Identifying and Managing Malnutrition, Frailty and Sarcopenia in the Community: A Narrative Review
Abstract
:1. Introduction
2. Methods
3. The Concepts of Malnutrition, Frailty and Sarcopenia
3.1. Protein-Energy Malnutrition
3.2. Frailty
3.3. Sarcopenia
3.4. Overlap between Malnutrition, Frailty and Sarcopenia
4. Prevalence of Malnutrition, Frailty and Sarcopenia
5. Impact and Effects of Malnutrition, Frailty and Sarcopenia
6. Causes/Risk Factors of Malnutrition, Frailty and Sarcopenia
7. Addressing Malnutrition, Frailty and Sarcopenia Using the Nutrition Care Process
7.1. Screening and Referral
7.2. Assessment and Diagnosis
7.3. Intervention
7.3.1. Strategies to Influence Knowledge and Behaviour
7.3.2. Strategies to Influence Intake
7.4. Monitoring and Evaluation (Including Documentation)
8. Hospital-to-Community Transition
9. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Domain | Malnutrition | Frailty | Sarcopenia |
---|---|---|---|
Nutritional | Poor appetite [12,66,67] Poor dentition [67] Dysphagia [36,66] Low intake of milk/milk alternatives [32,33] Food avoidance [33] Eating alone [32] Finding meal preparation a chore [33] | Being malnourished [68] Low protein intake [68,69] Poor diet quality [68] Poor dietary antioxidant intake [40,68] Higher DII® scores [70] Low number of teeth [71] Poor masticatory function [71] | Low protein intake [72] Poor diet quality [73] |
Physical function and form | Lower BMI/BW [36] Eating dependency [12,66] Poor physical function [12,34,66] Difficulty walking/climbing stairs [31] Unhealthy gait speed [36] Perceiving weight more than is [32,33] | Higher BMI [15,42,74] | Sedentary behaviour [75] |
Psychosocial | Living alone, widowed, divorced, separated or single [31,34,76] Dementia / cognitive decline [66] Loss of interest in life [66] Depression [34] | Widowed, divorced or never married [16] | |
Disease and care | Hospitalization [12,31,67] Parkinson disease [66] Constipation [66] Having no diabetes [67] Poor self-reported health [12,66,67] Polypharmacy [66] | Hospitalization [41] Multimorbidity [15,42,44] Polypharmacy [39,44,77] High mean DBI [39,77] More likely to have visited a HCP prior to a problem [41] | |
Demographic | Increasing age [31,36,66] Low income level [76] Low educational level [34,76] Women [34] | Increasing age [15,16,42,44] Women [16,42,44] Low educational level [44] Men [15] | Increasing age [44,47] |
Name | Description | Validity in Community Setting | Recommendation/Comment | |
---|---|---|---|---|
Malnutrition screening tools | ||||
Determine your Health Checklist (DETERMINE) | Self-completed, 10-question survey assessing dietary intake, nutrition impact symptoms, health conditions, medications, social/economic factors, weight changes and functional status [82]. | Reported criterion validity show 75–91% sensitivity and 11–54% specificity; however few studies used appropriate reference standards [83]. | Designed to assess nutritional status among community-dwelling older adults [82]; however predictive validity in community setting is poor (unable to predict mortality, hospitalisation, or weight loss of >5%). | |
Malnutrition Universal Screening Tool (MUST) | Considers BMI, weight loss and acute disease effect. | Two validation studies in community: 100% sensitivity, 98% specificity when validated against dietitian assessment; 58% sensitivity, 96% specificity when validated against unintentional weight loss or BMI [83]. | Has been validated in hospital, residential aged care and community settings [84]; but more validation studies are needed in community. | |
Mini Nutritional Assessment-short form (MNA-SF) | Six items on dietary intake, weight loss, mobility, disease/stress, neurological problems and BMI [85]. | Promising criterion validity in community setting, with high sensitivity (81–100%) and specificity (82–100%); however, studies used MNA-FF as reference standard so incorporation bias is present [83]. | Recommended for use with older adults and validated in hospital, residential aged care and community settings. | |
Malnutrition Screening Tool (MST) | Two questions on appetite and unintentional weight loss [86]. | Widely validated in hospital settings, with high sensitivity (90–98%) and specificity (85–89%); but not validated in community [83]. | Community validation studies needed. | |
Seniors in the Community: Risk Evaluation for Eating and Nutrition Questionnaire (SCREEN-II; now called SCREEN-14) | Considers weight, appetite, dietary intake, nutrition impact symptoms, ONS intake, social factors [87]. | Good validity among older community-dwelling Canadian and New Zealand adults, with reported sensitivity from 84–90% and specificity 62–86% when tested against clinical assessment by a trained dietitian. More validation studies needed in other settings [83]. | Was developed to assess general nutrition status in community-dwelling older adults, but is also validated as a malnutrition screening tool [88]. | |
Frailty screening/assessment tools ^ | ||||
The abbreviated Comprehensive Geriatric Assessment (aCGA) | 15 questions on functional status, cognitive status and depression [89]. | Good sensitivity (75–88%), moderate specificity (48–60%) for predicting functional decline/disability, mortality and institutionalisation in community-dwelling adults [90]. | Acceptable performance for predicting disability only, and not related to mortality or institutionalization; therefore, not recommended as first choice for screening. | |
FRAIL scale | Self-administered survey on ambulation, fatigue, illness, resistance and weight. | Using Fried frailty phenotype as a reference standard, sensitivity was 87–96% and specificity 64–86%, with a FRAIL scale score of 2 being the optimal cut-off point, among community-dwelling Australians [91] and Chinese [92]. | Good validity in community setting. A strength is that it does not require measurements nor administration by healthcare professionals [17,93]. | |
Frailty Index | Underpinned by biological causative theory, evaluates health deficits (comorbidities, symptoms, disabilities, diseases). | Adequately predicts adverse health outcomes and correlates strongly with other frailty measures. Sensitivity is 46–61% and specificity 84–90% when compared with Fried’s Frailty Phenotype [94]. | Considered gold standard for frailty screening due to high validity and ability to predict cause-specific mortality; however can be complex and time consuming to complete due to its mathematical nature, reducing its popularity clinically [17]. | |
Fried’s Frailty Phenotype (also known as Fried Scale) | Underpinned by biological causative theory and considers weight loss, exhaustion, grip strength, gait speed and physical activity [13]. | Can identify frailty and predict adverse clinical outcomes; hence is widely used in clinical and research settings. Low-moderate sensitivity (40–44%) and high specificity (85–94%) for predicting functional decline/ disability, mortality and institutionalisation in community-dwelling adults [90]. | Requires measurement of handgrip strength and gait speed, which are not always practical/feasible in community settings. | |
Gérontopôle Frailty Screening Tool | Involves two steps: questionnaire evaluating weight, exhaustion, slowness, cognition, dependence; and clinician judgement of frailty. | Good sensitivity (88%) and specificity (84%) when assessed against Cardiovascular Health Study criteria definition of frailty as reference standard [95]. Considered one of the most appropriate frailty screening tools for use in community-dwelling adults. | Designed for early frailty recognition in community-dwelling older people [96]; however, its lack of specific guidance for clinicians to identify frailty is a limitation [97]. | |
Groningen Frailty Indicator | 15 items covering physical, cognitive, social and psychological domains [98]. | Moderate sensitivity (52–63%) and specificity (69–77%) for predicting functional decline/disability, mortality and institutionalisation in community-dwelling adults [90]. | Can determine the level of frailty. | |
Tilburg Frailty Indicator | Self-administered 15-item questionnaire considering health, weight, walking difficulty, balance, hearing, sight, grip strength, fatigue, memory, sensory, anxiety, coping capacity, solitude and social support [99,100]. | Moderate to good predictive validity for disability (35–87% sensitivity; 61-89% specificity), needing residential care (81–86% sensitivity; 59–62% specificity) and hospitalisation (33–65% sensitivity; 60–86% specificity) in a range of community-dwelling, older adult populations [100,101,102,103,104]. | Developed for identifying frail community-dwelling older people, and is validated in this setting with high diagnostic accuracy (95% sensitivity; 86% specificity) for frailty [105]. A limitation is that it takes 14 mins to complete. | |
Vulnerable Elders Survey | Contains 13 questions on age, self-rated health, physical fitness and independence [106]. | Considered the most suitable tool to predict functional decline/disability, mortality and institutionalisation in community-dwelling adults, with high sensitivity (88–92%) but low-moderate specificity (47–59%) for predicting these outcomes [90]. | Developed to identify community-dwelling vulnerable elderly at risk for functional decline. Relatively short and easy to complete. | |
Vulnerable Elders Survey | Contains 13 questions on age, self-rated health, physical fitness and independence [106]. | Considered the most suitable tool to predict functional decline/disability, mortality and institutionalisation in community-dwelling adults, with high sensitivity (88–92%) but low-moderate specificity (47–59%) for predicting these outcomes [90]. | Developed to identify community-dwelling vulnerable elderly at risk for functional decline. Relatively short and easy to complete. | |
Sarcopenia screening tools | ||||
European Working Group on Sarcopenia in Older People (EWGSOP) | Two-step algorithm assessing gait speed and handgrip strength to identify people ‘at risk’ of sarcopenia who should then proceed to a full assessment [22]. | Reported sensitivity is between 33–71% and specificity 89–91% when validated against multiple criteria for diagnosing sarcopenia [107]. * | Highly specific, relatively simple tool (however requires measurement of gait speed and handgrip strength). Cut-off thresholds for skeletal muscle mass indexes are 9.2 kg/m2 and 7.4 kg/m2 and for hand grip-strength are 32 kg and 22 kg, for males and females, respectively [108]. | |
Goodman et al.’s screening grid | Assesses age and BMI to determine probability of low muscle mass [109]. | Reported sensitivity is between 41–67% and specificity 86–89% when validated against multiple criteria for diagnosing sarcopenia [107]. * | Simple tool requiring only age and BMI. Individuals with a probability >70% in men and >80% in women considered at risk of sarcopenia [109]; however Locquet et al. found great variation in cut-offs across different definitions (17–73%) [107]. | |
Ishii et al.’s score chart | Age, handgrip strength and calf circumference are used in a gender-adjusted equation to determine the probability of sarcopenia [110]. | Reported sensitivity is between 84–100% and specificity 74–81% when validated against multiple criteria for diagnosing sarcopenia [107]. * | Excellent performance in identifying sarcopenia risk; however, involves complex and time-consuming calculations, limiting its practicability. Diagnostic cut-off scores (recommended by Locquet et al.) are 111.1 for men and 128.5 for women [107]. | |
Mini Sarcopenia Risk Assessment (MSRA) | Considers age, hospitalisation in past year, activity level, number of meals/day, daily dairy intake, daily calorie intake, and weight loss in past year [111]. | Reported sensitivity is between 78–90% and reported specificity is between 38–71%. MSRA-5 consistently has higher specificity than MSRA-7 [111,112]. | Two versions; a five-item (MSRA-5) and a seven-item tool (MSRA-7). MSRA-5 is recommended due to easier completion and higher specificity than MSRA-7. Cut-offs to identify people with sarcopenia are 30 (MSRA-7) and 45 (MSRA-5). | |
The Strength, Assistance with walking, Rise from a chair, Climb stairs, and Falls (SARC-F) | Questionnaire on ability to carry a heavy load, walking, rising from a chair, climbing stairs, and frequency of falls [113]. | Initially validated in community-dwelling Chinese adults with low sensitivity but good specificity [113]. Other studies report sensitivities between 36–56% and specificity between 85–87% when validated against multiple criteria for diagnosing sarcopenia [107]. * | Relatively simple to complete and score; but requires assessment on stairs and of lifting a 4.5 kg load. Two versions; SARC-F-5 and SARC-F-3, comprising five and three questions, respectively. SARC-F-5 is recommended due to better diagnostic performance. Score of ≥4 (out of 10) on SARC-F-5 indicates sarcopenia risk. | |
Yu et al.’s prediction equation. | Uses weight, BMI, age and sex to identify people ‘at risk’ of sarcopenia [114]. | Reported sensitivity is between 16–83% and specificity 60–87% when validated against multiple criteria for diagnosing sarcopenia [107]. * | Recommended as a ‘rule-out’ screening test for sarcopenia (i.e., to reduce the number of costly DXA assessments undertaken). Cut-off values are dependent on the importance of DXA assessment cost vs. risk of missing sarcopenia cases [114]. |
Nutrition Assessment Domains | |
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Domain * | Description/Measures |
Food/nutrition-related history | Adequacy of food/nutrition intake (via diet history), with consideration of medications, complementary/alternative supplements, nutritional supplements, nutrition knowledge/beliefs, food access/availability, physical activity. Low/reduced food intake may indicate malnutrition. |
Anthropometric measurements | Height, weight, body mass index (BMI), weight history/weight change. Low BMI or unintentional weight loss may indicate malnutrition. |
Biochemical data/test results | Blood laboratory results (e.g., electrolytes, iron), clinical tests (e.g., gastric emptying time, metabolic rate). Abnormal blood or clinical results may indicate malnutrition/risk; but should be considered alongside other domains. |
Nutrition-focused physical findings | Physical appearance, appetite and other symptoms (swallow function, taste/smell changes, physical limitations). Thin appearance, muscle/fat wasting, reduced function, poor appetite and other nutrition impacting symptoms may indicate malnutrition. |
Frailty assessment domains | |
Domain | Description/measures |
Health | Co-morbidities/illnesses, age, self-reported health status, recent hospitalisation, polypharmacy, symptoms. |
Physical | Measures of weakness, exhaustion, decreased endurance/performance, slowness, balance, walking difficulty; weight loss, functional status, physical activity, dependence, disability (e.g., loss of hearing, sight). |
Nutritional | Appetite, dietary intake, nutrition impacting symptoms. |
Psychological | Cognition (memory, decision making), depression, anxiety. |
Social | Coping capacity, solitude, social relations/support. |
Sarcopenia assessment domains | |
Domain | Description/measures |
Health | Age, gender, recent hospitalisation |
Physical | Physical activity, muscle quantity and function, strength, gait, falls |
Nutritional | Weight, BMI, dietary intake, weight loss |
Study, Country | Study Design | Sample and Setting | Intervention | Assessment | Outcomes (Intervention vs. Control) |
---|---|---|---|---|---|
Malnutrition | |||||
Leggo et al., 2008 [135] | Pre/post intervention | 1145 adults (76.5 ± 9.2 years; 31% male) recruited from 16 Australian organisations caring for HACC clients in Australia | Adults identified as ‘at risk’ or ‘malnourished’ provided with at home, one-on-one individualized nutrition counselling from a dietitian for 6 months (median) | MST, PG-SGA |
|
Hamirudin et al., 2016 [136] | Mixed-method pre/post intervention | 143 adults (≥75 years; % male NS) recruited from 3 General Practices in NSW, Australia | Adults identified as ‘at risk’ or ‘malnourished’ provided with a resource kit a + other interventions (e.g., dietitian referral) by practice nurses for 6/12 months | MNA-SF, interviews |
|
Hamirudin et al., 2017 [137] | Pre/post intervention | 68 adults (85.5 ± 5.8 years; 47% male) recruited within 2 weeks post-discharge from hospitals in regional NSW, Australia | All adults provided with tailored individual dietary advice b at home by a dietitian for 3 months | MNA, body weight, BMI, diet history, food frequency checklist |
|
Charlton et al., 2013 [138] | Mixed-method pre/post pilot intervention | 12 adults (81.3 ± 10.9 years; 58% male) recruited from two MOW services in NSW, Australia | Provision of high protein, high-energy snacks five times a week, in addition to their usual MOW order, for 1 month | MNA, body weight, BMI, 24h diet recall, food frequency checklist, interviews |
|
Frailty | |||||
Cameron et al., 2013 [139] | RCT | 216 adults meeting FFP criteria (83.3 ± 5.9 years; 32% male) recruited from 16 organisations caring for HACC clients in Australia | Provision of an individualised, multifactorial, interdisciplinary exercise and nutrition program d for 12 months | FFP, Short Physical Performance Battery |
|
Milte et al., 2016 [140] | RCT | 175 adults recovering from hip fracture (≥70 years; 23% male) recruited from 3 acute care and 1 rehabilitation setting in SA and NSW, Australia | Provision of an individualized exercise and nutrition program e and fortnightly dietitian visit to review dietary intake and modify strategies for 6 months | HRQoL, QALY, costs |
|
Category | Outcome/Indicator |
---|---|
1. Direct nutrition | Knowledge gained Behaviour change Food & nutrient intake Nutritional status |
2. Clinical & health status | Biochemical data Weight/anthropometry Blood pressure Risk factor profile Disease signs and symptoms (e.g., muscle/fat wasting, appetite) |
3. Patient value-based care | Quality of life Patient satisfaction Self-efficacy / self-management Functional ability |
4. Healthcare utilisation/cost savings | Complications Medication changes Number of unplanned clinic visits Number of preventable hospital admissions Length of hospitalisation Nursing home admission |
Screening and Referral |
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Use a validated screening tool to identify patients at risk of malnutrition, frailty or sarcopenia. Tools that perform best in the community setting include:
|
Screening should be completed upon:
|
If it is not possible to screen using a validated tool, HCPs should consider the patient’s risk factors (see Table 1) to determine if they are at risk / would benefit from a full assessment (e.g., by a dietitian or exercise physiologist). |
Screening should be prioritised for patients who are older, have multiple comorbidities, have recently lost weight/are underweight, or who appear malnourished/frail/sarcopenic. |
Assessment and diagnosis |
Patients identified as ‘at-risk’ of malnutrition, frailty or sarcopenia should be referred to an appropriate health professional (e.g., dietitian, physiotherapist/exercise physiologist) for a full assessment and diagnosis, to inform nutrition and/or exercise interventions. |
These health professionals should use appropriate assessment tools or criteria to diagnose malnutrition, frailty or sarcopenia; and document the level/stage (if applicable). For example *:
|
If it is not possible to refer patients to appropriate allied health professionals, clinicians should use the domains in Table 3 to make an informal assessment, in order to guide care. |
Intervention |
A combination of nutrition- and exercise-based strategies should be adopted to manage malnutrition, frailty and sarcopenia. Common nutrition-based strategies used in combination include:
|
Strategies should be tailored to the individual, factoring in their preferences, needs and context/geographical area. |
Further work with consumers and HCPs should be undertaken to determine feasible and effective ways of improving nutrition among community-dwelling older adults in ANZ. |
Monitoring and evaluating |
HCPs should monitor/evaluate nutrition interventions using a combination of the indicators listed in Table 5, in conjunction with factoring in the indicators most important to patients to provide evidence-based, patient-centred care. This may include evaluating the following:
|
Qualitative and quantitative measures should be used to collect data, and, where available, validated tools. |
Findings should be communicated to all HCPs caring for the individual, as well as to the patient themselves. |
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Roberts, S.; Collins, P.; Rattray, M. Identifying and Managing Malnutrition, Frailty and Sarcopenia in the Community: A Narrative Review. Nutrients 2021, 13, 2316. https://doi.org/10.3390/nu13072316
Roberts S, Collins P, Rattray M. Identifying and Managing Malnutrition, Frailty and Sarcopenia in the Community: A Narrative Review. Nutrients. 2021; 13(7):2316. https://doi.org/10.3390/nu13072316
Chicago/Turabian StyleRoberts, Shelley, Peter Collins, and Megan Rattray. 2021. "Identifying and Managing Malnutrition, Frailty and Sarcopenia in the Community: A Narrative Review" Nutrients 13, no. 7: 2316. https://doi.org/10.3390/nu13072316