Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 3,171

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Multiple Wearable Sensors in Parkinson and Huntington Disease Individuals: A Pilot Study in Clinic and at Home

Open access
Adams, Jamie L., Dinesh, Karthik, Xiong, Mulin, Tarolli, Christopher G., Sharma, Saloni, Sheth, Nirav, Aranyosi, A.J., Zhu, William, Goldenthal, Steven, Biglan, Kevin M., Dorsey, E. Ray, Sharma, Gaurav·2017·Digital Biomarkers
DOI 10.1159/000479018
Open this paper in CoChat — free account

Abstract

Background: Clinician rating scales and patient-reported outcomes are the principal means of assessing motor symptoms in Parkinson disease and Huntington disease. However, these assessments are subjective and generally limited to episodic in-person visits. Wearable sensors can objectively and continuously measure motor features and could be valuable in clinical research and care. Methods: We recruited participants with Parkinson disease, Huntington disease, and prodromal Huntington disease (individuals who carry the genetic marker but do not yet exhibit symptoms of the disease), and controls to wear 5 accelerometer-based sensors on their chest and limbs for standardized in-clinic assessments and for 2 days at home. The study’s aims were to assess the feasibility of use of wearable sensors, to determine the activity (lying, sitting, standing, walking) of participants, and to survey participants on their experience. Results: Fifty-six individuals (16 with Parkinson disease, 15 with Huntington disease, 5 with prodromal Huntington disease, and 20 controls) were enrolled in the study. Data were successfully obtained from 99.3% (278/280) of sensors dispatched. On average, individuals with Huntington disease spent over 50% of the total time lying down, substantially more than individuals with prodromal Huntington disease (33%, p = 0.003), Parkinson disease (38%, p = 0.01), and controls (34%; p < 0.001). Most (86%) participants were “willing” or “very willing” to wear the sensors again. Conclusions: Among individuals with movement disorders, the use of wearable sensors in clinic and at home was feasible and well-received. These sensors can identify statistically significant differences in activity profiles between individuals with movement disorders and those without. In addition, continuous, objective monitoring can reveal disease characteristics not observed in clinic.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1159/000479018

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 5 references

Research works printed but not linked: 14

  1. 10.1016/j.drudis.2016.01.010 · DOI
  2. 10.1002/mds.25628 · DOI · Paper page
  3. 10.1016/S1474-4422(10)70245-3 · DOI · Paper page
  4. 10.1002/mds.22340 · DOI · Paper page
  5. 10.1002/mds.870110204 · DOI · Paper page
  6. 10.1016/j.gaitpost.2016.11.033 · DOI
  7. 10.1002/1531-8249(200001)47:1<64::AID-ANA11>3.3.CO;2-J · DOI
  8. 10.1016/j.jns.2016.03.021 · DOI
  9. 10.2217/nmt.15.45 · DOI
  10. 10.1002/ana.24495 · DOI
  11. 10.1016/j.jneumeth.2016.01.009 · DOI
  12. 10.2522/ptj.20140374 · DOI
  13. 10.1016/j.parkreldis.2013.05.014 · DOI
  14. 10.1007/s00415-011-6097-7 · DOI · Paper page

Cited by

Metadata

Authors
Adams, J. L., Dinesh, K., Xiong, M., Tarolli, C. G., Sharma, S., Sheth, N., Aranyosi, A. J., Zhu, W., Goldenthal, S., Biglan, K. M., Dorsey, E. R., & Sharma, G.
Venue
Digital Biomarkers
Publisher
S. Karger AG
Published
2017-08-17
Volume
1
Issue
1
Pages
52-63
ISSN
2504-110X (ISSN-L)
DOI
10.1159/000479018
OpenAlex
W2749718288
Cited by
3,171
References
5
FWCI
98.28
Type
journal article
Language
en
Open access
gold
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.