This study used a 2-arm, parallel, quasi-experimental design to examine the short-term effects of a mHealth intervention on healthy behaviors and cardiometabolic indicators among sedentary employees. The quasi-experimental design was selected to minimize contamination between groups while preserving the integrity of intervention delivery [25]. Recruitment and baseline assessments were conducted between April and May 2020 in 2 aerospace industrial workplaces in Taiwan.

Inclusion criteria were full-time employment in sedentary roles (sitting ≥6 hours per workday), age ≥20 years, absence of physical limitations restricting physical activity, and access to a smartphone with the LINE messaging app and an internet connection. Exclusion criteria were part-time employment, anticipated absence from work >2 weeks, relocation within 3 months, or pregnancy or planned pregnancy during the study period. A priori power analysis [26] (effect size=0.5; power=80%; correlation=0.5) indicated 48 participants per group were needed, accounting for a 20% dropout rate as suggested by prior work [21].

Approval to conduct the study was obtained from workplace senior management. Study invitations were disseminated via company websites, email, word-of-mouth, and on-site information sessions. Occupational health nurses introduced the study at both sites, and the nurse at the intervention site was trained as a research liaison using a standardized handbook.

Participants were enrolled by occupational health nurses after obtaining site-level consent from senior management. Worksite allocation to the intervention or control condition was determined by a coin toss conducted by an independent research assistant. Group assignments were concealed until completion of baseline assessments to minimize bias. Although the quasi-experimental design limited full allocation concealment, procedures were implemented postbaseline to reduce selection bias.

Participants and the site nurse were not blinded due to the nature of the mHealth intervention. However, all data collectors (except for 1 trained research assistant) and the dietitian analyzing dietary records were blinded. Statistical analyses were conducted on deidentified data to ensure analytical blinding.

A total of 101 employees were enrolled and assigned to the intervention group (n=50) or the control group (n=51). Intervention participants also received a Mi Smart Band 4 (Anhui Huami Information Technology Co) for daily activity tracking and a participant handbook.

Assessments were performed at baseline and 12 weeks during paid work hours. At the control site, an occupational health nurse distributed monthly educational booklets. At the intervention site, participants engaged with the SIMPLE HEALTH web app for 12 weeks. Both groups received comparable educational content, though the delivery format differed (print vs digital).

The mHealth intervention, SIMPLE HEALTH, was grounded in Social Cognitive Theory [23] and the Ecological Model [24], emphasizing self-regulation, self-efficacy, and multilevel behavioral influences. Program design was guided by dietary and physical activity guidelines [27-29], expert consultation, and prior participant feedback [21]. To facilitate sustainable behavior change, evidence-based behavior change techniques were systematically incorporated [30].

SIMPLE HEALTH was a smartphone-optimized web app developed through iterative testing. It included eight interactive components: (1) activity tracking, (2) goal setting, (3) behavior logging, (4) reminders, (5) personalized advice, (6) electronic educational booklets, (7) electronic motivational booklets, and (8) individual and team challenges. A summary of the intervention components is presented in Multimedia Appendix 1, with screenshots in Multimedia Appendix 2 and detailed development and usability findings reported elsewhere [31].

Intervention participants accessed educational content equivalent to that of the control group but in a digital format. They were instructed to wear the activity tracker daily, set biweekly goals, log diet and activity behaviors, and use app-based reminders. Notifications were delivered through LINE. For data security, participants created personal login credentials and linked their accounts to the study’s LINE Official Account using encrypted connections.

Control participants received 6 colorful print booklets over 3 months covering 2 themes—“sit less, move more” and “healthy eating.” These materials aimed to enhance knowledge, awareness, and practical skills for healthier behaviors. Control participants did not receive access to the app or activity trackers.

To assess the short-term effects of the intervention, we measured biomarkers of glucose metabolism, lipid metabolism, blood pressure, body composition, and physical activity and dietary behavior at baseline and 12 weeks. Data collection included an on-site assessment, activity monitoring, and a self-administered questionnaire.

The intention-to-treat principle was followed, where all participants were analyzed in the groups to which they were initially assigned, regardless of whether they completed the intervention or adhered to the protocol. This approach helps to minimize bias and provides a more realistic estimate of the intervention’s effect by reflecting real-world scenarios where not all participants fully comply with the study procedures [38]. Descriptive statistics summarized the characteristics of the participants. The equivalence of the 2 groups was assessed using chi-square tests for categorical variables and t tests for interval and continuous variables. To examine the effects of the mHealth intervention on healthy behaviors and cardiometabolic health indicators, a generalized estimating equation (GEE) model was used. GEE was chosen because it accounts for intracorrelation between repeated measurements and provides unbiased estimates despite missing data [39]. GEE models with robust estimators and unstructured correlation structures were used, adjusting for baseline potentially confounding variables. For consistency across the text and tables, the group × time interaction effect from the GEE model is reported as the adjusted mean difference (adjusted MD), representing the between-group difference in change from baseline to 12 weeks. A 2-tailed significance level was set at .05. All data analyses were conducted using SPSS version 26.0 (IBM Corp).

The study protocol was approved by the China Medical University and Hospital Research Ethics Center (CRREC-109‐017). Written informed consent was obtained from all participants before enrollment, detailing study objectives, procedures, potential risks and benefits, confidentiality, and voluntary participation rights. All data were anonymized using coded identifiers and stored on encrypted servers. Participants received gift vouchers valued at NT $100 (approximately US $3) after each assessment as recognition of their time. The trial was registered at ClinicalTrials.gov (NCT04347096; registered April 13, 2020).

This study evaluated the short-term effects of an mHealth program on physical activity, sitting time, dietary behavior, and cardiometabolic biomarker outcomes among sedentary employees. The intervention led to significant improvements in steps, moderate physical activity, and intake of calories, carbohydrates, fats, grains, and vegetables. Both the intervention and control groups demonstrated significant improvements in LDL cholesterol, body fat percentage, waist circumference, and occupational sitting and walking. However, the intervention group experienced unfavorable changes in triglyceride levels and HDL cholesterol. These findings contribute to the limited evidence on the benefits of mHealth programs targeting physical activity, sedentary behavior, and dietary habits, suggesting that such interventions may offer some cardiometabolic health benefits for sedentary employees.

The significant improvements in physical activity (steps and moderate physical activity) and dietary behavior (intake of calories, carbohydrates, fats, grains, and vegetables) observed in our study align with previous research on mHealth interventions and web-based health promotion programs targeting multiple health behaviors, including physical activity and diet [40-43]. Cook et al [40] evaluated the effectiveness of a web-based multimedia health promotion program aimed at improving dietary practices, increasing physical activity, and reducing stress in employees of a human resources company. The intervention group showed significant improvement in attitudes toward a healthy diet and dietary stage of change. Another study by Cook et al [41] examined the effectiveness of a web-based health promotion program, HealthyPast50, on diet, physical activity, stress, and tobacco use in older employees of an information technology company. The intervention group significantly improved diet behavior change self-efficacy, planning healthy eating, and mild exercise, but not physical activity. Deitz et al [42] tested the effectiveness of a web-based cardiovascular health promotion program targeting physical activity, diet, stress, mood, and smoking in hospital employees. The intervention group significantly improved dietary attitudes, intentions, self-efficacy, exercise self-efficacy, and overall level of physical activities, particularly strenuous physical activity. Finally, Oftedal et al [43] examined the preliminary efficacy of a mHealth intervention targeting shift workers’ physical activity, diet, and sleep quality. The intervention group significantly improved diet quality, though there were no significant changes in physical activity or sleep quality.

In this study, we observed inconsistent findings on the impact of increasing physical activity, reducing sitting time, and improving dietary behavior on cardiometabolic biomarkers, reflecting similar patterns found in previous research. Positive trends were noted in diastolic blood pressure, soft lean mass, and fasting glucose, though these were not statistically significant. In both intervention and control groups, there were significant improvements in LDL cholesterol, body fat percentage, and waist circumference. However, the intervention group showed unfavorable changes in triglycerides and HDL cholesterol.

The unfavorable changes in triglycerides and HDL cholesterol in the intervention group, despite promoting a low-carbohydrate diet that is high in healthy fats, may be due to several factors. Even small increases in carbohydrate intake can significantly impact lipid metabolism, potentially raising triglycerides and lowering HDL cholesterol, as the body’s lipid response is highly sensitive to carbohydrate changes [44]. The type of carbohydrates and fats consumed also matters; diets high in refined carbs or low-quality fats can negatively affect lipid profiles, even with balanced macronutrient ratios [45]. Additionally, individual metabolic responses and genetic factors can lead to varied lipid responses, further complicating outcomes [44]. In brief, these unfavorable changes likely result from subtle shifts in macronutrient intake and the types of foods consumed, underscoring the complexity of dietary impacts on lipid metabolism.

Previous research on the impact of mHealth interventions on cardiometabolic biomarkers has yielded mixed results. For example, Deitz et al [42] found no significant effects of a web-based program on waist circumference and blood pressure. In contrast, Fukuoka et al [46] evaluated a diabetes prevention intervention that combined a mobile phone app and a pedometer targeting physical activity and dietary intake in overweight adults at risk for type 2 diabetes. This intervention group showed significant reductions in hip circumference and blood pressure but no significant effects on fasting lipid levels (triglycerides, total cholesterol, LDL, and HDL) or glucose levels (hemoglobin A_1c and glucose).

A recent review by Jung et al [47] provided a comprehensive analysis of the effectiveness of mHealth interventions in promoting physical activity and weight loss among workers. While the mHealth intervention group significantly improved physical activity, they did not show a significant difference in weight loss. Another review by Buckingham et al [48] assessed the effectiveness of mHealth technology, including wearable activity monitors and smartphone apps, for promoting physical activity and reducing sedentary behavior in workplace settings. The review found that 69% of studies reported significant impacts on reductions in weight, systolic blood pressure, and total cholesterol.

These mixed findings highlight the complexity of mHealth interventions and their varied effects on cardiometabolic health indicators. While some studies demonstrate significant benefits in specific biomarkers, others show limited or no impact. This underscores the need for further research to identify the most effective components and contexts for mHealth interventions. This includes examining the specific features that contribute to behavioral changes and improvements in health indicators. Understanding how these interventions influence behavior and health at a physiological level can help in designing more targeted and effective programs. Furthermore, it is crucial to conduct longer-term studies to assess the sustainability of behavior changes and the long-term impact on cardiometabolic health.

Of particular interest was the finding that both the intervention and control groups showed significant improvements in LDL cholesterol, body fat percentage, waist circumference, and occupational sitting and walking from baseline to 12 weeks after the intervention. This suggests either (1) both interventions effectively improved healthy behaviors and cardiometabolic health indicators or (2) most participants in both groups were highly motivated to improve their health, contributing to the observed changes. Initially, we hypothesized that the mHealth intervention group would be more effective than the control group with print educational booklets. Since this study did not have a no-treatment control group, a cautious interpretation of the intervention effect is needed. The improvements could be attributed to assessment effects for both groups. For example, after the baseline assessment, both groups received feedback on biomarkers related to glucose metabolism, lipid metabolism, blood pressure, and body composition, as well as dietary assessment along with recommendations for calorie intake and portion sizes for the 4 food groups (whole grains, protein, vegetables, and fruits). This feedback likely served as a motivating factor and benefited the control group in addressing their health and behavioral issues. Thus, this study’s lack of a no-treatment control group limits our ability to definitively attribute the improvements to the interventions rather than other motivating factors or external influences. Future research should consider including a no-treatment control group to isolate the interventions’ effects better.

Although the control group receiving colorful print educational booklets was originally intended as a control, the scope and quality of these materials likely played a significant role in promoting behavioral regulation and self-efficacy. As a result, the print materials effectively functioned as interventions, posing a substantial rival to the mHealth program. The effectiveness of print materials in changing health behaviors and outcomes is well-supported by research [40].

Compared to the print materials, participants in the mHealth program received electronic versions of the 6 educational booklets and additional intervention components. However, the print materials offered distinct advantages: users had all the material in hand from the start without needing to access a web app and could read the booklets easily. This is particularly advantageous for middle-aged people with presbyopia, who may prefer print materials over smartphones due to the small screen size. The apparent efficacy of the print materials suggests that providing them to the mHealth intervention group could further enhance the program’s effectiveness. This finding highlights the importance of considering the format and accessibility of educational materials in health interventions, especially for populations that may have preferences or limitations related to technology use.

Integrating mHealth interventions into worksite health promotion programs can benefit employee well-being and productivity. The findings from this study suggest several practical implications for occupational health practitioners:

This study has several notable strengths. First, it is the first mHealth intervention study to target physical activity, sedentary behavior, and dietary behaviors in a workplace context. This comprehensive approach provides valuable insights into how these interventions can be integrated into employee wellness programs. Second, the study achieved good participant retention, enhancing the reliability of the findings. Third, using objective measures to assess most outcomes, including cardiometabolic biomarkers, physical activity, and dietary behavior, adds robustness to the results.

However, the study also has limitations. First, the relatively short duration of the intervention may not capture the long-term effects on cardiometabolic health. To mitigate this, we recommend future studies with extended follow-up periods to understand the observed improvements’ sustainability better. Second, the self-reported nature of the healthy eating behavior index and occupational sitting and physical activity questionnaire may introduce recall or social desirability bias. As we also included objective measures, the concern is minimal. Third, participant allocation was determined using a coin toss, which, although simple and transparent, is not an entirely unbiased method. Prior research has demonstrated that coin toss outcomes can be subtly manipulated or deviate from a true 50:50 probability due to human control or physical factors of the coin [49]. This limitation may have introduced a small but possible allocation bias, and future studies should use computer-generated randomization to minimize this risk. Finally, the generalizability of the results is limited as the study involved only 2 workplaces, and participants were not randomly selected. To enhance external validity, more studies with larger samples from diverse occupational settings and randomized sampling methods are recommended.

The mHealth intervention effectively improved physical activity, dietary behaviors, and certain cardiometabolic health indicators among sedentary employees. These findings suggest that integrating mHealth tools into workplace wellness programs can be beneficial. Future studies should explore long-term effects and broader applications across diverse occupational settings to confirm and extend these results.