Evidence map›Paper›PMID 28653659›Full record

ArticlePhysiological measurement2017

Continuous assessment of schizophrenia using heart rate and accelerometer data.

Erik Reinertsen, Maxim Osipov, Chengyu Liu, John M Kane, Georgios Petrides, Gari D Clifford

Abstract read
In one paragraph

Article in Physiological measurement, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–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

14 citing papers in PubMed.

  1. Article
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  4. Review
  5. Article
  6. Article
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  12. Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study.Technology and health care : official journal of the European Society for Engineering and Medicine · 2019
    Article
  13. Article
  14. Review
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

6 authors.

Erik ReinertsenDepartment of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America.
Maxim OsipovDepartment of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.
Chengyu LiuDepartment of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States of America.
John M KaneDepartment of Psychiatry, The Zucker Hillside Hospital and Hofstra Northwell School of Medicine, Hempstead, NY, United States of America.
Georgios PetridesDepartment of Psychiatry, The Zucker Hillside Hospital and Hofstra Northwell School of Medicine, Hempstead, NY, United States of America.
Gari D CliffordDepartment of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM008169 · NIGMS · EMORY UNIVERSITY · PI GROSS, ROBERT E · 1987 to 2021
$20.3M
NIGMS NIH HHS T32 GM008169Wellcome Trust
6 · The paper itself

Abstract

objectiveSchizophrenia has been associated with changes in heart rate (HR) and physical activity measures. However, the relationship between analysis window length and classifier accuracy using these features has yet to be quantified. APPROACH: Here we used objective HR and activity data to classify contiguous days of data as belonging to a schizophrenia patient or a healthy control. HR and physical activity recordings were made on 12 medicated subjects with schizophrenia and 12 healthy controls. Features derived from these data included classical statistical characteristics, rest-activity metrics, transfer entropy, and multiscale fuzzy entropy. We varied the analysis window length from two to eight days, and selected features via minimal-redundancy-maximal-relevance. A support vector machine was trained to classify schizophrenia from control windows on a daily basis. Model performance was assessed via subject-wise leave-one-out-crossfold-validation. MAIN

resultsAn analysis window length of eight days resulted in an area under a receiver operating characteristic curve (AUC) of 0.96. Reducing the analysis window length to two days only lowered the AUC to 0.91. The type of most predictive features varied with analysis window length. SIGNIFICANCE: Our results suggest continuous tracking of subjects with schizophrenia over short time scales may be sufficient to estimate illness severity on a daily basis.

Indexed as

AccelerometryHeart RateCase-Control StudiesHumansLocomotionSchizophreniaSupport Vector Machine

Identifiers

PMID28653659
PMCPMC5699450

What OpenQuestion holds

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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.