Evidence map›Paper›PMID 41248495›Full record

ArticleJournal of medical Internet research2025

Exploring Body Image Awareness With a Large Language Model-Based Conversational Agent: Qualitative Study With Young Adults.

Xuan Zhang, Ahmed Zayed, Josefin Rehn Hamrin, Arzu Güneysu, Sanna Kuoppamäki

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. 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

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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

5 authors.

Xuan ZhangDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden.ORCID https://orcid.org/0009-0004-6610-5785
Ahmed ZayedDepartment of Public Health and Primary Care, KU Leuven, Leuven, Belgium.ORCID https://orcid.org/0000-0001-7797-1655
Josefin Rehn HamrinDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden.ORCID https://orcid.org/0009-0005-4917-1518
Arzu GüneysuDepartment of Computing Science, Umeå University, Umeå, Sweden.ORCID https://orcid.org/0000-0003-2282-9939
Sanna KuoppamäkiDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden.ORCID https://orcid.org/0000-0001-7985-4057

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBody image plays a crucial role in both physical and mental health, influencing self-esteem, eating behaviors, and psychological well-being. Young adults are particularly vulnerable to body dissatisfaction, defined as negative thoughts or feelings about one's appearance. The benefits of positive body image, characterized by body appreciation and acceptance, are widely recognized, but few digital interventions are designed to support it for young adults.

objectiveWe designed a conversational artificial intelligence (AI) agent integrating biomedical information on eating disorders and the principles of cognitive behavioral therapy to enable open-domain conversations on body image. The study explores young adults' strategies to maintain a positive body image without the agent, the characteristics of conversations with the agent, and the advantages and drawbacks of having a conversation for body image concerns.

methodsA qualitative study consisting of in-depth interviews with young adults was conducted among 15 young adults (aged 20-30 years) who used the AI agent in their homes for a 1-week period. Data comprise preinterviews exploring young adults' maintenance of body image without the AI agent, text-based conversations with an AI agent (n=933 messages), and postinterviews on the perceived impact of conversations on body image awareness. Interview transcripts were analyzed through thematic analysis. Content analysis was applied to analyze the conversations with the AI agent.

resultsYoung adults' body image awareness was connected to self-acceptance, confidence, and valuing body functionality. Participants used several strategies to maintain body image without the AI agent, ranging from social support networks to exercise and positive self-talk. The conversations with the AI agent were categorized into (1) body image awareness, (2) body image-related eating and behavioral regulation, (3) body-focused mindfulness, and (4) social conversation with the agent. Three themes of perceived advantages and drawbacks regarding the conversations with the agent were identified as (1) facilitating body image awareness and self-reflection, (2) availability of conversational support, and (3) discontinuities in user engagement.

conclusionsYoung adults' body image awareness is closely linked to self-acceptance and self-appreciation. In this context, the AI agent was perceived as an available, accessible, and nonjudgmental conversational support in raising body image awareness through self-reflection and self-compassion. Challenges remain in sustaining long-term user engagement, which address the need for multidimensional personalization of the agent.

Indexed as

Artificial IntelligenceAwarenessBody ImageCommunicationLanguageAdultCognitive Behavioral TherapyFemaleHumansLarge Language ModelsMaleQualitative ResearchSelf ConceptYoung AdultAI agentsbody imageconversational agentsqualitative studyyoung adults

Identifiers

PMID41248495
PMCPMC12670058

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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.