Evidence map›Paper›PMID 39663281›Full record

ArticleBehavior research methods2024

Dance with me? Analyzing interpersonal synchrony and quality of interaction during joint dance.

Noemí Grinspun, Eden Landesman, Yonnhatan García, Tal-Chen Rabinowitch

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Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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

4 authors.

Noemí GrinspunThe School of Creative Arts Therapies, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel.ORCID 0000-0001-5956-6816
Eden LandesmanThe School of Creative Arts Therapies, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel.ORCID 0000-0001-5010-3276
Yonnhatan GarcíaMetropolitan University of Educational Sciences, 7760197, Santiago, Chile.ORCID 0000-0001-5911-9290
Tal-Chen RabinowitchThe School of Creative Arts Therapies, University of Haifa, Abba Khoushy Ave 199, 3498838, Haifa, Israel. trabinowi@univ.haifa.ac.il.ORCID 0000-0003-0112-5350

Funding

Israel Science Foundation 100008920
6 · The paper itself

Abstract

This methodological paper examines the assessment of interpersonal synchrony during a joint dancing task between mothers and their children (aged 4 to 5 years) using OpenPose. This pose estimation tool captures movement in naturalistic settings. The study analyzes 45 mother-child dyads, comparing two analytical methods for assessing synchrony, and examines their correlation with the Coding Interactive Behavior (CIB) measure of interaction quality. The first method employs cross-wavelet transform (CWT) coherence to assess synchrony based on vertical head movement. This straightforward and computationally efficient approach reveals a significant correlation between interpersonal synchrony and CIB scores, thus implying its potential as a reliable indicator of interaction quality and suggesting its potential as a measure of interaction quality. The second method, the generalized cross-wavelet transform (GCWT), analyzes synchrony across multiple body parts, offering a more complex and detailed analysis of interpersonal dynamics. However, it did not significantly correlate with the CIB scores. Our findings suggest that focusing on head movement using CWT can effectively capture critical elements of interpersonal synchrony linked to interaction quality. In contrast, despite its richness, the more complex GCWT approach may not align as closely with observed interactive behaviors as the CIB scores indicate. This study underscores the need to balance methodological complexity and ecological validity in research, offering insights into selecting analytical techniques based on research objectives and the nuances of interpersonal dynamics. Our results contribute to the field of interpersonal synchrony research, emphasizing the benefits of efficient methods in understanding mother-child interactions and interaction relationships in general.

Indexed as

DancingMother-Child RelationsAdultChild, PreschoolFemaleHumansInterpersonal RelationsMaleSocial InteractionWavelet AnalysisEcological settingsInterpersonal synchronyMother–child dyadMovementMusicOpenPoseWavelet transform

Identifiers

PMID39663281
PMCPMC11634922

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.