With the rapid increase in the aged population in South Korea, the number of long-term care (LTC) facilities has surged, increasing approximately 1,090% between 2004 (113 facilities) and 2013 (1,232 facilities) (National Health Insurance Service, 2014). Researchers have reported that the risk of malnutrition is higher for older adults residing in LTC facilities than those residing in the community (Ahmed & Haboubi, 2010; Cereda, 2012). In one study performed in a country other than Korea (Cereda, 2012), the prevalence rates of malnutrition and risk of malnutrition in older adults residing in LTC facilities were 27.2% and 52.1%, respectively, whereas those for older adults living at home were 4.2% and 27.4%, respectively. Health care providers in LTC facilities are likely to pay more attention to chronic illness or functional disability than nutritional status among older adult patients (Donini, Neri, De Chiara, Poggiogalle, & Muscaritoli, 2013), which may delay recognition of or timely intervention for malnutrition or malnutrition risk (Isenring, Banks, Ferguson, & Bauer, 2012; Pezzana et al., 2015). In one recent study of LTC institutions (Pezzana et al., 2015), approximately 50% of older adult residents requiring individualized nutritional care were not receiving it, probably because those institutions lacked systematic nutritional assessment and intervention. Therefore, providing effective nutrition management for institutionalized older adults in Korea is imperative, especially given that the number of older patients entering LTC facilities and their lengths of stay have accelerated due to the rapidly aging population (National Health Insurance Service, 2014).
It is well known that malnutrition in older adults is associated with many adverse health outcomes, including increased mortality, chronic disability, worsening of current illnesses, decreased quality of life, and increased health care costs (Brownie, 2006; Morley, 2012). Moreover, inadequate nutritional intake in older adults may result in deterioration of overall bodily functions, including respiratory, cardiovascular, gastrointestinal, and immune functions, as well as muscle strength (Cabrerizo et al., 2015; Saka, Kaya, Ozturk, Erten, & Karan, 2010; Singh et al., 2014). Although nutritional interventions and guidelines for institutionalized older adults have been developed and implemented in Western countries (Academy of Nutrition and Dietetics [AND], 2009; Amella & Aselage, 2012; American Medical Directors Association [AMDA], 2010; Buys, Flood, Real, Chang, & Locher, 2013; DiMaria-Ghalili, 2012), little research exists on the effects of interventional management of Korean older adults with or at risk of malnutrition (Kim, June, & Song, 2007).
As dietary requirements and patterns vary among cultures (Oh, No, & Kim, 2014), interventions meeting the specific nutritional demands of a population are needed. In addition, because medical systems and policies for older adults vary significantly among countries, such interventions' feasibility in terms of implementation must be considered. Korea has experienced a marked increase in the number of LTC facilities in response to its rapidly aging population. However, many LTC facilities have difficulty managing the quality of nutritional care, largely due to lack of resources. Such facilities often face understaffing, high nursing staff turnover, and time shortages; they provide few opportunities for continuing staff education on care of older adults (Chung & Lee, 2009; Hwang, 2013). For these reasons, a feasible nutritional intervention that can be readily implemented is a critical need for LTC facilities.
The current authors developed a nutritional intervention program based on the literature and recommendations of the Dietary Reference Intakes for Koreans (KDRI; Korean Nutrition Society, 2010) for malnourished older adults or those at high risk of malnutrition. The program included nutritional status assessment, nutrition management, and evaluation. The program was implemented in two Korean LTC facilities by instituting nursing staff education, facilitation, and a shared algorithm. The program's effects on the nutritional outcomes of older adults were examined.
The purpose of the current study was to investigate the effects of implementing a 4-week nutritional intervention for older adult patients with or at risk of malnutrition in two LTC facilities. It was hypothesized that the intervention group would demonstrate increased daily energy intake and levels of biochemical indicators, and improved overall nutritional status as measured by the Mini Nutritional Assessment (MNA; Guigoz, Vellas, & Garry, 1994).
Design and Sample
The current study used a quasi-experimental pre- and posttest design. Using a convenience sampling approach, two Korean LTC facilities were contacted for participation as an intervention site and a control site. The size and staffing of the two facilities were similar. The intervention facility had 195 beds and 87 nursing staff, including 22 RNs and 65 nursing assistants (30 nurse assistants [similar to licensed practical nurses] and 35 trained care workers), and two dietitians. The control facility had 195 beds and 85 nursing staff, including 27 RNs and 58 nursing assistants (28 nurse assistants and 30 trained care workers), and two dietitians.
According to the study's inclusion criteria, participants were 65 or older, at risk of malnutrition or had malnutrition (determined by a score of ≤23.5 on the MNA) (Guigoz et al., 1994), capable of verbal or written communication, had a score of ≥20 on the Korean version of the Mini-Mental State Examination (Kwon & Park, 1989), and had no dysphagia or chewing problems. Exclusion criteria were the presence of aspiration pneumonia and/or inability to take food by mouth.
The power analysis conducted to calculate the optimum sample size revealed that 52 participants, 26 in both the intervention and control groups, were required to detect a significant difference at the 5% level with 80% power. Therefore, allowing for a 15% dropout rate, 30 patients were recruited for both groups.
A total of 200 patients from the two LTC facilities were eligible for participation. One hundred one patients were willing to participate: 55 in the intervention facility and 46 in the control facility. Of these patients, 30 from both facilities were randomly selected for participation. A total of 45 participants completed the study: 23 in the intervention group (with seven dropouts) and 22 in the control group (with eight dropouts). Among the 15 dropouts, four were discharged to their homes, seven refused to provide blood samples for biochemical marker analysis, one was transferred to another facility, two were transferred to critical care settings, and one died. Figure 1 summarizes the sampling procedures.
Flowchart of participant selection process. Note. LTC = long-term care; MNA = Mini Nutritional Assessment; KMMSE = the Korean version of the Mini-Mental State Examination; NIEFA = nutritional intervention involving nursing staff education, facilitation, and a shared algorithm.
Developmental Procedures. The nutritional intervention program was derived from four previously published guidelines, literature reviews of nutrition-related studies for older adults, and KDRI nutrition recommendations distributed by the Korean Nutrition Society (2010). The guidelines selected were “Altered Nutritional Status in the Long-term Care Setting” (AMDA, 2010), “Unintended Weight Loss (UWL) in Older Adults Guideline” (AND, 2009), “Mealtime Difficulties” (Amella & Aselage, 2012), and “Nutrition in the Elderly” (DiMaria-Ghalili, 2012). For the literature reviews, PubMed, CINAHL, Cochrane Library, and ScienceDirect databases were searched for international studies and the National Assembly Library, KERIS, KMbase, and KoreaMed databases were searched for domestic studies. Using the keywords “aged,” “elderly,” “older adult,” “older age,” “older people,” “nutrition,” and “under-nutrition,” the search identified four systematic reviews, 10 randomized controlled trials, two cohort studies, eight case-control studies, and 20 review articles that were relevant to the study purposes.
Using the above-mentioned resources, contents of the draft nutritional intervention were selected and categorized into three phases: (a) assessment of nutritional status, including screening for malnutrition; (b) nutrition management, including oral care, chewing or swallowing problem care, mealtime assistance, creation of appropriate dining environments, referral to a dietitian, provision of nutritional supplements, training of care providers about nutrition management, and re-feeding syndrome monitoring; and (c) evaluation of the nutrition management provided, including continuous re-evaluation of participants' nutritional status with a specific time interval between re-evaluations.
During intervention finalization, experts in nutrition management (i.e., three physicians, six nurses, three nursing professors, one food and nutrition professor, and one dietitian) reviewed the contents and made recommendations for the final version of the program. The target users of the program were RNs and nursing assistants working in the two LTC facilities because these personnel had primary responsibility for monitoring the nutritional status of and providing mealtime assistance to participants. To optimize the program's feasibility, the current authors focused on exploiting existing resources in the intervention facility (i.e., the program included education of current nursing staff, adoption of facilitation strategies, and use of a shared algorithm to maintain continuity of care).
Facilitation has been defined as both a role and process (Dogherty, Harrison, & Graham, 2010); it involves helping health care providers improve their practice using strategies ranging from specific task-focused activities to more comprehensive processes of guiding providers and organizations to change (Harvey et al., 2002). In the current study, the use of change facilitators, algorithm posters, and a review of completed program checklists were involved in facilitation. The intervention (referred to as NIEFA [nutritional intervention involving nursing staff education, facilitation, and a shared algorithm]), including education, facilitation, and the algorithm, was implemented at the intervention facility for 4 weeks. Because one of the study measures for nutritional outcomes (i.e., serum albumin) has a half-life of 2 to 3 weeks (Rakel & Rakel, 2015), it was decided that at least 4 weeks was needed to examine the effects of the NIEFA program.
Implementation Procedures. The NIEFA program was primarily implemented by the principal investigator (PI; E.K.), who taught the nursing staff at the intervention facility how to integrate the program into their daily nursing activities. First, the PI educated RNs and nursing assistants about the nutritional intervention and its implementation strategies in two 1-hour sessions and additional individual sessions as necessary. The 1-hour sessions addressed the program's terminology, risk factors for malnutrition in older adults, problems related to malnutrition, assessment of nutritional status and amount of food intake, management of older adults with and at risk of malnutrition, the program evaluation method and process, and how to use the algorithm. Educational materials and program guidelines were provided to nursing staff who participated in these sessions. In addition, a total of 20 individual sessions were provided to RNs and nursing assistants who were off duty during the scheduled group sessions as well as to those who asked for more information or re-education about NIEFA program implementation.
Two nurses from the intervention facility were appointed as change facilitators. These nurses were recommended by the nursing director. Under the selection criteria, the change facilitators had to be enthusiastic about nursing practice, have good relationships with their colleagues, and often act as unofficial leaders of the nursing staff. To facilitate implementation of the NIEFA program, they regularly reviewed the program checklists that nursing staff completed, discussed nutrition management with them, and provided feedback. Over a 4-week period, the current authors met with the change facilitators weekly to check the extent of program implementation, identify any implementation issues, and discuss how to resolve those issues. The main issues that arose during program implementation were work overloads, lack of staff knowledge and skills, and lack of shared work priorities among staff. By brainstorming with the facilitators, they were able to overcome these issues, which to some degree reflected resistance to change, by improving work allocations for overworked staff, providing individual educational sessions for staff displaying lack of competency, and resetting staff work priorities in both clinical settings.
To further facilitate program implementation, algorithm posters illustrating the flow of the NIEFA program (including education, assessment, management, and evaluation) were hung in all wards of the intervention facility. The algorithm was expected to promote communication among nursing staff and maintain the continuity of nutritional care. Figure 2 summarizes the NIEFA program algorithm.
The NIEFA (nutritional intervention involving nursing staff education, facilitation, and a shared algorithm) program algorithm. Note. MNA = Mini Nutritional Assessment.
In addition, the program checklist that nursing staff had to complete when providing mealtime assistance was placed in participants' rooms. Program guidelines were also distributed to all nursing staff and copies were placed at each ward's nursing stations. Furthermore, to allow nursing staff to pay special attention to providing nutritional care during each mealtime (Heersink, Brown, DiMaria-Ghalili, & Locher, 2010), the current authors met with the nursing director and head nurses to reorganize the staff's work routines.
Daily Energy Intake. Five trained nurses in each facility measured and recorded participants' food intake by weighing the food remaining on their plates after they had finished eating; this procedure enabled the nurses to assess the daily dietary intake of carbohydrates, proteins, and fat. Snacks between meals were also recorded and included in the total amount of food intake; this snack information was obtained from participant interviews and nurses' direct observations. Because older adults are likely to eat more on weekends when families or friends visit, one weekend day and two weekdays were selected for calculation of average daily food intake. The average daily intake of energy was calculated using Korean food composition tables and the Computer Aided Nutritional Analysis Program for Professionals version 3.0 (The Korean Nutrition Society, 2005). Regarding daily energy intake, KDRI (The Korean Nutrition Society, 2010) has recommended a total of 1,600 kcal for older adult females and 2,000 kcal for older adult males.
Biochemical Indicators. Levels of serum albumin, total lymphocytes, hemoglobin, and hematocrit were measured in blood samples collected from participants at baseline and after 4 weeks. These biochemicals are sensitive indicators of nutritional status and have been frequently measured to assess malnutrition risk in older adults (Choi-Kwon et al., 2012; Park, Lim, Choi, Lee, & Lee, 2009; Saka et al., 2010). Both facilities measured the biochemical indicators based on the standardized protocol provided by the Korean Society for Laboratory Medicine. Serum albumin, total lymphocytes, hemoglobin, and hematocrit were considered to be at normal levels when they were within 3.3 to 4.7 g/dL, >1,500 cells/mm3, 11.7 to 14.5 g/dL, and 38.3% to 45.3%, respectively (The Korean Geriatric Society, 2005).
Overall Nutritional Status. The MNA developed by Guigoz et al. (1994) was used to evaluate participants' overall nutritional status. This screening tool comprises four domains and 18 items: four items in the anthropometry domain, six items in the general assessment domain, six items in the dietary domain, and two items in the self-assessment domain. Face-to-face interviews were conducted with each participant to complete the MNA. Participants took 15 minutes on average to respond to the entire questionnaire, for which the maximum score is 30; scores <17 indicate a malnourished state, scores 17 to 23.5 indicate risk of malnutrition, and scores ≥24 indicate being well nourished (Guigoz et al., 1994). The psychometric properties of the MNA scale have been tested in older adults in clinics, hospitals, and nursing homes, with sensitivity, specificity, and Cronbach's alpha values of 96%, 98%, and 0.78, respectively (Guigoz et al., 1994). The Korean-language MNA scale, which was translated and tested by Lee (2004), was used in the current study.
The current study was approved by a university hospital's institutional review board and by the two LTC facilities. All participants provided signed informed consent. Data were collected at baseline and 4 weeks after program implementation. A pretest was conducted at baseline to assess participants' demographic characteristics (including age, gender, education, religion, underlying diseases, number of comorbid illnesses, and length of stay) and nutritional status (including daily energy intake, levels of biochemical indicators, and MNA score). Using identical procedures, a posttest was conducted 4 weeks after program implementation to again assess participants' nutritional status.
Data were analyzed using SPSS version 19.0 for Microsoft Windows®. Homogeneity tests for demographic and nutrition-related characteristics of participants were conducted using frequencies, percentages, and chi-square and t tests, as appropriate. Because the homogeneity tests revealed significant differences in levels of proteins, fat, and serum albumin between the intervention and control groups, analysis of covariance (ANCOVA) was used to compare the two groups after controlling for the pretest values of these variables.
Demographic and Nutrition-Related Characteristics
Table 1 summarizes participants' demographic and nutrition-related characteristics. The mean age of participants was 78.6 years (range = 65 to 96 years), and most participants were female (68.9%, n = 31). A homogeneity test revealed that demographic characteristics did not differ significantly between the intervention and control groups. In addition, main admission diagnoses and frequencies of comorbidities were similar in the two groups. Based on pretest results, participants' daily energy intake averaged 1,333.45 kcal (range = 796 to 1,719 kcal), with no statistically significant difference between the two groups at baseline. Among the biochemical indicators, serum albumin levels were significantly higher in the control group (t = −8,894, p < 0.001) at baseline, but total lymphocyte, hemoglobin, and hematocrit levels did not significantly differ between groups. The total mean MNA score was 17.44 (range = 10 to 21.5), with no statistically significant difference between groups.
Comparison of Demographic and Nutrition-Related Characteristics between the Intervention and Control Groups (N = 45)
Changes in Daily Energy Intake, Biochemical Indicators, and Overall Nutritional Status
Changes in daily energy intake, biochemical indicators, and overall nutritional status in the two groups over 4 weeks are compared in Table 2. Intake of daily dietary energy in the form of carbohydrates, proteins, and fat significantly increased in the intervention group but not in the control group (t = 3.832, p < 0.001). The mean differences in total lymphocyte (t = 3.870, p < 0.001), hemoglobin (t = 4.991, p < 0.001), and hematocrit (t = 4.305, p < 0.001) levels over 4 weeks were significantly greater in the intervention group than in the control group. The mean differences in pre- and posttest serum albumin levels between the two groups were not significant (t = 0.131, p = 0.725). However, the serum albumin level of the intervention group significantly increased over 4 weeks (paired t = 3.579, p = 0.002), whereas that of the control group did not. Changes in the MNA scores were significantly greater in the intervention group than in the control group (t = 10.223, p < 0.001). In addition, the post-test MNA scores were significantly higher than the pretest scores in the intervention group but not in the control group. The number of malnourished participants decreased from seven (30.4%) to one (4.3%) in the intervention group and from six (27.3%) to five (22.7%) in the control group (data not shown).
Changes in Nutritional Status after NIEFA Program Implementation between the Intervention and Control Groups
The 4-week NIEFA program was designed to efficiently and effectively improve nutritional outcomes by exploiting existing organizational resources. Over 4 weeks, the program was effective in increasing the intervention group's daily energy intake, levels of biochemical indicators (serum albumin, total lymphocytes, hemoglobin, and hematocrit), and MNA scores. In contrast, in the control group, these parameters either significantly decreased or did not change during the 4-week period.
Program implementation resulted in an average daily energy intake increase of 1,393 to 1,693 kcal (p < 0.001). The baseline value of 1,393 kcal is similar to the average energy intake of Korean older adults living at home (Lee & Kim, 2003) and Korean older adults with dementia residing in nursing homes (Kim, 2010). This dietary energy intake of participants and Korean older adults in general is lower than the 1,600-kcal energy intake recommended in the Korean Nutrition Society's (2010) KDRI. These values show that Korean older adults are vulnerable to insufficient consumption of nutrients and have higher risk of nutrient deficiencies regardless of their living status. In particular, insufficient intake of calories may not only cause wasting or unintentional weight loss, but may also result in protein deprivation from muscle mass (Rakel & Rakel, 2011); thus, adequate calorie intake is imperative to maintain protein sparing in older adults.
Regarding biochemical indicators, changes in albumin levels over 4 weeks did not differ significantly between the intervention and control groups, although the within-group difference was significant in the intervention group. Despite the lack of a statistically significant difference between the groups, albumin levels were within normal ranges at baseline and after 4 weeks in both groups. In addition, levels of this indicator in participants were similar to those found in hospitalized older adults in previous studies (Park et al., 2009; Saka et al., 2010). Other studies of the nutritional status of Korean older adults have yielded similar results, showing that serum biochemical indicators, such as prealbumin (Kim & Choi, 2013), albumin, and total lymphocytes (Choi-Kwon et al., 2012), were not deficient. These findings may be associated with previous reports that Korean older adults have an adequate protein intake regardless of their socioeconomic status, a factor known to be related to malnutrition in older adults (Choi-Kwon et al., 2012; Kim et al., 2007; Oh, Jho, No, & Kim, 2015). One possible explanation for the protein intake is that food made from soy (e.g., tofu, miso soup) is popular in Korea (Oh et al., 2014) and the facilities participating in the current study served soy-based food as well as japgokbap, which is made of brown rice and beans, at every meal; this diet apparently met participants' protein needs. However, the quality of protein consumed must also be considered. Because plant protein, like that in soybeans and soy-based food, is likely to be deficient in one or more essential amino acids and is not a source of high biological-value protein, high-quality animal protein is more highly recommended for maintaining muscle mass in older adults (Oh et al., 2014).
Participants' baseline mean hemoglobin and hematocrit levels were 11.3 g/dL and 33.81%, respectively—both below normal ranges. After the 4-week NIEFA program, both indicators slightly increased in the intervention group, whereas they decreased in the control group. The increases in the intervention group could be explained by the fact that the program helped participants increase their oral intake of various nutrients, resulting in substantial increases in both biochemical indicators. However, the finding that these indicators did not reach normal ranges after 4 weeks even in the intervention group may indicate that the study period was not long enough to achieve these outcomes. Anemia, or below-normal hemoglobin concentrations, in older adults has been associated with increased risk of falls, prolonged hospitalization, and high mortality (Riva et al., 2009); thus, improvement of anemic status is necessary to prevent further negative health outcomes.
The finding that MNA scores also significantly increased in the intervention group suggests that implementing the NIEFA program improved participants' overall nutritional status. In addition, the finding that the nutritional status of the control group worsened somewhat over the 4-week study period suggests that older adults can become malnourished within a short period when appropriate nutritional intervention is not provided.
Regarding implementation of nutritional interventions in nursing settings, many studies have identified barriers and facilitators of such interventions. For example, many studies have found leadership and organizational support to be keys to successful intervention implementation (Gifford, Davies, Edwards, Griffin, & Lybanon, 2007), whereas commonly identified barriers include lack of time, nurse under-staffing, and lack of support from administrators (Bamford, Heaven, May, & Moynihan, 2012). Nurses in Korean LTC facilities, including the two that participated in the current study, are known to have similar problems, including low wages, high turnover, conflicts with patients or family members, and few educational opportunities, which are essential for improving quality of care (Chung & Lee, 2009; Hwang, 2013). Although the participating facilities exhibited these problems, the current results demonstrated that devoting sufficient time to staff education and providing frequent feedback to nursing staff about nutrition management for older adults encouraged the staff to actively implement the intervention. Furthermore, the nursing director of the intervention facility was enthusiastic about the study. Meeting educational needs of nursing staff and involving the nursing director likely played major roles in successful implementation of the intervention.
The current study is significant because nursing staff were successfully engaged in implementing the NIEFA program in two LTC facilities in Korea where nutrition intervention is rarely conducted, and because this systematic approach significantly improved the nutritional status of older adult patients with or at risk of malnutrition.
However, the current study had several limitations that should be considered when interpreting its findings. First, ANCOVA was used to control for baseline differences in pretest scores for albumin between the intervention and control groups. Although use of ANCOVA in quasi-experimental studies is fairly common, it is also controversial because the method's ability to control for covariates raises technical issues that may not meet the assumptions associated with homogeneity of regression (Jamieson, 2004). Second, participants' pre-existing comorbidities, which may have decreased their oral intake, were not fully assessed. However, the main admission diagnoses of diseases were assessed and did not include congestive heart failure, chronic obstructive pulmonary disease, or cancer, which can have serious effects on nutritional status. Moreover, participants' current overall nutritional status was measured by MNA, and the number of current comorbidities was analyzed. Third, the 4-week implementation period may have been insufficient for the NIEFA program's full effects to be observed. Finally, the relatively small number of participants may limit the generalizability of the study findings.
Implications for Geriatric Nursing
The NIEFA program involved integration of a nutritional intervention whose elements had been validated in previous studies into a LTC facility. Consequently, excessive time and costs associated with developing and testing a nutritional program were avoided. Furthermore, during program implementation, the current authors focused on exploiting existing organizational resources, such as nursing staff and work processes, to facilitate systematic integration of the intervention without making extensive changes in the organization. These strategies may make the NIEFA program suitable for widespread use in LTC facilities. More importantly, the program helped participating older adults improve their perceived and actual nutritional status. The NIEFA program and its implementation strategies offer a promising model for geriatric LTC facilities to readily initiate nutritional quality of care while overcoming barriers posed by nursing staff turnover, time shortages, and lack of continuing education opportunities.
The current study demonstrated that the NIEFA intervention could achieve beneficial outcomes in older adult patients, as most nutrition indicators significantly improved in the intervention group while declining or remaining unchanged in the control group. Future longitudinal research is needed to investigate the program's long-term effects in terms of patient (e.g., duration of hospital stay, morbidities, mortalities) and facility (e.g., nursing staff satisfaction, intervention cost-effectiveness) outcomes. In addition, the nutritional status of older adults is influenced by various factors and cannot be optimized by nursing staff alone; therefore, future intervention enhancement should involve multidisciplinary professionals, such as geriatricians, dietitians, physical therapists, psychologists, and nurses.
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Comparison of Demographic and Nutrition-Related Characteristics between the Intervention and Control Groups (N = 45)
|Variable||Intervention Group (n = 23)||Control Group (n = 22)||χ2 or t Test||p Value|
| Female||16 (69.6)||15 (68.2)||0.01||0.92|
| Male||7 (30.4)||7 (31.8)|
| Yes||16 (69.6)||17 (77.3)||0.342||0.559|
| No||7 (30.4)||5 (22.7)|
| No||14 (60.9)||11 (50)||0.538||0.463|
| Yes||9 (39.1)||11 (50)|
|Main admission diagnosis|
| Hypertension||5 (21.8)||6 (27.3)||1.079||0.982|
| Diabetes||4 (17.4)||4 (18.2)|
| Other||4 (17.4)||2 (9.1)|
| Cerebrovascular disease||3 (13)||4 (18.2)|
| Fracture||3 (13)||3 (13.6)|
| Arthritis||3 (13)||2 (9.1)|
| Parkinson's disease||1 (4.4)||1 (4.5)|
|No. of comorbidities|
| 1 to 2||5 (21.8)||4 (18.2)||1.96||0.375|
| 3 to 4||9 (39.1)||13 (59.1)|
| ≥5||9 (39.1)||5 (22.7)|
|Age (years)||78.7 (7.06)||78.5 (7.37)||0.091||0.928|
|Stay duration (days)||420.87 (276.96)||447.55 (399.45)||−0.261||0.795|
|Energy intake (kcal/day)||1,393.70 (180.54)||1,270.47 (276.89)||1.76||0.087|
|Serum albumin (g/dL)||3.90 (0.32)||4.66 (0.24)||−8.894||<0.001|
|Total lymphocytes (cells/mm3)||1,596.59 (428.69)||1,874.13 (642.05)||−1.712||0.094|
|Hemoglobin (g/dL)||10.89 (1.64)||11.78 (1.82)||−1.726||0.091|
|Hematocrit (%)||33.44 (4.9)||34.19 (5.58)||−0.475||0.637|
|Mini Nutritional Assessment||16.89 (2.14)||18.02 (2.65)||−1.579||0.122|
Changes in Nutritional Status after NIEFA Program Implementation between the Intervention and Control Groups
|Variable||Group||Baseline||Week 4||Paired t Test||p Value||Mean Difference (Mean, SD)||t Test||p Value|
|Energy intake (kcal/day)||Intervention||1,393.7 (180.54)||1,693.97 (181.42)||7.533||<0.001||300.28 (191.17)||3.832||<0.001|
|Control||1,270.47 (276.89)||1,341.89 (221.92)||1.599||0.125||71.42 (209.51)|
|Serum albumina (g/dL)||Intervention||3.9 (0.32)||4.1 (0.28)||3.579||0.002||0.2 (0.27)||0.131||0.725|
|Control||4.66 (0.24)||4.65 (0.27)||−0.472||0.642||−0.01 (0.14)|
|Total lymphocytes (cells/mm3)||Intervention||1,596.59 (428.69)||1,829.72 (496.15)||2.765||0.011||233.13 (404.43)||3.87||<0.001|
|Control||1,874.13 (642.05)||1,712.53 (545.56)||−2.914||0.008||−161.6 (260.08)|
|Hemoglobin (g/dL)||Intervention||10.89 (1.64)||11 (1.64)||1.187||0.248||0.11 (0.47)||4.991||<0.001|
|Control||11.78 (1.82)||11.07 (1.74)||−5.211||<0.001||−0.71 (0.64)|
|Hematocrit (%)||Intervention||33.44 (4.9)||34.27 (4.74)||2.116||0.046||0.82 (1.86)||4.305||<0.001|
|Control||34.19 (5.58)||32.16 (5.36)||−3.736||0.001||−2.03 (2.55)|
|Mini Nutritional Assessment||Intervention||16.89 (2.14)||20.7 (2.28)||4.214||<0.001||3.8 (1.48)||10.223||<0.001|
|Control||18.02 (2.65)||18.3 (2.68)||1.867||0.076||0.27 (0.69)|