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. 2024 Jun 6;8(2):e102–e112. doi: 10.3928/24748307-20240521-01

Association of Obesity With Health Literacy and Weight Perception Among Women Merchants in Abidjan, Côte d'Ivoire: A Cross-Sectional Study

Rui Usui ✉, Maki Aomori, Shogo Kanamori, Bi Tra Jamal Sehi, Setsuko Watabe
PMCID: PMC11235983  PMID: 38852072

Abstract

Background:

In Abidjan, Côte d'Ivoire's largest city, obesity rates among women are increasing, posing a major health challenge, especially for the working generation. Merchants represent 64.3% of working women and are a typical occupation for women with low- and middle-income. Health literacy is used to prevent and overcome chronic diseases and can be used as anti-obesity measures.

Objective:

The aim of this study was to examine the relationship between obesity, health literacy, and weight perception among women merchants in Abidjan.

Methods:

In this cross-sectional study, we conducted a complete enumeration survey among women merchants in a market in Abidjan from December 2020 to December 2021. In addition to anthropometric measurements, structured face-to-face interviews were conducted. The participants were asked about their weight perception, weight management behaviors, and sociodemographic attributes. They also responded to the Health Literacy Questionnaire (HLQ). Data were tabulated using descriptive statistics, and multiple logistic regression analysis was performed to examine obesity's association with HLQ scales, weight perception, and weight management behaviors.

Key Results:

Of the 873 participants, 259 (29.7%) were obese; 82% of them underestimated their weight. Obesity was associated with a higher rate of HLQ1 (Feeling understood and supported by health care providers) (odds ratio [OR] = 2.926, confidence interval [CI]:1.450–5.901, p = .03), a lower score of HLQ3 (Actively managing my health) (OR = 0.343, CI:0.165–0.716, p = 0.004), a lower rate of accurate weight perception (OR = 0.145, CI: 0.093–0.224, p < .001), and a lower rate of eating at least three meals per day (OR = 0.401, CI:0.260–0.617, p < .001).

Conclusions:

Findings from this study of Abidjan women merchants include obese participants' lack of a proactive attitude toward personal health management, and the association of factors such as inaccurate weight perception and eating fewer than three meals per day with obesity. These finding have important implications for future anti-obesity measures. [HLRP: Health Literacy Research and Practice. 2024;8(2):e102–e112.]

Plain language summary

Plain Language Summary: We found the relationship between obesity and health literacy among women merchants, Abidjan in Côte d'Ivoire. The results showed that participants with obesity lacked proactive attitude toward personal health management among health literacy skills. Anti-obesity measures in Abidjan need to incorporate this aspect of the health literacy skills.

Obesity is a global health issue as one of the risk factors (Must et al., 1999) for noncommunicable diseases (NCDs) such as cardiovascular disease and type 2 diabetes mellitus (World Health Organization, 2023b). In African countries, obesity rates are particularly high among women (13.2% among women and 4.8% among men) (World Health Organization, 2023a), a situation exacerbated by a combination of biological (Lovejoy & Sainsbury, 2009) and cultural factors (World Health Organization, 2013).

In Côte d'Ivoire, a West African country, the percentage of women with obesity more than doubled from 5.7% in 1996 to 13.1% in 2016 (World Health Organization, 2023a). Particularly in Abidjan, the country's largest city, owing to infrastructure development and urbanization, people have become physically less active and their calorie intake has increased, resulting in a higher risk of obesity (Amugsi et al., 2017). Abidjan's obesity rate is higher than the national average (the 2005 national average for women was 8.4% (World Health Organization, 2023a) and 11.6% in Abidjan) (STEPS/MNT Program Coordination Directorate, 2005).

A study of obesity risk among patients who are over-weight and obese in Abidjan found that being a merchant was a risk factor (Sable et al., 2020), suggesting that merchants are an occupation that requires obesity prevention. Many women work as merchants in Abidjan's approximately 120 markets (Poyau, 2005). Women merchants represent 64.3% of working women in Abidjan (Ministère de la Santé et de la Lutte contre le Sida et al., 2013) and typically belong to low- and middle-income households. Meanwhile, many people with low- and middle-income in Côte d'Ivoire do not have health insurance; therefore, they do not undergo screening for NCDs nor have access to medical care when affected. Access to essential information to prevent obesity, such as appropriate diet and exercise, is also limited for them. As such, anti-obesity measures, including medical and non-medical interventions, are especially important.

Some cultural factors have traditionally promoted obesity in African countries (Agyemang et al., 2016) but it is only in recent years that obesity has begun to be recognized as a risk factor for NCDs, and a tendency among African people to desire a normal weight or physique has also been reported (Agyapong et al., 2020; Gradidge et al., 2020). Weight misperception has been shown to hinder obesity prevention and weight loss among adults in the United States (Duncan et al., 2011). Previous studies in sub-Saharan Africa have primarily looked into the prevalence of weight misperception and degree of discrepancy with the reality (Agyapong et al., 2020; Gradidge et al., 2020; Tateyama et al., 2018). However, evidence on the association between obesity and weight perception is slight and limited (Mogre et al., 2013).

Health literacy (HL) is important in understanding the ability to overcome or prevent chronic conditions, such as obesity, which requires not only accurate health knowledge but also self-management (Nutbeam, 1998; Olesen et al., 2017). HL has been gaining attention in sub-Saharan Africa (Chrissini & Panagiotakos, 2021; World Health Organization, 2015). Although the literature has demonstrated a link between obesity and low HL (Chrissini & Panagiotakos, 2021) we could not find any relevant research conducted in sub-Saharan African countries.

HL measurement in obesity control, which requires lifestyle improvement, encompasses a wide variety of factors including not only the ability to read health information (functional HL) but also attitude toward health and support from others. The HL Questionnaire (HLQ) is a tool that allows for a multidimensional assessment of individual or group HL (Osborne et al., 2013). It has also been used to identify needs and to formulate intervention guidelines to address them (Beauchamp et al., 2015).

Given the foregoing, we aimed to elucidate the relationship between obesity and HL, as well as weight perception, with the goal of contributing to measures geared toward mitigating obesity among women merchants in Abidjan. Furthermore, we explore the applicability of the results to other groups in similar regions and cultures.

Methods

Study Design and Population

As part of this cross-sectional study, we conducted a complete enumeration survey of women merchants in Abidjan's Market A. The area where the market is located has traditionally been multiethnic and multiracial, with a large low- and middle-income population (Brenoum et al., 2017). It is a medium-sized general market with active product distribution and a wide variety of products for sale. The market can be described as a melting pot of merchants, representative of the multiethnic city of Abidjan. The target population consisted of women age 18 years or older who were using retail spaces in the market. Pregnant women and women less than 1 year postpartum were excluded owing to the impact of pregnancy and breastfeeding on weight.

Measurements

The participants underwent anthropometric measurements (height and weight) and structured face-to-face interviews. The anthropometric measurements were taken using a tape measure and a body composition analyzer, with the participants being asked to take off heavy clothing and socks. The interview consisted of questions regarding the participants' sociodemographic attributes (e.g., age, ethnicity, family size, education, income, marital history, childbirth history), weight perception (“Do you consider yourself slim, normal, overweight, or obese?”), desire to gain or lose weight, reasons for the desire to gain or lose weight, and weight management (weight management behavior, number of meals eaten the previous day, where and why the participants weighed themselves the last time, how frequently the participants weighed themselves, and last measured weight). For questions pertaining to sociodemographic attributes, we used items from the WHO STEPwise approach to surveillance (STEPS/MNT Program Coordination Directorate, 2005) implemented in Abidjan. Items related to weight perception and weight management were developed by the research team based on previous studies (Awosan et al., 2017; Joh et al., 2013; Shieh et al., 2016; Tateyama et al., 2018; Wang et al., 2009). For HL, we used the French version of the HLQ (Debussche et al., 2018; Osborne et al., 2013; Ousseine et al., 2017). The HLQ consists of 9 scales, each with 4 to 5 items. The score for each scale is the average of its items. Interview items were rated on a Likert scale, with options ranging from 1 = strongly disagree to 4 = strongly agree for the first to fifth scales and 1 = cannot do to 5 = very easy for the 6th to 9th scales. We adopted the HLQ because measures against obesity, in which various factors such as lifestyle are intricately involved, necessitate considering not only the ability to read and understand health information but also multifaceted abilities, such as attitude toward health and social support. The languages used in the markets and among merchants in Abidjan are French and Dioula (Aboa, 2015; Brenoum et al., 2017). Considering that the market district is not originally a Dioula-speaking area, and that the area is home to many immigrants from within the country and neighboring countries (Brenoum et al., 2017), we used the French version based on the assumption that all merchants working in the target market could communicate in French. To check the internal consistency of the HLQ scales, alpha coefficients for each scale were calculated from the sub-items.

Data were collected by trained researchers who visited the retail spaces in the market. During the anthropometric measurements, care was taken to ensure that the participants' weight was not made known to others. The researchers read the questions to them, and the participants' answers were filled in by the researchers. The survey period was from December 2020 to December 2021.

Statistical Analyses

We used SPSS Statistics for Windows, version 25 for all statistical analyses. We calculated the body mass index (BMI) from the participants' height and weight and classified it based on the World Health Organization's definitions (World Health Organization, 2023b): under-weight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), over-weight (25–29.9 kg/m2), and obese (≥30 kg/m2). For items pertaining to sociodemographic attributes and weight perception/management, we tabulated descriptive statistics for the aforementioned categories. Regarding weight perception, we calculated the Kappa coefficient to determine the degree of agreement between the BMI values and participants' personal weight perception (slim, normal, overweight, and obese). The results were also tabulated for agreement (correct weight perception), underestimation, and overestimation. For HLQ, we calculated the mean and standard deviations (SD) for each scale. We then conducted multiple logistic regression analysis using forced entry method to assess the association between obesity and the HLQ scales, as well as weight perception/management items. In addition, we calculated adjusted odds ratios (ORs) and their 95% confidence intervals (CIs). The dependent variable was obesity status, whereas the independent variables included HLQ scales 1–9 and items pertaining to weight perception and management. Participants' sociodemographic attributes, including age category, ethnic group, academic background, individual income, and number of family members, were used to control for possible confounders. We determined the goodness of fit of the regression model using the Hosmer–Lemeshow test. The significance level was set at p < .05.

Ethics and Consent

The present study was carried out with the approval of the relevant ethics committee (permit no. A200300003). Furthermore, we obtained a research permit from Chefferie d'Anono in Côte d'Ivoire, the chiefdom with jurisdiction over the target market. After explaining the study to the participants both verbally and using printed materials, we obtained informed consent by having them sign a participation consent form.

Results

After interviewing 913 women merchants with no known pregnancy at the time of the survey and applying the exclusion criteria, we included data from 873 participants in the analysis. There was no refusal to participate in the study. Table 1 shows the characteristics of the participants by BMI category, as well as items pertaining to weight perception and management. The participants' mean age was 39 (SD ± 10.5) years. Of the participants, 259 (29.7%) had obesity and 410 (47%) were overweight. The overall BMI mean was 27.7 (SD ± 4.29) and that those with obesity was 32.5 (SD ± 2.37). The accurate weight was perceived by only 46 (17.8%) of the participants with obesity, which is significantly smaller than 14 (70%) of participants who were underweight (p < .001), 80 (43.5%) of normal weight participants (p < .001) and 163 (39.8%) of overweight participants (p < .001). The Kappa coefficient, which measures the agreement between BMI values and personal weight perception, was 0.062 (p = .001), indicating virtually no agreement. Additionally, 47.9% of the participants with obesity weighed themselves less than once a year. The most common place where participants weighed themselves was a medical institution (55.2%), followed by a pharmacy (23.2%). Only 4 (0.5%) of the overall participants and 1 (0.4%) of the obese owned a scale.

Table 1.

Baseline Characteristics of Women Merchants in Abidjan (N = 873)

graphic file with name 10.3928_24748307-20240521-01-table1a.jpg

Characteristic Total Underweight Normal Overweight Obese
n (%)
Total 873 (100) 20 (2.3) 184 (21.1) 410 (46.9) 259 (29.7)
Sociodemographic data
Age (years)
  18–29 187 (21.4) 11 (55) 78 (42.4) 87 (21.2) 11 (4.2)
  30–39 259 (29.7) 5 (25) 64 (34.8) 140 (34.1) 50 (19.3)
  40–49 259 (29.7) 1 (5) 33 (17.9) 116 (28.3) 109 (42.1)
  ≥50 168 (19.2) 3 (15) 9 (4.9) 67 (16.3) 89 (34.4)
Ethnic group
  South 472 (54.1) 13 (65) 79 (42.9) 226 (55.1) 154 (59.5)
  North 234 (26.8) 2 (10) 59 (32.1) 125 (30.5) 48 (18.5)
  People of Burkina Faso, Ghana, Mali, Nigeria, and Guinea 167 (19.1) 5 (25) 46 (25) 59 (14.4) 57 (22)
Academic background
  <Secondary education 596 (68.3) 13 (65) 118 (64.1) 263 (64.1) 202 (78)
  Secondary education enrollment or higher 277 (31.7) 7 (35) 66 (35.9) 147 (35.9) 57 (22)
Individual income
  <75,000 XOFa/month 440 (50.4) 19 (95) 133 (72.3) 178 (43.4) 110 (42.5)
  ≥75,000 XOFa/month 433 (49.6) 1 (5) 51 (27.7) 232 (56.6) 149 (57.5)
Number of family members
  <5 people 345 (39.5) 11 (55) 100 (54.3) 181 (44.1) 53 (20.5)
  ≥5 people 528 (60.5) 9 (45) 84 (45.7) 229 (55.9) 206 (79.5)
Not nulliparity
  Yes 689 (78.9) 11 (55) 118 (64.1) 315 (76.8) 245 (94.6)
  No 184 (21.1) 9 (45) 66 (35.9) 95 (23.2) 14 (5.4)
Have a smartphone
  Yes 670 (76.7) 12 (60) 140 (76.1) 327 (79.8) 191 (73.7)
  No 203 (23.3) 8 (40) 44 (23.9) 83 (20.2) 68 (26.3)
Body weight estimation and behavioral characteristics
Meal frequency
  <3 times/day 325 (37.2) 6 (30) 58 (31.5) 132 (32.2) 129 (49.8)
  ≥3 times/day 548 (62.8) 14 (70) 126 (68.5) 278 (67.8) 130 (50.2)
Weight management
  By exercise
    Yes 216 (24.7) 3 (15) 47 (25.5) 107 (26.1) 59 (22.8)
    No 657 (75.3) 17 (85) 137 (74.5) 303 (73.9) 200 (77.2)
  By dietary control
    Yes 309 (35.4) 8 (40) 95 (51.6) 109 (26.6) 97 (37.5)
    No 564 (64.6) 12 (60) 89 (48.4) 301 (73.4) 162 (62.5)
Weighing
  ≥1 time/year 408 (46.7) 4 (20) 52 (28.3) 217 (52.9) 135 (52.1)
  <1 time/year 465 (53.3) 16 (80) 132 (71.7) 193 (47.1) 124 (47.9)
  ≥2 times/year 225 (25.8) 3 (15) 20 (10.9) 126 (30.7) 76 (29.3)
  <2 times/year 648 (74.2) 17 (85) 164 (89.1) 284 (69.3) 183 (70.7)
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graphic file with name 10.3928_24748307-20240521-01-table1b.jpg

Characteristic Total Underweight Normal Overweight Obese
n (%)
Have a scale at home
  Yes 4 (0.5) 0 (0) 1 (0.5) 2 (0.5) 1 (0.4)
  No 869 (99.5) 20 (100) 183 (99.5) 408 (99.5) 258 (99.6)
Previous place of weighing
  One's home 4 (0.5) 0 (0) 0 (0) 3 (0.7) 1 (0.4)
  Pharmacy 180 (20.6) 2 (10) 33 (17.9) 85 (20.7) 60 (23.2)
  Medical institution 442 (50.6) 11 (55) 67 (36.4) 221 (53.9) 143 (55.2)
  Campaigns and other events 57 (6.5) 2 (10) 13 (7.1) 30 (7.3) 12 (4.6)
  Paid scales in markets 173 (19.8) 5 (25) 61 (33.2) 69 (16.8) 38 (14.7)
  Acquaintance's house 8 (0.9) 0 (0) 6 (3.3) 1 (0.2) 1 (0.4)
  Other 9 (1) 0 (0) 4 (2.2) 1 (0.2) 1 (0.4)
Estimation of body weight
  Correctly estimated 303 (34.7) 14 (70) 80 (43.5) 163 (39.8) 46 (17.8)
  Overestimated 38 (4.4) 6 (30) 28 (15.2) 4 (1) 0 (0)
  Underestimated 532 (60.9) 0 (0) 76 (41.3) 243 (59.3) 213 (82.2)
Weight gain or loss desire
  Desire to lose weight 324 (37.1) 0 (0) 38 (20.7) 131 (32) 155 (59.8)
  Desire to gain weight 135 (15.5) 14 (70) 85 (46.2) 32 (7.8) 4 (1.5)
  Desire to maintain weight 411 (47.1) 6 (30) 59 (32.1) 246 (60) 100 (38.6)
  Other 3 (0.3) 0 (0) 2 (1.1) 1 (0.2) 0 (0)
About weight at last weighing
  Not remembered 517 (59.2) 16 (80) 112 (60.9) 236 (57.6) 153 (59.1)
  More than the actual weight at this time 28 (3.2) 3 (15) 17 (9.2) 7 (1.7) 1 (0.4)
  <Actual weight at this time 109 (12.5) 0 (0) 12 (6.5) 50 (12.2) 47 (18.1)
  Same as this time (within ± 5 kg) 219 (25.1) 1 (5) 43 (23.4) 117 (28.5) 58 (22.4)
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a

The international currency code for the African Financial Community franc.

Responses to the question on the reason for wanting to gain or lose weight are shown in Table 2. Overall, the most common response was “Normal weight is a sign of health” (38.6%), followed by “Normal weight is attractive” (11.9%). Among participants with obesity, the most common response was “Normal weight is a sign of health” (11.6%), followed by “Being overweight is attractive” (4.8%).

Table 2.

Ideal Weight Perceived by the Study Participants and Its Reasons (N = 873)

graphic file with name 10.3928_24748307-20240521-01-table2.jpg

Responses Total Underweight Normal Overweight Obese
n (%)
People like overweight women 24 (2.7) 2 (0.2) 11 (1.3) 9 (1) 2 (0.2)
People like normal women 9 (1) 0 (0) 2 (0.2) 6 (0.7) 1 (0.1)
People like thin women 3 (0.3) 0 (0) 1 (0.1) 1 (0.1) 1 (0.1)
My husband like overweight women 41 (4.7) 2 (0.2) 7 (0.8) 15 (1.7) 17 (1.9)
My husband like normal women 33 (3.8) 0 (0) 12 (1.4) 15 (1.7) 6 (0.7)
My husband like thin women 10 (1.1) 0 (0) 2 (0.2) 5 (0.6) 3 (0.3)
Being overweight is a sign of good health 25 (2.9) 0 (0) 9 (1) 10 (1.1) 6 (0.7)
Being normal is a sign of good health 337 (38.6) 9 (1) 76 (8.7) 151 (17.3) 101 (11.6)
Being thin is a sign of good health 50 (5.7) 2 0.2 8 (0.9) 20 (2.3) 20 (2.3)
Being overweight is a sign of wealth 18 (2.1) 0 (0) 4 (0.5) 8 (0.9) 6 (0.7)
Being normal is a sign of wealth 35 (4) 0 (0) 4 (0.5) 27 (3.1) 4 (0.5)
Being thin is a sign of wealth 1 (0.1) 0 (0) 0 (0) 0 (0) 1 (0.1)
Being overweight is a symbol of beauty 79 (9) 0 (0) 15 (1.7) 22 (2.5) 42 (4.8)
Being normal is a symbol of beauty 104 (11.9) 3 (0.3) 9 (1) 79 (9) 13 (1.5)
Being thin is a symbol of beauty 13 (1.5) 0 (0) 2 (0.2) 6 (0.7) 5 (0.6)
Other 91 (10.4) 2 (0.2) 22 (2.5) 36 (4.1) 31 (3.6)
Total 873 (100) 20 (2.3) 184 (21.1) 410 (47) 259 (29.7)
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Cronbach's alpha coefficients (number of sub-items) for each HLQ scale are as follows: HLQ1 = 0.766 (4), HLQ2 = 0.759 (4), HLQ3 = 0.723 (5), HLQ4 = 0.689 (4), HLQ5 = 0.803 (5), HLQ6 = 0.824 (5), HLQ7 = 0.820 (6), HLQ8 = 0.826 (5), and HLQ9 = 0.847 (5). Descriptive statistics from the HLQ are shown in Table 3. Although a simple comparison is not possible, the highest score was recorded for HLQ4, “Social support for health,” followed by HLQ6, “Ability to actively engage with health care providers.” Among the 4-point scales from HLQ1 to HLQ5, the lowest score was recorded for HLQ1, “Feeling understood and supported by health care providers.” Between HLQ6 and HLQ9, the lowest score was recorded for HLQ8, “Ability to find good health information.”

Table 3.

Health Literacy Scores of Women Market Traders in Abidjan

graphic file with name 10.3928_24748307-20240521-01-table3.jpg

HLQ Scale Mean SD
1. Feeling understood and supported by healthcare providers 2.95 0.51
2. Having sufficient information to manage my health 2.98 0.51
3. Actively managing my health 3.09 0.42
4. Social support for health 3.16 0.42
5. Appraisal of health information 3.02 0.54
6. Ability to actively engage with healthcare providers 3.15 0.59
7. Navigating the health care system 3.10 0.57
8. Ability to find good health information 3.05 0.59
9. Understand health information well enough to know what to do 3.09 0.80
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Note. HLQ = Health Literacy Questionnaire; SD = standard deviation.

Table 4 shows the results of multiple logistic regression analysis performed to determine the factors associated with obesity. It showed that HLQ1 (Feeling understood and supported by health care providers) (OR = 2.926, CI: 1.450–5.901, p = .03) and HLQ3 (Actively managing my health) (OR = 0.343, CI: 0.165–0.716, p = 0.004) were significantly associated with obesity among the 9 HLQ scales. Among items pertaining to weight perception and management, accurate weight perception (OR = 0.145, CI: 0.093–0.224, p < .001) and eating 3 or more meals a day (OR = 0.401, CI: 0.260–0.617, p < .001) were significantly associated with obesity. Other factors significantly associated with obesity included age (≥ 50 years, OR = 12.077, CI: 4.814–30.297, p < .001; being in their 40s, OR = 6.979, CI: 2.925–16.654, p < .001, being in their 30s, OR = 2.539, CI: 1.084–5.947, p = .032), northern region ethnicity (OR = 0.401, CI: 0.228–0.705, p = .002), and having five or more family members (OR = 2.107, CI: 1.375–3.228, p = .001).

Table 4.

Logistic Regression Model to Identify the Association Between Obesity and Its Correlates

graphic file with name 10.3928_24748307-20240521-01-table4a.jpg

Characteristic Odds Ratio Upper Lower p Value
95% CI
Age (years)
  18–29 (reference)
  30–39 2.539 1.084 5.947 .032*
  40–49 6.979 2.925 16.654 <.001*
  ≥50 12.077 4.814 30.297 <.001*
Ethnic group
  People of Burkina Faso, Ghana, Mali, Nigeria, and Guinea (reference)
  South ethnic groups 0.790 0.487 1.281 .339
  North ethnic groups 0.401 0.228 0.705 .002*
Number of family members
  ≥5 people 2.107 1.375 3.228 .001*
Educational level
  Secondary or above 0.603 0.362 1.006 .053
Individual income
  ≥75,000 XOFa/month 1.373 0.895 2.105 .146
Not nulliparity .318
  Yes 1.481 0.685 3.204
Meal frequency
  ≥3 times/day 0.401 0.260 0.617 <.001*
Have a smartphone
  Yes 0.689 0.435 1.093 .113
Exercise for weight control
  Yes 0.828 0.515 1.330 .435
Dietary restriction for weight control
  Yes 1.183 0.772 1.812 .440
Weighing
  ≥1 time/year 1.254 0.843 1.864 .264
Correct weight estimation
  Yes 0.145 0.093 0.224 <.001*
HLQ Scale
Scale 1. Feeling understood and supported by health care providers 2.926 1.450 5.901 .003*
Scale 2. Having sufficient information to manage my health 0.737 0.355 1.529 .413
Scale 3. Actively managing my health 0.343 0.165 0.716 .004*
Scale 4. Social support for health 1.401 0.771 2.546 .268
Scale 5. Appraisal of health information 1.486 0.722 3.059 .283
Scale 6. Ability to actively engage with healthcare providers 1.239 0.712 2.157 .448
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graphic file with name 10.3928_24748307-20240521-01-table4b.jpg

Characteristic Odds Ratio Upper Lower p Value
95% CI
Scale 7. Navigating the healthcare system 1.043 0.540 2.015 .900
Scale 8. Ability to find good health information 0.788 0.380 1.634 .522
Scale 9. Understand health information well enough to know what to do 0.819 0.524 1.281 .382
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Note. Nagelkerke R2 = 0.409, Predictive accuracy = 80.2%. CI = confidence interval; HLQ = Health Literacy Questionnaire.

a

The international currency code for the African Financial Community franc.

*

p < .05

Discussion

Our study is the first to demonstrate an association between obesity and HL in sub-Saharan Africa; specifically, among women merchants in Abidjan. In terms of the relation between obesity and HL, as well as weight perception, individuals with obesity scored lower on HLQ3 and higher on HLQ1; furthermore, they did not have accurate weight perception and did not consume more than three meals a day.

In this study, the obesity rate among the women merchants in the target market was 29.7%. This was roughly double the overall obesity rate of 13.2% among African women and 13.1% among women in Côte d'Ivoire in 2016 (World Health Organization, 2023a). It was also roughly 2.5 times the rate (11.6%) reported in the 2005 STEPwise approach to surveillance survey (STEPS/MNT Program Coordination Directorate, 2005) conducted in Abidjan, indicating that obesity among women merchants in Abidjan is a pressing issue that must be addressed.

Obesity was significantly associated with low scores on HLQ3 (Actively managing my health). This scale indicates the ability to set health goals, develop an action plan, and dedicate time to them. The validity of these findings is supported by previous research that found an association between high HLQ3 scores and high vegetable/fruit intake (Lim et al., 2017) and low HbA1c level (Olesen et al., 2017). Hence, programs emphasizing a positive attitude toward health management may be more important in supporting individuals with obesity than those that focus on the ability to communicate with health care providers (HLQ6) or the ability to gather and understand health information (HLQ scales 2, 5, 7, 8, and 9), which are domains of HLQ for which no association was established. Moreover, this finding suggests the necessity to follow up with people to help them set individual weight goals and plans for lifestyle improvement, as well as to determine how to make time to achieve them.

Meanwhile, obesity was associated with high scores on HLQ1 (Feeling understood and supported by health care providers), in contrast to previous findings (Faruqi et al., 2015; Lassetter et al., 2015; Michou et al., 2018; Zoellner et al., 2016). This discrepancy may be because residents receive fewer disease prevention services and have fewer opportunities to connect with health care providers outside of getting injured or sick. Individuals with obesity are more likely to visit a hospital owing to complications, such as high blood pressure and diabetes, which may have skewed the data.

The association between obesity and weight misperception established in our study supports previous findings (Duncan et al., 2011; Lynch et al., 2009; Mogre et al., 2013). We specifically established that weight perception is independently associated with obesity, with HL and sociodemographic attributes serving as moderating variables. This has important implications for intervention programs and approaches to participants with different attributes. Two underlying causes for the participants' weight misperception can be cited. The first is the failure to keep track of one's own weight. Of the participants with obesity, 47.9% did not weigh themselves even once a year (Helander et al., 2014; Jensen et al., 2014; Vuorinen et al., 2021). The most common place of weight measurement among participants with obesity was a medical institution (55.2%), but this may not have been voluntary as the measurements were likely taken during a medical visit. Given that more than half (59.1%) of the participants with obesity could not recall their last weight measurement and that only 0.4% of them owned a scale, they may be unconcerned about their weight. Therefore, the initial intervention should consist of creating an environment conducive for weight measurement, such as by placing scales in common areas at markets. Misconceptions about normal weight are the second cause. The fact that some participants responded that “being overweight is attractive” is consistent with a trend observed in other African countries as well (Okop et al., 2019; Tateyama et al., 2018). However, given that 82.2% of participants underestimated their weight, and that “Having normal weight is a sign of health” was the most common reason for wanting to gain or lose weight, the participants likely had misconceptions about normal weight. Information on BMI is not widely available in the country, suggesting the importance of disseminating information about healthy weight.

As a final factor, individuals with obesity were found to eat fewer than three meals per day. One possible explanation for this is that they eat more in one sitting and have irregular meal timings. Contradictory research has indicated that lower meal frequency both increases and decreases the risk of obesity (Kahleova et al., 2017; Ma et al., 2003). Our findings support the former. However, individuals with obesity tendency toward dietary underreporting has also been noted (Bellisle et al., 1997), making conclusions difficult to draw and necessitating further investigation.

Finally, the findings of this study provided implications that the results could be generalizable to low- and middle-income women within Abidjan and to West African countries with similar cultural backgrounds. In neighboring countries, merchants are the most common occupation for working women, accounting for more than half of the share in major urban areas: 56.4% in Senegal (Agence Nationale de la Statistique et de la Démographie & ICF, 2019), 59.2% in Benin (Institut National de la Statistique et de l'Analyse Économique & ICF, 2019), 50.2% in Burkina Faso (Institut National de la Statistique et de la Démographie & The DHS Program, 2023), and 74.3% in Togo (Ministère de la Planification et al., 2015). The cultural and social backgrounds of these countries are similar, and the results could be applicable to these regions as well, although this needs to be proven by further studies.

Study Limitations

One of the limitations of this study was that the data collection period was as long as 1 year, owing to the impact of the COVID-19 (coronavirus disease 2019) pandemic. Thus, we could not discount the impact of the participants aging 1 year over the study period. It is also undeniable that the initial participants may have shared the survey details with those who subsequently participated. However, as the survey was on individuals' thoughts and habits, the impact on the participants' responses was deemed minimal. Furthermore, the French version of the HLQ is not culturally adapted to Côte d'Ivoire. However, we found the internal consistency of each HLQ scale is mostly acceptable. Also, there were no issues, such as difficulties with understanding the questions during the pre-test or the main survey; thus, we can say that there was no serious problem with the use of the HLQ scales in this study. Finally, this study shows a cross-sectional association between health literacy and obesity. Longitudinal studies would be needed to establish causality, including temporal associations.

Conclusion

Our study is the first to investigate the link between obesity and HL in sub-Saharan Africa; specifically, among women merchants in Abidjan. It indicated that the obesity rate among the women merchants in the target market was roughly double the Côte d'Ivoire national average. As women merchants represent half of the country's working women, this finding implies the necessity to target them in the country's obesity control program. In addition, our findings established that the participants with obesity lacked a proactive attitude toward personal health management. Furthermore, known factors, such as inaccurate weight perception and eating fewer than three meals per day, were associated with obesity. These findings offer important implications for anti-obesity measures in Abidjan.

Acknowledgments

The authors thank Gnayoro Dah Berenger, Yoroko Eric, and Kouadio Firmin, who served as the research assistants during data collection and manuscript writing; the local government office of Anono for their cooperation with our data collection activities; and all of the merchants in the Anono market for their cooperation.

Funding Statement

Grant: This work was supported by grant JP19K19768 from the Japan Society for the Promotion of Science KAKENHI (Grants-in-Aid for Scientific Research of Japan).

References

  1. Aboa , A. L. A. ( 2015. ). La dynamique du français en milieu urbain à Abidjan [The dynamics of French in urban Abidjan.] . Revue electronique internationale de science du langage: Sudlangues [International Journal of Language Science Sudlangues] , 24 . [Google Scholar]
  2. Agence Nationale de la Statistique et de la Démographie, & ICF . ( 2020. ). Senegal: Enquête Démographique et de Santé Continue (EDS- Continue) 2019 . https://www.dhsprogram.com/pubs/pdf/FR368/FR368.pdf
  3. Agyapong , N. A. F. , Annan , R. A. , Apprey , C. , & Aduku , L. N. E. ( 2020. ). Body weight, obesity perception, and actions to achieve desired weight among rural and urban Ghanaian adults . Journal of Obesity , 2020 , 7103251 . Advance online publication. 10.1155/2020/7103251 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Agyemang , C. , Boatemaa , S. , Frempong , G. A. , & Aikins , A . ( 2016. ). Obesity in sub-Saharan Africa. Metabolic syndrome . Springer International Publishing; . 10.1007/978-3-319-12125-3_5-1 [DOI] [Google Scholar]
  5. Amugsi , D. A. , Dimbuene , Z. T. , Mberu , B. , Muthuri , S. , & Ezeh , A. C. ( 2017. ). Prevalence and time trends in overweight and obesity among urban women: An analysis of demographic and health surveys data from 24 African countries, 1991–2014 . BMJ Open , 7 ( 10 ), e017344 . 10.1136/bmjopen-2017-017344 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Awosan , K. J. , Adeniyi , S. A. , Bello , H. , & Bello-Ibrahim , Z. ( 2017. ). Nutritional status, weight perception and weight control practices among office employees in Sokoto, Nigeria . The Pan African Medical Journal , 27 , 279 . 10.11604/pamj.2017.27.279.12222 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Beauchamp , A. , Buchbinder , R. , Dodson , S. , Batterham , R. W. , Elsworth , G. R. , McPhee , C. , Sparkes , L. , Hawkins , M. , & Osborne , R. H. ( 2015. ). Distribution of health literacy strengths and weaknesses across socio-demographic groups: A cross-sectional survey using the Health Literacy Questionnaire (HLQ) . BMC Public Health , 15 , 678 . 10.1186/s12889-015-2056-z PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bellisle , F. , McDevitt , R. , & Prentice , A. M. ( 1997. ). Meal frequency and energy balance . British Journal of Nutrition , 77 ( Suppl 1 ), S57 – S70 . 10.1079/BJN19970104 PMID: [DOI] [PubMed] [Google Scholar]
  9. Brenoum , K. D. , Dihouegbeu , D. P. , Kobenan , A. C. , & Atta , K . ( 2017. ). Anono: From village to dormitory district (Abidjan-Côte d'ivoire) Ivorian Journal of Savanna Geography , No 3 , Alassane Ouattara university, Bouaké; , pp. 209 – 224 . [Google Scholar]
  10. Chrissini , M. K. , & Panagiotakos , D. B. ( 2021. ). Health literacy as a determinant of childhood and adult obesity: A systematic review . International Journal of Adolescent Medicine and Health , 33 ( 3 ), 9 – 39 . 10.1515/ijamh-2020-0275 PMID: [DOI] [PubMed] [Google Scholar]
  11. Debussche , X. , Lenclume , V. , Balcou-Debussche , M. , Alakian , D. , Sokolowsky , C. , Ballet , D. , Elsworth , G. R. , Osborne , R. H. , & Huiart , L. ( 2018. ). Characterisation of health literacy strengths and weaknesses among people at metabolic and cardiovascular risk: Validity testing of the Health Literacy Questionnaire . SAGE Open Medicine , 6 , 1 – 12 https://journals.sagepub.com/doi/full/10.1177/2050312118801250 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Duncan , D. T. , Wolin , K. Y. , Scharoun-Lee , M. , Ding , E. L. , Warner , E. T. , & Bennett , G. G. ( 2011. ). Does perception equal reality? Weight misperception in relation to weight-related attitudes and behaviors among overweight and obese US adults . The International Journal of Behavioral Nutrition and Physical Activity , 8 , 20 . 10.1186/1479-5868-8-20 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Faruqi , N. , Spooner , C. , Joshi , C. , Lloyd , J. , Dennis , S. , Stocks , N. , Taggart , J. , & Harris , M. F. ( 2015. ). Primary health care-level interventions targeting health literacy and their effect on weight loss: A systematic review . BMC Obesity , 2 , 6 . 10.1186/s40608-015-0035-7 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Gradidge , P. J. , Golele , P. , & Cohen , E. ( 2020. ). Body weight perceptions and obesity amongst university employed South African women . Women & Health , 60 ( 8 ), 851 – 862 . 10.1080/03630242.2020.1758287 PMID: [DOI] [PubMed] [Google Scholar]
  15. Helander , E. E. , Vuorinen , A. L. , Wansink , B. , & Korhonen , I. K. ( 2014. ). Are breaks in daily self-weighing associated with weight gain? PLoS One , 9 ( 11 ), e113164 . 10.1371/journal.pone.0113164 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Institut National de la Statistique et de l'Analyse Économique, & ICF . ( 2019. ). République Du Bénin Ciquième Enquête Démographique et de Santé au Bénin (EDSB-V) 2017–2018 . http://dhsprogram.com/pubs/pdf/FR350/FR350.pdf
  17. Institut National de la Statistique et de la Démographie, & the The DHS Program . ( 2023. ). Burkina Faso Enquête Démographique et de Santé 2021 . https://www.dhsprogram.com/pubs/pdf/FR378/FR378.pdf
  18. Jensen , M. D. , Ryan , D. H. , Apovian , C. M. , Ard , J. D. , Comuzzie , A. G. , Donato , K. A. , Hu , F. B. , Hubbard , V. S. , Jakicic , J. M. , Kushner , R. F. , Loria , C. M. , Millen , B. E. , Nonas , C. A. , Pi-Sunyer , F. X. , Stevens , J. , Stevens , V. J. , Wadden , T. A. , Wolfe , B. M. , Yanovski , S. Z. , Tomaselli , G. F. , & the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, & the Obesity Society . ( 2014. ). 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society . Circulation , 129 ( 25, Suppl. 2 ), S102 – S138 . 10.1161/01.cir.0000437739.71477.ee PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Joh , H. K. , Oh , J. , Lee , H. J. , & Kawachi , I. ( 2013. ). Gender and socioeconomic status in relation to weight perception and weight control behavior in Korean adults . Obesity Facts , 6 ( 1 ), 17 – 27 . 10.1159/000346805 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kahleova , H. , Lloren , J. I. , Mashchak , A. , Hill , M. , & Fraser , G. E. ( 2017. ). Meal frequency and timing are associated with changes in body mass index in adventist health study 2 . The Journal of Nutrition , 147 ( 9 ), 1722 – 1728 . 10.3945/jn.116.244749 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Lassetter , J. H. , Clark , L. , Morgan , S. E. , Brown , L. B. , VanServellen , G. , Duncan , K. , & Hopkins , E. S. ( 2015. ). Health literacy and obesity among native Hawaiian and Pacific Islanders in the United States . Public Health Nursing (Boston, Mass.) , 32 ( 1 ), 15 – 23 . 10.1111/phn.12155 PMID: [DOI] [PubMed] [Google Scholar]
  22. Lim , S. , Beauchamp , A. , Dodson , S. , O'Hara , J. , McPhee , C. , Fulton , A. , Wildey , C. , & Osborne , R. H. ( 2017. ). Health literacy and fruit and vegetable intake in rural Australia . Public Health Nutrition , 20 ( 15 ), 2680 – 2684 . 10.1017/S1368980017001483 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Lovejoy , J. C. , Sainsbury , A. , & the Stock Conference. 2008 Working Group . ( 2009. ). Sex differences in obesity and the regulation of energy homeostasis . Obesity Reviews , 10 ( 2 ), 154 – 167 . 10.1111/j.1467-789X.2008.00529.x PMID: [DOI] [PubMed] [Google Scholar]
  24. Lynch , E. , Liu , K. , Wei , G. S. , Spring , B. , Kiefe , C. , & Greenland , P. ( 2009. ). The relation between body size perception and change in body mass index over 13 years: The Coronary Artery Risk Development in Young Adults (CARDIA) study . American Journal of Epidemiology , 169 ( 7 ), 857 – 866 . 10.1093/aje/kwn412 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ma , Y. , Bertone , E. R. , Stanek , E. J. , III , Reed G. W. , Hebert J. R. , Cohen N. L. , Merriam P. A. , & Ockene , I. S. ( 2003. ). Association between eating patterns and obesity in a free-living US adult population . American Journal of Epidemiology , 158 ( 1 ), 85 – 92 . 10.1093/aje/kwg117 PMID: [DOI] [PubMed] [Google Scholar]
  26. Michou , M. , Panagiotakos , D. B. , & Costarelli , V. ( 2018. ). Low health literacy and excess body weight: A systematic review . Central European Journal of Public Health , 26 ( 3 ), 234 – 241 . 10.21101/cejph.a5172 PMID: [DOI] [PubMed] [Google Scholar]
  27. Ministère de la Planification, D. A. T.-M. T. Ministère de la Santé - MS/Togo, & ICF International . ( 2015. ). Togo Enquête Démographique et de Santé 2013–2014 . http://dhsprogram.com/pubs/pdf/FR301/FR301.pdf
  28. Ministère de la Santé et de la Lutte contre le Sida, Institut National de la Statistique, & Ministère d'État Ministère du Plan et du Développement [Ministry of Health and the Control of AIDS, National Institute of Statistic, Ministry of State Ministry of Planning and Development] . ( 2013. ). Enquête démographique et de santé et à indicateurs multiples [Demographic and Health Survey and Multiple Indicator Cluster Survey]. (EDS-MICS) 2011–2012 . https://dhsprogram.com/pubs/pdf/fr272/fr272.pdf
  29. Mogre , V. , Mwinlenna , P. P. , & Oladele , J. ( 2013. ). Distorted self-perceived weight status and its associated factors among civil servants in Tamale, Ghana: A cross-sectional study . Archives of Public Health , 71 ( 1 ), 30 . 10.1186/2049-3258-71-30 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Must , A. , Spadano , J. , Coakley , E. H. , Field , A. E. , Colditz , G. , & Dietz , W. H. ( 1999. ). The disease burden associated with overweight and obesity . Journal of the American Medical Association , 282 ( 16 ), 1523 – 1529 . 10.1001/jama.282.16.1523 PMID: [DOI] [PubMed] [Google Scholar]
  31. Nutbeam , D. ( 1998. ). Health promotion glossary . Health Promotion International , 13 , 394 – 364 . 10.1093/heapro/13.4.349 [DOI] [Google Scholar]
  32. Okop , K. J. , Levitt , N. , & Puoane , T. ( 2019. ). Weight underestimation and body size dissatisfaction among black African adults with obesity: Implications for health promotion . African Journal of Primary Health Care & Family Medicine , 11 ( 1 ), e1 – e8 . 10.4102/phcfm.v11i1.2022 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Olesen , K. , F Reynheim , A. L. , Joensen , L. , Ridderstråle , M. , Kayser , L. , Maindal , H. T. , Osborne , R. H. , Skinner , T. , & Willaing , I. ( 2017. ). Higher health literacy is associated with better glycemic control in adults with type 1 diabetes: A cohort study among 1399 Danes . BMJ Open Diabetes Research & Care , 5 ( 1 ), e000437 . 10.1136/bmjdrc-2017-000437 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Osborne , R. H. , Batterham , R. W. , Elsworth , G. R. , Hawkins , M. , & Buchbinder , R. ( 2013. ). The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ) . BMC Public Health , 13 , 658 . 10.1186/1471-2458-13-658 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Ousseine , Y. M. , Rouquette , A. , Bouhnik , A. D. , Rigal , L. , Ringa , V. , Smith , A. , & Mancini , J. ( 2017. ). Validation of the French version of the Functional, Communicative and Critical Health Literacy scale (FCCHL) . Journal of Patient-Reported Outcomes , 2 ( 1 ), 3 . 10.1186/s41687-018-0027-8 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Poyau , A . ( 2005. ). Les récentes mutations des marchés économiques dans la capitale économique ivoirienne [Recent changes in the economic markets of economic capital of Côte d'Ivoire. Space populations societies]. Espace populations sociétés . Space populations societies(2005/1) , 111 – 126 . 10.4000/eps.2744 [DOI]
  37. Sable , S. P. , Yan , K. , Yapi , A. , Kpebo , D. D. , Ekou , K. F. , Ake-Tano , P. O. S. , Tetchi , O. E. , Konan , E. Y. , Kouassi , D. , & Wan , C. ( 2020. ). Epide- miologic profile of overweight and obesity in Abidjan, Ivory Coast: A cross-sectional study . Annals of Global Health , 86 ( 1 ), 46 . 10.5334/aogh.2755 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Shieh , C. , Knisely , M. R. , Clark , D. , & Carpenter , J. S. ( 2016. ). Self-weighing in weight management interventions: A systematic review of literature . Obesity Research & Clinical Practice , 10 ( 5 ), 493 – 519 . 10.1016/j.orcp.2016.01.004 PMID: [DOI] [PubMed] [Google Scholar]
  39. STEPS/MNT Program Coordination Directorate . ( 2005. ). Survey on risk factors for non-communicable diseases in Côte d'Ivoire - 2005. Lagunes health regions survey report . https://extranet.who.int/ncdsmicrodata/index.php/catalog/625/download/4487 [Google Scholar]
  40. Tateyama , Y. , Techasrivichien , T. , Musumari , P. M. , Suguimoto , S. P. , Zulu , R. , Macwan'gi , M. , Dube , C. , Ono-Kihara , M. , & Kihara , M. ( 2018. ). Obesity matters but is not perceived: A cross-sectional study on cardiovascular disease risk factors among a population-based probability sample in rural Zambia . PLoS One , 13 ( 11 ), e0208176 . 10.1371/journal.pone.0208176 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Vuorinen , A.-L. , Helander , E. , Pietilä , J. , & Korhonen , I. ( 2021. ). Frequency of self-weighing and weight change: Cohort study with 10,000 smart scale users . Journal of Medical Internet Research , 23 ( 6 ), e25529 – e25529 . 10.2196/25529 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wang , Y. , Liang , H. , & Chen , X. ( 2009. ). Measured body mass index, body weight perception, dissatisfaction and control practices in urban, low-income African American adolescents . BMC Public Health , 9 , 183 . 10.1186/1471-2458-9-183 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. World Health Organization . ( 2013. ). Global action plan for the prevention and control of noncommunicable diseases 2013–2020 . https://www.who.int/publications/i/item/9789241506236 [Google Scholar]
  44. World Health Organization . ( 2015. ). Health literacy toolkit for lowand middle-income countries . https://www.who.int/publications/i/item/9789290224754 [Google Scholar]
  45. World Health Organization . ( 2023a. ). The Global Health observatory . https://www.who.int/data/gho/data/indicators [Google Scholar]
  46. World Health Organization . ( 2023b. ). Obesity and overweight . https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-over-weight [Google Scholar]
  47. Zoellner , J. , You , W. , Almeida , F. , Blackman , K. C. , Harden , S. , Glasgow , R. E. , Linnan , L. , Hill , J. L. , & Estabrooks , P. A. ( 2016. ). The influence of health literacy on reach, retention, and success in a worksite weight loss program . American Journal of Health Promotion , 30 ( 4 ), 279 – 282 . 10.1177/0890117116639558 PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

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