Preliminary psychometric testing of a novel cognitive decline risk screening tool for middle-aged adults of 40 to 60 years: a pilot study
DOI:
https://doi.org/10.18203/2394-6040.ijcmph20263274Keywords:
Health promotion, Risk assessment, Cognitive dysfunctionAbstract
Background: The cognitive decline risk screening tool (CDRST) was developed using the modified Delphi technique for adults aged 40 to 60 years to identify cognitive decline risk. This pilot study assessed the tool's internal consistency and developed preliminary cut-off scores.
Methods: Community-dwelling adults aged 40 to 60 years who attended the outpatient clinic were included through convenience sampling. Analysis included descriptive statistics, Cronbach’s alpha, an inter-item correlation matrix with Spearman's rank correlation, and scale cutoff scores identified using the standard deviation method (Mean±SD).
Results: 103 individuals participated, with a mean age of 50.1±6.55 years. Cronbach's alpha was 0.81, and the inter-item correlation matrix ranged from -0.02 to 0.62, indicating low to moderate correlations. A cut-off score of ≥43.64 (≥Mean+1SD) was noted, with 14.56% as high risk.
Conclusions: The CDRST showed good reliability, with all items contributing to the tool's overall construct, and exploratory score cutoffs provided a preliminary risk-stratification framework.
References
Longitudinal Ageing Study in India (LASI). India report. National Programme for Health Care of Elderly and International Institute for Population Sciences, Ministry of Health and Family Welfare, Government of India. 2020.
Angrisani M, Nichols E, Meijer E, Gross AL, Ehrlich J, Varghese M, et al. Modifiable risk factors for dementia in India: a cross-sectional study revisiting estimates and reassessing prevention potential and priorities. BMJ Public Health. 2024;2(2):e001362.
Belessiotis-Richards C, Livingston G, Marston L, Mukadam N. A cross-sectional study of potentially modifiable risk factors for dementia and cognitive function in India: a secondary analysis of 10/66, LASI, and SAGE data. Int J Geriatr Psychiatry. 2022;37(2):NA.
Khan J. Nutritional status, alcohol-tobacco consumption behaviour and cognitive decline among older adults in India. Sci Rep. 2022;12(1):21102.
Sharma M, Pradhan MR. Socioeconomic inequality in cognitive impairment among India’s older adults and its determinants: a decomposition analysis. BMC Geriatr. 2023;23(1):7.
Dominguez LJ, Veronese N, Vernuccio L, Catanese G, Inzerillo F, Salemi G, et al. Nutrition, physical activity, and other lifestyle factors in the prevention of cognitive decline and dementia. Nutrients. 2021;13(11):4080.
Omura JD, Brown DR, McGuire LC, Taylor CA, Fulton JE, Carlson SA. Cross-sectional association between physical activity level and subjective cognitive decline among US adults aged ≥45 years, 2015. Prev Med. 2020;141:106279.
Smith L, Shin JI, Jacob L, Carmichael C, López Sánchez GF, Oh H, et al. Sleep problems and mild cognitive impairment among adults aged ≥50 years from low-and middle-income countries. Exp Gerontol. 2021;154:111513.
Yadav J, Kumar K, Sahoo KC, Sinha A, Kumar LALD, Shalini S, et al. Multimorbidity, lifestyle factors, and risk of reduced cognitive performance among older adults: evidence from the longitudinal ageing study in India (LASI). Researchsquare. 2026.
De Looze C, Feeney J, Seeher KM, Thiyagarajan JA, Diaz T, Kenny RA. Assessing cognitive function in longitudinal studies of ageing worldwide: some practical considerations. Age Ageing. 2023;52(4):iv13-25.
Stubs J, Selbæk G, Strand BH, Livingston G, Anstey KJ, Deckers K, et al. Predicting cognitive decline: comparative analysis of ANU-ADRI, CAIDE, CogDrisk, LIBRA, LIBRA2, UKBDRS and Lancet-based dementia risk scores in the HUNT study. J Prev Alzheimers Dis. 2026;13(4):100524.
Andrade C. Understanding the difference between standard deviation and standard error of the mean, and knowing when to use which. Indian J Psychol Med. 2020;42(4):409-15.
Bonett DG, Wright TA. Cronbach’s alpha reliability: interval estimation, hypothesis testing, and sample size planning. J Organ Behav. 2015;36(1):3-15.
Hertzog MA. Considerations in determining sample size for pilot studies. Res Nurs Health. 2008;31(2):180-91.
Anthoine E, Moret L, Regnault A, Sbille V, Hardouin JB. Sample size used to validate a scale: a review of publications on newly-developed patient reported outcomes measures. Health Qual Life Outcomes. 2014;12(1):176.
Piedmont RL. Inter-item correlations. In: Michalos AC, editor. Encyclopedia of Quality of Life and Well-Being Research. Dordrecht: Springer. 2014;3303-4.
Shulruf B, Coombes L, Damodaran A, Freeman A, Jones P, Lieberman S, et al. Cut-scores revisited: feasibility of a new method for group standard setting. BMC Med Educ. 2018;18(1):126.
Deckers K. The role of lifestyle factors in primary prevention of dementia. Maastricht: Maastricht University; 2017. Available at: https://cris.maastrichtuniversity.nl/portal/en/publications/the-role-of-lifestyle-factors-in-primary-prevention-of-dementia(f1239282-82c6-4fea-b756-c1f5521254c5).html. Accessed on 15 June 2026.
Reas ET, Laughlin GA, Bergstrom J, Kritz-Silverstein D, McEvoy LK. Physical activity and trajectories of cognitive change in community-dwelling older adults: the Rancho Bernardo Study. J Alzheimers Dis. 2019;71(1):109-18.
Stinchcombe A, Hammond NG. Social determinants of memory change: a three-year follow-up of the Canadian Longitudinal Study on Aging (CLSA). Arch Gerontol Geriatr. 2023;104:104-12.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Agrima Aggarwal, Neha Jain

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.