Demographic Characteristics and Dietary Pattern of the Elderly in Ondo State, Nigeria

Type Journal Article - British Journal of Medicine and Medical Research
Title Demographic Characteristics and Dietary Pattern of the Elderly in Ondo State, Nigeria
Author(s)
Volume 3
Issue 4
Publication (Day/Month/Year) 2013
Page numbers 2173-2188
URL http://zenodo.org/record/8036/files/1374403137-Olayiwola342013BJMMR4111.pdf
Abstract
Objective: To investigate the demographic characteristics and dietary patterns of elderly
adults (>60 years) in Ondo State, Nigeria.
Methodology: This was a random sampling of 400 elderly individuals (>60 years) living
in Ondo State, Nigeria. Data on demographic and non-demographic variable
characteristics, including food habits, dietary patterns and food frequency, were collected
using an open-ended and structured questionnaire.
Results: Most individuals aged 60–69 years were married, with fewer than 25% having
primary education and the majority of the remainder having no formal education. Most
were employed in farming and their income was low (<200 USD per month). About 80%
ate three meals daily, 25% skipped meals, and 39% avoid certain foods and 87% had
favorite food which relates significantly with gender (?
2
=7.2; p<0.05) marital status
(?
2
=5.7; p<0.05) and health (rate of falling sick). Dietary pattern was significantly
associated with body ailments (?
2
=51.9; p<0.05). Certain habits, such as alcohol
ingestion, influenced the number of meals (?
2
=10; p<0.05). Memory loss was significantly
associated with skipping meals (?
2
=7.2; p<0.05), whereas depression was significantly
associated with the number of meals (?
2
=6.2; p<0.05). A logistic regression model found
that educational level, occupation and gender were significant independent predictors of
Research ArticleBritish Journal of Medicine & Medical Research, 3(4): 2173-2188, 2013
2174
dietary pattern.
Conclusion: Most elderly individuals in Ondo State, Nigeria, were of low socioeconomic
level and illiterate, with dietary patterns influenced by age, education, occupation and
gender. Age correlated inversely with bone mass, body mass index, body fat and body
water.

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