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Using the theoretical domains framework to inform strategies to support dietitians undertaking body composition assessments in routine clinical care

BMC Health Services Research volume 21, Article number: 518 (2021) Cite this article

Abstract

Background

Malnutrition, sarcopenia and cachexia are clinical wasting syndromes characterised by muscle loss. Systematic monitoring by body composition assessment (BCA) is recommended for the diagnosis, treatment and monitoring of the syndrome(s). This study investigated practices, competency, and attitudes of Australian dietitians regarding BCA, to inform a local implementation process.

Methods

Applying the Action cycle in the Knowledge to Action framework, surveys were distributed to the 26 dietitians of an 800-bed tertiary hospital. The survey assessed barriers and enablers to performing routine BCA in clinical care. Results were categorised using the Theoretical Domains Framework (TDF) and suitable interventions mapped using the Behaviour Change Wheel.

Results

Twenty-two dietitians (84.6%) completed the survey. Barriers to BCA were identified in all TDF domains, particularly in Knowledge, Skills, Social/professional role and identity, Beliefs about capabilities, and Environmental context and resources. Enablers existed in domains of: Skills; Beliefs about consequences; Goals; Environmental context and resources; Social influences; Intentions; Optimism; Reinforcement.

Conclusions

This study showed that hospital dietitians experience individual, team, and organisational barriers to adopt BCAs in clinical practice. We were able to formulate targeted implementation strategies to overcome these barriers to assist BCA adoption into routine practice.

Peer Review reports

Background

Malnutrition, sarcopenia and cachexia are clinical wasting syndromes, prevalent in patients with acute or chronic diseases and frail elderly [1,2,3]. Malnutrition occurs in 20–50% [1, 4] of patients in acute care settings, sarcopenia in 15–70% [5, 6] and cachexia in 5–80% [7,8,9]. Malnutrition is defined as “a state resulting from lack of intake or uptake of nutrition that leads to altered body composition (decreased fat free mass) and body cell mass leading to diminished physical and mental function and impaired clinical outcome from disease” [10]. Malnutrition is associated with reduced treatment efficacy and increased healthcare costs [11, 12]. Disease-related malnutrition is characterised by inflammation and can be acute or chronic. Chronic disease-related malnutrition is also called ‘cachexia’ and is characterised by “inflammation and ongoing loss of weight and muscle mass” [10]. Sarcopenia is a condition which is often associated with malnutrition. It is defined as “loss of skeletal muscle mass and strength related to ageing and/or chronic disease” [13, 14], and is associated with negative outcomes across health care settings including reduced survival, worse clinical outcomes and impaired quality of life in many clinical populations including oncology, surgical, hepatology, and older adults [15,16,17]. As sarcopenia is prevalent amongst elderly and chronically ill, assessment and treatment has been encouraged by several leading expert groups [13, 18]. To be able to identify sarcopenia, assessment of muscle strength and muscle quantity or quality is required.

The three syndromes of malnutrition, cachexia, and sarcopenia are present in hospital populations and although they have been well defined in clinical practice, the umbrella term ‘malnutrition’ is used for patients who show signs of inadequate food intake, weight loss, and muscle wasting. It is recommended to screen for malnutrition on admission to the hospital and regularly during hospital stay, and to treat malnutrition as early as possible [10, 19, 20]. Malnutrition is typically ‘managed’ with a two-step process of screening and assessment. The initial step uses a malnutrition screening tool, such as the Malnutrition Screening Tool (MST) or Nutritional Risk Screening (NRS) [10, 21]. Patients classified as ‘at risk of malnutrition’ are subsequently referred to a dietitian. The second step is a dietitian assessment using a validated tool, such as the Subjective Global Assessment (SGA) or Mini-Nutritional Assessment (MNA) [10, 11]. These assessment tools diagnose malnutrition by drawing on objective parameters such as weight and metabolic demand, as well as subjective parameters like weight history, nutrition impact symptoms, and physical examination of muscle mass and subcutaneous fat stores [22]. Nutritional assessment may include additional anthropometric assessments such as mid upper arm circumference, skin fold thickness and mid upper arm muscle circumference [23].

Whilst parameters of nutrition assessment tools are easy to gather and rate highly regarding sensitivity, specificity and inter-rater reliability, they do not provide objective data on body composition such as muscle mass [24]. In addition, there is a subset of patients who cannot be weighed and using an estimated weight leaves a margin for error in classifying malnutrition [25]. Nutrition assessment tools also fail to recognise that patients can have a low level of lean tissue with any BMI category [26].

Thus, measures of overall weight loss lack the sensitivity to detect the amount of lean mass an individual has and the potential loss of lean mass experienced. This introduces the potential to grossly underestimate the prevalence of hospital malnutrition if diagnosis is based on body weight and body weight changes alone. Other challenges that impact the correct identification of malnutrition are our ageing society and the global epidemic of overweight and obesity [27], resulting in a higher number of patients with sarcopenia, as well as overweight and obese patients with chronic or acute diseases [28,29,30]. Malnutrition in these patients is harder to recognise using the aforementioned assessment tools, but is nonetheless associated with worse outcomes [25, 31].

As a result of these shortcomings there is a lack of consensus amongst the dietetic and medical community surrounding malnutrition assessment methodologies [17, 32,33,34,35,36,37]. With societal changes and improved technologies available, it is prudent to consider additional objective ways to obtain information on lean mass, in order to diagnose and monitor the effectiveness of the treatment of malnutrition [17, 37, 38].

Routinely measuring lean mass provides an objective measure to diagnose and monitor malnutrition. This aligns with the international clinical nutrition community’s recognition of the need for BCA as part of malnutrition assessment [15, 17, 39]. Indeed, the new Global Leadership Initiative on Malnutrition (GLIM) criteria recommends the measuring of body composition and identifying loss of lean mass as one of the top five criteria to assist in diagnosing malnutrition [27, 33]. Since the launch of the GLIM criteria, several initiatives have been taken to validate the criteria. These showed that the GLIM criteria have a fair agreement with the reference standard [40,41,42].

Despite available evidence of the benefits of BCA, body composition is not routinely used by dietitians in clinical practice. This is reportedly due to incomplete knowledge and awareness, uncertainty of how and when to measure, poor availability of assessment tools and a lack of time [17, 43]. Given the role that lean mass plays in the clinical outcomes of certain illnesses, it is of critical importance that its assessment be added into the nutrition field [15, 17].

The most common BCA techniques that have been validated for use in humans are skinfold measurements, single and multi-frequency bio-impedance analysis, hydrodensitometry, Dual Energy X-ray Absorptiometry (DXA), computerized tomography (CT)-scans, and air displacement plethysmography (E.g. BodPod) [44]. These techniques are all non-invasive but vary with regards to cost, precision and validity, with skinfold measurements and bio-impedance analysis being relatively imprecise and DXA, CT-scans and air displacement plethysmography being more accurate but also more costly techniques, and less appropriate for bedside measurements [15, 44, 45]. Recently, ultrasound techniques have been used to assess body compartments at the bedside, for instance the upper quadriceps muscle [15].

It is widely recognised that the dissemination of information alone does not change practice [46]; thus drawing on an implementation science methodology facilitating this change and adoption process [47]. This theory-driven approach guides the rigorous and systematic processes of evidence selection, adapting knowledge to the local context, understanding barriers and enablers to its use, selecting appropriate interventions to support its adoption, and monitoring and evaluating outcomes, as well as sustaining knowledge use, as outlined in the Knowledge-to-Action (KTA) framework [48]. Within this framework additional theories, models, and frameworks can be applied to guide structured and systematic barrier identification and intervention selection, such as the Theoretical Domains Framework (TDF) and the Behaviour Change Wheel (BCW) [49, 50].

The aim of this project was to develop a department-wide strategy to incorporate BCA by dietitians into routine clinical care in an 800-bed tertiary hospital in South-East Queensland (Australia) using an Implementation Science approach. To inform this process we planned to investigate the current local practices, competency, and attitudes of our departmental clinical dietitians with regards to the utilisation of BCA.

Methods

The study was declared as Exempt from Review – Not Research according to the Human Research Ethics Committee of Mater Research Institute – UQ Human Research Ethics Committee (Project ID: EXMT/MML/58778). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants; instructions for survey completion indicated their completion implied consent.

This implementation planning project occurred in an 800-bed tertiary hospital in Brisbane, Australia. The hospital provides services to both private and public inpatients and outpatients and includes a variety of patient populations. At the start of this project (May 2017), The Dietetics and Foodservices department consisted of 20.55 full time equivalents (FTE) with 26 dietitians.

To develop our BCA implementation strategy we followed the KTA framework which is an iterative approach that allows building (Knowledge Creation) and application of knowledge (Action Cycle) [40, 48, 51]. The Action Cycle was the focus of this work; with steps that can occur sequentially or concurrently and involve identification of the problem, assessing knowledge use determinants, evaluating the impact of knowledge use or outcomes, and ensuring sustainability [40]. The KTA is a ‘process model’ that guides the process of translating research into practice [48]. The KTA is flexible enough to enable some of the steps (e.g. ‘assess barriers to knowledge use’ and ‘select, tailor, implement interventions’) to be guided by further application of ‘Determination Theories’ (i.e TDF) [50] and ‘Implementation Theories (Theories’ (i.e. BCW) [49] to assess barriers and enablers and design suitable interventions [51]. The TDF is used as a system for categorising and defining barriers, and the BCW as a system for guiding decision-making around designing behaviour change interventions based on the identified barriers.

Below, we outline the survey process which allowed determination of a dietetic departmental practices, competency, and attitudes. A survey was developed to assess barriers and enablers to BCA use within the dietetic department (Additional file 1). Questions were designed by the authors to map against domains of the TDF [50]. Questions covered knowledge attitudes on, and confidence in BCA device use, frequency and predicted time taken to use the devices, views on how it would change dietetic practice, and which patient cohorts would benefit from BCA, informed by current literature on barriers and enablers to undertaking BCA [43] and discussion within the research team. Each question also had an ‘other’ option. All department dietitians were invited to complete the survey via an email link to an online survey portal (Survey Monkey, San Mateo, CA, USA) in May 2018. The survey was open for two weeks and two reminders were sent prior to the closing date.

The results were summarised as frequencies and percentages of answers for each question. All authors reviewed the summarised survey results and tabulated the barriers and enablers identified (Table 1). This process involved an analysis using the TDF as the framework to categorise responses into domains; these responses were sorted into identified barriers and enablers, followed by documenting the source of the behaviour using the BCW (column 5), potential behaviour change techniques (BCT) in column 6, and finally, interventions designed drawing from the implementation science literature (column 7) [49, 52]. Definitions of the BCW intervention components (column 5) are as follows: Education (Increasing knowledge or understanding); Persuasion (Using communication to induce positive or negative feelings or stimulate action); Incentivisation (Creating expectation of reward); Coercion (Creating expectation of punishment or cost); Training (Imparting skills); Restriction (Using rules to reduce the opportunity to engage in the target behaviour (or to increase the target behaviour by reducing the opportunity to engage in competing behaviours)); Environmental restructuring (Changing the physical or social context); Modelling (Providing an example for people to aspire to or imitate); and Enablement (Increasing means/reducing barriers to increase capability or opportunity) [49]. Findings were refined through group discussion resulting in consensus, with subsequent operationalisation and prioritisation of strategies, listed in column 7, informed by BCTs in column 6. The group consisted of two clinician-researchers (one with expertise in implementation science and one in body composition) who were embedded in the department and three clinicians (including one senior team leader) with an interest in body composition assessment and who also had a strong clinical understanding of the department.

Table 1 Intervention mapping and operationalising after sorting of barriers and enablers to TDF domains from the dietitians surveyed
Full size table

Results

Twenty-two of 26 dietitians (84.6%) completed the survey. As shown in Table 2, more than half of clinicians had previous training in BCA, mostly in skinfold thickness and mid upper arm circumference (MUAC). Few had training in bioelectrical impedance spectroscopy (BIS) devices. The majority of clinicians were aware that skinfold calliper, BIS, PG-SGA physical exam, hand grip dynamometer and tape measure devices were available for use in their department. More clinicians felt confident using PG-SGA physical exam and tape measures with fewer feeling confident using the BIS, MUAC and handgrip devices and techniques. As seen in Fig. 1, the PG-SGA physical exam was the most common assessment reported to be performed, followed by the use of tape measures. The majority of clinicians reported that they never used skinfold measurement, BIS, MUAC or handgrip measures.

Table 2 Dietitian’s prior training, awareness of available devices and confidence in performing body composition assessments
Full size table
Fig. 1
figure 1

Frequency of reported device use by dietitians in routine clinical care

Full size image

Dietitians’ attitudes to use of BCA in routine practice were categorised barriers and enablers across TDF domains (Table 1). Around half of the dietitians reported not being sure who (54.5%), when (50%), what to do (50%), or how to interpret (45.5%) BCAs (TDF domain -Knowledge). Further, between 68.2–100% of BCA techniques were not used in their practice (Skills). Broadly, in their daily practice, the dietitians noted that undertaking BCAs were not in their daily routine (Belief about Capabilities; Memory, attention and decision processes; Intentions). Almost half (45.5%) felt they would need to change their practice to incorporate BCA into their assessments (Memory, attention and decision processes) and 40.9% reported it would be a hassle to find references ranges (Behavioural Regulation).

However, positively, over two-thirds of dietitians were aware of most of the devices in the department (Skills), and felt adding BCA into their practice would have a positive effect on a range of activities, including assessment of energy requirements (77.3%), providing objective measures of their interventions (72.3%) (Beliefs about Consequences). A large majority of dietitians reported they would like to learn more about BCAs (72.2%) and apply them in their practice (68.2%)(Goals), feeling it would make their practice more interesting (Optimism). The dietitians also agreed that the Body Composition team would make these changes possible (67.7%)(Reinforcement).

Table 1 shows the mapping of the identified barriers and enablers (columns 2 and 3) to the TDF domains (column 1). Interventions and how these can be operationalised, drawing from the literature [49, 52] are in columns 4 and 5. Barriers to use of BCA within our department were identified in all TDF domains. Enablers included: Skills; Beliefs about consequences; Goals; Environmental context and resources; Social influences; Intentions; Optimism; Reinforcement.

Through the detailed mapping process, these are summarised in and operationalised in column 6 of Table 1. They can broadly be grouped as: 1. Professional development strategy, 2. Body composition assessment clinical champion project, and 3. Departmental integration process.

Discussion

This study aimed to understand the attitudes, beliefs, and practices of clinicians in a tertiary hospital dietetics department regarding patients’ BCA practices to inform a process of integrating these practices into routine clinical care. Most dietitians rarely used BCA with their patients in a systematic way. Barriers and enablers existed in many of the same TDF domains. Many dietitians felt unsure of their skills, when and how to systematically use these BCA techniques, and some questioned their benefit for particular clinical areas (E.g. neonatal care) and/or outside of research projects. However, many dietitians were optimistic about the potential this process would provide to enabling evidence-based practice and noted it would add to the strength of assessments, recommendations, and ability to detect malnutrition and other wasting syndromes, and to clinically relevant improvements within the delivery of medical nutrition therapy.

To our knowledge, this is the first study to investigate barriers and enablers to systematic adoption of BCA techniques into routine dietetic clinical practice. While many papers have promoted the use of BCA to detect malnutrition [10, 15, 17, 33, 36, 37], and specific studies described the application of these techniques in clinical areas (e.g. elderly [38]; liver failure [53,54,55]; oncology [56, 57]; renal disease [58]; and respiratory disease [32]) none have applied this across a hospital dietetics department.

To our knowledge, only one study, by Reijnierse et al. (2017), documented barriers to BCA application in practice [43]. These were explored before and after a Dutch health professional training program on detection and management of sarcopenia [43]. Barriers included lack of availability of equipment, lack of knowledge, time constraints, and lack of collaboration with/awareness of other health professionals [43]. When Reijnierse’s study was repeated in a similar sized cohort of Australian and New Zealand health professionals (n = 250), as previously found, a lack of diagnostic tools was the main reason for not diagnosing sarcopenia [59]. Lack of sarcopenia awareness and lack of motivation among health-care professionals were also common barriers [43]. In addition to most of these, our study identified additional barriers relating to clinicians’ beliefs about the applicability of the techniques, personal ability to undertake the assessments and confidence in their abilities to incorporate these into their daily practice. Our more extensive suite of barriers may have resulted from a more profession-specific/department-wide assessment rather than training attendees of varied professions [43, 59].

Moreover, applicability issues also relate to BCA validity issues when used with acutely or chronically ill patients. American Society for Parenteral and Enteral Nutrition’s (ASPEN) recent systematic review showed minimal studies that have provided data on BCA in clinical populations. Out of BIA, DXA and ultrasound, DXA and CT scanning were recommended as ‘gold standard’, but the authors indicated that more research is required on the validity of BCA in specific patient populations [36].

Acknowledging the need for addressing all “bottlenecks” (barriers) in each phase of the implementation to ensure diagnosis and management of sarcopenia in daily practices, Reijnierse et al. (2017) highlight the need to draw on the implementation science literature in delivering effective interventions [43]. They highlight that this requires many factors such as acquisition of diagnostic measurement devices, reorganisation of care, collaboration between healthcare professionals, perceived needs and benefits of innovation and organizational factors [43]. Accordingly, we have adopted an implementation science approach to ensure we systematically select interventions that align with identified barriers and enhance existing enablers [48,49,50].

Following the operationalisation of the evidence-informed strategies to overcome the identified barriers and enablers, our team will progress the overarching interventions of upskilling (professional development strategy), modelling and reducing fear of change (clinical champion project) and embedding as usual practice (departmental integration) the use of BCA to complete a full ‘action cycle’ of the KTA [49, 52]. The details of these strategies are described in Table 1 (column 6). We will repeat our departmental survey in mid-2020 to re-assess adoption of, (perceived) competency in, and attitudes of clinical dietitians towards the utilisation of BCA devices within our department.

A study strength included the use of implementation science methodology and frameworks (KTA, TDF, BCW) [48,49,50] to map and inform our strategy. Many solutions may appear ‘common sense’ but the systematic assessment and rigour provided by the process provides confidence in the findings and interventions. The survey revealed numerous barriers and enablers to the adoption of BCA in routine clinical care. A greater understanding and/or a wider selection of barriers may have been identified through more qualitative approaches (E.g. focus groups, interviews). However, the methodological approaches were pragmatically chosen to be administered and analysed within routine practice without additional funding. The barriers and enablers identified may reflect specific local departmental issues and may not be generalisable to all sites. However, it is likely that many of these issues are common to other Australian and international sites, as highlighted by Reijnierse [43] and Yeung [59] and colleagues. Study limitations include potential reporting bias or answers reflecting social desirability despite being an anonymous due to the small team size, barriers not existing in the TDF domains allocated to them in the survey, plus lack of data on time burden for dietitians of performing measurements, booking devices, and carrying devices to clinics or wards. We also lack data on objective clinical practice change, resultant clinical outcomes, and cost-effectiveness. Another limitation is the potential impact of knowledge and practice loss with staff turnover; however this was attempted to be circumvented with handover and orientation processes.

Conclusions

In summary, malnutrition is associated with poorer clinical outcomes in hospitalised patients. BCA devices can be a useful addition to routine clinical care to detect muscle loss that can otherwise be undetected in current malnutrition screening and assessment processes. However, we identified numerous health professional, team, and organisational barriers to the systematic adoption of these processes. Through a process of barrier analysis and intervention mapping within an implementation science framework we have designed three-pronged strategy of dietitian upskilling, embedding and evaluating, and management-endorsement and support to facilitate adoption of practices that will support evidence-based care for these patients. Our next step will be to assess the process of implementation of BCA into routine dietetic practise in our hospital department and its impact on practices, competency, and attitudes of our departmental clinical dietitians with regards to the utilisation of BCA.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BCA:

Body Composition Assessment

BCW:

Behaviour Change Wheel

BIS:

Bioelectrical Impedance Spectroscopy

DXA:

Dual Energy X-ray Absorptiometry

FTE:

Full Time Equivalents

GLIM:

Global Leadership Initiative on Malnutrition

KTA:

Knowledge to Action

MUAC:

Mid Upper Arm Circumference

MUST:

Malnutrition Universal Screening Tool

MST:

Malnutrition Screening Tool

PG-SGA:

Patient-Generated Subjective Global Assessment

SGA:

Subjective Global Assessment

TDF:

Theoretical Domains Framework

References

  1. Barker L, Gout B, Crowe T. Hospital malnutrition: prevalence, identification and impact on patients and the healthcare system. Int J Environ Res Public Health. 2011;8(2):514–27. https://doi.org/10.3390/ijerph8020514.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lim SLOK, Chan YH, Loke WC, Ferguson M, Daniels L. Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality. Clin Nutr. 2012;31(3):345-50. Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality. Clin Nutr. 2012;31(3):345–50. https://doi.org/10.1016/j.clnu.2011.11.001.

    Article  PubMed  Google Scholar 

  3. Correia M, Perman M, Waitzberg D. Hospital malnutrition in Latin America: a systematic review. Hospital malnutrition in Latin America: A systematic review. Clin Nutr. 2017;36(4):958–67.

    Article  PubMed  Google Scholar 

  4. Agarwal E, Ferguson M, Banks M, Bauer J, Capra S, Isenring E. Nutritional status and dietary intake of acute care patients: results from the nutrition care day survey. Clin Nutr. 2012;31(1):41–7. https://doi.org/10.1016/j.clnu.2011.08.002.

    Article  PubMed  Google Scholar 

  5. Ryan A, Power D, Daly L, Cushen S, Ní Bhuachalla Ē, Prado C. Cancer-associated malnutrition, cachexia and sarcopenia: the skeleton in the hospital closet 40 years later. Proc Nutr Soc. 2016;75(2):199–211. https://doi.org/10.1017/S002966511500419X.

    Article  PubMed  Google Scholar 

  6. Peterson S, Braunschweig C. Prevalence of Sarcopenia and Associated Outcomes in the Clinical Setting. Nutr Clin Pract. 2016;31(1):40–8. https://doi.org/10.1177/0884533615622537.

    Article  CAS  PubMed  Google Scholar 

  7. Gingrich A, Volkert D, Kiesswetter E, Thomanek M, Bach S, Sieber C, et al. Prevalence and overlap of sarcopenia, frailty, cachexia and malnutrition in older medical inpatients. BMC Geriatr. 2019;19(1):120. https://doi.org/10.1186/s12877-019-1115-1.

    Article  PubMed  PubMed Central  Google Scholar 

  8. De Waele E, Demol J, Caccialanza R, Cotogni P, Spapen H, Malbrain M, et al. Unidentified cachexia patients in the oncologic setting: Cachexia UFOs do exist. Nutrition. 2019;63–64:200–4.

    Article  PubMed  Google Scholar 

  9. von Haehling S, Anker M, Anker S. Prevalence and clinical impact of cachexia in chronic illness in Europe, USA, and Japan: facts and numbers update. J Cachexia Sarcopenia Muscle. 2016;7(5):507–9. https://doi.org/10.1002/jcsm.12167.

    Article  Google Scholar 

  10. Cederholm T, Barazzoni R, Austin P, Ballmer P, Biolo G, Bischoff S, et al. ESPEN guidelines on definitions and terminology of clinical nutrition. Clin Nutr. 2017;36(1):49–64. https://doi.org/10.1016/j.clnu.2016.09.004.

    Article  CAS  PubMed  Google Scholar 

  11. Kellett J, Kyle G, Itsiopoulos C, Naunton M, Luff N. Malnutrition: the importance of identification, documentation, and coding in the acute care setting. J Nutr Metab. 2016;2016:9026098.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Cederholm T, Jensen G. To create a consensus on malnutrition diagnostic criteria: a report from the global leadership initiative on malnutrition (GLIM) meeting at the ESPEN congress. Clin Nutr. 2017;36(1):7–10. https://doi.org/10.1016/j.clnu.2016.12.001.

    Article  PubMed  Google Scholar 

  13. Cruz-Jentoft A, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16–31. https://doi.org/10.1093/ageing/afy169.

    Article  PubMed  Google Scholar 

  14. Bauer J, Morley J, Schols A, Ferrucci L, Cruz-Jentoft A, Dent E, et al. Sarcopenia: a time for action. An SCWD Position Paper. J Cachexia Sarcopenia Muscle. 2019;10(5):956–61. https://doi.org/10.1002/jcsm.12483.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Earthman CP. Body composition tools for assessment of adult malnutrition at the bedside: a tutorial on research considerations and clinical applications. JPEN J Parenter Enteral Nutr. 2015;39(7):787–822. https://doi.org/10.1177/0148607115595227.

    Article  PubMed  Google Scholar 

  16. Martin L, Gioulbasanis I, Senesse P, Baracos V. Cancer-associated malnutrition and CT-defined sarcopenia and Myosteatosis are endemic in overweight and obese patients. JPEN J Parenter Enteral Nutr. 2019.

  17. Deutz NEP, Ashurst I, Ballesteros MD, Bear DE, Cruz-Jentoft AJ, Genton L, et al. The underappreciated role of low muscle mass in the Management of Malnutrition. J Am Med Dir Assoc. 2019;20(1):22–7. https://doi.org/10.1016/j.jamda.2018.11.021.

    Article  PubMed  Google Scholar 

  18. Beaudart C, Rolland Y, Cruz-Jentoft A, Bauer J, Sieber C, Cooper C, et al. Assessment of muscle function and physical performance in daily clinical practice: a position paper endorsed by the European Society for Clinical and Economic Aspects of osteoporosis, osteoarthritis and musculoskeletal diseases (ESCEO). Calcif Tissue Int. 2019;105(1):1–14. https://doi.org/10.1007/s00223-019-00545-w.

    Article  CAS  PubMed  Google Scholar 

  19. Arends J, Bachmann P, Baracos V, Barthelemy N, Bertz H, Bozzetti F, et al. ESPEN guidelines on nutrition in cancer patients. Clin Nutr. 2017;36(1):11–48. https://doi.org/10.1016/j.clnu.2016.07.015.

    Article  PubMed  Google Scholar 

  20. White J, Guenter P, Jensen G, Malone A, Schofield M, 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). J Parenter Enteral Nutr. 2012;36(3):275–83.

    Article  Google Scholar 

  21. Ferguson M, Capra S, Bauer J, Banks M. Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutrition. 1999;15(6):458–64. https://doi.org/10.1016/S0899-9007(99)00084-2.

    Article  CAS  PubMed  Google Scholar 

  22. Bauer J, Capra S, Ferguson M. Use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer. EJCN. 2002;56(8):779–85. https://doi.org/10.1038/sj.ejcn.1601412.

    Article  CAS  PubMed  Google Scholar 

  23. BAPEN. (2020) NAhwboun-sa-a-pn-asAN. Nutritional Assessment 2020 [cited 2020 November 10]. Available from: https://www.bapen.org.uk/nutrition-support/assessment-and-planning/nutritional-assessment?showall=1.

  24. Kyle UG, Morabia A, Slosman DO, Mensi N, Unger P, Pichard C. Contribution of body composition to nutritional assessment at hospital admission in 995 patients: a controlled population study. Br J Nutr. 2001;86(6):725–31. https://doi.org/10.1079/BJN2001470.

    Article  CAS  PubMed  Google Scholar 

  25. Benítez Brito N, Suárez Llanos J, Fuentes Ferrer M, Oliva García J, Delgado Brito I, Pereyra-García Castro F, et al. Relationship between Mid-Upper Arm Circumference and Body Mass Index in Inpatients. PloS one. 2016;11(8):e0160480.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Martin L, Birdsell L, Macdonald N, Reiman T, Clandinin M, McCargar L, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol. 2013;31(12):1539–47. https://doi.org/10.1200/JCO.2012.45.2722.

    Article  PubMed  Google Scholar 

  27. Cederholm T, Bosaeus I, Barazzoni R, Bauer J, Van Gossum A, Klek S, et al. Diagnostic criteria for malnutrition - An ESPEN Consensus Statement Clinical nutrition. 2015;34(3):335–40. https://doi.org/10.1016/j.clnu.2015.03.001.

  28. Martin L, Gioulbasanis I, Senesse P, Baracos V. Cancer-associated malnutrition and CT-defined sarcopenia and Myosteatosis are endemic in overweight and obese patients. J Parenter Enter Nutr. 2020;44(2):227–38. https://doi.org/10.1002/jpen.1597.

    Article  Google Scholar 

  29. Vest A, Chan M, Deswal A, Givertz M, Lekavich C, Lennie T, et al. Nutrition, Obesity, and Cachexia in Patients With Heart Failure: A Consensus Statement from the Heart Failure Society of America Scientific Statements Committee. J Card Fail. 2019;25(5):380–400.

    Article  PubMed  Google Scholar 

  30. Ooi P, Hager A, Mazurak V, Dajani K, Bhargava R, Gilmour S, et al. Sarcopenia in chronic liver disease: impact on outcomes. Liver Transpl. 2019;25(9):1422–38. https://doi.org/10.1002/lt.25591.

    Article  PubMed  Google Scholar 

  31. World Health Organization. Overweight and obesity 2018. [Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.

  32. Walter-Kroker A, Kroker A, Mattiucci-Guehlke M, Glaab T. A practical guide to bioelectrical impedance analysis using the example of chronic obstructive pulmonary disease. Nutr J. 2011;10(1):35. https://doi.org/10.1186/1475-2891-10-35.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Cederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition - a consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1–9. https://doi.org/10.1016/j.clnu.2018.08.002.

    Article  CAS  PubMed  Google Scholar 

  34. Kyle U, Morabia A, Slosman D, Mensi N, Unger P, Pichard C. Contribution of body composition to nutritional assessment at hospital admission in 995 patients: a controlled population study. Br J Nutr. 2001;86(6):725–31. https://doi.org/10.1079/BJN2001470.

    Article  CAS  PubMed  Google Scholar 

  35. Kyle U, Pirlich M, Lochs H, Schuetz T, Pichard C. Increased length of hospital stay in underweight and overweight patients at hospital admission: a controlled population study. Clin Nutr. 2005;24(1):133–42. https://doi.org/10.1016/j.clnu.2004.08.012.

    Article  PubMed  Google Scholar 

  36. Sheean P, Gonzalez M, Prado C, McKeever L, Hall A, Braunschweig C. American Society for Parenteral and Enteral Nutrition clinical guidelines: the validity of body composition assessment in clinical populations. JPEN J Parenter Enteral Nutr. 2019;44(1):12–43.

    Article  PubMed  Google Scholar 

  37. Thibault R, Pichard C. The evaluation of body composition: a useful tool for clinical practice. Ann Nutri Metab. 2012;60(1):6–16. https://doi.org/10.1159/000334879.

    Article  CAS  Google Scholar 

  38. Dent E, Hoogendijk E, Visvanathan R, Wright O. Malnutrition Screening and Assessment in Hospitalised Older People: a Review. J Nutr Health Aging. 2019;23(5):431–44. https://doi.org/10.1007/s12603-019-1176-z.

    Article  CAS  PubMed  Google Scholar 

  39. Mareschal J, Achamrah N, Norman K, Genton L. Clinical value of muscle mass assessment in clinical conditions associated with malnutrition. J Clin Med. 2019;8(7):1040. https://doi.org/10.3390/jcm8071040.

    Article  PubMed Central  Google Scholar 

  40. Allard J, Keller H, Gramlich L, Jeejeebhoy K, Laporte M, Duerksen D. GLIM criteria has fair sensitivity and specificity for diagnosing malnutrition when using SGA as comparator. Clin Nutr. 2020;39(9):2771–7. https://doi.org/10.1016/j.clnu.2019.12.004.

    Article  PubMed  Google Scholar 

  41. Zhang XTM, Zhang Q, Zhang KP, Guo ZQ, Xu HX, et al. The GLIM criteria as an effective tool for nutrition assessment and survival prediction in older adult cancer patients. Clin Nutr. 2020;10.

  42. Clark A, Reijnierse E, Lim W, Maier A. Prevalence of malnutrition comparing the GLIM criteria, ESPEN definition and MST malnutition risk in geriatric rehabilitation patients: RESORT. Clin Nutr. 2020;39(11):3504–11. https://doi.org/10.1016/j.clnu.2020.03.015.

    Article  PubMed  Google Scholar 

  43. Reijnierse E, de van der Schueren M, Trappenburg M, Dvoes M, Meskers C, Maier A. Lack of knowledge and availability of diagnostic equipment could hinder the diagnosis of sarcopenia and its management. PLoS One. 2017;12:e0185837.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Academy of Nutrition and Dietetics. Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and Athletic Performance. J Acad Nutr Diet. 116:501–28.

  45. Fischer M, JeVenn A, Hipskind P. Evaluation of muscle and fat loss as diagnostic criteria for malnutrition. Nutr Clin Pract. 2015;30(2):239–48. https://doi.org/10.1177/0884533615573053.

    Article  PubMed  Google Scholar 

  46. Grol R, Wensing M. What drives change? Barriers to and incentives for achieving evidence-based practice. Med J Australia. 2004;180(6):s57–60.

    PubMed  Google Scholar 

  47. Eccles M, Grimshaw J, Walker A, Johnston M, Pitts N. Changing the behavior of healthcare professionals: the use of theory in promoting the uptake of research findings. J Clin Epidemiol. 2005;58(2):107–12. https://doi.org/10.1016/j.jclinepi.2004.09.002.

    Article  PubMed  Google Scholar 

  48. Straus S, Tetroe J, Graham I. Knowledge translation in health care. Moving from evidence to practice. Oxford: Wiley-Blackwell/BMJ Books; 2009. https://doi.org/10.1002/9781444311747.

    Book  Google Scholar 

  49. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6(1):42. https://doi.org/10.1186/1748-5908-6-42.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Francis JJ, O'Connor D, Curran J. Theories of behaviour change synthesised into a set of theoretical groupings: introducing a thematic series on the theoretical domains framework. Implement Sci. 2012;7(1):35. https://doi.org/10.1186/1748-5908-7-35.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci. 2015;10(1):53. https://doi.org/10.1186/s13012-015-0242-0.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Colquhoun H, Leeman J, Michie S, Lokker C, Bragge P, Hempel S, et al. Towards a common terminology: a simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies. Implement Sci. 2014;9:51.

    PubMed  PubMed Central  Google Scholar 

  53. Montano-Loza A. Clinical relevance of sarcopenia in patients with cirrhosis. World J Gastroenterol. 2014;20(25):8061–71. https://doi.org/10.3748/wjg.v20.i25.8061.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hsu C, Kao J. Sarcopenia and chronic liver diseases. Expert Rev Gastroenterol Hepatol. 2018;12(12):1229–44. https://doi.org/10.1080/17474124.2018.1534586.

    Article  CAS  PubMed  Google Scholar 

  55. Cichoz-Lach H, Michalak A. A Comprehensive Review of Bioelectrical Impedance Analysis and Other Methods in the Assessment of Nutritional Status in Patients with Liver Cirrhosis. Gastroenterol Res Pract. 2017;2017:6765856.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Crawford J. What are the criteria for response to cachexia treatment? Ann Palliat Med. 2019;8(1):43–9. https://doi.org/10.21037/apm.2018.12.08.

    Article  PubMed  Google Scholar 

  57. Carneiro I, Mazurak V, Prado C. Clinical Implications of Sarcopenic Obesity in Cancer. Curr Oncol Rep. 2016;18(10):62. https://doi.org/10.1007/s11912-016-0546-5.

    Article  PubMed  Google Scholar 

  58. Carrero J, Johansen K, Lindholm B, Stenvinkel P, Cuppari L, Avesani C. Screening for muscle wasting and dysfunction in patients with chronic kidney disease. Kidney Int. 2016;90(1):53–66. https://doi.org/10.1016/j.kint.2016.02.025.

    Article  PubMed  Google Scholar 

  59. Yeung S, Reijnierse E, Trappenburg M, Meskers C, Maier A. Current knowledge and practice of Australian and New Zealand health-care professionals in sarcopenia diagnosis and treatment: time to move forward! `. Australas J Ageing. 2020;39(2):e185–93. https://doi.org/10.1111/ajag.12730.

    Article  PubMed  Google Scholar 

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Acknowledgements

Queensland Health - Health Research Fellowship funding (2015-2020).

Funding

Dr. Shelley Wilkinson was supported by a Queensland Government Department of Health- Health Research Fellowship.

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Authors and Affiliations

  1. Department of Dietetics & Foodservices, Mater Health, South Brisbane, Queensland, Australia

    Chloe J. D. Jobber, Shelley A. Wilkinson, Elyssa K. Hughes, Fiona Nave & Barbara S. van der Meij

  2. School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Queensland, 4072, Australia

    Shelley A. Wilkinson

  3. Nutrition & Dietetics, Mater Research Institute - The University of Queensland, Brisbane, Queensland, Australia

    Shelley A. Wilkinson & Barbara S. van der Meij

  4. Department of Nutrition and Dietetics, Logan Hospital, Meadowbrook, Queensland, Australia

    Elyssa K. Hughes

  5. Bond University Nutrition and Dietetics Research Group, Gold Coast, Queensland, Australia

    Barbara S. van der Meij

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  1. Chloe J. D. Jobber
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  3. Elyssa K. Hughes
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  4. Fiona Nave
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  5. Barbara S. van der Meij
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Contributions

All authors have participated sufficiently in the article to take public responsibility for the content. Chloe Jobber was responsible for collecting and analysing data, interpreting results, writing the manuscript. A/Prof Shelley Wilkinson (corresponding author) and Dr. Barbara van der Meij were responsible for study design, project coordination, data interpretation and manuscript preparation. Elyssa Hughes was responsible for collecting and analysing data, and reviewing the manuscript. Fiona Nave was responsible for collecting data, coordinating the project, and reviewing the manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Shelley A. Wilkinson.

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Ethics approval and consent to participate

The study was declared as Exempt from Review – Not Research according to the Human Research Ethics Committee of Mater Research Institute – UQ Human Research Ethics Committee (Project ID: EXMT/MML/58778). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants; instructions for survey completion indicated their completion implied consent.

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The authors declare that they have no competing interests.

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Jobber, C.J.D., Wilkinson, S.A., Hughes, E.K. et al. Using the theoretical domains framework to inform strategies to support dietitians undertaking body composition assessments in routine clinical care. BMC Health Serv Res 21, 518 (2021). https://doi.org/10.1186/s12913-021-06375-7

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BMC Health Services Research

ISSN: 1472-6963

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