Evidence map›Paper›PMID 33539481›Full record

SynthesisPloS one2021

Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.

Marco Sica, Salvatore Tedesco, Colum Crowe, Lorna Kenny, Kevin Moore, Suzanne Timmons, John Barton, Brendan O'Flynn, Dimitrios-Sokratis Komaris

Abstract readSystematic Review
In one paragraph

Synthesis in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 7 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 7 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Observational
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Marco SicaTyndall National Institute, University College Cork, Cork, Ireland.ORCID 0000-0003-4188-4482
Salvatore TedescoTyndall National Institute, University College Cork, Cork, Ireland.
Colum CroweTyndall National Institute, University College Cork, Cork, Ireland.
Lorna KennyCentre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland.
Kevin MooreCentre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland.
Suzanne TimmonsCentre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland.
John BartonTyndall National Institute, University College Cork, Cork, Ireland.
Brendan O'FlynnTyndall National Institute, University College Cork, Cork, Ireland.
Dimitrios-Sokratis KomarisTyndall National Institute, University College Cork, Cork, Ireland.ORCID 0000-0003-4623-9060

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient's condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients' status and the disease's symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson's, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms' assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.

Indexed as

HumansHypokinesiaParkinson DiseaseWearable Electronic Devices

Identifiers

PMID33539481
PMCPMC7861548

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.