# Digital Technologies for Symptom Monitoring in Parkinson Disease

**Authors:** Natalia Chunga, Varun Reddy, William Barbosa, Jamie L. Adams

PMC · DOI: 10.1007/s11910-026-01481-7 · Current Neurology and Neuroscience Reports · 2026-03-04

## TL;DR

Digital health technologies can continuously and objectively monitor Parkinson's disease symptoms in real-life settings, offering advantages over traditional methods.

## Contribution

The paper reviews recent advances in digital technologies for monitoring both motor and non-motor symptoms of Parkinson's disease.

## Key findings

- Digital health technologies can accurately monitor PD symptoms and detect subtle changes over time.
- Portable technologies like wearables and mobile apps enable remote monitoring of symptoms in real-world settings.
- Standardization of digital measures is needed for widespread clinical use of these technologies.

## Abstract

Digital health technologies (DHT) are promising tools for symptom monitoring in Parkinson disease (PD), offering objective and continuous data in real-life settings. This article reviews recent literature on DHT for symptom monitoring in PD, with a focus on remote monitoring devices.

Research studies have demonstrated that DHT can accurately and reliably monitor both motor and non-motor symptoms of PD, surpassing the limitations of subjective and episodic traditional clinical assessments. Digital measures show promise in predicting PD before clinical diagnosis, differentiating between individuals with and without PD, and detecting subtle symptom changes over time. Portable and non-invasive technologies—such as mobile applications, wearable sensors, and radio wave activity trackers—offer the opportunity to assess symptoms remotely, capturing day-to-day changes and real-world experiences.

DHT have the potential to optimize monitoring of PD symptoms in clinical and research settings, which may help advance therapeutic development and tailor treatment interventions. As DHT continue evolving, standardization of the collection methods and selection of clinically relevant digital measures will be crucial for their wide-scale implementation.

## Linked entities

- **Diseases:** Parkinson disease (MONDO:0005180)

## Full-text entities

- **Diseases:** psychomotor slowing (MESH:D011596), Bradykinesia (MESH:D018476), Tremor (MESH:D014202), motor dysfunction (MESH:D000068079), RBD (MESH:D020187), Movement Disorder (MESH:D009069), cognitive decline (MESH:D003072), rigidity (MESH:D009127), Parkinson (MESH:D010302), dementia (MESH:D003704), depression (MESH:D003866), gait and balance impairment (MESH:D020234), dyskinesia (MESH:D004409), anxiety (MESH:D001007), Huntington (MESH:D006816), Psychiatric Symptoms (MESH:D001523), PD (MESH:D010300), Sleep disturbances (MESH:D012893), Mov Disord (MESH:D009358), Cognitive Symptoms (MESH:D019954), falls (MESH:C537863)
- **Chemicals:** DHT (-), Dopaminergic (MESH:D004298)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## References

6 references — full list in the complete paper: https://tomesphere.com/paper/PMC12960457/full.md

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Source: https://tomesphere.com/paper/PMC12960457