Osteoporosis-related life habits and knowledge about osteoporosis among women in El Salvador: a cross-sectional study

Abstract

Background: Osteoporosis is a systemic skeletal disorder, characterized by reduced bone mass, deterioration of bone structure, increased bone fragility, and increased fracture risk. It is more frequent to find among women than men at a 4:1 ratio. Evidence suggests that to adopt changes on some life habits can prevent or delay development of osteoporosis. Several osteoporosis-risk factors have been confirmed in the US and western Europe, but in El Salvador there are neither reliable epidemiological statistics about this skeletal disorder nor studies addressing osteoporosis-risk factors in women. The aim of this study was to determinate the extent of osteoporosis knowledge, the levels of both daily calcium intake and weight-bearing physical activity, and the influence of several osteoporosis-risk factors on these variables in three age groups of Salvadorean women.

Methods: In this exploratory cross-sectional study, an osteoporosis knowledge assessment questionnaire including a food frequency and a physical activity record section were used to collect data and it was delivered through a face-to-face interview. A convenience sample (n = 197) comprised of three groups of women aged 25-35 years, 36-49 years, and over 49 years was taken. Among-group comparisons of means were analyzed by two-way ANOVA. To determinate the overall influence of osteoporosis-risk factors, the multivariate analysis was used.

Results: Study results indicated that better educated women had more knowledge about osteoporosis than women with a low education level, regardless of age, even though this knowledge was rather fair. Older women got more weight-bearing physical activity at home and less at place of employment than reported by the younger women; however, neither group performed sufficient high-intensity WBPA to improve bone mass. Regardless of age, the most women consumed 60% or less than the Dietary Reference Intake of calcium and depend on household income, lactose intolerance and coffee rather than milk consumption.

Conclusion: In summary, the majority of women in this study have modest knowledge on osteoporosis. The knowledge base is not linked to preventive health habits, including sufficient calcium intake and performance of weight-bearing physical activities. They are thus at increased risk for low bone mass.

Figure 1

Semantic map showing an osteoporosis knowledge structure. Semantic map showing an osteoporosis knowledge structure related to several aspect concerning to concept, risk-factors, prevention, and diagnosis of this skeletal disorder.

Figure 2

Total osteoporosis knowledge scores. Total osteoporosis knowledge scores obtained by Salvadorean women in each age group (25 – 35 years, n = 73; 36 – 49 years, n = 74; over 49 years, n = 50), subclassified by educational level (none, primary, secondary, higher). Data are means ± SEM. Bars with distinct letters differ significantly within each age group (p < 0.05; two-way ANOVA followed by Tukey test).

Figure 3

Predictive model of influences of surveyed variables on total knowledge scores. Predictive model of the surveyed variables: age (Age), education (Educatn), family history of osteoporosis (Fam_Hist), household income (Income), menopause (Menopaus), and fecundity (Fecundity) influencing on total osteoporosis knowledge score (Tot_scor; R² = 0.37, p < 0.001, n = 197). The numbers on single- or double-headed arrows are regression weights or correlation coefficients, respectively.

Figure 4

Weight-bearing physical activities performed at worksite. Weight-bearing physical activity (hours/day) at worksite performed by Salvadorean women in each age group (25 – 35 years, n = 73; 36 – 49 years, n = 74; over 49 years, n = 50), subclassified by family history of osteoporosis (osteoporosis in family or no osteoporosis in family). Data are means ± SEM. Bars with distinct letters differ significantly (p < 0.05; Kruskal-Wallis' test followed by Dunn's test).

Figure 5

Weight-bearing physical activities performed at home. Weight-bearing physical activity (hours/day) at home performed by Salvadorean women in each age group (25 – 35 years, n = 73; 36 – 49 years, n = 74; over 49 years, n = 50), subclassified by family history of osteoporosis (osteoporosis in family or no osteoporosis in family). Data are means ± SEM. Bars with distinct letters differ significantly (p < 0.05; Kruskal-Wallis' test followed by Dunn's test).

Figure 6

Weight-bearing physical activity during exercise. Weight-bearing physical activity during exercise (hours/week) performed by Salvadorean women in each age group (Age 1: 25 – 35 years, n = 73; Age 2: 36 – 49 years, n = 74; Age 3: over 49 years, n = 50), subclassified by family history of osteoporosis (osteoporosis in family or no osteoporosis in family). Data are means ± SEM. Bars with distinct letters differ significantly (p < 0.05; Kruskal-Wallis' test followed by Dunn's test).

Figure 7

Predictive model of influences of surveyed variables on WBPA performed at worksite. Predictive model of the surveyed variables: age (Age), education (Educatn), family history of osteoporosis (Fam_Hist), household income (Income), menopause (Menopaus), and fecundity (Fecundity) influencing on amount of weight-bearing physical activities performed at worksite (WBPAwork; I; R² = 0.26, p < 0.001, n = 197). The numbers on single- or double-headed arrows are regression weights or correlation coefficients, respectively.

Figure 8

Predictive model of influences of surveyed variables on WBPA performed at home. Predictive model of the surveyed variables: age (Age), education (Educatn), family history of osteoporosis (Fam_Hist), household income (Income), menopause (Menopaus), and fecundity (Fecundity) influencing on amount of weight-bearing physical activities performed at home (WBPAhome; II; R² = 0.27, p < 0.001, n = 197). The numbers on single- or double-headed arrows are regression weights or correlation coefficients, respectively.

Figure 9

Total calcium intakes among Salvadorean women classified by age and household income. Total calcium intakes among Salvadorean women in each age group (Age 1: 25 – 35 years, n = 73; Age 2: 36 – 49 years, n = 74; Age 3: over 49 years, n = 50), subclassified by household income (above or below the poverty line). Shaded boxes extend from the 25^th to the 75^th percentile; whiskers indicate the 5^th and 95^th percentiles; dots indicate individuals lying outside these percentiles; the mean is indicated by a solid line, the median by a dashed line.

Figure 10

Total calcium intake among Salvadorean women classified by age and household income. Total calcium intake among Salvadorean women in each age group (25 – 35 years, n = 73; 36 – 49 years, n = 74; over 49 years, n = 50), subclassified by household income (above or below the poverty line). Data are means ± SEM. Bars with distinct letters differ significantly (p < 0.05; Kruskal-Wallis' test followed by Dunn's test).

Figure 11

Dairy calcium intake among Salvadorean women classified by age and household income. Dairy calcium intake among Salvadorean women in each age group (25 – 35 years, n = 73; 36 – 49 years, n = 74; over 49 years, n = 50), subclassified by household income (above or below the poverty line). Data are means ± SEM. Bars with distinct letters differ significantly (p < 0.05; Kruskal-Wallis' test followed by Dunn's test).

Figure 12

Predictive model of influences of surveyed variables on total calcium intake. Predictive model of the surveyed variables: age (Age), lactose intolerance (Lactose), coffee consumption (Coffee), use of aluminum antiacids (Antiacid), household income (Income), and education (Educatn) influencing on total calcium intake (Total_Ca; I; R² = 0.19, p < 0.001, n = 197). The numbers on single- or double-headed arrows are regression weights or correlation coefficients, respectively.

Figure 13

Predictive model of influences of surveyed variables on dairy calcium intake. Predictive model of the surveyed variables: age (Age), lactose intolerance (Lactose), coffee consumption (Coffee), use of aluminum antiacids (Antiacid), household income (Income), and education (Educatn) influencing on dairy calcium intake (Dairy_Ca; II; R² = 0.20, p < 0.001, n = 197). The numbers on single- or double-headed arrows are regression weights or correlation coefficients, respectively.