Evidence map›Paper›PMID 40686278›Full record

ArticleJMIR human factors2025

A Comprehensive Profiling System Integrating Myers-Briggs Type Indicator (MBTI) and Dominance, Influence, Steadiness, and Conscientiousness (DISC) for Personalized Health Training: Correlational Analysis and Usability Evaluation.

Donghyun Kim, Dong Hun Lee, Mi Kyung Hwang

Abstract read
In one paragraph

Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

3 authors.

Donghyun KimDepartment of Industrial and Systems Engineering, Dongguk University, Seoul, Republic of Korea.ORCID 0000-0003-0841-3830
Dong Hun LeeKOI Healthcare Co., Ltd., Seoul, Republic of Korea.ORCID 0009-0002-2234-9089
Mi Kyung HwangDepartment of Health and Medical Administration, Tongwon University, 268, Jungang-ro,, Gwangju-si, Gyeonggi-do, 12735, Republic of Korea.ORCID 0009-0007-9124-3882

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study proposes an integrated approach to developing personalized health behavior change programs by combining personality traits and behavior types. Existing tools, such as Myers-Briggs Type Indicator (MBTI) and Dominance, Influence, Steadiness, and Conscientiousness (DISC), have limitations: MBTI reflects internal tendencies but lacks behavioral insights, while DISC highlights behavior but overlooks deeper personality aspects. To address these gaps, the study integrates MBTI and DISC to create a comprehensive profiling system. Objective: The goal of this research is to design a novel profiling system that merges MBTI and DISC for personalized health management. This system aims to link personality traits with behavior patterns to enhance the effectiveness of tailored health behavior change programs. Methods: The study involved 3 phases: administering MBTI and DISC tests to 130 participants to analyze correlations, developing an integrated survey for health behavior analysis, and testing its usability with 20 experts for validation. Results: Significant correlations were observed between MBTI and DISC indicators, including a notable negative correlation between Thinking-Feeling (T/F) and Dominance (D), suggesting an inverse relationship between decision-making preferences and assertiveness. Usability testing results indicated high participant satisfaction, with an average SUS (System Usability Scale) score of 86.0. The SUS is a widely used questionnaire for measuring subjective assessments of usability. This score exceeded industry benchmarks for system usability. Expert evaluations further reinforced the system's practical applicability, highlighting its potential to enhance user engagement through personalized behavioral insights. Conclusions: This study presents a combined MBTI and DISC profiling system, offering both theoretical insights and practical tools for health behavior change programs. Future research should validate its effectiveness with larger samples and explore broader applications in various health domains.

Indexed as

Health BehaviorPersonalityAdultFemaleHumansMaleMiddle AgedPrecision MedicineSurveys and QuestionnairesYoung AdultDISCDominance, Influence, Steadiness, and Conscientiousnesshealth behavior changeintegrated profilingMBTIMyers-Briggs Type Indicatorpersonality assessmentpersonality typespersonalized programs

Identifiers

PMID40686278
PMCPMC12319163

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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.