Empowering Postpartum Women: The Role of mHealth Apps in Promoting Mental Health and Healthy Weight Management

Empowering Postpartum Women: The Role of mHealth Apps in Promoting Mental Health and Healthy Weight Management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Empowering Postpartum Women: The Role of mHealth Apps in Promoting Mental Health and Healthy Weight Management Huang Xiaocui, Ye Shengyao, Nadia Samsudin, Li Kuan, Lin Xuefen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6554586/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jul, 2025 Read the published version in BMC Women's Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background The proliferation of mobile health (mHealth) applications has markedly influenced self-management practices related to obesity and mental well-being. However, the effectiveness of fitness apps in enhancing health outcomes is closely tied to their frequency of usage, a factor that has been insufficiently explored, especially among postpartum populations. Objective This study aimed to propose and empirically test a structural equation modeling (SEM) framework to examine the moderating effects of fitness app usage frequency on the relationships among obesity, lifestyle behaviors, dietary habits, and mental health outcomes among postpartum women. Methods A cross-sectional online survey was administered to postpartum women in Malaysia within one year after childbirth, collecting 468 valid responses. Participants were categorized into four distinct groups based on their frequency of fitness app usage: daily, weekly, rarely, and never. Results The SEM analyses highlighted significant variations among the four user groups. The daily-user model exhibited the strongest explanatory power (R² = 0.82), followed by weekly (R² = 0.79), rarely (R² = 0.66), and never-user groups (R² = 0.59). Specifically, in the daily-user group, demographic factors, lifestyle behaviors, dietary intake, and Body Mass Index (BMI) explained 82% of the variance in mental health outcomes. Across all usage categories, BMI consistently demonstrated a significant negative relationship with mental health issues, suggesting better mental health among participants with lower BMI. Further, factor loading analyses identified screen time (0.89) and physical activity (0.81) as dominant indicators of lifestyle behaviors. Frequent app users (daily and weekly) displayed healthier dietary choices and lower BMI scores compared to infrequent users. Conclusions Regular engagement with fitness mHealth applications substantially enhances mental health and supports obesity management among postpartum women. This study underscores the critical moderating role of app usage frequency in optimizing health outcomes, providing practical implications for public health strategies and interventions targeting postpartum populations. non-communicable disease psychological well-being public health obesity health risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Innovations in information and communications technology (ICT) have enveloped health and healthcare areas over the last two decades ( 1 ). This allows healthcare professionals to use apps in clinical practice to monitor patients and obtain feedback ( 2 , 3 ). These advanced technologies at the fingertips support people in a variety of life spheres as well, including physical exercise, mood, work productivity and sleep quality ( 4 ). Mobile phone-based tools, or mHealth is a subset of electronic health that is widely utilized, and which is an affordable and convenient platform for promoting public health ( 5 ) especially in developing countries ( 6 ). The developed tablet-computer-enabled mHealth intervention apps are intended to be used by general healthcare professionals in primary care settings ( 7 , 8 ). However, the adoption of mHealth in high-income countries is much higher compared to upper-middle, lower-middle and low-income countries. High-income countries seem to have better awareness and knowledge of mHealth technology, as it is said that a country’s economic growth parallels the technological advances ( 9 ). Globally, an estimated 2.7 billion people will have and use smartphones by 2019 owing to the user-friendly app software of mobile devices ( 10 ). Besides, people choose to use mobile apps for daily reminders to support self-management activities ( 11 ), access educational materials, record their health behaviors, track health data, share tracked information with primary care providers ( 12 ) and improve the quality of care ( 13 ). Recently, mHealth has grown rapidly with hundreds of thousands of apps available for downloading from online stores. These apps have various fields and functions ( 14 ), for instance pain apps ( 15 ), medication reminder apps ( 16 ), and smoking cessation, mental health and transitional care apps ( 17 ). Studies have shown that the most downloaded apps are related to fitness, nutrition and behavior ( 14 , 18 ). In contrast to a few research studies on the impact of fitness apps on body mass index (BMI) or weight, there is a lack of studies on fitness apps associated with mental health. Based on data from WHO, more women are reportedly overweight and obese than men ( 19 ). Obesity is a threat to women during the antepartum, peripartum and postpartum stages ( 20 , 21 ). Maternal obesity has emerged as a significant global health challenge, contributing to serious implications for women's health during and after pregnancy ( 22 ). Pregnancy and the postpartum phase represent particularly vulnerable periods during which many women experience substantial weight gain and alterations in body composition. Previous research indicates that approximately 48% of pregnant women exceed recommended weight gain levels, increasing their risk of postpartum obesity ( 23 ). Elevated maternal BMI is linked to numerous postpartum complications, including sustained weight gain, persistent obesity, metabolic syndrome, cardiovascular disease, and an increased risk of developing type 2 diabetes ( 24 ). Garmendia et al. ( 25 ) emphasized that unhealthy lifestyle behaviors significantly contribute to postpartum weight gain and obesity among women. Such unhealthy behaviors, notably poor sleep quality and physical inactivity, have been consistently reported in recent literature ( 26 ). Wen et al. ( 27 ) identified poor sleep quality as a crucial factor that discourages physical activity among postpartum women. Supporting this viewpoint, Waring et al. ( 28 ), further reinforced the idea that reduced physical activity levels play a critical role in postpartum obesity. Evidence strongly suggests that engaging in regular physical activity effectively mitigates excessive weight gain during pregnancy and throughout the postpartum period ( 29 ). Furthermore, dietary behaviors significantly influence postpartum obesity risk. Kay et al. ( 30 ) found that low consumption of nutritious foods such as fruits, vegetables, whole grains, and lean proteins significantly contributes to obesity among postpartum women. Similarly, Harris et al. ( 31 ) provided quantitative evidence demonstrating a strong preference for sugar-sweetened beverages among postpartum women, which aligns with the findings of Kay et al. ( 30 ). Additionally, mental health disorders are frequently reported alongside obesity and overweight conditions ( 24 ). Given that postpartum mental health concerns are commonly observed, it becomes particularly important to address obesity within the broader context of women's mental health and overall postpartum well-being ( 21 ). Previous surveys have reported that women are more likely to download health and fitness apps ( 10 , 32 ). For this reason, the main group of subjects in the present study comprises postpartum women. Previous studies have investigated postpartum-related mHealth intervention regarding HIV prevention and postpartum family planning among couples ( 33 , 34 ). To the best of our knowledge, studies on fitness app use by postpartum women are very rare. Since there is a lack of study on SEM application for postpartum women’s fitness app use, SEM data analysis is applied in the current paper to better understand the relationships between variables. The SEM method is relevant to estimating the dependent variable interrelations with measurement and latent variables. The primary aim of this study is to assess the frequency of fitness app usage by postpartum women. More specifically, the goal is to identify and characterize the predictors of postpartum women’s obesity and mental health on account of frequent fitness app utilization. The initial phase of this study involves developing a novel conceptual framework to investigate obesity and mental health among postpartum women using SEM. In the subsequent analytical phase, the data are examined through moderation analysis, wherein the frequency of fitness app usage is considered a moderator. This categorization aligns with the characteristics of moderators in SEM, hypothesizing that app usage frequency significantly influences the relationships among variables within the research model. To evaluate this moderating effect, fitness app usage frequency was classified into four distinct categories: daily, weekly, rarely, and never. The final analytical phase involves a comparative evaluation of the outputs derived from these four categorized models. The comprehensive research framework, encompassing five latent variables and two measurement variables, is depicted in Fig. 1 . Demographics constitute the initial latent independent variable, whereas mental health represents the dependent latent variable. The variables lifestyle, healthy food intake, unhealthy food intake, and Body Mass Index (BMI) are incorporated as mediators within the framework. Additionally, mHealth app usage frequency is explicitly operationalized as the primary moderator variable. In statistical modeling, a moderator variable alters the strength or direction of the relationship between two latent variables. Moderators can be either naturally occurring variables, such as gender, or experimentally manipulated factors. Evaluating these moderating effects enables researchers to understand how relationships differ across subgroups. In the current study, frequency of mHealth fitness app usage fulfills this moderating role. SEM was employed as the analytical technique due to its capacity to model latent constructs and their interactions comprehensively. Unlike traditional regression analysis, SEM accommodates latent variables, which are theoretical constructs inferred indirectly from observed indicators. Additionally, SEM facilitates simultaneous assessment of multiple dependent and independent variables, allowing researchers to evaluate both direct and indirect associations. Consequently, SEM represents a robust methodological approach for elucidating the complex relationships among obesity, mental health, and technology engagement in postpartum populations. Methods Research Variable Measurement The questionnaire utilized in this study was developed by carefully combining and adapting validated measurement items from various previously published studies. Rather than employing a single existing questionnaire, relevant indicators from prior literature were specifically selected and integrated to ensure comprehensive and accurate measurement of each research variable. In this research work, demographics was measured with four indicators, i.e. age group, education background, working experience and household income per month. The age range was classified into four groups: 21 to 25 years old, 26 to 30, 31 to 35 and over 35. The education background of the respondents was categorized as less than high school, high school, diploma, Bachelor, and Master or Ph.D. The respondents’ working experience was denoted as no job experience, 1 to 3 years, 4 to 6 years, 7 to 10 years, and more than 10 years. The household income per month in Ringgit Malaysia (RM) were categorized as less than 2,000; 2,000 to 3,000; 3,000 to 4,000; 4,000 to 5,000 and over 5,000. The lifestyle variable was measured based on previous studies ( 35 , 36 ), from which the authors selected a few indicators, such as average working hours per day, physical activity per week and average sleep hours per day. In addition, corresponding to Khajeheian et al.’s research ( 37 ), screen time (e.g. TV, smartphone, tablet, etc.) per day was added into lifestyle indicators. The frequency of physical activity per week was denoted by “none,” “1 time,” “2 times,” “3 times,” “4 times” and “more than 4 times.” The average screen time per day was indicated by “less than 1 hour,” “1 to 2 hours,” “2 to 3 hours,” “3 to 4 hours” and “more than 4 hours.” The average sleep hours per day was categorized as “less than 6 hours,” “6 to 7 hours,” “7 to 8 hours,” “8 to 9 hours,” and “more than 9 hours.” The average work hours per day consisted of five categories, “none,” “less than 7 hours,” “7 to 8 hours,” “8 to 9 hours” and “more than 9 hours.” In this study, dietary intake variables were categorized broadly into healthy and unhealthy food groups. Healthy foods included fruits, vegetables, and whole grains, while unhealthy foods encompassed fast food, sweets, chips, and soft drinks ( 30 , 36 , 38 ). Respondents indicated their food intake frequency using a five-point Likert scale ranging from "never" to "always." This categorization was deliberately selected to reflect general dietary preferences rather than precise consumption quantities or serving sizes. By focusing on broad food categories rather than detailed dietary records, the study aimed to reduce respondent recall bias and reporting inaccuracies. Moreover, this method allowed respondents to reflect more accurately on their typical dietary choices without being influenced by perceived desirable eating behaviors. This approach aligns well with the research objective of examining general dietary preferences and their associations with postpartum obesity and mental health outcomes. To measure the BMI range of an individual, the indicators to be calculated are (weight in kilograms)/(height in meters) ². The BMI categories were applied in line with the standardized measurement ( 39 ). Hence, this study on postpartum obesity contains the following categories: <18.5 underweight, ≥ 18.5 to < 25.0 normal, ≥ 25.0 to < 30.0 overweight and ≥ 30.0 obese. Three indicators were utilized to measure mental health as the dependent variable, guided by Broadman’s theory ( 40 ). Respondents assessed their mental health status over the past year by self-reporting the number of serious problems faced, perceived stress levels, and overall happiness in life. The problems encountered were classified into three categories: “more than two serious problems,” “one or two serious problems,” and “no serious problems.” Stress levels were categorized as “high stress,” “medium stress,” and “normal stress,” while happiness in life was categorized as “not happy,” “average,” and “happy.” The decision to utilize self-reported subjective indicators instead of clinical diagnostic criteria was deliberate, as the primary objective of this research was to understand respondents' perceived mental health conditions rather than clinically diagnosed mental health disorders. This approach aligns with the non-clinical nature of the study, reflecting the broader population’s experiences and perceptions, thereby providing insights relevant to preventive and public health contexts ( 41 ). For the mHealth variable (moderator), the frequency of respondents using fitness apps was categorized into “daily,” “weekly,” “rarely” and “never used,” as applied in previous studies ( 42 ). Sampling Hair et al. ( 43 ) recommended determining the minimum required sample size based on the number of latent variables and associated indicators included within a structural model. Specifically, they suggested that: (a) a minimum of 100 respondents is required when the model comprises five or fewer latent variables, each measured by at least three indicators; (b) at least 150 respondents are recommended for models with seven or fewer latent variables, each including at least three indicators; (c) a minimum of 300 respondents is necessary if the model has seven or fewer latent variables, with some containing fewer than three indicators; and (d) at least 500 respondents are advised when more than seven latent variables are present, with some containing fewer than three indicators. The current research framework includes five latent variables, and the moderating variable (fitness app usage frequency) is divided into four categories. Thus, it was anticipated that each usage category (daily, weekly, rarely, and never) would require a minimum of 100 respondents, yielding a total required sample size of at least 400. An online questionnaire was distributed to postpartum women residing in Kuala Lumpur, and a total of 468 completed questionnaires were obtained. All research procedures were conducted in accordance with relevant ethical guidelines and regulations. Respondents were informed clearly about the study's purpose, and informed consent was duly obtained from each participant. Results The data were collected via online questionnaire distributed to a total of 468 Malaysian postpartum women living in Kuala Lumpur, with no missing data. The data obtained for this study can be found in the descriptive statistical analysis in Table 1 . Table 1 Descriptive statistics Number, Percentage BMI (kg/m²) Underweight (n, %) 40 10.3% Normal (n, %) 115 29.7% Overweight (n, %) 134 34.6% Obese (n, %) 179 46.3% Age (n, %) 21 to 25 years old 31 6.6% 26 to 30 years old 106 22.6% 31 to 35 years old 181 38.7% Over 35 years old 150 32.1% Education (n, %) Less than high school 83 17.7% High school 54 11.5% Diploma 120 25.6% Bachelor 170 36.3% Master or PhD 41 8.8% Income (n, %) Less than RM 2,000 52 11.1% RM 2,000–3,000 62 13.2% RM 3,000–4,000 150 32.1% RM 4,000–5,000 168 35.9% Over RM 5,000 36 7.7% Job Experience (n, %) No job experience 39 8.3% 1–3 years 86 18.4% 4–6 years 112 23.9% 7–10 years 144 30.8% More than 10 years 87 18.6% Physical Activity (n, %) None 118 25.2% 1 time 89 19.0% 2 times 118 25.2% 3 times 54 11.5% 4 times 54 11.5% More than 4 times per week 35 7.5% Screen Time (n, %) Less than 1 hour 0 0.0% 1–2 hours 0 0.0% 2–3 hours 153 32.7% 3–4 hours 128 27.4% More than 4 hours per day 187 40.0% Sleep (n, %) Less than 6 hours 30 6.4% 6–7 hours 56 12.0% 7–8 hours 193 41.2% 8–9 hours 149 31.8% More than 9 hours per day 40 8.5% Work (n, %) None 43 9.2% Less than 7 hours 30 6.4% 7–8 hours 90 19.2% 8–9 hours 265 56.6% More than 9 hours per day 40 8.5% Problems (n, %) More than 2 serious problems 126 26.9% 1 or 2 serious problems 141 30.1% No serious problems in the past year 201 42.9% Stress (n, %) High stress 116 24.8% Medium 131 28.0% Normal 221 47.2% Happiness (n, %) Not happy 221 47.2% Average 183 39.1% Happy 64 13.7% Fitness app frequency of use (n, %) Daily 100 21.4% Weekly 113 24.2% Rarely 151 32.3% Never used 104 22.3% Number (%) Never Rarely Sometimes Mostly Always Healthy Food Fruits 0 (0.0%) 46 (9.8%) 148 (31.6%) 208 (44.4%) 66 (14.1%) Vegetables 0 (0.0%) 28 (6.0%) 161 (34.4%) 172 (36.8%) 107 (22.9%) Whole grains 0 (0.0%) 34 (7.3%) 135 (28.8%) 187 (40.0%) 112 (23.9%) Unhealthy Food Fast food 0 (0.0%) 0 (0.0%) 32 (6.8%) 231 (49.4%) 205 (43.8%) Sweets 0 (0.0%) 0 (0.0%) 40 (8.5%) 229 (48.9%) 199 (42.5%) Chips 0 (0.0%) 0 (0.0%) 54 (11.5%) 195 (41.7%) 219 (46.8%) Soft drinks 0 (0.0%) 0 (0.0%) 82 (17.5%) 181 (38.7%) 205 (43.8%) SEM Analysis Validity and Reliability The validity and reliability of a survey need to fit some of the conditions for SEM analysis. To examine the validity, every latent variable in the research should attain Cronbach’s alpha value of 0.7 or more ( 44 ). According to Table 2 , the Cronbach’s alpha value for every latent variable is aligned with the condition that reinforces the validity of this research. Table 2 Cronbach’s alpha output Variables Value Demographics 0.71 Lifestyle 0.76 Healthy Food 0.77 Unhealthy Food 0.79 Mental Health 0.81 Next, to test the reliability of this research, every latent variable indicator should obtain factor loading value more than 0.7 ( 45 ). Based on Table 3 , age and working indicator showed to have factor loading value less than 0.7. So, these indicators should be eliminated from the rest of SEM analysis. Table 3 Factor loading analysis Items Value Age 0.59 Education 0.73 Income 0.78 Job Experience 0.72 Fruits 0.77 Vegetables 0.73 Whole grains 0.74 Fast food 0.74 Sweets 0.79 Chips 0.81 Soft drinks 0.76 Physical Activity 0.81 Screen Time 0.89 Sleeping 0.76 Working 0.66 Problem 0.76 Stress 0.78 Happiness 0.73 Subsequently, the reliability of each latent variable was assessed using the Average Variance Extracted (AVE). After eliminating indicators with insufficient factor loadings, all latent variables were required to achieve an AVE value of 0.5 or higher to ensure adequate reliability. As presented in Table 4 , the AVE values obtained in this study clearly exceed the recommended threshold of 0.5, confirming satisfactory reliability for all latent variables. Table 4 AVE output Variables Value Demographics 0.71 Lifestyle 0.61 Healthy Food 0.55 Unhealthy Food 0.62 Mental Health 0.59 Model Fitting A suitable research model should attain fit values above 0.9 ( 46 ). The goodness of fit index (GFI), relative fit index (RFI), incremental fit index (IFI), comparative fit index (CFI), Tucker-Lewis index (TLI) and normed fit index (NFI) values in this study are within acceptable ranges. Therefore, the model-data fit in this research is accepted. Table 5 Model fitting analysis Items Value GFI 0.921 RFI 0.905 IFI 0.911 CFI 0.945 TLI 0.961 NFI 0.923 Structural Modeling This study employed SEM to explore and highlight meaningful connections among variables within a carefully designed conceptual model of postpartum obesity and mental health. To provide clear insights into how fitness app usage frequency influences these relationships, Figs. 3 to 6 illustrate the structural model outputs corresponding to the daily, weekly, rarely, and never fitness app usage groups, respectively. In these diagrams, significant relationships among variables are depicted by solid arrows, while non-significant relationships are indicated by dashed arrows. In the daily usage model (Fig. 3 ), nearly all relationships between the variables demonstrated significance, except for the direct link between demographics and BMI. The weekly usage model (Fig. 4 ) showed fewer significant connections, with two relationships identified as non-significant, specifically the effects of demographics on BMI and lifestyle on unhealthy food intake. Further diminishing of significance appeared in the rarely usage model (Fig. 5 ), wherein five out of fourteen possible relationships were not significant. These included the impacts of demographics on unhealthy food intake, BMI, and mental health, as well as the impacts of lifestyle on healthy food consumption and BMI. Lastly, the never usage model (Fig. 6 ) revealed the most limited relationships, with half of the associations lacking significance. Specifically, demographics showed no significant influence on any other variable, and lifestyle demonstrated no significant impact on healthy food intake or BMI. Interestingly, the strongest influence observed within the daily and weekly usage models was the positive impact of lifestyle on mental health, with coefficients of 0.63 and 0.67, respectively. Conversely, in the rarely and never use models, the strongest relationships were negative, demonstrating that BMI significantly affected mental health, with coefficients of -0.66 and − 0.68, respectively. These nuanced findings underscore the profound moderating role fitness app usage frequency plays in shaping relationships between lifestyle, demographic characteristics, BMI, dietary habits, and mental health outcomes. By exploring these structural relationships, the present study offers valuable insights into postpartum women's health behaviors and provides evidence-based suggestions for improving mental health and obesity management through increased engagement with mHealth technologies. Discussion This article presents the latest empirical research employing a multilevel analytical framework specifically tailored for postpartum women, focusing on the moderating role of fitness-related mHealth app usage frequency. Initially, the study introduced an innovative postpartum obesity framework, emphasizing critical factors associated with obesity and mental health. The analysis was carried out through SEM, followed by examining key variables influencing mental health according to varying frequencies of fitness app usage. Data was collected from Malaysian respondents within the first postpartum year, consistent with previous research by Kubota et al. ( 47 ). The results indicated substantial rates of overweight (34.6%) and obesity (46.3%) among postpartum women in the sample. The frequency of mHealth app use significantly moderated the relationships among the variables in the research model. Based on previous studies examining obesity, improvements were made specifically for postpartum women, carefully incorporating Malaysian cultural context ( 36 , 46 ). The conceptual model integrated seven primary variables: demographics, lifestyle, healthy food intake, unhealthy food intake, BMI, mental health, and mHealth app usage frequency. Demographic variables (age, education level, income, and work experience) acted as initial independent variables, while mental health (measured through problems, stress, and happiness indicators) served as the dependent variable. Mediators included lifestyle behaviors (physical activity, screen time, sleep duration, and working hours), and dietary habits classified as healthy foods (fruits, vegetables, whole grains) or unhealthy foods (fast foods, sweets, chips, soft drinks). Previous studies confirm the associations between maternal obesity and mental health, lifestyle behaviors and postpartum weight changes, as well as postpartum dietary patterns ( 48 , 49 ). For detailed SEM analysis, respondents were divided based on app usage frequency into four distinct groups: daily, weekly, rarely, and never. Factor loading analysis led to the removal of age and working hours indicators. Consequently, four final SEM models were derived and presented separately. In the "daily" model, demographic variables had significant positive impacts on lifestyle (0.41), healthy food intake (0.29), and unhealthy food intake (0.19). However, demographics showed no significant impact on BMI. Lifestyle positively influenced both healthy (0.35) and unhealthy (0.23) food consumption but negatively affected BMI (-0.23). Healthy (0.18) and unhealthy (0.19) food intakes both significantly influenced BMI positively. In the "weekly" model, similar trends appeared: demographics positively influenced lifestyle (0.35), healthy food intake (0.27), and unhealthy food intake (0.16). Lifestyle positively impacted healthy food intake (0.32) and negatively influenced BMI (-0.16). Additionally, healthy (0.22) and unhealthy (0.23) food intake both positively affected BMI. Factor loading analysis revealed screen time (0.89) and physical activity (0.81) as the highest lifestyle indicators, consistent with healthier dietary choices and lower BMI among frequent app users. The "rarely use" model showed demographic variables significantly impacting lifestyle (0.17) and healthy food intake (0.16). Lifestyle negatively influenced unhealthy food consumption (-0.19). Healthy (0.19) and unhealthy food (0.46) had significant positive impacts on BMI, confirming previous literature on dietary influences on BMI. The "rarely use" group indicated lifestyle and healthy food intake positively correlated with increased mental health issues, whereas unhealthy food intake and higher BMI correlated with fewer mental health issues. In the "never use" model, demographics showed no significant impact on other variables. Lifestyle negatively impacted unhealthy food intake (-0.26) and positively impacted mental health (0.43). Healthy and unhealthy food intakes both positively influenced BMI significantly. The factor loading analysis highlighted that the "chips" indicator strongly influenced BMI, aligning with previous findings on snack consumption among postpartum women. Overall, comparative analysis across the four models clearly demonstrates that frequent fitness app usage contributes significantly to improved lifestyle behaviors, healthier dietary patterns, and better mental health outcomes, thus positively influencing postpartum obesity management. To conclude the discussion, two main interpretations are presented based on the analysis of the four models. First, Fig. 7 displays all four models together and the non-significance arrows are eliminated. The R-squared (R²) values from the SEM analysis were 0.82 for the "daily" model, 0.79 for the "weekly" model, 0.71 for the "rarely" model, and 0.59 for the "never use" model (Figs. 3 to 6 ). This indicates that in the daily usage model, 82% of the variance in mental health among postpartum women can be explained collectively by BMI, demographics, lifestyle behaviors, and dietary intake (both healthy and unhealthy food). In comparison, only 59% of mental health variance is explained by these factors in the group of respondents who never used fitness apps. Therefore, it appears that increasing the frequency of fitness app usage is associated with improved explanatory power of the research model regarding mental health outcomes. Furthermore, the influence of demographic variables varied significantly across models. Demographics showed a significant effect on four essential variables, specifically healthy food intake, unhealthy food intake, lifestyle, and mental health, in both the daily and weekly fitness app user groups (Figs. 3 and 4 ). However, the influence of demographics was less prominent in groups with less frequent app usage. In the rarely used model, demographics significantly impacted only two variables, lifestyle and healthy food intake (Fig. 5 ). In contrast, demographics had no significant relationships with any variable in the never use group (Fig. 6 ). This trend indicates that as the frequency of fitness app usage decreases among Malaysian postpartum women, the role of demographic characteristics within the research model diminishes. Additionally, a noteworthy trend emerged regarding the relationship between unhealthy food intake and BMI across the different models. The impact of unhealthy food consumption on BMI was lowest in the daily app usage group (0.19, Fig. 3 ), increased slightly in the weekly group (0.23, Fig. 4 ), and further increased substantially in the rarely group (0.46, Fig. 5 ), reaching its highest value in the never use group (0.67, Fig. 6 ). These results highlight that decreasing frequency of fitness app engagement is linked to a stronger positive relationship between unhealthy dietary patterns and increased BMI. Correspondingly, BMI’s effect on mental health outcomes decreased as app usage frequency increased, suggesting that regular fitness app usage might mitigate the negative impact of BMI on mental health among postpartum women. As a result, the frequency of fitness app use by Malaysian postpartum women not only has significant impact on BMI and mental health management but also leads to unhealthy food consumption management by the women. Conclusions Pregnancy is frequently identified as a significant contributor to weight gain and obesity among women during the postpartum period, representing a substantial public health challenge globally ( 21 , 50 ). The present study introduces an improved conceptual framework compared to previous research by emphasizing the frequency of mHealth app use as a moderator influencing demographics, lifestyle habits, dietary choices, BMI, and mental health outcomes among postpartum women. Advances in technology have facilitated the emergence of mHealth apps, which effectively assist users in managing lifestyle aspects such as diet, physical fitness, and health behaviors ( 46 ). Findings from this study highlight the valuable role these apps play in improving quality of life, especially by promoting healthier lifestyles and better mental health among postpartum women. Additionally, regular physical activity remains widely recognized as an effective strategy for weight reduction and overall health enhancement ( 51 ). However, certain limitations should be acknowledged. First, parity was not specifically assessed, despite its known influence on postpartum obesity. The participants in this study were standardized based on a postpartum period of one year following childbirth. Although this approach is consistent with previous literature, allowing mothers adequate time for physical and psychological recovery, future research could incorporate parity as an additional factor to better understand its influence on postpartum weight dynamics. Second, self-reported measurements of weight and height potentially impacted data validity. While prior research supports the reliability of self-reported anthropometric data, incorporating objective measurements in future studies would improve accuracy, enhancing research validity. Accurate anthropometric data collection is an essential component in ensuring the robustness and credibility of findings. To the authors’ knowledge, this research is the first to explore postpartum obesity using mHealth app usage frequency as a moderating variable through SEM combined with moderation analyses. By highlighting key relationships between postpartum obesity, mental health, and technology-driven health management, this study significantly contributes to current academic literature. Its insights provide valuable groundwork for future studies and interventions aimed at addressing postpartum obesity and mental health, two prominent public health issues requiring focused preventive approaches. Abbreviations The following abbreviations are used in this manuscript: mHealth Mobile health SEM Structural Equation Modeling BMI Body Mass Index RM Ringgit Malaysia R² R-square CFI Comparative Fit Index NFI Normed Fit Index RFI Relative Fit Index IFI Incremental Fit Index GFI Goodness of Fit Index TLI Tucker Lewis Index AVE Average Variance Extracted Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of UCSI University (protocol code IEC-2023-FOSSLA-0202 on 9 April 2024). Informed consent was obtained from all subjects involved in the study. The research methods were performed in accordance with the relevant guidelines and regulations. Participants of the study were informed about the purpose, objectives, and their right to participate, decline participation, or withdraw their participation in the research activities by verbal. Respondents have been notified that the information given was private and confidential which only going to use for academic purposes only. Written informed consent was obtained from all respondents. Consent for publication Not applicable. Availability of data and materials The data are not publicly available due to the Institutional Ethics Committee of UCSI University rules and regulations. The data that support the findings of this research are available upon reasonable request from the corresponding author and with permission of the Institutional Ethics Committee of UCSI University. Competing interests The authors declare no conflicts of interest. Funding This research received no external funding Authors' contributions Conceptualization, H.X. and Y.S.; methodology, L.K. and L.X..; software, H.X.; validation, Y.S. and N.S.; formal analysis, N.S.; writing—original draft preparation, H.X., Y.S., N.S., L.K., and L.X; writing—review and editing, H.X., Y.S., and N.S; supervision and project administration, N.S. All authors have read and agreed to the published version of the manuscript. Acknowledgements All authors would like to express their sincere appreciation and gratitude to the participants for their valuable cooperation and contribution to this research. References Varma DS, Mualem M, Goodin A, Gurka KK, Wen TS, Gurka MJ, et al. Acceptability of an mHealth App for Monitoring Perinatal and Postpartum Mental Health: Qualitative Study With Women and Providers. JMIR Form Res. 2023;7:e44500. El Ayadi AM, Diamond-Smith NG, Duggal M, Singh P, Sharma P, Kaur J, et al. Preliminary impact of an mHealth education and social support intervention on maternal health knowledge and outcomes among postpartum mothers in Punjab, India. BMC Pregnancy Childbirth. 2025;25(1):239. Yan C, Cao R, Hu X, Hu Y, Liu H. A longitudinal study of a mHealth app and regional policies on the uptake of postpartum depression referral after positive screening in Shenzhen. BMC Pregnancy Childbirth. 2024;24(1):536. Hutton L, Price BA, Kelly R, McCormick C, Bandara AK, Hatzakis T, et al. Assessing the Privacy of mHealth Apps for Self-Tracking: Heuristic Evaluation Approach. Jmir Mhealth and Uhealth. 2018;6(10). Arakawa Y, Haseda M, Inoue K, Nishioka D, Kino S, Nishi D, et al. Effectiveness of mHealth consultation services for preventing postpartum depressive symptoms: a randomized clinical trial. BMC Med. 2023;21(1):221. Gibson AD, Mazeri S, Lohr F, Mayer D, Bailey JLB, Wallace RM, et al. One million dog vaccinations recorded on mHealth innovation used to direct teams in numerous rabies control campaigns. Plos One. 2018;13(7). Jindal D, Gupta P, Jha D, Ajay VS, Goenka S, Jacob P, et al. Development of mWellcare: an mHealth intervention for integrated management of hypertension and diabetes in low-resource settings. Global Health Action. 2018;11(1). Zhu G, Liu H, Feng MN. An Evolutionary Game-Theoretic Approach for Assessing Privacy Protection in mHealth Systems. International Journal of Environmental Research and Public Health. 2018;15(10). Feroz A, Kadir MM, Saleem S. Health systems readiness for adopting mhealth interventions for addressing non-communicable diseases in low- and middle-income countries: a current debate. Global Health Action. 2018;11(1). Liew MS, Zhang J, See J, Ong YL. Usability Challenges for Health and Wellness Mobile Apps: Mixed-Methods Study Among mHealth Experts and Consumers. Jmir Mhealth and Uhealth. 2019;7(1). Yu DH, Parmanto B, Dicianno B. An mHealth App for Users with Dexterity Impairments: Accessibility Study. Jmir Mhealth and Uhealth. 2019;7(1). Khan AI, Gill A, Cott C, Hans PK, Gray CS. mHealth Tools for the Self-Management of Patients With Multimorbidity in Primary Care Settings: Pilot Study to Explore User Experience. Jmir Mhealth and Uhealth. 2018;6(8). Ruton H, Musabyimana A, Gaju E, Berhe A, Grepin KA, Ngenzi J, et al. The impact of an mHealth monitoring system on health care utilization by mothers and children: an evaluation using routine health information in Rwanda. Health Policy and Planning. 2018;33(8):920-7. Giunti G. 3MD for Chronic Conditions, a Model for Motivational mHealth Design: Embedded Case Study. Jmir Serious Games. 2018;6(3). Lalloo C, Hundert A, Harris L, Pham Q, Campbell F, Chorney J, et al. Capturing Daily Disease Experiences of Adolescents With Chronic Pain: mHealth-Mediated Symptom Tracking. Jmir Mhealth and Uhealth. 2019;7(1). Chong EYC, Palanisamy UD, Jacob SA. A qualitative study on the design and development of an mHealth app to facilitate communication with the Deaf community: perspective of community pharmacists. Patient Preference and Adherence. 2019;13:195-207. Jessen S, Mirkovic J, Ruland CM. Creating Gameful Design in mHealth: A Participatory Co-Design Approach. Jmir Mhealth and Uhealth. 2018;6(12). Crico C, Renzi C, Graf N, Buyx A, Kondylakis H, Koumakis L, et al. mHealth and telemedicine apps: in search of a common regulation. Ecancermedicalscience. 2018;12. Orgnization WH. Obesity and overweight: World Health Organization; 2018, February 16 [Available from: http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Moussa HN, Alrais MA, Leon MG, Abbas EL, Sibai BM. Obesity epidemic: impact from preconception to postpartum. Future Science Oa. 2016;2(3). Khadka N, Fassett MJ, Oyelese Y, Mensah NA, Chiu VY, Yeh M, et al. Trends in Postpartum Depression by Race, Ethnicity, and Prepregnancy Body Mass Index. JAMA Netw Open. 2024;7(11):e2446486. Pendeloski KPT, Ono E, Torloni MR, Mattar R, Daher S. Maternal obesity and inflammatory mediators: A controversial association. American Journal of Reproductive Immunology. 2017;77(5). Meng Y, Groth SW, Li DM. The Association between Obesity-Risk Genes and Gestational Weight Gain Is Modified by Dietary Intake in African American Women. Journal of Nutrition and Metabolism. 2018. Geusens F, Van Uytsel H, Ameye L, Devlieger R, Jacquemyn Y, Van Holsbeke C, et al. The impact of self-monitoring physical and mental health via an mHealth application on postpartum weight retention: Data from the INTER-ACT RCT. Health Promot Perspect. 2024;14(1):44-52. Garmendia ML, Zamudio C, Araya M, Kain J. Association between prepregnancy obesity and metabolic risk in Chilean premenopausal women 10 y postpartum. Nutrition. 2017;38:20-7. Wilcox S, Liu J, Sevoyan M, Parker-Brown J, Turner-McGrievy GM. Effects of a behavioral intervention on physical activity, diet, and health-related quality of life in postpartum women with elevated weight: results of the HIPP randomized controlled trial. BMC Pregnancy Childbirth. 2024;24(1):808. Wen S-Y, Ko Y-L, Jou H-J, Chien L-Y. Sleep quality at 3 months postpartum considering maternal age: A comparative study. Women Birth. 2018. Waring ME, Simas TAM, Oleski J, Xiao RS, Mulcahy JA, May CN, et al. Feasibility and Acceptability of Delivering a Postpartum Weight Loss Intervention via Facebook: A Pilot Study. Journal of Nutrition Education and Behavior. 2018;50(1):70-+. Awoke MA, Earnest A, Skouteris H, Moran LJ, Wycherley TP. Modeling the effect of diet and physical activity on body mass index in prepregnant and postpartum women. Nutrition. 2023;111:112026. Kay MC, Wasser H, Adair LS, Thompson AL, Siega-Riz AM, Suchindran CM, et al. Consumption of key food groups during the postpartum period in low-income, non-Hispanic black mothers. Appetite. 2017;117:161-7. Harris A, Chilukuri N, West M, Henderson J, Lawson S, Polk S, et al. Obesity-Related Dietary Behaviors among Racially and Ethnically Diverse Pregnant and Postpartum Women. J Pregnancy. 2016;2016:9832167. Kamarudin SS, Idris IB, Ahmad N, Sharip S. Exploring Asian maternal experiences and mHealth needs for postpartum mental health care. Digit Health. 2024;10:20552076241292679. Harrington EK, McCoy EE, Drake AL, Matemo D, John-Stewart G, Kinuthia J, et al. Engaging men in an mHealth approach to support postpartum family planning among couples in Kenya: a qualitative study. Reproductive Health. 2019;16(1):17. Gupta N, Gupta A, Bhagia S, Singh S. Mobile Technology for increasing Postpartum Family Planning Acceptability: The Development of a Mobilebased (mHealth) Intervention through a Dedicated Counselor—A Pilot Innovative Study conducted in a Tertiary Teaching Hospital of Agra, Uttar Pradesh, India. Journal of South Asian Federation of Obstetrics Gynaecology. 2018:74-80. Nakayama K, Yamaguchi, K., Maruyama, S., & Morimoto, K. The relationship of lifestyle factors, personal character, and mental health status of employees of a major Japanese electrical manufacturer. Environmental health and preventive medicine. 2001;5(4):144-9. Wan Mohamed Radzi C, Salarzadeh Jenatabadi H, Samsudin N. Postpartum depression symptoms in survey-based research: a structural equation analysis. BMC Public Health. 2021;21(1):27. Khajeheian D, Colabi AM, Shah N, Radzi C, Jenatabadi HS. Effect of Social Media on Child Obesity: Application of Structural Equation Modeling with the Taguchi Method. International Journal of Environmental Research and Public Health. 2018;15(7). Huang H, Radzi C, Jenatabadi HS. Family Environment and Childhood Obesity: A New Framework with Structural Equation Modeling. International Journal of Environmental Research and Public Health. 2017;14(2). Schrijvers JK, McNaughton SA, Beck KL, Kruger R. Exploring the Dietary Patterns of Young New Zealand Women and Associations with BMI and Body Fat. Nutrients. 2016;8(8). Boardman JD. Stress and physical health: the role of neighborhoods as mediating and moderating mechanisms. Social science & medicine (1982). 2004;58(12):2473-83. Salarzadeh Jenatabadi H, Bt Wan Mohamed Radzi CWJ, Samsudin N. Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach. Int J Environ Res Public Health. 2020;17(14). Liu Y, Ren W, Qiu Y, Liu J, Yin P, Ren J. The Use of Mobile Phone and Medical Apps among General Practitioners in Hangzhou City, Eastern China. JMIR mHealth and uHealth. 2016;4(2):e64-e. Hair J, Black W, Babin B, Anderson R. Multivariate data analysis: Pearson new international edition. New Jersey: Pearson/Prentice Hall; 2014. Miranda AR, Scotta AV, Cortez MV, Soria EA. Two-years mothering into the pandemic: Impact of the three COVID-19 waves in the Argentinian postpartum women's mental health. PLoS One. 2025;20(3):e0294220. Guo H, Zhou Z, Ma F, Chen X. Doctoral students' academic performance: The mediating role of academic motivation, academic buoyancy, and academic self-efficacy. Heliyon. 2024;10(12):e32588. Bt Wan Mohamed Radzi CWJ, Salarzadeh Jenatabadi H, Samsudin N. mHealth Apps Assessment among Postpartum Women with Obesity and Depression. Healthcare (Basel). 2020;8(2). Kubota C, Inada T, Nakamura Y, Shiino T, Ando M, Aleksic B, et al. Stable factor structure of the Edinburgh Postnatal Depression Scale during the whole peripartum period: Results from a Japanese prospective cohort study. Sci Rep. 2018;8(1):17659. Chen HH, Hsiung Y, Lee CF, Huang JP, Chi LK, Weng SS. Effects of an mHealth intervention on maternal and infant outcomes from pregnancy to early postpartum for women with overweight or obesity: A randomized controlled trial. Midwifery. 2024;138:104143. Nicklas JM, Pyle L, Soares A, Leiferman JA, Bull SS, Tong S, et al. The Fit After Baby randomized controlled trial: An mHealth postpartum lifestyle intervention for women with elevated cardiometabolic risk. PLoS One. 2024;19(1):e0296244. Stanhope KK, Stallworth T, Forrest AD, Vuncannon D, Juarez G, Boulet SL, et al. Planning for the forgotten fourth trimester of pregnancy: A parallel group randomized control trial to test a postpartum planning intervention vs. standard prenatal care. Contemp Clin Trials. 2024;143:107586. Samsudin N, Bailey RP, Ries F, Hashim S, Fernandez JA. Assessing the impact of physical activity on reducing depressive symptoms: a rapid review. BMC Sports Sci Med Rehabil. 2024;16(1):107. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Jul, 2025 Read the published version in BMC Women's Health → Version 1 posted Editorial decision: Revision requested 02 Jun, 2025 Reviewers agreed at journal 22 May, 2025 Reviews received at journal 21 May, 2025 Reviews received at journal 20 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers invited by journal 20 May, 2025 Editor assigned by journal 18 May, 2025 Editor invited by journal 02 May, 2025 Submission checks completed at journal 30 Apr, 2025 First submitted to journal 30 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6554586","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460136217,"identity":"2ad48153-3374-4d11-a050-214dc9de7512","order_by":0,"name":"Huang Xiaocui","email":"","orcid":"","institution":"Wenzhou No.7 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huang","middleName":"","lastName":"Xiaocui","suffix":""},{"id":460136218,"identity":"078aaed1-2db5-4359-9bfc-19a2dfaa1874","order_by":1,"name":"Ye Shengyao","email":"","orcid":"","institution":"Wenzhou Vocational College of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Shengyao","suffix":""},{"id":460136219,"identity":"bcf8375e-6066-406a-8e6c-2ee723942deb","order_by":2,"name":"Nadia Samsudin","email":"data:image/png;base64,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","orcid":"","institution":"UCSI University","correspondingAuthor":true,"prefix":"","firstName":"Nadia","middleName":"","lastName":"Samsudin","suffix":""},{"id":460136220,"identity":"f397d679-4fbd-49f7-9941-5d19171de629","order_by":3,"name":"Li Kuan","email":"","orcid":"","institution":"Wenzhou Vocational College of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Kuan","suffix":""},{"id":460136221,"identity":"e0970aca-4148-498f-aea2-646aa3b2f3e6","order_by":4,"name":"Lin Xuefen","email":"","orcid":"","institution":"Wenzhou Vocational College of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xuefen","suffix":""}],"badges":[],"createdAt":"2025-04-29 09:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6554586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6554586/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-025-03907-9","type":"published","date":"2025-07-16T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83436109,"identity":"c1e1c858-e234-4d0c-9d26-d3643626a96a","added_by":"auto","created_at":"2025-05-26 08:31:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35120,"visible":true,"origin":"","legend":"\u003cp\u003eResearch framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6554586/v1/20ea2815a90109e1c666ca5b.png"},{"id":83436116,"identity":"8b4b6810-6242-4f49-8ebb-b824faa18b80","added_by":"auto","created_at":"2025-05-26 08:31:42","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":295454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3.\u003c/strong\u003eDaily fitness app use model\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6554586/v1/54f9f19d4310ff5725a46e1f.jpeg"},{"id":83437430,"identity":"2864b976-7c16-4bc1-88a7-b0535737a405","added_by":"auto","created_at":"2025-05-26 08:47:42","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":296717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4.\u003c/strong\u003eWeekly fitness app use model\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6554586/v1/dfa51072b61f5fc47476729e.jpeg"},{"id":83437105,"identity":"9079ba9c-acb3-43cb-9785-c12f9f6d4a37","added_by":"auto","created_at":"2025-05-26 08:39:42","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":268659,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003eRarely fitness app use model\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6554586/v1/0b525117b835fc5ab1bc4a26.jpeg"},{"id":83436112,"identity":"ce2fbdfd-171b-4fdd-94cc-3079321b65cb","added_by":"auto","created_at":"2025-05-26 08:31:42","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":280749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6.\u003c/strong\u003eNever use fitness app model\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6554586/v1/992cfeab97c466010afaf5f6.jpeg"},{"id":83437431,"identity":"36ac193d-723b-4791-aefa-69a9b7fbfd6b","added_by":"auto","created_at":"2025-05-26 08:47:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":253402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7.\u003c/strong\u003eImpact significance in all models\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6554586/v1/adecf402bef34bf1702e62b0.png"},{"id":87219331,"identity":"5ae9cd56-6112-4a9b-911c-c5a0686fae40","added_by":"auto","created_at":"2025-07-21 16:03:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2315182,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6554586/v1/918784bd-e50d-4a04-b5d2-8f99d79112ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Empowering Postpartum Women: The Role of mHealth Apps in Promoting Mental Health and Healthy Weight Management","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eInnovations in information and communications technology (ICT) have enveloped health and healthcare areas over the last two decades (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This allows healthcare professionals to use apps in clinical practice to monitor patients and obtain feedback (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These advanced technologies at the fingertips support people in a variety of life spheres as well, including physical exercise, mood, work productivity and sleep quality (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Mobile phone-based tools, or mHealth is a subset of electronic health that is widely utilized, and which is an affordable and convenient platform for promoting public health (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) especially in developing countries (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The developed tablet-computer-enabled mHealth intervention apps are intended to be used by general healthcare professionals in primary care settings (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, the adoption of mHealth in high-income countries is much higher compared to upper-middle, lower-middle and low-income countries. High-income countries seem to have better awareness and knowledge of mHealth technology, as it is said that a country\u0026rsquo;s economic growth parallels the technological advances (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Globally, an estimated 2.7\u0026nbsp;billion people will have and use smartphones by 2019 owing to the user-friendly app software of mobile devices (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Besides, people choose to use mobile apps for daily reminders to support self-management activities (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), access educational materials, record their health behaviors, track health data, share tracked information with primary care providers (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and improve the quality of care (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecently, mHealth has grown rapidly with hundreds of thousands of apps available for downloading from online stores. These apps have various fields and functions (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), for instance pain apps (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), medication reminder apps (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and smoking cessation, mental health and transitional care apps (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Studies have shown that the most downloaded apps are related to fitness, nutrition and behavior (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In contrast to a few research studies on the impact of fitness apps on body mass index (BMI) or weight, there is a lack of studies on fitness apps associated with mental health.\u003c/p\u003e \u003cp\u003eBased on data from WHO, more women are reportedly overweight and obese than men (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Obesity is a threat to women during the antepartum, peripartum and postpartum stages (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Maternal obesity has emerged as a significant global health challenge, contributing to serious implications for women's health during and after pregnancy (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Pregnancy and the postpartum phase represent particularly vulnerable periods during which many women experience substantial weight gain and alterations in body composition. Previous research indicates that approximately 48% of pregnant women exceed recommended weight gain levels, increasing their risk of postpartum obesity (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Elevated maternal BMI is linked to numerous postpartum complications, including sustained weight gain, persistent obesity, metabolic syndrome, cardiovascular disease, and an increased risk of developing type 2 diabetes (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGarmendia et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) emphasized that unhealthy lifestyle behaviors significantly contribute to postpartum weight gain and obesity among women. Such unhealthy behaviors, notably poor sleep quality and physical inactivity, have been consistently reported in recent literature (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Wen et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) identified poor sleep quality as a crucial factor that discourages physical activity among postpartum women. Supporting this viewpoint, Waring et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), further reinforced the idea that reduced physical activity levels play a critical role in postpartum obesity. Evidence strongly suggests that engaging in regular physical activity effectively mitigates excessive weight gain during pregnancy and throughout the postpartum period (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Furthermore, dietary behaviors significantly influence postpartum obesity risk. Kay et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) found that low consumption of nutritious foods such as fruits, vegetables, whole grains, and lean proteins significantly contributes to obesity among postpartum women. Similarly, Harris et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) provided quantitative evidence demonstrating a strong preference for sugar-sweetened beverages among postpartum women, which aligns with the findings of Kay et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Additionally, mental health disorders are frequently reported alongside obesity and overweight conditions (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Given that postpartum mental health concerns are commonly observed, it becomes particularly important to address obesity within the broader context of women's mental health and overall postpartum well-being (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious surveys have reported that women are more likely to download health and fitness apps (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). For this reason, the main group of subjects in the present study comprises postpartum women. Previous studies have investigated postpartum-related mHealth intervention regarding HIV prevention and postpartum family planning among couples (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). To the best of our knowledge, studies on fitness app use by postpartum women are very rare. Since there is a lack of study on SEM application for postpartum women\u0026rsquo;s fitness app use, SEM data analysis is applied in the current paper to better understand the relationships between variables. The SEM method is relevant to estimating the dependent variable interrelations with measurement and latent variables. The primary aim of this study is to assess the frequency of fitness app usage by postpartum women. More specifically, the goal is to identify and characterize the predictors of postpartum women\u0026rsquo;s obesity and mental health on account of frequent fitness app utilization.\u003c/p\u003e \u003cp\u003eThe initial phase of this study involves developing a novel conceptual framework to investigate obesity and mental health among postpartum women using SEM. In the subsequent analytical phase, the data are examined through moderation analysis, wherein the frequency of fitness app usage is considered a moderator. This categorization aligns with the characteristics of moderators in SEM, hypothesizing that app usage frequency significantly influences the relationships among variables within the research model. To evaluate this moderating effect, fitness app usage frequency was classified into four distinct categories: daily, weekly, rarely, and never. The final analytical phase involves a comparative evaluation of the outputs derived from these four categorized models. The comprehensive research framework, encompassing five latent variables and two measurement variables, is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Demographics constitute the initial latent independent variable, whereas mental health represents the dependent latent variable. The variables lifestyle, healthy food intake, unhealthy food intake, and Body Mass Index (BMI) are incorporated as mediators within the framework. Additionally, mHealth app usage frequency is explicitly operationalized as the primary moderator variable.\u003c/p\u003e \u003cp\u003eIn statistical modeling, a moderator variable alters the strength or direction of the relationship between two latent variables. Moderators can be either naturally occurring variables, such as gender, or experimentally manipulated factors. Evaluating these moderating effects enables researchers to understand how relationships differ across subgroups. In the current study, frequency of mHealth fitness app usage fulfills this moderating role. SEM was employed as the analytical technique due to its capacity to model latent constructs and their interactions comprehensively. Unlike traditional regression analysis, SEM accommodates latent variables, which are theoretical constructs inferred indirectly from observed indicators. Additionally, SEM facilitates simultaneous assessment of multiple dependent and independent variables, allowing researchers to evaluate both direct and indirect associations. Consequently, SEM represents a robust methodological approach for elucidating the complex relationships among obesity, mental health, and technology engagement in postpartum populations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Variable Measurement\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe questionnaire utilized in this study was developed by carefully combining and adapting validated measurement items from various previously published studies. Rather than employing a single existing questionnaire, relevant indicators from prior literature were specifically selected and integrated to ensure comprehensive and accurate measurement of each research variable.\u003c/p\u003e \u003cp\u003eIn this research work, demographics was measured with four indicators, i.e. age group, education background, working experience and household income per month. The age range was classified into four groups: 21 to 25 years old, 26 to 30, 31 to 35 and over 35. The education background of the respondents was categorized as less than high school, high school, diploma, Bachelor, and Master or Ph.D. The respondents\u0026rsquo; working experience was denoted as no job experience, 1 to 3 years, 4 to 6 years, 7 to 10 years, and more than 10 years. The household income per month in Ringgit Malaysia (RM) were categorized as less than 2,000; 2,000 to 3,000; 3,000 to 4,000; 4,000 to 5,000 and over 5,000.\u003c/p\u003e \u003cp\u003eThe lifestyle variable was measured based on previous studies (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), from which the authors selected a few indicators, such as average working hours per day, physical activity per week and average sleep hours per day. In addition, corresponding to Khajeheian et al.\u0026rsquo;s research (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), screen time (e.g. TV, smartphone, tablet, etc.) per day was added into lifestyle indicators. The frequency of physical activity per week was denoted by \u0026ldquo;none,\u0026rdquo; \u0026ldquo;1 time,\u0026rdquo; \u0026ldquo;2 times,\u0026rdquo; \u0026ldquo;3 times,\u0026rdquo; \u0026ldquo;4 times\u0026rdquo; and \u0026ldquo;more than 4 times.\u0026rdquo; The average screen time per day was indicated by \u0026ldquo;less than 1 hour,\u0026rdquo; \u0026ldquo;1 to 2 hours,\u0026rdquo; \u0026ldquo;2 to 3 hours,\u0026rdquo; \u0026ldquo;3 to 4 hours\u0026rdquo; and \u0026ldquo;more than 4 hours.\u0026rdquo; The average sleep hours per day was categorized as \u0026ldquo;less than 6 hours,\u0026rdquo; \u0026ldquo;6 to 7 hours,\u0026rdquo; \u0026ldquo;7 to 8 hours,\u0026rdquo; \u0026ldquo;8 to 9 hours,\u0026rdquo; and \u0026ldquo;more than 9 hours.\u0026rdquo; The average work hours per day consisted of five categories, \u0026ldquo;none,\u0026rdquo; \u0026ldquo;less than 7 hours,\u0026rdquo; \u0026ldquo;7 to 8 hours,\u0026rdquo; \u0026ldquo;8 to 9 hours\u0026rdquo; and \u0026ldquo;more than 9 hours.\u0026rdquo;\u003c/p\u003e \u003cp\u003eIn this study, dietary intake variables were categorized broadly into healthy and unhealthy food groups. Healthy foods included fruits, vegetables, and whole grains, while unhealthy foods encompassed fast food, sweets, chips, and soft drinks (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Respondents indicated their food intake frequency using a five-point Likert scale ranging from \"never\" to \"always.\" This categorization was deliberately selected to reflect general dietary preferences rather than precise consumption quantities or serving sizes. By focusing on broad food categories rather than detailed dietary records, the study aimed to reduce respondent recall bias and reporting inaccuracies. Moreover, this method allowed respondents to reflect more accurately on their typical dietary choices without being influenced by perceived desirable eating behaviors. This approach aligns well with the research objective of examining general dietary preferences and their associations with postpartum obesity and mental health outcomes.\u003c/p\u003e \u003cp\u003eTo measure the BMI range of an individual, the indicators to be calculated are (weight in kilograms)/(height in meters) \u0026sup2;. The BMI categories were applied in line with the standardized measurement (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Hence, this study on postpartum obesity contains the following categories: \u0026lt;18.5 underweight, \u0026ge;\u0026thinsp;18.5 to \u0026lt;\u0026thinsp;25.0 normal, \u0026ge;\u0026thinsp;25.0 to \u0026lt;\u0026thinsp;30.0 overweight and \u0026ge;\u0026thinsp;30.0 obese.\u003c/p\u003e \u003cp\u003eThree indicators were utilized to measure mental health as the dependent variable, guided by Broadman\u0026rsquo;s theory (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Respondents assessed their mental health status over the past year by self-reporting the number of serious problems faced, perceived stress levels, and overall happiness in life. The problems encountered were classified into three categories: \u0026ldquo;more than two serious problems,\u0026rdquo; \u0026ldquo;one or two serious problems,\u0026rdquo; and \u0026ldquo;no serious problems.\u0026rdquo; Stress levels were categorized as \u0026ldquo;high stress,\u0026rdquo; \u0026ldquo;medium stress,\u0026rdquo; and \u0026ldquo;normal stress,\u0026rdquo; while happiness in life was categorized as \u0026ldquo;not happy,\u0026rdquo; \u0026ldquo;average,\u0026rdquo; and \u0026ldquo;happy.\u0026rdquo; The decision to utilize self-reported subjective indicators instead of clinical diagnostic criteria was deliberate, as the primary objective of this research was to understand respondents' perceived mental health conditions rather than clinically diagnosed mental health disorders. This approach aligns with the non-clinical nature of the study, reflecting the broader population\u0026rsquo;s experiences and perceptions, thereby providing insights relevant to preventive and public health contexts (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the mHealth variable (moderator), the frequency of respondents using fitness apps was categorized into \u0026ldquo;daily,\u0026rdquo; \u0026ldquo;weekly,\u0026rdquo; \u0026ldquo;rarely\u0026rdquo; and \u0026ldquo;never used,\u0026rdquo; as applied in previous studies (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHair et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) recommended determining the minimum required sample size based on the number of latent variables and associated indicators included within a structural model. Specifically, they suggested that: (a) a minimum of 100 respondents is required when the model comprises five or fewer latent variables, each measured by at least three indicators; (b) at least 150 respondents are recommended for models with seven or fewer latent variables, each including at least three indicators; (c) a minimum of 300 respondents is necessary if the model has seven or fewer latent variables, with some containing fewer than three indicators; and (d) at least 500 respondents are advised when more than seven latent variables are present, with some containing fewer than three indicators. The current research framework includes five latent variables, and the moderating variable (fitness app usage frequency) is divided into four categories. Thus, it was anticipated that each usage category (daily, weekly, rarely, and never) would require a minimum of 100 respondents, yielding a total required sample size of at least 400. An online questionnaire was distributed to postpartum women residing in Kuala Lumpur, and a total of 468 completed questionnaires were obtained. All research procedures were conducted in accordance with relevant ethical guidelines and regulations. Respondents were informed clearly about the study's purpose, and informed consent was duly obtained from each participant.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data were collected via online questionnaire distributed to a total of 468 Malaysian postpartum women living in Kuala Lumpur, with no missing data. The data obtained for this study can be found in the descriptive statistical analysis in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eNumber, Percentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eUnderweight (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNormal (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e29.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eOverweight (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e34.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eObese (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e46.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e21 to 25 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e6.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e26 to 30 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e22.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e31 to 35 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e38.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eOver 35 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e32.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e17.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e11.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e25.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e36.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMaster or PhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e8.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLess than RM 2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e11.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRM 2,000\u0026ndash;3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e13.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRM 3,000\u0026ndash;4,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e32.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRM 4,000\u0026ndash;5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e35.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eOver RM 5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e7.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob Experience\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNo job experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e18.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e4\u0026ndash;6 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e23.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e7\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e30.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMore than 10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e18.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical Activity\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e25.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e1 time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e19.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e2 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e25.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e3 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e11.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e4 times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e11.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMore than 4 times per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e7.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScreen Time\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLess than 1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e32.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e27.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMore than 4 hours per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e40.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLess than 6 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e6\u0026ndash;7 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e12.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e7\u0026ndash;8 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e41.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e8\u0026ndash;9 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e31.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMore than 9 hours per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e8.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e9.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLess than 7 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e7\u0026ndash;8 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e19.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e8\u0026ndash;9 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e56.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMore than 9 hours per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e8.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProblems\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMore than 2 serious problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e26.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e1 or 2 serious problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e30.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNo serious problems in the past year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e42.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStress\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHigh stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e24.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e28.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e47.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHappiness\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNot happy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e47.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e39.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHappy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e13.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFitness app frequency of use\u003c/b\u003e (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e21.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eWeekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e24.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e32.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNever used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e22.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMostly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealthy Food\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e148 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e208 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e161 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e172 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e107 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole grains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e135 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e187 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e112 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnhealthy Food\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFast food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e32 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e231 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e205 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e40 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e229 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e199 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e54 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e195 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e219 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft drinks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e82 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e181 (38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e205 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSEM Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eValidity and Reliability\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe validity and reliability of a survey need to fit some of the conditions for SEM analysis. To examine the validity, every latent variable in the research should attain Cronbach\u0026rsquo;s alpha value of 0.7 or more (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). According to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the Cronbach\u0026rsquo;s alpha value for every latent variable is aligned with the condition that reinforces the validity of this research.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha output\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifestyle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHealthy Food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUnhealthy Food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNext, to test the reliability of this research, every latent variable indicator should obtain factor loading value more than 0.7 (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Based on Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, age and working indicator showed to have factor loading value less than 0.7. So, these indicators should be eliminated from the rest of SEM analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor loading analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eJob Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole grains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFast food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSoft drinks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eScreen Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSleeping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProblem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHappiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSubsequently, the reliability of each latent variable was assessed using the Average Variance Extracted (AVE). After eliminating indicators with insufficient factor loadings, all latent variables were required to achieve an AVE value of 0.5 or higher to ensure adequate reliability. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the AVE values obtained in this study clearly exceed the recommended threshold of 0.5, confirming satisfactory reliability for all latent variables.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAVE output\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifestyle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHealthy Food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUnhealthy Food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel Fitting\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA suitable research model should attain fit values above 0.9 (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The goodness of fit index (GFI), relative fit index (RFI), incremental fit index (IFI), comparative fit index (CFI), Tucker-Lewis index (TLI) and normed fit index (NFI) values in this study are within acceptable ranges. Therefore, the model-data fit in this research is accepted.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fitting analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStructural Modeling\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study employed SEM to explore and highlight meaningful connections among variables within a carefully designed conceptual model of postpartum obesity and mental health. To provide clear insights into how fitness app usage frequency influences these relationships, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e to \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrate the structural model outputs corresponding to the daily, weekly, rarely, and never fitness app usage groups, respectively. In these diagrams, significant relationships among variables are depicted by solid arrows, while non-significant relationships are indicated by dashed arrows.\u003c/p\u003e \u003cp\u003eIn the daily usage model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), nearly all relationships between the variables demonstrated significance, except for the direct link between demographics and BMI. The weekly usage model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed fewer significant connections, with two relationships identified as non-significant, specifically the effects of demographics on BMI and lifestyle on unhealthy food intake. Further diminishing of significance appeared in the rarely usage model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), wherein five out of fourteen possible relationships were not significant. These included the impacts of demographics on unhealthy food intake, BMI, and mental health, as well as the impacts of lifestyle on healthy food consumption and BMI. Lastly, the never usage model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed the most limited relationships, with half of the associations lacking significance. Specifically, demographics showed no significant influence on any other variable, and lifestyle demonstrated no significant impact on healthy food intake or BMI.\u003c/p\u003e \u003cp\u003eInterestingly, the strongest influence observed within the daily and weekly usage models was the positive impact of lifestyle on mental health, with coefficients of 0.63 and 0.67, respectively. Conversely, in the rarely and never use models, the strongest relationships were negative, demonstrating that BMI significantly affected mental health, with coefficients of -0.66 and \u0026minus;\u0026thinsp;0.68, respectively.\u003c/p\u003e \u003cp\u003eThese nuanced findings underscore the profound moderating role fitness app usage frequency plays in shaping relationships between lifestyle, demographic characteristics, BMI, dietary habits, and mental health outcomes. By exploring these structural relationships, the present study offers valuable insights into postpartum women's health behaviors and provides evidence-based suggestions for improving mental health and obesity management through increased engagement with mHealth technologies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis article presents the latest empirical research employing a multilevel analytical framework specifically tailored for postpartum women, focusing on the moderating role of fitness-related mHealth app usage frequency. Initially, the study introduced an innovative postpartum obesity framework, emphasizing critical factors associated with obesity and mental health. The analysis was carried out through SEM, followed by examining key variables influencing mental health according to varying frequencies of fitness app usage. Data was collected from Malaysian respondents within the first postpartum year, consistent with previous research by Kubota et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The results indicated substantial rates of overweight (34.6%) and obesity (46.3%) among postpartum women in the sample.\u003c/p\u003e \u003cp\u003eThe frequency of mHealth app use significantly moderated the relationships among the variables in the research model. Based on previous studies examining obesity, improvements were made specifically for postpartum women, carefully incorporating Malaysian cultural context (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The conceptual model integrated seven primary variables: demographics, lifestyle, healthy food intake, unhealthy food intake, BMI, mental health, and mHealth app usage frequency. Demographic variables (age, education level, income, and work experience) acted as initial independent variables, while mental health (measured through problems, stress, and happiness indicators) served as the dependent variable. Mediators included lifestyle behaviors (physical activity, screen time, sleep duration, and working hours), and dietary habits classified as healthy foods (fruits, vegetables, whole grains) or unhealthy foods (fast foods, sweets, chips, soft drinks). Previous studies confirm the associations between maternal obesity and mental health, lifestyle behaviors and postpartum weight changes, as well as postpartum dietary patterns (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor detailed SEM analysis, respondents were divided based on app usage frequency into four distinct groups: daily, weekly, rarely, and never. Factor loading analysis led to the removal of age and working hours indicators. Consequently, four final SEM models were derived and presented separately.\u003c/p\u003e \u003cp\u003eIn the \"daily\" model, demographic variables had significant positive impacts on lifestyle (0.41), healthy food intake (0.29), and unhealthy food intake (0.19). However, demographics showed no significant impact on BMI. Lifestyle positively influenced both healthy (0.35) and unhealthy (0.23) food consumption but negatively affected BMI (-0.23). Healthy (0.18) and unhealthy (0.19) food intakes both significantly influenced BMI positively. In the \"weekly\" model, similar trends appeared: demographics positively influenced lifestyle (0.35), healthy food intake (0.27), and unhealthy food intake (0.16). Lifestyle positively impacted healthy food intake (0.32) and negatively influenced BMI (-0.16). Additionally, healthy (0.22) and unhealthy (0.23) food intake both positively affected BMI. Factor loading analysis revealed screen time (0.89) and physical activity (0.81) as the highest lifestyle indicators, consistent with healthier dietary choices and lower BMI among frequent app users.\u003c/p\u003e \u003cp\u003eThe \"rarely use\" model showed demographic variables significantly impacting lifestyle (0.17) and healthy food intake (0.16). Lifestyle negatively influenced unhealthy food consumption (-0.19). Healthy (0.19) and unhealthy food (0.46) had significant positive impacts on BMI, confirming previous literature on dietary influences on BMI. The \"rarely use\" group indicated lifestyle and healthy food intake positively correlated with increased mental health issues, whereas unhealthy food intake and higher BMI correlated with fewer mental health issues.\u003c/p\u003e \u003cp\u003eIn the \"never use\" model, demographics showed no significant impact on other variables. Lifestyle negatively impacted unhealthy food intake (-0.26) and positively impacted mental health (0.43). Healthy and unhealthy food intakes both positively influenced BMI significantly. The factor loading analysis highlighted that the \"chips\" indicator strongly influenced BMI, aligning with previous findings on snack consumption among postpartum women.\u003c/p\u003e \u003cp\u003eOverall, comparative analysis across the four models clearly demonstrates that frequent fitness app usage contributes significantly to improved lifestyle behaviors, healthier dietary patterns, and better mental health outcomes, thus positively influencing postpartum obesity management. To conclude the discussion, two main interpretations are presented based on the analysis of the four models. First, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e displays all four models together and the non-significance arrows are eliminated.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe R-squared (R\u0026sup2;) values from the SEM analysis were 0.82 for the \"daily\" model, 0.79 for the \"weekly\" model, 0.71 for the \"rarely\" model, and 0.59 for the \"never use\" model (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e to \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This indicates that in the daily usage model, 82% of the variance in mental health among postpartum women can be explained collectively by BMI, demographics, lifestyle behaviors, and dietary intake (both healthy and unhealthy food). In comparison, only 59% of mental health variance is explained by these factors in the group of respondents who never used fitness apps. Therefore, it appears that increasing the frequency of fitness app usage is associated with improved explanatory power of the research model regarding mental health outcomes.\u003c/p\u003e \u003cp\u003eFurthermore, the influence of demographic variables varied significantly across models. Demographics showed a significant effect on four essential variables, specifically healthy food intake, unhealthy food intake, lifestyle, and mental health, in both the daily and weekly fitness app user groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, the influence of demographics was less prominent in groups with less frequent app usage. In the rarely used model, demographics significantly impacted only two variables, lifestyle and healthy food intake (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, demographics had no significant relationships with any variable in the never use group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This trend indicates that as the frequency of fitness app usage decreases among Malaysian postpartum women, the role of demographic characteristics within the research model diminishes.\u003c/p\u003e \u003cp\u003eAdditionally, a noteworthy trend emerged regarding the relationship between unhealthy food intake and BMI across the different models. The impact of unhealthy food consumption on BMI was lowest in the daily app usage group (0.19, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), increased slightly in the weekly group (0.23, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and further increased substantially in the rarely group (0.46, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), reaching its highest value in the never use group (0.67, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These results highlight that decreasing frequency of fitness app engagement is linked to a stronger positive relationship between unhealthy dietary patterns and increased BMI. Correspondingly, BMI\u0026rsquo;s effect on mental health outcomes decreased as app usage frequency increased, suggesting that regular fitness app usage might mitigate the negative impact of BMI on mental health among postpartum women.\u003c/p\u003e \u003cp\u003eAs a result, the frequency of fitness app use by Malaysian postpartum women not only has significant impact on BMI and mental health management but also leads to unhealthy food consumption management by the women.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePregnancy is frequently identified as a significant contributor to weight gain and obesity among women during the postpartum period, representing a substantial public health challenge globally (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The present study introduces an improved conceptual framework compared to previous research by emphasizing the frequency of mHealth app use as a moderator influencing demographics, lifestyle habits, dietary choices, BMI, and mental health outcomes among postpartum women. Advances in technology have facilitated the emergence of mHealth apps, which effectively assist users in managing lifestyle aspects such as diet, physical fitness, and health behaviors (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Findings from this study highlight the valuable role these apps play in improving quality of life, especially by promoting healthier lifestyles and better mental health among postpartum women. Additionally, regular physical activity remains widely recognized as an effective strategy for weight reduction and overall health enhancement (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, certain limitations should be acknowledged. First, parity was not specifically assessed, despite its known influence on postpartum obesity. The participants in this study were standardized based on a postpartum period of one year following childbirth. Although this approach is consistent with previous literature, allowing mothers adequate time for physical and psychological recovery, future research could incorporate parity as an additional factor to better understand its influence on postpartum weight dynamics. Second, self-reported measurements of weight and height potentially impacted data validity. While prior research supports the reliability of self-reported anthropometric data, incorporating objective measurements in future studies would improve accuracy, enhancing research validity. Accurate anthropometric data collection is an essential component in ensuring the robustness and credibility of findings. To the authors\u0026rsquo; knowledge, this research is the first to explore postpartum obesity using mHealth app usage frequency as a moderating variable through SEM combined with moderation analyses. By highlighting key relationships between postpartum obesity, mental health, and technology-driven health management, this study significantly contributes to current academic literature. Its insights provide valuable groundwork for future studies and interventions aimed at addressing postpartum obesity and mental health, two prominent public health issues requiring focused preventive approaches.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003emHealth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMobile health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStructural Equation Modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRinggit Malaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eR-square\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eComparative Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNormed Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRelative Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIncremental Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGoodness of Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTLI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTucker Lewis Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAverage Variance Extracted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of UCSI University (protocol code IEC-2023-FOSSLA-0202 on 9 April 2024).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all subjects involved in the study. The research methods were performed in accordance with the relevant guidelines and regulations. Participants of the study were informed about the purpose, objectives, and their right to participate, decline participation, or withdraw their participation in the research activities by verbal. Respondents have been notified that the information given was private and confidential which only going to use for academic purposes only. Written informed consent was obtained from all respondents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are not publicly available due to the Institutional Ethics Committee of UCSI University rules and regulations. The data that support the findings of this research are available upon reasonable request from the corresponding author and with permission of the Institutional Ethics Committee of UCSI University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, H.X. and Y.S.; methodology, L.K. and L.X..; software, H.X.; validation, Y.S. and N.S.; formal analysis, N.S.; writing\u0026mdash;original draft preparation, H.X., Y.S., N.S., L.K., and L.X; writing\u0026mdash;review and editing, H.X., Y.S., and N.S; supervision and project administration, N.S. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors would like to express their sincere appreciation and gratitude to the participants for their valuable cooperation and contribution to this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVarma DS, Mualem M, Goodin A, Gurka KK, Wen TS, Gurka MJ, et al. Acceptability of an mHealth App for Monitoring Perinatal and Postpartum Mental Health: Qualitative Study With Women and Providers. JMIR Form Res. 2023;7:e44500.\u003c/li\u003e\n\u003cli\u003eEl Ayadi AM, Diamond-Smith NG, Duggal M, Singh P, Sharma P, Kaur J, et al. Preliminary impact of an mHealth education and social support intervention on maternal health knowledge and outcomes among postpartum mothers in Punjab, India. BMC Pregnancy Childbirth. 2025;25(1):239.\u003c/li\u003e\n\u003cli\u003eYan C, Cao R, Hu X, Hu Y, Liu H. A longitudinal study of a mHealth app and regional policies on the uptake of postpartum depression referral after positive screening in Shenzhen. BMC Pregnancy Childbirth. 2024;24(1):536.\u003c/li\u003e\n\u003cli\u003eHutton L, Price BA, Kelly R, McCormick C, Bandara AK, Hatzakis T, et al. Assessing the Privacy of mHealth Apps for Self-Tracking: Heuristic Evaluation Approach. Jmir Mhealth and Uhealth. 2018;6(10).\u003c/li\u003e\n\u003cli\u003eArakawa Y, Haseda M, Inoue K, Nishioka D, Kino S, Nishi D, et al. Effectiveness of mHealth consultation services for preventing postpartum depressive symptoms: a randomized clinical trial. BMC Med. 2023;21(1):221.\u003c/li\u003e\n\u003cli\u003eGibson AD, Mazeri S, Lohr F, Mayer D, Bailey JLB, Wallace RM, et al. One million dog vaccinations recorded on mHealth innovation used to direct teams in numerous rabies control campaigns. Plos One. 2018;13(7).\u003c/li\u003e\n\u003cli\u003eJindal D, Gupta P, Jha D, Ajay VS, Goenka S, Jacob P, et al. Development of mWellcare: an mHealth intervention for integrated management of hypertension and diabetes in low-resource settings. Global Health Action. 2018;11(1).\u003c/li\u003e\n\u003cli\u003eZhu G, Liu H, Feng MN. An Evolutionary Game-Theoretic Approach for Assessing Privacy Protection in mHealth Systems. International Journal of Environmental Research and Public Health. 2018;15(10).\u003c/li\u003e\n\u003cli\u003eFeroz A, Kadir MM, Saleem S. Health systems readiness for adopting mhealth interventions for addressing non-communicable diseases in low- and middle-income countries: a current debate. Global Health Action. 2018;11(1).\u003c/li\u003e\n\u003cli\u003eLiew MS, Zhang J, See J, Ong YL. Usability Challenges for Health and Wellness Mobile Apps: Mixed-Methods Study Among mHealth Experts and Consumers. Jmir Mhealth and Uhealth. 2019;7(1).\u003c/li\u003e\n\u003cli\u003eYu DH, Parmanto B, Dicianno B. An mHealth App for Users with Dexterity Impairments: Accessibility Study. Jmir Mhealth and Uhealth. 2019;7(1).\u003c/li\u003e\n\u003cli\u003eKhan AI, Gill A, Cott C, Hans PK, Gray CS. mHealth Tools for the Self-Management of Patients With Multimorbidity in Primary Care Settings: Pilot Study to Explore User Experience. Jmir Mhealth and Uhealth. 2018;6(8).\u003c/li\u003e\n\u003cli\u003eRuton H, Musabyimana A, Gaju E, Berhe A, Grepin KA, Ngenzi J, et al. The impact of an mHealth monitoring system on health care utilization by mothers and children: an evaluation using routine health information in Rwanda. Health Policy and Planning. 2018;33(8):920-7.\u003c/li\u003e\n\u003cli\u003eGiunti G. 3MD for Chronic Conditions, a Model for Motivational mHealth Design: Embedded Case Study. Jmir Serious Games. 2018;6(3).\u003c/li\u003e\n\u003cli\u003eLalloo C, Hundert A, Harris L, Pham Q, Campbell F, Chorney J, et al. Capturing Daily Disease Experiences of Adolescents With Chronic Pain: mHealth-Mediated Symptom Tracking. Jmir Mhealth and Uhealth. 2019;7(1).\u003c/li\u003e\n\u003cli\u003eChong EYC, Palanisamy UD, Jacob SA. A qualitative study on the design and development of an mHealth app to facilitate communication with the Deaf community: perspective of community pharmacists. Patient Preference and Adherence. 2019;13:195-207.\u003c/li\u003e\n\u003cli\u003eJessen S, Mirkovic J, Ruland CM. Creating Gameful Design in mHealth: A Participatory Co-Design Approach. Jmir Mhealth and Uhealth. 2018;6(12).\u003c/li\u003e\n\u003cli\u003eCrico C, Renzi C, Graf N, Buyx A, Kondylakis H, Koumakis L, et al. mHealth and telemedicine apps: in search of a common regulation. Ecancermedicalscience. 2018;12.\u003c/li\u003e\n\u003cli\u003eOrgnization WH. Obesity and overweight: World Health Organization; 2018, February 16 [Available from: http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.\u003c/li\u003e\n\u003cli\u003eMoussa HN, Alrais MA, Leon MG, Abbas EL, Sibai BM. Obesity epidemic: impact from preconception to postpartum. Future Science Oa. 2016;2(3).\u003c/li\u003e\n\u003cli\u003eKhadka N, Fassett MJ, Oyelese Y, Mensah NA, Chiu VY, Yeh M, et al. Trends in Postpartum Depression by Race, Ethnicity, and Prepregnancy Body Mass Index. JAMA Netw Open. 2024;7(11):e2446486.\u003c/li\u003e\n\u003cli\u003ePendeloski KPT, Ono E, Torloni MR, Mattar R, Daher S. Maternal obesity and inflammatory mediators: A controversial association. American Journal of Reproductive Immunology. 2017;77(5).\u003c/li\u003e\n\u003cli\u003eMeng Y, Groth SW, Li DM. The Association between Obesity-Risk Genes and Gestational Weight Gain Is Modified by Dietary Intake in African American Women. Journal of Nutrition and Metabolism. 2018.\u003c/li\u003e\n\u003cli\u003eGeusens F, Van Uytsel H, Ameye L, Devlieger R, Jacquemyn Y, Van Holsbeke C, et al. The impact of self-monitoring physical and mental health via an mHealth application on postpartum weight retention: Data from the INTER-ACT RCT. Health Promot Perspect. 2024;14(1):44-52.\u003c/li\u003e\n\u003cli\u003eGarmendia ML, Zamudio C, Araya M, Kain J. Association between prepregnancy obesity and metabolic risk in Chilean premenopausal women 10 y postpartum. Nutrition. 2017;38:20-7.\u003c/li\u003e\n\u003cli\u003eWilcox S, Liu J, Sevoyan M, Parker-Brown J, Turner-McGrievy GM. Effects of a behavioral intervention on physical activity, diet, and health-related quality of life in postpartum women with elevated weight: results of the HIPP randomized controlled trial. BMC Pregnancy Childbirth. 2024;24(1):808.\u003c/li\u003e\n\u003cli\u003eWen S-Y, Ko Y-L, Jou H-J, Chien L-Y. Sleep quality at 3 months postpartum considering maternal age: A comparative study. Women Birth. 2018.\u003c/li\u003e\n\u003cli\u003eWaring ME, Simas TAM, Oleski J, Xiao RS, Mulcahy JA, May CN, et al. Feasibility and Acceptability of Delivering a Postpartum Weight Loss Intervention via Facebook: A Pilot Study. Journal of Nutrition Education and Behavior. 2018;50(1):70-+.\u003c/li\u003e\n\u003cli\u003eAwoke MA, Earnest A, Skouteris H, Moran LJ, Wycherley TP. Modeling the effect of diet and physical activity on body mass index in prepregnant and postpartum women. Nutrition. 2023;111:112026.\u003c/li\u003e\n\u003cli\u003eKay MC, Wasser H, Adair LS, Thompson AL, Siega-Riz AM, Suchindran CM, et al. Consumption of key food groups during the postpartum period in low-income, non-Hispanic black mothers. Appetite. 2017;117:161-7.\u003c/li\u003e\n\u003cli\u003eHarris A, Chilukuri N, West M, Henderson J, Lawson S, Polk S, et al. Obesity-Related Dietary Behaviors among Racially and Ethnically Diverse Pregnant and Postpartum Women. J Pregnancy. 2016;2016:9832167.\u003c/li\u003e\n\u003cli\u003eKamarudin SS, Idris IB, Ahmad N, Sharip S. Exploring Asian maternal experiences and mHealth needs for postpartum mental health care. Digit Health. 2024;10:20552076241292679.\u003c/li\u003e\n\u003cli\u003eHarrington EK, McCoy EE, Drake AL, Matemo D, John-Stewart G, Kinuthia J, et al. Engaging men in an mHealth approach to support postpartum family planning among couples in Kenya: a qualitative study. Reproductive Health. 2019;16(1):17.\u003c/li\u003e\n\u003cli\u003eGupta N, Gupta A, Bhagia S, Singh S. Mobile Technology for increasing Postpartum Family Planning Acceptability: The Development of a Mobilebased (mHealth) Intervention through a Dedicated Counselor\u0026mdash;A Pilot Innovative Study conducted in a Tertiary Teaching Hospital of Agra, Uttar Pradesh, India. Journal of South Asian Federation of Obstetrics Gynaecology. 2018:74-80.\u003c/li\u003e\n\u003cli\u003eNakayama K, Yamaguchi, K., Maruyama, S., \u0026amp; Morimoto, K. The relationship of lifestyle factors, personal character, and mental health status of employees of a major Japanese electrical manufacturer. Environmental health and preventive medicine. 2001;5(4):144-9.\u003c/li\u003e\n\u003cli\u003eWan Mohamed Radzi C, Salarzadeh Jenatabadi H, Samsudin N. Postpartum depression symptoms in survey-based research: a structural equation analysis. BMC Public Health. 2021;21(1):27.\u003c/li\u003e\n\u003cli\u003eKhajeheian D, Colabi AM, Shah N, Radzi C, Jenatabadi HS. Effect of Social Media on Child Obesity: Application of Structural Equation Modeling with the Taguchi Method. International Journal of Environmental Research and Public Health. 2018;15(7).\u003c/li\u003e\n\u003cli\u003eHuang H, Radzi C, Jenatabadi HS. Family Environment and Childhood Obesity: A New Framework with Structural Equation Modeling. International Journal of Environmental Research and Public Health. 2017;14(2).\u003c/li\u003e\n\u003cli\u003eSchrijvers JK, McNaughton SA, Beck KL, Kruger R. Exploring the Dietary Patterns of Young New Zealand Women and Associations with BMI and Body Fat. Nutrients. 2016;8(8).\u003c/li\u003e\n\u003cli\u003eBoardman JD. Stress and physical health: the role of neighborhoods as mediating and moderating mechanisms. Social science \u0026amp; medicine (1982). 2004;58(12):2473-83.\u003c/li\u003e\n\u003cli\u003eSalarzadeh Jenatabadi H, Bt Wan Mohamed Radzi CWJ, Samsudin N. Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach. Int J Environ Res Public Health. 2020;17(14).\u003c/li\u003e\n\u003cli\u003eLiu Y, Ren W, Qiu Y, Liu J, Yin P, Ren J. The Use of Mobile Phone and Medical Apps among General Practitioners in Hangzhou City, Eastern China. JMIR mHealth and uHealth. 2016;4(2):e64-e.\u003c/li\u003e\n\u003cli\u003eHair J, Black W, Babin B, Anderson R. Multivariate data analysis: Pearson new international edition. New Jersey: Pearson/Prentice Hall; 2014.\u003c/li\u003e\n\u003cli\u003eMiranda AR, Scotta AV, Cortez MV, Soria EA. Two-years mothering into the pandemic: Impact of the three COVID-19 waves in the Argentinian postpartum women\u0026apos;s mental health. PLoS One. 2025;20(3):e0294220.\u003c/li\u003e\n\u003cli\u003eGuo H, Zhou Z, Ma F, Chen X. Doctoral students\u0026apos; academic performance: The mediating role of academic motivation, academic buoyancy, and academic self-efficacy. Heliyon. 2024;10(12):e32588.\u003c/li\u003e\n\u003cli\u003eBt Wan Mohamed Radzi CWJ, Salarzadeh Jenatabadi H, Samsudin N. mHealth Apps Assessment among Postpartum Women with Obesity and Depression. Healthcare (Basel). 2020;8(2).\u003c/li\u003e\n\u003cli\u003eKubota C, Inada T, Nakamura Y, Shiino T, Ando M, Aleksic B, et al. Stable factor structure of the Edinburgh Postnatal Depression Scale during the whole peripartum period: Results from a Japanese prospective cohort study. Sci Rep. 2018;8(1):17659.\u003c/li\u003e\n\u003cli\u003eChen HH, Hsiung Y, Lee CF, Huang JP, Chi LK, Weng SS. Effects of an mHealth intervention on maternal and infant outcomes from pregnancy to early postpartum for women with overweight or obesity: A randomized controlled trial. Midwifery. 2024;138:104143.\u003c/li\u003e\n\u003cli\u003eNicklas JM, Pyle L, Soares A, Leiferman JA, Bull SS, Tong S, et al. The Fit After Baby randomized controlled trial: An mHealth postpartum lifestyle intervention for women with elevated cardiometabolic risk. PLoS One. 2024;19(1):e0296244.\u003c/li\u003e\n\u003cli\u003eStanhope KK, Stallworth T, Forrest AD, Vuncannon D, Juarez G, Boulet SL, et al. Planning for the forgotten fourth trimester of pregnancy: A parallel group randomized control trial to test a postpartum planning intervention vs. standard prenatal care. Contemp Clin Trials. 2024;143:107586.\u003c/li\u003e\n\u003cli\u003eSamsudin N, Bailey RP, Ries F, Hashim S, Fernandez JA. Assessing the impact of physical activity on reducing depressive symptoms: a rapid review. BMC Sports Sci Med Rehabil. 2024;16(1):107.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"non-communicable disease, psychological well-being, public health, obesity, health risk","lastPublishedDoi":"10.21203/rs.3.rs-6554586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6554586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe proliferation of mobile health (mHealth) applications has markedly influenced self-management practices related to obesity and mental well-being. However, the effectiveness of fitness apps in enhancing health outcomes is closely tied to their frequency of usage, a factor that has been insufficiently explored, especially among postpartum populations.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to propose and empirically test a structural equation modeling (SEM) framework to examine the moderating effects of fitness app usage frequency on the relationships among obesity, lifestyle behaviors, dietary habits, and mental health outcomes among postpartum women.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional online survey was administered to postpartum women in Malaysia within one year after childbirth, collecting 468 valid responses. Participants were categorized into four distinct groups based on their frequency of fitness app usage: daily, weekly, rarely, and never.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe SEM analyses highlighted significant variations among the four user groups. The daily-user model exhibited the strongest explanatory power (R\u0026sup2; = 0.82), followed by weekly (R\u0026sup2; = 0.79), rarely (R\u0026sup2; = 0.66), and never-user groups (R\u0026sup2; = 0.59). Specifically, in the daily-user group, demographic factors, lifestyle behaviors, dietary intake, and Body Mass Index (BMI) explained 82% of the variance in mental health outcomes. Across all usage categories, BMI consistently demonstrated a significant negative relationship with mental health issues, suggesting better mental health among participants with lower BMI. Further, factor loading analyses identified screen time (0.89) and physical activity (0.81) as dominant indicators of lifestyle behaviors. Frequent app users (daily and weekly) displayed healthier dietary choices and lower BMI scores compared to infrequent users.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eRegular engagement with fitness mHealth applications substantially enhances mental health and supports obesity management among postpartum women. This study underscores the critical moderating role of app usage frequency in optimizing health outcomes, providing practical implications for public health strategies and interventions targeting postpartum populations.\u003c/p\u003e","manuscriptTitle":"Empowering Postpartum Women: The Role of mHealth Apps in Promoting Mental Health and Healthy Weight Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 08:31:37","doi":"10.21203/rs.3.rs-6554586/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-02T10:50:08+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"241724818926988564809035410354654269326","date":"2025-05-22T14:49:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-22T00:56:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T10:35:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69330176434370804329953576704620778436","date":"2025-05-20T10:15:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63019845834311278941941503099235764617","date":"2025-05-20T09:38:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-20T09:26:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-18T18:24:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-02T06:46:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-30T15:30:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-04-30T15:28:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f80a4ac-762e-449a-b5e5-88b57f670eb1","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T16:00:03+00:00","versionOfRecord":{"articleIdentity":"rs-6554586","link":"https://doi.org/10.1186/s12905-025-03907-9","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2025-07-16 15:57:15","publishedOnDateReadable":"July 16th, 2025"},"versionCreatedAt":"2025-05-26 08:31:37","video":"","vorDoi":"10.1186/s12905-025-03907-9","vorDoiUrl":"https://doi.org/10.1186/s12905-025-03907-9","workflowStages":[]},"version":"v1","identity":"rs-6554586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6554586","identity":"rs-6554586","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}