Globally, the older adult population is steadily growing, with increased average lifespans, due to continual improvement of the standard of living and development of medical technology (Amarya, Singh, & Sabharwal, 2015). According to the Korea National Statistical Office (2015), South Korea is fast approaching the aged society boundary in which >14% of the population is 65 and older. Korea will likely become a post-aged society in 2026, with >20% of its population comprising older adults (Kim, Kim, & Byun, 2015).
The older adult population represents a vulnerable group at risk for nutritional deficiencies (Leslie & Hankey, 2015). Specifically, older adults are more prone to malnutrition due to lack of financial support, decreased mobility, chronic diseases, polypharmacy, social changes, and age-related physiological changes (Agarwalla, Saikia, & Baruah, 2015; Edfors & Westergren, 2012). However, limited information is available regarding malnutrition risk in Korean older adults. Older adult women are less educated and more likely to have had a lower-status occupation, leading to lower social economic status than their male counterparts (Doumit, Nasser, & Hanna, 2014; Kvamme, Grønli, Florholmen, & Jacobsen, 2011). As such, older adult women can have difficulties maintaining good nutrition. Unfortunately, few studies focusing on malnutrition risk in Korean older adults exist.
Malnutrition is a state in which a deficiency, excess, or imbalance of energy, protein, and other nutrients causes adverse effects on body form, function, and clinical outcome, including morbidity, mortality, and hospital length of stay (Boulos, Salameh, & Barberger-Gateau, 2016; D'Amelio et al., 2014; Heuberger, van Eeden-Moorefield, & Wong, 2013; Vafaei et al., 2013). Thus, maintaining good nutritional status has significant implications for health, delay and reduction of risk of disease development, and maintenance of functional independence (Boulos et al., 2016a; Romero-Ortuno et al., 2011). Nevertheless, the nutrition and health of older adults is often overlooked (Chang & Lin, 2016; Leslie & Hankey, 2015).
Currently, in Korea, few older adults with functional impairments move to nursing homes; most choose or have to remain in their own homes (Park, Kim, & Kim, 2014). Many of these older adults are disabled and dependent on others for acquiring, preparing, and/or consuming their food (Edfors & Westergren, 2012; Kim et al., 2015). This problem remains evident, with an approximately 10% to 38% malnutrition risk in older adults living at home and needing help (Boulos et al., 2016b; Phillips, Foley, Barnard, Isenring, & Miller, 2010).
Several factors of malnutrition have been identified: physical status, disability, disease presence, and psychosocial factors (e.g., affective status, social relations, psychosocial well-being) (Amarya et al., 2015; Fares, Barbosa, Borgatto, Coqueiro, & Fernandes, 2012; Fávaro-Moreira et al., 2016; Söderhamn, Flateland, Jessen, & SÖderhamn, 2011; Tan et al., 2016). Within this context, the current study identified the prevalence and associated factors of malnutrition risk regarding sociodemographic and health-related characteristics, and self-reported health status in home-dwelling Korean older adult women.
Study Design and Participants
A cross-sectional descriptive study was conducted from October 2013 to December 2013. One hundred sixty-eight individuals were recruited using convenience sampling by public announcement from five elderly welfare centers or public health centers in rural areas of Chungchung province in South Korea. The sample was selected according to the following inclusion criteria: age ≥65 years; able to read, speak, and write Korean; no cognitive impairment (according to the Korean version of the Mini-Mental State Examination [Kang, Na, & Hahn, 1997], scores ≥24); and no physical impairment and independent living (modified Korean version of the Barthel Index score = 100 [Jung et al., 2007]).
To verify the statistical power of the sample size, G*Power software version 3.1.2 was used (Faul, Erdfelder, Buchner, & Lang, 2009). The sample size required for the multiple regression method was 150, according to the following parameters (number of independent variables = 18, power = 80%, and effect size = 0.15). A 10% dropout rate was considered. Therefore, a sample size of 168 participants was suitable for this study.
Sociodemographic and Health-Related Characteristics. Participants were required to provide sociodemographic characteristics, including age, spouse, education level, job, monthly income, perceived financial status (above average, average, below average), and religion (Table 1). Regarding health-related characteristics, participants were asked about use of magnifying glasses, hearing aids, and medicine, and comorbidities for >6 months. Comorbidity was measured if participants had been diagnosed by physicians and were currently receiving treatment for cancer, heart disease, stroke, hypertension, diabetes, osteoporosis, arthritis, trauma, respiratory disease, gastrointestinal disease, liver disease, mental illness, incontinence, and others. Comorbidity was categorized as yes or no. Participants' weight (kg) and height (cm) were measured to the nearest decimal by trained research assistants to calculate body mass index (BMI). During these measurements, participants wore light clothing and no shoes. BMI was calculated as the weight divided by height squared (kg/m2).
Participant Characteristics by Nutritional Status (N = 168)
Self-Reported Health Status. The Korean Health Status Measure for the Elderly (KoHSME; Shin et al., 2002) was used to assess participants' perceived health status. KoHSME comprises a list of 44 items with a 5-point Likert response scale (1 = not at all, 2 = a little, 3 = moderately, 4 = quite a lot, 5 = greatly). Pain intensity uses a visual analogue scale. The composite score is the sum of six dimension scores: physical function (19 items), emotional function (13 items), social function (six items), sexual function (one item), general health perception (three items), and bodily pain (two items). Higher scores indicate better health status. However, regarding bodily pain, a higher score indicates higher perceived pain intensity. Cronbach's alpha was 0.81.
Malnutrition Risk. The Nutrition Screening Initiative (NSI) checklist (Posner, Jette, Smith, & Miller, 1993) measures malnutrition risk. The current study used a validated Korean version of the NSI checklist (Yu & Kim, 2002). The NSI checklist has been widely used internationally to identify older adults at nutritional risk because of its simplicity, capacity to be self-administered or applied to the patient by health professionals, and usefulness in health services of different levels of complexity (Kim et al., 2011; Tan et al., 2016; Vieira, Assuncão, Schäfer, & Santos, 2016). This tool comprises 10 questions on risk factors related to nutrition, including inappropriate intake of food, social isolation, poverty, dependence or disorders, acute or chronic diseases, chronic administration of drugs, and old age (≥80 years). Answer choices were either yes (1 to 4 points depending on risk level) or no (0 points). Cumulative scores ranged from 0 to 21 points, with higher scores indicating higher nutritional risk and imbalanced nutrition. Scores of 0 to 2 indicated low risk, scores of 3 to 5 indicated moderate risk, and scores ≥6 indicated high risk. Cronbach's alpha was 0.78.
Data Collection and Ethical Considerations
The local institutional review board approved the current study. All participants received verbal information about the study and voluntarily agreed to participate by providing their written informed consent. The three trained research assistants guaranteed participants' anonymity, confidentiality, and freedom to withdraw from participating at any time. Data were collected using face-to-face interviews with self-reported questionnaires. It took approximately 10 to 15 minutes to complete the questionnaire.
Data were analyzed using SPSS version 23.0. Descriptive statistics were displayed as counts with percentages to present patient characteristics. Chi-square tests were performed to compare the differences in sociodemographic characteristics between good nutrition and moderate and high nutritional risk groups. One-way analysis of variance was performed to test for significant differences in the means of perceived health status among three different nutritional risk groups. A hierarchical logistic regression was performed to identify predictors of malnutrition risk using the enter method, in which a hierarchical modeling approach enters variables in logically connected blocks, with blocks and their order of entry selected on a priori basis; values were presented as odds ratios (ORs) with 95% confidence intervals (CI). Nagelkerke R2 was used to evaluate each regression analysis model. Statistical significance was defined as a two-tailed p value of <0.05.
Prevalence of Malnutrition Risk
Using NSI cutoff points, moderate to high nutritional risk was present in 69.6% of participants (good nutrition group = 30.4% [n = 51], moderate nutritional risk group = 33.3% [n = 56], and high nutritional risk group = 36.3% [n = 61]).
Differences in Sociodemographic and Health-Related Characteristics by Nutritional Risk Groups
There was a significant difference in perceived financial status (p = 0.028), medicine use (p = 0.014), and comorbidity (p = 0.043) among the three groups. There were also significant differences in general health perception (F = 15.49, p < 0.001), physical function (F = 25.98, p < 0.001), emotional function (F = 26.42, p < 0.001), social function (F = 25.89, p < 0.001), bodily pain (F = 4.15, p = 0.002), and sexual function (F = 6.33, p < 0.001) among the groups (Table 2).
Difference in Self-Reported Health Status by Nutritional Status Groups (N = 168)
Multinomial Hierarchical Logistic Regression Analysis of Factors Associated With Nutritional Risk Group
The model outcome was the nutritional risk group (moderate or high), with the good nutrition group as the reference group. The first block entered into the multinomial hierarchical logistic regression model comprised perceived financial status, medicine use, and comorbidity as significantly different variables in univariate analysis. The second block entered the six subdomains of perceived health status (Table 3).
Predictors of Malnutrition Risk in Korean Older Adult Women (N = 168)
The following variables remained in the model as significant predictors. Physical function was significantly associated with the nutritional status group. The high risk group was more likely to have low physical (ß = −1.09, OR = 0.34, 95% CI = [0.12, 0.92]) and emotional (ß = −1.17, OR = 0.31, 95% CI [0.13, 0.74]) function compared to the good nutrition group. The high risk group was also more likely to have severe bodily pain (ß = 1.01, OR = 2.75, 95% CI [1.35, 5.59]) than the good nutrition group (Table 3). The moderate risk group was less likely to have good social function than the good nutrition group (ß = −0.96, OR = 0.38, 95% CI [0.16, 0.93]).
Malnutrition is a major geriatric problem associated with poor health status and high mortality; the impact of nutritional condition on health outcome in older adults has been widely recognized (Agarwalla et al., 2015; Amarya et al., 2015; D'Amelio et al., 2014). As a key factor of healthy aging, information about nutritional status will be a crucial public health concern because of the growing older adult population worldwide (Kaiser et al., 2010; Torres et al., 2014). In addition, the relative impact of factors contributing to nutritional status may be different from one population to another depending on cultural background (Volkert, 2013). To the current authors' knowledge, the nutritional status of home-dwelling Korean older adult women remains unexplored compared with institutionalized and hospitalized older adults. According to previous studies, older adult women are more likely to be malnourished than their male counterparts because of multiple pregnancies, low social status, poverty, lack of education, and lack of attention to health (Doumit et al., 2014; Kvamme et al., 2011).
In the current study, the prevalence of moderate/high malnutrition risk using the NSI was approximately 69.6%. This finding was higher than that in previous studies, which showed a risk of 37.7% for American older adults in rural areas (Kaiser et al., 2010) and 58% for geriatric outpatients in The Netherlands (van Bokhorst-de van der Schueren et al., 2013). Moreover, the current finding is considerably higher than that in other hospital inpatient studies, which have estimated the risk to be 29% in England and 36% in Sweden (Meier & Stratton, 2008). Some studies have reported that all older adults living in institutions tend to experience higher prevalences of malnutrition compared to community-dwelling older adults (Agarwalla et al., 2015; Doumit et al., 2014). These differences in prevalence rates of malnutrition risk may be due to differences in selection criteria, definition of malnutrition risk, assessment tools, and sociodemographic backgrounds. In addition, the reason for the relatively high nutritional risk among participants could be the exclusion of older adults in urban areas. Several studies found that the prevalence of malnutrition was higher among older adults in rural areas (Boulos et al., 2016a; Doumit et al., 2014; Wojszel, 2012). Generally, older adults in rural communities in Korea have been disadvantaged by less access to health care and senior welfare services compared with those in urban settings (Jung et al., 2016; Park et al., 2014). For example, relatively lower socioeconomic status in rural older adults has been reported, including lower levels of education and lower income (Kim, Kim, & Lee, 2012). Malnutrition risk among older adults in rural areas may be due to reduced food availability, consumption of food low in essential nutrients, and growing food insecurity. Therefore, further research to compare characteristics of individuals at risk of malnutrition living in rural versus urban communities may investigate social and environmental determinants of malnutrition risk in older adults.
Determinants of poor nutritional status are varied and numerous (Torres et al., 2014; Vafaei et al., 2013). The current study showed that worse physical and emotional function and greater perceived pain intensity were significantly associated with an increased malnutrition risk after adjusting for financial status, medicine use, and comorbidity. These results support previous studies showing that low self-perceived health, decreased physical mobility, impaired activities of daily living, and depression symptoms predicted malnutrition risk (Edfors & Westergren, 2012; Heuberger et al., 2013; Vafaei et al., 2013). Decreased physical function can affect nutritional status by limiting the ability of an older adult to produce, acquire, and/or prepare food (Romero-Ortuno et al., 2011; Timpini et al., 2011). Further, persistent or intermittent chronic pain may reduce appetite and pleasure related to food intake (Romero-Ortuno et al., 2011). Poor emotional function, including depressed mood, loneliness, anxiety, and stress, was identified as an independent determinant of malnutrition (Vafaei et al., 2013; Volkert, 2013), meaning poorer emotional function can lead to malnutrition from lack of appetite, loss of interest in self-care, apathy, and physical weakness.
Participants with moderate nutritional risk were less likely to have good social function than the good nutrition group. Boulos et al. (2016b) found that social isolation, few social contacts, and malnutrition risk were positively associated with each other among older adults. Recent studies reported that informal support from family, friends, and community members served as a necessary component in maintaining food security, often through food gifts of prepared meals or bags of groceries—a form of aid considered socially acceptable (Edfors & Wesgergren, 2012; Fares et al., 2012). Individuals who are socially isolated are exposed to various dangers, potentially leading to negative health conditions such as malnutrition, re-hospitalization, and social impairments (Romero-Ortuno et al., 2011). Older adults without family or insufficient informal systems are isolated (Edfors & Westergren, 2012). Therefore, health care providers should be aware of the importance of overall geriatric assessment, including demographic factors, housing arrangements, social activities, and nutritional assessments.
The current study used self-reported health status to measure the overall health status of Korean older adult women. Previous studies have reported that self-reported health status is a reliable and valid indicator for general health, comorbidities, and mortality in older adults (Heuberger et al., 2013; SÖderhamn et al., 2011). Wu et al. (2013) reported that older adults with more positive self-reported health status show higher well-being and low mortality, whereas other studies report positively self-reported health status affects malnutrition risk (Doumit et al., 2014; Heuberger et al., 2013), which is consistent with the current results.
Although perceived financial status, medication use, and comorbidity did not remain significant predictors of malnutrition risk in the hierarchical logistic regression model, there were statistically significant differences among the three groups. More than one half (55.7%) of participants who had lower incomes were at high risk of malnutrition. This result was supported by Fávaro-Moreira et al. (2016) and Kim et al. (2015), who noted lower income is significantly associated with nutritional risk. In addition, findings from previous research show that malnutrition is more pronounced among older patients with multiple chronic diseases (van Bokhorst-de van der Schueren et al., 2013; Volkert, 2013). Further, a high nutritional risk was found in 80.3% of participants who had taken medicines over 6 months, which was consistent with Wojszel's (2012) finding that the most common malnutrition risk factor was multidrug therapy that has side effects affecting nutrient intake. Accordingly, specific training courses should be organized to enhance knowledge about nutrition; community-based routine screening of more vulnerable groups, such as poor individuals living in underprivileged regions and those experiencing multiple comorbidities, should be implemented.
Nutritional education may be needed to improve the nutritional status of older adult women in rural settings; they may need an array of resources to supply their daily life necessities and be able to make healthier choices. Those resources might include psychological aspects, education, and financial and social support based on individuals' physical abilities. It is important to identify older adults with poor nutritional status because malnutrition is related to negative consequences, such as increased morbidity, poor quality of life, and increased health care costs (Amarya et al., 2015; Volkert, 2013). Future intervention research is needed to empirically validate what is suggested by the current correlational study.
There are several limitations of the current study. First, the cross-sectional design used a convenience sample; therefore, the sample may be biased. Second, participants were a community-dwelling, voluntary group of Korean older adult women. Thus, the current conclusions cannot be extrapolated toward older adult women experiencing serious chronic illness or living in residential care. Third, the use of multiple inferential tests may have increased the likelihood of Type I error. Fourth, the NSI checklist related to components of nutritional status, medical history, and medicine use were self-reported. Further studies should use tools that include overall nutrition criteria, such as clinical evaluation, anthropometric measures, dietary evaluation, body composition analysis, and laboratory investigations. Self-reported health status may be potentially too narrow to encapsulate true objectively measurable physical, sexual, and psychosocial health. Therefore, future research should consider more comprehensive objective health variables combined with subjective health status.
Korean older adult women are specifically more likely to be malnourished than their male counterparts because of relatively lower income, less education, poor health, and fewer social networks. In the current community-based study, more than one half of participants were considered at risk of malnutrition. These findings highlight a need for early detection of malnutrition risk and nutritional assessment considering multifactorial factors in primary care settings. In addition, the intervention should preferably target nutritional status and underlying problems in physical, functional, psychological, and social domains. Further research is needed to develop appropriate and concrete guidelines for nutrition among older adults and intervention programs.
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Participant Characteristics by Nutritional Status (N = 168)
|Characteristic||n (%)||χ2||p Value|
|Total||Good Nutrition Group||Moderate Risk Group||High Risk Group|
| 65 to 74||73 (43.5)||28 (54.9)||20 (35.7)||25 (41)||4.24||0.12|
| ≥75||95 (56.5)||23 (45.1)||36 (64.3)||36 (59)|
| No||102 (60.7)||30 (58.8)||33 (58.9)||39 (63.9)||0.42||0.812|
| Yes||66 (39.3)||21 (41.2)||23 (41.1)||22 (36.1)|
| Illiterate||35 (20.8)||14 (27.5)||9 (16.1)||12 (19.7)||5.29||0.507|
| Elementary school||78 (46.4)||24 (47.1)||26 (46.2)||28 (45.9)|
| Middle school||33 (19.6)||9 (17.6)||10 (17.9)||14 (23)|
| High school||22 (13.1)||4 (7.8)||11 (19.6)||7 (11.5)|
| No||129 (76.8)||34 (66.7)||48 (85.7)||47 (77)||5.44||0.666|
| Yes||39 (23.2)||17 (33.3)||8 (14.3)||14 (23)|
| <1,000,000₩||141 (83.9)||41 (80.4)||48 (85.7)||52 (85.2)||0.93||0.628|
| ≥1,000,000₩||27 (16.1)||10 (19.6)||8 (14.3)||9 (14.8)|
|Perceived financial status|
| Above average||5 (3)||1 (2)||3 (5.4)||1 (1.6)||10.89||0.028|
| Average||92 (54.8)||36 (70.6)||30 (53.6)||26 (42.6)|
| Below average||71 (42.3)||14 (27.5)||23 (41.1)||34 (55.7)|
| No||45 (26.8)||16 (31.4)||15 (26.8)||14 (23)||1.9||0.397|
| Yes||123 (73.2)||35 (68.6)||41 (73.2)||47 (77)|
|Use of magnifying glass|
| No||62 (36.9)||24 (47.1)||21 (37.5)||17 (27.9)||4.41||0.11|
| Yes||106 (63.1)||27 (52.9)||35 (62.5)||44 (72.1)|
|Use of hearing aid|
| No||151 (89.9)||44 (86.3)||51 (91.1)||56 (91.8)||1.06||0.587|
| Yes||17 (10.1)||7 (13.7)||5 (8.9)||5 (8.2)|
| No||44 (26.2)||21 (41.2)||11 (19.6)||12 (19.7)||8.51||0.014|
| Yes||124 (73.8)||30 (58.8)||45 (80.4)||49 (80.3)|
| No||56 (33.3)||24 (47.1)||16 (28.6)||16 (26.2)||6.28||0.043|
| Yes||112 (66.7)||27 (52.9)||40 (71.4)||45 (73.8)|
|Body mass index|
| Underweight||13 (7.7)||3 (5.9)||4 (7.1)||6 (9.8)||2.26||0.895|
| Normal||71 (42.3)||21 (41.2)||25 (44.6)||25 (41)|
| Overweight||39 (23.2)||14 (27.5)||10 (17.9)||15 (24.6)|
| Obesity||45 (26.8)||13 (25.5)||17 (30.4)||15 (24.6)|
Difference in Self-Reported Health Status by Nutritional Status Groups (N = 168)
|Subdomain||Mean (SD)||F (p Value)||Scheffe Testa|
|Good Nutrition Group (n = 51)||Moderate Risk Group (n = 56)||High Risk Group (n = 61)|
|General health perception||3.27 (0.57)||2.88 (0.66)||2.56 (0.74)||15.49 (<0.001)||a > b, c|
|Physical function||3.92 (0.66)||3.21 (0.83)||2.83 (0.88)||25.89 (<0.001)||a > b > c|
|Emotional function||3.97 (0.79)||3.26 (0.87)||2.76 (0.96)||26.42 (<0.001)||a > b > c|
|Social function||4.05 (0.67)||3.12 (0.89)||2.78 (1.2)||25.89 (<0.001)||a > b, c|
|Bodily pain||2.2 (1.05)||2.27 (0.9)||2.69 (0.88)||4.15 (0.002)||a < c|
|Sexual function||3.49 (1.42)||2.74 (1.39)||2.58 (1.43)||6.33 (<0.001)||a > b, c|
|Total||4.93 (0.87)||4.06 (0.99)||3.62 (1.17)||22.92 (<0.001)||a > b, c|
Predictors of Malnutrition Risk in Korean Older Adult Women (N = 168)a
|Predictor||Moderate Risk Group||High Risk Group|
|ß||SE||OR||95% CI||p Value||ß||SE||OR||95% CI||p Value|
|Block 1: Sociodemographic and Health-Related Factors|
|Perceived financial status|
| Average||0.73||0.45||0.28||[0.23, 3.02]||0.106||−0.58||0.53||0.73||[0.04, 13.22]||0.269|
| Below average||0.17||0.47||0.68||[0.06, 57.61]||0.721||0.86||0.54||3.1||[0.17, 57.6]||0.114|
|Medicine use (1 = Yes)||0.49||0.3||2.66||[0.82, 8.63]||0.103||0.48||0.3||2.62||[0.80, 8.52]||0.11|
|Comorbidity (1 = Yes)||0.17||0.27||1.41||[0.48, 4.14]||0.527||0.27||0.28||1.74||[0.59, 5.14]||0.319|
|Block 2: Self-Reported Health Status|
|Perceived financial status|
| Average||0.42||0.5||0.52||[0.03, 7.8]||0.401||−0.04||0.65||1.73||[0.05, 63.35]||0.955|
| Below average||0.2||0.57||0.97||[0.05, 17.38]||0.733||0.62||0.7||3.34||[0.08, 134.05]||0.372|
|Medicine use (1 = Yes)||0.56||0.39||3.1||[0.68, 14.11]||0.144||0.33||0.41||1.93||[0.39, 9.6]||0.424|
|Comorbidity (1 = Yes)||0.26||0.34||1.69||[0.44, 6.53]||0.447||0.4||0.37||2.23||[0.51, 9.66]||0.286|
|Self-reported health status|
| General health perception||0.18||0.49||1.2||[0.46, 3.16]||0.712||−0.37||0.53||0.69||[0.24, 1.95]||0.481|
| Physical function||0.65||0.49||0.53||[0.22, 1.38]||0.189||−1.09||0.52||0.34||[0.12, 0.92]||0.034|
| Emotional function||0.38||0.41||0.68||[0.31, 1.52]||0.346||−1.17||0.45||0.31||[0.13, 0.74]||0.009|
| Social function||−0.96||0.45||0.38||[0.16, 0.93]||0.034||−0.53||0.48||0.59||[0.23, 1.53]||0.277|
| Bodily pain||0.53||0.33||1.71||[0.88, 3.27]||0.112||1.01||0.36||2.75||[1.35, 5.59]||0.005|
| Sexual function||0.13||0.19||0.88||[0.61, 1.26]||0.476||−0.05||0.2||0.95||[0.64, 1.41]||0.797|