The Associations between Multi-Theory Model (MTM) Constructs and Health Behavior Change among College Students: A Systematic Review and Meta-Analysis

Summary

This meta-analysis of 13 studies (2,605 college students) quantifies MTM construct associations with health behavior change and identifies key stage-specific factors.

Abstract

This study aims to explore the role of the Multi-Theory Model (MTM) in health behavior change among college students, to quantify the strength of the association between each MTM construct and such changes, and to provide empirical evidence for designing stage-specific intervention strategies. Databases including CNKI, Wanfang, PubMed, Web of Science, Scopus, and Embase were searched from their inception to October 2024. After the studies were screened according to the inclusion and exclusion criteria and the data were extracted, a meta-analysis was performed using RevMan 5.4 software. Statistical significance was set at P < 0.05. Thirteen studies involving 2,605 participants were included. In the initiation stage, behavioral confidence (BC) demonstrated the strongest association with health behavior change (OR=1.12). In the maintenance stage, practice for change (PC) had the strongest association (OR=1.15), whereas changes in the social environment (CSE) exhibited high stability (I²=0%). The Multi-Theory Model (MTM) constructs are significantly associated with health behavior change among college students. This study provides quantitative evidence on the stage-specific associations of MTM constructs, which may inform the future design of targeted interventions. It further recommends focusing on behavioral confidence (BC) for initiation and practice for change (PC), along with changes in the social environment (CSE) for maintenance to improve adherence.

Introduction

College students, as a core group of young adults with high educational potential, play a pivotal role in driving global social progress and sustainable development¹. They are in a critical period of physical and psychological development, and face challenges such as academic pressure, social adaptation, and independent living. Consequently, their health is increasingly influenced by lifestyle habits and campus environments². For example, heavy study loads, irregular routines, a lack of exercise, and poor self-management often lead to both mental and physical health issues³. A growing body of evidence underscores the significant mental health challenges faced by college students. A large-scale meta-analysis revealed pooled prevalence rates of 34% for depression and 32% for anxiety among this demographic⁴. These rates are substantially higher than those in the general population, underscoring the unique pressures of this life stage, a pattern that is also evident in East Asia⁵. This period of transition often disrupts healthy habits, making the cultivation of positive health behaviors during this time crucial for shaping lifelong well-being.

The WHO notes that healthy behaviors increase health awareness, self-management, and quality of life⁶. Health behaviors are actions that are associated with physical and mental well-being while individuals adapt to the environment⁷. They play a vital role in the development of healthy lifestyles. Effective theoretical models are necessary to understand the factors associated with improvements and the potential facilitation of replacing unhealthy habits with healthy habits in both the short and long term.

Among the various health behavior intervention models, the Multi-Theory Model (MTM) of health behavior change, first proposed by Professor Manoj Sharma in 2015⁸, is prominent. Compared with other behavior change theories, such as the Transtheoretical Model (TTM), the Theory of Planned Behavior (TPB), and Social Cognitive Theory (SCT), the MTM has unique advantages, particularly in addressing the specific needs of college students. While the TTM effectively outlines stages of change, it offers limited actionable strategies for sustaining new behaviors, a particular challenge for students navigating fluctuating academic and social demands⁹. The TPB emphasizes behavioral intention as a key predictor but does not explicitly differentiate between the initiation and maintenance of behavior, a distinction that is critical in the dynamic context of student life¹⁰. SCT, despite highlighting self-efficacy and observational learning, provides a less structured framework for designing stage-specific interventions¹¹. In contrast, the MTM not only bifurcates the change process into initiation and maintenance but also introduces specific, measurable constructs for each stage, rendering it particularly suitable for developing targeted and sustainable health behavior interventions in college settings¹². This theory divides health behavior change into two stages: initiation and maintenance, thereby addressing the complex and diverse needs of health behavior interventions¹³.

The development of the MTM has progressed through four stages. Early theories focused on knowledge dissemination and awareness-raising to change behavior through information provision¹⁴. Second-generation theories incorporated skill training and consciousness improvement¹⁵. Third-generation theories emerged in the 1990s, introducing evidence-based techniques and emphasizing empirically grounded frameworks such as Social Cognitive Theory¹¹. In the fourth and latest stage, the MTM integrates core elements from multiple classical theories to comprehensively analyze and predict the initiation and maintenance of health behaviors¹⁶. The MTM combines key components from various theories to better predict and explain how individuals initiate and sustain healthy behaviors.

The MTM categorizes behavior change into two stages: initiation and maintenance. Initiation is influenced by participatory dialog (PD), behavioral confidence (BC), and changes in the physical environment (CPE). Maintenance is affected by emotional transformation (ET), practice for change (PC), and changes in the social environment (CSE)¹⁷. The MTM has been successfully applied in areas such as diet management¹⁸,handwashing behavior¹⁹, smoking cessation²⁰, alcohol abstinence¹², and gambling cessation²¹. However, most existing studies have focused on qualitative descriptions of its applicability or single-factor correlation analysis^17,22,23 and lack a quantitative synthesis of the strength of the association of each MTM construct with health behaviors among college students.

Given the unique challenges faced by college students, exploring the role of the MTM in their health behaviors is necessary. This study begins with the two stages, initiation and maintenance, and focuses on the six key constructs of the model. It aims to quantify the strength of the cross-sectional associations between each MTM construct and health behavior change among college students. By quantitatively integrating the data, this study provides comparable evidence on the strength of the associations of various MTM constructs and their cross-sectional relationships with college students' health behaviors. By synthesizing correlational data, this study aims to generate hypotheses about which constructs are most critical for each stage and thus should be tested in future longitudinal and experimental studies. This study addresses the limitations of previous systematic reviews, which remained largely at the qualitative description level and lacked a quantitative synthesis of evidence regarding the cross-sectional associations between MTM constructs and college students' health behaviors. Additionally, it fills a gap in quantitative research on the MTM, specifically in the college student population, by offering a more precise theoretical basis for health interventions aimed at this group. Furthermore, this study identifies stage-specific intervention priorities through quantitative analysis, providing direct preliminary evidence for the design of phased health behavior intervention strategies for college students.

Protocol

This systematic review and meta-analysis protocol was registered on PROSPERO with a registration number of CRD 420251012795. This study was designed and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)²⁴ guidelines (Supplementary File 1).

1. Literature search strategy

The relevant literature was retrieved by searching PubMed, Embase, Web of Science, Scopus, Wanfang, and Chinese National Knowledge Infrastructure (CNKI) databases for studies published from the inception of each database to October 2024. The Chinese search terms used were "duo li lun mo xing" AND "da xue sheng" AND "jian kang xing wei". The English search terms used were (("Multi-Theory Model") OR ("MTM")) AND (("college student*") OR ("university student*") OR ("undergraduate*") OR ("academician")) AND (("health behavior*") OR ("healthy behavior*") OR ("health-related behavior*")).

2. Literature inclusion and exclusion criteria

The inclusion criteria were as follows: (1) the study subjects were college students; (2) the Multi-Theory Model (MTM) was applied as a predictive framework for health behaviors; (3) sufficient data were provided for calculating association measures (ORs); (4) the study design was a cross-sectional study; and (5) The included articles were limited to English and Chinese. The exclusion criteria were as follows: (1) duplicate publications; (2) studies with incomplete or inaccessible data; (3) studies not utilizing the Multi-Theory Model (MTM); and (4) reviews, case reports, conference abstracts, animal studies, etc.

3. Literature screening and data extraction

The identified records were imported into EndNote X21 software for literature review and duplicate removal. The study selection process was performed independently by two reviewers (X.L.H. and K.X.H.). First, they screened titles and abstracts against the eligibility criteria. Afterward, the full texts of potentially relevant articles were retrieved and assessed. Any discrepancies at each stage were resolved through consensus or, when necessary, by adjudication from a third senior reviewer (X.X.Z.). Data extraction was conducted independently by the same two reviewers (X.L.H. and K.X.H.) using a prepiloted, standardized data extraction form. The extracted data included the following: first author, publication year, country of study, sample size, demographic characteristics (e.g., sex and mean age), specific health behavior investigated, measurement tools for each MTM construct, and effect size data (e.g., odds ratios with 95% confidence intervals) for the association between each construct and behavior change.

4. Appraisal of methodological quality

The methodological quality of the included studies was assessed using a combined evaluation tool specifically designed for cross-sectional studies²⁵. The tool comprises seven criteria: (1) scientifically sound design; (2) reasonable data collection strategy; (3) reporting of sample response rate; (4) good representativeness of the sample relative to the population; (5) appropriate research objectives and methods; (6) reporting of statistical power; and (7) reasonable statistical methods. Responses were categorized as "yes", "no", or "unclear" and assigned scores of 1, 0, and 0.5, respectively. If an indicator was not applicable, it was denoted by "_" and scored as 1.0 points²⁶. The total score of the scale was 7.0 points. A score of 6.0-7.0 indicated Grade A quality, 4.0-5.5 indicated Grade B quality, and a score below 4.0 indicated Grade C quality. The quality assessment was performed independently by two reviewers (X.L.H. and X.Z.). Disagreements in scoring were discussed until a consensus was reached.

5. Statistical analysis

Data were analyzed using Review Manager 5.3 software. Heterogeneity among studies was assessed using P values and I² statistics. If P > 0.1 and I² < 50.0%, indicating acceptable heterogeneity among the study results, a fixed-effects model was used²⁷. If P ≤ 0.1 and I² ≥ 50.0%, indicating substantial heterogeneity, a random-effects model was applied²⁸. The odds ratio (OR) was used as the quantitative estimator throughout the study. Sensitivity analyses or subgroup analyses were conducted to explore the sources of heterogeneity when necessary. Statistical significance was set at P < 0.05. Publication bias was assessed both visually using funnel plots and statistically using Egger's linear regression test for outcome measures that included a sufficient number of studies (n ≥ 10). The primary data analysis, including the generation of forest and funnel plots, was performed using Review Manager (RevMan) version 5.3 software. Additionally, the statistical assessment for publication bias (Egger's test) was conducted using Stata version 18. Publication bias was assessed using funnel plots for outcome measures with a sufficient number of studies (n ≥ 10), with a significance level set at α = 0.05.

Results

Literature search
A total of 90 articles were initially identified. After 43 duplicate records were removed using EndNote software combined with manual screening, 47 articles were retained. By reviewing the titles and abstracts, 19 articles that were clearly irrelevant or did not meet the study's subject and design criteria were excluded. After the full texts were read, 15 additional articles were excluded because of irrelevance to the research topic, incomplete data, or unavailability of full texts....

Discussion

This meta-analysis quantified the cross-sectional associations between MTM constructs and health behavior change among college students. A novel finding is the identification of key stage-specific and behavior-specific constructs: behavioral confidence was paramount for initiation, whereas practice for change (PC), particularly for physical activity (OR=1.22), had the strongest association for maintenance. Furthermore, changes in the social environment (CSE) demonstrated high stability across studies (I²=0%), highli...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
EndNote	Clarivate	X21	Software used for literature management and screening
Review Manager (RevMan)	The Cochrane Collaboration	5.3	Software used for meta-analysis
stata	StataCorp LLC	18	Software used for statistical analysis and data management