Evidence map›Paper›PMID 30008027›Full record

ArticleMedical & biological engineering & computing2019

Cardiovascular assessment of supportive doctor-patient communication using multi-scale and multi-lag analysis of heartbeat dynamics.

M Nardelli, A Greco, O P Danzi, C Perlini, F Tedeschi, E P Scilingo, L Del Piccolo, G Valenza

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Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

8 authors.

M NardelliBioengineering and Robotics Research Centre E. Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy. mimma.nardelli@for.unipi.it.ORCID http://orcid.org/0000-0003-0453-7465
A GrecoBioengineering and Robotics Research Centre E. Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy.
O P DanziDepartment of Neurological, Biomedical and Movement Sciences, University of Verona, Verona, Italy.
C PerliniDepartment of Neurological, Biomedical and Movement Sciences, University of Verona, Verona, Italy.
F TedeschiDepartment of Neurological, Biomedical and Movement Sciences, University of Verona, Verona, Italy.
E P ScilingoBioengineering and Robotics Research Centre E. Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy.
L Del PiccoloDepartment of Neurological, Biomedical and Movement Sciences, University of Verona, Verona, Italy.
G ValenzaBioengineering and Robotics Research Centre E. Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emphatic doctor-patient communication has been associated with improved psycho-physiological well-being involving cardiovascular and neuroendocrine responses. Nevertheless, a comprehensive assessment of heartbeat linear and nonlinear dynamics throughout the communication of a life-threatening disease has not been performed yet. To this extent, we studied linear heartbeat dynamics through the extraction of time-frequency domain measurements, as well as heartbeat nonlinear and complex dynamics through novel approaches to compute multi-scale and multi-lag series analyses: namely, the multi-scale distribution entropy and lagged Poincaré plot symbolic analysis. Heart rate variability series were recorded from 54 healthy female subjects who were blind to the aim of the experiment. Participants were randomly assigned into two groups: 27 subjects watched a video where an oncologist discloses the diagnosis of a cancer metastasis to a patient, whereas the remaining 27 watched the same video including four additional supportive comments by the clinician. Considering differences between the beginning and the end of each communication video, results from non-parametric Wilcoxon tests demonstrated that, at a group level, significant differences occurred in heartbeat linear and nonlinear dynamics, with lower complexity during nonsupportive communication. Furthermore, a support vector machine algorithm, validated using a leave-one-subject-out procedure, was able to discern the supportive experience at a single-subject level with an accuracy of 83.33% when nonlinear features were considered, dropping to 51.85% when using standard HRV features only. In conclusion, heartbeat nonlinear and complex dynamics can be a viable tool for the psycho-physiological evaluation of supportive doctor-patient communication. Graphical Abstract Scheme of the three main stages of the study: signal acquisition during doctor-patient communication, ECG signal processing and pattern recognition results.

Indexed as

Physician-Patient RelationsSignal Processing, Computer-AssistedSocial SupportAdultAnxietyCommunicationFemaleHeart RateHumansMaleMiddle AgedPattern Recognition, AutomatedSupport Vector MachineVideo RecordingDistribution entropyLagged Poincaré plotMulti-scale entropyPattern recognitionSupportive communicationSupport vector machine

Identifiers

PMID30008027

What OpenQuestion holds

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Registered trials

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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.