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Screening Community-Living Older Adults for Protein Energy Malnutrition and Frailty: Update and Next Steps


Protein-energy malnutrition (PEM)/undernutrition and frailty are prevalent, overlapping conditions impacting on functional and health outcomes of older adults, but are frequently unidentified and untreated in community settings in the United States. Using the World Health Organization criteria for effective screening programs, we reviewed validity, reliability, and feasibility of data-driven screening tools for identifying PEM and frailty risk among community-dwelling older adults. The SCREEN II is recommended for PEM screening and the FRAIL scale is recommended as the most promising frailty screening tool, based on test characteristics, cost, and ease of use, but more research on both tools is needed, particularly on predictive validity of favorable outcomes after nutritional/physical activity interventions. The Malnutrition Screening Tool (MST) has been recommended by one expert group as a screening tool for all adults, regardless of age/care setting. However, it has not been tested in US community settings, likely yields large numbers of false positives (particularly in community settings), and its predictive validity of favorable outcomes after nutritional interventions is unknown. Community subgroups at highest priority for screening are those at increased risk due to prior illness, certain demographics and/or domiciliary characteristics, and those with BMI < 20 kg/m2 or < 22 if > 70 years or recent unintentional weight loss > 10% (who are likely already malnourished). Community-based health professionals can better support healthy aging by increasing their awareness/use of PEM and frailty screening tools, prioritizing high-risk populations for systematic screening, following screening with more definitive diagnoses and appropriate interventions, and re-evaluating and revising screening protocols and measures as more data become available.


As they age, most older adults want to retain their independence and remain in their own homes and communities for as long as possible. Life expectancy is increasing and arguably many older adults are healthier than their counterparts of comparable ages from past generations, but disability-free life expectancy still lags behind [1]. Because a growing number of older adults suffer from multiple acute/chronic diseases and disabilities, there is significant risk for both protein-energy malnutrition (PEM, often referred to as undernutrition or malnutrition) and physical frailty.

Screening for PEM/undernutrition and frailty is important because when these conditions go unrecognized and untreated, the risks of adverse outcomes and decreased functionality increase [2]. The rationale for screening for PEM and frailty risk together is due to the conditions’ common origins and signs/symptoms, their similar treatments and outcomes, and their frequent co-occurrence among both very old and lower-income minority groups [3, 4], as further described in Table 1. A systematic review assessing malnutrition and physical frailty found significant associations between the two conditions among 80% of the available studies of community-dwelling older adults [5]. The prognostic value of PEM and frailty screening tools used together was also good for predicting mortality among older patients with various medical and surgical conditions including acute heart failure [6], gastric cancer (post surgery) [7], and advanced colorectal cancer (receiving chemotherapy) [8]. Yet, although screening tools exist, they are inconsistently used as part of public health screening programs targeted toward community-dwelling older adults.

Table 1 Similarities between characteristics of protein-energy malnutrition (PEM)/undernutrition and frailty in older adults

An additional challenge is that the screening tools currently used have dissimilar operating definitions for PEM and frailty, as well as different measures and cutoffs. Many are impractical for community settings and most are unvalidated, with high false positive rates and much misclassification. What is needed are data-driven approaches, combined with expert judgement, that focus on evaluating the validity and reliability of the available screening tools to identify PEM and frailty.

A half century has passed since the World Health Organization (WHO) summarized the criteria needed for effective screening programs [9]. Using this WHO framework, we reviewed published evidence on PEM and frailty screening of community-living older adults. This article describes the prevalence of these conditions and screening challenges, promising PEM and frailty screening tools that overcome some but not all difficulties in screening community-living populations, and next steps to promote systematic PEM and frailty screening and intervention to support healthier aging and decreased disability in the United States (US).

Defining Effective Screening

Decades ago, the WHO established the following criteria (that are still in use today) for effective public health screening programs:

  • There need to be agreed-upon definitions and objective criteria that can be used to describe the condition.

  • The condition screened for must be a significant health problem and, if the condition is detected early by screening, effective treatments must exist.

  • Validated tools to measure risk of occurrence must be available so those likely to be ill can be given priority for further assessment/treatment. Therefore, screening measures need to have satisfactory prevalence, criterion validity, sensitivity, specificity, and predictive validity.

  • Populations most likely to benefit from screening must be identifiable and reachable.

  • Screening tools must be suitable for the setting in which they are used (feasibility, performance, cost) and reflect changes in status that will result from effective interventions.

  • Screening tools must be simple/noninvasive and a cost-effective standardized plan should specify the screening tool(s) and processes [9].

Developing community screening protocols starts with defining the elements of effective screening that include detection of a problem when early treatment can be more effective than after signs and symptoms develop, and identification of risk factors and use of this information to prevent/lessen the problem by modifying the risk factors [10].

Nutritional screening, the first step in the nutrition care process, involves the systematic identification of individuals at risk to establish whether a full nutritional assessment is needed [11]. Since the malnutrition screening tools described in this article focus on PEM, and not the entire panoply of all forms of malnutrition, they will henceforth be referred to as PEM screening tools. Screening for frailty–which has been viewed as a cornerstone of geriatric medicine–is also often undertaken to identify risk of adverse health outcomes [12].

Unfortunately, most available PEM and frailty screening tools have limitations for use with community-living older adults, since the tools were not developed/tested for effectiveness in this population or setting. Additional challenges to effective screening include:

Fragmented Healthcare Delivery

The capability and will to screen may be lacking because comprehensive assessment and intervention as an integrated part of overall healthcare delivery may not be available at the community level.

Limited Awareness that Problems Exist

Health professionals may be unaware that validated and feasible screening measures exist or believe PEM and frailty screening is outside their scope of practice [13, 14]. Fortunately, multidisciplinary education and advocacy efforts directed toward earlier intervention and treatment of preventable malnutrition and frailty are increasingly acknowledged [15].

Lack of National Survey Data

There is a lack of standard core measures on national surveys that could serve as benchmarks of PEM, other forms of malnutrition, and frailty prevalence or signals of risk. In the US Healthy People 2020 and other national health objectives, neither malnutrition nor frailty are singled out as conditions with targeted goals for older adults, in part because reliable prevalence estimates of these conditions are not available to assess the effectiveness of prevention and treatment interventions. Gahche et al. found no US national surveys of older adults that provided complete measures for both PEM and frailty risk screening, and thus they recommended adding measures for unintentional weight loss, loss of appetite, and grip-strength to national surveys to allow for risk screening for the conditions [16]. These simple measures provide the data needed to help monitor the nutritional health of older adults.

Failure to Prioritize Highly Vulnerable Groups in the Community for Screening

Efforts are most efficient and effective when concentrated on community groups whose demographics, residence, or health status suggest vulnerability and who may otherwise be overlooked. Most older Americans (93.5%) live independently in homes, apartments, or other community settings [17].

Table 2 summarizes how PEM and frailty screening compares to the WHO criteria for screening. Further considerations are described below, including common measures, prevalence, promising screening tools, and next steps for community-level PEM and frailty screening.

Table 2 Characteristics of PEM and frailty screening tools and programs for older persons in the community in comparison to World Health Organization criteria for screening for disease [9]

PEM Screening in the Community

Definitional challenges can be confusing both as to the type and treatability of malnutrition. In this article, the term older adult malnutrition refers solely to PEM or undernutrition (International Classification of Disease (ICD-10) codes E43, E44, E46, E64) rather than to nutritional disorders characterized by a broader classification that also includes overweight, obesity, and other excessive alimentation (ICD-10 codes E65, E66, E67, E68) [18]. This distinction is critical since treatments for under and overnutrition are different. Even when the definition of malnutrition is restricted to PEM/undernutrition, there is frequent disagreement on specific cut-points for identification and criteria used to measure it, although for some screening measures, such as body mass index (BMI) and weight loss over time, there appears to be relatively high consensus on cut-points.

Another challenge is whether screening should identify only risk of PEM/undernutrition that can be treated solely by dietary means (primary PEM), or whether it should also include PEM due to other causes that requires different treatments (secondary PEM), if it responds to treatment at all [19]. In the US and other Western countries, the primary cause of undernutrition is disease accompanied by such factors as inflammatory activity, comorbidities, and dependency [20]. The problem of separating out individuals with primary PEM (who are likely to require only nutritional interventions) is especially salient for community programs. Community-based providers typically do not have access to the clinical services needed for assessment, diagnosis, and subsequent treatment of secondary malnutrition. The European Union Joint Programming Initiative Knowledge Hub project on Malnutrition in the Elderly (MaNuEL) defines malnutrition as primary malnutrition–a condition that is treatable by dietary means [19]. Malnutrition indicators evaluated through MaNuEL include low BMI, previous weight loss, moderate and severe decrease in food intake, and for older adults aged > 65, combined BMI < 20 kg/m2 and/or weight loss. MaNuEL researchers concluded that the criteria used strongly affected prevalence and, thus, suggested it may be preferable to look at each criterion separately, as each may indicate a different nutritional etiology [21].

PEM/undernutrition were somewhat similarly considered in the 2012 consensus approach for diagnosing and documenting malnutrition in hospitalized adults that was published jointly by the Academy of Nutrition and Dietetics (AND) and the American Society for Parenteral and Enteral Nutrition (ASPEN). The approach described six characteristics of severe malnutrition in adult patients: weight loss, insufficient energy intake, loss of muscle mass, loss of subcutaneous fat, fluid accumulation, and diminished functional status, and it required the presence of two of these six criteria for a malnutrition diagnosis [22].

In contrast, the Global Leadership Initiative in Malnutrition (GLIM) recommends that any validated screening tool of those it lists can be used to screen for PEM [23]. However, most tools on the GLIM list were validated in hospitalized populations, and thus they are unsuitable for community settings without further validation, particularly because the prevalence of secondary malnutrition is typically much lower in the community than in chronically ill/hospitalized populations, and even if identified, resources for further assessment and treatment are often lacking. Another challenge with the GLIM approach is that aggregated measures taken with various heterogenous tools cannot be used for valid prevalence estimates. Moreover, in using many different screening tools, there is the risk of identifying different individuals at risk, even in the same population, since the tools’ measurement criteria vary.


More needs to be known about the prevalence of PEM, as that will determine how costly community screening may be. Prevalence needs to be high among screened populations so that the relative costs of screening programs in relation to the number of true positive cases detected that are amenable to treatment are justifiable for decreasing or eliminating adverse health consequences. The documented prevalence of PEM in community settings depends on what measures are used, as was illustrated in a recent MaNuEL study that employed harmonized definitions of malnutrition to assess prevalence in over 5000 community-dwelling, older European adults [21]. About 4.2% had BMI < 20 kg/m2, between 2.3 and 10.5% (depending on the country) had weight loss, and severe decreases in food intake were reported by up to 9.6%. The prevalence of BMI < 20 kg/m2 and weight loss occurring together never exceeded 2.6%. Thus, the criteria used strongly affected prevalence estimates. In the MaNuEL group’s most recent report, the prevalence of PEM risk) was found to be 8.5% in European community settings. It was higher in adults > 80 years, in women, and in those with one or multiple morbidities, and differed by geographic location and the screening tool employed [20].

PEM Screening Tools

Table 3 summarizes several common PEM screening tools (sometimes referred to as malnutrition screening tools), major components of the tools, and the items/types of questions employed. The validity of PEM screening tools for older adults in community settings was reviewed as part of MaNuEL. A scoring system that included ratings for validity, the parameters used (what was measured), and evidence the measures were suitable for detecting malnutrition in older adults was applied [24]. Thirty-six unique studies were found validating 20 different malnutrition screening tools. The authors concluded that due to poor validation study design and results, there was insufficient evidence to make strong recommendations for any of the malnutrition screening tools. However, they did identify SCREEN II (Table 4), initially developed in Canada where it is still widely used, as having the greatest evidence of validity in the community [25].

Table 3 Malnutrition screening tools: components/domains and measurement items/questions employed
Table 4 Description of Seniors in the Community: Risk Evaluation for Eating and Nutrition (SCREEN II) questionnaire [68]

The MaNuEL research framework is specific to older adults. In contrast, AND conducted a systematic review on the validity, agreement, and reliability of tools to screen all adults for malnutrition regardless of age, medical history, or physical location (care setting) using AND’s evidence analysis process [26]; 69 studies met their inclusion criteria. The SCREEN II was not evaluated in AND’s review. The Malnutrition Screening Tool (MST) (Table 5) was found to exhibit moderate validity, agreement, and reliability with Grade I (Strong) evidence. The evidence supporting other screening tools was reported as Grade II (Fair) [27]. AND’s draft position paper on malnutrition screening concluded “based upon current evidence, the Malnutrition Screening Tool (MST) should be used to screen adults for malnutrition regardless of their age, medical history, or location” [28].

Table 5 Description of the Malnutrition Screening Tool (MST) [63]

Frailty Screening in the Community

Frailty (ICD-10 CM code R54) is variously referred to as old age senescence, senile asthenia, and senile debility. Sarcopenia (ICD‐10‐CM M62.84) is closely related to frailty and malnutrition and is a condition involving age-related muscle wasting and/or an underlying disease if one is present [18].

Muscle mass is the biological substrate of physical frailty that leads to functional impairment. The operational definitions of frailty and the measures used to identify it are highly variable. One popular description is the Fried Frailty Phenotype which defines frailty based on physical frailty characteristics (weakness, decreased endurance, slow performance, exhaustion, and weight loss) that are unique and separate from disability and comorbidities alone. Using the Fried Frailty Phenotype, a score of three of the five measures present defines the individual as frail and a score of one or two as prefrail [29].

Other definitions view frailty as related to the accumulation of various deficits, such as mental, social, and physical deficits, rather than as a specific and distinct set of criteria [30]. In the accumulation of deficits models, severity of frailty is scored by the number of accumulated disabilities or comorbid conditions (up to 30 or more depending on the specific model).


Although frailty seems to be common in later life, it is often poorly documented in clinical records. Because of the different definitions used, populations screened, and variable cut-off measures employed, prevalence rates vary widely between studies. Understandably, when frailty is measured based solely on physical measures (such as a low BMI and physical activity), the documented prevalence is lower than for frailty measured based on definitions that also include other dimensions [31]. In a systematic review of cross-sectional studies using various definitions of frailty, Colllard et al. evaluated the prevalence of pre-frailty and frailty in community-dwelling adults > 65 years in the US, and in other countries [32]. The reported prevalence in the community varied significantly, from 4 to 59%, with an overall weighted frailty prevalence of 10.7% (95% confidence interval (CI) = 10.5–10.9) in 21 studies totaling 61,500 participants. Frailty prevalence increased with age and was higher in women (9.6%) than in men (5.2%). Prevalence rates for sarcopenia–one component of physical frailty–are reportedly lower than for frailty. However, the true prevalence of sarcopenia is likely unknown. Similar to frailty, it is not yet routinely measured in community settings and when it is, different measures are used for detecting its presence making it difficult to consolidate data and establish accurate prevalence estimates.Consensus is gradually emerging as previous definitions are revised, at least in Europe [33].

Frailty Screening Tools

The characteristics of an ideal or “best” frailty screening tool are similar to those already discussed above, including strong criterion validity, reliability, feasibility, low cost, and predictive ability. Multiple frailty screening tools exist (Table 6), although compared to PEM screening tools, fewer are valid/feasible and fewer consensus papers/large systematic reviews have been published. The predictive validity of frailty screening tools varies. Studies of very old nursing home residents in France found frailty measures to be related particularly to balance and ability to rise from a chair without assistance [34]. In a large British cohort study, low physical capability predicted future mortality risk both in those under and over 70 years of age, even when physical capability was not associated with comorbidities [35].

Table 6 Frailty screening tools: components/domains and measurement items/questions employed

Two promising tools for frailty screening that are easy to use–particularly in high-risk settings in the community–are described below. Since both tools are based on self-reports, one unanswered question is whether individuals can report signs/symptoms validly and reliably enough to be helpful. The FRAIL scale (Table 7) is a short, self-administered questionnaire [36]. Although the FRAIL scale has fair validity and is feasible for community settings, it has a relatively low specificity; it overestimates the number of individuals who are frail in a population. Both the FRAIL Scale and the SARC-F (described below) were found to be useful screens for a stepped care approach to detecting frailty among older community residents of Hong Kong [37].

Table 7 Description of the Fatigue, Resistance, Ambulation Illnesses, and Loss of Weight (FRAIL) Scale screening tool [36]

Sarcopenia is closely related to frailty and has been considered a precursor to the physical manifestation of frailty [38]. Morley et al. developed the SARC-F screening tool (Table 8) for rapid identification of sarcopenia risk [39]. The SARC-F has good test–retest reliability [40], and high specificity, but low sensitivity [36, 41]. It did have some prognostic value after discharge to home among elderly Japanese patients who had been hospitalized with cardiovascular disease [42]. Ida, Keneko and Murata recently reviewed articles from 1960 to date that included data on the sensitivity and specificity of SARC-F’s diagnostic criteria for sarcopenia in older adults. Seven studies with a total of 12,800 subjects met their study eligibility criteria. Overall, these studies achieved similar pooled results of sensitivity and specificity using definitions of both the International Working Group on Sarcopenia and the Asian Working Group for Sarcopenia. Because few studies were calibrated to the Foundation of the National Institutes of Health reference standards (which are based on appendicular lean mass) a meta-analysis was not performed on the aggregated data. Although the screening sensitivity of SARC-F was poor, its specificity was high [43]; thus, it may be an effective tool for identifying those who should undergo further assessment to confirm a sarcopenia diagnosis.

Table 8 Description of the Strength, Assistance in walking, Rise from chair, Climb stairs, Falls (SARC-F) screening tool [39]

Assessment of those Screened to be at Risk

Screening with standardized and well-validated tools identifies those at risk for PEM and/or frailty, but an assessment must be conducted to complete a definitive diagnosis, establish etiology, and plan and implement an intervention.

PEM Assessment

PEM assessment measures as well as those for malnutrition in general often focus on changes in body composition which may result from many causes. The simplest cause is primary undernutrition uncomplicated by other factors and due to insufficient dietary intake (particularly protein and energy). The resulting atrophy of muscle tissue can be further exacerbated by a sedentary lifestyle and/or the aging process itself. More complicated causes of malnutrition include secondary malnutrition due to chronic or acute disease or injury with or without inflammation, or mixes of primary and secondary malnutrition.

The GLIM included inflammation in its definition of malnutrition because the presence of inflammation may vitiate nutritional efforts unless the inflammation is also treated. The criteria established by GLIM to diagnose, grade, and assess malnutrition include non-volitional weight loss, low BMI, and reduced muscle mass (all easily observable phenotypical physical characteristics) as well as measurements linked more closely to etiology (reduced food intake, inflammation, and disease burden). GLIM outlined a definitive diagnosis to consist of at least one phenotypical and one etiological criterion and recommended that individual patients should then be assessed more thoroughly to determine causality because such diagnoses will not all respond to the same interventions [23].

Frailty Assessment

Neither PEM, frailty, nor scarcopenia screening yields a definitive diagnosis. Further clinical assessment is necessary to confirm the presence, causes, treatments, and interventions. In US community settings, it is rarely possible to screen for physical frailty using functional tests because of lack of time and equipment; thus, functional tests should always be included in frailty assessment in clinical settings. The comprehensive geriatric assessment (CGA) is often considered the clinical “gold standard” for assessing frailty, but it is very time consuming. Other frailty tools have been suggested as useful in clinical settings, yet at present there is little consensus about the best method for diagnosing frailty. In addition, like malnutrition, frailty is complicated by chronic/acute disease or injury that can affect the ultimate diagnosis and intervention, and sarcopenia (which can result from pathologies involved in both malnutrition and frailty) further confounds diagnoses.

Clegg, Rogers, and Young completed a systematic review of the diagnostic test accuracy of simple tools for identifying frailty in prospective studies of community-dwelling older adults. Three studies were identified, involving 3261 participants with a median frailty prevalence of 1.5% based on either the cumulative deficits frailty index or CGA, which were used as the reference standards. Several different screening tools were examined, including gait speed, timed up and go (TUG) test, and the PRISMA 7 questionnaire. The tools were highly sensitive for identifying frailty, but had very limited specificity, suggesting that they lacked accuracy as single tests to identify frailty [44]. Nevertheless, the tools may be useful. In 2019, Ahlund et al. studied 408 frail elders, mean age 85 years, with a high comorbidity burden who needed inpatient emergency medical care. They assessed aerobic capacity and muscle strength during patients’ hospital stays and 3 months later. Both higher aerobic capacity (measured by a 6 minute walk test) and muscle strength (measured by hand-grip strength) were associated with lower mortality at 1 year. Moreover, a change for the better in these variables over the first few months post-hospitalization was also identified as important [45], pointing to the need for effective screening tools and interventions in the community to help treat frailty.

Community-Based Interventions for PEM and Frailty

A number of different community-based interventions exist for both PEM and frailty and can be accessed by various providers and service organizations. Systematic reviews on several effective interventions are described below.


Just as critical as the diagnostic assessment, the selected interventions for PEM are important for effective clinical outcomes. Many of the original papers on the topic refer to malnutrition although upon inspection it is clear that the term refers more narrowly to PEM/undernutrition. PEM interventions vary in their ability to prevent or improve relevant and meaningful outcomes such as nutritional status, morbidity, functional status, and mortality [46]. As part of MaNuEL, quality assessments were conducted using Cochrane and GRADE criteria on 18 primary intervention studies taken from 17 systematic reviews. Correa-Perez et al. reported the overall quality of the evidence was very low due to risk of bias, small sample size, and heterogeneous outcome measures and populations and this precluded relevant meta-analyses (except for body weight and BMI measures). Based on their meta-analysis of the few studies comparing the effect of oral nutrition supplements (ONS) versus usual nutritional care on nutritional status (measured by changes in body weight and BMI) the researchers identified small gains in body weight after interventions, but changes in BMI or percent change in body weight were not evident. They also identified two randomized controlled trials that showed improvements in functional status (measured by TUG and activities of daily living) in the ONS treated group. The researchers concluded there is a clear need for well-designed, randomized controlled trials that follow standard criteria for reporting interventions on relevant outcomes for treating the condition in older people [46].

An evidence profile review on PEM/malnutrition was recently undertaken as part of the WHO Integrated Care for Older People (ICOPE) project which formulated recommendations for the prevention and management of undernutrition among older people in community and primary care settings. The WHO ICOPE workgroup concluded there is adequate, moderate-quality evidence to suggest that ONS with or without dietary advice improves the nutritional status of undernourished older adults and that for older adults at risk for it there is adequate, but low-quality evidence to suggest that ONS with or without dietary advice may improve nutritional status [47].

These results are similar to those from a recent systematic review of randomized clinical trials of nutritional interventions (provision of dietary counseling and/or ONS) in older adults at risk of PEM. Interventions were viewed as effective if they improved nutritional status by an increase in energy intake of 250 kcal/day and a weight gain of at least 1.0 kg. The intervention effect was significant for weight gain (odds ratio [OR] 1.58, 95%; CI 1.16, 2.17), but not for energy intake (OR 1.59; CI 0.95, 2.66). After stratifying by the type of intervention, the intervention effect was significant only for an increase in energy intake when dietary counseling was given in combination with ONS (OR 2.28; CI 1.90, 2.73). Therefore, for older adults at risk of PEM, they identified nutritional interventions had a positive effect on energy intake and body weight, and dietary counseling combined with ONS was the most effective intervention [48].


For frailty, the evidence is still inconclusive on the effectiveness of specific interventions (except for physical exercise, particularly strength-bearing exercise) for older adults at high risk of frailty in the community. A recent review considered 21 randomized studies (totaling 5275 older adults > 65 years who were prefrail/frail) involving interventions to prevent frailty progression compared to alternative interventions, usual care, or no care. Physical exercise programs both reduced and postponed frailty, but only when the programs were conducted with groups. Favorable effects on frailty indicators were also achieved with interventions that combined physical exercise with ONS, with cognitive training, or combinations of these treatments [49].

Just as frailty prevalence studies are confounded by the different operational definitions and measures used to identify it, so too are frailty intervention studies. A recent scoping review of interventions and policies aimed to prevent/reduce the level of frailty identified 14 studies (12 randomized controlled trials and two cohort studies) but because six different definitions of frailty were used in the studies, it was difficult to assess the combined effects of interventions. The interventions that significantly reduced the number of frailty markers present or the prevalence of frailty included all types and combinations of physical activity interventions (with and without nutrition supplementation and/or memory training), and pre-habilitation (e.g. physical therapy plus exercise and home modifications) [50].

In a systematic review of the effects of health care interventions on quality of life in the frail elderly, van Rijckevorsel-Scheele et al. screened relevant articles and found 19 intervention studies that assessed intervention effects on quality of life. Not surprisingly, the studies were heterogeneous in their design and involved many different interventions and, thus, the results were inconclusive with respect to the effects of exercise interventions on the quality of life for frail elders [51]. By limiting its focus, a systematic review of 46 studies (totaling 15,690 participants) that only analyzed randomized controlled trials/cohort studies with primary care frailty interventions, identified strength training and protein supplement interventions (that also included physical activity) as the best interventions, both in terms of relative effectiveness and ease of implementation [52].

Both PEM and Frailty

The efficacy of interventions or treatment of individuals who were assessed as suffering from both PEM and frailty has also been studied. One critical factor in evaluating interventions for both together is whether those receiving the interventions were frail because they were undernourished and thus likely to benefit from nutritional interventions [4] versus being frail because of complex chronic disease and requiring appropriate medical treatment. Certainly, patients who are at risk of PEM should be screened for frailty since PEM may also play a significant role in the prevention and management of sarcopenia [53, 54] and equally important, patients who are at risk of frailty should be screened for PEM as a potential underlying cause contributing to their frailty.

All frailty criteria are affected to some degree by poor eating habits, and frailty itself may have a negative effect on eating (due to decreased appetite which is related to lower basal metabolic rate secondary to loss of muscle) and ultimately diminish nutritional status [55]. In a recent systematic review of the impacts of interventions on both nutritional and frailty status of vulnerable older individuals, diet (especially energy intake and overall diet nutritional quality) was identified as a helpful intervention. Yet, the efficacy of nutritional interventions in treating frailty could not be verified because most of the studies were cross sectional, and longitudinal outcomes were unavailable [56].

To date, the evidence on the role of nutrition interventions in preventing or reversing frailty consists of small studies of short duration; more studies are needed that are adequately powered to assess the effects of nutritional interventions in preventing and/or treating frailty [55]. The data are strongest for the combined interventions of dietary protein and physical activity as key anabolic stimuli for muscle protein synthesis. Although dose and duration effects are not yet clear, recommendations for adequate diets that ensure ample intakes of protein (perhaps also vitamin D, antioxidant nutrients, and long chain fatty acids) as well as a physically active lifestyle (with strength bearing exercise) can be made for all older adults to preserve their quality of life [54].

Next Steps and Conclusions

Like blood pressure and other vital signs, simple screening measures for PEM and frailty are important indicators of health risk. Screening at the community level to identify and treat preventable malnutrition and frailty risk among vulnerable older adults is feasible. Screening for these conditions should be a part of routine health care for older adults living in the community. The way forward begins with awareness and education to ensure development of core competency in screening for such health risks as part of everyday clinical practice among all those who care for older adults.

Since PEM and frailty are closely interrelated in older adults, meaningful prevalence estimates and benchmarks are needed to evaluate the effectiveness of interventions and to make comparisons between international, national and local findings. Incorporating common core screening measures for malnutrition and frailty–such as unintentional weight loss, poor appetite, and hand-grip strength–will strengthen existing measures in national surveys of older adults to generate such estimates [16].

Using these same measures in screening tools that are valid, feasible, easy and inexpensive to administer, in concert with appropriate diagnostic, assessment, intervention and follow-up services, will go far in helping prevent and treat malnutrition and frailty. The AND workgroup highlighted that when data gathered with non-validated tools enter large databases alongside data from validated tools, it compromises accuracy and raises questions about the overall quality of screening processes [28]. The same could be said for frailty screening, a consistent approach and use of validated tools is necessary. As more data become available, it will be important to reevaluate screening tools and processes for both malnutrition and frailty screening and to modify recommendations as appropriate to ultimately support healthy aging in the community.


  1. Chang, A. Y., Skirbekk, V. F., Tyrovolas, S., Kassebaurm, N. J., & Dieleman, J. L. (2019). Measuring population ageing: An analysis of the Global burden of Disease Study 2017. The Lancet Public Health,4(3), e159–e167.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Wei, K., Nyunt, M. S. Z., Gao, Q., Wee, S. L., Yap, K. B., & Ng, T. P. (2018). Association of frailty and malnutrition with long term functional and mortality outcomes among community dwelling older adults: Results from the Singapore Longitudinal Aging Study. JAMA Network Open,1(3), e180650.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Vellas, B., Villars, H., Abellan, G., et al. (2006). Overview of the MNA–its history and challenges. Journal of Nutritional Health and Aging,10(6), 456–463.

    CAS  Google Scholar 

  4. Laur, C. V., McNicholl, T., Valaltis, R., & Keller, H. H. (2017). Malnutrition or frailty? Overlap and evidence gaps in the diagnosis and treatment of frailty and malnutrition. Applied Physiology, Nutrition, and Metabolism,42(5), 449–458.

    Article  PubMed  Google Scholar 

  5. Verlaan, S., Ligthart-Melis, G. C., Wijers, J. L. J., Cederholm, T., Maier, A. B., & de van der Schueren, M. A. E. (2017). High prevalence of physical frailty among community-dwelling malnourished older adults—a systematic review and meta-analysis. Journal of the American Medical Directors Association,18(5), 374–382.

    Article  PubMed  Google Scholar 

  6. Sze, S., Zhang, J., Pellicori, P., Morgan, D., Hove, A., & Clark, A. L. (2017). Prognostic value of simple frailty and malnutrition screening tools in patients with acute heart failure due to left ventricular systolic dysfunction. Clinical Research in Cardiology,106(7), 533–541.

    CAS  Article  PubMed  Google Scholar 

  7. Tegels, J. J., de Maat, M. F., Hulsewe, K. W., Hoofwijk, A. G. M., & Stoot, J. H. (2014). Value of geriatric frailty and nutritional status assessment in predicting postoperative mortality in gastric cancer surgery. Journal of Gastrointestinal Surgery,18(3), 439–446.

    Article  PubMed  Google Scholar 

  8. Aaldriks, A. A., van der Geest, L. G. M., Giltay, E. J., et al. (2013). Frailty and malnutrition predictive of mortality risk in older patients with advanced colorectal cancer receiving chemotherapy. Geriatric Oncology,4(3), 218–226.

    Article  Google Scholar 

  9. Wilson, J. M. G., & Junger, G. (1968) Principles and practice of screening for disease, World Health Organization, Geneva. Available: Accessed 8 July 2019.

  10. Herman, C. R., Gill, H. K., Eng, J., & Fajardo, L. L. (2002). Screening for preclinical disease: Test and disease characteristics. American Journal of Roentgenology,179(4), 825–831.

    Article  Google Scholar 

  11. Malnutrition Quality Improvement Initiative (MQii). MQii Toolkit. 2018. Available: Accessed 8 July 2019

  12. Watson, J., Buta, B., & Xue, Q. L. (2018). Frailty screening and interventions: Considerations for clinical practice. Clinics in Geriatric Medicine,34(1), 25–38.

    Article  Google Scholar 

  13. Weiler, M., Arensberg, M., Comee, L., Krok-Schoen, J., Gahche, J., & Dwyer, J. (2018). Views of dietetics professionals (DPs) on risk factors/screening tools for identifying malnutrition and frailty in older adults. Journal of the Academy of Nutrition and Dietetics,118(10), A130.

    Article  Google Scholar 

  14. Eglseer, D., Halfens, R. J. G., Schussler, S., Visser, M., Volkert, D., & Lohrmann, C. (2018). Is the topic of malnutrition in older adults addressed in the European nursing curricula? A MaNuEL study. Nurse Education Today,68, 13–18.

    Article  PubMed  Google Scholar 

  15. Watson, K., Farrell, M., Arensberg, M. B., & Dwyer, J. (2015). Nutrition as a vital sign: Progress since the 1990 multidisciplinary nutrition screening initiative and opportunities for nursing. Journal of Nursing and Care,4, 224.

    Article  Google Scholar 

  16. Gahche, J., Weiler, M., Arensberg, M., & Dwyer, J. (2018). Defining opportunities for national survey data to identify risks for frailty and malnutrition. Innovation in Aging,2(Supplement 1), 716.

    Article  PubMed Central  Google Scholar 

  17. Wellman, N. (2010) Food preparation and consumption habits of community-dwelling populations. In: Providing healthy and safe foods as we age: Workshop Summary. Washington, D.C.: National Academies Press. Available online: Accessed 8 July 2019.

  18. Centers for Medicare & Medicaid Services. (2019). 2018 ICD-10 Procedural Coding System. Available: Accessed 8 July 2019.

  19. Visser, M., Volkert, D., Corish, C., et al. (2017). Tackling the increasing problem of malnutrition in older persons: The malnutrition in the elderly (MaNuEL) knowledge hub. Nutrition Bulletin,42(2), 178–186.

    Article  Google Scholar 

  20. Leij-Halfwerk, S., Verwijs, M. H., van Houdt, S., et al. (2019). Prevalence of protein-energy malnutrition risk in European older adults in community, residential and hospital settings, according to 22 malnutrition screening tools validated for use in adults > 65 years: A systematic review and meta-analysis. Maturitas,126, 80–89.

    Article  Google Scholar 

  21. Wolters, M., Volkert, D., Streicher, M., et al. (2018). Prevalence of malnutrition using harmonized definitions in older adults from different settings—a MaNuEL study. Clinical Nutrition.

    Article  PubMed  Google Scholar 

  22. White, J. V., Guenter, P., Jensen, G., et al. (2012). Consensus statement of the 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. Journal of Parenteral and Enteral Nutrition,36(3), 275–283.

    Article  PubMed  Google Scholar 

  23. Cederholm, T., Jensen, G. L., Correia, M. I. T. D., et al. (2019). GLIM criteria for the diagnosis of malnutrition: A consensus report from the global nutrition community. Clinical Nutrition,38(1), 1–9.

    CAS  Article  PubMed  Google Scholar 

  24. Power, L., de van der Schueren, M. A., Leij-Halfwerk, S., et al. (2019). Development and application of a scoring system to rate malnutrition screening tools used in older adults in community and healthcare settings—A MaNuEL study. Clinical Nutrition,38(4), 1807–1819.

    Article  PubMed  Google Scholar 

  25. Power, L., Mullally, D., Gibney, E. R., et al. (2018). A review of the validity of malnutrition screening tools used in older adults in community and healthcare settings—A MaNuEL study. Clinical Nutrition ESPEN,24, 1–13.

    Article  PubMed  Google Scholar 

  26. Handu, D., Moloney, L., Wolfram, T., Ziegler, P., Acosta, A., & Steiber, A. (2016). Academy of nutrition and dietetics methodology for conducting systematic reviews for the Evidence Analysis Library. Journal of the Academy of Nutrition and Dietetics,116(2), 311–318.

    Article  PubMed  Google Scholar 

  27. Skipper A, Charney P, Coltman AE, et al. (2018) Nutrition screening adults systematic review, Academy of Nutrition and Dietetics. Available: Accessed 8 July 2019

  28. Academy of Nutrition and Dietetics (2019) Draft position paper: malnutrition screening tools for adults. Available: Accessed 17 May 2019

  29. Fried, L. P., Tangen, C. M., Walston, J., et al. (2001). Frailty in older adults: Evidence for a phenotype. The Journals of Gerontology Series A Biological Sciences and Medical Sciences,56(3), M146–M156.

    CAS  Article  Google Scholar 

  30. Bergman, H., Ferrucci, L., Guralnik, J., et al. (2007). Frailty: An emerging research and clinical paradigm—Issues and controversies. The Journals of Gerontology Series A Biological Sciences and Medical Sciences,62(7), 731–737.

    Article  PubMed  Google Scholar 

  31. Aguayo, G. A., Donneau, A. F., Vaillant, M. T., et al. (2017). Agreement between 35 published frailty scores in the general population. American Journal of Epidemiology,186(4), 420–434.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Collard, R. M., Boter, H., Shoevers, R. A., & Oude Voshaar, R. C. (2012). Prevalence of frailty in community-dwelling older persons: A systematic review. Journal of the American Geriatric Society,60(8), 1487–1492.

    Article  Google Scholar 

  33. Cruz-Jentoft, A. J., Bahat, G., Bauer, J., et al. (2019). Sarcopenia: Revised European consensus on definition and diagnosis. Age and Aging,48, 16–31.

    Article  Google Scholar 

  34. Tabue-Teguo, M., Kelaiditi, E., Demougeot, L., Dartigues, J. F., Vellas, B., & Cesari, M. (2015). Frailty index and mortality in nursing home residents in France: Results from the INCUR study. Journal of the American Medical Directors Association,16(7), 603–606.

    Article  PubMed  Google Scholar 

  35. Keevil, V. L., Luben, R., Hayat, S., Sayer, A. A., Wareham, N. J., & Khaw, K. T. (2018). Physical capability predicts mortality in late mid-life as well as in old age: Findings from a large British cohort study. Archives of Gerontology and Geriatrics,74, 77–82.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Abellan van Kan, G., Rolland, Y. M., Morley, J. E., & Vellas, B. (2008). Frailty: Toward a clinical definition. Journal of the American Medical Directors Association,9(2), 71–72.

    Article  PubMed  Google Scholar 

  37. Woo, J., Yu, R., Wong, M., Yeung, F., Wong, M., & Lum, C. (2015). Frailty screening in the community using the FRAIL Scale. Journal of the American Medical Directors Association,16(5), 412–419.

    Article  PubMed  Google Scholar 

  38. Wilson, D., Jackson, T., Sapey, E., & Lord, J. M. (2017). Frailty and sarcopenia: The potential role of an aged immune system. Ageing Research Reviews,36, 1–10.

    Article  PubMed  Google Scholar 

  39. Morley, J. E., Rolland, Y., Tolson, D., & Vellas, B. (2012). Increasing awareness of the factors producing falls: The mini falls assessment. Journal of the American Medical Directors Association,13(2), 87–90.

    Article  PubMed  Google Scholar 

  40. Morley, J. E., Vellas, B., Abellan van Kan, G., et al. (2013). Frailty consensus: A call to action. Journal of the American Medical Directors Association,14(6), 392–397.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Rapp, K., Becker, C., Cameron, I. D., et al. (2012). Epidemiology of falls in residential aged care: Analysis of more than 70,000 falls from residents of Bavarian nursing homes. Journal of the American Medical Directors Association,13(2), 187.

    Article  PubMed  Google Scholar 

  42. Tanaka, S., Kamiya, K., Hamazaki, N., et al. (2018). SARC-F questionnaire identifies physical limitations and predicts post discharge outcomes in elderly patients with cardiovascular disease. Journal of Cachexia, Sarcopenia and Muscle-Clinical Reports,3(1), e00056.

    Google Scholar 

  43. Ida, S., Kaneko, R., & Murata, K. (2017). SARC-F for screening of sarcopenia among older adults: A meta-analysis of screening test accuracy. Journal of the American Medical Directors Association,19(8), 685–689.

    Article  Google Scholar 

  44. Clegg, A., Rogers, L., & Young, J. (2015). Diagnostic test accuracy of simple instruments for identifying frailty in community-dwelling older people: A systematic review. Age and Ageing,44(1), 148–152.

    Article  PubMed  Google Scholar 

  45. Ahlund, K., Ekerstad, N., Back, M., Karlson, B. W., & Oberg, B. (2019). Preserved physical fitness is associated with lower 1-year mortality in frail elderly patients with a severe comorbidity burden. Clinical Interventions in Aging,14, 577–586.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Correa-Perez, A., Abraha, I., Cherubini, A., et al. (2019). Efficacy of non-pharmacological interventions to treat malnutrition in older persons: A systematic review and meta-analysis. The SENATOR project ONTOP series and MaNuEL knowledge hub project. Ageing Research Reviews,49, 27–48.

    Article  PubMed  Google Scholar 

  47. World Health Organization (2017) Integrated care for older people (ICOPE) Guidelines on community-level interventions to manage declines in intrinsic capacity. Evidence profile: Malnutrition. Available: Accessed 8 July 2109

  48. Reinders, I., Volkert, D., de Groot, L. C., et al. (2019). Effectiveness of nutritional interventions in older adults at risk of malnutrition across different health care settings: Pooled analyses of individual participant data from nine randomized controlled trials. Clinical Nutrition,38(4), 1797–1806.

    Article  PubMed  Google Scholar 

  49. Apostolo, J., Cooke, R., Bobrowicz-Campos, E., et al. (2018). Effectiveness of interventions to prevent pre-frailty and frailty progression in older adults: A systematic review. JBI Database of Systematic Reviews and Implementation Reports,16(1), 140–232.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Puts, M. T. E., Toubasi, S., Andrew, M. K., et al. (2017). Interventions to prevent or reduce the level of frailty in community-dwelling older adults: A scoping review of the literature and international policies. Age and Ageing,46(3), 383–392.

    Article  PubMed  PubMed Central  Google Scholar 

  51. van Rijckevorsel-Scheele, J., Willems, R. C. W. J., Roelofs, P. D. D. M., Koppelaar, E., Gobbens, R. J. J., & Goumans, M. J. B. M. (2019). Effects of health care interventions on quality of life among frail elderly: A systematized review. Clinical Interventions in Aging,14, 643–658.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Travers, J., Romero-Ortuno, R., Bailey, J., & Cooney, M. T. (2019). Delaying and reversing frailty: A systematic review of primary care interventions. British Journal of General Practice,69(678), e61–e69.

    Article  PubMed  Google Scholar 

  53. Gabrovec, B., Veninsek, G., Samaniego, L. L., Carriazo, A. M., Antoniadou, E., & Jelenc, M. (2018). The role of nutrition in ageing: A narrative review from the perspective of the European Joint Action on Frailty: ADVANTAGE JA. European Journal of Internal Medicine,56, 26–32.

    Article  PubMed  Google Scholar 

  54. Robinson, S. M., Reginster, J. Y., Rizzoli, R., et al. (2018). Does nutrition play a role in the prevention and management of sarcopenia? Clinical Nutrition,37(4), 1121–1132.

    CAS  Article  PubMed  Google Scholar 

  55. Yannakoulia, M., Ntanasi, E., Anastasiou, C. A., & Scarmeas, N. (2017). Frailty and nutrition: From epidemiological and clinical evidence to potential mechanisms. Metabolism Clinical and Experimental,68, 64–76.

    CAS  Article  PubMed  Google Scholar 

  56. Lorenzo-Lopez, L., Maseda, A., de Labra, C., Regueiro-Folgueira, L., Rodriguez-Villamil, J. L., & Millan-Calenti, J. C. (2017). Nutritional determinants of frailty in older adults: A systematic review. BMC Geriatrics,17(1), 108.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Streicher, M., van Zwienen-Pot, J., Bardon, L., et al. (2018). Determinants of incident malnutrition in community-dwelling older adults: A MaNuEL multicohort meta-analysis. Journal of the American Geriatrics Society,66(12), 2335–2343.

    Article  PubMed  Google Scholar 

  58. Zhang, Y., Alonso-Coello, P., Guyatt, G. H., et al. (2019). GRADE Guidelines: 19. Assessing the certainty of evidence in the importance of outcomes or values and preferences—risk of bias and indirectness. Journal of Clinical Epidemiology,11, 94–104.

    Article  Google Scholar 

  59. Corish, C. A., & Bardon, L. A. (2018). Malnutrition in older adults: Screening and determinants. Proceedings of the Nutrition Society,3, 1–8.

    Article  Google Scholar 

  60. van Bokhorst-de van der Schueren, M. A., Guaitoli, P. R., Jansma, E. P., & de Vet, H. C. (2014). Nutrition screening tools: Does one size fit all? A systematic review of screening tools for the hospital setting. Clinical Nutrition,33(1), 39–58.

    Article  PubMed  Google Scholar 

  61. Wilson, M. M., Thomas, D. R., Rubenstein, L. Z., et al. (2005). Appetite assessment: Simple appetite questionnaire predicts weight loss in community-dwelling adults and nursing home residents. American Journal of Clinical Nutrition,82(5), 1074–1081.

    CAS  Article  PubMed  Google Scholar 

  62. DeGroot, L. C., Beck, A. M., Schroll, M., & Staveren, W. A. (1998). Evaluating the DETERMINE your nutritional health checklist and the mini nutritional assessment as tools to identify nutritional problems in elderly Europeans. European Journal of Clinical Nutrition,52(12), 877.

    CAS  Article  Google Scholar 

  63. Ferguson, M., Capra, S., Bauer, J., & Banks, M. (1999). Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutrition,15(6), 458–464.

    CAS  Article  Google Scholar 

  64. Elia M (2003) The ‘MUST’ report, Nutritional screening of adults: A multidisciplinary responsibility. Development and use of the ‘Malnutrition Universal Screening Tool’ (‘MUST’) for adults. British Alliance for Enteral and Parenteral Nutrition (BAPEN), London. Available:

  65. Guigoz, Y., Vellas, B., & Garry, P. J. (1996). Assessing the nutritional status of the elderly: The mini nutritional assessment as part of the geriatric evaluation. Nutrition Reviews,54, S59–S65.

    CAS  Article  PubMed  Google Scholar 

  66. Murphy, M. C., Brooks, C. N., New, S. A., & Lumbers, M. L. (2000). The use of the Mini-Nutritional Assessment (MNA) tool in elderly orthopaedic patients. European Journal of Clinical Nutrition,54, 555–562.

    CAS  Article  PubMed  Google Scholar 

  67. Morley, J. E. (1991). Why do physicians fail to recognize and treat malnutrition in older persons? Journal of the American Geriatric Society,39, 1139–1140.

    CAS  Article  Google Scholar 

  68. Keller, H. H., Goy, R., & Kane, S. L. (2005). Validity and reliability of SCREEN II (Seniors in the community: Risk evaluation for eating and nutrition, Version II). European Journal of Clinical Nutrition,59(10), 1149–1157.

    CAS  Article  PubMed  Google Scholar 

  69. Kruizenga, H. M., Seidell, J. C., de Vet, H. C., Wierdsma, N. J., & van Bokhorst-de van der Schueren, M. A. (2005). Development and validation of a hospital screening tool for malnutrition: The short nutritional assessment questionnaire (SNAQ). Clinical Nutrition,24(1), 75–82.

    CAS  Article  PubMed  Google Scholar 

  70. Keller, H. H., McKenzie, J. D., & Goy, R. E. (2001). Construct validation and test–retest reliability of the seniors in the community: Risk evaluation for eating and nutrient questionnaire. The Journals of Gerontology: Series A, Biological Sciences and Medical Sciences,56(9), M552–M558.

    CAS  Article  Google Scholar 

  71. Keller, H. H., Ostbye, T., & Goy, R. (2004). Nutritional risk predicts quality of life in elderly community-living Canadians. The Journals of Gerontology, Series A, Biological Sciences and Medical Sciences, 59(1), 66–74.

    Article  Google Scholar 

  72. Keller, H. H. (2007). Promoting food intake in older adults living in the community: A review. Applied Physiology, Nutrition, and Metabolism,32(6), 991–1000.

    Article  PubMed  Google Scholar 

  73. Wham, C. A., Redwood, K. M., & Kerse, N. (2014). Validation of the nutrition screening tool ‘Seniors in the Community: Risk evaluation for Eating and Nutrition Screening II’ among octogenarians. Journal of Nutrition, Health and Aging,18(1), 39–43.

    CAS  Article  PubMed  Google Scholar 

  74. Akhtar, U., Keller, H. H., Tate, R. B., & Lenyei, C. O. (2015). Construct validation of three nutrition questions using health and diet ratings in older Canadian males living in the community. Canadian Journal of Dietetic Practice and Research,76(4), 194–199.

    Article  PubMed  Google Scholar 

  75. Ramage-Morin, P. L., Gilmour, H., & Rotermann, M. (2017). Nutritional risk, hospitalization and mortality among community-dwelling Canadians aged 65 or older. Health Reports,28(9), 17–27.

    PubMed  Google Scholar 

  76. Broeska, V. E., Lengyel, C. O., & Tate, R. B. (2013). Nutritional risk and 5-year-mortality of older community-dwelling Canadian men: The Manitoba Follow-Up Study. Journal of Nutrition in Gerontology and Geriatrics,32(4), 317–329.

    Article  PubMed  Google Scholar 

  77. Hogan, D., Lan, L. T., Diep, D. T., Gallegos, D., & Collins, P. F. (2017). Nutritional status of Vietnamese outpatients with chronic obstructive pulmonary disease. Journal of Human Nutrition and Dietetics,30(1), 83.

    CAS  Article  PubMed  Google Scholar 

  78. Isenring, E., Cross, G., Daniels, L., Kellett, E., & Koczwara, B. (2006). Validity of the malnutrition screening tool as an effective predictor of nutritional risk in oncology outpatients receiving chemotherapy. Supportive Care in Cancer,14(11), 11152.

    Article  Google Scholar 

  79. Kerse, N., Boyd, M., Mclean, C., Koziol-Mclain, J., & Robb, G. (2008). The BRIGHT tool. Age and Ageing,37(5), 553–588.

    Article  PubMed  Google Scholar 

  80. Burn, R., Hubbard, R. E., Scrase, R. J., et al. (2018). A frailty index derived from a standardized comprehensive geriatric assessment predicts mortality and aged residential care admission. BMC Geriatrics,18, 319.

    Article  Google Scholar 

  81. Vellas, B., Balardy, L., Gillette-Guyonnet, S., et al. (2013). Looking for frailty in community-dwelling older persons: The gerontopole frailty screening tool (GFST). Journal of Nutrition, Health, and Aging,17(7), 629–631.

    CAS  Article  PubMed  Google Scholar 

  82. Bielderman, A., van der Schans, C. P., van Lieshout, M. R. J., et al. (2013). Multidimensional structure of the Groningen Frailty Indicator in community-dwelling older people. BMC Geriatrics,13, 86.

    Article  Google Scholar 

  83. Raiche, M., Hebert, R., & Dubois, M. F. (2008). PRISMA-7: A case-finding tool to identify older adults with moderate to severe disabilities. Archives of Gerontology and Geriatrics,47(1), 9–18.

    Article  PubMed  Google Scholar 

  84. Strawbridge, W. J., Shema, S. J., Balfour, J. L., Higby, H. R., & Kaplan, G. A. (1998). Antecedents of frailty over three decades in an older cohort. Journal of Gerontology,53(1), S9–S16.

    CAS  Article  Google Scholar 

  85. Gobbens, R. J., van Assen, M. A., Luijkx, K. G., Wijnen-Sponselee, M. T., & Schols, J. M. (2009). The Tilburg Frailty Indicator: Psychometric properties. Journal of the American Medical Directors Association,11(5), 344–355.

    Article  Google Scholar 

  86. Susanto, M., Hubbard, R. E., & Gardiner, P. A. (2018). Validity and responsiveness of the FRAIL Scale in middle aged women. Journal of the American Medical Directors Association,19(1), 65–69.

    Article  PubMed  Google Scholar 

  87. Morley, J. E., Malmstrom, T. K., & Miller, D. K. (2012). A simple frailty questionnaire (FRAIL) predicts outcomes in middle aged African Americans. Journal of Nutrition, Health, and Aging,16(7), 601–608.

    CAS  Article  PubMed  Google Scholar 

  88. Gardiner, P. A., Mishra, G. D., & Dobson, A. J. (2015). Validity and responsiveness of the FRAIL scale in a longitudinal cohort study of older Australian women. Journal of the American Medical Directors Association,16(9), 781–783.

    Article  PubMed  Google Scholar 

  89. Aprahamian, I., de Castro Cezar, N. O., Izbicki, R., et al. (2017). Screening for frailty with the FRAIL scale: A comparison with the phenotype criteria. Journal of the American Medical Directors Association,18(7), 592–596.

    Article  PubMed  Google Scholar 

  90. Kojima, G. (2018). Frailty defined by FRAIL Scale as a predictor of mortality: A systematic review and meta-analysis. Journal of the American Medical Directors Association,19(8), 685–689.

    Article  Google Scholar 

  91. Aprahamian, I., Lin, S. M., Suemoto, C. K., et al. (2017). Feasibility and factor structure of the FRAIL Scale in older adults. Journal of the American Medical Directors Association,18(4), 367.

    Article  PubMed  Google Scholar 

  92. Dong, L., Qiao, X. Q., Tian, X., et al. (2018). Cross-cultural adaptation and validation of the FRAIL Scale in Chinese community-dwelling older adults. Journal of the American Medical Directors Association,19(1), 12–17.

    Article  PubMed  Google Scholar 

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We thank Clare A. Corish RD, PhD, FINDI and her colleagues at the School of Public Health, Physiotherapy and Sports Science, University College Dublin for their helpful comments on the manuscript.


There was no funding provided for this research.

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Correspondence to Mary Beth Arensberg.

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Conflict of interest

M Weiler and MB Arensberg are employees of the Abbott Nutrition Division of Abbott and stockholders of Abbott. J Dwyer is a member of the Scientific Advisory Boards of McCormick Science Institute, Bay State Millling, and the Mushroom Council. She was paid expenses and a fee by the Dairy Council for moderating a symposium in 2018 and by Gerber/Nestle for a presentation at the American Society for Nutrition Annual Meeting in 2019. She is editor of Nutrition Today, and holds stock in several food and drug companies. J Gahche has no apparent conflicts of interest.

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Dwyer, J.T., Gahche, J.J., Weiler, M. et al. Screening Community-Living Older Adults for Protein Energy Malnutrition and Frailty: Update and Next Steps. J Community Health 45, 640–660 (2020).

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  • Protein-energy malnutrition
  • PEM
  • Undernutrition
  • Malnutrition
  • Screening
  • Frailty screening
  • Community-living
  • Older adults