Introduction

The growing prevalence of metabolic diseases such as hypertension, type 2 diabetes mellitus, and obesity are a major global public health concern [1]. Disparities remain in the burden of metabolic diseases, with low- to middle-income countries bearing a greater burden [1,2]. In particular, low- to middle-income countries experience higher mortality rates related to type 2 diabetes mellitus and hypertension [1]. Therefore, appropriate interventions are needed, including enhancing health infrastructure and providing healthy lifestyle programs. The prevalence of having two or more risk factors of cardiovascular disease is 76.0% in Cambodia [3]. Hypertension emerges as the major metabolic risk factor in Cambodia [3]. According to the World Health Organization (WHO), non-communicable diseases such as type 2 diabetes and cardiovascular disease were estimated to account for 64% of all deaths in Cambodia, 2019 [4]. The WHO emphasizes that an important way to control or prevent non-communicable diseases included metabolic diseases is to focus on reducing risk factors associated with these diseases [5]. In Cambodia, 29.6% of men aged 18-69 years reported being current smokers, and 49.5% of adult men aged 18-69 engaged in heavy drinking during the past 30 days [4]. These results indicate the need to reduce lifestyle related risk factors. Lifestyle changes can be a simple and effective way to delay these metabolic diseases and control them effectively.

Self-help groups are informal groups of residents who come together to address their common conditions or problems [6]. Peer support groups are a key element of community-based interventions [6]. This means that community-based interventions emphasize a sense of ownership by residents with health problems, and to enhance their participation in the process of solving health problems. Community-based interventions have been reported to promote health among residents in low-income or developing countries [7,8]. The interaction, motivation, and empowerment by participants in self-help groups have a positive effect, because continuous healthy lifestyle practices are influenced by an individual’s will. In Cambodia, the Village Health Support Groups (VHSGs) play a key role in delivering health service, including maternal, newborn, and child health, to vulnerable residents [9]. In addition, the roles of VHSGs are expanding to address health inequalities by promoting accessibility of health services [9]. These systems provide an essential foundation for facilitating the formation, operation and expansion community-based self-help groups.

Mobile health (mHealth) is considered to overcome the barriers of time, environment, and cost accessibility compared to traditional face-to-face interventions [10]. Previous studies have reported that mHealth intervention can increase physical activity and promote lifestyle changes in participants with metabolic diseases [11,12]. In particular, mHealth based interventions for at risk groups with metabolic diseases could be an alternative to resolve difficulties such limited accessibility to health promotion policies and systems low- or middle-income countries. A 12-week fitness walking program reduced the metabolic syndrome components, such as blood pressure and triglyceride, in postmenopausal women [13], and 12,000 steps per day also improved the metabolic health, such as fasting glucose level, HDL-C, and triglyceride, in college students [14]. In this respect, it is necessary to retrospectively identify health changes associated with participation in community-based self-help groups using mHealth, which can collect daily step data in real time, particularly in low- or middle-income countries. Accordingly, the present study aimed to investigate daily step counts in relation to metabolic health indicators and lifestyle practices among participants of mHealth-based healthy lifestyle clubs in Cambodia.

Methods

Study Design

A single-group repeated-measures observational design was used to examine temporal changes in metabolic health and health behaviors among participants in an mHealth-based healthy lifestyle program over a 2-year period.

This study adopted an observational approach to investigate naturally occurring changes in health indicators among community residents who voluntarily participated in the healthy lifestyle clubs. All participants received the same program components, and no experimental manipulation of step counts or random assignment to different intervention conditions was performed. Participants were retrospectively categorized into a high-step volume group (HSG) and a low-step volume group (LSG) using 8,000 steps as the threshold [15], based on the average daily step counts recorded during the program period for descriptive comparison. This stratification served as a descriptive and exploratory approach to examine whether temporal patterns in metabolic and behavioral outcomes differed according to naturally occurring walking levels.

Participants

The study participants were local residents 40 years or older who were enrolled in the project, entitled “noncommunicable disease (NCD) prevention and management program using mHealth in Prek Pnov of Cambodia”, which was a Public-Private Partnership (PPP) program by Korea International Cooperation Agency (KOICA) in 2022-2024. The project was conducted by a consortium between Korea Association of Health Promotion (KAHP) and Jeonbuk National University. Participants were selected if they met the following criteria: (a) ≥ 40 years of age (b) high-risk group for non-communicable diseases (c) able to move independently, and (d) capable of using a smartphone and applications. 275 Cambodians (171 women, 104 men) volunteered to participate in the project at the baseline, and 207 of them (129 women, 78 men) participated at 2-year follow-up. Finally, 204 Cambodians (129 women, 75 men) were selected as the study participants who completed fasting for the health exam at each time point through the 2-year follow-up.

Procedure

A single-group repeated-measures observational design was used for this study. Participants were retrospectively categorized as either the low-step volume group (LSG) or the high-step volume group (HSG) based on daily step counts. This classification was based on the 8,000-step criterion [15], informed by evidence that daily step counts associated with reduced mortality differ by age. A previous study reported that mortality risk was reduced when adults aged 60 years and older walked 6,000-8,000 steps per day, whereas adults younger than 60 years benefited at approximately 8,000-10,000 steps per day. Considering that the mean age of the participants was 56.60 years, 8,000 steps was used as an appropriate criterion for categorizing participants into two groups. Daily step counts were collected using the step-tracking function in the mobile application developed for the NCD prevention and management program. The average daily step counts for each participant were calculated based on the step-count data continuously recorded throughout the study period. All participants received the same healthy lifestyle program, aside from naturally occurring differences in daily step counts. Changes in metabolic factors and health behaviors were analyzed at three measurement points. The baseline assessment was performed in August 2022, and two follow-up assessments were conducted annually in June-July 2023 and in July 2024, respectively.

Healthy lifestyle club

A total of 10 clubs were operated for residents who were at high risk or had hypertension or diabetes who owned and could use smartphones. Participants were selected based on health screening results. Participants were classified as meeting one or more of the following criteria: (1) systolic and diastolic blood pressure ≥130/85mmHg or taking antihypertensive medication; (2) FBG ≥100mg/dL or taking anti-diabetes medication. The healthy lifestyle clubs were formed after obtaining informed consent from residents who met the eligibility criteria at baseline examination in August 2022, and the clubs continued to operate until December 2024. Each club was comprised of 20-25 residents living in the same village and functioned as a self-help group with common health problems. Club members met daily to participate physical activities such as dancing, walking, and aerobics and shared their experiences through monthly team meetings. To promote participation in the clubs, club members elected a club representative, and set the goals and meeting rules, schedules, places of club meetings, and club member roles. An mHealth system was used to efficiently operate the clubs and to encourage continuous participation. The system included a notification service, health information delivery, clinical assessment tools, and a tracker for recording participants’ daily step counts. Daily step counts were automatically recorded through a smart band (Huawei Technologies Co., Ltd., China), which was linked to the developed mobile application. The tracker was the key component of the system; it recorded the daily step counts from club participants and sent the step counts to the project manager for management of participants. A real-time alarm function also encouraged continuous walking for club members. In addition, education on healthy lifestyle practices such as physical activities and diet was provided, and each club selected and used exercise video programs of their choice.

The clubs were operated in cooperation with local medical staff and VHSG in Cambodia. Members received health education and counseling through mHealth system during regular monthly club meetings. They also participated in an annual non-communicable disease prevention campaign. Clubs that were successfully operated were selected as excellent clubs and rewarded, and the excellent club operation methods were shared with other club members (Figure 1).

Measure

1. Definition of metabolic syndrome

Metabolic syndrome (MetS) was defined by a combination of elevated waist circumference (WC), impaired fasting blood glucose (FBG), hypertriglyceridemia, low high-density lipoprotein (HDL) cholesterol, and elevated blood pressure (BP). The 2009 Joint Interim Statement (JIS) criteria require at least 3 of the following components: (1) elevated waist circumference ≥90cm for men, or ≥ 85cm for women (South Asian); (2) triglycerides (TG) ≥150mg/dL or treatment for elevated TG; (3) HDL-cholesterol<40mg/dL for men, or <50mg/dL for women; (4) systolic and diastolic blood pressure ≥130/85mmHg or antihypertensive treatment; and (5) FBG ≥100mg/dL or diagnosis of type 2 diabetes [16].

2. Anthropometric measurements

Body height in cm and body weight in kg were recorded to the nearest 0.1 units using an automatic portable stadiometer (UL Trosonic Height Weight machine, SH-300G, Healicom, China), while the participants were dressed in light clothing without shoes. BMI was calculated as the weight (kg) divided by height squared (m²). WC was measured in a standard manner using soft tape at the point between the upper iliac crest and the lowest rib after normal expiration. BP was recorded using an automatic blood pressure monitor after maintaining a stable state for more than 5 minutes.

3. Laboratory Measurements

After an overnight 10-hour fast, blood samples were collected for measurement of TG, HDL-cholesterol, and FBG. TG, HDL-cholesterol and FBG were measured by Auto Chemistry Analyzer (BK-280, Biospace, Shandong, China). The Auto Chemistry Analyzer was provided to Prek Pnov Referral Hospital of Cambodia as the part of the project. The laboratory indices were measured by well-trained laboratory staff from both Korea and Cambodia. For this, Korean experts visited Cambodia to provided professional techniques and capacity building for laboratory staff, periodically.

4. General characteristics and behavior assessment

We used a structured questionnaire to assess the general characteristics and healthy lifestyle of the study participants. The general characteristics included sex, age, marital status, education level, occupation, monthly income. Health behaviors items included cigarette smoking, alcohol drinking, exercising, eating fruits and vegetables, drinking sugar-sweetened beverages (SSB) or artificially sweetened beverages (ASB), salty food eating.

We defined current smokers as those who currently smoke cigarettes and smoked at least one cigarette during the last 30 days prior to the survey. High risk alcohol use is defined by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) as 5 or more drinks per day or 15 or more drinks in a week for men, 4 or more drinks per day or 8 or more drinks in a week for women [17]. The consumption of SSB or ASB beverages was categorized based on whether participants consumed one or more drinks per week. And we assessed eating fruits and vegetables by measuring whether fruits and vegetables were consumed daily. Finally, regular physical activity was defined as 30 minutes or more of moderate-intensity activity on 5 or more days per week, according to the adult physical activity guidelines provided by the U.S. Centers for Disease Control and Prevention [18].

The structured questionnaire developed by Korean experts was first translated into English, and then translated and reviewed into Khmer (the Cambodian language) by two medical experts (one doctor and one nurse) fluent in three languages (English, Korean and Khmer). For some questions, related images were shown to help participants understand the question accurately.

Statistical analysis

The collected data for this study were analyzed using SPSS for Windrows, version 26.0 (SPSS Inc, Chicago, IL, USA) and SAS for Windows, version 9.3 (SAS Institute, Carey, NC, USA). A p-value <.05 was considered statistically significant. The descriptive characteristics of the participants were analyzed by frequency (percentage) and mean (standard deviation). The test of homogeneity for descriptive characteristics was conducted using the chi-square test and independent t-test. To identify temporal changes in metabolic parameters across the three measurement points over the 2-year follow-up, the linear mixed models (LMM) with fixed effects for intercepts and time were applied. Moreover, the generalized estimating equation (GEE) were used to identify changes in the prevalence of metabolic syndrome component and health behaviors from baseline to 2-year follow-up. The LMM and GEE models were adjusted for sex, age, marital status, and occupation. Theses covariates were selected based on factors previous shown to be associated with MetS among Cambodian adults [19].

Ethics consideration

The study protocol was approved by the National Ethics Committee for Health Research (NECFR) of the Ministry of Health in Cambodia (No. 080 NECHR) and the institutional review board (IRB) of Jeonbuk National University (IRB No. 2022-02-006-001). All participants read and signed an informed consent form approved by NECFR and Jeonbuk National University IRB. We also informed the participants that they could withdraw from the study at any time, if they wanted to stop participating in the study, even after providing consent, without any penalty.

Results

Characteristics of participants

Table 1 presents the descriptive characteristics of study participants. A total of 204 participants were categorized by step counts into 90 in the LSG and 114 in the HSG. The age of the participants was 56.60±9.79 years (LSG 57.60±11.13, HSG 55.81±8.57), and 63.2% of the participants were women. Respondents were mostly married (71.6%), while 26.5% were previously married. More than half of the participants had a lower level of education; 15.7% were uneducated and 42.6% completed only elementary school. Most of the respondents were businessmen or sellers (44.1%) or civil servants/police officers (31.9%).

Among the participants, 10.8% were current smokers and the same proportion were high-risk drinkers. Most respondents consumed SSB/ASB (62.3%), and 25.0% of the participants reported no engagement in physical activity. There were no statistically significant differences in demographic characteristics, lifestyle behaviors and metabolic parameters between LSG and HSG at baseline (Table 1).

Changes in metabolic syndrome and metabolic parameters across time points.

The primary outcomes were metabolic syndrome and metabolic parameters. Table 2 describes the changes in prevalence of MetS and its components by step-volume groups. The prevalence of MetS decreased significantly over time in both groups (p<.01). In HSG, the prevalence of all components declined significantly, whereas in LSG, no within-group reductions were observed for high WC and high FBG. The degree of reduction was greater in HSG for high WC, high BP, and high TG, while decreases in low HDL-cholesterol, high TG, and MetS were more evident in LSG. For both groups, the prevalence of high BP and MetS declined steadily, showing larger reductions at 1-year follow-up and smaller reductions at 2-year follow-up (p<.05). High FBG and low HDL-cholesterol in HSG, and high TG in both groups, declined overall but partially rebounded at the 2-year follow-up after marked decrease at 1 year (p<.05). In contrast, high WC increased slightly at 1-year and then decreased at 2-year (p<.01).

As shown in Table 3 no significant group-by-time interactions were observed for any metabolic parameters, indicating that temporal patterns did not differ significantly between the two step- volume groups. Likewise, no significant differences were observed between LSG and HSG at each of the three measurement points (baseline, 1-year, and 2-year follow-up). In the HSG, significant temporal changes were observed for WC, BMI, SBP, DBP, FBG and HDL-cholesterol over the 2-year period (p<.01), whereas in LSG, no within-group changes were identified for BMI, SBP, and TG. The magnitude of change was greater in HSG than in LSG for most parameters, except WC and FBG. Moreover, some parameters (WC, SBP, FBG) showed larger reductions at 1-year follow-up compared with the 2-year follow-up.

Changes in health behaviors across time points

The secondary outcomes included health behaviors such as smoking, high-risk drinking, healthy eating and regular exercise Table 4. Within-group temporal changes showed significant increases in daily fruit intake and regular exercise in both groups over the 2-year period (p<.001), with more pronounced changes at 1-year and slight attenuation at 2-years. Among all health behavior indicators examined, regular exercise demonstrated the most substantial temporal improvement in both groups. Weekly SSB/ASB consumption showed a significant reduction from baseline to 2-year follow-up in the high-step volume group (p <.001), and this parameter differed significantly between groups at 2-year (p=.031).

No significant group-by-time interactions were observed for most health behaviors, indicating that temporal patterns did not differ significantly between the high-step and low-step volume groups. Similarly, no between-group differences were found at any of the three measurement points (baseline, 1-year, and 2-year follow-up), with one exception.

Discussion

This 2-year longitudinal observational study identified temporal patterns of metabolic health and healthy behaviors among Cambodian adults participating in mHealth-based healthy lifestyle clubs. The findings provide important insights into the relationships among community-based health promotion, metabolic health, and healthy lifestyle practices in resource-limited settings in low- or middle-income countries.

Overall, significant within-group changes were observed in metabolic parameters and health behaviors over the 2-year period, in both the LSG and the HSG. In the HSG, significant temporal improvements were observed in all metabolic parameters except TG. In the LSG, significant changes were also found, although fewer parameters improved compared with the HSG. These finding are consistent with previous observational studies, reporting that participants in community-based self-help groups or peer-support programs is associated with improvements metabolic health [20-22].

In terms of health behaviors, significant temporal changes were observed in SSB/ASB consumption, daily fruit intake, and regular exercise in both LSG and HSG. Notably, regular exercise showed the most substantial increase among lifestyle indicators. These behavioral changes may reflect the combined influence of self-help group- or peer-support participation, mHealth-based self-monitoring, and health education components [10-12,23].

The peer-support or community-based self-help group involvement has been associated with positive changes in metabolic health, particularly in low- or middle-income settings where access to health care services is relatively limited. The effects of peer-support or shared empathy have been explained in social cognitive theory [24]. The theory emphasizes that learning occurs in social context through observing, imitating, and reinforcing the behaviors of others. Mechanisms of peer support in self-help groups include sharing lived experiences, identifying participants’ strengths, providing social and practical support, and the unique role of peers who share similar health challenges in contrast to professional health providers [25]. Because participants in self-help groups face many similar difficulties, peer-support or feedback can serve as practical strategies for solving health problems and overcoming barriers [21].

However, no significant differences were found for group-by-time interaction in this study. These findings indicate that the temporal change patterns between HSG and LSG did not differ significantly. Given the single-group repeated-measures observational design, these results should be interpreted cautiously.

At the same time, this pattern offers important implications for future research. It suggests that continuous participation in community-based health promotion programs that combine peer support, self-monitoring, and health education may be more important than the absolute daily step count. These findings are consistent with recent systematic reviews reporting that incremental increases in daily step counts are associated with reduced risk of all-cause mortality and cardiovascular events [26,27]. These reviews noted that increases of 500-1,000 steps per day contribute to reductions in health risks, although the magnitude of the reduction may vary depending on baseline health status [26,27]. In this respect, community-based programs may consider promoting continuous walking as a primary goal rather than emphasizing the attainment of an absolute daily step count target.

In our study, notable temporal patterns were identified. Some metabolic parameters and health behavior indicators showed substantial improvement at the 1-year follow-up. At the 2-year follow-up, however, these improvements slightly diminished or partially rebounded, although the overall trend remained favorable. The modest decline observed at the 2-year follow-up may reflect natural fluctuations in health indicators, seasonal or environmental influences, or reduced adherence to the health promotion program over time. However, due to limitations of the single-group repeated-measures observational design, it is not possible to determine what this pattern at the 2-year follow-up reflects. Therefore, these findings should be interpreted cautiously. This temporal pattern is consistent with findings from previous systematic reviews, which reported that improvements in metabolic indicators or health behaviors often tend to weaken over time [28,29]. These results can also be interpreted through theoretical perspectives on the maintenance of behavior change. Long-term lifestyle changes may produce smaller effects than short-term changes because maintaining these behaviors over time can be influenced by reduced satisfaction with health outcomes, difficulties in self-regulation, environmental and social barriers, and limited or inconsistent access to resources [30]. These findings illustrate that community-based programs may require more intensive support in later stages of implementation than in the early phase. Moreover, a notable finding of this study is that most metabolic and health behavior indicators remained significantly improved compared with baseline, despite a slight decline at the 2-year follow-up. The advantages of mHealth, such as self-monitoring, timely notification and feedback, accessible health information, and facilitated remote support, may have helped motivate participants to maintain healthy behaviors [31-34].

Strengths of the present study include demonstrating the feasibility of implementing and sustaining mHealth-based healthy lifestyle clubs as community-based health promotion programs in resourced-limited environments. Despite limited access to health care services, the program achieved a 74.2% retention rate (204/275) over a 2-year period among health-vulnerable residents in Cambodia. This suggests that smartphone-based approaches can overcome barriers in infrastructure-limited environments. The successful implementation of mHealth-based healthy lifestyle clubs was supported by contextual factors such as cooperating with existing medical staff and VHSGs, peer-supported activities, and cost-effective, measurable digital tools that promoted participants engagement and empowered self-management. These strengths indicate that similar community-based programs using mHealth may be applicable in other low- and middle-income countries.

Despite these strengths, our study has several limitations. First, this study adopted a single-group repeated-measures observational design. Therefore, the results cannot be interpreted causally. And the observed changes may reflect natural temporal variations or secular trends, as well as the influence of unmeasured factors, rather than the effects of the participation itself. To address this limitation, cluster randomized controlled trials at the village level could be considered in the future research. Second, the HSG and the LSG were retrospectively divided based on naturally occurring behaviors after program participation, rather than through random or experimental assignment. In this respect, comparisons of two groups should be considered exploratory and descriptive, and the findings should be interpreted with caution. Future studies should include randomized controlled trials to determine the causal effects of health promotion program participation on improvements in health status. In addition, further research could aim to compare the relative contributions of peer support, mHealth based programs, and health education, as well as strategies for maintaining engagement and improvements after beginning of participation. Third, the results of this study were obtained from residents in Prek Pnov of Cambodia, so the findings may not be generalizable to populations in other countries or regions. Fourth, diagnostic criteria for MetS vary between populations belonging to different ethnic groups. Study results may differ according to other diagnostic MetS criteria. Finally, changes in health behaviors were assessed using self-reported questionnaires. Thus, social desirability bias may have influenced the responses.

Nevertheless, our findings provide important implications. The results of this study suggest that sustained engagement in walking activities, rather than focusing only on total step count volume, may be relevant to improved metabolic health. In addition, these results offer insight into how community-based health promotion programs using mHealth can support sustainable lifestyle practices among populations in low-income settings. These findings may serve as a foundation for future policies and studies aimed at developing sustainable lifestyle-supporting strategies to promote metabolic health.

Conclusions

Overall, this 2-year follow-up observational study identified temporal changes in metabolic health and health behaviors among adults 40 years or older who participated in an mHealth-based healthy lifestyle program in Cambodia. Significant within-group improvements were observed over 2-year follow-up in several metabolic indicators (WC, BP, FBG, HDL-cholesterol) and health behaviors (regular exercise, daily fruit intake, and SSB/ASB consumption). These changes were more pronounced at the 1-year follow-up, with a slight decline at the 2-year follow-up. The temporal patterns were broadly similar between the HSG and the LSG, suggesting that community-based health promotion programs may be associated with improvements in metabolic health. However, these findings are exploratory and should be confirmed in randomized controlled trials.

The results of this study also highlight the feasibility of implementing and sustaining health promotion programs using mHealth, self-help groups, and peer support in resourced-limited environments. The observed temporal patterns provided insight into the components of such programs and raise important questions for future research. Moreover, the findings contribute to the limited evidence on longitudinal metabolic health trajectories in low- and middle-income countries in Southeast Asia and support the potential expansion of mHealth-based community health promotion in similar settings.

Figure 1.

Conceptual framework for healthy lifestyle club development and sustainability.

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