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M.B. Beyene1,2, R. Visvanathan2,3, A.T. Amare1,2


1. Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia; 2. Adelaide Geriatrics Training and Research with Aged Care Centre (GTRAC), Faculty of Health and Medical Sciences, University of Adelaide, Woodville, SA, 5011, Australia; 3. Aged and Extended Care Services, The Queen Elizabeth Hospital, Central Adelaide Local Health Network, Adelaide, SA, Australia; ORCID Numbers: Melkamu Bedimo Beyene 0000-0003-4349-9887; Renuka Visvanathan 0000-0002-1303-9479; Azmeraw T. Amare 0000-0002-7940-0335

Corresponding Author: Azmeraw T. Amare, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5000, Australia, Tel: +61 8 83137438, E-Mail: azmeraw.amare@adelaide.edu.au



BACKGROUND: In 2015, the World Health Organization (WHO) introduced the concept of intrinsic capacity (IC) to define healthy aging based on functional capacity. In this scoping review, we summarized available evidence on the development and validation of IC index scores, the association of IC with health-related factors, and its biological basis. The review specifically focused on identifying current research gaps, proposed strategies to leverage biobank datasets, and opportunities to study the genetic mechanisms and gene-environment interactions underlying IC.
METHODS: The literature search was conducted across six databases, including PubMed, CINAHL, Web of Science, Scopus, AgeLine, and PsycINFO, using keywords related to IC.
RESULTS: This review included 84 articles, and most of them (n=38) adopted the 5-domains approach to operationalize IC, utilizing correlated five factors or bifactor structures. Intrinsic capacity has consistently shown significant associations with socio-demographic and health-related outcomes, including age, sex, wealth index, nutrition, exercise, smoking, alcohol use, ADL, IADL, frailty, multimorbidity, and mortality. While studies on the biological basis of the composite IC are limited, with only one study finding a significant association with the ApoE gene variants, studies on specific IC domains — locomotor, vitality, cognitive, psychological, and sensory suggest a heritability of 20-85% of IC and several genetic variants associated with these subdomains have been identified. However, evidence on how genetic and environmental factors influence IC is still lacking, with no available study to date.
CONCLUSION: Our review found that there was inconsistency in the use of standardized IC measurement tools and indicators, but the IC indices had shown good construct and predictive validity. Research into the genetic and gene-to-environment interactions underlying IC is still lacking, which calls for the use of resources from large biobank datasets in the future.

Key words: Intrinsic capacity, healthy aging, functional ability, genetics.



In 2020, one billion of the world’s population was 60 years or older, with an increase of 400 million expected between 2021 and 2030 (1-3). As this demographic shift continues, exploring innovative mechanisms to promote healthy aging is an important global health and economic policy agenda. Advocacy for improved health across the lifespan to increase the likelihood of older people being functionally able and capable of doing what they value in older age is increasing.
The World Health Organisation (WHO) redefined Healthy Aging in 2015, taking a life-course approach in preparation for the predicted demographic shift globally. It was redefined as the life-long process of developing and maintaining functional ability (1), determined by intrinsic capacity (IC), the environment, and the interaction between these two factors (1). Intrinsic capacity refers to the composite of an individual’s physical and mental capacities across the five domains: locomotor, vitality, cognitive, psychological, and sensory (4). Higher IC levels are associated with decreased disability risk and better overall quality of life (5-7).
In the last two years, four scoping reviews relating to IC have been published (8-11). These reviews have focused on the sensitivity and specificity of WHO’s Integrated Care for Older People (ICOPE) step 1 tool in detecting loss of IC (11), demonstrated that IC predicts physical function, frailty, falls and quality of life over time (10), highlighted that there was a lack of consistency in terms of the domains and metrics used across studies (9), and queried if IC was as an underlying latent trait of all capacities rather than an aggregate summary measure of the sub-domain capacities (8). The scoping reviews thus far are yet to address IC’s biological (genetic) underpinnings. Intrinsic capacity is influenced by the person’s underlying genetic as well as the interaction between the person’s genetic make-up and their environment (including lifestyle). Also, research on IC is rapidly increasing, providing a basis for more recent reviews.
Our research group is researching to understand IC genetics better, leveraging existing cohorts where genetic data were collected. Within that context, the primary aim of this scoping review was to explore the existing literature to identify factors (especially genetics) relating to IC and to provide a current overview of knowledge regarding the measurement of IC, along with its predictive and construct validities.



Scoping Review Framework

We used the Joanna Brigs Institute’s (JBI’s) Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) and Arksey and O’Malley methodological framework (12-14). The process involved five stages, including defining the purpose, the research question, and the search terms (Stage 1); identifying relevant studies (stage 2); selecting studies that met the predetermined inclusion criteria (stage 3); mapping and charting the data obtained from the selected studies (stage 4); and collating, summarizing, and reporting of the review findings (stage 5).

Stage 1: Defining the research questions and search terms

This initial phase involved refining the scope and direction of the review based on a preliminary search conducted on Google Scholar. Through this step, we had background information on studies and search terms related to IC measurement, IC measures’ validity, health and health-related functional outcomes associated with IC, and its underlying biologic(genetic) basis.
“Generally, the research questions for this review were:
1. What are the IC measurement tools in literature? What are the approaches to computing composite IC scores and assessing the validity of the scores?
This question aims to summarize research findings on IC domains used/found, their indicators, approach to developing composite scores, and the validation of indexes.
2. What are the different sociodemographic, health, and health-related factors associated with intrinsic capacity?
3. Does IC have a biological/genetic basis? What are the biomarkers associated with IC?

Inclusion and exclusion criteria

The Participant, Concept, Context (PCC) approach was employed to develop the eligibility criteria for study inclusion. The inclusion criteria were; studies conducted on human subjects of all ages (populations), focusing on the measurement of IC, its validation, association with socio-economic and health outcomes (concept), and in any setting – whether the studies were conducted in the community or institutional setting (context), published in the English language and published between 01/01/2015 – 20/10/2023. However, abstracts, conference proceedings, commentary, editorials, reviews, and personal opinions were excluded. No research records were available until WHO experts released the initial article operationalizing IC measurement (15).

Stage 2: Identifying relevant studies (Search strategy)

The literature search was conducted across Six Databases: PubMed, CINAHL, Web of Science, Scopus, AgeLine, and PsycINFO using the mesh terms and keywords “intrinsic capacity”, “intrinsic capacity decline”, “intrinsic capacity domains”, “intrinsic capacity impairment”, “intrinsic capacity index”, “intrinsic capacity model”, and “intrinsic capacity score” in the context of Aging. Each database’s detailed search string is presented in Supplementary Table 1 (Supplementary Table- 1).

Stage 3: Selection of relevant articles

Identified articles were imported into Clarivate Analytics EndNote 20 after the completion of the search, and duplicates were removed. Following this, two researchers (MB and AT) independently evaluated the titles and abstracts of the articles against the inclusion criteria. The two assessors thoroughly reviewed the full text of the selected articles. Articles that did not meet the inclusion criteria were excluded, and the reasons for their exclusion were documented and reported. Disagreements that arose during the selection process were resolved through discussion. The search outcomes and procedure for selecting or excluding studies can be observed in the PRISMA-ScR flowchart (Figure 1).

Figure 1. PRISMA flow chart showing the steps of the literature search


Stage 4: Data extraction and synthesis

Using a data extraction tool, MB and AT collected various information from selected articles, including name of authors, publication year, characteristics of study participants, design and setting of the study, domains of IC measured, method used to calculate composite IC scores, validation approaches, and other relevant information. Supplementary Table 2 provides the data extraction form and the summary of information collected from the articles (Supplementary Table 2). The results collected from these selected articles were presented primarily using narrative descriptions and tables.



Descriptive Summary

Our search strategy yielded 1498 articles, of which 398 were identified as duplicates and, thus, removed. After screening titles and abstracts (Figure 1), 986 publications were excluded, and full-text screening of 114 articles was conducted, resulting in 72 publications for full-text review. With a targeted citation search strategy, we found 12 additional articles relevant to our topic, and the final list for this scoping review was 84 articles.
Of the total 84 articles reviewed, the majority, 77 articles (92%), were published in the last two years, between 2021 and 2023.
The majority (51 articles, 61%) of the publications were carried out using samples sourced from Asia, of which 33 were from China. The remaining studies utilized study participants distributed across other geographical regions, including 20 (24%) studies in Europe (France (n=9), UK (n=4), Belgium (n=3), Spain (n=2), and Netherlands (n=1), and Norway (n=1)), North America (n=3), South America (n=5), New Zealand (n=1) (Supplementary Table-2). Four articles involving study samples from multiple continents. Out of the 84 reviewed articles, 33 were cross-sectional studies, 43 had a longitudinal approach (involving cohort, case-control, or longitudinal designs), and 8 were randomized control trials (Supplementary Table- 2).
Most (64 studies) have explored the validity of IC measurement in different ways. Some studies assessed the predictive validity (16-20) by assessing if IC predicts future health outcomes, whereas others assessed construct validity through the cross-sectional association of IC with socio-demographic variables (21-23), health and health-related functional outcomes (7, 20, 24, 25), mortality (26-29), and quality of life (7).
Some of the studies have inquired into the structural validity of IC (30, 31), sensitivity and specificity analyses (23, 32-34), tested internal consistency using Cronbach alpha (20, 35), performed ROC curve analysis (19), assessed criterion validity through logistic regression analysis (28), and conducted validation analysis by dividing the population into two as 70% for training and 30% validation cohort (26). The WHO has also published an expert consensus article on the measurement and validation of IC, providing a comprehensive working definition of vitality capacity (36).

The association of biological and environmental factors with IC

Biological markers with IC

Eight studies explored the association of IC with aging-related biomarkers. While specific studies estimating the heritability of IC are currently lacking, evidence based on the five IC domains suggests a heritability estimate of 20-85% (37-54). Thus far, only one candidate gene study has been conducted, and this study showed a significant association of IC with ApoE carriage (26).
Research conducted by Lu WH, et al. (2023) showed an increased level of inflammatory markers such as Plasma C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor receptor-1 (TNFR-1), monocyte chemoattractant protein-1 (MCP-1) and growth differentiation factor-15 (GDF-15) in individuals with lower IC (55). Another study by Lee WJ, et al. (2023) found that high serum levels of IL-6, CRP, hyperglycemia, and low dehydroepiandrosterone sulfate (DHEA-S) were associated with low IC (26, 56). Lower levels of serum albumin and folate (26), high homocysteine (55), Tumor Necrosis Factor Receptor 1 level (TNFR1) (57), Plasma N-Terminal Pro-B-Type Natriuretic Peptide level (58), or E-selectin (26) and increased allostatic load (26) were significantly associated with low IC. Recent studies have also reported associations between IC and plasma biomarkers reflecting inflammation (such as CRP, IL-6, TNFR-1, and MCP-1) and mitochondrial impairment (such as GDF-15, IF1) such that elevated levels of plasma interleukin-6 (IL-6), tumor necrosis factor receptor-1 (TNFR-1), CRP, growth differentiation factor-15 (GDF-15) and IF1 were associated with lower IC or faster decline in IC (59-61). Details of all factors associated with intrinsic capacity are presented in Table 1 (Table 1).

Table 1. Association of intrinsic capacity with health and health-related outcomes

Abbreviations: FHI-Functional health index, DQI – Diet quality index, BMI – Body mass index, ADL-Activities of daily living, IADL-Instrumental activities of daily living, LSM – Life space mobility, QoL – Quality of life, DHEA-S – dehydroepiandrosterone sulfate, TNFR1- Serum Tumor necrosis factor receptor 1 level, APOE+4 – apolipoprotein E gene, GDF-15 – Growth differentiation factor 15, CRP – C-reactive protein, IF1-Mitochondrial inhibitory factor 1.


Lifestyle and socio-economic factors with IC

Exercise and lifestyle choices play a crucial role in IC, with studies revealing significant associations. Smoking is linked to lower IC (23, 64, 68, 70-73), as is alcohol consumption (23, 71), and reduced meat intake (64). Interventional and cohort studies, respectively, underscore the positive impact of healthy eating (77) and fruits and vegetables and protein-rich diets on IC (96). A multidomain intervention has also been shown to enhance IC (102).
The exploration of socio-economic factors demonstrates noteworthy associations with IC. Age is inversely correlated, with lower IC found in older age (7, 20-23, 32, 57, 62-69), and women tend to exhibit lower IC (7, 20-23, 62, 63, 66, 67). Lower educational status (7, 20, 21, 23, 32, 64, 66-68), low economic status (20-22, 62, 64, 65, 67, 91), unmarried status (21, 23, 32, 64, 68), and urban residence are associated with lower IC.; however, being white race was associated with a higher IC (23, 64), and higher IC was observed in Chinese individuals compared to non- Chinese in Singapore (23, 97).
Social factors also play a role in IC. Lower social engagement, lower subjective social status, fewer social activities, and lower housing index are linked to lower IC (7, 32, 66).

Mortality and morbidity with IC

IC is a significant predictor of various health outcomes. It predicts multimorbidity (20, 65), mortality (10, 27, 28, 35, 56, 65, 74, 81, 93-95), quality of life (5, 6, 10, 76), risk of dementia (87), cardiovascular diseases mortality (17), respiratory disease mortality (16), hospitalization (27), and complications related to hospitalization (95). Conversely, multimorbidity predicts declines in IC (68). In addition to predictive relationships, IC index has shown cross-sectional associations with quality of life (7), medication adherence (89), sleep health (21), nursing home-acquired pneumonia (90), polypharmacy (82), hypertension status (32), hospitalization (90), presence of chronic neurological illness (69), low self-rated health (82) and various health conditions, such as insomnia, memory loss, constipation, slowness, chronic obstructive pulmonary disease, and osteoarthritis (64).

Functional ability with IC

IC predicted functional difficulty parameters, including the future incidence of ADL (20, 24, 27, 35, 74-81), IADL (20, 24, 27, 31, 66, 74, 76, 78, 82, 83), Frailty (24, 84-88), disability/functional decline/dependence (6, 28, 65, 75), and have shown cross-sectional associations with ADL (7, 23, 25, 69), frailty (7, 25, 63) and IADL (7, 23, 25, 63). Moreover, IC was associated with fragility fracture (99), nursing home stay (35), life-space mobility (30), falls (24), incontinence (64, 82), and sarcopenia (18).

Measurement and validation of IC and its domains

Various approaches have been utilized in the process of constructing IC, each involving a different number and type of domain. Although most studies (n=38; 45%) utilized the five domains methodology (6, 7, 24, 26-28, 30, 31, 55-57, 62, 64, 71, 74, 79, 81, 84, 85, 94, 96, 98, 103-107), seven studies utilized the bifactor structure (with one general domain (IC) and five sub-domains) (20, 22, 23, 35, 66, 67, 86). Five of these studies (20, 23, 35, 66, 67) comparing the goodness of fit when the bifactor approach was applied instead of the correlated factors and hierarchical model options found that the bifactor structure has better model fit statistics. Eight other studies adopted the six domains construct by dividing the sensory domain into two components, namely hearing and vision (21, 33, 75, 83, 87, 89, 91, 108), whereas eleven studies considered only four domains by excluding the sensory domain (34, 59-61, 70, 73, 77, 88, 90, 93, 102) and another two studies used four domains excluding the cognition domain (16, 17). Three studies utilized other methods, including seven domains (65), eight domains (78), and no domain but summing up indicators directly (5).
Studies comparing the traditional (correlated factors) method, the bifactor method, and the hierarchical method consistently demonstrate that the bifactor model provides a superior fit compared to both the hierarchical model and the correlated factors models (20, 23, 35, 66, 67). In measuring IC, various methods have been used, ranging from simple approaches involving a single variable to complex composite assessments. Details of all methods used to operationalize each domain under the reviewed articles are shown in Table 2 (Table 2).

Table 2. Variables used to define/measure intrinsic capacity and/or domains

Abbreviations: CES-D – center for epidemiological studies depression score, FEV1 – Forced expiratory volume in one second, MMSE-Mini mental state examination, GDS- Geriatric depression sale, MNA- Mini nutritional assessment, ADL-Activities of daily living, QoL – quality of life, ICOPE- integrated care for older people, AMTS-Abbreviated mental test score, SPMSQ- short portable mental state questionnaire, PHQ-9 -Patient health questionnaire, CSI-D – Community screening instrument for dementia, CIDI -composite international diagnostic interview, PEF- Peak expiratory flow, BMI – body mass index, GAD-7 – Generalized anxiety disorder-7, B-POMA –Tinetti’s balance subscale of performance-oriented mobility assessment, ENIGMA – Elderly nutritional indicators for geriatric malnutrition assessment, MoCA – Montreal cognitive assessment, GSES- General self-efficacy scale, HADS-A-7 – anxiety sub-scale of hospital anxiety and depression Scale


Computation of IC scores

Sixty-seven articles have created IC composite scores. Standard methods for constructing IC composite scores involve using factor analysis or principal component analysis (7, 25, 73), in line with original research relating to IC (20). Out of the nine studies utilizing factor analysis methods, two employed the traditional correlated five-factors approach (30, 98), while the remaining seven utilized the bifactor method, incorporating five specific factors and one general factor (20, 22, 23, 35, 66, 67, 86).
However, the majority 32 (47.8%) papers calculated the IC score by summing individual IC domains scores without weighting, using either a two-point scale (0-impaired/bad and 1-unimpaired/good) or a three-point scale (0-impaired, 1-slightly impaired, or 2-unimpaired) (18, 19, 21, 24, 32, 33, 55-58, 61-64, 68, 69, 71, 74, 75, 80, 82-85, 94, 95, 97, 100, 106, 109, 113, 114). Other ways employed to compute IC were averaging the z-scores of the domains (70, 72, 81, 90, 93, 96, 102), direct summation of the values of indicators (5, 16, 17, 88, 101, 115), the latent growth modeling (LGM) method (31), weighted linear combination of indicators with loading greater than 0.3 (78), direct summation of indicators associated with domains in the regression model (26, 65, 79), averaging the domains’ average values (28, 59, 60), and a 2-parameter domains item response theory (IRT) which refers to direct categorization of IC as “0” for impairment in any domain and “1” for no impairment in any of the domains (6, 27).



Some eight years since the WHO redefined Healthy Aging, there has been an exponential growth in research relating to IC, with almost all publications in the last 2 years and the majority (61%) leveraging data from Asia. Whilst studies have explored the association and predictive ability of IC with inflammatory, lifestyle, health, and socio-economic factors, research relating to genes and IC is rare. Many methods have been used to assess and score IC, making it vital that a consensus is reached globally.
While specific studies estimating the heritability of IC are currently lacking, evidence based on the five IC domains suggests a heritability range of 20-85%; specifically, genetic factors contribute to the variability in cognitive 50-70% (37-39), sensory 20-30% (40-43), locomotor 30-85% (44-47), vitality 25– 65% (48-50) and psychological 35–70% (51-54) domains. There has been one attempt, through a candidate gene study approach, to identify genes associated with the broader IC domain. This particular study showed a significant association of IC with ApoE carriage (26). Understanding the interaction between genes and environment (including lifestyle factors) and IC is important. Before this, it is essential to identify genetic markers associated with IC. Such research may confirm the benefits of lifestyle or behavioral changes in helping people age well, including allowing the personalization of this intervention to the individual.
As outlined in the results section, our scoping review introduces new elements beyond those explored in the initial review addressing adverse health outcomes associated with IC. In addition to investigating biological and inflammatory biomarkers which are new, this review also identifies additional factors previously unexplored, which are related to socio-economic (such as educational status, economic standing, marital status, ethnicity, residence, housing index, and social engagement); functional ability (such as fragility fracture, life-space mobility, nursing home stay, incontinence, and sarcopenia are also assessed); morbidity and mortality (such as multimorbidity, medication adherence, polypharmacy, hospitalization and its associated complications, sleep health, cardiovascular diseases mortality, and respiratory diseases mortality which were not addressed in the previous review) and behavioral and lifestyle-related factors (such as exercise, alcohol consumption, smoking, eating habits, and dietary patterns).
In the context of developing IC indices, no consistent method has been used thus far. New indicators for IC domains continue to be used, often without goodness of fit testing. Goodness of fit tests must be done while new indicators are used to determine whether those indicators are appropriate (118). Similarly, some studies used quick and direct tools, and others employed more complex and time-consuming composite measures. Balancing the precision of variable measurement with tool simplicity is crucial, and in the realm of measurement science, it is recommended to utilize straightforward yet robust instruments to assess variables like IC (119, 120). Studies also reveal that the bifactor method correlates with the five factors method and performs better than the correlated factors and hierarchical methods (20, 23, 35, 66, 67). There is also evidence of better explanatory power of the bifactor model when compared to the other two methods (121-123). Whilst evidence supports that the bifactor method results have better conceptual clarity and fit than the other structures, the lack of standardization may introduce bias (128).
Furthermore, there needs to be an effort to reach a consensus in defining indicators for individual domains. Consensus about the indicators and methods is necessary to inform the planning of future cohort studies. Planned cohort studies, as opposed to leveraging already collected data, may have the advantage of enabling the collection of appropriate indicators to measure IC. Two primary approaches are being applied to compute the IC composite score: the CFA (20, 22, 23, 30, 66, 67, 86, 96) and the arithmetic sum/average of the values of the domains (19, 24, 32, 55-58, 62-64, 71, 74, 75, 82, 84, 85, 94, 106, 114). Both have demonstrated good construct and predictive validity. Concerning the approaches for measurement, the reflective versus formative nature of the IC measurement (i.e. whether IC should be considered as an underlying latent trait of all capacities or an aggregate summary measure of the subdomain capacities) shall also be considered in future studies (124).

Limitations and Strengths

The major strength of this review is that we followed a rigorous and stepwise screening process with the involvement of independent assessors. The findings were reported following the JBI’s PRISMA guideline extension for scoping review (PRISMA-ScR). The scope of the literature search was limited to peer-reviewed articles, and as a result, unpublished studies and organizational reports were not included. Due to logistic limitations, only articles published in the English language were reviewed, although studies published in other languages may have provided information on the external validity of IC tools in a multicultural setting.


Conclusion and recommendations

Since the introduction of IC for defining healthy aging and functional capacity, extensive research has been conducted to measure these indices and validate them, as well as to assess their relevance in medical, social, and behavioral sciences. This review identifies that there is a knowledge gap (no evidence) when it comes to our understanding of the genetics of IC. Such understanding can be improved by leveraging existing longitudinal studies but there is also a need for planned cohort studies where IC measurements are collected prospectively, and genetic data is also available. This review highlights the current difficulties in comparing existing studies given the lack of standardization in defining indicators that ought to be collected for each IC domain and how best to assess and score IC. Reaching such consensus is imperative because it will not only define the approach for the use of already collected data but will also support the planning and conduct of new longitudinal studies focused on IC and functional ability. The pooling of data globally to advance our understanding collectively would also be more likely through a standardized approach.


Acknowledgments: MB Beyene is Ph.D. student with scholarship support from University of Adelaide (The University Adelaide Research Scholarship).

Funding: AT Amare is currently supported by the National Health and Medical Research Council (NHMRC) Emerging Leadership (EL1) Investigator Grant (APP2008000). Open Access funding enabled and organized by CAUL and The University of Adelaide​.

Conflicts of interest: Professor Renuka Visvanathan is a member of the WHO Clinical Consortium in Healthy Aging, but the work presented in this manuscript does not represent the views of the consortium.

Ethical standards: This review was conducted in accordance with established ethical procedures and standards.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.






1. WHO. Decade of Healthy Ageing: Baseline Report. WHO; 2020.
2. WHO: Ageing and health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health (2022). Accessed 10/04 2023.
3. Nations U. Leaving No One Behind In An Ageing World.World Social Report 2023
4. Organization WH. World report on ageing and health. World Health Organization; 2015.
5. Stephens C, Allen J, Keating N, Szabó Á, Alpass F. Neighborhood environments and intrinsic capacity interact to affect the health-related quality of life of older people in New Zealand. Maturitas. 2020;139:1-5. doi: 10.1016/j.maturitas.2020.05.008.
6. Salinas-Rodríguez A, González-Bautista E, Rivera-Almaraz A, Manrique-Espinoza B. Longitudinal trajectories of intrinsic capacity and their association with quality of life and disability. Maturitas. 2022;161:49-54. doi: 10.1016/j.maturitas.2022.02.005.
7. Cheong CY, Yap P, Nyunt MSZ, Qi G, Gwee X, Wee SL, et al. Functional health index of intrinsic capacity: multi-domain operationalisation and validation in the Singapore Longitudinal Ageing Study (SLAS2). Age & Ageing. 2022;51(3):1-10. doi: 10.1093/ageing/afac011.
8. Koivunen K, Schaap LA, Hoogendijk EO, Schoonmade LJ, Huisman M, van Schoor NM. Exploring the conceptual framework and measurement model of intrinsic capacity defined by the World Health Organization: A scoping review. AGEING RESEARCH REVIEWS. 2022;80. doi: 10.1016/j.arr.2022.101685.
9. Liang Y, Shang S, Gao Y, Zhai J, Cheng X, Yang C, et al. Measurements of Intrinsic Capacity in Older Adults: A Scoping Review and Quality Assessment. Journal of the American Medical Directors Association. 2023;24(3):267-. doi: 10.1016/j.jamda.2022.09.011.
10. Zhou J, Chang H, Leng M, Wang Z. Intrinsic Capacity to Predict Future Adverse Health Outcomes in Older Adults: A Scoping Review. Healthcare (2227-9032). 2023;11(4):450. doi: 10.3390/healthcare11040450.
11. de Oliveira VP, Ferriolli E, Lourenço RA, González-Bautista E, Barreto PD, de Mello RGB. The sensitivity and specificity of the WHO’s ICOPE screening tool, and the prevalence of loss of intrinsic capacity in older adults: A scoping review. MATURITAS. 2023;177. doi: 10.1016/j.maturitas.2023.107818.
12. Aromataris E MZE: JBI Manual for Evidence Synthesis. https://synthesismanual.jbi.global/ Accessed 23/11 2022.
13. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of internal medicine. 2018;169(7):467-73.
14. Arksey H, O’Malley L. Scoping studies: towards a methodological framework. International journal of social research methodology. 2005;8(1):19-32.
15. Cesari M, Araujo de Carvalho I, Amuthavalli Thiyagarajan J, Cooper C, Martin FC, Reginster JY, et al. Evidence for the Domains Supporting the Construct of Intrinsic Capacity. J Gerontol A Biol Sci Med Sci. 2018;73(12):1653-60.
16. Ramírez-Vélez R, Iriarte-Fernandez M, Santafé G, Malanda A, Beard JR, Garcia-Hermoso A, et al. Association of intrinsic capacity with respiratory disease mortality. Respiratory Medicine. 2023;212. doi: 10.1016/j.rmed.2023.107243.
17. Ramírez-Vélez R, Iriarte-Fernández M, Santafé G, Malanda A, Beard JR, Garcia-Hermoso A, et al. Association of intrinsic capacity with incidence and mortality of cardiovascular disease: Prospective study in UK Biobank. Journal of Cachexia, Sarcopenia and Muscle. 2023;14(5):2054-63. doi: 10.1002/jcsm.13283.
18. Zhu L, Zong X, Shi X, Ouyang X. Association between Intrinsic Capacity and Sarcopenia in Hospitalized Older Patients. Journal of Nutrition, Health & Aging. 2023;27(7):542-9. doi: 10.1007/s12603-023-1946-5.
19. Lu F, Liu S, Liu X, Li J, Jiang S, Sun X, et al. Comparison of the predictive value of intrinsic capacity and comorbidity on adverse health outcome in community-dwelling older adults. Geriatric Nursing. 2023;50:222-6. doi: 10.1016/j.gerinurse.2023.02.001.
20. Beard JR, Jotheeswaran AT, Cesari M, Araujo De Carvalho I. The structure and predictive value of intrinsic capacity in a longitudinal study of ageing. BMJ Open. 2019;9(11). doi: 10.1136/bmjopen-2018-026119.
21. Chang YH, Chen YC, Ku LE, Chou YT, Chen HY, Su HC, et al. Association between sleep health and intrinsic capacity among older adults in Taiwan. Sleep Med. 2023;109:98-103. doi: 10.1016/j.sleep.2023.06.016.
22. Yu R, Lai D, Leung G, Woo J. Trajectories of Intrinsic Capacity: Determinants and Associations with Disability. Journal of Nutrition, Health & Aging. 2023;27(3):174-81. doi: 10.1007/s12603-023-1881-5.
23. Aliberti Márlon JR, Bertola L, Szlejf C, Oliveira Db, Piovezan Ronaldo D, Cesari M, et al. Validating intrinsic capacity to measure healthy aging in an upper middle-income country: Findings from the ELSI-Brazil. The Lancet Regional Health – Americas. 2022;12:100284.
24. Tay L, Tay EL, Mah SM, Latib A, Koh C, Ng YS. Association of Intrinsic Capacity with Frailty, Physical Fitness and Adverse Health Outcomes in Community-Dwelling Older Adults. JOURNAL OF FRAILTY & AGING. 2023;12(1):7-15. doi: 10.14283/jfa.2022.28.
25. Gutiérrez-Robledo LM, García-Chanes RE, González-Bautista E, Rosas-Carrasco O. Validation of Two Intrinsic Capacity Scales and Its Relationship with Frailty and Other Outcomes in Mexican Community-Dwelling Older Adults. Journal of Nutrition, Health & Aging. 2021;25(1):33-40. doi: 10.1007/s12603-020-1555-5.
26. Meng LC, Huang ST, Peng LN, Chen LK, Hsiao FY. Biological Features of the Outcome-Based Intrinsic Capacity Composite Scores From a Population-Based Cohort Study: Pas de Deux of Biological and Functional Aging. Front Med (Lausanne). 2022;9:851882.
27. Campbell CL, Cadar D, McMunn A, Zaninotto P. Operationalization of Intrinsic Capacity in Older People and Its Association With Subsequent Disability, Hospital Admission and Mortality: Results From The English Longitudinal Study of Ageing. Journals of Gerontology Series A: Biological Sciences & Medical Sciences. 2023;78(4):698-703. doi: 10.1093/gerona/glac250.
28. Koivunen K, Hoogendijk Emiel O, Schaap Laura A, Huisman M, Heymans Martijn W, van Schoor Natasja M. Development and validation of an intrinsic capacity composite score in the Longitudinal Aging Study Amsterdam: a formative approach. Aging Clinical and Experimental Research. 2023.
29. Charles A, Buckinx F, Locquet M, Reginster J-Y, Petermans J, Gruslin B, et al. Prediction of Adverse Outcomes in Nursing Home Residents According to Intrinsic Capacity Proposed by the World Health Organization. Journals of Gerontology Series A: Biological Sciences & Medical Sciences. 2020;75(8):1594-9. doi: 10.1093/gerona/glz218.
30. Lee JQ, Ding YY, Latib A, Tay L, Ng YS. INtrinsic Capacity and its RElAtionship With Life-SpacE Mobility (INCREASE): a cross-sectional study of community-dwelling older adults in Singapore. BMJ OPEN. 2021;11(12). doi: 10.1136/bmjopen-2021-054705.
31. Lu SY, Liu YQ, Guo YQ, Ho HC, Song YM, Cheng W, et al. Neighbourhood physical environment, intrinsic capacity, and 4-year late-life functional ability trajectories of low-income Chinese older population: A longitudinal study with the parallel process of latent growth curve modelling. ECLINICALMEDICINE. 2021;36. doi: 10.1016/j.eclinm.2021.100927.
32. Leung AYM, Su JJ, Lee ESH, Fung JTS, Molassiotis A. Intrinsic capacity of older people in the community using WHO Integrated Care for Older People (ICOPE) framework: a cross-sectional study. BMC Geriatrics. 2022;22(1):1-12. doi: 10.1186/s12877-022-02980-1.
33. Lu F, Li J, Liu X, Liu S, Sun X, Wang X. Diagnostic performance analysis of the Integrated Care for Older People (ICOPE) screening tool for identifying decline in intrinsic capacity. BMC Geriatr. 2023;23(1):509. doi: 10.1186/s12877-023-04180-x.
34. Sanchez-Rodriguez D, Demonceau C, Bruyère O, Cavalier E, Reginster JY, Beaudart C. Intrinsic capacity and risk of death: Focus on the impact of using different diagnostic criteria for the nutritional domain. MATURITAS. 2023;176. doi: 10.1016/j.maturitas.2023.107817.
35. Stolz E, Mayerl H, Freidl W, Roller-Wirnsberger R, Gill TM. Intrinsic Capacity Predicts Negative Health Outcomes in Older Adults. Journals of Gerontology Series A: Biological Sciences & Medical Sciences. 2022;77(1):101-5. doi: 10.1093/gerona/glab279.
36. Bautmans I, Knoop V, Amuthavalli Thiyagarajan J, Maier AB, Beard JR, Freiberger E, et al. WHO working definition of vitality capacity for healthy longevity monitoring. Lancet Healthy Longev. 2022;3(11):e789-e96.
37. Tucker-Drob EM, Briley DA, Harden KP. Genetic and Environmental Influences on Cognition Across Development and Context. Curr Dir Psychol Sci. 2013;22(5):349-55. doi: 10.1177/0963721413485087.
38. Reynolds CA, Finkel D. A Meta-analysis of Heritability of Cognitive Aging: Minding the “Missing Heritability” Gap. Neuropsychology Review. 2015;25(1):97-112. doi: 10.1007/s11065-015-9280-2.
39. McGue M, Christensen K. The heritability of cognitive functioning in very old adults: evidence from Danish twins aged 75 years and older. Psychology and aging. 2001;16(2):272-80. doi: 10.1037//0882-7974.16.2.272.
40. Vuckovic D, Dawson S, Scheffer DI, Rantanen T, Morgan A, Di Stazio M, et al. Genome-wide association analysis on normal hearing function identifies PCDH20 and SLC28A3 as candidates for hearing function and loss. Hum Mol Genet. 2015;24(19):5655-64. doi: 10.1093/hmg/ddv279.
41. Kvestad E, Czajkowski N, Krog NH, Engdahl B, Tambs K. Heritability of hearing loss. Epidemiology (Cambridge, Mass). 2012;23(2):328-31. doi: 10.1097/EDE.0b013e318245996e.
42. Hogg RE, Dimitrov PN, Dirani M, Varsamidis M, Chamberlain MD, Baird PN, et al. Gene–Environment Interactions and Aging Visual Function: A Classical Twin Study. Ophthalmology. 2009;116(2):263-9.e1. doi: https://doi.org/10.1016/j.ophtha.2008.09.002.
43. Hammond CJ, Duncan DD, Snieder H, de Lange M, West SK, Spector TD, et al. The heritability of age-related cortical cataract: the twin eye study. Investigative ophthalmology & visual science. 2001;42(3):601-5.
44. Roth SM. Genetic aspects of skeletal muscle strength and mass with relevance to sarcopenia. Bonekey Rep. 2012;1:58. doi: 10.1038/bonekey.2012.58.
45. Zempo H, Miyamoto-Mikami E, Kikuchi N, Fuku N, Miyachi M, Murakami H. Heritability estimates of muscle strength-related phenotypes: A systematic review and meta-analysis. Scandinavian journal of medicine & science in sports. 2017;27(12):1537-46. doi: 10.1111/sms.12804.
46. Peter I, Papandonatos GD, Belalcazar LM, Yang Y, Erar B, Jakicic JM, et al. Genetic modifiers of cardiorespiratory fitness response to lifestyle intervention. Med Sci Sports Exerc. 2014;46(2):302-11. doi: 10.1249/MSS.0b013e3182a66155.
47. Willems SM, Wright DJ, Day FR, Trajanoska K, Joshi PK, Morris JA, et al. Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness. Nature Communications. 2017;8(1):16015. doi: 10.1038/ncomms16015.
48. Hanscombe KB, Persyn E, Traylor M, Glanville KP, Hamer M, Coleman JRI, et al. The genetic case for cardiorespiratory fitness as a clinical vital sign and the routine prescription of physical activity in healthcare. Genome Med. 2021;13(1):180. doi: 10.1186/s13073-021-00994-9.
49. Doris PA. The genetics of blood pressure and hypertension: the role of rare variation. Cardiovasc Ther. 2011;29(1):37-45. doi: 10.1111/j.1755-5922.2010.00246.x.
50. Robinson MR, English G, Moser G, Lloyd-Jones LR, Triplett MA, Zhu Z, et al. Genotype–covariate interaction effects and the heritability of adult body mass index. Nature Genetics. 2017;49(8):1174-81. doi: 10.1038/ng.3912.
51. Polderman TJ, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47(7):702-9. doi: 10.1038/ng.3285.
52. Amare AT, Vaez A, Hsu Y-H, Direk N, Kamali Z, Howard DM, et al. Bivariate genome-wide association analyses of the broad depression phenotype combined with major depressive disorder, bipolar disorder or schizophrenia reveal eight novel genetic loci for depression. Molecular Psychiatry. 2020;25(7):1420-9. doi: 10.1038/s41380-018-0336-6.
53. Mullins N, Forstner AJ, O’Connell KS, Coombes B, Coleman JRI, Qiao Z, et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nature Genetics. 2021;53(6):817-29. doi: 10.1038/s41588-021-00857-4.
54. Cardno AG, Marshall EJ, Coid B, Macdonald AM, Ribchester TR, Davies NJ, et al. Heritability estimates for psychotic disorders: the Maudsley twin psychosis series. Archives of general psychiatry. 1999;56(2):162-8. doi: 10.1001/archpsyc.56.2.162.
55. Lin S, Wang F, Zheng J, Yuan Y, Huang F, Zhu P. Intrinsic Capacity Declines with Elevated Homocysteine in Community-Dwelling Chinese Older Adults. Clinical Interventions in Aging. 2022;17:1057-68. doi: 10.2147/CIA.S370930.
56. Lee WJ, Peng LN, Lin MH, Loh CH, Hsiao FY, Chen LK. Intrinsic capacity differs from functional ability in predicting 10-year mortality and biological features in healthy aging: results from the I-Lan longitudinal aging study. AGING-US. 2023;15(3):748-64.
57. Ma L, Liu P, Zhang Y, Sha G, Zhang L, Li Y. High Serum Tumor Necrosis Factor Receptor 1 Levels Are Related to Risk of Low Intrinsic Capacity in Elderly Adults. Journal of Nutrition, Health & Aging. 2021;25(4):416-8. doi: 10.1007/s12603-020-1533-y.
58. Ma L, Zhang Y, Liu P, Li S, Li Y, Ji T, et al. Plasma N-Terminal Pro-B-Type Natriuretic Peptide is Associated with Intrinsic Capacity Decline in an Older Population. Journal of Nutrition, Health & Aging. 2021;25(2):271-7. doi: 10.1007/s12603-020-1468-3.
59. Lu WH, Guyonnet S, Martinez LO, Lucas A, Parini A, Vellas B, et al. Association between aging-related biomarkers and longitudinal trajectories of intrinsic capacity in older adults. GEROSCIENCE. 2023. doi: 10.1007/s11357-023-00906-2.
60. Lu WH, Gonzalez-Bautista E, Guyonnet S, Lucas A, Parini A, Walston JD, et al. Plasma inflammation-related biomarkers are associated with intrinsic capacity in community-dwelling older adults. Journal of Cachexia, Sarcopenia and Muscle. 2023;14(2):930-9. doi: 10.1002/jcsm.13163.
61. da Silva JA, Martinez LO, Rolland Y, Najib S, Croyal M, Perret B, et al. Plasma Level of ATPase Inhibitory Factor 1 and Intrinsic Capacity in Community-Dwelling Older Adults: Prospective Data From the MAPT Study. JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES. 2023. doi: 10.1093/gerona/glad142.
62. Gutiérrez-Robledo LM, García-Chanes RE, Pérez-Zepeda MU. Allostatic Load as a Biological Substrate to Intrinsic Capacity: A Secondary Analysis of CRELES. Journal of Nutrition, Health & Aging. 2019;23(9):788-95. doi: 10.1007/s12603-019-1251-5.
63. Ma L, Chhetri JK, Zhang Y, Liu P, Chen Y, Li Y, et al. Integrated Care for Older People Screening Tool for Measuring Intrinsic Capacity: Preliminary Findings From ICOPE Pilot in China. Frontiers in Medicine. 2020;7. doi: 10.3389/fmed.2020.576079.
64. Ma L, Chhetri JK, Zhang L, Sun F, Li Y, Tang Z. Cross-sectional study examining the status of intrinsic capacity decline in community-dwelling older adults in China: prevalence, associated factors and implications for clinical care. BMJ Open. 2021;11(1):e043062.
65. Prince MJ, Acosta D, Guerra M, Huang Y, Jacob KS, Jimenez-Velazquez IZ, et al. Intrinsic capacity and its associations with incident dependence and mortality in 10/66 Dementia Research Group studies in Latin America, India, and China: A population-based cohort study. PLoS Med. 2021;18(9):e1003097.
66. Yu R, Amuthavalli Thiyagarajan J, Leung J, Lu Z, Kwok T, Woo J. Validation of the Construct of Intrinsic Capacity in a Longitudinal Chinese Cohort. J Nutr Health Aging. 2021;25(6):808-15.
67. Beard JR, Si Y, Liu Z, Chenoweth L, Hanewald K. Intrinsic Capacity: Validation of a New WHO Concept for Healthy Aging in a Longitudinal Chinese Study. Journals of Gerontology Series A: Biological Sciences & Medical Sciences. 2022;77(1):94-100. doi: 10.1093/gerona/glab226.
68. Jiang X, Chen F, Yang X, Yang M, Zhang X, Ma X, et al. Effects of personal and health characteristics on the intrinsic capacity of older adults in the community: a cross-sectional study using the healthy aging framework. BMC Geriatrics. 2023;23(1):1-10. doi: 10.1186/s12877-023-04362-7.
69. Rarajam Rao A, Waris M, Saini M, Thakral M, Hegde K, Bhagwasia M, et al. Prevalence and Factors Associated with Impairment in Intrinsic Capacity among Community-Dwelling Older Adults: An Observational Study from South India. Current Gerontology & Geratrics Research. 2023;2023:1-9. doi: 10.1155/2023/4386415.
70. Huang CH, Umegaki H, Makino T, Uemura K, Hayashi T, Kitada T, et al. Effect of Various Exercises on Intrinsic Capacity in Older Adults With Subjective Cognitive Concerns. Journal of the American Medical Directors Association. 2021;22(4):780-. doi: 10.1016/j.jamda.2020.06.048.
71. Muneera K, Muhammad T, Althaf S. Socio-demographic and lifestyle factors associated with intrinsic capacity among older adults: evidence from India. BMC Geriatrics. 2022;22(1):1-16. doi: 10.1186/s12877-022-03558-7.
72. Zhou M, Kuang L, Hu N. The Association between Physical Activity and Intrinsic Capacity in Chinese Older Adults and Its Connection to Primary Care: China Health and Retirement Longitudinal Study (CHARLS). International Journal of Environmental Research and Public Health. 2023;20(7). doi: 10.3390/ijerph20075361.
73. Yu R, Lai D, Leung G, Tam LY, Cheng C, Kong S, et al. Moving towards the ICOPE Approach: Evaluation of Community-Based Intervention Activities on Improving Intrinsic Capacity. JOURNAL OF NUTRITION HEALTH & AGING. 2023. doi: 10.1007/s12603-023-2003-0.
74. Zeng X, Shen S, Xu L, Wang Y, Yang Y, Chen L, et al. The Impact of Intrinsic Capacity on Adverse Outcomes in Older Hospitalized Patients: A One-Year Follow-Up Study. Gerontology. 2021;67(3):267-75. doi: 10.1159/000512794.
75. Chen JJ, Liu LF, Chang SM. Approaching person-centered long-term care: The trajectories of intrinsic capacity and functional decline in Taiwan. Geriatrics & Gerontology International. 2022;22(7):516-22. doi: 10.1111/ggi.14391.
76. Yu J, Si H, Jin Y, Qiao X, Ji L, Bian Y, et al. Patterns of intrinsic capacity among community-dwelling older adults: Identification by latent class analysis and association with one-year adverse outcomes. Geriatric Nursing. 2022;45:223-9. doi: 10.1016/j.gerinurse.2022.04.021.
77. Lim K-Y, Lo H-C, Cheong I-F, Wang Y-Y, Jian Z-R, Chen IC, et al. Healthy Eating Enhances Intrinsic Capacity, Thus Promoting Functional Ability of Retirement Home Residents in Northern Taiwan. Nutrients. 2022;14(11):2225-. doi: 10.3390/nu14112225.
78. Waris M, Upadhyay AD, Chatterjee P, Chakrawarty A, Kumar P, Dey AB. Establishment of Clinical Construct of Intrinsic Capacity in Older Adults and Its Prediction of Functional Decline. Clin Interv Aging. 2022;17:1569-80.
79. Zhao J, Chhetri Jagadish K, Chang Y, Zheng Z, Ma L, Chan P. Intrinsic Capacity vs. Multimorbidity: A Function-Centered Construct Predicts Disability Better Than a Disease-Based Approach in a Community-Dwelling Older Population Cohort. Frontiers in Medicine. 2021;8.
80. Jiang YS, Shi H, Kang YT, Shen J, Li J, Cui J, et al. Impact of age-friendly living environment and intrinsic capacity on functional ability in older adults: a cross-sectional study. BMC GERIATRICS. 2023;23(1). doi: 10.1186/s12877-023-04089-5.
81. Sánchez-Sánchez JL, Rolland Y, Cesari M, de Souto Barreto P. Associations Between Intrinsic Capacity and Adverse Events Among Nursing Home Residents: The INCUR Study. Journal of the American Medical Directors Association. 2022;23(5):872-. doi: 10.1016/j.jamda.2021.08.035.
82. Yu R, Leung G, Leung J, Cheng C, Kong S, Tam LY, et al. Prevalence and Distribution of Intrinsic Capacity and Its Associations with Health Outcomes in Older People: The Jockey Club Community eHealth Care Project in Hong Kong. J Frailty Aging. 2022;11(3):302-8.
83. Yu J, Jin Y, Si H, Bian Y, Liu Q, Qiao X, et al. How does social support interact with intrinsic capacity to affect the trajectory of functional ability among older adults? Findings of a population-based longitudinal study. Maturitas. 2023;171:33-9. doi: 10.1016/j.maturitas.2023.03.005.
84. Liu S, Kang L, Liu X, Zhao S, Wang X, Li J, et al. Trajectory and Correlation of Intrinsic Capacity and Frailty in a Beijing Elderly Community. Frontiers in Medicine. 2021;8.
85. Tay L, Tay EL, Mah SM, Latib A, Ng YS. Intrinsic capacity rather than intervention exposure influences reversal to robustness among prefrail community-dwelling older adults: A non-randomized controlled study of a multidomain exercise and nutrition intervention. FRONTIERS IN MEDICINE. 2022;9. doi: 10.3389/fmed.2022.971497.
86. Yu R, Leung J, Leung G, Woo J. Towards Healthy Ageing: Using the Concept of Intrinsic Capacity in Frailty Prevention. Journal of Nutrition, Health & Aging. 2022;26(1):30-6. doi: 10.1007/s12603-021-1715-2.
87. Gonzalez-Bautista E, Llibre-Guerra JJ, Sosa AL, Acosta I, Andrieu S, Acosta D, et al. Exploring the natural history of intrinsic capacity impairments: longitudinal patterns in the 10/66 study. Age and Ageing. 2023;52(7):1-9.
88. Chew J, Lim JP, Yew S, Yeo A, Ismail NH, Ding YY, et al. Disentangling the Relationship between Frailty and Intrinsic Capacity in Healthy Community-Dwelling Older Adults: A Cluster Analysis. Journal of Nutrition, Health & Aging. 2021;25(9):1112-8. doi: 10.1007/s12603-021-1679-2.
89. Meng LC, Hsiao FY, Huang ST, Lu WH, Peng L-N, Chen LK. Intrinsic Capacity Impairment Patterns and their Associations with Unfavorable Medication Utilization: A Nationwide Population-Based Study of 37,993 Community-Dwelling Older Adults. Journal of Nutrition, Health & Aging. 2022;26(10):918-25. doi: 10.1007/s12603-022-1847-z.
90. Sanchez-Sánchez JL, Rolland Y, Cesari M, de Souto Barreto P. Impact of nursing home-acquired pneumonia on the domains of the novel construct of intrinsic capacity: The INCUR study. J Am Geriatr Soc. 2022;70(12):3436-46.
91. Pages A, Costa N, Gonzalez-Bautista E, Mouni M, Juillard-Condat B, Molinier L, et al. Screening for deficits on intrinsic capacity domains and associated healthcare costs. ARCHIVES OF GERONTOLOGY AND GERIATRICS. 2022;100. doi: 10.1016/j.archger.2022.104654.
92. Liao X, Shen J, Li M. Effects of multi-domain intervention on intrinsic capacity in older adults: A systematic review of randomized controlled trials (RCTs). Experimental Gerontology. 2023;174. doi: 10.1016/j.exger.2023.112112.
93. Locquet M, Sanchez-Rodriguez D, Bruyère O, Geerinck A, Lengelé L, Reginster JY, et al. Intrinsic Capacity Defined Using Four Domains and Mortality Risk: A 5-Year Follow-Up of the SarcoPhAge Cohort. Journal of Nutrition, Health & Aging. 2022;26(1):23-9. doi: 10.1007/s12603-021-1702-7.
94. Yu R, Lai ETC, Leung G, Ho SC, Woo J. Intrinsic capacity and 10-year mortality: Findings from a cohort of older people. EXPERIMENTAL GERONTOLOGY. 2022;167. doi: 10.1016/j.exger.2022.111926.
95. Nagae M, Umegaki H, Komiya H, Nakashima H, Fujisawa C, Watanabe K, et al. Intrinsic capacity in acutely hospitalized older adults. Experimental Gerontology. 2023;179. doi: 10.1016/j.exger.2023.112247.
96. Huang CH, Okada K, Matsushita E, Uno C, Satake S, Martins BA, et al. Dietary patterns and intrinsic capacity among community-dwelling older adults: a 3-year prospective cohort study. European Journal of Nutrition. 2021;60(6):3303-13. doi: 10.1007/s00394-021-02505-3.
97. Plácido J, Marinho V, Ferreira JV, Teixeira IA, Costa EC, Deslandes AC. Association among race/color, gender, and intrinsic capacity: results from the ELSI-Brazil study. Revista de Saude Publica. 2023;57(1). doi: 10.11606/S1518-8787.2023057004548.
98. Yeung SSY, Sin D, Yu R, Leung J, Woo J. Dietary Patterns and Intrinsic Capacity in Community-Dwelling Older Adults: A Cross-Sectional Study. Journal of Nutrition, Health & Aging. 2022;26(2):174-82. doi: 10.1007/s12603-022-1742-7.
99. Astrone P, Perracini MR, Martin FC, Marsh DR, Cesari M. The potential of assessment based on the WHO framework of intrinsic capacity in fragility fracture prevention. Aging Clinical & Experimental Research. 2022;34(11):2635-43. doi: 10.1007/s40520-022-02186-w.
100. Lin S, Huang M, Yang L, Chen S, Huang X, Zheng J, et al. Dietary diversity and overweight are associated with high intrinsic capacity among Chinese urban older adults (2020−2021). Experimental Gerontology. 2023;177. doi: 10.1016/j.exger.2023.112194.
101. Su H, Xu L, Yu H, Zhou Y, Li Y. Social isolation and intrinsic capacity among left-behind older adults in rural China: The chain mediating effect of perceived stress and health-promoting behavior. Frontiers in Public Health. 2023;11. doi: 10.3389/fpubh.2023.1155999.
102. Giudici KV, Barreto PD, Beard J, Cantet C, de Carvalho IA, Rolland Y, et al. Effect of long-term omega-3 supplementation and a lifestyle multidomain intervention on intrinsic capacity among community-dwelling older adults: Secondary analysis of a randomized, placebo-controlled trial (MAPT study). MATURITAS. 2020;141:39-45. doi: 10.1016/j.maturitas.2020.06.012.
103. Ramirez-Velez R, Correa-Bautista JE, Garcia-Hermoso A, Cano CA, Izquierdo M. Reference values for handgrip strength and their association with intrinsic capacity domains among older adults. JOURNAL OF CACHEXIA SARCOPENIA AND MUSCLE. 2019;10(2):278-86. doi: 10.1002/jcsm.12373.
104. Arokiasamy P, Selvamani Y, Jotheeswaran AT, Sadana R. Socioeconomic differences in handgrip strength and its association with measures of intrinsic capacity among older adults in six middle-income countries. Sci Rep. 2021;11(1):19494.
105. Nascimento LMD, Cruz TGCD, Silva JFDLE, Silva LP, Inácio BB, Sadamitsu CMO, et al. Use of Intrinsic Capacity Domains as a Screening Tool in Public Health. International Journal of Environmental Research and Public Health. 2023;20(5). doi: 10.3390/ijerph20054227.
106. Jia S, Zhao W, Ge M, Xia X, Hu F, Hao Q, et al. Associations between transitions of intrinsic capacity and frailty status, and 3-year disability. BMC Geriatrics. 2023;23(1):1-8. doi: 10.1186/s12877-023-03795-4.
107. Luque XRI, Blancafort-Alias S, Casanovas SP, Forne S, Vergara NM, Povill PF, et al. Identification of decreased intrinsic capacity: Performance of diagnostic measures of the ICOPE Screening tool in community dwelling older people in the VIMCI study. BMC GERIATRICS. 2023;23(1). doi: 10.1186/s12877-023-03799-0.
108. Mathur A, Bhardwaj P, Joshi NK, Jain YK, Singh K. Intrinsic Capacity of Rural Elderly in Thar Desert using World Health Organization Integrated Care for Older Persons Screening Tool: A Pilot Study. Indian Journal of Public Health. 2022;66(3):337-40. doi: 10.4103/ijph.ijph_731_22.
109. Muneera K, Muhammad T, Pai M, Ahmed W, Althaf S. Associations between intrinsic capacity, functional difficulty, and fall outcomes among older adults in India. Sci Rep. 2023;13(1):9829. doi: 10.1038/s41598-023-37097-x.
110. Yu J, Si H, Qiao X, Jin Y, Ji L, Liu Q, et al. Predictive value of intrinsic capacity on adverse outcomes among community-dwelling older adults. Geriatric Nursing. 2021;42(6):1257-63. doi: 10.1016/j.gerinurse.2021.08.010.
111. Langballe EM, Skirbekk V, Strand BH. Subjective age and the association with intrinsic capacity, functional ability, and health among older adults in Norway. European Journal of Ageing. 2023;20(1):1-10. doi: 10.1007/s10433-023-00753-2.
112. Hu XH, Ruan J, Zhang WB, Chen J, Bao ZJ, Ruan QW, et al. The overall and domain-specific quality of life of Chinese community-dwelling older adults: the role of intrinsic capacity and disease burden. FRONTIERS IN PSYCHOLOGY. 2023;14. doi: 10.3389/fpsyg.2023.1190800.
113. Zhang N, Zhang H, Sun M-Z, Zhu Y-S, Shi G-P, Wang Z-D, et al. Intrinsic capacity and 5-year late-life functional ability trajectories of Chinese older population using ICOPE tool: the Rugao Longevity and Ageing Study. Aging Clinical and Experimental Research. 2023;35(10):2061-8.
114. Gonzalez-Bautista E, de Souto Barreto P, Andrieu S, Rolland Y, Vellas B. Screening for intrinsic capacity impairments as markers of increased risk of frailty and disability in the context of integrated care for older people: Secondary analysis of MAPT. Maturitas. 2021;150:1-6.
115. Liu S, Yu X, Wang X, Li J, Jiang S, Kang L, et al. Intrinsic Capacity predicts adverse outcomes using Integrated Care for Older People screening tool in a senior community in Beijing. Archives of Gerontology & Geriatrics. 2021;94:N.PAG-N.PAG. doi: 10.1016/j.archger.2021.104358.
116. Gonzalez-Bautista E, de Souto Barreto P, Virecoulon Giudici K, Andrieu S, Rolland Y, Vellas B. Frequency of Conditions Associated with Declines in Intrinsic Capacity According to a Screening Tool in the Context of Integrated Care for Older People. J Frailty Aging. 2021;10(2):94-102.
117. Rodríguez-Laso A, García-García FJ, Rodríguez-Mañas L. The ICOPE Intrinsic Capacity Screening Tool: Measurement Structure and Predictive Validity of Dependence and Hospitalization. JOURNAL OF NUTRITION HEALTH & AGING. 2023. doi: 10.1007/s12603-023-1985.
118. Schermelleh-Engel K, Moosbrugger H, Müller H. Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online. 2003;8(2):23-74.
119. Thorndike RM. Book review: psychometric theory by Jum Nunnally and Ira Bernstein New York: McGraw-hill, 1994, xxiv+ 752 pp. Applied psychological measurement. 1995;19(3):303-5.
120. DeVellis RF, Thorpe CT. Scale development: Theory and applications. Sage publications; 2021.
121. Markon KE. Bifactor and hierarchical models: Specification, inference, and interpretation. Annual review of clinical psychology. 2019;15:51-69.
122. Chen FF, Hayes A, Carver CS, Laurenceau JP, Zhang Z. Modeling general and specific variance in multifaceted constructs: A comparison of the bifactor model to other approaches. Journal of personality. 2012;80(1):219-51.
123. Zhang B, Sun T, Cao M, Drasgow F. Using bifactor models to examine the predictive validity of hierarchical constructs: Pros, cons, and solutions. Organizational Research Methods. 2021;24(3):530-71.
124. Koivunen K, Schaap LA, Hoogendijk EO, Schoonmade LJ, Huisman M, van Schoor NM. Exploring the conceptual framework and measurement model of intrinsic capacity defined by the World Health Organization: A scoping review. Ageing Research Reviews. 2022;80:101685. doi: https://doi.org/10.1016/j.arr.2022.101685.

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