A Bibliometric Mapping of Mindfulness, Exercise, and Mental Health Research: A Scienometric Analysis Based on Web of Science and Scopus (2012–2026)

Abstract

Background and Purpose: The intersection of mindfulness practices, physical activity, and mental health has garnered substantial scholarly attention over the past decade. Although empirical studies have proliferated, a systematic mapping of the intellectual structure, collaborative networks, and evolving research fronts in this interdisciplinary domain remains limited. This preregistered bibliometric study aims to comprehensively delineate the knowledge landscape, identify core research hotspots, and detect emerging trends within the literature on mindfulness, meditation, and exercise interventions for mental health from 2012 to 2026. Data Sources and Search Strategy: Primary literature will be retrieved from the Web of Science Core Collection and Scopus databases. The retrieval period spans from January 1, 2012, to May 9, 2026. The search string in WOS is: TS= (Mindfulness OR Meditation OR "Mindfulness-Based Stress Reduction" OR "Mindfulness-Based Cognitive Therapy" OR "Self-Compassion") AND TS= ("Mental Health" OR "Mental Hygiene") AND TS= (Exercise* OR "Physical Activit*"), with an equivalent strategy adapted for Scopus using TITLE-ABS-KEY fields. All search terms have been verified against Medical Subject Headings (MeSH) to ensure semantic precision. Inclusion/Exclusion Criteria and Screening: The study will include original research articles and reviews published in English within the specified timeframe. Non-English publications, early-access/online-first articles, and non-original document types (e.g., editorials, meeting abstracts, and letters) will be excluded. After initial retrieval, deduplication and harmonization of datasets from both databases will be performed using BibexPy (a Python-based tool) via exact DOI matching. Analytical Methods and Software: Quantitative analyses will be conducted using CiteSpace (version 6.4.R1) and VOSviewer (version 1.6.20). The analytical framework includes: 1. Performance Analysis: Annual publication trends and productivity distributions of leading authors, institutions, and countries. 2. Co-authorship and Collaboration Network Analysis: Visual mapping of collaborative relationships at the author, institutional, and country levels. 3. Reference Co-citation Analysis: Cluster analysis to identify core thematic domains (e.g., relaxation, self-compassion, depression, mhealth) using LSI, LLR, and MI labeling algorithms. 4. Keyword Burst Detection: Identification of citation surges to capture emerging research fronts and transient intellectual movements. Parameter Settings: The time frame for CiteSpace analysis will be set from January 2012 to June 2026, with a "Years Per Slice" parameter configured to 2 years to mitigate short-term fluctuations. Node selection will be based on a modified g-index (scale factor K=25) to dynamically capture high-impact literature based on actual citation distributions, thereby overcoming the rigidity of fixed numerical cutoffs. Pruning methods will include Minimum Spanning Tree (MST) and Pruned Sliced Network. Anticipated Outcomes and Contribution: This study is expected to reveal the knowledge diffusion paths, predominant research clusters (e.g., mindfulness-based interventions, digital health applications, student mental health, and post-pandemic resilience), and shifting paradigms in the application of contemplative practices to sports and clinical mental health settings. The findings will offer researchers, clinicians, and policymakers a clear roadmap of the field’s evolution and provide valuable references for future interdisciplinary collaboration and translational research. All analysis procedures and data processing scripts will be documented transparently to ensure full replicability.