Abstract
Objectives
The associations between nutritional status and lifestyle factors have not been well established. This study aimed to investigate the prevalence of poor nutrition and to examine the relationships between nutritional status and unhealthy lifestyle and other related factors among the elderly.
Methods
This cross-sectional study was conducted in Liaobu Town, Dongguan city, China. A total of 708 community-dwelling older adults aged ≥60 years were recruited by stratified random sampling. Data on sociodemographic characteristics, health and lifestyle factors, and the Mini Nutritional Assessment (MNA) scores were collected using structured questionnaires via face-to-face interviews. A multivariate logistic regression model was constructed to identify the risk factors of poor nutrition.
Results
The prevalence of malnutrition among the elderly adults in this study was 1.3%, and 24.4% were at risk of malnutrition (RM). Poor nutrition was significantly associated with female gender, older age, lower education, a high number of self-reported chronic diseases, and hospitalization in the last year. Unhealthy lifestyle factors associated with poor nutrition included current smoking status, higher alcohol consumption, lack of physical activity, longer duration of sitting, negative attitude towards life, and a poor family relationship.
Conclusions
While the prevalence of malnutrition was low, RM was high in the elderly population in China. The determinants of malnutrition were explored and the relationships between nutritional status and unhealthy lifestyle factors were examined. The results of this study provide information for future longitudinal studies with multi-factorial interventional design in order to determine the effects of the causal relationships.
Key words: Nutritional status, prevalence, risk factors, unhealthy lifestyle factors, elderly adults
List of abbreviations
- MNA
Mini nutritional assessment
- RM
Risk of malnutrition
- BMI
Body mass index
- BP
Blood pressure
- SBP
Systolic blood pressure
- DBP
Diastolic blood pressure
Introduction
The population of elderly adults aged ≥60 years is increasing worldwide and is projected to reach 2 billion by 2050 (1). A similar situation was noted in China, the China Research Center on Ageing reported that the population elderly adults in China in 2013 was 202 million (approximately 15% of the total population) (2). Aging not only results in changes in an individual's physiology and psychology but it may also be changes in an individual's physiology and psychology but may also be a risk factor for malnutrition (3).
Previous research has shown that malnutrition can have serious consequences. It can exacerbate disease progress, reduce immune function, increase the risk of infection and complications, delay recovery, and prolong hospitalization. Moreover, malnutrition may lead to increased disease morbidity and mortality as well as increased healthcare expenditures and result in poor quality of life (3, 4, 5, 6, 7, 8). Although malnutrition is costly and harmful for the elderly, it is frequently unrecognized and neglected (9). This may be due to a lack of awareness as well as poor knowledge among elderly adults (10). According to the 2016 Global Nutrition Report, malnutrition affects one in 3 people worldwide and is increasing in nearly every country, making it a growing public health challenge (11, 12); therefore, more attention is needed to develop prevention, detection, and treatment strategies.
During the past decades, a number of methods for nutritional risk assessment have been developed. Meanwhile, mixed findings have been reported regarding the prevalence of malnutrition and the risk of malnutrition (RM). Based on the Mini Nutritional Assessment (MNA) scale, the incidence of malnutrition in community-dwelling elderly adults varies between 0.2% and 13.7%, as well as 8% to 50.3% of elderly individuals have RM (10, 13, 14, 15, 16, 17, 18). Differences in sample size, age distribution, diversified social environment, and geographical differences may all contribute to the variations between studies (14). Nonetheless, these findings indicate the urgency to explore malnutrition, especially RM, and the potential risk factors that have contributed to this phenomenon.
Several factors have been associated with nutritional status in elderly adults (19). Sociodemographic factors such as female gender, older age, widowed status, and lower education and income levels may contribute to a poor nutritional status (10). Several health-related factors such as chronic diseases, physical and social barriers, risk of depression, and a lack of healthrelated knowledge are also related to malnutrition (16, 20). However, the associations between malnutrition and other factors, such as hospitalization in the last year and various unhealthy lifestyle factors, remain unclear. The present study assessed unhealthy lifestyle factors in 3 dimensions: behavioral factors such as smoking, alcohol consumption, exercise, duration of sitting, and sleep quality; attitudinal factors (e.g., towards life); and social factors such as family relationship. The effect of unhealthy lifestyle factors on malnutrition among community-dwelling elderly adults has not been well established in China (20).
It is necessary to identify the nutritional status of community-dwelling older adults, especially those at RM, as well as risk factors that could be determined by a comprehensive geriatric evaluation. Indeed, effective nutritional interventions may reverse the course of malnutrition, thus preventing and controlling the serious consequences associated with its progression. However, data on the nutritional status and associated risk factors for malnutrition among communitydwelling elderly adults are limited in China.
Thus, we investigated elderly adults aged ≥60 years living in different communities with the following aims: 1) to assess the prevalence of malnutrition and RM; 2) to identify the relationships between nutritional status and unhealthy lifestyle factors; and 3) to determine the factors associated with poor nutrition among elderly adults.
Methods
Study design and subjects
In April 2013, a cross-sectional study on the nutritional and health status of elderly adults was carried out in Liaobu Town, Dongguan City, Southern China. Since the reform and opening-up, Liaobu Town is a well-known and representative town in Dongguan city, with a fast-growing manufacturing industry. Liaobu Town consists of 30 villages, corresponding to a population of approximately 71,000 residents and an area of 71.38 square kilometers (21). These villages are managed by 21 community health service centers; therefore, we call the elderly living in Liaobu Town as community-dwelling elderly adults in this paper.
This study used stratified random sampling to generate the study samples. According to local economic levels (21), we divided the 30 villages into 3 levels, with 10 villages per level. Three villages were selected randomly in each level (3/10) and a total of 9 villages were generated (9/30). All of the elderly adults (aged ≥60 years) in the 9 villages were investigated. The sample distribution is presented in Additional file 1: Figure S1.
Figure S1.
Map of Liaobu Town, the figure of sample distribution
A total of 792 community-dwelling elderly adults lived in the 9 villages, according to the city's household registration system. The following selection criteria were used to select the final sample of 708 study subjects (Table 1): 1) longterm residents of Liaobu Town; 2) aged ≥60 years; 3) with measurable weight and height; 4) without acute disease or immediate emergency care requirements; 5) no serious cognitive impairments and with an ability to communicate normally. A flowchart illustrating the selection of the study subjects is demonstrated in Figure 1.
Table 1.
The results of stratified random sampling method of Liaobu Town, Dongguan City, China
| Economical level | Poor nutrition a | Well-nourished b | All participants | p c | p d |
|---|---|---|---|---|---|
| Level 1 | 0.237 | ||||
| Village 1 | 9 (12.7) | 62 (87.3) | 71 (10.0) | ||
| Village 2 | 19 (23.2) | 63 (76.8) | 82 (11.7) | ||
| Village 3 | 20 (20.4) | 78 (79.6) | 98 (13.8) | ||
| Total 1 | 48 (19.1) | 203 (80.9) | 251 (35.5) | ||
| Level 2 | 0.872 | ||||
| Village 4 | 16 (25.0) | 48 (75.0) | 64 (9.1) | ||
| Village 5 | 14 (28.6) | 35 (71.4) | 49 (6.9) | ||
| Village 6 | 36 (28.3) | 91 (71.7) | 127 (17.9) | ||
| Total 2 | 66 (27.5) | 174 (72.5) | 240 (33.9) | ||
| Level 3 | 0.147 | ||||
| Village 7 | 24 (24.7) | 73 (75.3) | 97 (13.6) | ||
| Village 8 | 27 (38.6) | 43 (61.4) | 70 (9.9) | ||
| Village 9 | 17 (34.0) | 33 (66.0) | 50 (7.1) | ||
| Total 3 | 68 (31.3) | 149 (68.7) | 217 (30.6) | ||
| Total |
182 (25.7) |
526 (74.3) |
708 (100.0) |
0.008** |
Note: Data presented are n (%); MNA: Mini nutritional assessment; a. Poor nutrition: MNA scores ≤ 23.5, included 9 elders of malnutrition (MNA scores < 17.0); b. Well-nourished: MNA scores ≥ 24; c. P values in Chi square differences between the same group; d. P values in Chi square differences between the three groups; * p < 0.05; ** p < 0.01; *** p < 0.001.
Figure 1.
Flow chart in the selection of study subjects
Ethics Statement
This study was approved by the ethics board of community health service center of Liaobu Town. Written informed consent was obtained from each study participant before the investigation.
Procedures
All interview groups included a local healthcare staff member (community health service center) and a medical student who were trained prior to the commencement of investigation in order to standardize data collection and recording. In addition, each group contained a supervisor to ensure that the interviews were conducted properly and that missing data were identified in a timely manner. The interviews took place in the participants' homes and data were collected from each participant through a structured study questionnaire that was administered verbally by the staff member and medical student. The entire interview process lasted approximately 30 minutes for each elderly adult who participated in the study.
Measurements and instruments
Sociodemographic characteristics
Data on sociodemographic characteristics including gender, age, marital status, educational level, retirement employment, health insurance, and living arrangements were collected for each participant.
Health and lifestyle factors
The health and lifestyle section of the questionnaire included 2 parts: unhealthy lifestyle factors and health-related factors. The unhealthy lifestyle factors included questions pertaining to current smoking status, alcohol consumption, physical activity, time of sitting, sleep status, attitude towards life, and family relationships. Current smoking status was defined as participants who had smoked ≥1 cigarette(s) per day for at least 6 months. Alcohol consumption was defined as drinking alcohol for participants who reported consuming alcohol an average of more than once a week within the last year. Participants who performed moderate exercise lasting no less than 30 minutes ≥3 times per week, including activities such as walking, jogging, square dancing, tai chi, and ba duan jin, were considered to be physically active. Time of sitting was defined by the response to the question: how long do you sit on an average day? Sleep status was defined based on the self-reported response to the question: How do you feel about your sleep status in the past month? a) good, b) fair, c) poor. Attitude towards life was defined by the response to the following question: What is your attitude toward your life in the past month? a) positive, b) neutral, c) negative. Finally, family relationships were defined by responses to the question: How have you gotten along with your family members in the past month? a) good, b) fair, c) poor.
The health-related factors included questions on the number of self-reported chronic diseases (e.g., hypertension and diabetes), whether they had undergone a physical examination at least once per year, whether they had been hospitalized in the last year, and body mass index (BMI), blood pressure (BP), and blood glucose levels. The Chinese reference was used to categorize BMI, as follows: underweight (<18.5 kg/m2), normal weight (18.5–23.9 kg/m2), overweight (24.0–27.9 kg/m2), and obese (≥28.0 kg/m2) (22). BP was measured on the right arm using a mercury sphygmomanometer using the average of 3 readings for analysis.
Assessment of nutritional status:
The MNA was specifically designed to assess the nutritional status of elderly adults based on responses to 18 items (23, 24, 25). The tool is divided into 4 parts. The first is anthropometric assessment (items 1 to 4; e.g., BMI and mid-arm and calf circumferences), with the circumference measurements performed twice using a portable tape with the smallest division of 0.1 cm. In the present study, the mid-arm circumference was measured as follows: first, the elderly adult was instructed to bend the arm at the elbow at a right angle with the palm facing up. Next, the health staff marked the midpoint between the acromial surface of the scapula and the olecranon process of the elbow on the back of the nondominant arm. Finally, each participant was instructed to allow the arm hang loosely, and the mid-arm circumference was measured. Calf circumference was measured with the tape wrapped around the widest part of the calf when subject was sitting with the leg hanging loosely. In order to ensure that the widest point was measured, the circumference was also measured at point above and below the widest point (26). The second part of the MNA is a general assessment (items 5 to 10; e.g., medication use mobility, and the presence of a skin ulcer); the third is dietary assessment (items 11 to 16; e.g., meals, food, and fluid intake); and the fourth is self-assessment (items 17 and 18; e.g., health and nutritional status). Given that lower scores predict a higher risk of malnutrition (27), the total score (of 30 points) can be used to classify elderly adults as malnourished individuals (<17 points), individuals at RM (17-23.5 points), or well-nourished individuals (≥ 24 points) (28). In this paper, MNA scores ≤23.5 points represented poor nutrition. Additionally, the MNA had been reported to be one of the most valid and frequently used nutritional screening tools in the elderly age group, and it is reported to have good reliability and validity (13, 15, 29).
Statistical analysis
Data were presented as means and standard deviations (SD) for continuous variables, while categorical variables were presented as frequency and percentages. All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS), version 13.0 (SPSS Inc., Chicago, IL).
First, 9 elderly participants with malnutrition were classified as individuals at RM. We then categorized nutritional status into 2 categories: well-nourished and poor nutrition. Second, the chi-square and t-tests were used to assess the differences in sociodemographic, health, and lifestyle factors among the elderly adults. In addition, we calculated Spearman correlation coefficients to assess the relationship between unhealthy lifestyle factors and MNA scores. Finally, all statistically significant variables identified in univariate analysis were included in a multivariate logistic regression analysis, except for BMI, which is an important part of the MNA scale. The logistic regression models used a forward stepwise selection strategy. The odds ratios (ORs) with 95% confidence intervals (95% CI) and Nagelkerke R2 values were also calculated. Twotailed P values < 0.05 were considered statistically significant.
Results
Participant characteristics
A total of 708 elderly adults from 9 villages in Liaobu Town were included; the sample distribution is shown in Table 1 and Figure S1 (Additional File 1). Of the 708 total subjects, 337 (47.6%) were male and 371 (52.4%) were female. All participants were between 60 and 100 years of age, with an average age of 70.19 ± 8.25 years. Their sociodemographic and descriptive characteristics are shown in Table 2.
Table 2.
The sociodemographic characteristics association with MNA score and its prevalence
| Variable | Poor nutrition a | Well-nourished b | All participants | p |
|---|---|---|---|---|
| Gender | 0.030* | |||
| Male | 74 (22.0) | 263 (78.0) | 337 (47.6) | |
| Female | 108 (29.1) | 263 (70.9) | 371 (52.4) | |
| Age, years | 73.73 ± 9.39 | 68.97 ± 7.45 | 70.19 ± 8.25 | < 0.001*** |
| Age, years | < 0.001*** | |||
| 60~74 | 99 (19.6) | 405 (80.4) | 504 (71.2) | |
| 75~84 | 54 (35.5) | 98 (64.5) | 152 (21.5) | |
| ≥ 85 | 29 (55.8) | 23 (44.2) | 52 (7.3) | |
| Marital status | < 0.001** | |||
| Single c | 58 (49.2) | 96 (50.8) | 154 (21.8) | |
| Married | 124 (22.4) | 430 (77.6) | 554 (78.2) | |
| Education level | < 0.001*** | |||
| No education | 106 (35.2) | 195 (64.8) | 301 (42.5) | |
| Primary school | 65 (21.9) | 232 (78.1) | 297 (42.0) | |
| Middle school or above | 11 (10.0) | 99 (90.0) | 110 (15.5) | |
| Retirement employment | 0.043* | |||
| Yes | 32 (19.6) | 131 (80.4) | 163 (23.0) | |
| No | 150 (27.5) | 395 (72.5) | 545 (77.0) | |
| Health insurance | ||||
| Yes | 180 (25.8) | 519 (74.2) | 699 (98.7) | 0.810 |
| No | 2 (22.2) | 7 (77.8) | 9 (1.3) | |
| Living arrangement | < 0.001*** | |||
| Living alone | 52 (40.9) | 75 (59.1) | 127 (17.9) | |
| Living with spouse only | 86 (23.2) | 284 (76.8) | 370 (52.3) | |
| Living with children | 44 (20.9) | 167 (79.1) | 211 (29.8) | |
| MNA scores | 22.00 ± 2.03 | 26.65 ± 1.26 | 25.45 ± 2.52 | < 0.001*** |
| MNA status |
182 (25.7) |
526 (74.3) |
708 (100.0) |
Note: Data presented are mean ± SD or n (%); MNA: Mini nutritional assessment; a. Poor nutrition: MNA scores ≤ 23.5, included 9 elders of malnutrition (MNA scores < 17.0); b. Well-nourished: MNA scores ≥ 24; c. Single: unmarried, divorced or widowed; * p < 0.05; ** p < 0.01; *** p < 0.001.
Overall prevalence
Only 9 of the elderly participants (1.3%) were classified as malnourished, while 173 (24.4%) were classified as those with RM. Hence, a total of 182 participants were categorized as having poor nutrition, corresponding to a prevalence of 25.7%. The mean MNA score was 25.45 ± 2.52. The epidemiological characteristics of the individuals within the poor nutrition category are reported in Table 2.
Univariate analysis
The results of the univariate analysis for poor nutrition are shown in Table 1, 2, and 3. Increasing age was accompanied by a decreasing MNA score, as shown in Figure 2.
Table 3.
Health and lifestyle factors of participants related to MNA score
| Variable | Poor nutrition a | Well-nourished b | All participants | p |
|---|---|---|---|---|
| unhealthy lifestyle factors | ||||
| Current smoking | 0.006** | |||
| Yes | 67 (32.8) | 137 (67.2) | 204 (28.8) | |
| No | 115 (22.8) | 389 (77.2) | 504 (71.2) | |
| Alcohol consumption | < 0.001*** | |||
| Yes | 24 (47.1) | 27 (52.9) | 51 (7.2) | |
| No | 158 (24.0) | 499 (76.0) | 619 (92.8) | |
| Physical activity | < 0.001*** | |||
| Yes | 95(21.3) | 351 (78.7) | 446 (63.0) | |
| No | 87 (33.2) | 175 (66.8) | 262 (37.0) | |
| Time of sitting, hours | 0.001** | |||
| < 6 | 96 (21.6) | 349 (78.4) | 445 (62.9) | |
| ≥ 6 | 86 (32.7) | 177 (67.3) | 263 (37.1) | < 0.001*** |
| Time of sitting, hours | 6.05 ± 3.34 | 5.18 ± 2.59 | 5.40 ± 2.82 | |
| Sleep status | 0.001** | |||
| Good | 96 (23.0) | 321 (77.0) | 417 (58.9) | |
| Fair | 49 (24.1) | 154 (75.9) | 203 (28.7) | |
| Poor | 37 (42.0) | 51 (58.0) | 88 (12.4) | |
| Attitude toward life | < 0.001*** | |||
| Positive | 117 (20.7) | 448 (79.3) | 565 (79.8) | |
| Neutral | 48 (42.5) | 65 (57.5) | 113 (16.0) | |
| Negative | 17 (56.7) | 13 (43.3) | 30 (4.2) | |
| Family relationship | < 0.001*** | |||
| Good | 61 (21.2) | 227 (78.8) | 288 (40.7) | |
| Fair | 85 (24.4) | 263 (75.6) | 348 (49.1) | |
| Poor | 36 (50.0) | 36 (50.0) | 72 (10.2) | |
| Health-related factors | ||||
| No. of self-reported chronic disease | < 0.001*** | |||
| 0 | 64 (18.8) | 277 (81.2) | 341 (48.1) | |
| 1 | 72 (28.9) | 177 (71.1) | 249 (35.2) | |
| ≥ 2 | 46 (39.0) | 72 (61.0) | 118 (16.7) | |
| Physical examination | 0.875 | |||
| Yes | 137 (25.6) | 399 (74.4) | 536 (75.7) | |
| No | 45 (26.2) | 127 (73.8) | 172 (24.3) | |
| Hospitalization in the last year | < 0.001*** | |||
| Yes | 52 (40.6) | 76 (59.4) | 128 (18.1) | |
| No | 130 (22.4) | 450 (77.6) | 580 (81.9) | |
| BMI, kg/m2 | < 0.001*** | |||
| Underweight | 26 (89.7) | 3 (10.3) | 29 (4.1) | |
| Normal weight | 111 (32.4) | 232 (67.6) | 343 (48.4) | |
| Overweight | 31 (13.1) | 206 (86.9) | 237 (33.5) | |
| Obese | 14 (14.1) | 85 (85.9) | 99 (14.0) | < 0.001*** |
| BMI, kg/m2 | 22.06 ± 3.98 | 24.86 ± 3.32 | 24.14 ± 3.71 | |
| SBP, mmHg | 132.79 ± 17.11 | 132.30 ± 17.65 | 132.42 ± 17.50 | 0.746 |
| DBP, mmHg | 79.07 ± 9.60 | 80.12 ± 9.31 | 79.85 ± 9.39 | 0.194 |
| Blood Glucose, mmol/L |
6.22 ± 1.75 |
6.36 ± 2.04 |
6.32 ± 1.97 |
0.385 |
Note: Data presented are mean ± SD or n (%); MNA: Mini nutritional assessment; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; a. Poor nutrition: MNA scores ≤ 23.5, included 9 elders of malnutrition (MNA scores < 17.0); b .Well-nourished: MNA scores ≥ 24; * p < 0.05; ** p < 0.01; *** p < 0.001.
Figure 2.
Changes of Age with MNA score for men and women
Spearman correlation analysis
Table 4 shows the Spearman correlation coefficients between unhealthy lifestyle factors and the MNA score. Current smoking status, alcohol consumption, lack of physical activity, long stretches of time spent sitting, poor sleep quality, a negative attitude towards life, and a poor family relationship were significantly negatively correlated with the MNA score (p < 0.05).
Table 4.
The spearman correlation coefficients between unhealthy lifestyle factors and MNA score
| Variable | MNA score | p |
|---|---|---|
| Current smoking (No = 0, Yes = 1) | -0.080 | 0.033* |
| Alcohol consumption (No = 0, Yes = 1) | -0.094 | 0.013* |
| Lack of physical activity (No = 0, Yes = 1) | -0.182 | < 0.001*** |
| Long stretches of time sitting per day (< 6 hours = 0, ≥ 6 hours = 1) | -0.107 | 0.005** |
| The poor sleep quality (Good = 0, Medium = 1, Poor = 2) | -0.163 | < 0.001*** |
| Negative attitude toward life (Positive = 0, Medium = 1, Negative = 2) | -0.272 | < 0.001*** |
| The poor family relationship (Good = 0, Medium = 1, Poor = 2) |
-0.180 |
< 0.001*** |
Note: MNA: Mini nutritional assessment; * p < 0.05; ** p < 0.01; *** p < 0.001.
Multivariate logistic regression analysis
Table 5 shows the results of the regression, after adjustment for gender, age, educational level, current smoking status, alcohol consumption, physical activity, and number of selfreported chronic diseases. Elderly adults who were female (OR = 2.06), were aged ≥85 years, (OR = 4.45), had a lower education (OR = 1.94), had more self-reported chronic diseases (OR = 2.65), and had been hospitalized in the last year (OR = 2.10) were more likely to suffer from poor nutrition. The risk of poor nutrition increased significantly with the presence of the following unhealthy lifestyle factors: current smoking status (OR = 2.84), alcohol consumption (OR = 3.50), lack of physical activity (OR = 1.67), long stretches of time spent sitting (OR = 1.08), a negative attitude towards life (OR = 5.00), and a poor family relationship (OR = 1.97).
Table 5.
Variables related to MNA score from multivariate logistic regression modelsa
| Variable | β | OR b (95% CI) | P | Nagelkerke R2 |
|---|---|---|---|---|
| Gender | 0.078 | |||
| Male | Reference | |||
| Female | 0.720 | 2.06 (1.22 - 3.48) | 0.007** | |
| Age, years | 0.150 | |||
| 60~74 | Reference | |||
| 75~84 | 0.729 | 2.07 (1.30 - 3.30) | 0.002** | |
| ≥ 85 | 1.494 | 4.45 (2.24 - 8.87) | < 0.001*** | |
| Education level | 0.179 | |||
| Middle school or above | Reference | |||
| Primary school | 0.825 | 2.28 (1.07 – 4.85) | 0.032* | |
| No education | 1.133 | 3.10 (1.42 – 6.77) | 0.004** | |
| No. of self-reported chronic disease | 0.211 | |||
| 0 | Reference | |||
| 1 | 0.566 | 1.76 (1.12 – 2.77) | 0.014* | |
| ≥ 2 | 0.973 | 2.65 (1.52 – 4.61) | 0.001** | |
| Hospitalization in the last year | 0.236 | |||
| No | Reference | |||
| Yes | 0.743 | 2.10 (1.29 - 3.41) | 0.003** | |
| Current smoking | 0.255 | |||
| No | Reference | |||
| Yes | 1.045 | 2.84 (1.67 - 4.83) | < 0.001*** | |
| Alcohol consumption | 0.273 | |||
| No | Reference | |||
| Yes | 1.253 | 3.50 (1.74 – 7.03) | < 0.001*** | |
| Physical activity | 0.285 | |||
| Yes | Reference | |||
| No | 0.515 | 1.67 (1.10 – 2.54) | 0.015* | |
| Time of sitting, hours | 0.079 | 1.08 (1.01 – 1.16) | 0.024* | 0.297 |
| Attitude toward life | 0.310 | |||
| Positive | Reference | |||
| Neutral | 0.947 | 2.58 (1.56 - 4.26) | < 0.001*** | |
| Negative | 1.610 | 5.00 (2.08 – 12.04) | < 0.001*** | |
| Family relationship | 0.318 | |||
| Good | Reference | |||
| Fair | -0.172 | 0.84 (0.55 – 1.30) | 0.439 | |
| poor |
0.680 |
1.97 (1.04 – 3.75) |
0.038* |
Note: OR = Odds ratio; 95%CI = 95% confidence interval; MNA: Mini nutritional assessment; a. Well-nourished = 0, Poor nutrition = 1; b. Adjusted OR; * p < 0.05; ** p < 0.01; *** p < 0.001.
Discussion
Main findings
In the present study, 25.7% of the elderly adults suffered from poor nutrition. The following factors were identified as possible predictors for poor nutrition in this population: female gender, increasing age, lower education level, more selfreported chronic diseases, and hospitalization in the previous year. The relationships between poor nutrition and the majority of unhealthy lifestyle factors (e.g., current smoking status, alcohol consumption, and time of sitting) were also identified in the current study of elderly participants.
Comparison with previous studies
A longitudinal study conducted over 11 years demonstrated that malnutrition or RM were both significantly associated with shorter survival among community-dwelling elderly adults; the multi-adjusted HRs of mortality for malnutrition and RM were 2.40 and 1.49, respectively (8). These findings underscore the need to develop a better understanding of the prevalence of malnutrition, RM, and the associated risk factors for malnutrition in elderly adults.
In the present study, the incidence of malnutrition and RM was 1.3% and 24.4%, respectively, among communitydwelling elders. Not surprisingly, our values were similar to the findings of several other studies (13, 20, 30) using the same assessment tool (MNA), cut-off score (≤23.5 points), and research subjects (community-dwelling elderly adults). Specifically, the incidence of malnutrition and RM in our study were similar to findings from Shanghai (1.7% and 19.1%, respectively), Guangzhou (1.0% and 29.2%, respectively), and Beijing (0.2% and 32.3%, respectively) in China, as well as in Japan (0.2% and 20.6%, respectively) and Singapore (2.8% and 50.3%, respectively) [6, 14, 16, 17, 31]. Conversely, a study by Han and colleagues in Wuhan, China reported a higher prevalence of malnutrition and RM (8% and 36.4%, respectively) (10). There are 4 possible explanations for these differences. First, the current study did not include elderly individuals who experienced serious or acute diseases, or cognitive impairment. Second, there are differences in age distributions and sample sizes between studies. In the Wuhan study, the average age was 74.14 (SD, 5.95) years and included 162 participants, but the current study recruited 708 participants with an average age of 70.19 (SD, 8.25) years. Third, the results may be influenced by geographical differences and climates. Wuhan city in China is known for its hot and humid summers; the survey was conducted during the hot summer months, which may have affected individual participant appetites and weights, resulting in a higher incidence of malnutrition (10). Lastly, different economic levels may contribute to poor nutrition (7, 32). More specifically, Shanghai, Beijing, and Guangzhou are prosperous cities in China, as is Dongguan, which was the focus of the current study. However, Wuhan city is less prosperous in comparison.
The characteristics assessed in this sample population were of interest. With its accelerated industrialization and urbanization, Liaobu Town is becoming an affluent town that has benefited from the policy of reform and opening-up. Despite these advancements, a high prevalence of poor nutrition was identified in this study; more importantly, it seems to be neglected. The majority of elderly people (long-term residents) have experienced a transition from poverty to affluence with the reform and opening-up. We speculated that elderly adults would maintain their frugal living habits that were developed during the period of poverty and that the unhealthy lifestyle factors might also play an important role in poor nutrition. In the present study, the high prevalence of poor nutrition was predicted by 11 risk factors, which were divided into sociodemographic characteristics, health-related factors, and unhealthy lifestyle factors in order to make comparisons.
In regards to the sociodemographic characteristics assessed in the present study, female gender, older age, and lower education level were associated with poor nutrition. Previous studies also reported older age to be associated with a higher prevalence of poor nutrition (6, 17). Han and colleagues demonstrated that aging was negatively correlated with MNA scores in China (10) Furthermore, the results of other studies have revealed that aging is accompanied by changes in physical composition, declining gastrointestinal function, and reduced feeding drive, which may also affect digestion and absorption (33, 34). Poor nutrition was more commonly observed among those with a single marital status (unmarried, divorced, or widowed) (Table 2); however, our study failed to observe an association between marital status and nutritional status in the multivariate logistic analysis (20, 35). A recent systematic review of 28 observational studies provided strong evidence for the lack of association between the death of a spouse and malnutrition (19). While some studies have suggested that there is a relationship between marital and nutritional status (10), these results remain controversial.
With regard to health-related factors, correlations have been reported between poor nutrition and the number of selfreported chronic diseases and hospitalization in the previous year. Consistent with the finding for chronic diseases (30, 36, 37), Shi and colleagues reported that the presence of ≥2 comorbidities contributed to the prevalence of malnutrition or RM (20). Generally, poor physical health is positively associated with poor nutrition. Therefore, hospitalization within the previous year may have impacted individual nutritional status (19). Dorner and colleagues also reported that 76.7% of elderly patients who were acutely hospitalized were malnourished or had RM (38); it is likely that those individuals may require a longer time to recover after their hospital discharge.
Surprisingly, we found that that the majority of unhealthy lifestyle factors were independently associated with poor nutrition. In addition, the results of the Spearman correlation analysis (Table 4) further supported this phenomenon. Unhealthy lifestyle factors may play a predictive role in malnutrition among the elderly. A smoker's taste and appetite might be influenced by tobacco or the pro-inflammatory effect of smoking, which may lead to malnutrition (37, 39). Interestingly, a study from the Netherlands reported that alcohol consumption decreased the risk of malnutrition. However, our results supported the opposite association: drinking increased the risk of poor nutrition. The high energy content of alcohol may prevent or slow weight loss (37); however, data supporting this phenomenon in elderly adults are lacking. Arif and colleagues reported that moderate alcohol consumption has a protective effect on obesity; in other words, drinking may lead to weight loss (40). Another explanation for our observations might be related to the limited number of current alcohol consumers in our study. Moreover, other studies in China have suggested that smoking and drinking do not influence nutritional status (20, 41). Thus, further research is necessary to confirm this relationship.
Another finding of the current study was that a lack of exercise and sedentary behavior were independent risk factors for poor nutrition. During the survey period, we found that some elders were not willing to exercise, but preferred sitting for long stretches every day, especially those who were older and frail and those with diseases such as CVD. A recently systematic review supported the hypothesis that an exercise program for elderly patients may help to improve function and healthcare (42). Therefore, exercise may ameliorate nutritional status. Fuzeki and colleagues found that sedentary behavior was associated with overweight or weight gain (43); however, this observation may also be applied to young and middle-aged individuals. With aging, muscle mass and muscle strength are progressively lost (44); as a result, elderly adults lose their ability to be active, gradually leading them to sit for extended periods of time. Van Cauwenberg and Buman reported that individuals aged ≥75 years had longer sedentary duration than those aged 65-74 years (45, 46). A lack of exercise and long stretches of time spent sitting may lead to a decrease in energy consumption and loss of appetite, which establishes a vicious cycle that may eventually lead to malnutrition. This cycle may also occur in elderly individuals who are frail or who have various diseases. In this study, the MNA score decreased with increasing age (Figure 2). Therefore, for the reasons suggested above, a lack of exercise and long stretches of time spent sitting might contribute to a higher risk of poor nutrition in elderly adults.
We also explored the association between an individual's attitude towards life and nutritional status. Previous studies have demonstrated that depression is a contributory factor for the development of undernutrition (16, 47). However, even if an individual was not depressed, those who felt negatively about life were at an increased risk of developing poor nutrition in this study. These findings may be explained by decreased food cravings, reduced appetite, and finding an enjoyment in sitting for a long time in elderly adults with a negative outlook (47). This finding suggests that maintaining a positive attitude towards life may contribute to improved nutritional status, which may be a useful finding for health education provided to elderly individuals.
Additionally, our study assessed the effects of a good family relationship on nutritional status. Having a family relationship was found to be an indispensable part of an individual's social network. Having a poor relationship with family members, as an unhealthy lifestyle factor, may cause social isolation and lead to appetite loss, malnutrition, and increased mortality (7, 48, 49). However, Ferdous and Posner found that, among the number of social networks an individual has, belonging to a social club was not associated with malnutrition (50, 51). The current study assessed whether the individuals had a positive relationship with their family members rather than the number of social networks, a difference that resulted in the observed association. However, the findings regarding family relationships may be better supported by additional studies on this topic.
Based on these findings, the results of our study suggest that promotion of healthy lifestyles may be a prevention and intervention strategy to reverse the progression from having a RM to malnutrition or from being well-nourished to having a RM among the elderly.
Limitations
This study has several limitations. First, the study was conducted in a single town, which likely does not represent the entire population of China. Second, we did not collect income data for each individual, which is a very sensitive problem in China and may affect an individual's nutritional status. However, given that these data are unreliable in China, it would be difficult to draw accurate conclusions regarding the effect on an individual's nutritional status. In order to begin understanding the relationship between individual income level and nutritional status, we divided the participants into several groups according to their economic status. Third, because of the cross-sectional nature of this study design, causal relationships could not be inferred. Additionally, although the data were collected through face-to-face interviews, recall bias may have been introduced by the elderly participants. Future research is needed, which should utilize a larger and prospective cohort design to infer the causal relationships.
Conclusion
Although the prevalence of malnutrition was low, a high prevalence of RM was observed in elderly adults in this study population in China. Poor nutrition was associated with female gender, older age, lower education, more self-reported chronic diseases, hospitalization in the last year, current smoking status, alcohol consumption, lack of physical activity, long times spent sitting, negative attitude towards life, and a poor family relationship. These findings may provide useful information for future longitudinal studies to establish the effects and causal relationships regarding malnutrition prevalence, as well as multifactorial interventions to prevent malnutrition among elderly adults.
Authors' contributions: All authors contributed to the development of the study framework, interpretation of the results, and revision of successive drafts of the manuscript, and all have approved the version submitted for publication. JLL, MJJ, JT, and BKY were responsible for data collection. WQL and JT conducted the data analyses. WQL and LXY drafted the manuscript. HHXW, BL, and PXW finalized the manuscript with input from all authors.
Competing interests: The authors have no potential conflicts of interest.
Acknowledgments: This study was supported by the Science and Technology Program of Guangzhou (201607010136) and the Guangzhou 121 Talents program. We gratefully acknowledged Songtao Tang, Jianhu Zhong for their excellent work in study coordination, data collection and management.
References
- 1.Ayers E, Verghese J. Locomotion, cognition and influences of nutrition in ageing. Proc Nutr Soc. 2014;73(2):302–308. doi: 10.1017/S0029665113003716. 10.1017/S0029665113003716 PubMed PMID: 24176094. [DOI] [PubMed] [Google Scholar]
- 2.Wang XQ, Chen PJ. Population ageing challenges health care in China. Lancet. 2014;383(9920):870. doi: 10.1016/S0140-6736(14)60443-8. 10.1016/S0140-6736(14)60443-8 PubMed PMID: 24607099. [DOI] [PubMed] [Google Scholar]
- 3.Tsai AC, Chang JM, Lin H, Chuang YL, Lin SH, Lin YH. Assessment of the nutritional risk of >53-year-old men and women in Taiwan. Public Health Nutr. 2004;7(1):69–76. doi: 10.1079/phn2003519. 10.1079/PHN2003519 PubMed PMID: 14972074. [DOI] [PubMed] [Google Scholar]
- 4.Visvanathan R, Macintosh C, Callary M, Penhall R, Horowitz M, Chapman I. The nutritional status of 250 older Australian recipients of domiciliary care services and its association with outcomes at 12 months. J Am Geriatr Soc. 2003;51(7):1007–1011. doi: 10.1046/j.1365-2389.2003.51317.x. 10.1046/j.1365-2389.2003.51317.x PubMed PMID: 12834523. [DOI] [PubMed] [Google Scholar]
- 5.Margetts BM, Thompson RL, Elia M, Jackson AA. Prevalence of risk of undernutrition is associated with poor health status in older people in the UK. Eur J Clin Nutr. 2003;57(1):69–74. doi: 10.1038/sj.ejcn.1601499. 10.1038/sj.ejcn.1601499 PubMed PMID: 12548299. [DOI] [PubMed] [Google Scholar]
- 6.Kaburagi T, Hirasawa R, Yoshino H, Odaka Y, Satomi M, Nakano M, et al. Nutritional status is strongly correlated with grip strength and depression in community-living elderly Japanese. Public Health Nutr. 2011;14(11):1893–1899. doi: 10.1017/S1368980011000346. 10.1017/S1368980011000346 PubMed PMID: 21426623. [DOI] [PubMed] [Google Scholar]
- 7.Chen CC, Schilling LS, Lyder CH. A concept analysis of malnutrition in the elderly. J Adv Nurs. 2001;36(1):131–142. doi: 10.1046/j.1365-2648.2001.01950.x. 10.1046/j.1365-2648.2001.01950.x PubMed PMID: 11555057. [DOI] [PubMed] [Google Scholar]
- 8.Shakersain B, Santoni G, Faxen-Irving G, Rizzuto D, Fratiglioni L, Xu W. Nutritional status and survival among old adults: an 11-year population-based longitudinal study. Eur J Clin Nutr. 2015 doi: 10.1038/ejcn.2015.109. [DOI] [PubMed] [Google Scholar]
- 9.White JV, Guenter P, Jensen G, Malone A, Schofield M. Consensus statement: Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition) JPEN J Parenter Enteral Nutr. 2012;36(3):275–283. doi: 10.1177/0148607112440285. 10.1177/0148607112440285 PubMed PMID: 22535923. [DOI] [PubMed] [Google Scholar]
- 10.Han Y, Li S, Zheng Y. Predictors of nutritional status among community-dwelling older adults in Wuhan, China. Public Health Nutr. 2009;12(8):1189–1196. doi: 10.1017/S1368980008003686. 10.1017/S1368980008003686 PubMed PMID: 18789169. [DOI] [PubMed] [Google Scholar]
- 11.Gulland A. Malnutrition and obesity coexist in many countries, report finds. BMJ. 2016;353:i3351. doi: 10.1136/bmj.i3351. 10.1136/bmj.i3351 PubMed PMID: 27306276. [DOI] [PubMed] [Google Scholar]
- 12.International Food Policy Research Institute. 2016. Global Nutrition Report 2016: From Promise to Impact: Ending Malnutrition by 2030.Washington, DC: IFPRI, 2016.
- 13.Guigoz Y. The Mini Nutritional Assessment (MNA) review of the literature—What does it tell us? J Nutr Health Aging. 2006;10(6):466–485. PubMed PMID: 17183419. [PubMed] [Google Scholar]
- 14.Wang XY, Lu XC, Li JY, Gan Y, Wang MY, Deng H. Nutritional status of residents in Guangzhou community. Chinese Journal of Gerontology. 2015;7:1958–1960. [Google Scholar]
- 15.Kaiser MJ, Bauer JM, Ramsch C, Uter W, Guigoz Y, Cederholm T, et al. Frequency of malnutrition in older adults: a multinational perspective using the mini nutritional assessment. J Am Geriatr Soc. 2010;58(9):1734–1738. doi: 10.1111/j.1532-5415.2010.03016.x. 10.1111/j.1532-5415.2010.03016.x PubMed PMID: 20863332. [DOI] [PubMed] [Google Scholar]
- 16.Koo YX, Kang ML, Auyong A, Liau GZ, Hoe J, Long M, et al. Malnutrition in older adults on financial assistance in an urban Asian country: a mixed methods study. Public Health Nutr. 2014;17(12):2834–2843. doi: 10.1017/S1368980013002413. 10.1017/S1368980013002413 PubMed PMID: 24008061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Li Y, Chen B, Guan SC, Zhang J. Nutritional status and related factors of elderly people in Beijing community. Chinese Journal of Gerontology. 2012;20:4479–4481. [Google Scholar]
- 18.Boulos C, Salameh P, Barberger-Gateau P. Malnutrition and frailty in community dwelling older adults living in a rural setting. Clin Nutr. 2016;35(1):138–143. doi: 10.1016/j.clnu.2015.01.008. 10.1016/j.clnu.2015.01.008 PubMed PMID: 25649256. [DOI] [PubMed] [Google Scholar]
- 19.van der Pols-Vijlbrief R, Wijnhoven HA, Schaap LA, Terwee CB, Visser M. Determinants of protein-energy malnutrition in community-dwelling older adults: a systematic review of observational studies. Ageing Res Rev. 2014;18:112–131. doi: 10.1016/j.arr.2014.09.001. 10.1016/j.arr.2014.09.001 PubMed PMID: 25257179. [DOI] [PubMed] [Google Scholar]
- 20.Shi R, Duan J, Deng Y, Tu Q, Cao Y, Zhang M, et al. Nutritional status of an elderly population in Southwest China: a cross-sectional study based on comprehensive geriatric assessment. J Nutr Health Aging. 2015;19(1):26–32. doi: 10.1007/s12603-014-0471-y. 10.1007/s12603-014-0471-y PubMed PMID: 25560813. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.People's Government of Liaobu Town, Duangguan City. [EB/OL]. http://liaobu.dg.gov.cn/rsbl.asp?lm=49&lm2=57. In.
- 22.Chen CM. Overview of obesity in Mainland China. Obes Rev. 2008;9:14–21. doi: 10.1111/j.1467-789X.2007.00433.x. 10.1111/j.1467-789X.2007.00433.x PubMed PMID: 18307694. [DOI] [PubMed] [Google Scholar]
- 23.Guigoz Y, Vellas B, Garry PJ. Assessing the nutritional status of the elderly: The Mini Nutritional Assessment as part of the geriatric evaluation. Nutr Rev. 1996;54:S59–S65. doi: 10.1111/j.1753-4887.1996.tb03793.x. 10.1111/j.1753-4887.1996.tb03793.x PubMed PMID: 8919685. [DOI] [PubMed] [Google Scholar]
- 24.Guigoz Y, Vellas B, Garry PJ. Mini Nutritional Assessment: a practical assessment tool for grading the nutritional state of elderly patients. Facts Res Gerontol. 1994;4:15–59. [Google Scholar]
- 25.Morley JE. Assessment of malnutrition in older persons: a focus on the Mini Nutritional Assessment. J Nutr Health Aging. 2011;15(2):87–90. doi: 10.1007/s12603-011-0018-4. 10.1007/s12603-011-0018-4 PubMed PMID: 21365159. [DOI] [PubMed] [Google Scholar]
- 26.Nijs KA, de Graaf C, Siebelink E, Blauw YH, Vanneste V, Kok FJ, et al. Effect of family-style meals on energy intake and risk of malnutrition in dutch nursing home residents: a randomized controlled trial. J Gerontol A Biol Sci Med Sci. 2006;61(9):935–942. doi: 10.1093/gerona/61.9.935. 10.1093/gerona/61.9.935 PubMed PMID: 16960024. [DOI] [PubMed] [Google Scholar]
- 27.Izawa S, Kuzuya M, Okada K, Enoki H, Koike T, Kanda S, et al. The nutritional status of frail elderly with care needs according to the mini-nutritional assessment. Clin Nutr. 2006;25(6):962–967. doi: 10.1016/j.clnu.2006.05.006. 10.1016/j.clnu.2006.05.006 PubMed PMID: 16859814. [DOI] [PubMed] [Google Scholar]
- 28.Vellas B, Guigoz Y, Baumgartner M, Garry PJ, Lauque S, Albarede JL. Relationships between nutritional markers and the mini-nutritional assessment in 155 older persons. J Am Geriatr Soc. 2000;48(10):1300–1309. doi: 10.1111/j.1532-5415.2000.tb02605.x. 10.1111/j.1532-5415.2000.tb02605.x PubMed PMID: 11037019. [DOI] [PubMed] [Google Scholar]
- 29.Vellas B, Lauque S, Andrieu S, Nourhashemi F, Rolland Y, Baumgartner R, et al. Nutrition assessment in the elderly. Curr Opin Clin Nutr Metab Care. 2001;4(1):5–8. doi: 10.1097/00075197-200101000-00002. 10.1097/00075197-200101000-00002 PubMed PMID: 11122552. [DOI] [PubMed] [Google Scholar]
- 30.Tsai AC, Chang TL, Yang TW, Chang-Lee SN, Tsay SF. A modified mini nutritional assessment without BMI predicts nutritional status of community-living elderly in Taiwan. J Nutr Health Aging. 2010;14(3):183–189. doi: 10.1007/s12603-010-0046-5. 10.1007/s12603-010-0046-5 PubMed PMID: 20191250. [DOI] [PubMed] [Google Scholar]
- 31.Fei XF, Cheng QM, Shi YM, Cao WX. Nutritional assessment and intervene points for retired residents in a community. Parenteral & Enteral Nutrition. 2003;10(1):28–31. [Google Scholar]
- 32.Donini LM, Scardella P, Piombo L, Neri B, Asprino R, Proietti AR, et al. Malnutrition in elderly: social and economic determinants. J Nutr Health Aging. 2013;17(1):9–15. doi: 10.1007/s12603-012-0374-8. 10.1007/s12603-012-0374-8 PubMed PMID: 23299371. [DOI] [PubMed] [Google Scholar]
- 33.Brownie S. Why are elderly individuals at risk of nutritional deficiency? Int J Nurs Pract. 2006;12(2):110–118. doi: 10.1111/j.1440-172X.2006.00557.x. 10.1111/j.1440-172X.2006.00557.x PubMed PMID: 16529597. [DOI] [PubMed] [Google Scholar]
- 34.Visvanathan R. Anorexia of Aging. Clin Geriatr Med. 2015;31(3):417–427. doi: 10.1016/j.cger.2015.04.012. 10.1016/j.cger.2015.04.012 PubMed PMID: 26195100. [DOI] [PubMed] [Google Scholar]
- 35.Kabir ZN, Ferdous T, Cederholm T, Khanam MA, Streatfied K, Wahlin A. Mini Nutritional Assessment of rural elderly people in Bangladesh: the impact of demographic, socio-economic and health factors. Public Health Nutr. 2006;9(8):968–974. doi: 10.1017/s1368980006009906. 10.1017/PHN2006990 PubMed PMID: 17125558. [DOI] [PubMed] [Google Scholar]
- 36.Kuzuya M, Kanda S, Koike T, Suzuki Y, Satake S, Iguchi A. Evaluation of Mini-Nutritional Assessment for Japanese frail elderly. Nutrition. 2005;21(4):498–503. doi: 10.1016/j.nut.2004.08.023. 10.1016/j.nut.2004.08.023 PubMed PMID: 15811771. [DOI] [PubMed] [Google Scholar]
- 37.van Bokhorst-de VDSM, Lonterman-Monasch S, de Vries OJ, Danner SA, Kramer MH, Muller M. Prevalence and determinants for malnutrition in geriatric outpatients. Clin Nutr. 2013;32(6):1007–1011. doi: 10.1016/j.clnu.2013.05.007. 10.1016/j.clnu.2013.05.007 [DOI] [PubMed] [Google Scholar]
- 38.Dorner TE, Luger E, Tschinderle J, Stein KV, Haider S, Kapan A, et al. Association between nutritional status (MNA(R)-SF) and frailty (SHARE-FI) in acute hospitalised elderly patients. J Nutr Health Aging. 2014;18(3):264–269. doi: 10.1007/s12603-013-0406-z. 10.1007/s12603-013-0406-z PubMed PMID: 24626753. [DOI] [PubMed] [Google Scholar]
- 39.Collins PF, Stratton RJ, Elia M. The influence of smoking status on malnutrition risk and 1-year mortality in outpatients with chronic obstructive pulmonary disease. Journal of Human Nutrition and Dietetics. 2011;24(4):382–383. [Google Scholar]
- 40.Arif AA, Rohrer JE. Patterns of alcohol drinking and its association with obesity: data from the Third National Health and Nutrition Examination Survey, 1988-1994. BMC Public Health. 2005;5:126. doi: 10.1186/1471-2458-5-126. 10.1186/1471-2458-5-126 PubMed PMID: 16329757; PMCID 1318457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Ji L, Meng H, Dong B. Factors associated with poor nutritional status among the oldest-old. Clin Nutr. 2012;31(6):922–926. doi: 10.1016/j.clnu.2012.03.007. 10.1016/j.clnu.2012.03.007 PubMed PMID: 22521179. [DOI] [PubMed] [Google Scholar]
- 42.Martinez-Velilla N, Cadore L, Casas-Herrero A, Idoate-Saralegui F, Izquierdo M. Physical Activity and Early Rehabilitation in Hospitalized Elderly Medical Patients: Systematic Review of Randomized Clinical Trials. J Nutr Health Aging. 2016;20(7):738–751. doi: 10.1007/s12603-016-0683-4. 10.1007/s12603-016-0683-4 PubMed PMID: 27499308. [DOI] [PubMed] [Google Scholar]
- 43.Fuzeki E, Vogt L, Banzer W. Sedentary behaviour and health. Gesundheitswesen. 2015;77(3):148–160. doi: 10.1055/s-0034-1370951. PubMed PMID: 24696372. [DOI] [PubMed] [Google Scholar]
- 44.Visvanathan R, Yu S, Field J, Chapman I, Adams R, Wittert G, et al. Appendicular Skeletal Muscle Mass: Development and Validation of Anthropometric Prediction Equations. J Frailty Aging. 2012;1(4):147–151. doi: 10.14283/jfa.2012.23. PubMed PMID: 27093315. [DOI] [PubMed] [Google Scholar]
- 45.Van Cauwenberg J, Van Holle V, De Bourdeaudhuij I, Owen N, Deforche B. Diurnal Patterns and Correlates of Older Adults' Sedentary Behavior. PLoS One. 2015;10(8):e133175. doi: 10.1371/journal.pone.0133175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Buman MP, Hekler EB, Haskell WL, Pruitt L, Conway TL, Cain KL, et al. Objective light-intensity physical activity associations with rated health in older adults. Am J Epidemiol. 2010;172(10):1155–1165. doi: 10.1093/aje/kwq249. 10.1093/aje/kwq249 PubMed PMID: 20843864; PMCID 3004766. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Chen CC. A framework for studying the nutritional health of community-dwelling elders. Nurs Res. 2005;54(1):13–21. doi: 10.1097/00006199-200501000-00003. 10.1097/00006199-200501000-00003 PubMed PMID: 15695935. [DOI] [PubMed] [Google Scholar]
- 48.McIntosh WA, Shifflett PA, Picou JS. Social support, stressful events, strain, dietary intake, and the elderly. Med Care. 1989;27(2):140–153. doi: 10.1097/00005650-198902000-00005. 10.1097/00005650-198902000-00005 PubMed PMID: 2918765. [DOI] [PubMed] [Google Scholar]
- 49.Martin CT, Kayser-Jones J, Stotts N, Porter C, Froelicher ES. Factors contributing to low weight in community-living older adults. J Am Acad Nurse Pract. 2005;17(10):425–431. doi: 10.1111/j.1745-7599.2005.00073.x. 10.1111/j.1745-7599.2005.00073.x PubMed PMID: 16181265. [DOI] [PubMed] [Google Scholar]
- 50.Posner BM, Jette A, Smigelski C, Miller D, Mitchell P. Nutritional risk in New England elders. J Gerontol. 1994;49(3):M123–M132. doi: 10.1093/geronj/49.3.m123. 10.1093/geronj/49.3.M123 PubMed PMID: 8169334. [DOI] [PubMed] [Google Scholar]
- 51.Ferdous T, Kabir ZN, Wahlin A, Streatfield K, Cederholm T. The multidimensional background of malnutrition among rural older individuals in Bangladesh—a challenge for the Millennium Development Goal. Public Health Nutr. 2009;12(12):2270–2278. doi: 10.1017/S1368980009005096. 10.1017/S1368980009005096 PubMed PMID: 19257922. [DOI] [PubMed] [Google Scholar]