The potential of digital communication tools in enhancing health literacy and improving health outcomes for both patients and healthcare providers is significant. Research demonstrates the effectiveness of various digital communication tools including mobile health apps, in promoting health literacy and digital literacy. These tools have been shown to facilitate patient education, self-management, and clinical decision-making26,27,28.
Many health-related behaviors and patterns of behavior often take root during adolescence and young adulthood, persisting well into adulthood. The period coinciding with college or university attendance holds particular significance for the cultivation of healthy lifestyle habits, carrying potentially significant implications for long-term health outcomes29.
According to our research, participants in the traditional program were 19.97 years old on average, but those in the mHealth program were 20.5 years old. Regarding the distribution of genders, 40% of participants in the mHealth program were male and 60% were female, compared to 46.3% male and 53.7% female in the traditional program. Approximately two-thirds of the participants in the mHealth group hailed from rural areas, while 59.1% came from rural areas in the traditional group. Moreover, the majority of both groups belonged to a moderate socioeconomic status, with percentages of 45.4% and 47.3% in the mHealth and traditional groups respectively.
In examining the lifestyle patterns of the participants, we observed that approximately 24% of individual in the traditional group, and 20% of individual in the mHealth group smoked whether they smoked cigarettes or used waterpipes, this finding is in agreement with Al Ali and Khazaaleh30. According to the WHO’s findings, 16% of students were found to be cigarette smokers, while 17% were waterpipe smokers. Consistent with earlier research, waterpipe smoking prevalence exceeded that of cigarette smoking among students. This trend could be linked to the perception of waterpipe smoking as less harmful than cigarette smoking, coupled with its social acceptability and expected behavior31.
In terms of fast food consumption, 40% of individuals in the mHealth group were fast food consumers, while the other group had a slightly higher rate of 45%. Additionally, 46.8% and 44% of participants in the mHealth and traditional groups respectively exhibited poor dietary choices, this is in the same line with Al Ali and Khazaaleh30 who revealed that over half of the students had consumed unhealthy items within the past 24 h, often prioritizing fast food over fruits and vegetables. These findings closely resembled those of Alzahrani et al.32 who reported that around two-thirds of students frequented fast-food restaurants occasionally, with 28.0% doing so often, and 6.1% abstaining altogether. Similarly, national surveys among Jordanian33 and Lebanese students34 corroborated these results. Fast food consumption among students can be attributed to its ready availability in and around university campuses, where healthier food options may be limited, therefore raising the chance of gaining weight and developing chronic illnesses.
Our findings indicate that a significant proportion of participants exhibited low levels of physical activity, characterized as being under 600 MET minutes. Around 48% of people in the traditional group and 45% of those in the mHealth group showed this trend. These findings align with research by Verma et al.35 that used the International Physical Activity Questionnaire Long Form and discovered that 14.5% of all students had low levels of physical activity, with 14.2% falling into the moderate category and thus, indicating that 28.7% of participants had low physical activity levels. Similarly, Mahfouz et al.36 reported that over half of their study participants (62.7%) engaged in low physical activity. Notably, college students exhibit a high rate of physical inactivity, with 52.2% reported as inactive, and 34.4% categorized as overweight or at risk of becoming overweight/obese later in life37. These findings align with Awadalla et al.’s observations that 48% are of low levels of physical exercise among Saudi Arabian students at King Khalid University38. The prevalence of low physical activity in this demographic can be attributed to the limited time students have available for regular participation in physical activity programs. However, in contrast, Ali and Khazaaleha30 found that 58% of the undergraduate students surveyed regularly engaged in sports activities.
It is essential to assess how well different mHealth tactics work to enhance risk factor management and general lifestyle. Our research showed that both the mHealth and traditional groups experienced significant improvements in physical activity after the intervention (p values < 0.001, < 0.001), and that there was a statistically significant difference between the two groups, with the mHealth group showing greater improvement. These findings in harmony with Al-Nawaiseh et al.39 who found that the randomized controlled trial demonstrates that a 12-week mHealth intervention utilizing a mobile health application led to a significant enhancement in physical activity (measured by step counts) and reduction in body weight among college students. Our findings align with those of Safran Naimark et al., who observed a 26.9% increase in physical activity (step counts) following a pedometer-based mHealth intervention compared to baseline levels. This evidence serves as a foundation for designing an optimal mHealth intervention aimed at maximizing improvements in physical activity engagement, if sustained, is anticipated to yield various health benefits, including reduced risks of obesity, heart disease, and type 2 diabetes mellitus (T2DM).
In addition, our results declared that significant improvement in (FFQ) post-intervention in both groups (p < 0.001, < 0.001) respectively, and sleep quality scale (SQS) (p-value 0.019 and 0.012). Additionally, mHealth showed a statistically significant improvement over the traditional group in terms of (IPAQ), (FFQ) and (SQS) (p-value 0.03, 0.01, 0.009) respectively. Our results, consistent with Zhang et al.18 demonstrated that lifestyle interventions greatly enhanced the four characteristics of behaviors’ self-efficacy, as well as the individual dimensions and general health-promoting behaviors (nutrition, psychological well-being, stress management, and physical activity). Also, our results were similar to the characteristics of another patient-centered transitional care program run by nurses, which demonstrated significant effects on self-efficacy40. Various behavioral theories propose similar strategies for enhancing self-efficacy, including social persuasion, modeling, and mastery of experience41,42,
There was a slight improvement in BMI post-intervention but still statistically insignificant, this is in the same line with Al-Nawaiseh et al.39 who found that there is no significant improvement post-intervention and between the intervention and control group regarding weight, fat (%) and BMI. Also, Cruz-Cobo et al.43 revealed the implementation of mHealth interventions did not result in a notable decrease in patients’ BMI and waist circumference, unlike the findings reported by Chen et al.44, which showed that BMI significantly improved as a result of mHealth interventions.
On the contrary, the mHealth group and the traditional group showed statistically significant improvements in their systolic blood pressure (p-values < 0.001, < 0.001) and diastolic blood pressure (p values < 0.002) after the intervention, respectively, compared to the pre-intervention (p-value 0.003) regarding systolic blood pressure. In consistence with the previous findings, David et al.45 found that a lifestyle program enhanced with mHealth interventions featuring blood pressure monitoring and supportive text messages resulted in statistically significant and clinically meaningful improvements in adherence to achieving at least four lifestyle goals, compared to standard clinical treatment (UCT) alone. Chen et al.46, conducted a study in China and found that eHealth interventions significantly impacted systolic blood pressure (SBP), with six studies reporting a standardized mean difference (SMD) of -0.35 (95% CI: -0.66 to -0.04, p = 0.03). Similarly, research by Zha et al., Haas et al., and Nolan et al.47,48,49 demonstrated that eHealth interventions effectively improved SBP levels. This positive effect may be attributed to the flexibility of eHealth interventions, enabling them to support increased adherence to lifestyle programs and fit in with students’ lifestyles. These positive results are further supported by other advantages of eHealth, including its wide appeal, accessibility, ability to reach a variety of demographics, and high compliance at a cheap cost.
Regarding lipid profile, there was a statistically significant decrease in (TC (< 0.001, < 0.001), LDL (< 0.001, < 0.001), HDL (0.04, 0.005), and RBS (< 0.001, < 0.001) post-intervention compared to pre-intervention in mHealth group and traditional group respectively. Also, in comparing the mHealth group versus the traditional group, there was a statistically significant improvement in the mHealth than the traditional group in TC and LDL (p-value 0.04, 0.05) respectively. The effectiveness of ongoing counseling via mHealth may be responsible for the notable decrease in cholesterol. Communication between patients and healthcare professionals was made possible via mHealth, which improved intervention compliance and gave people access to health information.
Our results are consistent with earlier studies showing that technology-based treatments are more successful than conventional advice at encouraging adherence to healthy lifestyle choices46,50. Specifically, when this paradigm was used for cardiometabolic risk management, cholesterol levels improved51. Furthermore, two research investigated the efficacy of nutrition-only platforms. One study examined the effects of a computerized meal planning and nutritional tracking platform on lipid markers in people with dyslipidemia and found improvements in all parameters that were tested52.
Furthermore, one study investigated the impact of a mobile application that offered health education and tracked step counts on various cardiovascular risk factors within a presumably healthy population53. The findings indicated that increased daily step counts associated with app usage led to a reduction of 0.07 mmol/L in LDL cholesterol and a rise of 0.05 mmol/L in HDL cholesterol53. By improving lipid profiles, this study contributes to the scant data currently available demonstrating the efficacy of digital lifestyle interventions in lowering cardiovascular risk and correcting hormonal imbalances. These results demonstrate the potential of such interventions for primary and secondary prevention of cardiometabolic risk, in conjunction with previous clinical population-focused investigations.
Compared to a recent meta-analysis, our results showed no significant differences in triglycerides (P = 0.72), total cholesterol (P = 0.44), LDL cholesterol (P = 0.35), or HDL cholesterol (P = 0.21). Nonetheless, positive results were noted in the mHealth groups54,55. A meta-analysis by Gencer et al.56 demonstrated that a reduction of 1 mmol/L in LDL cholesterol yields significant benefits. Ettehad et al.’s meta-analysis57 similarly suggests that a 10 mmHg decrease in SBP correlates with a lowered risk of cardiovascular events. Turan Kavradim et al.58 further bolstered this idea by pointing out that improvements were seen in both SBP and DBP. Our results are consistent with those of Akinosun et al. regarding lipid metrics59 who observed enhancements in LDL, HDL, and total cholesterol levels. However, Xu et al.60 considered that only HDL and total cholesterol levels showed improvement, however, LDL cholesterol did not alter significantly. Furthermore, in contrast to our findings, Turan Kavradim et al.58 reported improvements in triglycerides and total cholesterol but not in LDL or HDL cholesterol. Despite this, a meta-analysis found no evidence of a significant advantage of smartphone technology over traditional medical methods in terms of blood pressure and cholesterol variables. High heterogeneity across the studies measuring total and LDL cholesterol levels, however, may be the cause of this disparity59,61.
Yang et al.62 from Shandong University’s Health Management Center in China showed that after the intervention, individuals in the health management group had significantly lower levels of low-density lipoprotein, systolic and diastolic blood pressure, waist circumference, and BMI. High-density lipoprotein levels, on the other hand, significantly rose, and these indices were lower than those of the control group. Following a 2-year follow-up, the control group’s BMI, waist circumference, systolic and diastolic blood pressure, and low-density lipoprotein levels did not alter significantly from baseline.
Limitations
Many students refused to participate due to long measurement tools and invasive laboratory investigations and post-session electronic responses collection represented a load on the researcher due to the large number of participants and different methods of receiving it. The study design didn’t support the assessment of long-term sustainability.
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