Tanzania’s NCD Transition: A Comparative Analysis of Healthy Lifestyle Behaviors for Hypertension Prevention Between Rural and Urban Adults

Abstract

Introduction

Hypertension remains a major public health concern in Tanzania, yet evidence on the combined adoption of healthy lifestyle behaviors across rural and urban populations remains limited. This study assessed healthy lifestyle adoption and associated socio-demographic factors among adults in contrasting rural and urban settings in Morogoro Region, Tanzania, using a composite healthy lifestyle index (HLI) approach.

Methods

An analytical cross-sectional study involving 838 adults was conducted in Kilombero District Council and Morogoro Municipal Council. A composite HLI incorporating body mass index, physical activity, smoking, alcohol use, and fruit and vegetable intake was constructed. Participants were divided into high or low behavior adoption groups, and stratified multivariable logistic regression models were used to estimate adjusted odds ratios (aORs) with 95% confidence intervals (CIs).

Results

High healthy lifestyle adoption was more common in rural Kilombero than urban Morogoro (55.5% vs. 47.0%; p=0.017). In rural areas, lifestyle adoption varied by income, employment, and age, while in urban Morogoro, income was the primary socio-demographic factor associated with healthy lifestyle adoption. The findings also suggest that behavioral transitions commonly associated with urbanization may already be emerging in rural settings.

Conclusions

Healthy lifestyle adoption differed between rural and urban populations, highlighting the need for context-specific and integrated prevention strategies. Urban interventions should prioritize improving access to healthy foods and opportunities for physical activity, while rural strategies should support the preservation of currently favorable lifestyle behaviors alongside prevention of emerging behavioral risks.

Keywords

hypertension healthy lifestyle rural population urban population noncommunicable diseases Tanzania

Background

Hypertension, characterized by persistently elevated blood pressure, affects more than 1.4 billion adults globally and is a leading cause of cardiovascular disease and premature mortality, particularly in low- and middle-income countries where awareness, treatment, and control remain low.¹ In sub-Saharan Africa (SSA), rapid urbanization and dietary transitions have driven rising prevalence, ranging from 19.3% to 39.6% across countries.^2,3 Tanzania mirrors this pattern, with prevalence estimates ranging from 11% among adults aged 15–49 to 34% among those aged 30–79 (World Health Organization (WHO) modeled estimates), highlighting the burden of largely preventable complications such as stroke and myocardial infarction.^4,5

In Morogoro Region, hypertension remains a significant burden. Rural Kilombero reports a prevalence of 29.3%, yet only about one-third of affected individuals are aware of their condition.¹ By contrast, urban Morogoro Municipal Council (MC) records much higher prevalence, reaching 45%,⁶ consistent with wider SSA patterns.⁷ While pharmacological treatment is essential for those diagnosed, its population-level impact is constrained by low detection, limited access to care, and poor long-term adherence. As a result, primary prevention through healthy lifestyle behaviors remains crucial, including maintaining a healthy body weight, engaging in regular physical activity, avoiding tobacco, limiting alcohol consumption, and maintaining adequate intake of fruits and vegetables.^8,9

In urban Tanzania, sedentary work, time pressure, reliance on purchased foods, and greater exposure to energy-dense processed products are associated with lower physical activity, dietary changes, and higher prevalence of overweight and obesity compared with rural populations.^8-11 Urban diets are characterized by higher consumption of processed and animal-source foods, whereas rural populations tend to maintain more plant-rich diets.^8-11 Similar rural–urban patterns have been reported across SSA, where urban populations show higher processed-food consumption, lower physical activity, and greater adiposity than rural counterparts.^12-15 Consistent patterns are observed in Asia using composite healthy-lifestyle scores that combine BMI, smoking, alcohol, physical activity, and diet, with rural populations scoring higher on physical activity and plant-based diet components and urban populations showing higher adiposity and processed-food intake.¹⁶ These patterns indicate that rural–urban differences in lifestyle behaviors reflect broader epidemiological and food system transitions across low- and middle-income countries.¹⁶

Socio-demographic factors further shape lifestyle behaviors and hypertension management. Older age, female sex, higher education, and higher socioeconomic status are generally associated with greater awareness of hypertension and preventive strategies.^1,17,18 However, increased awareness does not consistently lead to adherence, especially where social or environmental constraints restrict healthy choices. Conversely, lower education or income may limit access to health information, preventive resources, and continuity of care, exacerbating inequities in both prevention and control.^1,17,18

Despite these insights, evidence from Tanzania on the combined adoption of multiple healthy behaviors remains limited, particularly in rural–urban comparisons. Most studies focus on individual behaviors rather than composite lifestyle profiles.^19,20 As a result, they may not fully capture the co-occurrence and cumulative distribution of multiple modifiable lifestyle behaviors within individuals. By using a composite healthy lifestyle index (HLI), this study provides a more comprehensive assessment of lifestyle behavior patterns and their socio-demographic distribution across rural and urban settings in Tanzania. Addressing this gap is important for informing integrated prevention strategies aligned with Tanzania’s National Strategic Plan for the Prevention and Control of Non-Communicable Diseases (NCDs) (2021–2026), which emphasizes the prevention and reduction of behavioral risk factors associated with NCDs, and broader efforts toward achieving Sustainable Development Goal (SDG) 3.4 on reducing premature mortality from NCDs.²¹ Therefore, this study aimed to assess the adoption of healthy lifestyle behaviors for hypertension prevention among adults in rural and urban settings in Morogoro Region, Tanzania, and to examine socio-demographic factors associated with healthy lifestyle adoption using a composite healthy lifestyle index approach.

Methods

Study Design and Approach

This study employed an analytical cross-sectional design to assess the adoption of healthy lifestyle behaviors for hypertension prevention among adults in urban and rural areas of Morogoro Region, Tanzania. A quantitative approach was used. A community-based cross-sectional household survey was conducted using an interviewer-administered, structured questionnaire. The questionnaire captured socio-demographic characteristics and lifestyle behaviors. This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies.²²

Study Area and Population

The study was conducted in Morogoro MC and Kilombero District Council (DC) to capture urban–rural differences in hypertension. Morogoro MC represents an urban setting, where a hospital-based study reported a hypertension prevalence of 45%.⁶ Kilombero DC represents a rural context, with community-based data indicating a prevalence of 29.3%.¹ The study population comprised adults aged 18 years and older residing in households within the selected councils. These sites provide contrasting yet relevant settings for examining hypertension-related lifestyle behaviors in Tanzania.

Sample Size and Sampling Technique

The sample size (n) was calculated using the formula for two independent sample proportions.²³

n = \frac{z^{2} \times (p (1 - p 1) + p 2 (1 - p 2))}{d^{2}} \times d e f f

This approach was used to compare the adoption of healthy lifestyle behaviors between urban adults in Morogoro Municipal Council (MC) and rural adults in Kilombero District Council (DC). In the absence of published estimates on the prevalence of composite healthy lifestyle behaviors combining multiple risk factors (BMI, physical activity, smoking, alcohol consumption, and fruit and vegetable intake) in comparable Tanzanian rural and urban populations, hypertension prevalence was used as a proxy indicator. Although hypertension is a downstream clinical outcome rather than a direct behavioral measure, it reflects cumulative exposure to multiple modifiable lifestyle risks that were also included in the composite HLI and has well-documented population-based estimates in both settings. It was therefore considered a pragmatic and epidemiologically relevant proxy for sample size estimation in the absence of locally available data on combined healthy lifestyle behaviors. However, this approach may not precisely represent the prevalence of composite healthy lifestyle behaviors and may have introduced some imprecision in the estimated sample size.

Hypertension prevalence estimates of 45% in urban Morogoro MC and 29.3% in rural Kilombero DC were used. Assuming a z-score of 1.96, a 5% margin of error, and a design effect of 1.2 to account for cluster sampling, the minimum required sample size was 838 respondents (456 urban and 382 rural).

A multistage cluster sampling approach was employed. First, Morogoro Municipal Council and Kilombero District Council were purposively selected to represent urban and rural settings, respectively. Second, two wards were randomly selected from each district (Morogoro MC: Kihonda and Mkundi; Kilombero DC: Lumemo and Kibaoni), yielding a total of four wards. Third, one street (urban) or village (rural) was randomly selected within each ward. Finally, households were selected using systematic sampling. Within each selected household, the household head or another eligible adult aged 18 years or older was invited to participate.

If an eligible participant was unavailable at the time of the visit, up to two repeat visits were conducted. In cases where no eligible respondent was available or a household declined participation, the next household in the sampling sequence was approached. All eligible individuals who were successfully contacted agreed to participate, resulting in a 100% response rate among contacted participants.

Inclusion and Exclusion Criteria

The study included adults aged 18 years and older who had lived in the study areas for at least one year. Participation was not restricted by ethnicity or race; all eligible adults in selected households were included regardless of ethnic background. Individuals were eligible whether or not they had been diagnosed with hypertension, as the study focused on lifestyle behaviors across different levels of cardiovascular risk. Participants needed to communicate in Kiswahili or English, either directly or with the support of an interpreter. Those with severe illness, including serious mental health or cognitive conditions that limited communication, informed consent, or meaningful participation, were excluded. Pregnant women were excluded because pregnancy-related physiological changes can affect hypertension risk and related behaviors, based on self-report. Individuals with active substance use disorders that interfered with participation were also excluded.

Data Collection Procedure and Tools

Data were collected using a structured and systematic approach to ensure accuracy and consistency. Ten Research Assistants (RAs) with backgrounds in public health, social sciences, or clinical healthcare were recruited and trained over two days on the study objectives, ethical considerations, and data collection techniques, including KoboToolbox. Household visits were conducted to administer structured questions about respondents’ lifestyle behaviors, including the frequency, intensity, and type of physical activity, duration of exercise sessions, dietary habits with a focus on fruit and vegetable consumption, as well as smoking and alcohol use. During these visits, anthropometric measurements were obtained using standardized procedures. Height and weight were measured once for each participant using a portable height meter and calibrated Seca® scale, respectively, with participants wearing light clothing and no shoes.²⁴ Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²) and classified according to WHO international cut-offs, with underweight defined as <18.5 kg/m², normal weight as 18.5–24.9 kg/m², overweight as 25.0–29.9 kg/m², and obesity as ≥30.0 kg/m².^24-26

Data Analysis

Data were analyzed to assess adherence to low-risk lifestyle behaviors for the prevention of hypertension. Key behavioral variables included BMI, physical activity, smoking, alcohol consumption, and fruit and vegetable intake. BMI was categorized as low risk (<25 kg/m²) or high risk (≥25 kg/m²).^24-26 Physical activity was classified as low risk if respondents reported at least 150 minutes per week of moderate-intensity activity, 75 minutes of vigorous activity, or an equivalent combination totaling ≥600 Metabolic Equivalent of Task (MET)-minutes per week, calculated using standard MET values.²⁷ Smoking status was categorized as low risk for never or former smokers and high risk for current smokers.²⁸ Alcohol consumption was assessed using the WHO Alcohol Use Disorders Identification (AUDIT), with scores <8 considered low risk and higher scores indicating increasing risk.²⁹ Fruit and vegetable intake was classified as low risk if respondents reported consuming at least 2 servings per day of fruits and at least 2 servings per day of vegetables, consistent with the Tanzania Mainland Food-Based Dietary Guidelines,³⁰ which recommend a minimum of two daily servings each of fruits and vegetables, excluding starchy vegetables and limiting fruit juice to one serving daily. Fruit and vegetable intake was assessed using self-reported usual daily consumption, adapted from the WHO STEPwise approach to NCD risk factor surveillance (STEPS),²⁴ in which participants reported their average number of servings consumed per day rather than via a 24-hour recall.

Socio-demographic characteristics were summarized using descriptive statistics. A composite healthy lifestyle score was constructed based on established HLI approaches, which combine multiple modifiable behavioral risk factors into a single summary measure. Each lifestyle component (BMI, physical activity, smoking, alcohol consumption, fruit intake, and vegetable intake) was dichotomized according to established public health recommendations and assigned one point for low-risk status and zero otherwise,^31-33 yielding a total score ranging from 0 to 6. Equal weighting was applied to all components, consistent with widely used HLI approaches, which treat each behavior as an independent modifiable determinant of cardiovascular risk and capture the cumulative effect of multiple lifestyle behaviors. This approach prioritizes assessment of overall lifestyle behavior patterns and comparability across studies rather than estimating the relative biological contribution of individual behaviors to hypertension risk. However, equal weighting assumes that each lifestyle component contributes similarly to overall risk, which may oversimplify the complex and potentially unequal relationships between specific behaviors and hypertension.³⁴ Scores of 0–3 were classified as low adoption of a healthy lifestyle, while scores of 4–6 indicated high adoption. Continuous behavioral variables were categorized using established international and national public health thresholds to facilitate interpretation and ensure comparability with existing epidemiological studies and clinical recommendations.

Healthy lifestyle adoption (low vs high) was defined as the primary outcome variable. Socio-demographic variables, including age, sex, education, marital status, income, employment, and location, were treated as independent predictor variables and were included in multivariable models to assess their independent associations and adjust for potential confounding. Complete-case analysis was used for the regression models. Participants with missing data on outcome or predictor variables were excluded from the respective stratified analyses.

Associations between socio-demographic factors and healthy lifestyle adoption were examined using multivariable logistic regression models, stratified by location to assess potential differences between rural and urban populations, and reported as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Analyses were conducted without sampling weights, as the study aimed to estimate associations rather than population-level prevalence. Because cluster identifiers were not retained in the analytical dataset, clustering at the ward and household levels could not be accounted for in the regression analyses. Socio-demographic variables were selected based on theoretical relevance and prior literature and were entered simultaneously into the models to assess their independent associations with healthy lifestyle adoption. This approach allowed estimation of independent associations while adjusting for potential confounding between socio-demographic variables. Multicollinearity was assessed using variance inflation factors (VIF), and no significant multicollinearity was detected, with all adjusted VIF values below 1.2. Statistical significance was set at p<0.05. To assess the robustness of the composite healthy lifestyle classification, sensitivity analyses were conducted using a stricter threshold for high lifestyle adoption (scores 5–6 vs. 0–4), and the stratified multivariable logistic regression models were re-estimated using the alternative classification. All analyses were conducted using RStudio version 4.5.1.

Results

Socio-Demographic Characteristics of the Study Population

A total of 838 adults were recruited through multistage cluster sampling. This included 456 participants from Morogoro MC and 382 participants from Kilombero DC. All eligible adults approached consented to participate, and the full planned sample was included in the descriptive analysis. Overall, median age was similar in both settings (Kilombero: 32 years, IQR 25–47; Morogoro: 31 years, IQR 25–41). In terms of sex distribution, women were more prevalent in Morogoro than in Kilombero (68.6% vs 54.5%). Similarly, educational attainment was higher in Morogoro, with 39.3% reporting secondary education, compared with 33.5% in Kilombero. Meanwhile, marital status was comparable across sites, with approximately half of the participants married. With respect to economic status, median monthly income was higher in Morogoro (200,000 TZS; IQR 100,000–400,000) than in Kilombero (150,000 TZS; IQR 80,000–300,000). Finally, informal employment predominated in both settings, accounting for just over half of participants (Table 1).

Table 1.

Socio-Demographic Characteristics of Study Participants by Residence (n=838)

Variable	Kilombero DC (n=382)	Morogoro MC (n=456)
Age (years)	Median (IQR) 32 (25 – 47)	Median (IQR) 31 (25 – 41)
18 – 24	85 (22.3)	106 (23.2)
25 – 34	122 (31.9)	173 (37.9)
35 – 44	62 (16.2)	97 (21.3)
45 – 54	45 (11.8)	39 (8.6)
55+	68 (17.8)	41 (9.0)
Sex
Female	208 (54.5)	313 (68.6)
Male	174 (45.5)	143 (31.4)
Education level
Primary education or less	229 (60.0)	231 (50.6)
Secondary education	128 (33.5)	179 (39.3)
Tertiary education or above	25 (6.5)	46 (10.1)
Marital status
Single	120 (31.4)	153 (33.6)
Married	199 (52.1)	235 (51.5)
Co-habiting	14 (3.7)	26 (5.7)
Divorced	20 (5.2)	15 (3.3)
Widowed	29 (7.6)	27 (5.9)
Monthly income (TZS)	Median (IQR) 150000 (80000 – 300000)	Median (IQR) 200000 (100000 – 400000)
No income	37 (11.9)	42 (10.9)
Low income (>0 ≤ 500,000)	263 (84.3)	288 (74.8)
Middle/High income (>500,000)	12 (3.8)	55 (14.3)
Employment status
Student	22 (5.8)	23 (5.0)
Housewife	61 (16.0)	69 (15.1)
Unemployed	35 (9.2)	66 (14.5)
Formal employment	20 (5.2)	32 (7.0)
Informal employment	202 (52.9)	236 (51.8)
Other	42 (10.9)	30 (6.6)

IQR = Interquartile Range; TZS = Tanzanian Shillings; n = number of participants.

Rural–Urban Differences in Adoption of Healthy Lifestyle Behaviors

Overall adoption of healthy lifestyle behaviors differed significantly by setting, with a higher proportion of participants in rural Kilombero achieving high adoption compared with urban Morogoro (212/382, 55.5% vs 213/456, 47.0%; p=0.017). Overall, 425 of 838 participants (50.7%) achieved high adoption. Consistent with this pattern, normal BMI was more common among rural participants than urban participants (51.6% vs 36.6%), while overweight and obesity were more prevalent in urban Morogoro (58.3%) than rural Kilombero (45.6%). Overall, the distribution of BMI categories differed significantly between rural and urban populations (p<0.001). Similarly, physical activity levels were higher in Kilombero, where 63.1% achieved at least 600 MET-minutes per week, compared with 53.5% in Morogoro (p<0.006).

In contrast, smoking patterns differed only modestly between settings. Most participants in both areas had never smoked; however, current smoking was slightly more common in Kilombero (8.9%), whereas former smoking was more frequently reported in Morogoro (5.7%), with a statistically significant difference (p<0.043). Dietary behaviors showed a different pattern. Although fruit consumption was low overall, a higher proportion of urban participants met the recommended intake of at least two servings per day compared with rural participants (19.8% vs 13.4%; p<0.017) (Table 2).

Table 2.

Healthy Lifestyle Behaviors Among Adults by Residence (n=838)

Variable	Kilombero DC (n=382)	Morogoro MC (n=456)	p-Value
Adoption of healthy behaviors
Low adoption	170 (44.5)	240 (53.0)	0.017
High adoption	212 (55.5)	213 (47.0)
BMI (Kg/m ² )
Underweight (<18.5)	11 (2.9)	23 (5.0)	<0.001
Normal weight ≥18.5 to <25.0)	197 (51.6)	167 (36.6)
Overweight (25.0–29.9)	98 (25.7)	140 (30.7)
Obese (≥30.0)	76 (19.9)	126 (27.6)
Physical activity category (MET-minute week)
Active (≥600)	241 (63.1)	244 (53.5)	0.006
Inactive (<600)	141 (36.9)	212 (46.5)
Smoking status
Never smoke	337 (88.2)	401 (88.3)	0.043
Former smoker	11 (2.9)	26 (5.7)
Current smoker	34 (8.9)	27 (5.9)
Alcohol consumption category
Low risk (AUDIT score < 8)	381 (99.7)	453 (99.3)	0.630
Increasing risk (AUDIT score ≥ 8 ≤ 15)	1 (0.3)	3 (0.7)
Fruit consumption
≥ 2 servings/day	51 (13.4)	90 (19.8)	0.017
< 2 servings/day	331 (86.6)	365 (80.2)
Vegetable consumption
Consuming ≥ 2 servings/day	180 (47.1)	195 (42.8)	0.232
Consuming < 2 servings/day	202 (52.9)	261 (57.2)

BMI = Body Mass Index; MET = Metabolic Equivalent of Task; AUDIT = Alcohol Use Disorders Identification Test; n = number of participants. Symbols: ≥ greater than or equal to; ≤ less than or equal to.

Socio-Demographic Determinants of Healthy Lifestyle Adoption by Residential Location

Stratified multivariable logistic regression identified predictors of healthy lifestyle adoption by residence. In rural Kilombero, low-income participants were more likely to adopt healthy lifestyles than those with no income (aOR = 4.79; 95% CI: 1.91–12.50). Employed participants had lower odds of adoption compared with unemployed participants (aOR = 0.37; 95% CI: 0.18–0.70), and adults aged 35–44 years were less likely to adopt healthy lifestyles than those aged 18–24 (aOR = 0.37; 95% CI: 0.15–0.92). In urban Morogoro, income was the only significant predictor, with middle or high income associated with higher odds compared with no income (aOR = 2.71; 95% CI: 1.04–7.24) (Table 3).

Table 3.

Stratified Multivariable Logistic Regression Analysis of Healthy Lifestyle Adoption by Residence

	Kilombero DC			Morogoro MC
Variable	aOR	95% CI	p-value	aOR	95% CI	p-value
Age Group
18–24	ref.			ref.
25–34	0.56	0.26–1.20	0.143	1.06	0.56–2.01	0.857
35–44	0.37	0.15–0.92	0.035	1.13	0.55–2.32	0.748
45–54	0.66	0.29–1.51	0.322	1.20	0.54–2.66	0.660
55+	0.39	0.14–1.05	0.065	0.85	0.32–2.23	0.742
Sex
Male	ref.			ref.
Female	0.96	0.58–1.60	0.875	0.96	0.61–1.53	0.875
Education
Primary or less	ref.			ref.
Secondary education	1.26	0.74–2.15	0.399	0.98	0.62–1.55	0.944
Tertiary or above	2.62	0.91–8.68	0.090	1.45	0.71–3.03	0.311
Marital Status
Single	ref.			ref.
Co-habiting	1.14	0.32–4.35	0.845	1.73	0.60–5.41	0.323
Divorced	1.08	0.34–3.47	0.900	0.59	0.17–1.89	0.382
Married	1.08	0.58–2.04	0.804	0.76	0.45–1.27	0.290
Widowed	1.31	0.42–4.18	0.648	1.29	0.43–3.89	0.651
Income
No income	ref.			ref.
Low income	4.79	1.91–12.50	0.001	1.61	0.72–3.67	0.249
Middle/High income	1.74	0.34–8.56	0.498	2.71	1.04–7.24	0.043
Employment status
Unemployed	ref.			ref.
Employed	0.37	0.18–0.70	0.003	0.80	0.46–1.38	0.421

aOR = adjusted Odds Ratio; CI = Confidence Interval; ref. = reference category.

Sensitivity Analysis

Sensitivity analyses using a stricter threshold for healthy lifestyle adoption (scores 5–6 vs. 0–4) showed similar overall rural–urban patterns. High adoption remained more common in rural Kilombero than urban Morogoro (81/382, 21.2% vs. 65/456, 14.3%; p=0.012). In rural Kilombero, adults aged 35–44 years remained less likely to report high healthy lifestyle adoption, while tertiary education became positively associated with adoption under the stricter classification. In urban Morogoro, most socio-demographic associations were attenuated and no longer statistically significant, although the direction of associations remained broadly consistent with the primary analysis. Wider confidence intervals were observed in both rural and urban models (Supplementary Table S1).

Discussion

This study aimed to assess the adoption of healthy lifestyles for hypertension prevention and examine associated socio-demographic factors among adults in the rural and urban areas of the Morogoro Region, Tanzania. Using a composite HLI, the findings indicate that rural residents had higher overall adoption than urban residents. However, only about half of the participants in both settings met the recommended criteria. Socio-demographic predictors differed by residence, with income and employment showing context-specific associations. These findings should be interpreted in light of the cross-sectional design and potential self-report bias, which limit causal inference.

This study contributes to Tanzanian and SSA NCD literature by examining combined healthy lifestyle adoption using a composite index across contrasting rural and urban populations. While most studies in Tanzania and the wider SSA region have focused on individual behavioral risk factors or specific cardiovascular risk profiles in isolation,^10,18-20 this study provides a broader assessment of overall lifestyle behavior patterns and their socio-demographic distribution within the same population context. The findings also suggest that behavioral transitions commonly associated with urbanization may already be emerging in rural settings, highlighting the need for prevention strategies that address multiple co-occurring lifestyle risks across both rural and urban populations.^11,35,36

The observed rural–urban differences are consistent with evidence from Tanzania and other SSA settings showing higher levels of overweight and obesity and less favorable lifestyle patterns among urban populations compared with rural populations.^11,15,36-38 These differences may reflect variations in occupational patterns, food environments, and lifestyle contexts, although these factors were not directly measured in this study.³⁸ National data also indicate that overweight and obesity are rising, particularly among women, and excess adiposity, physical inactivity, and low fruit and vegetable intake are established drivers of hypertension, whereas higher fruit and vegetable consumption lowers risk.³⁷ However, comparisons of fruit and vegetable intake across studies should be interpreted cautiously because this study applied the Tanzania-specific threshold of ≥2 servings per day rather than the WHO recommendation of ≥5 servings per day. Although this threshold may limit direct comparability with international studies, it reflects locally adapted dietary guidance for the Tanzanian setting.

However, evidence suggests that these urban-associated lifestyle patterns are emerging in rural areas as well, driven by market integration, changing food environments,^11,35 and media influences, particularly among younger populations.³⁹ Nyaruhucha et al and Muhomba et al reported rising overweight among school-aged children and adults in Morogoro,^40,41 indicating that unhealthy lifestyle behaviors may be emerging early in both rural and urban populations. This suggests that the higher healthy lifestyle adoption observed in rural Kilombero may reflect a transitional pattern rather than a stable protective advantage. This highlights the need for preventive strategies in both settings.

In addition, the very low prevalence of risky alcohol use observed in this study may reflect underreporting rather than the true absence of risk. In many Tanzanian settings, alcohol consumption is socially sensitive, and respondents may underreport intake in interviewer-administered surveys due to stigma or social desirability bias. As a result, alcohol-related risk may be underestimated in this study.⁴²

Only about half of adults in either setting achieved high healthy lifestyle adoption, indicating that multiple lifestyle-related risk factors frequently co-occurred within individuals. This finding highlights the importance of integrated prevention approaches that address multiple behaviors simultaneously rather than targeting individual risk factors in isolation.³⁶ Practically, this means improving food environments, creating safe spaces for activity, and promoting healthy diets in urban Morogoro while supporting the preservation of currently more favorable lifestyle patterns observed in rural Kilombero.

Socio-demographic analyses reveal context-specific patterns. In rural Kilombero, low-income individuals were more likely to adopt healthy behaviors than those with no income, contrasting with evidence from rural China, where higher household income is associated with greater dietary diversity, a key component of healthy lifestyles.⁴³ This pattern may reflect the continued reliance of lower-income rural households on more physically active livelihoods and less processed diets compared with individuals experiencing greater economic transition and lifestyle change. In this context, higher income may not necessarily correspond to healthier lifestyle practices, particularly where increasing income is accompanied by reduced physical activity and greater access to processed foods. However, income alone may not fully capture economic conditions or lifestyle practices in this setting.

In urban Morogoro, by contrast, middle- and high-income individuals were more likely to adopt healthy lifestyles than those with no income, consistent with findings from Korea, where higher income groups, particularly among women, had greater odds of engaging in healthy behaviors.⁴⁴ This may reflect differences in access to healthier food options, recreational opportunities, and health-related information in urban environments, as well as reduced structural barriers to healthy lifestyle adoption among higher-income groups.

Employed rural participants had lower odds of adopting healthy lifestyles compared with unemployed participants; however, employment status in rural areas encompasses a wide range of activities, and the type and intensity of work were not assessed in this study. Therefore, these findings should be interpreted cautiously. Moreover, adults aged 35–44 years were also less likely to adopt healthy behaviors than younger adults, aligning with Malaysian findings where younger adults (18–30 years) had higher odds of a healthy lifestyle than older age groups.⁴⁵ In this rural context, sex, education, and marital status were not significant predictors, suggesting that material and time constraints may matter more than socio-demographic position. These differences may be related to occupational and dietary patterns commonly observed in rural settings, although these factors were not directly measured in this study.⁴⁶

These rural–urban contrasts highlight the need for tailored interventions by supporting time-efficient healthy practices and strengthening nutrition education in rural areas, and increasing affordable access to healthy diets and opportunities for physical activity among low-income populations in urban areas. These findings have direct implications for the implementation of Tanzania’s National Strategic Plan for the Prevention and Control of NCDs (2021–2026),²¹ which emphasizes population-level prevention and reduction of behavioral risk factors associated with NCDs.

In urban settings such as Morogoro, efforts should focus on improving access to affordable healthy foods, promoting physical activity through built environment interventions, and targeting low-income populations who are at greater risk of unhealthy behaviors. In rural areas, strategies should aim to support the preservation of currently favorable lifestyle patterns while addressing emerging risks associated with dietary transitions and reduced physical activity. Strengthening community-based health promotion, integrating behavior change interventions into primary healthcare, and leveraging existing community structures are critical for effective implementation.

Sensitivity analyses using a stricter threshold for healthy lifestyle adoption showed similar overall rural–urban patterns, supporting the robustness of the primary findings. However, several socio-demographic associations were attenuated under the stricter classification, suggesting that some predictor-specific findings may be sensitive to the definition of healthy lifestyle adoption and should therefore be interpreted cautiously.

The findings may be relevant to similar rural and urban populations in Tanzania and other low- and middle-income countries undergoing comparable nutrition and epidemiological transitions. However, context-specific differences should be considered when interpreting these results.

Study Limitations and Mitigation

This study has several limitations. First, its cross-sectional design means that causal relationships cannot be established, and the direction of associations remains uncertain. Although multistage cluster sampling improved representativeness and a design effect was incorporated into the sample size calculation, clustering at ward and household levels was not accounted for in the regression analysis because cluster identifiers were not retained in the analytical dataset. This may have resulted in underestimated standard errors and overstatement of statistical significance. Therefore, the regression findings should be interpreted cautiously.

Second, the sample size was calculated using hypertension prevalence as a proxy due to the absence of published estimates of composite healthy lifestyle behaviors in comparable Tanzanian rural and urban populations. Although hypertension is a downstream clinical outcome rather than a direct behavioral measure, it reflects cumulative exposure to multiple modifiable lifestyle risk factors that were also incorporated into the composite healthy lifestyle index and was therefore considered a pragmatic and locally relevant proxy for sample size estimation. However, hypertension prevalence may not precisely represent the prevalence of combined healthy lifestyle behaviors, which could have introduced some imprecision in the estimated sample size.

Third, lifestyle behaviors were self-reported and may be influenced by recall and social desirability bias. The very low prevalence of risky alcohol use observed in this study, in particular, may reflect underreporting due to cultural norms and the interviewer-administered nature of the survey. As a result, alcohol-related risk may have been underestimated, potentially leading to overestimation of healthy lifestyle adoption and attenuation of associations involving alcohol-related behaviors.

In addition, the composite HLI simplifies complex behaviors and assigns equal weight to all components, even though their contributions to hypertension risk may differ. Although this approach is widely used in population-based studies, it may oversimplify context-specific behavioral patterns and differences in the relative contribution of individual lifestyle components to hypertension risk. A sensitivity analysis using a stricter threshold for healthy lifestyle adoption showed broadly similar overall rural–urban patterns. However, alternative weighting schemes and more extensive index specifications were not explored and may influence the classification of healthy lifestyle adoption. Measures of fruit and vegetable intake did not capture portion size, seasonality, or access, and physical activity may not fully reflect occupational or domestic activity. The use of Tanzania-specific dietary guidelines (≥2 servings/day), rather than WHO recommendations (≥5 servings/day), may affect comparability with international studies and could have resulted in a larger proportion of participants being classified as having adequate fruit and vegetable intake. However, the selected threshold reflects locally adapted national dietary guidance and was considered more contextually relevant for assessing dietary behaviors within the Tanzanian setting.

Finally, broader environmental and structural factors, such as food availability, built environment, and cultural norms, were not directly assessed. Despite these limitations, the study provides useful insights into rural–urban differences in lifestyle behaviors and highlights the need for context-specific prevention strategies and further longitudinal research.

Conclusion

This study shows clear rural–urban differences in lifestyle behaviors for hypertension prevention in Morogoro Region. Adults in rural Kilombero were more likely to report healthier lifestyle behaviors compared with those in urban Morogoro, highlighting differences between rural and urban populations. Income influenced behaviors differently across settings, supporting healthier choices in urban areas and traditional, active routines in rural communities. These findings suggest potential areas for future interventions and highlight the need for context-specific strategies to promote healthy lifestyle behaviors. In Kilombero, strategies could focus on protecting traditional diets, strengthening nutrition education, and targeting employed adults. In Morogoro, efforts could include improving access to fruits and vegetables and promoting opportunities for physical activity. These measures may help inform Tanzania’s NCD prevention efforts and support progress toward SDG 3.4 and Universal Health Coverage (UHC).

Supplemental Material

Supplemental Material - Tanzania’s NCD Transition: A Comparative Analysis of Healthy Lifestyle Behaviors for Hypertension Prevention Between Rural and Urban Adults

Supplemental Material for Tanzania’s NCD Transition: A Comparative Analysis of Healthy Lifestyle Behaviors for Hypertension Prevention Between Rural and Urban Adults by Salim J. Mpimbi, Mangi J. Ezekiel, Idda H. Mosha, Gilbert Fokou and Bonfoh Bassirou in INQUIRY: The Journal of Health Care Organization, Provision, and Financing.

Supplemental Material

Supplemental Material - Tanzania’s NCD Transition: A Comparative Analysis of Healthy Lifestyle Behaviors for Hypertension Prevention Between Rural and Urban Adults

Footnotes

Acknowledgment

This study received financial support from the Science for Africa Foundation through the Developing Excellence in Leadership, Training and Science in Africa (DELTAS Africa) programme, under the Afrique One-REACH initiative. We also acknowledge the institutional backing and academic guidance provided by the MUHAS. The views expressed in this publication are solely those of the authors and do not necessarily reflect the positions of DELTAS Africa, Afrique One-REACH, or the Science for Africa Foundation.

Ethical Considerations

Ethical clearance for this study was obtained from the Research Ethics Committee of the Muhimbili University of Health and Allied Sciences (MUHAS) (Ref. No. DA.282/298/01.C/2582). Additional approvals were obtained from the respective municipal and district authorities before data collection. All participants provided written informed consent after receiving detailed information about the study’s purpose, the procedures involved, potential risks and benefits, and their freedom to withdraw at any time. Confidentiality was maintained by anonymizing all data, storing transcripts on encrypted, password-protected servers accessible only to the research team, keeping offline backups securely, and ensuring that interviews were conducted in private and secure environments.

Author Contributions

SJM, GF, and BB contributed to the conceptualization and project administration. SJM performed data collection, curation, formal analysis, investigation, and methodology. MJE and IHM co-supervised the field study. SJM wrote the original draft, and MJE, IHM, GF, and BB reviewed and edited the manuscript. BB acquired a project grant. All authors read and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded in full by the Science for Africa Foundation to the Developing Excellence in Leadership, Training and Science in Africa (DELTAS Africa) program [Afrique One-ASPIRE, Del- 15–008 and Afrique One-REACH, Del- 22–011] with support from Wellcome Trust and the UK Foreign, Commonwealth & Development Office and is part of the EDCPT2 program supported by the European Union.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.*

Supplemental Material

Supplemental material for this article is available online.

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