

Digital endpoints & wearable monitoring for Parkinson’s and Movement Disorder Trials

Digital endpoints are objective, sensor-based measurements captured using wearable devices and apps, to track disease progression and treatment response in real-world settings.

Digital endpoints & wearable monitoring for Parkinson’s and Movement Disorder Trials

How digital endpoints are transforming Parkinson's disease trials

Digital endpoints are transforming how Parkinson’s disease is measured in clinical research, with growing applications across other movement disorders. For sponsors and CROs running trials on Parkinson’s and other movement disorders, digital endpoints derived through wearable sensors provide objective, real-world data collection, and offer deeper insight into symptom progression, treatment response, and overall quality of life.

Digital endpoints for Parkinson’s clinical trials

Digital endpoints for Parkinson’s clinical trials

Parkinson’s disease is defined by its cardinal motor symptoms: bradykinesia, tremor, rigidity, and postural instability, which are key biomarkers in clinical trials. Digital endpoints are increasingly used across trials on dopaminergic therapies (e.g. levodopa), disease-modifying treatments, and deep brain stimulation (DBS) and neuromodulation.

Why continuous wearable monitoring matters in movement disorders

Movement disorders are a group of nervous system conditions that affect movement. Parkinson’s disease is a progressive neurodegenerative disorder characterized by motor symptoms such as bradykinesia, tremor, rigidity, and postural instability, alongside non-motor complications that significantly impact quality of life. Parkinson’s symptoms fluctuate significantly throughout the day due to factors such as medication cycles, activity levels, and disease progression. Digital biomarkers and wearable devices, such as watches, apps, and other monitoring tools can address these challenges:

Capturing daily motor fluctuations

Detect “on/off” states and symptom variability throughout the day.

Objective measurement

Effectively supplement clinician-rated scales such as MDS-UPDRS.

Improved sensitivity to change

Identify subtle treatment effects that may not be visible during site visits.

Patient burden

Frequent site visits can be challenging, particularly for patients with mobility impairments. For trials, this can result in lower participation.

Key digital measures in Parkinson’s monitoring include:

It is possible to track real-time treatment response and symptoms in Parkinson’s patients while they are at home, using 30+ validated movement disorder algorithms, developed by PKG Health and provided by Empatica

Download full list of digital biomarkers
Key digital endpoints in movement disorder clinical trials

Key digital endpoints in movement disorder clinical trials

It is possible to track real-time treatment response and symptoms in Parkinson’s patients while they are at home, using 30+ validated movement disorder algorithms, developed by PKG Health and provided by Empatica

Bradykinesia

  • What it is: Slowness of movement, a core diagnostic feature of Parkinson’s disease.
  • Why it’s important: Bradykinesia is strongly linked to functional impairment and is a primary endpoint in many trials.
  • Why continuous monitoring helps: Wearables can quantify movement speed and amplitude throughout the day, revealing fluctuations tied to medication timing and disease severity.
  • Relevance across movement disorders: Reduced movement speed and hypokinesia are also observed in conditions such as Huntington’s disease and atypical parkinsonian syndromes.

Tremor

  • What it is: Involuntary rhythmic oscillations, often occurring at rest.
  • Why it’s important: Tremor severity is a key indicator of disease burden and treatment efficacy.
  • Why continuous monitoring helps: Sensor-based measurements provide objective tremor frequency and amplitude data, enabling precise tracking beyond subjective reporting.
  • Relevance across movement disorders: Tremor is the defining feature of Essential Tremor (ET), the most prevalent movement disorder globally. It is also present in dystonia and other neurological conditions.

Dyskinesia

  • What it is: Involuntary movements often associated with long-term dopaminergic treatment.
  • Why it matters: Dyskinesia is a major side effect that impacts treatment optimization and patient quality of life.
  • Why continuous monitoring helps: Continuous data allows differentiation between dyskinesia and other movements, supporting better assessment of treatment-related complications.
  • Relevance across movement disorders: While most commonly studied in Parkinson’s disease, abnormal involuntary movements are also relevant in Huntington’s disease and drug-induced movement disorders.

Gait and Postural Stability

  • What it reflects: Walking patterns, balance, and risk of falls.
  • Why it matters: Gait impairment and falls are leading causes of morbidity in Parkinson’s disease.
  • Why continuous monitoring helps: Wearables can capture real-world gait patterns and detect abnormalities such as freezing of gait that may not appear during site assessments.
  • Relevance across movement disorders: Gait and balance impairments are also central features of ataxia, progressive supranuclear palsy (PSP), and other movement disorders.

Motor fluctuations

  • What it reflects: Variability in motor symptoms throughout the day, often linked to medication cycles.
  • Why it matters: A key outcome in evaluating treatment effectiveness, particularly in advanced Parkinson’s disease.
  • Why continuous monitoring helps: Wearables enable detection of “on/off” states and transitions in real-world settings.
  • Relevance across movement disorders: Motor variability is a defining feature of Huntington's disease, where chorea and bradykinesia can fluctuate across the day.

What are the limitations of traditional Parkinson’s assessments?

Missing real-world context

Evaluations capture only brief snapshots of a patient’s status, without necessarily capturing how symptoms affect quality of life outside the clinic.

Intermittent data collection

Infrequent assessments miss symptom variability and transient events. This is key in Parkinson’s patients where variability appears on a daily basis.

Clinician and patient subjectivity

Ratings depend on clinician interpretation and patient self-reports, which provide valuable information but are affected by recall.

Patient burden

Frequent site visits can be challenging, particularly for patients with mobility impairments. For trials, this can result in lower participation.

Effectiveness of wearable devices in monitoring Parkinson’s Disease symptoms

Effectiveness of wearable devices in monitoring Parkinson’s Disease symptoms

Wearable devices equipped with accelerometers and gyroscopes used in Parkinson’s research have demonstrated strong potential in accurately and continuously monitoring key symptoms such as tremor, bradykinesia and dyskinesia.

  • 95% sensitivity and 88% specificity comparing BKS with UPDRS III (Unified Parkinson's Disease Rating Scale).¹
  • Highly significant correlation (R = 0.80, p < 0.0001) between DKS and AIMS (Abnormal Involuntary Movement Scale).¹
    92.5% sensitivity and 92.9% specificity identifying tremor (PTT ≥ 0.8%).²
  • Wearable devices identify uncontrolled symptoms 72% of the time.³
    Accelerometry data from wrist-worn sensors can measure step count in PD with <3% error rate.⁴

See publications

1. Griffiths RI, Kotschet K, Arfon S, et al. Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. J Parkinsons Dis. 2012;2(1):47-55.
2. Braybrook M, O’Connor S, Churchward P, et al. An Ambulatory Tremor Score for Parkinson’s Disease. J Parkinsons Dis. 2016;6(4):723-731.
3. Woodrow H, Horne M, Fernando C, Kotschet K, Treat to Target Study Group. A blinded, controlled trial of objective measurement in Parkinson’s disease. NPJ Parkinson’s Disease. 2020;6:35. https://doi.org/10.1038/s41531-020-00136-9
4. Shokouhi N, Khodakarami H, Fernando C, et al. Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring. Front Aging Neurosci. 2022;14:904895. https://doi.org/10.3389/fnagi.2022.904895

Enabling scalable remote monitoring for Parkinson’s trials

Enabling scalable remote monitoring for Parkinson’s trials

Empatica provides a clinical-grade wearable platform designed to support continuous, remote monitoring in neurological clinical trials.

  • FDA-cleared and CE-marked platform, devices and apps
  • Continuous actigraphy and movement analysis for motor symptom tracking
  • Continuous data collection for up to 14 days
  • High patient adherence through comfortable, wrist-worn design
  • 38 validated movement disorder measures
  • Over 200 sleep and activity measures
  • Secure data infrastructure supporting compliance requirements (GDPR, HIPAA)

Learn about our Clinical Trials offering

Additional resources

Advancing objective Parkinson’s monitoring in clinical practice: Evolving the PKG within the Empatica platform

Advancing objective Parkinson’s monitoring in clinical practice: Evolving the PKG within the Empatica platform

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Empatica secures CE MDR Certification for Parkinson’s monitoring using PKG measures

Empatica secures CE MDR Certification for Parkinson’s monitoring using PKG measures

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Beyond the clinic: Using continuous monitoring to better manage ‘OFF’ in Parkinson’s

Beyond the clinic: Using continuous monitoring to better manage ‘OFF’ in Parkinson’s

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The future of Parkinson’s care: Integrating and scaling wearable monitoring

The future of Parkinson’s care: Integrating and scaling wearable monitoring

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Empatica Health Monitoring Platform for Parkinson's

Empatica Health Monitoring Platform for Parkinson's

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Empatica Acquires PKG Health to Transform Parkinson’s Disease Monitoring

Empatica Acquires PKG Health to Transform Parkinson’s Disease Monitoring

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Frequently Asked Questions

What are relevant digital endpoints in Parkinson’s clinical trials?

Digital endpoints are objective, sensor-derived measurements such as tremor and gait speed, collected via wearable devices to assess disease progression and treatment response. Common digital endpoints in Parkinson’s clinical trials include bradykinesia scores (BKS), dyskinesia scores (DKS), tremor metrics (PTT), and sleep measures such as WASO. These endpoints can be used as primary, secondary, or exploratory outcomes depending on study design.

Are wearable devices validated for Parkinson’s research?

Yes, many wearable-derived measures have shown correlation with clinical scales and are increasingly used in clinical trials. Empatica’s EmbraceMini and EmbracePlus wearables have also shown near-100% equivalence with the PKG Watch when measuring Bradykinesia, Dyskinesia and Tremor. In addition, the Empatica Health Monitoring Platform is FDA-cleared and purpose-made for remote patient monitoring.

How are movement disorders traditionally measured in Parkinson’s trials?

They are traditionally assessed using clinical scales like MDS-UPDRS, but increasingly supplemented with continuous wearable-based measurements.

What are the benefits of continuous monitoring in Parkinson’s disease?

Continuous monitoring captures real-world symptom variability, improves sensitivity to change, and reduces reliance on subjective assessments.

Can digital biomarkers replace clinical scales?

Digital biomarkers are increasingly used as complementary endpoints to enhance trial sensitivity and patient-centricity.

How effective are wearable devices in monitoring Parkinson's symptoms?

Wearable devices have proven highly effective in monitoring Parkinson’s disease symptoms, particularly for capturing motor fluctuations and real-world patient experience. Equipped with sensors such as accelerometers and gyroscopes, wearable devices can continuously measure key motor symptoms including tremor, bradykinesia, dyskinesia, and gait impairment. These measurements provide objective, high-frequency data that supplements traditional clinical scales like MDS-UPDRS.