East Sumba District was selected as the study area because it is classified as a high malaria-endemic region [64]. The district consists of 22 subdistricts and 156 villages [65]. The total population in the district is 248,776, with an almost equal distribution of male (51.4%) and female (48.6%) residents. Approximately one-half of the population is engaged in the agricultural sector. The district has 4 hospitals, 22 public health centers, and 78 subsidiary public health centers across the region [65]. This study was conducted in 4 selected subdistricts where there is 1 public health center in each subdistrict, as indicated in Figure 1.

This study used a cluster-assigned quasi-experimental design with pretest and posttest assessments in both control and intervention groups. The cluster unit for this study was the village, which was not randomized because the research team aimed to implement the intervention across all villages within a selected subdistrict, where the subdistrict leader would serve as a role model to encourage the community to improve malaria awareness. To obtain a representative sample, the research team randomly selected 4 of the 22 subdistricts, of which 2 were assigned as the intervention groups and 2 served as the control groups. The first 3 subdistricts each had 6 villages, and the fourth subdistrict had 7 villages; therefore, a total of 25 villages participated in the study, representing about 16% of all the villages in the district. Of these, 13 villages were assigned to the intervention group, while 12 villages formed the control group. The number of sampled households selected from each village was proportional to the number of households in that village. Within each selected village, households were randomly sampled to serve as an experimental unit as indicated in Figure 2.

To determine the required sample size for the baseline prevalence of study outcomes, the research team applied the Leslie-Kish formula for calculating the sample size in cross-sectional studies [66]:

where n₀ is the initial sample size; p is the prevalence of malaria awareness in ENT Province (48.8%) [39], Z is the confidence level at 95% (standard value of 1.96), and d is the relative precision (0.05); therefore, the initial sample size was 384 participants. For the population survey, a community correction factor of 2 was applied, doubling the required sample size to 768 participants. Additionally, a 15% buffer was added to account for potential incomplete data, primarily due to nonresponse or failure to complete interviews. Thus, the final sample size was determined to be 884 participants with 442 individuals in the control group and 442 individuals in the intervention group. This number served as a guideline for recruiting participants for the household survey, which was conducted both before and after the intervention.

Individuals were eligible for recruitment if they had resided in the selected villages for at least 12 months and did not intend to migrate or relocate within the next 6 months after initial contact. Furthermore, participants were required to be at least 18 years old. All participants were required to provide written informed consent. For those who were unable to read or write, the consent form was read aloud by trained research assistants (RAs), and participants provided consent using a thumbprint on an ink pad. The RAs graduated from public health degrees and were local nurses with prior experience in data collection and employment at local public health centers. These professionals were recruited to ensure the study’s data collection process was conducted effectively.

This study followed a 4-stage process to achieve its primary outcomes, as described in the following sections.

Data were collected in face-to-face interviews, guided by a questionnaire adapted from a previously published study [67]. The questionnaire consists of 5 sections covering demographic characteristics, general knowledge of malaria, treatment-seeking behavior, practice of malaria prevention measures, and practice of malaria treatment-seeking behaviors. Each section contained questions to capture key behavioral aspects of malaria. A detailed list of the questions for each section is provided in Multimedia Appendix 1. Furthermore, this data collection was conducted by local nurses and health promotion workers from the selected health centers. These enumerators participated in a 1-day intensive training to improve their understanding of the study background and objectives. Moreover, enumerators were trained how to approach the potential participants in this study and how to conduct the interview following the guidance of this study and local culture. The same enumerators were responsible for recruiting participants during both baseline data collection and the postintervention phase. For secondary data collection, malaria register books from each public health center were reviewed to obtain data on the number of malaria cases and malaria-related deaths over a 10- year period from 2013 to 2023. Data on the number of malaria cases in each public health center throughout the district were collected from the Health Department of East Sumba District for the same period.

This study considers demographic and environmental factors as independent variables. Demographic variables include gender, age, education level, occupation, and socioeconomic status (SES). Respondents’ SES will be measured based on ownership of durable assets such as a television, refrigerator, car, electricity, telephone, and radio; the number of bedrooms; and home infrastructure conditions such as access to drinking water sources and the presence of water channels around the house [68]. Using principal component analysis, respondents’ SES will be categorized into 5 quantiles ranging from the lowest (quantile 1) to the highest (quantile 5) [69]. Environmental variables in the study include the location of the residence, categorized as rice fields, coastal areas, or hilly areas, and accessibility to villages, classified as easy, moderate, or difficult to access. Additionally, the study considered proximity to health facilities, including the type and distance of the nearest health facilities. For secondary data from the health center, patient records from 2013 to 2023 were reviewed for all patients with suspected malaria, and positive or negative cases were confirmed based on laboratory examination results. The number of malaria cases based on gender, age group, type of Plasmodium, time (year and month), location of residence, and type of treatment and the number of deaths since the period under review were collected from the 4 selected health centers.

The study considers 10 potential outcomes variables, with the first 7 serving as the primary outcomes of the intervention, while the remaining 3 represent secondary findings. The first primary outcome of the study is improvement in the malaria awareness index among rural communities. If the malaria awareness index after the intervention is higher than that before the intervention, it indicates that the health education campaign effectively enhanced malaria awareness. This index will be assessed based on 12 questions covering general knowledge of malaria, treatment-seeking behavior, and knowledge of malaria prevention measures, following the methodology of a previous study [39]. Each correct response will be assigned a score of 1, while incorrect answers will receive a score of 0, with a total possible score of 12. Participants’ knowledge level will be categorized based on their accuracy rate: 0% (zero awareness), 1% to 59% (poor awareness), 60% to 79% (good awareness), and at least 80% (excellent awareness). Those classified as having excellent or good awareness will be considered aware of malaria, while participants in the other categories will be classified as unaware of malaria [39,70]. The malaria awareness index will be calculated before and after the intervention in both the intervention and control groups to evaluate the effectiveness of the health education campaign.

The second outcome variable in this study is improvement in AMTSB. This improvement will be demonstrated if the proportion of rural adults exhibiting AMTSB after the intervention is higher than before the intervention. AMTSB is defined as seeking malaria treatment at professional health facilities within 24 hours of experiencing malaria symptoms [41]. The proportion of rural adults engaging in AMTSB will be calculated both before and after the intervention in both the intervention and control groups. This assessment will be based on the 3 key questions from the malaria treatment-seeking behavior section of the questionnaire.

The third outcome variable in this study is improvement in good MPMK. This improvement will be demonstrated if the percentage of the rural population with good MPMK after the intervention is higher than before the intervention. A good level of MPMK is defined as the ability to correctly answer at least one-half of the total questions in the MPMK section, as indicated in previous studies [41,59]. The level of MPMK will be assessed before and after the intervention in both the intervention and control groups.

The fourth outcome variable is the use of bed nets during the dry season in high malaria-endemic settings. Community behaviors regarding bed net use may exhibit seasonal variations, as individuals may adjust their practices depending on weather conditions [71]. This outcome will be assessed based on the participants’ self-reported responses to a survey question asking whether they slept under a bed net the night before the survey.

The fifth outcome variable in this study is the variation in the use of a bed net in the dry and wet seasons. The literature indicates that bed net use might fluctuate based on seasonal weather conditions, as individuals adapt their malaria prevention behaviors accordingly [71,72]. This malaria awareness campaign began at the end of August 2024, during the dry season, and concluded in the second week of January 2025, which corresponds to the wet season in the study area. The study will examine seasonal differences in bed net use by analyzing responses to 4 key questions from the malaria prevention practice section of the questionnaire. The disparity in bed net use between dry and wet seasons will be assessed for both the intervention and control groups.

The sixth outcome variable in this study is improvement in good MPMP. Improvement will be demonstrated if the percentage of the rural population practicing good MPMP after the intervention is higher than before the intervention. A good level of MPMP is defined as the ability to correctly answer at least one-half of the total questions in the MPMP section, as indicated in a previous study [73,74].

The seventh outcome variable in this study is variation in access to basic drinking water and its associated factors among rural communities in ENT Province, Indonesia. Household access to basic drinking water is defined based on the WHO Joint Monitoring Program guidelines, which classify water sources into 4 categories: no service, unimproved, limited, and basic [75]. To analyze this variable, a dichotomous classification will be used, where households will be categorized as either having access to an improved drinking water source (Yes=1) or lacking access to an improved drinking water source (No=0). This classification will help determine disparities in access to safe drinking water and identify key factors influencing these differences.

The secondary outcomes of this study are derived from the analysis of patient medical records from 4 selected health centers, along with additional data from the Department of Health, East Sumba District. The eighth outcome variable focuses on the monthly number of malaria cases recorded over the past 10 years, from 2013 to 2023. By integrating data from both health centers and the Meteorological Agency of East Sumba, this study aims to explore the impact of climatic variables on malaria case fluctuations in this region, following the methodology of a previous study [76]. This analysis will provide valuable insights into seasonal and long-term trends in malaria transmission, which could help inform future malaria prevention and control strategies.

The ninth outcome variable in this study examines the demographic variations in malaria prevalence among communities residing in hilly and coastal areas over the past 10 years (2013-2023). Among the 4 selected health centers, Baing and Nggongi Public Health centers are located in coastal areas, whereas Mahu and Kananggar Public Health Centers are situated in hilly regions. Given that vector distribution and species composition may vary between different geographic settings [77], previous studies in Indonesia have reported significant differences in malaria prevalence between coastal and hilly areas [78]. By analyzing malaria case records from these health centers, this study aims to assess regional disparities in malaria burden and determine how demographic and environmental factors contribute to these differences. This information may help tailor malaria prevention and control efforts to the specific needs of different geographic regions.

The tenth outcome variable in this study investigates malaria prevalence and malaria-related mortality rates in East Sumba district over the past 10 years (2013-2023). Data on the number of malaria cases and malaria-related deaths were obtained from malaria register books at the health centers and malaria reports from the Health Department of East Sumba District. Therefore, the trends in malaria prevalence and mortality rates over the 10-year period in the study area will be revealed, following the methods of previous studies [79,80].

All statistical analyses will be conducted using SPSS version 27 (IBM Corp), and P values <.05 will be considered statistically significant.

For the first outcome variable, descriptive statistics will be used to summarize participants’ sociodemographic characteristics and environmental variables including gender, age group, SES, education level, family size, distance to the nearest health facilities, village accessibility, and household location. A chi-square test will be applied within each study group (intervention and control) to evaluate the association of basic malaria understanding, basic malaria knowledge, the level of malaria knowledge, and the level of malaria awareness before and after the intervention. Unpaired sample t tests will be used to investigate the statistical differences. Comparisons will be performed within groups (pre- and postintervention) and between groups to determine the impact of the intervention [61]. Furthermore, a binary logistic regression model will be used to identify the potential factors associated with malaria awareness in both the intervention and control groups [39].

For the second outcome variables, descriptive statistics will be used to summarize participants’ sociodemographic and environmental characteristics including gender, age group, SES, education level, family size, distance to the nearest health facilities, access to village, and household location for both study groups. A chi-square test will be used to evaluate the association between AMTSB and the sociodemographic and environmental characteristics of participants. Unpaired samples t tests will be used to assess the statistical differences. Comparisons will be conducted within and between groups for both pre- and postintervention periods. Moreover, binary logistic regression analysis will be used to identify predictors associated with AMTSB among participants in both study groups [41]. Only predictors that demonstrate statistically significant differences (P<.05) will be retained in the final model.

For the third variable response, participants’ sociodemographic characteristics and environmental variables in each group will be reported using descriptive statistics. The association between a good level of MPMK with predictors will be investigated using a chi-square test. Unpaired samples t tests will be used to investigate the statistical differences. Comparisons will be conducted within and between groups for both pre- and post-intervention periods. Furthermore, binary logistic regression analysis will be performed to identify key risk factors influencing MPMK [44,81] among rural communities in both study groups.

For the fourth outcome variable, descriptive statistics will be used to summarize participants’ sociodemographic characteristics and environmental variables based on the awareness that malaria can be prevented. Associations between bed net use and its predictors will be examined using a chi-square test. Binary logistic regression analysis will then be conducted to identify the primary risk factors associated with bed net use during the dry season among rural communities [71].

For the fifth outcome variable, participants’ sociodemographic characteristics, environmental variables, and malaria-related behavioral aspects in both the intervention and control groups will be analyzed. Data will be presented as proportions for each season (dry and wet seasons) to examine the seasonal variations in malaria prevention behaviors. The associations between bed net use and its predictors will be evaluated using a chi-square test for each season. The main risk factors for bed net use during the dry and wet seasons will be investigated using binary logistic regression analysis. The adjusted odds ratio will be used to quantify the strength of the associations between bed net use and its predictors in each season [71,72].

For the sixth outcome variable, participants’ sociodemographic characteristics, environmental variables, and behavioral aspects of malaria in each group will be reported using descriptive statistics. The association between a good level of MPMP and its predictors will be investigated using chi-square tests. Unpaired samples t tests will be used to investigate the statistical differences. Comparations will be performed within and between groups for both pre- and postintervention periods. Moreover, binary logistic regression analysis will be performed to identify the primary risk factors influencing MPMP [73,74] among rural communities in both study groups.

For the seventh outcome variables, descriptive statistics will be used to summarize participants’ sociodemographic characteristics, environmental variables, and malaria-related behaviors based on their source of drinking water. The associations between access to improved drinking water and its predictors will be assessed using chi-square tests. Furthermore, a binary logistic regression model will be used to determine predictors influencing access to improved drinking water [75].

For the eighth outcome variable, descriptive analyses of climatic factors across different subdistricts will be performed. Data will be aggregated at the subdistrict level, and statistical estimates will be generated for each variable. Specifically, the total number of malaria cases and the average malaria incidence will be computed. Climatic variables, including maximum, minimum, and mean temperature values, will be calculated. Moreover, data from all 22 subdistricts will be analyzed, focusing on malaria cases and climatic parameters, including maximum, minimum, and average temperature values. To depict the seasonal dynamics, seasonal variations in climatic parameters and malaria cases will be plotted, covering the 2 main seasons. A time series for both predictor and outcome variables will be plotted to deliver an overall perspective. A map of malaria cases will be generated. Scatterplots will be developed to illustrate relationships between malaria cases and predictor variables, following the methods of a previous study [76].

For the ninth outcome variable, malaria cases records from 2013 to 2023 will be analyzed across different geographic areas. Data from Baing and Nggongi Public Health Centers, representing coastal regions, will be combined, while records from Mahu and Kananggar Public Health Centers, representing hilly areas, will be aggregated separately. Malaria prevalence will be calculated on a monthly, yearly, and cumulative basis over the study period. To visualize the demographic trends, malaria prevalence will be plotted against key demographic variables. The disparity of malaria prevalence in demographic variables will be investigated using a chi-square test [10,82].

For the tenth outcome variable, descriptive statistics will be used to calculate frequencies and percentages of overall malaria prevalence and trends in malaria transmission considering variations by season, month, year, sex, age, and species of malaria parasite. Epidemiological characteristics, including age group, sex, and Plasmodium species–adjusted malaria-related mortality rates in East Sumba District will be presented as percentages followed by their 95% confidence intervals. Chi-square tests will be used to compare the associations of malaria burden by age groups and sex. The malaria prevalence and mortality rate will be illustrated in a map to assess the distributions of the cases geographically [79,80].

We adhered to the Declaration of Helsinki to ensure that the rights, integrity, and confidentiality of the respondents are strictly protected. All respondents signed consent forms before being interviewed. For this interventional study, we received human ethics approval from the Human Ethics Committee of Nusa Cendana University Kupang ENT Province Indonesia (ethics ID: 42/UN15.21/KEPK/2024) as shown in Multimedia Appendix 2. All respondents who participated in this study were involved voluntarily without compensation in the form of material; however, they received additional knowledge about malaria. Data obtained from this study will be strictly kept by the principal investigator, and no personal respondent information will be shared during the dissemination of the study results. The research team secured official permission letters from relevant authorities at the provincial, district, subdistrict, and village levels to conduct the study within the designated areas. Written consent forms were collected from all participants participating in this study.

The findings of this interventional study will be disseminated through national and international conferences and published in peer-reviewed journals.

This research endeavors to examine the impact of integrating multiple media into an interventional study designed to influence malaria-related behavioral changes among rural communities. The intervention incorporated local governmental leaders, traditional music, and loudspeaker announcements, recognizing their potential to enhance community engagement and facilitate effective malaria awareness campaigns. Local authorities play a pivotal role in public health initiatives as they serve as trusted figures within their communities and possess extensive experience in mobilizing the local population [63,83]. Furthermore, music has been shown to be a powerful tool for fostering social connection and disseminating health information, particularly in efforts to promote preventive behaviors for infectious diseases [62]. The use of loudspeakers as a communication medium has also been identified as an effective approach for delivering malaria health messages and ensuring continuous information dissemination in rural settings [61]. Moreover, integrating music-based education enhances learning outcomes among populations with low literacy rates [84], making it a particularly effective strategy for engaging rural communities. This is particularly relevant in ENT Province, where high school dropout rates remain a challenge [85,86]. By leveraging these culturally relevant and widely accessible communication methods, this quasi-experimental study has the potential to generate a significant impact on improving the malaria awareness index, AMTSB, knowledge of malaria prevention measures, and practice of malaria prevention measures among rural adults in ENT Province, Indonesia.

Interventional studies have demonstrated a positive impact on improving malaria-related behaviors at the community level across various settings [50,51,55,60,87-89]. In SEA, several interventional studies have shown significant contributions to behavioral change in malaria prevention and treatment [52,61,90,91]. For instance, a study conducted in Northern Myanmar found that loudspeaker announcements delivering malaria prevention and treatment messages over 6 months significantly increased malaria awareness among villagers [61]. Similarly, a school-based educational intervention in India demonstrated a substantial improvement in malaria prevention and control knowledge among students in grades 9 to 12 [90]. In Pakistan, an interventional study targeting pregnant women indicated that the use of LLINs increased significantly following a 12-week health education program [52]. In Bhutan, a community-directed educational intervention on malaria prevention, implemented over 6 months, led to improvements in malaria-related knowledge, attitudes, and prevention practices among rural communities in Sarpang district [91]. In Indonesia, there is a notable gap in interventional studies focused on malaria education. Most existing research has been conducted in school settings [60] and is primarily concentrated in the western region of the country [59]. To the best of our knowledge, this is the first study in SEA to investigate the combined impact of local leadership, traditional music, and loudspeaker announcements as intervention media to enhance malaria knowledge, improve prevention practices, and encourage early treatment-seeking behaviors among rural communities affected by malaria.

The data collection instruments used in this study were adapted from a validated questionnaire, with modifications made to ensure ease of administration. The measurement of key variables, including the malaria awareness index, AMTSB, MPMK, and MPMP, was based on standardized indicators from reputable international journals. This methodological approach allows for comparability with national and international studies, enhancing the credibility and applicability of the findings. Consequently, the study’s results will serve as an important reference for guiding malaria health policy in rural Indonesia and supporting the national commitment to achieving malaria elimination by 2030.

Despite its strengths, this study has several limitations. First, village selection was not randomized for the control and intervention groups, which may introduce potential selection bias. However, the allocation strategy ensured that the distribution of villages between study groups was comparable in terms of malaria endemicity, and the intervention was designed to maximize its impact by including all villages within a selected subdistrict. Second, most of the data collected in this interventional study relied on self-reported responses from participants, covering aspects such as general malaria knowledge, symptoms, diagnosis, and prevention measure knowledge. This reliance on self-reported data introduces the potential for recall bias. However, to mitigate this limitation, all MPMP were directly observed by RAs during data collection, improving the reliability of reported behavioral measures.

This study is expected to provide significant findings to comprehensively investigate the change in behavioral aspects of malaria among rural communities following the implementation of a local wisdom-based malaria intervention. Changes in the malaria awareness index, MPMK, MPMP, disparity in using core prevention measures such as LLINs in 2 different seasons, and malaria treatment–seeking behaviors among rural communities due to the implementation of a local wisdom-based health education campaign will be recognized. These findings will provide evidence-based insights into the effectiveness of locally driven health education strategies in malaria-endemic regions. Therefore, this research will contribute to the development of contextually relevant malaria policies in Indonesia. Furthermore, it aligns with global malaria elimination efforts, supporting Indonesia’s commitment to achieving a malaria-free status by 2030.