Skip to content
DraconiticResearch
Search Sources API
197,578 studies31 sourcesUpdated 59 min ago
Peer-reviewedCC BY-NC-ND 4.0NPJ Parkinson's Disease

Monitoring nocturnal movement and sleep in Parkinson’s disease: a systematic review of movement sensors

  • Manon D. Mijnsbergen1
  • Paulien J. van Tilborg1
  • Luuk Kerckhaert1
  • Rolf Fronczek1,2
  • Bob J. van Hilten1
  • Dagmar H. Hepp1
  • Roel H. A. Weijer1,3
  1. 1Department of Neurology, Leiden University Medical Center (LUMC), Leiden, the Netherlands
  2. 2Stichting Epilepsie Instellingen Nederland (SEIN), Sleep-wake Center, Heemstede, the Netherlands
  3. 3Department of Rehabilitation Medicine, VUmc, Amsterdam Movement Sciences, Amsterdam University Medical Center, Amsterdam, the Netherlands
Read full textDOICite

Abstract

Movement sensors are increasingly used to assess sleep by detecting nocturnal (in)activity in Parkinson’s disease (PD) research, but these studies are methodologically highly heterogenous and lack standardization. This systematic reviews evaluates 70 studies to inform standardization protocols for both research and clinical application. Results of the meta-analyses highlight that, at group level, actigraphy can differentiate people with PD (PwPD) from healthy controls. Sensitivity to detect change in wearable-derived measures is still unclear as meta-analyses for intervention studies were not done due to high heterogeneity. Based on methodological considerations and supported by other studies, reliable standardized assessments require at least seven nights of monitoring, appropriate sensor placement, and validated PD-specific algorithms. Studies comparing wearable and polysomnography parameters show that actigraphy can accurately estimate sleep parameters for individual PwPD. Additionally, actigraphy is valuable for detecting nocturnal behaviors such as getting out of bed or turning in bed. As suggested by other studies, though not empirically supported, combining actigraphy with sleep diaries improves the accuracy of sleep parameter estimation, but future improvements in validated PD-specific algorithms may reduce the need for diaries.

Related

  1. Eye-movement-based machine learning for identifying Parkinson’s disease dementia in a retrospective cohort: an interpretable model-development study
    Jing Wu, Qing Liu, Song Yang +5
    Frontiers in aging neurosciencePeer-reviewedCC BYFull textGeneral medicineEurope PMC
  2. Polysomnographic and nocturnal heart rate correlates of cognitive impairment in Parkinson’s disease: a cross-sectional study
    Zhang Linwei, Lv Pu, Wang Lu +3
    Frontiers in neurologyPeer-reviewedCC BYFull textNeurologyEurope PMCCrossref
  3. Co‐ and Multi‐Pathologies in Parkinson's Disease: An International Parkinson and Movement Disorder Society Scientific Issues Committee Review
    Michele Matarazzo, Per Borghammer, Inas Elsayed +7
    Movement DisordersPeer-reviewedCC BY-NC-ND11 citedNeurologyOpenAlex
  4. Machine Learning Applications for Wearable Sensor Data in Tennis and Padel: A Systematic Review of Movement Recognition, Performance Monitoring, and Emerging Injury-Related Applications.
    Lucrezia Moggio, Antonio Ammendolia, Alessandro de Sire +6
    Journal of functional morphology and kinesiologyPeer-reviewedCC BYFull textGeneral medicineEurope PMC
  5. Occupational pesticide exposure and risk of Parkinson’s disease: protocol for a systematic review and meta-analysis
    Frederikke Oxenvad Schultz, Annett Dalbøge, Johan Hviid Andersen +2
    BMJ OpenPeer-reviewedLicence not statedGeneral medicineCrossref

Cite

BibTeXRIS
BibTeX
@article{mijnsbergen2026monitoring,
  title = {{Monitoring nocturnal movement and sleep in Parkinson’s disease: a systematic review of movement sensors}},
  author = {Manon D. Mijnsbergen and Paulien J. van Tilborg and Luuk Kerckhaert and Rolf Fronczek and Bob J. van Hilten and Dagmar H. Hepp and Roel H. A. Weijer},
  journal = {NPJ Parkinson's Disease},
  year = {2026},
  month = jul,
  doi = {10.1038/s41531-026-01431-7},
  url = {https://doi.org/10.1038/s41531-026-01431-7},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Monitoring nocturnal movement and sleep in Parkinson’s disease: a systematic review of movement sensors
AU  - Manon D. Mijnsbergen
AU  - Paulien J. van Tilborg
AU  - Luuk Kerckhaert
AU  - Rolf Fronczek
AU  - Bob J. van Hilten
AU  - Dagmar H. Hepp
AU  - Roel H. A. Weijer
PY  - 2026
DA  - 2026/07/09
JO  - NPJ Parkinson's Disease
DO  - 10.1038/s41531-026-01431-7
UR  - https://doi.org/10.1038/s41531-026-01431-7
AB  - Movement sensors are increasingly used to assess sleep by detecting nocturnal (in)activity in Parkinson’s disease (PD) research, but these studies are methodologically highly heterogenous and lack standardization. This systematic reviews evaluates 70 studies to inform standardization protocols for both research and clinical application. Results of the meta-analyses highlight that, at group level, actigraphy can differentiate people with PD (PwPD) from healthy controls. Sensitivity to detect change in wearable-derived measures is still unclear as meta-analyses for intervention studies were not done due to high heterogeneity. Based on methodological considerations and supported by other studies, reliable standardized assessments require at least seven nights of monitoring, appropriate sensor placement, and validated PD-specific algorithms. Studies comparing wearable and polysomnography parameters show that actigraphy can accurately estimate sleep parameters for individual PwPD. Additionally, actigraphy is valuable for detecting nocturnal behaviors such as getting out of bed or turning in bed. As suggested by other studies, though not empirically supported, combining actigraphy with sleep diaries improves the accuracy of sleep parameter estimation, but future improvements in validated PD-specific algorithms may reduce the need for diaries.
SN  - 2373-8057
ER  -