Skip to main content
SpringerLink
Log in

The ICOPE Intrinsic Capacity Screening Tool: Measurement Structure and Predictive Validity of Dependence and Hospitalization

  • Original Research
  • Published:
The journal of nutrition, health & aging

Abstract

Objectives

To evaluate the measurement structure of the ICOPE screening tool (IST) of intrinsic capacity and to find out whether the IST as a global measure adds explanatory power over and above its domains in isolation to predict the occurrence of adverse health outcomes such as dependence and hospitalization in community-dwelling older people.

Design

Secondary analysis of a cohort study, the Toledo Study of Healthy Ageing.

Setting

Province of Toledo, Spain.

Participants

Community-dwelling older people.

Measurements

Items equal or similar to those of the IST were introduced as a reflective-formative construct in a Structural Equation Model to evaluate its measurement structure and its association with dependence for basic and instrumental activities and hospitalization over a three-year period.

Results

A total of 1032 individuals were analyzed. Mean age was 73.5 years (sd 5.4). The least preserved indicators were ability to recall three words (18%) and to perform chair stands (54%). Vision and hearing items did not form a single sensory domain, so six domains were considered. Several cognition items did not show sufficiently strong and univocal associations with the domain. After pruning the ill-behaved items, the measurement model fit was excellent (Satorra-Bentler scaled chi-square: 10.3, degrees of freedom: 11, p=0.501; CFI: 1.000; RMSEA: 0.000, 90% CI: 0.000–0.031, p value RMSEA<=0.05: 1; SRMR: 0.055). In the structural model, the cognition domain items were not associated as expected with age (p values 0.158 and 0.293), education (p values 0.190 and 0.432) and dependence (p values 0.654 and 0.813). The IST included as a composite in a model with the individual domains showed no statistically significant associations with any of the outcomes (dependence for basic activities: 0.162, p=0.167; instrumental: −0.052, p=0.546; hospitalization: 0.145, p=0.167), while only the mobility domain did so for dependence (basic: −0.266, p=0.005; instrumental: −0.138, p=0.019). The model fit of the last version was good (Satorra-Bentler scaled chi-square: 52.1, degrees of freedom: 52, p=0.469; CFI: 1.000; TLI: 1.000; RMSEA: 0.01, 90% CI: 0.000–0.02, p value RMSEA<=0.05: 1; SRMR: 0.071). The IST operationalized as the sum of non-impaired domains was not associated after covariate adjustment (dependence for basic activities: −0.065, p=0.356; instrumental: −0.08, p=0.05; hospitalization: −0.003, p=0.949) either.

Conclusion

The cognitive domain of the IST, and probably other of its items, may need a reformulation. A global measure of intrinsic capacity such as the IST does not add explanatory power to the individual domains that constitute it. So far, our results confirm the importance of checking the findings of the IST with a second confirmatory step, as described in the WHO’s ICOPE strategy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Figure 1
Figure 2

Similar content being viewed by others

References

  1. World Health Organization (2015). World Report on Ageing and Health. Geneva

  2. Cesari M, Araujo de Carvalho I, Amuthavalli Thiyagarajan J, Cooper C, Martin FC, Reginster JY, Vellas B, Beard JR. Evidence for the domains supporting the construct of intrinsic capacity. J Gerontol A Biol Sci Med Sci 2018;10;73:1653–60. doi:https://doi.org/10.1093/gerona/gly011

    Article  Google Scholar 

  3. World Health Organization (2019). Integrated care for older people (ICOPE): Guidance for person-centred assessment and pathways in primary care. Geneva

  4. Flora DB. Your coefficient alpha Is probably wrong, but which coefficient omega is right? A tutorial on using R to obtain better reliability estimates. Adv Methods Pract Psychol Sci 2020;3:484–501. doi:https://doi.org/10.1177/2515245920951

    Article  Google Scholar 

  5. 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:e026119. doi:https://doi.org/10.1136/bmjopen-2018-026119

    Article  PubMed  PubMed Central  Google Scholar 

  6. Yu R, Amuthavalli Thiyagarajan J, Leung J, Lu Z, Kwok T, Woo JR. Validation of the construct of intrinsic capacity in a longitudinal Chinese cohort. J Nutr Health Aging 2021;25:808–15. doi:https://doi.org/10.1007/s12603-021-1637-z

    Article  CAS  PubMed  Google Scholar 

  7. Aliberti MJR, Bertola L, Szlejf C, Oliveira D, Piovezan RD, Cesari M, de Andrade FB, Lima-Costa MF, Perracini MR, Ferri CP, Suemoto CK. Validating intrinsic capacity to measure healthy aging in an upper middle-income country: Findings from the ELSI-Brazil. Lancet Reg Health Am 2022;12:100284. doi:https://doi.org/10.1016/j.lana.2022.100284

    PubMed  PubMed Central  Google Scholar 

  8. Liu S, Yu X, Wang X, Li J, Jiang S, Kang L, Liu X. Intrinsic Capacity predicts adverse outcomes using Integrated Care for Older People screening tool in a senior community in Beijing. Arch Gerontol Geriatr 2021;94:104358. doi:https://doi.org/10.1016/j.archger.2021.104358

    Article  PubMed  Google Scholar 

  9. Pagès A, Costa N, González-Bautista E, Mounié M, Juillard-Condat B, Molinier L, Cestac P, Rolland Y, Vellas B, De Souto Barreto P; MAPT/DSA Group. Screening for deficits on intrinsic capacity domains and associated healthcare costs. Arch Gerontol Geriatr 2022;100:104654. doi:https://doi.org/10.1016/j.archger.2022.104654

    Article  PubMed  Google Scholar 

  10. Ma L, Chhetri JK, Zhang Y, Liu P, Chen Y, Li Y, Chan P. Integrated Care for Older People Screening Tool for measuring intrinsic capacity: Preliminary findings from ICOPE pilot in China. Front Med (Lausanne) 2020;7:576079. doi:https://doi.org/10.3389/fmed.2020.576079

    Article  PubMed  Google Scholar 

  11. González-Bautista E, de Souto Barreto P, Andrieu S, Rolland Y, Vellas B; MAPT/DSA group (members are listed under ‘Contributors’). 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. doi:https://doi.org/10.1016/j.maturitas.2021.05.011

    Article  PubMed  Google Scholar 

  12. Tavassoli N, de Souto Barreto P, Berbon C, Mathieu C, de Kerimel J, Lafont C, Takeda C, Carrie I, Piau A, Jouffrey T, Andrieu S, Nourhashemi F, Beard JR, Soto Martin ME, Vellas B. Implementation of the WHO integrated care for older people (ICOPE) programme in clinical practice: a prospective study. Lancet Healthy Longev 2022;3(6):e394–e404. doi:https://doi.org/10.1016/S2666-7568(22)00097-6

    Article  PubMed  Google Scholar 

  13. Henseler J (2021) Second-order constructs. In: Composite-based structural equation modeling. The Guilford Press, New York, pp. 219–54

    Google Scholar 

  14. 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 Res Rev 2022;80:101685. doi:https://doi.org/10.1016/j.arr.2022.101685

    Article  CAS  PubMed  Google Scholar 

  15. Koivunen K, Hoogendijk EO, Schaap LA, Huisman M, Heymans MW, van Schoor NM. Development and validation of an intrinsic capacity composite score in the Longitudinal Aging Study Amsterdam: a formative approach. Aging Clin Exp Res 2023;35:815–825. doi:https://doi.org/10.1007/s40520-023-02366-2

    Article  PubMed  PubMed Central  Google Scholar 

  16. Bollen KA, Bauldry S. Three Cs in measurement models: Causal indicators, composite indicators, and covariates. Psychol Methods 2011;16:265–84. doi:https://doi.org/10.1037/a0024448

    Article  PubMed  PubMed Central  Google Scholar 

  17. Schuberth F, Rademaker ME, Henseler J. Estimating and assessing second-order constructs using PLS-PM: the case of composites of composites. Industrial Management & Data Systems 2020;120:2211–2241. doi:https://doi.org/10.1108/IMDS-12-2019-0642

    Article  Google Scholar 

  18. Garcia-Garcia FJ, Gutierrez Avila G, Alfaro-Acha A, Amor Andres MS, De Los Angeles De La Torre Lanza M, Escribano Aparicio MV, Humanes Aparicio S, Larrion Zugasti JL, Gomez-Serranillo Reus M, Rodriguez-Artalejo F, Rodriguez-Manas L. The prevalence of frailty syndrome in an older population from Spain. The Toledo Study for Healthy Aging. J Nutr Health Aging 2011;15:852e856. doi:https://doi.org/10.1007/s12603-011-0075-8

    Article  Google Scholar 

  19. Guigoz Y, Vellas B, Garry PJ (1994) Mini Nutritional Assessment: A practical Assessment Tool for Grading the Nutritional State of Elderly Patients. In: Vellas B, Ed., The Mini Nutritional Assessment (MNA), Supplement No 2. Serdi Publisher, Paris, pp. 15–59

    Google Scholar 

  20. Muñoz Díaz B, Molina-Recio G, Romero-Saldaña M, Redondo Sánchez J, Aguado Taberné C, Arias Blanco C, Molina-Luque R, Martínez De La Iglesia J. Validation (in Spanish) of the Mini Nutritional Assessment survey to assess the nutritional status of patients over 65 years of age. Fam Pract 2019;36:172–178. doi:https://doi.org/10.1093/fampra/cmy051. Erratum in: Fam Pract 2019;36:528

    Article  PubMed  Google Scholar 

  21. Folstein MP, Folstein SE, McHugh PR. Mini-Mental State: A practical method for grading the cognitive state of patient for the clinician. J Psychiatr Res 1975;12:189–98. doi:https://doi.org/10.1016/0022-3956(75)90026-6

    Article  CAS  PubMed  Google Scholar 

  22. Escribano-Aparicio MV, Pérez-Dively M, García-García FJ, Pérez-Martín A, Romero L, Ferrer G, Martín-Correa E, Sánchez-Ayala MI. Validación del MMSE de Folstein en una población española de bajo nivel educativo. Rev Esp de Geriatr Gerontol 1999;34:319–26

    Google Scholar 

  23. Sheikh JI, Yesavage JA (1986) Geriatric Depression Scale (GDS). Recent evidence and development of a shorter version. In: Brink TL (ed) Clinical gerontology: A guide to assessment and intervention. The Haworth Press Inc, New York, pp. 165–73

    Google Scholar 

  24. Fernández-San Martín M, Andrade-Rosa C, Molina JD, Muñoz PE, Carretero B, Rodríguez M, Silva A. Validation of the Spanish version of the geriatric depression scale (GDS) in primary care. Int J Geriatr Psychiatry 2002;17:279–87. doi:https://doi.org/10.1002/gps.588. Erratum in: Int J Geriatr Psychiatry 2007;22:704. Andrade, C [corrected to Andrade-Rosa, C]; Molina, J [corrected to Molina, J D]

    Article  PubMed  Google Scholar 

  25. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged. The Index of ADL: A standardized measure of biological and psychosocial function. JAMA 1963;185:914–19. doi:https://doi.org/10.1001/jama.1963.03060120024016

    Article  CAS  PubMed  Google Scholar 

  26. Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 1969;9:179–86

    Article  CAS  PubMed  Google Scholar 

  27. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373–83. doi:https://doi.org/10.1016/0021-9681(87)90171-8

    Article  CAS  PubMed  Google Scholar 

  28. Kline RB (2016) Analyses of confirmatory factor analysis models, In: Principles and practice of structural equation modeling, 4th edition. The Guilford Press, New York, pp.300–37

    Google Scholar 

  29. Kline RB (2016) Global fit testing, In: Principles and practice of structural equation modeling, 4th edition. The Guilford Press, New York, pp.26260

    Google Scholar 

  30. Kline RB (2016) Estimation and local fit testing, In: Principles and practice of structural equation modeling, 4th edition. The Guilford Press, New York, pp.231–60

    Google Scholar 

  31. Satorra A, Bentler PM. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika 2001;66:507–14. doi:https://doi.org/10.1007/BF02296192

    Article  Google Scholar 

  32. Lee D, Lim WY, Park S, Jin YW, Lee WJ, Park S, Seo S. Reliability and validity of a nationwide survey (the Korean Radiation Workers Study). Saf Health Work 2021;12: 445–51. doi:https://doi.org/10.1016/j.shaw.2021.07.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Katz JN, Chang LC, Sangha O, Fossel AH, Bates DW. Can comorbidity be measured by questionnaire rather than medical record review? Med Care 1996;34:73–84. doi:https://doi.org/10.1097/00005650-199601000-00006

    Article  CAS  PubMed  Google Scholar 

  34. Rosseel Y. An R package for Structural Equation Modeling. J Stat Softw 2012;48:1–36. doi:https://doi.org/10.18637/jss.v048.i02

    Article  Google Scholar 

  35. López-Ortiz S, Lista S, Peñín-Grandes S, Pinto-Fraga J, Valenzuela PL, Nisticò R, Emanuele E, Lucia A, Santos-Lozano A. Defining and assessing intrinsic capacity in older people: A systematic review and a proposed scoring system. Ageing Res Rev 2022;79:101640. doi:https://doi.org/10.1016/j.arr.2022.101640

    Article  PubMed  Google Scholar 

  36. Wu W, Sun L, Li H, Zhang J, Shen J, Li J, Zhou Q. Approaching person-centered clinical practice: A cluster analysis of older inpatients utilizing the measurements of intrinsic capacity. Front Public Health 2022;11;10:1045421. doi:https://doi.org/10.3389/fpubh.2022.1045421

    Article  Google Scholar 

  37. Yu J, Si H, Qiao X, Jin Y, Ji L, Liu Q, Bian Y, Wang W, Wang C. Predictive value of intrinsic capacity on adverse outcomes among community-dwelling older adults. Geriatr Nurs 2021;42:1257–63. doi:https://doi.org/10.1016/j.gerinurse.2021.08.010

    Article  PubMed  Google Scholar 

  38. Vermeulen J, Neyens JC, van Rossum E, Spreeuwenberg MD, de Witte LP. Predicting ADL disability in community-dwelling elderly people using physical frailty indicators: a systematic review. BMC Geriatr 2011;11:33. doi:https://doi.org/10.1186/1471-2318-11-33

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

To Jotheeswaran A. Thiyagarajan and Yuka Sumi of the WHO for proposing the analysis of the measurement structure of the ICOPE tool and to Keith A. Markus of the John Jay College of Criminal Justice, CUNY, for his advice on the SEM analyses.

Funding

Funding: This work was supported by the Thematic Area for Frailty and Healthy Ageing of the Network of Biomedical Research Centers (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain.

Author information

Authors and Affiliations

  1. CIBERFES, ISCIII, C/ Sinesio Delgado, 4, 28029, Madrid, Spain

    Á. Rodríguez-Laso, F. J. García-García & Leocadio Rodríguez-Mañas

  2. Geriatric Department, Hospital Virgen del Valle, Complejo Hospitalario de Toledo, Ctra. de Cobisa, S/N, 45071, Toledo, Spain

    F. J. García-García

  3. Biomedical Research Foundation, Hospital Universitario de Getafe, Ctra de Toledo Km12,5, 28905, Getafe, Madrid, Spain

    Leocadio Rodríguez-Mañas

  4. Geriatric Department, Hospital Universitario de Getafe, Ctra de Toledo Km12,5, 28905, Getafe, Madrid, Spain

    Leocadio Rodríguez-Mañas

Authors
  1. Á. Rodríguez-Laso
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. J. García-García
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leocadio Rodríguez-Mañas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leocadio Rodríguez-Mañas.

Ethics declarations

Conflict of interest: Ángel Rodríguez-Laso, Francisco José García-García, and Leocadio Rodríguez-Mañas declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rodríguez-Laso, Á., García-García, F.J. & Rodríguez-Mañas, L. The ICOPE Intrinsic Capacity Screening Tool: Measurement Structure and Predictive Validity of Dependence and Hospitalization. J Nutr Health Aging 27, 808–816 (2023). https://doi.org/10.1007/s12603-023-1985-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12603-023-1985-y

Key words

Search

Navigation