This was a hospital-based cross-sectional study, whereby data were collected at one point in time.

A representative number of adolescent pregnant girls aged 15-19 years attending the RCH clinic at SFRRH was included in the study. Adolescent pregnant women aged below 15 years were excluded since there is a high prevalence of pregnancies among the age group 15-19 years 17. Moreover, only participants who accepted and signed written informed consent were included.

Purposive sampling was used to obtain a sample. The sample size was obtained using Cochran’s formula which is: n = Z2×p(1-p)d2, where Z is standard normal variance (1.95 for 95%), n is the desired sample size, p is the proportion of the target population estimated to have a particular characteristic, q is the proportion of the target population estimated not to have a particular characteristic and d is the degree of accuracy desired (0.05 of 5%) 18. The proportion of adolescent pregnant girls in Tanzania is 22%.

Hence, n = (1.95)2×0.22(1-0.22)(0.05)2

= 261 adolescent pregnant girls

But due to limited time and resources the desired sample size was reduced by 50%.

Hence the desired sample size was 131 adolescent pregnant girls.

Qualitative and quantitative data were obtained using a structured questionnaire comprising four sections with demographic information, 24hrs dietary food recall information, anthropometric parameters and factors influencing dietary diversity and nutritional status respectively. Anthropometric data was obtained by measuring Mid-Upper Arm Circumference (MUAC) using a MUAC tape. The questionnaire was written in English and then translated to Swahili language for easy understanding. During sessions with pregnant adolescent, the researcher was the one filling the questionnaire.

Data collected was coded and entered in SPSS version 20 then imported to R software version 4.4.0 for statistical analysis. Microsoft Excel software was utilized to generate charts and graphs for visualization purposes. Descriptive statistics were employed to summarize the demographic characteristics of the 131 adolescent pregnant women, including their age distribution, marital status, gravidity, educational background, and occupation. Dietary diversity was assessed using the minimum dietary diversity score calculated from 24-hour recall data. Chi-square tests were performed to examine the association between dietary diversity score, demographic variables, including age, marital status, gravidity, education level, and occupation, and socioeconomic and cultural factors. This test was used due to the presence of categorical variables such as demographic factors (age, marital status, education, and occupation), and its ability to assess the association of these variables with MUAC and dietary diversity score.

The ethical approval for conducting this research was obtained from the Institution Review Board at St Francis University College of Health and Allied Sciences.

A total of 131 adolescent pregnant women aged 15-19 years participated in this study. The mean age of participants was (18 ± 0.9 SD) years. A large proportion aged 19 years (n=63, 48.1%), followed by 18 years (n=34, 26.0%), and the rest (n=34, 26.0 %) had 16-17 years. Majority of the women were married (n=83, 63.4%) while the rest (n=48, 36.6%) were unmarried. Regarding the gravidity (n= 86, 65.6%) were prime gravid while the rest (n=45, 34.4%) were gravida 2 and 3. Most of the women had primary education (n=85,64.9%) followed by secondary education (n=42,32.1%) and the rest (n=4,3.1%) had university and informal education (Figure 2). Moreover, majority of the participants were farmers (n= 56, 42.7%) followed by unemployed (n= 28, 21.4%) and the rest (n=47, 35.9%) were small business owners. Concerning their occupation, most women (n= 130, 99.2%) had an income less than Tshs.100,000/= (Figure 1).

Figure 2

This study found that the mean DD score was 6 (SD = 1.65). Out of 131 adolescent pregnant women (n =122, 93.1%) met the minimum dietary diversity score about the 24-hour recall period. The proportion of dietary diversity was similar across the age of participants (χ₁² = 31.78, p = 0.240), with the majority (n=122, 93.1%) meeting the minimum dietary diversity score. The proportion of dietary diversity score statistically differed with the number of children born (χ₁² = 48.44, p < 0.001), with prime gravida (n =80, 93.0%) meeting most of the dietary diversity score compared to the rest. There was a statistically significant difference in the proportions of dietary diversity score with marital status (χ₁² = 18.25, p = 0.032). With (n= 76, 91.6%) married and (n=46, 95.8%) unmarried adolescent pregnant women met the minimum dietary diversity score while (n=7, 8.4%) married and (n=2, 4.2%) unmarried failed to meet the minimum dietary diversity score on a 24-hour recall period. Moreover, the majority (n= 77, 90.5%) participants with primary education followed by (n= 42, 100%) secondary education and the rest (n= 2, 100%) university and informal education met the minimum dietary diversity score. This observed difference was statistically significant (χ₁² = 74.67, p < 0.001). Furthermore, the DD score was similar across the participants’ occupations (χ₁² = 43.047, p = 0.555) (Table 1).

Overall, out of 131 adolescent pregnant women (n =123, 93.9% and n= 8, 6.1%) fell under normal and undernourished MUAC category (Figure 3). The mean MUAC was 24 (SD = 2.37). The proportion of MUAC was similar across the age of participants (χ₁² = 3.02, p = 0.389, Table 2). Similarly, the proportion of MUAC across the number of children born was not statistically significant (χ₁² = 4.32, p = 0.115). Moreover, the proportion of MUAC across the marital status was not statistically significant (χ₁² = 0.11, p = 0.744). Furthermore, the proportion of MUAC across education status was not statistically significant (χ₁² = 0.53, p = 0.913). Additionally, the proportion of MUAC across participants’ occupations was not statistically significant (χ₁² = 3.38, p = 0.642) (Table 2).

Overall, out of 121 adolescent pregnant women who met the minimum dietary diversity score only (n= 115, 95.0%) had normal MUAC while the rest (n= 6, 5.0%) were undernourished (Figure 3). Moreover, out of 10 women who did not meet the minimum dietary diversity score, only (n= 8, 80.0%) had normal MUAC while the rest (n= 2, 20.0%) were undernourished. This observed difference was statistically significant (χ₁² = 21.19, p = 0.012).

Among the factors assessed to influence dietary diversity score nutrition knowledge provided at the health facility was observed statistically significant (χ₁² = 25.016, p = 0.003). Moreover, women’s declaration of nutrition knowledge sufficiency was statistically significant as well (χ₁² =24.46, p = 0.004). However, family and friend support was observed not to be statistically significant in influencing DD Score among pregnant adolescent women (χ₁² = 35.24, p = 0.505). But also, additional resources to support women during pregnancy were observed not to be statistically significant (χ₁² = 31.99, p = 0.928).

Several challenges were identified that hindered adolescent pregnant participants from maintaining a balanced diet. Majority of them (n= 36, 27.5%) stated that they failed to access recommended foods due to financial constraints. Moreover, some women (n= 32, 24.4%) complained of being sick, food aversions and lack of appetite therefore not preferring most foods hindering their diversity. Additionally, other women (n= 10, 7.6%) stated being ignorant of healthy eating while others (n= 27, 19.8%) mentioned lacking access to the recommended foods due to the resident setting and cultural limitations as well. However, other women (n=26, 20.6%) stated that they faced no challenge in meeting the recommended diet (Figure 4). These observed challenges associated with meeting the minimum dietary diversity score were marginally statistically significant (χ₁² = 61.316, p = 0.053).

Table 1

Factors		Dietary diversity score		p -value
	Total (n=131)	Inadequate (<5) (n=9)	Adequate (≥ 5) (n=122)
	n (%)	n (%)	n (%)
Age
15	0(0)	0(0)	0(0)
16	6(4.6)	0(4.9)	6(5.0)
17	28(21.4)	0(22.8)	28(23.0)
18	34(26.0)	4(25.2)	30(24.6)
19	63(48.1)	5(47.2)	58(47.5)	0.24
Education Status
Primary	85(64.9)	8(64.3)	77(63.1)
Secondary	42(32.1)	0(32.5)	42(34.5)
University/college	1(0.8)	0(0.8)	1(0.8)
Informal	3(2.3)	1(2.4)	2(1.6)	<0.001*
Ethnicity
Bena	2(1.5)	0(1.6)	2(1.6)
Chaga	5(3.8)	0(0)	5(4.1)
Hehe	5(3.8)	0(0)	5(4.1)
Ndamba	14(10.7)	0(0)	14(11.5)
Ndewe	9(6.9)	1(6.5)	8(6.6)
Ngindo	25(19.1)	3(19.5)	22(18.0)
Ngoni	1(0.8)	0(0)	1(0.8)
Nyakyusa	6(4.6)	0(0)	6(4.9)
Pare	5(3.8)	0(0)	5(4.1)
Pogoro	40(30.5)	4(31.7)	36(29.5)
Sambaa	4(3.1)	0(0)	4(3.3)
Sukuma	15(11.5)	2(11.4)	13(10.7)	0.216
Income level
Less than 100,000/=	130(99.2)	9(99.2)	121(99.2)
Over 100,000/=	1(0.8)	0(0)	1(0.8)	<0.001*
Marital status
Married	83(63.4)	7(62.6)	76(62.3)
Unmarried	48(36.6)	2(37.4)	46(37.8)	0.032
Number of children
0	86(65.7)	5(64.2)	81(66.4)
1	39(8)	0(0)	39(32.0)
2	6(4.6)	4(4.1)	2(1.6)	<0.001*
Occupation
Business women	20(15.3)	2(14.6)	18(14.8)
Farmer	56(42.8)	4(41.5)	52(42.6)
Loan Officer	1(0.8)	0(0)	1(0.8)
No Job	28(21.4)	1(22.0)	27(22.1)
Entrepreneur	6(4.6)	0(0)	6(4.9)
Tailor	20(15.3)	2(16.3)	18(14.8)	0.555

Table 2

Factors		MUAC
	Total (n=131)	Normal (n=123)	Undernourished (n=8)	p-value
	n (%)	n (%)	n (%)
Age
15	0(0)	0(0)	0(0)
16	6(4.6)	6(4.9)	0(0)
17	28(21.4)	28(22.8)	0(0)
18	34(26.0)	31(25.2)	3(37.5)
19	63(48.0)	58(47.1)	5(62.5)	0.389
Education Status
Primary	85(64.9)	79(64.2)	6(75)
Secondary	42(32.1)	40(32.5)	2(25)
University/college	1(0.8)	1(0.8)	0(0)
Informal	3(2.3)	3(2.5)	0(0)	0.9129
Ethnicity
Bena	2(1.5)	2(1.6)	0(0)
Chaga	5(3.8)	5(4.1)	0(0)
Hehe	5(3.9)	5(4.1)	0(0)
Ndamba	14(10.7)	13(10.6)	1(12.5)
Ndewe	9(6.9)	8(6.5)	1(12.5)
Ngindo	25(19.1)	24(19.5)	1(12.5)
Ngoni	1(0.8)	1(0.8)	0(0)
Nyakyusa	6(4.6)	5(4.1)	1(12.5)
Pare	5(3.9)	3(2.4)	2(25)
Pogoro	40(30.5)	39(31.7)	1(12.5)
Sambaa	4(3.1)	4(3.3)	0(0)
Sukuma	15(11.5)	14(11.4)	1(12.5)	0.3125
Income level
Less than 100,000/=	130(99.2)	122(99.2)	8(100)
Over 100,000/=	1(0.8)	1(0.8)	0(0)	1
Marital status
Married	83(63.4)	77(62.6)	6(75)
Unmarried	48(36.6)	46(37.4)	2(25)	0.744
Number of children
0	86(65.6)	79(64.2)	7(87.5)
1	39(29.8)	39(31.7)	0(0)
2	6(4.6)	5(4.1)	1(12.5)	0.1151
Occupation
Business women	20(15.3)	18(14.6)	2(25)
Farmer	56(42.7)	51(41.5)	5(62.5)
Loan Officer	1(0.8)	1(0.8)	0(0)
No Job	28(21.4)	27(22.0)	1(12.5)
Entrepreneur	6(4.6)	6(4.9)	0(0)
Tailor	20(15.3)	20(16.3)	0(0)	0.6422

Figure 3

Figure 4

This study sought to determine dietary diversity and nutrition status along with influencing factors such as socio-economic and demographic among adolescent pregnant. Given the dual nutritional demands faced by pregnant adolescents, understanding dietary patterns is crucial to ensuring they receive adequate nutrition, which in turn supports the health and well-being of both mother and child Although the sample size was reduced by 50%, the results are based on this reduced sample and show no impact on the significance of the findings.

The results reveal that approximately 93% of the participants met the Minimum Dietary Diversity for Women of Reproductive Age (MDD-W) during the 24-hour recall period, indicating that most adolescent pregnant women had access to a relatively diverse diet. Nutrition education, demonstrations, and counselling provided during monthly antenatal clinic visits likely contributed to this adherence to a diverse diet. This finding contrasts with a study in Tanzania by Heri et al. 19, which found that only 28% of pregnant women met the minimum dietary diversity score, largely due to limited nutrition education within the general population, underscoring the importance of nutrition education in promoting dietary diversity. Moreover, a study done by Mruma et al. 20 in Tanzania, revealed that some pregnant women fail to utilize the given nutritional information at the clinic, and thereafter fail to adhere to a diverse diet further posing risks of deficiencies and poor health outcomes.

Furthermore, several factors were identified to influence dietary diversity, including socio-demographic aspects such as age, education level, and marital status, as well as socio-economic factors like economic status and occupation. When analysing age in dietary diversity scores, the study found no significant differences across age groups, suggesting that age does not substantially impact dietary diversity. However, studies in Ethiopia suggested that younger pregnant women under 20 years old may adhere better to dietary diversity guidelines due to their lack of experience compared to older women 14. Therefore, it can be evident that the younger a woman is the more she adheres to the guideline but also considering the number of children born of the mother also could entail adherence level.

The results indicated that dietary diversity was different depending on gravidity, with primigravida women (those pregnant for the first time) more likely to achieve the minimum dietary diversity score compared to multiparous women (those with more than one child). This largely is attributed to novice pregnancy experience that constricts these mothers to listen and follow their health instructor to ensure better nutrition and their well-being during the pregnancy period. A study done in Somalia also reported that primigravida women had a better adherence to diverse diets per direction contributing to their nutritional status 15. Similarly, a study from Eastern Ethiopia suggested that first-time mothers often have more resources and support, which may facilitate better dietary practices compared to women who have had multiple pregnancies 21. These findings highlight the importance of considering parity when addressing dietary diversity in pregnant women.

Additionally, marital status significantly influenced dietary diversity, with a significant difference observed between married and unmarried women. Married women have their husbands’ support emotionally and physically as well, this greatly gives assurance and psychological support that is essential during the time 22. This finding aligns with a study done in Ethiopia, which found that marital status had a more substantial impact on dietary diversity, possibly due to social support systems involved 14.

Moreover, educational status was a notable factor in dietary diversity, with women who had at least a secondary education showing better adherence to diverse diets. This can be attributed to their enhanced understanding of the importance of nutrition. This finding aligns with a study done in Western Ethiopia, which emphasized the role of education in fostering nutritional knowledge and making healthier dietary choices 23. Conversely, occupation did not have a significant impact on dietary diversity, suggesting that regardless of employment status, women had equal opportunities to maintain a diverse diet. This may be because most women had similar occupations of peasantry as the study took place in a rural setting. Moreover, access to the same food markets purchasing the same kind of food items, along with societal norms regarding dietary practices during pregnancy. However, this finding contrasts with a study done in Nepal, which found that employment status significantly influenced dietary diversity due to increased income, which allowed for better food access 24. The interplay between education and occupation highlights the complex nature of dietary diversity determinants.

Furthermore, this study found a significant correlation between nutritional status, as measured by Mid-Upper Arm Circumference (MUAC), and dietary diversity score, highlighting the crucial role of dietary diversity in maternal nutrition. This supports findings from a study in Kenya, which argued that dietary diversity is closely linked to nutrient adequacy and improved maternal nutritional status 25. Similarly, A study done in Ethiopia indicated that dietary diversity positively impacts both maternal and fetal health 14. Therefore, it is essential for pregnant women, particularly adolescents, to ensure they consume a diverse diet to meet their increased nutritional needs and support fetal development, leading to better health outcomes for both mother and baby.

Moreover, while socio-economic and demographic factors did not significantly account for nutritional status as measured by MUAC, studies from Western Ethiopia and Indonesia have shown that these factors can significantly influence nutritional status through the adoption of good dietary practices 26, 23. Despite this contradiction with other research on the influence of these factors, they remain crucial for ensuring the health of both mother and fetus. Additionally, maintaining good nutritional status is vital for infant health, particularly given the increased nutrient requirements of adolescent mothers. These findings underscore the need for comprehensive approaches to maternal nutrition.

As well, nutrition knowledge provided at health facilities and participants' self-reported nutrition knowledge was significantly associated with higher dietary diversity. This finding is consistent with a study done in Tanzania, that highlighted the critical role of nutrition education in improving dietary outcomes 19. Moreover, a study done in Ethiopia also revealed the positive impact of nutrition knowledge given at antenatal clinics on improving dietary diversity 27.

Not only that but also, family and friend support, as well as additional resources for pregnant women, were not significantly associated with dietary diversity scores. This can be attributed to the fact that most women reported having less support from family and friends since most self-supported themselves, but also required no additional resources or support, due to acknowledgement of clinic support being sufficient. Al-Mutawal et al. nailed the significant factor of social support to enhance the well-being of pregnant women hence contributing to overall health outcomes 28. These contrasts suggest that while nutrition education is pivotal, social support mechanisms should not be overlooked as it may lead to psychological support hence avoiding the risk of depression, which may result in incidences such as miscarriage 28, 29.

Additionally, financial constraints, health issues such as illness, food aversions, lack of appetite, ignorance about healthy eating, lack of access to recommended foods due to residential settings, and cultural restrictions were identified as challenges hindering dietary diversity among adolescent pregnant women. Although these challenges were only marginally statistically significant, addressing them could potentially improve dietary diversity in this population. Similar barriers were reported where the emphasis was on the importance of overcoming socioeconomic and educational barriers to enhance dietary practices 24, 30.

This study was hospital-based, with the sample size being reduced by 50% due to practical constraints of time and funds, hence may not fully reflect the dietary diversity of adolescent women in the broader community. Additionally, the study's cross-sectional design and reliance on a 24-hour dietary recall period recorded on random days may not capture the full variability in daily food consumption, but also as individuals attending hospitals may tend to have higher education levels or income than those in the general population, this may have potentially introduced biases and affect the generalizability of the findings.

The findings of this study revealed that most adolescent pregnant women were able to achieve adequate dietary diversity. However, socio-demographic factors such as age, occupation, and education, along with challenges like loss of appetite and illness, influenced both their dietary diversity and overall nutritional status. Therefore, in order to improve dietary outcomes, it is essential to address these challenges. Furthermore, future research should take a community-based longitudinal approach to better understand how these factors impact dietary patterns among adolescent pregnant women. This could lead to the development of targeted interventions aimed at promoting positive health outcomes for both mothers and their new-borns.