Abstract
Background
Community-living older adults may be susceptible to malnutrition (undernutrition) due to both physiological and non-physiological causes. The condition develops over time and the early signs and symptoms may not be obvious. Therefore awareness and early identification of nutrition risk factors may prevent, or at least slow, the progression of malnutrition.
Objective
To describe community-living older adults' understanding of the signs of malnutrition, where they would seek malnutrition information and their selfperception of body weight.
Design
Older adults (aged ≥ 65 years) living in the community setting completed an online or paper based questionnaire between May and August 2016. The questionnaire contained a mix of closed and open questions which related to weight perception, weight changes, perceived signs of malnutrition and sources of malnutrition information. Body mass index (BMI) from self-reported data was classified using BMI reference ranges for older adults and compared to self-perceived weight status. Textual data regarding the signs of malnutrition were analysed and reviewed by two authors using content analysis. Descriptive statistics were used to describe participant characteristics.
Results
A total of 344 responses were received, the majority (90%) completed online. Mean participant age was 73 years and 57% of participants were female. Most (92%) reported their health to be good/very good. Body weight was perceived to be just right or more than it should be by 87% of underweight women and 97% of underweight men. Although 71% of the participants indicated their body weight had remained stable in the past six months, 37% reported they had been trying to change their weight. Signs of malnutrition resulted in four key categories of (i) psychological, (ii) physical appearance, (ii) bodily function and (iv) weight change. Very few reported the need to locate malnutrition information and indicated the top three sources for information would be (i) general practitioner, (ii) dietitian or (iii) internet.
Conclusion
This paper has presented useful data about malnutrition from the perspective of the community-living older adult. We found there may be uncertainty about the best weight, for older age. As many indicated they had been trying to change their weight, awareness needs to be raised regarding the impact of weight changes on health outcomes in this population. In this study, the internet appeared to be a key provider of nutrition information. Healthcare professionals need to consider how this can be used in an informative manner among community living older adults as a tool for raising awareness about nutrition risk and malnutrition.
Key words: Older adults, community, malnutrition signs, body mass index, weight perception
Introduction
Malnutrition, also referred to as undernutrition (1), is not only an issue for older adults in care, it is also present among community-living older adults (2). Forms of undernutrition include deficiencies of protein and/or energy (kilojoules), referred to as protein energy malnutrition (PEM), or deficiencies of specific micronutrients (3). In industrialised nations such as Australia and the United Kingdom, undernutrition is more likely to be seen in those with acute or chronic disease, or in older adults (3). In many cases treatable causes of undernutrition in older adults may be overlooked (3) and the reasons for this are not clear. However, left untreated, overall physical functioning, health and quality of life can be negatively affected (4).
Many community-living older adults may be susceptible to malnutrition due to both physiological and non-physiological causes (5). These can include social factors, such as inability to prepare and cook meals, psychological factors such as anxiety, depression or bereavement and medical factors such as infection or multiple medications (5). However, malnutrition is a continuum; the condition develops gradually over time and so the early signs and symptoms may not be as obvious as in other diseases (6, 7). Awareness and identification of risk factors that occur early in the malnutrition continuum may prevent, or at least slow, the progression. Therefore, the process of malnutrition screening is commonly used to determine if there is risk of malnutrition and to raise awareness of the factors associated with risk.
Various nutrition screening tools have been validated to detect the risk of developing malnutrition in older adults (8). Validated tools for use in community-living older adults include the Mini Nutritional Assessment Short Form (MNASF) (9), the Malnutrition Universal Screening Tool (MUST) (10) and the Seniors in the Community: Risk Evaluation for Eating and Nutrition (SCREEN II) (11). Screening tools do not diagnose malnutrition; their purpose is to identify risk factors that may lead to the development of malnutrition and the need for further assessment (6). Within the screening tool, a score is assigned to each risk factor and the total score determines the course of action (12). Scores that identify a level of risk should lead to a full nutritional assessment with targeted interventions to address the risk factors (6).
Malnutrition screening is designed to be quick whereas the ensuing nutrition assessment is not such a simple task, expertise is required (6). It does not rely exclusively on the comparison of dietary intake with estimated nutrient requirements. Other measurements such as anthropometry, biochemical data, social and medical history should be considered in the assessment (6). Currently there is lack of consensus on the definition for the term malnutrition and the diagnosis of the condition (13, 14). Some guidelines consider an aetiology-based definition (1) whereas others provide criteria independent of aetiology (15). Low body weight, unintentional weight loss, inadequate energy intake or poor appetite have been defined as accepted criteria of PEM (16). However, undernutrition can occur at any body weight (1) and it is possible to be overweight and at risk for malnutrition. This was highlighted in a recent Australian study of 225 community-living older adults screened for malnutrition using the MNA-SF. Over one-third of the cohort found whose screening score indicated risk for malnutrition had a body mass index (BMI) in the overweight or obese range (17).
It has been suggested that older adults who are told they are at risk for malnutrition do not necessarily acknowledge this indicates a potential problem and may not view it as a health-care priority (18). In Australia, dietitians have reported a poor understanding of malnutrition as a barrier to conducting malnutrition screening of community-living older adults (19). Furthermore, it has been reported that seniors who have been screened for malnutrition and found to have risk factors present may deny the positive screening result (20). Others have declined the recommended nutrition assessment which should follow positive malnutrition screening (21, 22).
There are many signs of malnutrition including low body weight, weight loss and reduced food intake (12), and these are components of numerous malnutrition screening tools (8). Yet little research has examined what community-living older adults consider to be the signs of malnutrition. Additionally, it is not generally known where older adults would, or perceive they would, seek information regarding malnutrition. This is important to understand because perceptions about malnutrition, nutrition risk factors and where individuals seek nutrition information may play an important role in raising awareness and improving the management of this condition. Therefore, the aim of this study was to describe community-living older adults' (aged ≥ 65 years) understanding of the signs of malnutrition, where they would seek information regarding malnutrition, and their self-perception of body weight.
Methods
Our exploratory cross-sectional study was conducted throughout the Sunshine Coast region, Queensland, Australia. Eligible participants were older adults aged ≥ 65 years living independently in the community, not nursing homes or aged care facilities. Recruitment and data collection were carried out simultaneously between May and August 2016. During this three-month period, the lead researcher contacted several community-based recreational and social activity groups for seniors. Invitations to participate were distributed via informal presentations, email, and printed information sheets. The questionnaire length was approximately ten minutes and was available for completion either online using SurveyMonkey Inc. (Palo Alto, California, USA main website: www. surveymonkey.com), or as a paper based version, returned in a self-sealed envelope. To assist with response rate, the option to go into the draw to win one of ten $50 gift cards was offered to all participants.
To answer the research questions, the authors drafted a questionnaire specifically for this study. Prior to distribution, the questionnaire was piloted on two community-living older adults and two accredited practising dietitians with community and aged care dietetic experience (data not shown). The final questionnaire contained a mix of closed (multiple answer and dichotomous) and open (free text) questions. Basic demographic information (age, gender, living arrangement, marital status, highest education level and weekly income) was collected. Eleven questions related to weight perception, weight changes, current weight and height, health status, perceived signs of malnutrition and sources of malnutrition information (see Appendix).
Data Analysis
Online survey responses were exported to Excel 2016 (Microsoft Corporation). Paper based surveys were entered manually into the Excel spreadsheet. Final data were checked by two authors and imported to IBM SPSS Statistics version 24.0 (IBM Corporation). Descriptive statistics were used to describe participant characteristics and reported as frequencies and rounded percentages. Chi-square tests of independence were used with significance set to p < 0.05 and effect size calculated using Cramer's V.
The open-ended question, regarding signs of malnutrition, was analysed using content analysis (23). The analysis followed three main phases of preparation, organisation, and reporting (24). The process of constant comparison (25, 26) was used during the preparation phase whereby the entire data set of written responses was first read several times by the first author for familiarisation. During the organisation phase, an inductive approach was used to generate and apply descriptive codes to the comments according to the content. Related codes were combined to produce sub-categories and summarised into key categories based on the content. Quantitative results were generated by counting the number of items in each category and results reported as percentages of the overall number of coded comments. The first author initially analysed the data with review by another member of the research team, enabling both category refinement and research rigour.
BMIs were calculated from self-reported weights and heights using the formula: weight in kilograms/height in metres2. Results were classified based on BMI categories appropriate for older adults: underweight (BMI < 23kg/m2), normal weight (23-30 kg/m2) and overweight (> 30 kg/m2) (27) and the WHO BMI classifications for adults: underweight (<18.5), normal range (18.5-24.99), overweight (≥25) and obese (≥30) (28).
Ethics
Ethical approval was granted by the University of the Sunshine Coast Human Ethics Committee (approval number S/16/870). A research information sheet was provided to all participants and informed consent was implied upon questionnaire completion.
Results
In total, 390 responses (349 completed online and 41 completed on paper) were received. Forty-six were excluded as they were either blank, age was not provided or was less than 65 years. The final number of participants was 344 with the majority (90%) of questionnaires completed online. Just over half (57%) the participants were female (n = 196). The mean participant age was 73 years (range 65-91). Almost half (49%) had tertiary education, 65% were married and 92% reported their health to be good/very good. Participant demographics are presented in Table 1.
Table 1.
Characteristics of 344 community-living older adults aged ≥65 years
| Total n (%) | Male n (%) | Female n (%) | |
|---|---|---|---|
| Age Category (n=344) | |||
| 65-69 | 108 (31) | 34 (23) | 74 (38) |
| 70-74 | 119 (35) | 53 (36) | 66 (34) |
| 75-79 | 69 (20) | 35 (24) | 34 (17) |
| 80+ | 48 (14) | 26 (18) | 22 (11) |
| Self-Rated Health Status (n=344) | |||
| Very Good | 144 (42) | 52 (35) | 92 (47) |
| Good | 171 (50) | 77 (52) | 94 (48) |
| Fair | 28 (8) | 18 (12) | 10 (5) |
| Poor | 1 (<1) | 1 (1) | 0 (0) |
| Marital Status (n=344) | |||
| Married | 223 (65) | 113 (76) | 110 (56) |
| Widowed | 52 (15) | 9 (6) | 43 (22) |
| Divorced/separated | 41 (12) | 10 (7) | 31 (16) |
| Partnership | 19 (6) | 11 (7) | 8 (4) |
| Single | 9 (3) | 5 (3) | 4 (2) |
| Living Arrangement (n=339) | |||
| Live alone | 94 (28) | 25 (17) | 69 (36) |
| Live with spouse or partner | 236 (70) | 121 (83) | 115 (60) |
| Live with family or friends | 9 (3) | 0 (0) | 9 (5) |
| Highest Education Level (n=342) | |||
| University Post Graduate Level | 61 (18) | 32 (22) | 29 (15) |
| University Bachelor Degree | 91 (27) | 46 (32) | 45 (23) |
| Advanced Diploma or Diploma | 17 (5) | 3 (2) | 14 (7) |
| Secondary Education Years 10 or above | 93 (27) | 27 (18) | 66 (34) |
| Secondary Education Years 9 and below | 4 (1) | 1 (1) | 3 (2) |
| Trade or TAFE Equivalent | 64 (19) | 31 (21) | 33 (17) |
| Other | 12 (4) | 6 (4) | 6 (3) |
| Weekly Income (n=343)* | |||
| >$2000 | 6 (2) | 6 (4) | 0 (0) |
| $1500 - $1999 | 19 (6) | 12 (8) | 7 (4) |
| $1250-$1499 | 13 (4) | 7 (5) | 6 (3) |
| $1000 - $1249 | 30 (9) | 18 (12) | 12 (6) |
| $800-$999 | 33 (10) | 16 (11) | 17 (9) |
| $600-$799 | 51 (15) | 23 (16) | 28 (14) |
| $400-$599 | 81 (24) | 21 (14) | 60 (31) |
| $300-$399 | 32 (9) | 12 (8) | 20 (10) |
| $200-$299 | 6 (2) | 3 (2) | 3 (2) |
| $1-$199 | 6 (2) | 1 (1) | 5 (3) |
| Nil income | 4 (1) | 2 (1) | 2 |
| Prefer not to answer | 62 (18) | 27 (18) | 35 (18) |
Australian dollars
Signs of malnutrition
The majority (98%) of participants indicated they had heard of the term malnutrition. A small proportion (7%) indicated they had been screened for malnutrition. Nearly all participants (96%) provided a response to the open-question “what do you think are the signs of malnutrition?. Responses ranged from single words to longer lists and sentences, overall providing approximately 3275 words of written text for analysis. As some responses contained more than one sign, the number of codes assigned to each response ranged from 1 – 13, thereby resulting in more codes (n = 1168) than individual responses (n = 331). Females (n = 188) provided 723 comments (62% of total) and males (n = 143) provided 445 comments (38% of total). Four key categories emerged: (i) psychological; (ii) physical appearance; (iii) bodily function, and (iv) weight change (Table 2).
Table 2.
Content analysis of written comments (n = 1168) to the question: What do you think are the signs of malnutrition? provided by 331 community-living older adults (aged ≥65 years). Results organised by key and sub-categories for total sample and per gender
| Signs of malnutrition (1168 comments) | Total Comments % (n) | Male % (n) | Female % (n) |
|---|---|---|---|
| Psychological | 34 (398) | 31 (139) | 36 (259) |
| Feeling tired or having little energy | 21 (244) | 20 (89) | 21 (155) |
| Cognitive function and mood | 10 (121) | 9 (36) | 12 (85) |
| Appetite disorders | 3 (33) | 3 (14) | 3 (19) |
| Physical Appearance | 27 (313) | 28 (125) | 26 (188) |
| Changes in face, eyes, skin, nails and teeth | 17 (197) | 71 (16) | 17 (126) |
| Underweight appearance | 6 (73) | 8 (34) | 5 (39) |
| Signs of physical wasting | 3 (33) | 4 (16) | 2 (17) |
| Overweight appearance | 1 (10) | 1 (4) | 1 (1) |
| Bodily Function | 24 (282) | 23 (102) | 25 (180) |
| Health and illness related | 16 (191) | 15 (66) | 17 (125) |
| Reduced physical functioning | 7 (79) | 7 (30) | 7 (49) |
| Bowel functioning | 1 (12) | 1 (6) | 1 (6) |
| Weight Change | 15 (175) | 18 (79) | 13 (96) |
| Losing weight | 15 (170) | 17 (75) | 13 (95) |
| Gaining weight | <1 (5) | 1 (4) | 1 (1) |
| Total Comments | 100 (1168) | 100 (445) | 100 (723) |
Most participants (95%, n = 328) identified psychological signs. Signs that related to physical appearance were identified by 80% (n = 264), bodily functions were reported by 64% (n = 213) and weight change was reported by 52% (n = 173) of participants (Table 2).
Malnutrition information
Most participants (91%) indicated they had not sourced information about malnutrition for themselves (n = 314). Those who had attempted to find information (n = 28; 13 male and 15 female) selected a total of 59 sources. Many (64%) selected more than one source of information. ‘Dietitian' (n = 12; 20%), ‘internet' (n = 11; 19%) and ‘general practitioner' (GP) (n = 10; 17%) were the most selected sources of malnutrition information. Hospital, nurse, and pharmacist were least selected. Of those who had not sourced information about malnutrition, a total of 816 likely sources were selected, with 77% selecting more than one potential source of information. ‘GP' (n = 272; 33%), ‘dietitian' (n = 195; 24%) and ‘internet' (n = 150; 18%) were the most selected sources. Hospital, magazines, and books/journals were the least selected sources (Figure 1).
Figure 1.
Actual (n = 28) and proposed (n = 314) sources of malnutrition information reported by community-living, older adults aged ≥65 years
BMI and weight perception
BMI was calculated for 338 participants (146 males; 192 females). BMI scores ranged from 17 to 42 kg/m2 (mean 25.5). When compared to BMI classifications for older adults, over half (54.7%) of females (n = 105) and 67.8% of males (n = 99) were within the healthy weight range. Only 14.1% of females (n = 27) and 11.6% of males (n = 17) were classified as overweight and 31.3% of females (n = 60) and 20.5% of males (n = 30) were classified as underweight. A chisquare test of independence indicated a significant association between gender and BMI classification, χ2(2) = 6.306, p = 0.43, however the association was weak, Cramer's V = 0.137. A similar yet slightly stronger effect was also apparent when using the WHO BMI categories, χ2(3) = 25.436, p < 0.05, Cramer's V = 0.274 (Table 3).
Table 3.
Body mass index (BMI) calcuated from self-reported height and weight data as reported by community-living older adults aged ≥ 65 years (n = 338) categorised by age and gender according to BMI reference ranges recommended for older adults (aged ≥65 years) and WHO BMI reference ranges for adults (aged ≥18 years)
| BMI Category Older Adults (27) | Underweight BMI <23 % (n) | Healthy weight BMI 23-30 % (n) | Overweight BMI >30 % (n) | |
|---|---|---|---|---|
| Males | ||||
| 65-69 | 3.4 (5) | 15.8 (23) | 4.1 (6) | |
| 70-74 | 5.5 (8) | 24.7 (36) | 5.5 (8) | |
| 75-79 | 7.5 (11) | 15.1 (22) | 1 .4(2) | |
| 80+ | 4.1 (6) | 12.3 (18) | 0.7 (1) | |
| Total (n=146) | 20.5 (30) | 67.8 (99) | 11.6 (17) | |
| Females | ||||
| 65-69 | 9.9 (19) | 22.4 (43) | 5.7 (11) | |
| 70-74 | 10.9 (21) | 17.7 (34) | 5.7 (11) | |
| 75-79 | 7.8 (15) | 7.8 (15) | 1.0 (2) | |
| 80+ | 2.6 (5) | 6.8 (13) | 1.6 (3) | |
| Total (n=192) | 31.3 (60) | 54.7 (105) | 14.1 (27) | |
| WHO BMI Category Adults (28) | Underweight BMI <18.5 % (n) | Healthy weight BMI 18.5 – 24.9 % (n) | Overweight BMI ≥25 % (n) | Obese BMI ≥30 % (n) |
| Males | ||||
| 65-69 | 0.0 (0) | 8.9 (13) | 10.3 (15) | 4.1 (6) |
| 70-74 | 0.0 (0) | 11.0 (16) | 19.2 (28) | 5.5 (8) |
| 75-79 | 0.7 (1) | 9.6 (14) | 12.3 (18) | 1.4 (2) |
| 80+ | 0.0 (0) | 6.2 (9) | 10.3 (15) | 0.7 (1) |
| Total (n=146) | 0.7 (1) | 35.6 (52) | 52.1 (76) | 11.6 (17) |
| Females | ||||
| 65-69 | 2.1 (4) | 18.2 (35) | 11.5 (22) | 6.3 (12) |
| 70-74 | 0.5 (1) | 19.3 (37) | 8.9 (17) | 5.7 (11) |
| 75-79 | 0.0(0) | 13.5 (26) | 2.1 (4) | 1.0 (2) |
| 80+ | 0.5 (1) | 5.2 (10) | 3.6 (7) | 1.6 (3) |
| Total (n=192) | 3.1 (6) | 56.3 (108) | 26.0 (50) | 14.6 (28) |
Weight change in the past six months was reported by 29% of the sample and 37% indicated they had been trying to change their weight in the past six months. The majority (71%) reported their weight had remained stable and a small number indicated unintentional weight change (5%). There were no significant differences between gender and weight changes (Table 4).
Table 4.
Weight changes in the past six months as reported by Australian community-living older adults aged ≥ 65 years
| Variable | Total % (n) | Males % (n) | Females % (n) |
|---|---|---|---|
| Have you been trying to change your weight in the past 6 months? | |||
| Yes | 37 (127) | 40 (59) | 35 (68) |
| No | 58 (201) | 53 (79) | 62 (122) |
| No, but it changed anyway | 5 (16) | 7 (10) | 3 (6) |
| Total | 100 (344) | 100 (148) | 100 (196) |
| Has your weight changed in the past 6 months? | |||
| Yes, gained weight | 10 (35) | 8 (11) | 12 (24) |
| Yes, lost weight | 19 (64) | 24 (36) | 14 (28) |
| Weight remained stable | 71 (242) | 68 (101) | 72 (141) |
| Don’t know | <1 (2) | 0 (0) | 1 (2) |
| Total | 100 (343) | 100 (148) | 100 (195) |
Comparisons between perceived weight status compared to BMI reference ranges for older adults and WHO ranges are presented in table 5. When compared to reference ranges for older adults, all overweight males (n = 17) and 93% of overweight females (n = 25) correctly perceived their weight as more than it should be. Half (52%) the healthy weight women and 57% of healthy weight men perceived themselves as overweight. For underweight males, 13% perceived their weight as more than it should be whilst most (83%) perceived their weight as just right. Similarly, 23% of underweight females perceived their weight as more than it should be whilst 63% of underweight females perceived their weight as just right.
Table 5.
Proportion of community-living older adults aged ≥65 years (n = 337) in self-reported BMI categories for older adults and WHO adult BMI ranges per perceived weight status
| Perceived weight status compared to BMI reference ranges for older adults (27) | |||
|---|---|---|---|
| More than it should be % (n) | Just right % (n) | Less than it should be % (n) | |
| Women (n=192) | |||
| Underweight | 23 (14) | 63 (38) | 13 (8) |
| Healthy weight | 52 (55) | 48 (50) | 0 (0) |
| Overweight | 93 (25) | 7 (2) | 0 (0) |
| Men (n=145) | |||
| Underweight | 13 (4) | 83 (25) | 3 (1) |
| Healthy weight | 57 (56) | 42 (41) | 1 (1) |
| Overweight | 100 (17) | 0 (0) | 0 (0) |
| Perceived weight status compared to WHO BMI reference ranges (28) | |||
| More than it should be % (n) | Just right % (n) | Less than it should be % (n) | |
| Women (n=192) | |||
| Underweight | 0 (0) | 17 (1) | 83 (5) |
| Healthy weight | 27 (29) | 70 (76) | 3 (3) |
| Overweight | 78 (39) | 22 (11) | 0 (0) |
| Obese | 93 (26) | 7 (2) | 0 (0) |
| Men (n=145) | |||
| Underweight | 0 (0) | 100 (1) | 0 (0) |
| Healthy weight | 19 (10) | 77 (40) | 4 (2) |
| Overweight | 67 (50) | 33 (25) | 0 (0) |
| Obese | 100 (17) | 0 (0) | 0 (0) |
Discussion
The aim of this study was to describe community-living older adults' understanding of the signs of malnutrition, where they would locate malnutrition information and their selfperception of body weight. In this sample of 344 communityliving older adults, participants focussed on psychological signs of malnutrition with nearly all commenting in this category. Most malnutrition screening tools for older adults consider weight change as a risk factor for malnutrition (8). However in this sample weight change as a sign of malnutrition ranked lower than other factors. This may indicate changes in body weight were not generally associated with malnutrition.
Regarding body weight, a BMI within the range of 18.5-24.9 kg/m2 is considered normal for adults aged ≥ 18 years (29). However, this may not apply to adults aged ≥ 65 years. A recent meta-analysis (27) found a steep increase in mortality for those with a BMI less than 23 or higher than 30, suggesting a higher BMI range for older adults, which is applicable to this study. We found clear discrepancies in BMI classification and perception of weight. Of those who were classified in the healthy weight range for older adults as defined by Winter et al. (27), less than 50% of males and females perceived their body weight as just right. Further, 87% of women and 97% of men classified as underweight perceived their weight to be just right or more than it should be. Similarly, GPs and practice nurses have been found to identify inappropriate weight ranges for older adults (30). This proposes the BMI of 23-30 kg/m2 as a healthy weight range for older adults, may not be generally accepted, known, or applied clinically.
In relation to weight changes, a high proportion (71%) indicated their weight had remained stable in the past six months. This is positive as in both Western and Asian countries, weight stable individuals have lower mortality risk compared to those who experience weight changes (31). On the other hand, over one third of participants indicated they had been trying to change their weight in the past six months. Unintentional weight change was reported by a small number in this study (5%) and it was not determined if this was weight gain or loss. This may require further investigation as weight loss and weight cycling are associated with higher mortality risk among community-living older adults (32) and unintentional weight loss can be a signal of underlying disease (33).
With regard to malnutrition information, very few participants in our study indicated they had tried to find information for themselves. This may be explained by the overall wellness of this sample as the majority reported their health status to be good/very good. Potential sources of information on malnutrition were spread across several different areas, however seeking advice from a GP was most commonly reported by over a third of participants. This is consistent with a smaller Australian qualitative study of twelve participants who reported they would contact their GP first if they had dietary concerns (34). However in our study, of those who did locate malnutrition information, ‘dietitian' and ‘internet' were selected more times as an information source than ‘GP'. This may indicate discrepancies between perception and practice when it comes to locating malnutrition information.
In this study, the internet was found to be one of the most frequently used, or likely to be used, sources of malnutrition information. Although this survey was widely distributed throughout the local community, it cannot be overlooked that most respondents chose to participate using the online version. As the internet ranked highly as an information source in this study and participants were highly educated, respondent bias is likely to be present. Nonetheless, it is important that older adults are proactively guided to reliable online sources to avoid being misinformed. Work is currently being undertaken in this area. Valid online screening tools designed to assist with self-management of nutrition risk are highly utilised by older adults (35). As GPs and practice nurses often provide nutritonal advice to older adults (30), promotion of such online tools could easily be incorporated as an additional educational resource.
This research does have some limitations. It was undertaken in only one Australian state and therefore the results may not be generalizable throughout the wider community-living older population within Australia or beyond. Additionally, BMI scores were derived from self-reported data which has obvious limitations (36) and our findings regarding weight perceptions could be strengthened by accurately measuring weight and height. However, this paper has presented useful data about malnutrition from the perspective of the community-living older adult.
In conclusion, we found community-living older adults are uncertain about the best weight for age when compared to the recently proposed BMI range of 23-30 (27) for older adults. The signs of malnutrition identified by this cohort were more subjective (for example feeling tired) than objective (for example weight loss) and did not necessarily align with malnutrition screening tool risk factors. As the internet appeared to be a key provider of malnutrition information, healthcare professionals need to consider how this can be used in an informative fashion among community-living older adults as a tool for raising awareness about nutrition risk and malnutrition.
Conflict of Interest
This research was funded by a Nutrition and Dietetics PhD research scholarship from the School of Health and Sport Sciences, University of the Sunshine Coast. All authors declare no conflicts of interest.
Electronic supplementary material
Supplementary material is available for this article at https://doi.org/10.1007/s12603-017-0942-z and is accessible for authorized users.
Appendix
References
- 1.White JV, Guenter P, Jensen G, Malone A. Academy malnutrition work group et al. 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]
- 2.Agarwal E, Miller M, Yaxley A, Isenring E. Malnutrition in the elderly: a narrative review. Maturitas. 2013;76(4):296–302. doi: 10.1016/j.maturitas.2013.07.013. 10.1016/j.maturitas.2013.07.013 PubMed PMID: 23958435. [DOI] [PubMed] [Google Scholar]
- 3.Morley JE. Undernutrition in older adults. Family Practice. 2012;29:i89–i93. doi: 10.1093/fampra/cmr054. 10.1093/fampra/cmr054 PubMed PMID: 22399563. [DOI] [PubMed] [Google Scholar]
- 4.Kvamme JM, Olsen JA, Florholmen J, Jacobsen BK. Risk of malnutrition and healthrelated quality of life in community-living elderly men and women: The Tromso study. Qual Life Res. 2011;20(4):575–582. doi: 10.1007/s11136-010-9788-0. 10.1007/s11136-010-9788-0 PubMed PMID: 21076942. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ahmed T, Haboubi N. Assessment and management of nutrition in older people and its importance to health. Clin Interv Aging. 2010;5:207–216. doi: 10.2147/cia.s9664. PubMed PMID: 20711440, PMCID 2920201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Field LB, Hand RK. Differentiating malnutrition screening and assessment: a nutrition care process perspective. J Acad Nutr Diet. 2015;115(5):824–828. doi: 10.1016/j.jand.2014.11.010. 10.1016/j.jand.2014.11.010 PubMed PMID: 25582410. [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.Phillips MB, Foley AL, Barnard R, Isenring EA, Miller MD. Nutritional screening in community-dwelling older adults: a systematic literature review. Asia Pac J Clin Nutr. 2010;19(3):440–449. PubMed PMID: 20805090. [PubMed] [Google Scholar]
- 9.Rubenstein LZ, Harker JO, Salva A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short form mini nutritional assessment (MNA-SF) J Geront. 2001;56:366–377. doi: 10.1093/gerona/56.6.m366. 10.1093/gerona/56.6.M366 [DOI] [PubMed] [Google Scholar]
- 10.British Association of ParenteralEnteral Nutrition., The ‘MUST’ explanatory booklet, A guide to the ‘Malnutrition Universal Screening Tool’ (MUST) for adults: 7 BAPEN, 2001
- 11.Keller HH, Goy R, Kane SL. Validity and reliability of SCREEN II (Seniors in the community: risk evaluation for eating and nutrition, Version II) Eur J Clin Nutr. 2005;59(10):1149–1157. doi: 10.1038/sj.ejcn.1602225. 10.1038/sj.ejcn.1602225 PubMed PMID: 16015256. [DOI] [PubMed] [Google Scholar]
- 12.Kondrup J, Allison S, Elia M, Vellas B, Plauth M. ESPEN guidelines for nutrition screening 2002. Clin Nutr. 2003;22(4):415–421. doi: 10.1016/s0261-5614(03)00098-0. 10.1016/S0261-5614(03)00098-0 PubMed PMID: 12880610. [DOI] [PubMed] [Google Scholar]
- 13.Soeters PB, Reijven PLM, van Bokhorst-de van der Schueren MAE, Schols JMGA, Halfens RJG, Meijers JMM, van Gemert WG. A rational approach to nutritional assessment. Clin Nutr. 2008;27(5):706–716. doi: 10.1016/j.clnu.2008.07.009. 10.1016/j.clnu.2008.07.009 PubMed PMID: 18783855. [DOI] [PubMed] [Google Scholar]
- 14.Matarese LE, Charney P. Capturing the elusive diagnosis of malnutrition. Nutr Clin Prac. 2017;32(1):11–14. doi: 10.1177/0884533616671856. 10.1177/0884533616671856 [DOI] [PubMed] [Google Scholar]
- 15.Cederholm T, Bosaeus I, Barazzoni R, Bauer J, Van Gossum A, Klek S, et al. Diagnostic criteria for malnutrition -an ESPEN consensus statement. Clin Nutr. 2015;34(3):335–340. doi: 10.1016/j.clnu.2015.03.001. 10.1016/j.clnu.2015.03.001 PubMed PMID: 25799486. [DOI] [PubMed] [Google Scholar]
- 16.van der Pols-Vijlbrief R, Wijnhoven HAH, 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]
- 17.Winter J, Flanagan D, McNaughton SA, Nowson C. Nutrition screening of older people in a community general practice, using the MNA-SF. J Nutr Health Aging. 2013;17(4):322–325. doi: 10.1007/s12603-013-0020-0. 10.1007/s12603-013-0020-0 PubMed PMID: 23538653. [DOI] [PubMed] [Google Scholar]
- 18.Bales CW. What does it mean to be “at nutritional risk Seeking clarity on behalf of the elderly. Am J Clin Nutr. 2001;74:155–156. doi: 10.1093/ajcn/74.2.155. 10.1093/ajcn/74.2.155 PubMed PMID: 11470713. [DOI] [PubMed] [Google Scholar]
- 19.Craven DL, Pelly FE, Isenring E, Lovell GP. Barriers and enablers to malnutrition screening of community-living older adults: a content analysis of survey data by Australian dietitians. Aust J Prim Health. 2017;23:196–201. doi: 10.1071/PY16054. 10.1071/PY16054 PubMed PMID: 27784507. [DOI] [PubMed] [Google Scholar]
- 20.Reimer H, Keller H, Tindale J. Learning you are “at risk: seniors’ experiences of nutrition risk screening. Eur J Ageing. 2012;9(1):81–89. doi: 10.1007/s10433-011-0208-2. 10.1007/s10433-011-0208-2 PubMed PMID: 28804409. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Craven DL, Pelly FE, Lovell GP, Ferguson M, Isenring E. Malnutrition screening of older adults in the community setting: Practices reported by Australian dietitians. Nutr Diet. 2016;73(4):383–388. 10.1111/1747-0080.12269 [Google Scholar]
- 22.Leggo M, Banks M, Isenring E, Stewart L, Tweeddale M. A quality improvement nutrition screening and intervention program available to Home and Community Care eligible clients. Nutr Diet. 2008;65(2):162–167. 10.1111/j.1747-0080.2008.00239.x [Google Scholar]
- 23.O’Cathain A, Thomas KJ. “Any other comments? Open questions on questionnaires -a bane or a bonus to research. BMC Med Res Methodol. 2004;4:25–31. doi: 10.1186/1471-2288-4-25. 10.1186/1471-2288-4-25 PubMed PMID: 15533249, PMCID 533875. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107–115. doi: 10.1111/j.1365-2648.2007.04569.x. 10.1111/j.1365-2648.2007.04569.x PubMed PMID: 18352969. [DOI] [PubMed] [Google Scholar]
- 25.Pope C, Ziebland S, Mays N. Qualitative research in health care: Analysing qualitative data. BMJ. 2000;320(7227):114–116. doi: 10.1136/bmj.320.7227.114. 10.1136/bmj.320.7227.114 PubMed PMID: 10625273, PMCID 1117368. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Leech NL, Onwuegbuzie AJ. An array of qualitative data analysis tools: a call for data analysis triangulation. Sch Psychol Q. 2007;22(4):557–584. 10.1037/1045-3830.22.4.557 [Google Scholar]
- 27.Winter JE, MacInnis RJ, Wattanapenpaiboon N, Nowson CA. BMI and all-cause mortality in older adults: a meta-analysis. Am J Clin Nutr. 2014;99(4):875–890. doi: 10.3945/ajcn.113.068122. 10.3945/ajcn.113.068122 PubMed PMID: 24452240. [DOI] [PubMed] [Google Scholar]
- 28.World Health Organization., BMI classification. The International classification of adult underweight, overweight and obestity according to BMI, 2017
- 29.World Health Organization., Global Health Observatory (GHO) data, 2017
- 30.Winter J, McNaughton SA, Nowson CA, Nutritional care of older patients: experiences of general practitioners and practice nurses, Aust J Prim Health., 2016 [DOI] [PubMed]
- 31.Cheng FW, Gao X, Jensen GL. Weight change and all-cause mortality in older adults: a meta-analysis. J Nutr Gerontol Geriatr. 2015;34(4):343–368. doi: 10.1080/21551197.2015.1090362. 10.1080/21551197.2015.1090362 PubMed PMID: 26571354. [DOI] [PubMed] [Google Scholar]
- 32.Murphy RA, Patel KV, Kritchevsky SB, Houston DK, Newman AB, Koster A, et al. Weight change, body composition and risk of mobility disability and mortality in older adults: a population-based cohort study. JAGS. 2014;62:1476–1483. doi: 10.1111/jgs.12954. 10.1111/jgs.12954 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Yu E, Ley SH, Manson JE, Willett W, Satija A, Hu FB, Stokes A, Weight history and call-cause and cause-specific mortality in three prospective cohort studies, Ann Intern Med., 2017 [DOI] [PMC free article] [PubMed]
- 34.Winter J M, McNaughton SA, Nowson CA. Older adults’ attitudes to food and nutrition: a qualitative study. J Aging Res Clin Practice. 2016;5(2):114–119. [Google Scholar]
- 35.Keller HH, Nutri-eSCREEN®: descriptive analysis of a self-management site for older adults (50+ years), BMC Nutrition., 2016
- 36.Gosse MA. How accurate is self-reported BMI. Nutr Bull. 2014;39(1):105–114. 10.1111/nbu.12075 [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Appendix