Remember me
College students are in a critical developmental stage characterized by the transition from adolescence to adulthood, during which they encounter multiple challenges, including increased academic demands and evolving social roles. Evidence suggests that college students often exhibit insufficient self-management capacity related to healthy lifestyle behaviors [], with inadequate physical activity, prolonged sedentary behavior, irregular diet patterns, and sleep disturbances being particularly prevalent. Previous research has demonstrated that health behaviors established during this developmental period tend to exhibit substantial stability and continuity over time []. The adoption of unhealthy lifestyle behaviors during this stage has been shown to significantly increase the risk of chronic diseases, depression, and anxiety later in adulthood [,]. Therefore, the implementation of early and effective interventions targeting these 4 key lifestyle behaviors among college students is of substantial public health significance [].
With the rapid advancement of digital technologies and the widespread adoption of smart devices, digital health interventions (DHIs) have emerged as an innovative approach to health promotion and are increasingly recognized as an important means of improving lifestyle behaviors among college students [,]. Particularly in the post–COVID-19 era, DHIs have demonstrated greater adaptability and broader application potential than traditional face-to-face health intervention models [-]. In recent years, a growing body of empirical evidence has shown that DHIs are effective in promoting physical activity among college students [-], reducing sedentary time [,], improving diet behaviors [,], and enhancing sleep quality [,]. These interventions—encompassing mobile apps, wearable devices, online platforms, and social media—offer several advantages, including low cost, high scalability, and a high degree of personalization [,], and have been shown to enhance user engagement and facilitate sustained behavior change [,]. Concurrently, advancements in emerging technologies, such as artificial intelligence, continue to drive the refinement of DHI implementation strategies and further enhance intervention effectiveness [].
However, the existing body of research on DHIs targeting lifestyle behaviors among college students remains subject to several limitations. On the one hand, the majority of original intervention studies have focused on single lifestyle behaviors or specific technological modalities, with a relative lack of comprehensive designs that integrate multiple behaviors and intervention approaches. At the same time, key intervention dimensions—such as functional characteristics, intervention duration, participant demographics, and adherence—have yet to reach unified standards or methodological consensus [,]. On the other hand, existing systematic reviews and meta-analyses in this field also demonstrate limitations in terms of specificity and methodological rigor. First, systematic syntheses that specifically target the college student population remain relatively scarce, with insufficient attention paid to lifestyle behaviors such as sedentary behavior and sleep. Second, existing analyses have not adequately synthesized the combined effects of multiple lifestyle behaviors across diverse DHI intervention formats [].
In light of the current research context and identified limitations, this review is guided by the following research questions: (1) What is the current state of the literature on DHIs targeting 4 key lifestyle behaviors among college students (physical activity, sedentary behavior, diet, and sleep)? (2) What are the specific implementation strategies and modalities of DHIs addressing these behaviors? (3) To what extent are DHIs effective in influencing these 4 target lifestyle behaviors among college students? Through a comprehensive synthesis and analysis of relevant primary research evidence, this review will explicitly consider the characteristics of college students as “digital natives” []. The review will systematically examine the forms, functions, and key components of different DHIs, and comprehensively evaluate their effects on the 4 target behaviors that are closely related to college student health. This review aims to clarify the applicability and effectiveness of DHIs within this population, thereby providing evidence-based recommendations for optimizing DHI tools, informing health promotion strategies in higher education settings, and guiding future research.
This systematic review was prospectively registered in PROSPERO (International Prospective Register of Systematic Reviews) on August 4, 2025 (registration number: CRD420251119078), and the reporting of the review findings adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines (see ). A comprehensive literature search was conducted across 10 major English-language electronic databases, including Scopus, Web of Science, PubMed, ProQuest Central, and 6 databases accessed via the EBSCOhost platform (MEDLINE, PsycINFO, SPORTDiscus, APA PsycArticles, ERIC, and Academic Search Premier), with Google Scholar used as a supplementary search source. In addition, the reference lists of relevant articles were screened to identify potentially missed studies. The initial search was completed on August 5, 2025, covering studies published between January 1, 2010, and June 1, 2025, for primary study identification, and an updated search was conducted on December 27, 2025, to capture studies published within the most recent 6 months; the same search strategy was applied consistently across both searches. No published search filters were used, and the search strategy was neither adapted from nor reused, in whole or in part, from previous reviews. The search strategy was initially developed by the authors and subsequently peer reviewed by an experienced searcher with expertise in scientific information retrieval. Beyond these approaches, no study registries were searched, no purposeful searching or browsing (eg, table of contents screening, print conference proceedings, or website searches) was conducted, and no additional information was sought by contacting authors, experts, manufacturers, or other relevant parties.
The literature search strategy was systematically developed in accordance with the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Search Extension) guidelines to ensure transparency and reproducibility of the search process. The strategy combined Medical Subject Headings terms with free-text terms and constructed keyword combinations around 3 core concepts: (1) intervention formats (eg, digital health, digital intervention, eHealth, and mobile health [mHealth]); (2) target behaviors (eg, physical activity, sedentary behavior, sleep, and diet); and (3) study populations (eg, university students, college students, and undergraduate students). Boolean operators (AND and OR) were applied to balance search sensitivity and specificity. Using Scopus as an example, the search query was as follows: TITLE-ABS-KEY (“digital health” OR “eHealth” OR “mHealth” OR “mobile health” OR “digital intervention” OR “health app”) AND TITLE-ABS-KEY (“college students” OR “university students” OR “undergraduate students” OR “young adults”) AND TITLE-ABS-KEY (“lifestyle behavior” OR “health behavior” OR “physical activity” OR “exercise” OR “diet” OR “nutrition” OR “sleep” OR “sedentary behavior”). The complete English-language search terms used across all databases are provided in . To ensure comprehensive coverage, no geographical restrictions were applied during the literature search, allowing for the inclusion of relevant studies from diverse global regions.
Inclusion and Exclusion CriteriaThe study inclusion criteria were developed in accordance with the PICOS (Population-Intervention-Comparison-Outcome -Study Design) framework, as outlined in .
The exclusion criteria were as follows: (1) The study population was not explicitly identified as “college students,” “university students,” or individuals enrolled in higher education institutions. (2) The nondigital components constituted the majority of the intervention (≥50%), or the study relied solely on wearable devices for passive behavioral monitoring without incorporating feedback mechanisms or active intervention strategies. (3) The study did not implement a behavioral intervention, or the intervention description lacked sufficient detail to determine its content and implementation procedures. (4) Studies that did not report any lifestyle behavior–related outcome measures. (5) Conference abstracts, theses, unpublished manuscripts, and other forms of gray literature. (6) Full-text articles were unavailable, or the publication was not in English.
Textbox 1. Study inclusion criteria.1. Population
Participants were required to be aged ≥18 years and explicitly identified as “college students,” “university students,” or “young adults enrolled in higher education.”2. Intervention
Studies were required to evaluate at least one health intervention primarily delivered through digital health technologies and targeting lifestyle-related behaviors. Digital health interventions included, but were not limited to, mobile apps, web-based platforms, SMS text message reminders, online courses, virtual coaches, digital gamification strategies, social media, and other eHealth/mHealth tools.3. Comparison
The presence of a control group was not mandatory; all original studies reporting intervention effects were eligible for inclusion.4. Outcomes
The primary outcomes included lifestyle behavior indicators, specifically physical activity, sedentary behavior, diet, and sleep.Secondary outcomes included physical and mental health indicators, such as weight, waist circumference, and self-efficacy.5. Study design
Original empirical studies targeting 1 or more of the 4 lifestyle behavior domains among college students and implementing digital health interventions were included.No restrictions were placed on study design; however, intervention content, participant characteristics, and relevant outcome measures were required to be clearly reported.Study SelectionAll retrieved records were imported into EndNote 20 (Clarivate Plc) reference management software for duplicate removal and standardized record numbering. Subsequently, 2 reviewers (QYZ and JJJ) independently screened titles and abstracts for initial eligibility. Records that passed the initial screening were subjected to full-text assessment to determine final eligibility for inclusion. To ensure standardization and consistency in the screening process, all reviewers received standardized training on the predefined inclusion and exclusion criteria. Interrater reliability between the 2 reviewers was assessed using the Cohen κ coefficient, yielding a value of 0.86, which indicates a high level of screening agreement. In cases of disagreement regarding individual records, a third reviewer (ZHY) was consulted to facilitate discussion and achieve a final consensus.
Data Extraction and SynthesisTo ensure standardization and consistency in the data extraction process, the research team developed a structured data extraction form in advance, covering the study title, first author, publication year, study region, study design, intervention population characteristics, intervention protocol characteristics, outcome measures, intervention effectiveness, and study conclusions. The data extraction form was pilot-tested using 5 studies to assess its feasibility. During the formal data extraction process, 2 reviewers (QYZ and JJJ) independently extracted the data. In cases of missing data or discrepancies in interpretation, a third reviewer (ZHY) was consulted to resolve disagreements. The final extracted data were consolidated into a standardized table and are presented in .
Quality AssessmentAll included studies were initially assessed for methodological quality using the Mixed Methods Appraisal Tool (MMAT, 2018 version) to obtain an overall preliminary appraisal of study quality. The MMAT is designed to evaluate 5 categories of study designs: qualitative research (QR), quantitative randomized controlled trials (QRCTs), quantitative nonrandomized studies (QNRSs), quantitative descriptive studies (QDSs), and mixed methods studies (MMSs), each comprising 5 appraisal criteria []. To enhance specificity and methodological rigor, the Risk of Bias 2 (RoB 2) tool was further applied to assess the risk of bias in QRCTs. For all other study designs, the Joanna Briggs Institute (JBI) critical appraisal tools were applied. This 2-stage quality assessment approach was intended to balance breadth and depth in methodological evaluation. Quality assessments were conducted independently by 2 reviewers (QYZ and JJJ), with discrepancies resolved through discussion. To ensure consistency, both reviewers received standardized training on the MMAT, RoB 2, and JBI critical appraisal tools and completed pilot scoring exercises before the formal assessment.
In this study, a total of 2998 records were retrieved from 10 major English-language databases. After deduplication and initial title and abstract screening, 273 articles were selected for full-text review. Based on the predefined exclusion criteria, exclusions were made for the following reasons: nonuniversity samples (n=68); interventions not primarily digital-based (n=20); wearable devices only, without active intervention components (n=5); absence of behavioral interventions (n=83); lack of relevant behavioral outcomes (n=35); and protocol or abstract only (n=19). Additionally, 3 more articles were identified through manual reference tracing of relevant review papers. Ultimately, 46 publications met the inclusion criteria and were included in the final analysis, as depicted in .
Figure 1. PRISMA flowchart of the study selection process. Quality Assessment ResultsFollowing 2 rounds of quality assessment, the first-round MMAT evaluation indicated that the 46 included studies demonstrated an overall high level of methodological quality. Specifically, 28 (61%) studies were rated as high quality, 14 (30%) as moderate quality, and 4 (9%) as low quality (see ; also see [,,-,,,,-]). Major methodological concerns identified during the assessment were primarily concentrated in MMAT items C4 and C5. Item C4 was primarily related to the implementation of blinding procedures, the adequacy of outcome interpretation, and the control of risk of bias, whereas item C5 reflected issues such as insufficient intervention adherence and the lack of rigorous statistical analyses. In the second round of assessment, the RoB 2 tool was applied to evaluate 30 QRCTs, indicating that the primary sources of bias were related to outcome measurement, deviations from intended interventions, and the handling of missing outcome data (see ; see also [,,,-,-,,,-,-,,,,,-]). Concurrently, the JBI critical appraisal of the remaining 16 studies indicated that key factors influencing study quality primarily included sample representativeness, intervention adherence, and the objectivity of outcome measurement (see ; see also [,,,,,,, -,,,,,,]).
The emergence of these methodological issues can be primarily attributed to 2 factors. On the one hand, the behavioral nature of DHIs makes the implementation of blinding inherently challenging, and several key behavioral outcomes rely on participant self-report measures. On the other hand, relatively high dropout rates associated with DHIs contribute to issues such as low intervention adherence and elevated loss-to-follow-up rates in some studies. When combined with insufficiently rigorous statistical analyses, these challenges may result in suboptimal handling of missing data or deviations from intended interventions. Although studies rated as moderate to low quality constitute a notable proportion of the included literature, it is important to recognize that many of their methodological limitations are closely related to the inherent characteristics of DHIs. Moreover, many of these studies primarily aimed to explore the feasibility and applicability of DHIs rather than to provide definitive evidence of intervention efficacy. Therefore, these studies retain substantial value for informing future research and intervention development. Given these considerations, no studies were excluded from this review solely based on methodological quality. Instead, all eligible studies were included, and findings from risk-of-bias assessments were systematically incorporated into the narrative synthesis. This approach allows for a comprehensive presentation of the current evidence landscape while explicitly identifying both its strengths and limitations.
Figure 2. Quality assessment results of the Mixed Methods Appraisal Tool. 
Figure 3. Quality assessment results of the Risk of Bias 2 tool. 
Figure 4. Quality assessment results of Joanna Briggs Institute critical appraisal tools. N/A: not applicable. Data Extraction ResultsThis review included a total of 46 studies. The basic characteristics of the included studies are summarized in , with detailed data extraction results provided in . Given the substantial heterogeneity among the included studies with respect to study design, target behaviors, intervention formats, core functions, and primary outcome measures, as well as variations in methodological quality, a meta-analysis was not conducted. Instead, a comprehensive analysis was performed using descriptive synthesis and comparative approaches. By systematically organizing and describing key characteristics of DHIs—including intervention targets, participant characteristics, sample sizes, formats, functions, durations, outcomes, and effects—this review delineates the overall patterns and heterogeneity within the field. Specific details are elaborated in the subsequent sections and illustrated through relevant tables, charts, and figures.
Table 1. Summary of data extraction from included studies.Study design and relevant studiesTotal completed, NParticipant age (years), mean (SD)Intervention(s)Target behavior(s)FunctionEffectivenessaLimited: limited evidence of effectiveness, based on reported effect measures, CIs, and authors’ conclusions (see ).
bYes: evidence of effectiveness, based on reported effect measures, CIs, and authors’ conclusions (see ).
cNo: no evidence of effectiveness, based on reported effect measures and authors’ conclusions (see ).
dNR: not reported.
In terms of annual distribution (see ), the number of studies during the early period (2014-2015) was low, with only 1 publication per year. Since 2016, the number of publications increased markedly, reaching a first minor peak in 2016 (n=8), possibly associated with the rapid adoption of smartphones and mobile apps among college students. From 2017 to 2020, the number of studies fluctuated between 1 and 5 annually, maintaining an overall moderate level. The number increased again and stabilized in 2021-2022, declined slightly in 2023, reached a second peak in 2024 (n=8), and remained high in 2025 (n=4). Publications from the last 5 years accounted for more than half of all studies identified.
Figure 5. Annual and cumulative publication counts of the included studies. The regional and country distribution of the included studies demonstrates a clear geographical concentration. At the regional level, most studies were conducted in North America (n=18, 39%), followed by Oceania (n=10, 22%) and Europe (n=9, 20%). Asia accounted for 6 (13%) studies, while Africa contributed the smallest share with 3 (7%) studies. At the country level, the United States recorded the highest number of publications (n=15, 33%), followed by Australia (n=9, 20%). The United Kingdom, Germany, Canada, South Korea, and India each contributed 2 studies. The remaining countries were represented by a single study, indicating a relatively dispersed distribution beyond the leading contributors.
The distribution of study design types among the included studies exhibited a clear structural pattern. The largest proportion comprised QRCTs (n=30, 65%). This was followed by QNRSs (n=10) and QDSs (n=4), which were primarily used for exploratory analyses and descriptive accounts of phenomena. By contrast, QR and MMS were represented by only 1 article each, accounting for less than 2% of the total. Overall, DHI studies addressing college students’ lifestyle behaviors are predominantly quantitative, with a marked preference for QRCTs.
With respect to ethical compliance, all included studies adhered to relevant ethical guidelines, with all 46 (100%) explicitly reporting informed consent procedures and ethics committee approval or review status. Regarding privacy protection and data security, 24 (52%) studies explicitly reported the implementation of protective measures, including secure server storage compliant with data safety standards, encrypted data transmission, data deidentification, and strict access control mechanisms. With respect to adverse events and intervention-related risks, no serious adverse events were reported across the included studies. Only a small number of studies reported minor negative issues related to technology use, such as fluctuations in intervention engagement, higher dropout rates, or reduced compliance attributable to participants’ competing academic or personal commitments. No health risks were identified that were directly attributable to the DHIs.
Intervention Design and Implementation ResultsIntervention ObjectivesAmong the intervention objectives examined in the included studies, 30 addressed physical activity, 26 addressed diet, 10 targeted sedentary behavior, and 6 targeted sleep. Single-behavior interventions accounted for a large proportion of the studies; however, multibehavior crossover interventions were also substantial, with combined physical activity and diet interventions being the most common (n=18). Notably, physical activity was both the most frequent single-behavior intervention target and the primary entry point for multibehavior combined interventions, whereas sleep was relatively underemphasized in intervention design.
Intervention ParticipantsBased on the PROGRESS-Plus (Place of Residence, Race/Ethnicity, Occupation, Gender/Sex, Religion, Education, Socioeconomic Status, Plus Other Relevant Factors) framework, a synthesis of sociodemographic characteristics from 46 DHI studies identified 10 primary participant categories (see ), including health status (n=46), age (n=45), gender/sex (n=45), education (n=41), occupation (n=39), place of residence (n=36), race/ethnicity (n=28), socioeconomic status (n=14), social capital (n=8), and religion (n=1). The analysis revealed the following: (1) All participants were college students, predominantly aged 18-30 years, which is consistent with typical college student demographics and showed no substantial deviation across studies. (2) Most interventions targeted students with generally healthy status, whereas 14 out of 46 (30%) focused on subpopulations with specific health risks or special needs, such as overweight or obesity, sleep disorders, psychological stress, or disordered eating behaviors. (3) Gender/sex distribution was relatively balanced across studies, whereas education and occupation exhibited limited variability owing to the homogeneity of the study population. (4) By contrast, PROGRESS-Plus dimensions such as race/ethnicity, socioeconomic status, social capital, and religion received notably limited attention, with a lack of systematic analysis from a health equity perspective.
Intervention SampleThe sample sizes of the included studies varied considerably. Histograms indicated that most studies had sample sizes concentrated below 200 participants, with a median of approximately 95, whereas a few studies had small (<50) or extremely large (>400) samples. As shown in , box-and-whisker plots further revealed an uneven distribution with long-tailed characteristics. Variations in sample size were closely associated with study design. Rigorous QRCTs typically require larger samples to ensure statistical power and therefore tend to employ medium- to large-scale sample sizes. By contrast, QDSs and QR are more inclined toward small-sample explorations, sometimes recruiting only a few dozen participants, and are more susceptible to selection bias.
Figure 6. Sample size distribution of the included studies. Intervention ModalitiesThe intervention formats in the included studies fell into 3 main categories. The first category, single, referred to interventions employing only 1 digital health technology (n=29), such as mobile apps. The second category, multiple, involved combining multiple digital health technologies within the same intervention (n=10). For example, the TXT2BFiT program integrated phone calls, websites, apps, and SMS text messaging simultaneously to achieve intervention goals. The third category, combined (n=7), compared the effectiveness of different combinations of digital health technologies, such as a “web-based nutrition intervention only” versus a “web-based intervention combined with daily SMS text message reminders.” Regarding the types of intervention technologies, these could be categorized into 7 groups: (1) mobile apps, used 21 times; (2) web-based platforms, including websites (13 times), online forums (3 times), and digital learning or eHealth tools (4 times); (3) mobile communications, including SMS text messages (11 times), emails (5 times), and phone calls (3 times); (4) social media (7 times); (5) wearable devices (4 times); (6) gamification and multimedia, including gamification and exergames (5 times), videos (2 times), and virtual reality (1 time); and (7) intelligent technologies, represented only by artificial intelligence (1 time). Overall, mobile apps and web-based platforms were the most frequently used technologies.
Intervention FunctionalitiesThe technological functions of the DHIs included in this review exhibited distinct patterns of emphasis. Educational and guidance-related functions predominated across most interventions, followed by monitoring and prompting functions; by contrast, feedback and interactive functions were used less frequently, while immersive, screening, and engagement-related functions were rarely incorporated. Coding these interventions using the Behavior Change Technique Taxonomy version 1 (BCTTv1) indicated that the most frequently employed techniques were “4.1 Instruction on how to perform the behavior” and “5.1 Information about health consequences,” suggesting that current DHIs primarily emphasize foundational behavioral support functions. Further frequency analysis of BCT coding among effective intervention studies (see ) showed that BCTTv1 codes 4.1 (16/87, 18%), 5.1 (14/87, 16%), and 2.3 (13/87, 15%) constituted the core set of techniques, collectively accounting for nearly half of all techniques used in effective interventions.
Table 2. Frequency distribution of codes in effective intervention studies (N=87).Behavior Change Technique Taxonomy version 1 codeDescriptionFrequency, n (%)4.1Instruction on how to perform behavior16 (18)5.1Information about health consequences14 (16)2.3Self-monitoring of behavior13 (15)2.2Feedback on behavior8 (9)7.1Prompts/cues8 (9)6.1Demonstration of behavior4 (5)2.1Monitoring by others (no feedback)3 (3)3.1Social support (unspecified)3 (3)5.3Social/environmental consequences3 (3)6.2Social comparison3 (3)12.1Restructuring physical environment3 (3)1.2Problem solving2 (2)1.1Goal setting (behavior)1 (1)1.6Discrepancy between current behavior and goal1 (1)2.4Self-monitoring of outcomes1 (1)2.6Biofeedback1 (1)2.7Feedback on outcomes1 (1)5.6Emotional consequences1 (1)9.1Credible source1 (1)Intervention DurationThe duration of interventions varied considerably across the included studies (see ; see also [,,-,,,,-]), with the majority concentrated in the short- to medium-term range (1-16 weeks). Studies involving long-term interventions (>16 weeks) were relatively scarce, with only 4 studies identified. Among these studies, most incorporated follow-up periods, and medium- to long-term interventions were typically associated with more systematic follow-up protocols. With respect to study design, randomized controlled trials predominantly employed interventions of medium duration (8-16 weeks). Among the QDSs (n=4) and MMS (n=1) analyzed, some studies employed longer intervention durations to observe behavioral maintenance; however, these accounted for a relatively small proportion of the evidence base. Subgroup analysis demonstrated a progressive increase in the proportion of studies classified as “effective” with increasing intervention duration (see ): 2 out of 4 (50.0%) for ultra-short-term (<1 week), 10 out of 16 (63%) for short-term (>1 and <8 weeks), 12 out of 18 (67%) for medium-term (8-16 weeks), and 3 out of 4 (75%) for long-term (>16 weeks). Notably, medium-duration interventions (8-16 weeks) not only represented the largest proportion of the existing evidence but also demonstrated both a relatively high “effective” rate (12/18, 67%) and a low “ineffective” rate (1/18, 6%). These findings indicate that current DHI research remains skewed toward short- and medium-term interventions, with the 8-16-week category standing out in terms of evidence volume and the apparent stability of intervention effects.
Figure 7. Chart of intervention duration and follow-up duration. Table 3. Subgroup analysis of intervention duration.Duration group (weeks)Number, nEffective (yes), n (%)Limited effect, n (%) Not effective (no), n (%) Ultrashort (≤1)42 (50.0)2 (50.0)0 (0) Short (>1 and <8)1610 (63)4 (25)2 (13)Medium (8-16)1812 (67)5 (28)1 (6)Long (>16)43 (75)0 (0)1 (25)Subtotal (analyzed)4227 (64)11 (26)4 (10)Excluded: not reported4N/AaN/AN/AaN/A: not applicable.
Intervention OutcomesAs a result of substantial heterogeneity among the included studies with respect to outcome measurement instruments, outcome definitions, and assessment time points, it was not feasible to define a unified primary outcome or to conduct a statistically valid meta-analysis. Accordingly, this review adopted a descriptive synthesis framework to summarize and integrate the relevant outcomes. The outcome metrics in the included studies were classified into 2 main categories. The primary outcomes focused on lifestyle behaviors, including physical activity (eg, activity level, step count, and activity intensity), sedentary behaviors (eg, total sedentary time and frequency of breaks from sitting or resting), diet (eg, dietary quality; intake of fruits, vegetables, and sugar-sweetened beverages; energy intake; and nutritional knowledge), and sleep (eg, sleep quality, duration, efficiency, and severity of insomnia). These indicators directly reflect changes in core health behaviors resulting from the intervention and serve as a key basis for evaluating its effectiveness. Secondary outcomes, serving as supplementary indicators, were more diverse and encompassed physical health status and psychosocial dimensions, such as weight and body composition (eg, weight, BMI, waist circumference, and body fat percentage), physical fitness indicators (eg, flexibility, muscle strength, and cardiorespiratory fitness), cardiometabolic indicators (eg, blood pressure, blood glucose, and blood lipid profiles), and psychological and self-perception measures (eg, self-efficacy, body image, and life satisfaction). Overall, current studies remain primarily focused on primary outcomes, while secondary outcomes have expanded but continue to exhibit limited coverage.
Intervention EffectivenessBased on the reported effect measure types, effect estimates, confidence levels (%), and CIs across the included studies, together with a comprehensive assessment of the authors’ conclusions (see ), the results indicated that 31 (67%) studies demonstrated evidence of intervention effectiveness, suggesting that DHIs are generally associated with positive outcomes in improving lifestyle behaviors among college students. Four studies reported no statistically significant effects, with limitations primarily attributed to small sample sizes or short intervention durations. The remaining 11 studies demonstrated limited effectiveness, with improvements observed only in selected secondary outcomes or during short-term follow-up periods.
Based on a comprehensive assessment of each behavioral domain using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework, the certainty of evidence for the physical activity and diet domains was rated as “moderate,” whereas the evidence for the sedentary behavior and sleep domains was rated as “low.” With respect to evidence credibility, this review indicates a moderate level of confidence in the overall estimate that DHIs are effective in improving lifestyle behaviors among college students. The certainty of evidence in some domains was downgraded due to methodological limitations in the existing primary studies, including small sample sizes, challenges in implementing blinding, and inconsistencies in outcome assessment tools. Nevertheless, these GRADE assessments provide an accurate reflection of the current state of the evidence and its overall strength for DHIs among college students, thereby offering valuable guidance for interpretation and future research.
In terms of temporal trends, research on DHIs targeting college students’ lifestyle behaviors has gradually emerged since 2014, expanded rapidly after 2016, and reached a peak in the past 5 years []. This trend has been driven primarily by 4 categories of factors. First, technological advances have laid a solid foundation for DHIs, with the proliferation of smartphones, wearable devices, and app ecosystems significantly enhancing their accessibility and operability []. Second, conceptual advancements have accelerate
Comments (0)