Household food insecurity and physically demanding work during pregnancy are risk factors for low birth weight in north Shewa zone public hospitals, Central Ethiopia, 2021: a multicenter cross-sectional study

BMC Pediatrics, Volume 22, No. 1, Year 2022

Interpretation

Background: Despite numerous efforts to improve the quality of maternal and child health medical services, over 20 million babies are born with low birth weights each year globally. However, factors related to low birth weight like physically demanding work during pregnancy, intimate partner violence, and food insecurity have not been explored well in Ethiopia. Thus, this study aimed to assess the prevalence of low birth weight and associated factors among neonates born in public Hospitals in North Shewa Zone, Central Ethiopia. Methods: A hospital-based cross-sectional study design was conducted from June 15 –to July 15, 2021, in North Shewa public hospitals. A total of 441 mothers and newborn pairs were selected by systematic random sampling. Data were collected using a pretested and structured interviewer-administered questionnaire with chart reviewing. Data entry and analysis were done using Epi Data version 3.1 and Statistical Package for the Social Sciences version 26 respectively. Binary logistic regression was done to identify factors associated with low birth weight. Adjusted odds ratio with its 95% confidence interval and a p-value less than 0.05 was considered to declare the statistically significant association. Results: The prevalence of low-birth-weight was 17.7% (95% CI: 14.3, 21.5). Pregnancy-related complication [AOR = 2.16; 95% CI:(1.12,4.18)], grand-multiparty [AOR = 2.57; 95% CI:(1.12,5.88)], physically demanding work during pregnancy [AOR = 2.19; 95% CI:(1.11,4.33)], midd-upper arm circumference less than 23 cm [AOR = 2.54; 95% CI:(1.26,5.10)], partner violence during pregnancy [AOR = 3.77; 95% CI:(1.81,7.88)], and being member of household with food insecure [AOR = 2.31; 95% CI:(1.12,4.75)] were factors significantly associated with low birth weight. Conclusions: This study showed that the magnitude of low birth weight was relatively high. Women with pregnancy-related complications, grand multiparty, physically demanding work during pregnancy, intimate partner violence, mid-upper arm circumference less than 23 cm, and food insecurity should be prioritized for mitigating LBW. Health care professionals should focus on Screening pregnant women for intimate partner violence, physically demanding activities, undernutrition and providing appropriate treatment during all maternal continuum of care might be helpful. This study was conducted at public hospitals in North Shewa Zone. North Shewa zone is one of the 20 zones in Oromia Regional State and its administrative town is Fiche town, which is located 112 km away from Addis Ababa, the capital city of Ethiopia. The zone is administratively divided into 13 districts and two town administrations. As per the 2021 census, the zone has a total population of 1,786,067, of whom 876,252 were men and 909,815 were women [24]. In terms of health facilities, the North Shewa zone has five public hospitals, 63 health centres, and 267 health posts [25]. These Health facilities provide multidimensional health care services for the catchment’s area population. The study was conducted from June 15 –to July 30/ 2021. A public hospitals-based cross-sectional study was done among 448 mothers with their newborns who were selected by systematic random sampling technique. All alive newborns with their mothers who gave birth in selected Public Hospitals in North Shewa Zone from June 15, 20,201, to July 30, 2021, were the study population. Newborns of mothers in critical medical conditions and newborns with visible congenital anomalies were excluded from the study. The sample size was determined for both objectives separately and a 10% non-response rate was added. The adequate sample size was obtained using a single population proportion formula with the assumptions of, a 21.6% prevalence of LBW [26], 95% confidence interval (CI), 4% margin of error, and adding a 10% non-response rate, the final sample size became 448. The average total births during the study period were approximately 1080 as estimated from the preceding months’ delivery flow of each hospital. The sampling interval was estimated by dividing the total study population of 1080 by the sample size (n = 448). The sampling fraction or K-value was 2. The first study participant was selected by lottery method for each hospital independently and the next participants were selected every other. Four public hospitals from the North Shewa zone were included in the study. The number of study participants was proportionally allocated to each respective hospital based on estimations obtained from the previous delivery report as indicated in the figure below (Fig. 1). Schematic representation of the sampling procedure among newborns delivered in North Shewa zone public hospitals, Central Ethiopia, 2021 A pretested and structured interviewer-administered questionnaire and medical record extraction were used. The questionnaire was adapted and modified from other peer-reviewed articles [6, 21, 27–31]. The questionnaire comprises different sections like women’s socio-demographic characteristics, obstetric-related factors, nutritional-related factors, intimate partner violence, household food insecurity, neonatal, and maternal behavior-related factors. The data were collected by four BSc midwives and two BSc nurses through face to face interviews and variables like neonatal birth weight, pregnancy complication, maternal hemoglobin, and gestational age were extracted from the delivery registration book and mother’s card. The Maternal-Upper Arm Circumference (MUAC) was measured on the left arm using a non-stretchable standard tape. Intimate Partner Violence during Pregnancy (IPVP): was measured using a standardized tool developed by WHO [32]. women who replied “yes” to at least one of the 13 questions related to sexual, psychological, and physical violence were coded as “having experienced IPVP, whereas women who answered “no” to all of the questions were coded as not exposed to IPVP [32, 33]. Household food insecurity: was assessed using a Household Food Insecurity Access Scale (HFIAS) developed by Food and Nutritional Technical Assistance (FANTA) [31]. The tools were tested and validated in Ethiopia with Cronbach’s alpha value of 0.85 for both rural and urban samples [34]. The tools consist of nine items with a (Yes or No) response. All “Yes” replies were given a score of one, while “No” responses were given a score of zero. Finally, all responses were added together and participants who scored > 2 affirmative responses were considered as from a household’s food insecure whereas those who replied ≤2 affirmative responses were from a household’s food secure [31]. Physically demanding work during pregnancy: was measured using the following eleven items related to domestic and other activities performed during the pregnancy period with Yes or No responses [15, 30, 35]. Daily household chores, fetching water with large buckets, lifting heavy loads (> 20 kg), chopping woods, cleaning the land, planting seeds, cutting grass for cattle feeding, washing clothes/utensils for long periods, standing for longer hours (> 3 hr), squatting during routine daily activity, and bathing and milking cattle. The sum of scores ranging from 0 to 11, which further classified into two categories; participants who replied ≤3 affirmative responses were coded as not engaged in physically demanding work whereas those who replied ≥4 positive responses were coded as engaged in physically demanding work 4–11 [15, 30]. In this study, the internal consistency of physically demanding work items was (Cronbach’s alpha = 0.82). Daily household chores: Whether the mother did her housework alone or with the assistance of a relative person throughout her pregnancy period. Undernutrition: Mothers with mid-upper arm circumference (MUAC) < 23 cm [36]. Low birth weight: Newborns who weighed less than 2500 g [1]. It coded as “1” for LBW whereas “0” for others since it was an outcome variable for this study. Birth-to-pregnancy interval is the period between the start of the index pregnancy and the preceding live birth. It has three categories, these are: <24monthes, between 24-47monthes, and ≥ 48 months are the three categories [37]. It was estimated by subtracting the duration of the current pregnancy from the period between the preceding childbirth and the current birth. Alcohol use: use of any amount unit of alcohol whether it is locally manufactured drinks (Tela, Teje, Areka), or beer, wine, and any alcoholic-liquors beverages [18]. The data were cleaned, checked, coded, and entered into Epi data statistical software version 3.1 and then exported to SPSS version 26 for analysis. Simple frequency, and summary statistics such as median, and interquartile range were generated as descriptive statistical analysis. The results were presented using frequencies, tables, and figures. Bivariable and multivariable binary logistic regression analyses were performed to see the association between independent variables and the outcome. To control all possible confounders, variables which have a P-value < 0.25 in the bivariable analysis were retained for the final model multivariable analysis. The multi co-linearity test was also done using VIF and tolerance tests to see the presence of correlation between independent variables. However, no variables with VIF > 10 and tolerance test < 0.1 were found. The Hosmer-Lemeshow and the Omnibus test were used to test the model’s goodness of fit. The model was deemed to be a good fit since the result was found to be insignificant for the Hosmer-Lemeshow test (p = 0.616), but significant for the Omnibus test (p = 0.000). In multivariable analysis, a P-value less than 0.05 was considered to declare a statistically significant association. The strength and direction of statistical association were reported using an adjusted odds ratio with it is 95% CI. The questionnaire was evaluated by experts in the related field. It was first developed in English language, then translated into Afaan Oromo and Amharic, with re-translation into English to ensure it is consistency. A pre-test was conducted in Chancho primary Hospital, on 5% of the total sample size to check for language clarity, estimate the time required for the interview, and necessary amendments were done accordingly. The training was given to data collectors and supervisors on the study’s objective, ethical principles, sample procedure, questionnaire content, confidentiality, and respondent rights. The principal investigator together with the supervisor checked the data for completeness on daily basis.

AI Digest

Study Justification:

– Despite efforts to improve maternal and child health services, there is still a high prevalence of low birth weight (LBW) globally.
– Factors related to LBW, such as physically demanding work during pregnancy, intimate partner violence, and food insecurity, have not been well explored in Ethiopia.
– This study aimed to assess the prevalence of LBW and associated factors among neonates born in public hospitals in North Shewa Zone, Central Ethiopia.

Highlights:

– The study found that the prevalence of LBW was 17.7% in the study population.
– Factors significantly associated with LBW included pregnancy-related complications, grand multiparty, physically demanding work during pregnancy, mid-upper arm circumference less than 23 cm, partner violence during pregnancy, and being a member of a household with food insecurity.
– Health care professionals should prioritize screening pregnant women for intimate partner violence, physically demanding activities, undernutrition, and providing appropriate treatment during all stages of maternal care.

Recommendations:

– Women with pregnancy-related complications, grand multiparty, physically demanding work during pregnancy, intimate partner violence, mid-upper arm circumference less than 23 cm, and food insecurity should be prioritized for interventions to mitigate LBW.
– Health care professionals should focus on screening pregnant women for intimate partner violence, physically demanding activities, undernutrition, and providing appropriate treatment during all stages of maternal care.

Key Role Players:

– Health care professionals (doctors, nurses, midwives) for screening and providing appropriate treatment.
– Social workers for addressing intimate partner violence.
– Nutritionists for addressing undernutrition.
– Policy makers for implementing interventions and allocating resources.

Cost Items for Planning Recommendations:

– Training and capacity building for health care professionals on screening and treatment protocols.
– Hiring and training of social workers.
– Provision of nutritional support and counseling services.
– Development and implementation of policies and guidelines.
– Monitoring and evaluation of interventions.
– Public awareness campaigns.
– Data collection and analysis.
– Infrastructure and equipment for health facilities.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study design is a hospital-based cross-sectional study, which limits the ability to establish causality. However, the study used a large sample size and employed systematic random sampling to select participants, which enhances the generalizability of the findings. The statistical analysis included binary logistic regression to identify factors associated with low birth weight, and adjusted odds ratios with 95% confidence intervals were reported. The study also provided a clear description of the data collection methods and tools used. To improve the evidence, future research could consider using a longitudinal design to establish causality and include a control group for comparison. Additionally, conducting the study in multiple settings or regions could further enhance the generalizability of the findings.

Abstract

This study was conducted at public hospitals in North Shewa Zone. North Shewa zone is one of the 20 zones in Oromia Regional State and its administrative town is Fiche town, which is located 112 km away from Addis Ababa, the capital city of Ethiopia. The zone is administratively divided into 13 districts and two town administrations. As per the 2021 census, the zone has a total population of 1,786,067, of whom 876,252 were men and 909,815 were women [24]. In terms of health facilities, the North Shewa zone has five public hospitals, 63 health centres, and 267 health posts [25]. These Health facilities provide multidimensional health care services for the catchment’s area population. The study was conducted from June 15 –to July 30/ 2021. A public hospitals-based cross-sectional study was done among 448 mothers with their newborns who were selected by systematic random sampling technique. All alive newborns with their mothers who gave birth in selected Public Hospitals in North Shewa Zone from June 15, 20,201, to July 30, 2021, were the study population. Newborns of mothers in critical medical conditions and newborns with visible congenital anomalies were excluded from the study. The sample size was determined for both objectives separately and a 10% non-response rate was added. The adequate sample size was obtained using a single population proportion formula with the assumptions of, a 21.6% prevalence of LBW [26], 95% confidence interval (CI), 4% margin of error, and adding a 10% non-response rate, the final sample size became 448. The average total births during the study period were approximately 1080 as estimated from the preceding months’ delivery flow of each hospital. The sampling interval was estimated by dividing the total study population of 1080 by the sample size (n = 448). The sampling fraction or K-value was 2. The first study participant was selected by lottery method for each hospital independently and the next participants were selected every other. Four public hospitals from the North Shewa zone were included in the study. The number of study participants was proportionally allocated to each respective hospital based on estimations obtained from the previous delivery report as indicated in the figure below (Fig. 1). Schematic representation of the sampling procedure among newborns delivered in North Shewa zone public hospitals, Central Ethiopia, 2021 A pretested and structured interviewer-administered questionnaire and medical record extraction were used. The questionnaire was adapted and modified from other peer-reviewed articles [6, 21, 27–31]. The questionnaire comprises different sections like women’s socio-demographic characteristics, obstetric-related factors, nutritional-related factors, intimate partner violence, household food insecurity, neonatal, and maternal behavior-related factors. The data were collected by four BSc midwives and two BSc nurses through face to face interviews and variables like neonatal birth weight, pregnancy complication, maternal hemoglobin, and gestational age were extracted from the delivery registration book and mother’s card. The Maternal-Upper Arm Circumference (MUAC) was measured on the left arm using a non-stretchable standard tape. Intimate Partner Violence during Pregnancy (IPVP): was measured using a standardized tool developed by WHO [32]. women who replied “yes” to at least one of the 13 questions related to sexual, psychological, and physical violence were coded as “having experienced IPVP, whereas women who answered “no” to all of the questions were coded as not exposed to IPVP [32, 33]. Household food insecurity: was assessed using a Household Food Insecurity Access Scale (HFIAS) developed by Food and Nutritional Technical Assistance (FANTA) [31]. The tools were tested and validated in Ethiopia with Cronbach’s alpha value of 0.85 for both rural and urban samples [34]. The tools consist of nine items with a (Yes or No) response. All “Yes” replies were given a score of one, while “No” responses were given a score of zero. Finally, all responses were added together and participants who scored > 2 affirmative responses were considered as from a household’s food insecure whereas those who replied ≤2 affirmative responses were from a household’s food secure [31]. Physically demanding work during pregnancy: was measured using the following eleven items related to domestic and other activities performed during the pregnancy period with Yes or No responses [15, 30, 35]. Daily household chores, fetching water with large buckets, lifting heavy loads (> 20 kg), chopping woods, cleaning the land, planting seeds, cutting grass for cattle feeding, washing clothes/utensils for long periods, standing for longer hours (> 3 hr), squatting during routine daily activity, and bathing and milking cattle. The sum of scores ranging from 0 to 11, which further classified into two categories; participants who replied ≤3 affirmative responses were coded as not engaged in physically demanding work whereas those who replied ≥4 positive responses were coded as engaged in physically demanding work 4–11 [15, 30]. In this study, the internal consistency of physically demanding work items was (Cronbach’s alpha = 0.82). Daily household chores: Whether the mother did her housework alone or with the assistance of a relative person throughout her pregnancy period. Undernutrition: Mothers with mid-upper arm circumference (MUAC) < 23 cm [36]. Low birth weight: Newborns who weighed less than 2500 g [1]. It coded as “1” for LBW whereas “0” for others since it was an outcome variable for this study. Birth-to-pregnancy interval is the period between the start of the index pregnancy and the preceding live birth. It has three categories, these are: <24monthes, between 24-47monthes, and ≥ 48 months are the three categories [37]. It was estimated by subtracting the duration of the current pregnancy from the period between the preceding childbirth and the current birth. Alcohol use: use of any amount unit of alcohol whether it is locally manufactured drinks (Tela, Teje, Areka), or beer, wine, and any alcoholic-liquors beverages [18]. The data were cleaned, checked, coded, and entered into Epi data statistical software version 3.1 and then exported to SPSS version 26 for analysis. Simple frequency, and summary statistics such as median, and interquartile range were generated as descriptive statistical analysis. The results were presented using frequencies, tables, and figures. Bivariable and multivariable binary logistic regression analyses were performed to see the association between independent variables and the outcome. To control all possible confounders, variables which have a P-value 10 and tolerance test < 0.1 were found. The Hosmer-Lemeshow and the Omnibus test were used to test the model’s goodness of fit. The model was deemed to be a good fit since the result was found to be insignificant for the Hosmer-Lemeshow test (p = 0.616), but significant for the Omnibus test (p = 0.000). In multivariable analysis, a P-value less than 0.05 was considered to declare a statistically significant association. The strength and direction of statistical association were reported using an adjusted odds ratio with it is 95% CI. The questionnaire was evaluated by experts in the related field. It was first developed in English language, then translated into Afaan Oromo and Amharic, with re-translation into English to ensure it is consistency. A pre-test was conducted in Chancho primary Hospital, on 5% of the total sample size to check for language clarity, estimate the time required for the interview, and necessary amendments were done accordingly. The training was given to data collectors and supervisors on the study’s objective, ethical principles, sample procedure, questionnaire content, confidentiality, and respondent rights. The principal investigator together with the supervisor checked the data for completeness on daily basis.

Materials

https://efashare.b-cdn.net/materials/5ec9feef0804668f44224168a0aad50633d226e2868e37d3e5d395bfd1a596fb/12887_2022_3480_MOESM1_ESM.docx

Innovations

Based on the information provided, here are some potential innovations that could improve access to maternal health:

1. Mobile health (mHealth) applications: Develop mobile applications that provide pregnant women with information on nutrition, prenatal care, and warning signs during pregnancy. These apps can also send reminders for prenatal appointments and provide access to telemedicine consultations.

2. Community health workers: Train and deploy community health workers to provide education and support to pregnant women in rural areas. These workers can conduct home visits, provide counseling on nutrition and prenatal care, and refer women to health facilities when necessary.

3. Telemedicine services: Establish telemedicine services that allow pregnant women in remote areas to consult with healthcare providers through video calls. This can help overcome geographical barriers and provide timely medical advice and support.

4. Maternal health clinics: Set up dedicated maternal health clinics in underserved areas to provide comprehensive prenatal care, including regular check-ups, screenings, and counseling. These clinics can also offer family planning services and postnatal care.

5. Maternal health vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal health services. These vouchers can cover the cost of prenatal visits, laboratory tests, and emergency obstetric care.

6. Maternal waiting homes: Establish maternal waiting homes near health facilities to accommodate pregnant women who live far away and need to travel for prenatal care or delivery. These homes can provide a safe and supportive environment for women during the critical period before and after childbirth.

7. Public-private partnerships: Foster collaborations between public health authorities and private healthcare providers to expand access to maternal health services. This can involve subsidizing private clinics to offer affordable prenatal care or partnering with private transportation companies to provide transportation vouchers for pregnant women.

8. Health education campaigns: Launch targeted health education campaigns to raise awareness about the importance of prenatal care, nutrition, and healthy behaviors during pregnancy. These campaigns can use various media channels, including radio, television, and social media, to reach a wide audience.

9. Maternal health financing mechanisms: Develop innovative financing mechanisms, such as microinsurance or community-based health financing schemes, to ensure financial protection for pregnant women and their families. This can help reduce out-of-pocket expenses and increase access to quality maternal health services.

10. Integration of services: Promote the integration of maternal health services with other healthcare services, such as family planning, HIV/AIDS prevention and treatment, and child immunization. This can improve efficiency and ensure comprehensive care for women and their families.

It is important to note that the implementation of these innovations should be context-specific and tailored to the local healthcare system and cultural norms.

AI Innovations Description

Based on the description provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Implement targeted interventions for pregnant women engaged in physically demanding work: Develop programs and policies that specifically address the needs of pregnant women who are engaged in physically demanding work. This can include providing education and training on safe work practices during pregnancy, offering alternative job assignments or modified work schedules, and ensuring access to appropriate support and resources.

2. Strengthen screening and support for intimate partner violence during pregnancy: Integrate screening for intimate partner violence into routine prenatal care visits and provide comprehensive support services for women who disclose experiencing violence. This can include training healthcare providers on how to identify signs of intimate partner violence, establishing referral pathways to specialized services, and implementing evidence-based interventions to address violence and promote safety.

3. Address household food insecurity: Develop and implement strategies to address household food insecurity among pregnant women. This can include expanding access to nutritious food through initiatives such as food assistance programs, community gardens, and nutrition education. Additionally, integrating screening for food insecurity into prenatal care visits and connecting women to appropriate resources and support can help address this risk factor for low birth weight.

4. Enhance antenatal care services: Strengthen antenatal care services to ensure comprehensive and holistic care for pregnant women. This can include improving access to prenatal care, increasing the frequency and quality of antenatal visits, and providing education and support on various aspects of maternal and child health, including nutrition, hygiene, and birth preparedness.

5. Foster collaboration and coordination among stakeholders: Establish partnerships and collaborations among healthcare providers, community organizations, government agencies, and other stakeholders to improve access to maternal health services. This can involve sharing resources, expertise, and best practices, as well as coordinating efforts to address the social determinants of health that contribute to low birth weight.

By implementing these recommendations, it is possible to develop innovative approaches that can improve access to maternal health and reduce the prevalence of low birth weight in the study area and beyond.

AI Innovations Methodology

Based on the information provided, here are some potential recommendations to improve access to maternal health:

1. Increase awareness and education: Implement comprehensive education programs to raise awareness about the importance of maternal health, including the risks associated with physically demanding work during pregnancy, intimate partner violence, and household food insecurity. This can be done through community outreach programs, workshops, and media campaigns.

2. Strengthen antenatal care services: Enhance antenatal care services by ensuring that pregnant women receive regular check-ups, screenings, and counseling on nutrition, physical activity, and violence prevention. This can help identify and address risk factors for low birth weight early on.

3. Improve access to nutritious food: Implement initiatives to improve access to nutritious food for pregnant women, especially those from households with food insecurity. This can include providing food vouchers, nutritional supplements, or support for income-generating activities to improve food security.

4. Address intimate partner violence: Develop and implement strategies to identify and address intimate partner violence during pregnancy. This can involve training healthcare providers to screen for violence, providing support services for victims, and promoting community awareness and prevention programs.

5. Provide support for physically demanding work: Develop policies and guidelines to protect pregnant women from engaging in physically demanding work that may pose risks to their health and the health of their unborn child. This can include workplace accommodations, job reassignments, or temporary leave options for pregnant women.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the target population: Determine the specific population group that will be affected by the recommendations, such as pregnant women in the North Shewa Zone of Central Ethiopia.

2. Collect baseline data: Gather data on the current access to maternal health services, prevalence of risk factors, and outcomes such as low birth weight. This can be done through surveys, interviews, and medical record reviews.

3. Develop a simulation model: Create a mathematical or statistical model that represents the relationships between the recommendations and the outcomes of interest. This model should consider factors such as the population size, intervention coverage, and potential impact on access to maternal health services.

4. Input data and parameters: Input the collected baseline data into the simulation model, along with relevant parameters such as the effectiveness of the recommendations, implementation timelines, and resource requirements.

5. Run simulations: Use the simulation model to run multiple scenarios that simulate the impact of implementing the recommendations. This can involve varying parameters such as the coverage of interventions, the level of community engagement, or the availability of resources.

6. Analyze results: Analyze the simulation results to assess the potential impact of the recommendations on improving access to maternal health. This can include evaluating changes in outcomes such as the prevalence of low birth weight, access to antenatal care, or reduction in risk factors.

7. Refine and validate the model: Review the simulation model and results with relevant stakeholders, such as healthcare providers, policymakers, and community representatives. Incorporate feedback and make adjustments to the model as necessary to improve its accuracy and relevance.

8. Communicate findings: Present the findings of the simulation study to key stakeholders and decision-makers. Use the results to advocate for the implementation of the recommendations and inform policy and programmatic decisions.

By following these steps, a simulation study can provide valuable insights into the potential impact of recommendations on improving access to maternal health and guide decision-making processes.

Author

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.1186/s12887-022-03480-2

Research Areas:

Disparities, Environmental, Food Security, Health System and Policy, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse, Violence and Injury, Workforce

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female