Evidence map›Paper›PMID 41708671›Full record

ArticleScientific reports2026

Health tourism model in the digital age: emotional healing effects of disembodied landscape perception through social media.

Ruimin Guo, Yiming Qi, Xubin Xie, Ruirui Liu

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Ruimin GuoSchool of Architecture and Art, Central South University, Changsha, 410083, Hunan, China.
Yiming QiPsychology Department, University of British Columbia, Vancouver, BC, V6T1Z4, Canada.
Xubin XieSchool of Architecture and Art, Central South University, Changsha, 410083, Hunan, China. xiexubin301@163.com.
Ruirui LiuSchool of Humanities, Central South University, Changsha, 410083, Hunan, China.

Funding

Central South University 2022znzk12Hengyang Normal University 2024HSKFJJ005
6 · The paper itself

Abstract

In the digital era, increasing attention has been paid to how social media reshapes virtual tourism, yet little research has explored the mechanisms of digital healing in disembodied travel and the differentiated effects of landscape types. This study takes Erhai Scenic Area in China as an example and combines text coding with questionnaire survey. Study 1 used web crawling to collect review texts, followed by semantic analysis and landscape coding. Study 2, based on the classifications derived from Study 1, conducted video experiments and questionnaire surveys. The results of both studies were then compared and analyzed. Findings indicate that emotion plays a partial mediating role in the mechanism of "disembodied landscape perception—embodied emotional change—embodied travel intention," with personality traits (openness and neuroticism) serving as moderators. Moreover, the four landscape types: ecological-natural, commercial-leisure, historical-cultural, and rural-pastoral demonstrate differentiated effects in emotional healing, promoting travel intentions, and facilitating the transformation from disembodied to embodied perception. This study provides a theoretical foundation for destination marketing, healthy landscape design, and digital healing, contributing to the development of new health tourism models in the social media context.

Indexed as

EmotionsMedical TourismSocial MediaAdultChinaDigital MediaFemaleHumansMalePerceptionSurveys and QuestionnairesDigital healingDisembodied experienceEmbodied cognitionHealth tourismLandscape perception

Identifiers

PMID41708671
PMCPMC12916797

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.