To ensure relevance and comparability, studies were included if they met the following criteria:

I. Evaluated mHealth applications designed for general wellness, preventive health, or chronic disease management.

II. Used validated usability instruments (e.g., MAUQ or SUS).

III. Reported numeric usability scores (mean and standard deviation) and sample size.

IV. Focused on adult populations (18 years and above).

Studies were excluded if they:

I. Included specialized clinical populations only (e.g., hospitalized patients, specific disease groups) that were not representative of general users.

II. Did not report sufficient numeric data for analysis.

III. Were conference abstracts, letters, or non-peer-reviewed sources.

From each included study, the following information was extracted:

I. App characteristics (name, type, platform).

II. Study population and sample size.

III. Usability scores (mean, standard deviation).

IV. Instrument used for usability assessment (MAUQ, SUS, or other).

V. Year and country of publication.

Numeric data were analyzed using descriptive statistics, including mean scores, standard deviations, and frequency distributions [3], [6]. Where possible, usability scores were aggregated across studies to compare trends between different app types (e.g., wellness apps vs. chronic disease management apps) [5]. Open-ended qualitative feedback reported in studies was summarized to highlight common usability strengths and weaknesses. Limitations regarding heterogeneity of study populations, app types, and measurement instruments were explicitly acknowledged to ensure transparency and interpretability.

In addition to quantitative aggregation, where studies provided qualitative feedback (e.g., participants’ comments on interface design, navigation, ease of use, aesthetic or content issues), these qualitative data were synthesized thematically. Common usability strengths and weaknesses identified across studies were tabulated and compared, following procedures recommended in umbrella-reviews of mHealth usability studies [3], [7]. If data necessary for quantitative pooling were missing (e.g., standard deviation, sample size), those studies were included only in qualitative synthesis and were excluded from pooled quantitative analysis. This ensures integrity and transparency in statistical reporting and avoids bias from incomplete data [3], [5].

Finally, given the heterogeneity in apps' purpose (wellness vs. disease management), populations (healthy adults vs. patients), and instruments used (MAUQ vs. SUS), subgroup analyses were planned. Means and standard deviations are reported separately for each subgroup, and where sufficient data existed, comparisons between subgroups (e.g., wellness apps vs. disease-management apps) were made to explore possible differences in usability performance. These subgroup comparisons were described using simple comparative statistics (mean differences), and where possible, statistical tests (e.g., t-tests) were referenced in line with prior meta-analytic usability research [6], [8].

Analysis of the extracted data indicated that the mean SUS score across all included digital health applications was 76.64 (SD = 15.12), exceeding the commonly accepted SUS benchmark of 68, indicating above-average usability [3], [8]. For studies employing the MAUQ, overall scores were similarly high. For instance, in a cognitive-assessment app for individuals with multiple sclerosis, the mean SUS score was 84.5 (SD = 13.34) and the total MAUQ score was 104.02 (SD = 17.69), reflecting excellent usability [3]. Subscale analyses in this study indicated that ease of use scored 30.57 (SD = 5.18), interface & satisfaction scored 42.96 (SD = 7.93), and usefulness scored 30.49 (SD = 7.14) [3].

When analyzed by app category, physical-activity and fitness apps consistently received higher usability ratings compared to non–activity apps. In the pooled analysis, physical-activity apps achieved a mean SUS score of 83.28 (SD = 12.39) [9]. Conversely, non–physical-activity apps, including wellness, educational, and disease-management applications, averaged a SUS score of 68.05 (SD = 14.05) [9], approximately meeting the usability benchmark.

Several studies also reported qualitative feedback regarding app usability. Commonly identified strengths included intuitive navigation, clear interface design, ease of use, and perceived usefulness for daily health or disease self-management [10]. In a longitudinal usability assessment of a cancer-support app, participants initially rated the app highly (Week 4: SUS = 89.15, SD = 9.65), with slight reductions observed over time (Week 20: SUS = 85.57, SD = 12.88), highlighting that usability perceptions may evolve with continued use [10].

The aggregated findings indicate that most mHealth applications, particularly those focused on physical activity, achieve usability scores above accepted benchmarks, suggesting that they are generally well-received by users. Apps outside this category demonstrated more variability in usability, yet generally met minimum standards. Across all studies, ease of use and interface satisfaction were consistently rated positively, whereas perceived usefulness exhibited more variation, particularly in longitudinal assessments. These trends suggest that developers should prioritize ongoing evaluation of app usability to maintain user engagement and satisfaction over time [3], [5], [6].

Several limitations of this secondary analysis should be noted. First, the data were extracted from published studies and raw user-level data were not available, which may limit the precision of pooled estimates. Second, heterogeneity across studies—including differences in app types, populations, measurement instruments, and cultural contexts—reduces the generalizability of the findings. Finally, publication bias may have led to overrepresentation of apps with higher reported usability scores, as studies reporting lower usability may be underpublished [7], [8]. Table 1 provides a consolidated overview of the usability metrics reported across the included studies, summarizing key characteristics such as app type, evaluation instrument, sample size, and mean usability scores, thereby offering a clear comparative perspective on the performance of various mHealth applications.

Fig. 1 illustrates the comparative mean usability scores across different categories of mHealth applications, providing a visual representation of the relative performance patterns observed in the secondary analysis and complementing the detailed numerical data presented in Table 1.

The findings of this secondary analysis demonstrate that most mHealth applications achieve usability scores that meet or exceed established benchmarks, particularly those focused on physical activity. These results are consistent with previous research indicating that user-centered design and straightforward task structures contribute to higher usability ratings in fitness-oriented applications [3], [8]. In contrast, wellness and educational apps exhibited more variability, which may be attributed to greater diversity in content, interaction complexity, and user expectations. The higher scores observed in disease-management apps, such as cancer-support platforms, align with studies suggesting that apps developed for clinical populations often undergo more rigorous usability testing due to their integration with care pathways [10].

From an applied perspective, these findings highlight the importance of maintaining simplicity, clarity, and perceived usefulness in mHealth app design. Developers and system architects should prioritize iterative usability testing, as longitudinal studies indicate that user experience may decline over time if design elements fail to address evolving user needs [10]. For health practitioners and policymakers, the results underscore the value of adopting apps with validated usability scores, as higher usability is associated with improved adherence and more consistent engagement in digital health programs [6]. Furthermore, educational institutions seeking to integrate digital health tools into student wellness programs may benefit from selecting physical-activity apps, which consistently demonstrate strong usability outcomes.

Despite these insights, several limitations warrant consideration. First, the analysis relies on published secondary data, limiting access to raw datasets that could enhance analytic precision. Second, heterogeneity across studies, including differences in target populations, app categories, and usability instruments, may reduce comparability. Third, publication bias may favor studies reporting positive outcomes, potentially inflating aggregated usability scores [7]. Lastly, the inclusion of studies from diverse cultural and geographical contexts may restrict generalizability to specific populations, such as university students.