Skip to main content

Advertisement

SpringerLink
Log in

Advancing Cognitive Health in Aging Populations by Leveraging Digital Assessment

  • Published:
Journal of Health Service Psychology Aims and scope Submit manuscript

Abstract

As the number of older adults is expected to rise, there exists a need for accessible tools that capture nuanced changes in cognition. Modern digital assessment tools provide a flexible, cost-effective framework that captures the trajectories, dynamics, and dimensionality of individual cognitive profiles, which can be employed both in clinics and even in home environments. These next-generation tools allow for early and accurate identification of preclinical indicators of dementia. Elucidated through the experiences of Alex and his wife Isabella, modern technology unveils an alternative model that will undoubtedly play a major role in promoting and monitoring cognitive health across the lifespan.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., & Vecchio, A. (2012). A smartphone-based fall detection system. Pervasive and Mobile Computing, 8(6), 883–899. https://doi.org/10.1016/j.pmcj.2012.08.003

    Article  Google Scholar 

  • Alzheimer’s and Related Dementias Resources for Professionals. (2023). National Institute on Aging. https://www.nia.nih.gov/health/health-care-professionals-information/alzheimers-and-related-dementias-resources

  • Amieva, H., & Ouvrard, C. (2020). Does treating hearing loss in older adults improve cognitive outcomes? A review. Journal of Clinical Medicine, 9(3). https://doi.org/10.3390/jcm9030805

  • Amini, S., Hao, B., Zhang, L., Song, M., Gupta, A., Karjadi, C., Kolachalama, V. B., Au, R., & Paschalidis, I. Ch. (2023). Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach. Alzheimer’s & Dementia, 19(3), 946–955. https://doi.org/10.1002/alz.12721

    Article  Google Scholar 

  • Anderson, M. & Perrin, A. (2017). Tech Adoption Climbs Among Older Adults. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/2017/05/17/tech-adoption-climbs-among-older-adults/

  • Barnett, J. H., Blackwell, A. D., Sahakian, B. J., & Robbins, T. W. (2016). The paired associates learning (PAL) test: 30 years of CANTAB translational neuroscience from laboratory to bedside in dementia research. Current Topics in Behavioral Neurosciences, 28, 449–474. https://doi.org/10.1007/7854_2015_5001

    Article  CAS  PubMed  Google Scholar 

  • Bastawrous, A., Rono, H. K., Livingstone, I. A. T., Weiss, H. A., Jordan, S., Kuper, H., & Burton, M. J. (2015). Development and validation of a smartphone-based visual acuity test (Peek Acuity) for clinical practice and community-based fieldwork. JAMA Ophthalmology, 133(8), 930. https://doi.org/10.1001/jamaophthalmol.2015.1468

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Berron, D., Ziegler, G., Vieweg, P., Billette, O., Güsten, J., Grande, X., Heneka, M. T., Schneider, A., Teipel, S., Jessen, F., Wagner, M., & Düzel, E. (2022). Feasibility of digital memory assessments in an unsupervised and remote study setting. Frontiers in Digital Health, 4, 892997. https://doi.org/10.3389/fdgth.2022.892997

    Article  PubMed  PubMed Central  Google Scholar 

  • Bertone, A., Bettinelli, L., & Faubert, J. (2007). The impact of blurred vision on cognitive assessment. Journal of Clinical and Experimental Neuropsychology, 29(5), 467–476.

    Article  PubMed  Google Scholar 

  • Betancourt, J. R., Green, A. R., & Carrillo, J. E. (2002). Cultural competence in health care: Emerging frameworks and practical approaches (Vol. 576). Commonwealth Fund, Quality of Care for Underserved Populations New York, NY.

  • Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., Friend, S. H., & Trister, A. D. (2016). The mPower study, Parkinson disease mobile data collected using ResearchKit. Scientific Data, 3(1), 160011. https://doi.org/10.1038/sdata.2016.11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Casanova, R., Saldana, S., Lutz, M. W., Plassman, B. L., Kuchibhatla, M., & Hayden, K. M. (2020). Investigating predictors of cognitive decline using machine learning. The Journals of Gerontology: Series B, 75(4), 733–742. https://doi.org/10.1093/geronb/gby054

    Article  Google Scholar 

  • Chan, J. Y. C., Yau, S. T. Y., Kwok, T. C. Y., & Tsoi, K. K. F. (2021). Diagnostic performance of digital cognitive tests for the identification of MCI and dementia: A systematic review. Ageing Research Reviews, 72, 101506. https://doi.org/10.1016/j.arr.2021.101506

    Article  PubMed  Google Scholar 

  • Christianson, K., Prabhu, M., Popp, Z. T., Rahman, M. S., Drane, J., Lee, M., Lathan, C., Lin, H., Au, R., & Sunderaraman, P. (2023). Adherence type impacts completion rates of frequent mobile cognitive assessments among older adults with and without cognitive impairment. Research Square.

  • Dahmen, J., Cook, D., Fellows, R., & Schmitter-Edgecombe, M. (2017). An analysis of a digital variant of the Trail Making Test using machine learning techniques. Technology and Health Care, 25(2), 251–264. https://doi.org/10.3233/THC-161274

    Article  PubMed  PubMed Central  Google Scholar 

  • Ding, H., Mandapati, A., Hamel, A. P., Karjadi, C., Ang, T. F. A., Xia, W., Au, R., & Lin, H. (2023). Multimodal machine learning for 10-year dementia risk prediction: The Framingham heart study. Journal of Alzheimer’s Disease, 96(1), 277–286. https://doi.org/10.3233/JAD-230496

    Article  PubMed  Google Scholar 

  • Duke Han, S., Nguyen, C. P., Stricker, N. H., & Nation, D. A. (2017). Detectable neuropsychological differences in early preclinical Alzheimer’s disease: A meta-analysis. Neuropsychology Review, 27(4), 305–325. https://doi.org/10.1007/s11065-017-9345-5

    Article  CAS  PubMed  Google Scholar 

  • Fox, R. S., Manly, J. J., Slotkin, J., Devin Peipert, J., & Gershon, R. C. (2021). Reliability and validity of the Spanish-language version of the NIH Toolbox. Assessment, 28(2), 457–471. https://doi.org/10.1177/1073191120913943

    Article  PubMed  Google Scholar 

  • Gammon, K. (2023). Undiagnosed: More than 7 million Americans u | EurekAlert! https://www.eurekalert.org/news-releases/1005499

  • Gates, N., Valenzuela, M., Sachdev, P., & Singh, F. (2014). Psychological well-being in individuals with mild cognitive impairment. Clinical Interventions in Aging, 779. https://doi.org/10.2147/CIA.S58866

  • Ghazirad, M., Hewitt, O., & Walden, S. (2022). What outcome measures are most useful in measuring the effectiveness of anti-dementia medication in people with intellectual disabilities and dementia? Advances in Mental Health and Intellectual Disabilities, 16(2), 87–101. https://doi.org/10.1108/AMHID-10-2021-0038

    Article  Google Scholar 

  • Goetz, C. G., Tilley, B. C., Shaftman, S. R., Stebbins, G. T., Fahn, S., Martinez‐Martin, P., Poewe, W., Sampaio, C., Stern, M. B., Dodel, R., Dubois, B., Holloway, R., Jankovic, J., Kulisevsky, J., Lang, A. E., Lees, A., Leurgans, S., LeWitt, P. A., Nyenhuis, D., … LaPelle, N. (2008). Movement Disorder Society‐sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS): Scale presentation and clinimetric testing results. Movement Disorders, 23(15), 2129–2170. https://doi.org/10.1002/mds.22340

  • Gold, M., Amatniek, J., Carrillo, M. C., Cedarbaum, J. M., Hendrix, J. A., Miller, B. B., Robillard, J. M., Rice, J. J., Soares, H., Tome, M. B., Tarnanas, I., Vargas, G., Bain, L. J., & Czaja, S. J. (2018). Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer’s disease clinical trials. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 4(1), 234–242. https://doi.org/10.1016/j.trci.2018.04.003

    Article  Google Scholar 

  • Haque, R. U., Pongos, A. L., Manzanares, C. M., Lah, J. J., Levey, A. I., & Clifford, G. D. (2021). Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment. IEEE Transactions on Biomedical Engineering, 68(1), 11–18. https://doi.org/10.1109/TBME.2020.2990734

    Article  PubMed  Google Scholar 

  • Honea, R. A., Vidoni, E. D., Swerdlow, R. H., & Burns, J. M. (2012). Maternal Family History is Associated with Alzheimer’s Disease Biomarkers. Journal of Alzheimer’s Disease : JAD, 31(3), 659–668. https://doi.org/10.3233/JAD-2012-120676

    Article  CAS  PubMed  Google Scholar 

  • Houts, C. R., Patrick-Lake, B., Clay, I., & Wirth, R. J. (2020). The Path Forward for Digital Measures: Suppressing the Desire to Compare Apples and Pineapples. Digital Biomarkers, 4(Suppl. 1), 3–12. https://doi.org/10.1159/000511586

    Article  PubMed  PubMed Central  Google Scholar 

  • Ichii, S., Nakamura, T., Kawarabayashi, T., Takatama, M., Ohgami, T., Ihara, K., & Shoji, M. (2020). CogEvo, a cognitive function balancer, is a sensitive and easy psychiatric test battery for age‐related cognitive decline. Geriatrics & Gerontology International, 20(3), 248–255. https://doi.org/10.1111/ggi.13847

    Article  Google Scholar 

  • Jayakumar, S., Maniglia, M., Guan, Z., Green, S., & Aaron, S. (2024). PLFest: A new platform for accessible, reproducible and open perceptual learning research. https://doi.org/10.31234/osf.io/kmh87

  • Kandiah, N., Zhang, A., Bautista, D. C., Silva, E., Ting, S. K. S., Ng, A., & Assam, P. (2016). Early detection of dementia in multilingual populations: Visual Cognitive Assessment Test (VCAT). Journal of Neurology, Neurosurgery, and Psychiatry, 87(2), 156–160. https://doi.org/10.1136/jnnp-2014-309647

    Article  PubMed  Google Scholar 

  • Korolev, I. O., Symonds, L. L., Bozoki, A. C., & Alzheimer’s Disease Neuroimaging Initiative. (2016). Predicting progression from mild cognitive impairment to Alzheimer’s dementia using clinical, MRI, and plasma biomarkers via probabilistic pattern classification. PloS One, 11(2), e0138866.

    Article  PubMed  PubMed Central  Google Scholar 

  • Krafka, K., Khosla, A., Kellnhofer, P., Kannan, H., Bhandarkar, S., Matusik, W., & Torralba, A. (2016). Eye tracking for everyone. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2176–2184. https://doi.org/10.1109/CVPR.2016.239

    Article  Google Scholar 

  • Lee, A. T. C., Richards, M., Chan, W. C., Chiu, H. F. K., Lee, R. S. Y., & Lam, L. C. W. (2020). Higher dementia incidence in older adults with poor visual acuity. The Journals of Gerontology: Series A, 75(11), 2162–2168. https://doi.org/10.1093/gerona/glaa036

    Article  Google Scholar 

  • Lelo de Larrea-Mancera, E. S., Stavropoulos, T., Hoover, E. C., Eddins, D. A., Gallun, F. J., & Seitz, A. R. (2020). Portable Automated Rapid Testing (PART) for auditory assessment: Validation in a young adult normal-hearing population. The Journal of the Acoustical Society of America, 148(4), 1831–1851. https://doi.org/10.1121/10.0002108

    Article  PubMed  PubMed Central  Google Scholar 

  • Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., Brayne, C., Burns, A., Cohen-Mansfield, J., Cooper, C., Costafreda, S. G., Dias, A., Fox, N., Gitlin, L. N., Howard, R., Kales, H. C., Kivimäki, M., Larson, E. B., Ogunniyi, A., … Mukadam, N. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet, 396(10248), 413–446. https://doi.org/10.1016/S0140-6736(20)30367-6

  • Loughrey, D. G., Kelly, M. E., Kelley, G. A., Brennan, S., & Lawlor, B. A. (2018). Association of age-related hearing loss with cognitive function, cognitive impairment, and dementia: A systematic review and meta-analysis. JAMA Otolaryngology–Head & Neck Surgery, 144(2), 115–126. https://doi.org/10.1001/jamaoto.2017.2513

    Article  Google Scholar 

  • Lumsden, J., Edwards, E. A., Lawrence, N. S., Coyle, D., & Munafò, M. R. (2016). Gamification of cognitive assessment and cognitive Ttaining: A systematic review of applications and efficacy. JMIR Serious Games, 4(2), e5888. https://doi.org/10.2196/games.5888

    Article  Google Scholar 

  • Manly, J. J., Jones, R. N., Langa, K. M., Ryan, L. H., Levine, D. A., McCammon, R., Heeringa, S. G., & Weir, D. (2022). Estimating the prevalence of dementia and mild cognitive impairment in the US: the 2016 health and retirement study harmonized cognitive assessment protocol project. JAMA Neurology, 79(12), 1242–1249. https://doi.org/10.1001/jamaneurol.2022.3543

    Article  PubMed  PubMed Central  Google Scholar 

  • Martyr, A., Nelis, S. M., Quinn, C., Wu, Y.-T., Lamont, R. A., Henderson, C., Clarke, R., Hindle, J. V., Thom, J. M., Jones, I. R., Morris, R. G., Rusted, J. M., Victor, C. R., & Clare, L. (2018). Living well with dementia: A systematic review and correlational meta-analysis of factors associated with quality of life, well-being and life satisfaction in people with dementia. Psychological Medicine, 48(13), 2130–2139. https://doi.org/10.1017/S0033291718000405

    Article  PubMed  Google Scholar 

  • Matthews, K. A., Xu, W., Gaglioti, A. H., Holt, J. B., Croft, J. B., Mack, D., & McGuire, L. C. (2019). Racial and ethnic estimates of Alzheimer’s disease and related dementias in the United States (2015–2060) in adults aged ≥65 years. Alzheimer’s & Dementia, 15(1), 17–24. https://doi.org/10.1016/j.jalz.2018.06.3063

    Article  Google Scholar 

  • Medici, A. C. (2021). Health sector challenges and policies in the context of ageing populations. United Nations Department of Economic and Social Affairs.

  • Molitor, R. J., Ko, P. C., & Ally, B. A. (2015). Eye movements in Alzheimer’s disease. Journal of Alzheimer’s Disease, 44(1), 1–12.

    Article  PubMed  Google Scholar 

  • Ng, K. P., Chiew, H. J., Lim, L., Rosa-Neto, P., Kandiah, N., & Gauthier, S. (2018). The influence of language and culture on cognitive assessment tools in the diagnosis of early cognitive impairment and dementia. Expert Review of Neurotherapeutics, 18(11), 859–869. https://doi.org/10.1080/14737175.2018.1532792

    Article  CAS  PubMed  Google Scholar 

  • Nichols, E., Steinmetz, J. D., Vollset, S. E., Fukutaki, K., Chalek, J., Abd-Allah, F., Abdoli, A., Abualhasan, A., Abu-Gharbieh, E., Akram, T. T., Al Hamad, H., Alahdab, F., Alanezi, F. M., Alipour, V., Almustanyir, S., Amu, H., Ansari, I., Arabloo, J., Ashraf, T., … Vos, T. (2022). Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. The Lancet Public Health, 7(2), e105–e125. https://doi.org/10.1016/S2468-2667(21)00249-8

  • Öhman, F., Hassenstab, J., Berron, D., Schöll, M., & Papp, K. V. (2021). Current advances in digital cognitive assessment for preclinical Alzheimer’s disease. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1), e12217. https://doi.org/10.1002/dad2.12217

    Article  Google Scholar 

  • Opwonya, J., Doan, D. N. T., Kim, S. G., Kim, J. I., Ku, B., Kim, S., Park, S., & Kim, J. U. (2022). Saccadic eye movement in mild cognitive impairment and Alzheimer’s disease: A systematic review and meta-analysis. Neuropsychology Review, 32(2), 193–227. https://doi.org/10.1007/s11065-021-09495-3

    Article  PubMed  Google Scholar 

  • Parsey, C. M., & Schmitter-Edgecombe, M. (2013). Applications of technology in neuropsychological assessment. The Clinical Neuropsychologist, 27(8), 1328–1361. https://doi.org/10.1080/13854046.2013.834971

    Article  PubMed  Google Scholar 

  • Peek, S. T., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., Van Der Voort, C. S., Aarts, S., Van Hoof, J., Vrijhoef, H. J., & Wouters, E. J. (2016). Older adults’ reasons for using technology while aging in place. Gerontology, 62(2), 226–237.

    Article  PubMed  Google Scholar 

  • Peng, Z. E., Waz, S., Buss, E., Shen, Y., Richards, V., Bharadwaj, H., Stecker, G. C., Beim, J. A., Bosen, A. K., Braza, M. D., Diedesch, A. C., Dorey, C. M., Dykstra, A. R., Gallun, F. J., Goldsworthy, R. L., Gray, L., Hoover, E. C., Ihlefeld, A., Koelewijn, T., … Venezia, J. H. (2022). FORUM: Remote testing for psychological and physiological acoustics. The Journal of the Acoustical Society of America, 151(5), 3116–3128. https://doi.org/10.1121/10.0010422

  • Popp, Z. T., Low, S., Kolachalama, V. B., Lin, H., Rahman, M. S., Prabhu, M., Chan, C., Salgado, M., & Au, R. (2023). Multimodal, longitudinal digital brain health platform using participant-driven study design. Alzheimer’s & Dementia, 19(S4), e066121. https://doi.org/10.1002/alz.066121

    Article  Google Scholar 

  • Possin, K. L., Moskowitz, T., Erlhoff, S. J., Rogers, K. M., Johnson, E. T., Steele, N. Z. R., Higgins, J. J., Stiver, J., Alioto, A. G., Farias, S. T., Miller, B. L., & Rankin, K. P. (2018). The Brain Health Assessment for detecting and diagnosing neurocognitive disorders. Journal of the American Geriatrics Society, 66(1), 150–156. https://doi.org/10.1111/jgs.15208

    Article  PubMed  PubMed Central  Google Scholar 

  • Pywell, J., Vijaykumar, S., Dodd, A., & Coventry, L. (2020). Barriers to older adults’ uptake of mobile-based mental health interventions. DIGITAL HEALTH, 6, 205520762090542. https://doi.org/10.1177/2055207620905422

    Article  Google Scholar 

  • Rajan, K. B., Weuve, J., Barnes, L. L., McAninch, E. A., Wilson, R. S., & Evans, D. A. (2021). Population estimate of people with clinical Alzheimer’s disease and mild cognitive impairment in the United States (2020–2060). Alzheimer’s & Dementia, 17(12), 1966–1975. https://doi.org/10.1002/alz.12362

    Article  Google Scholar 

  • Rasmussen, J., & Langerman, H. (2019). Alzheimer’s disease – Why we need early diagnosis. Degenerative Neurological and Neuromuscular Disease, Volume 9, 123–130. https://doi.org/10.2147/DNND.S228939

    Article  PubMed  PubMed Central  Google Scholar 

  • Reitan, R. M. (1971). Trail making test results for normal and brain-damaged children. Perceptual and Motor Skills, 33(2), 575–581. https://doi.org/10.2466/pms.1971.33.2.575

    Article  CAS  PubMed  Google Scholar 

  • Rentz, D. M., Parra Rodriguez, M. A., Amariglio, R., Stern, Y., Sperling, R., & Ferris, S. (2013). Promising developments in neuropsychological approaches for the detection of preclinical Alzheimer’s disease: A selective review. Alzheimer’s Research & Therapy, 5(6), 58. https://doi.org/10.1186/alzrt222

    Article  Google Scholar 

  • Rodríguez-Salgado, A. M., Llibre-Guerra, J. J., Tsoy, E., Peñalver-Guia, A. I., Bringas, G., Erlhoff, S. J., Kramer, J. H., Allen, I. E., Valcour, V., Miller, B. L., Llibre-Rodríguez, J. J., & Possin, K. L. (2021). A brief digital cognitive assessment for detection of cognitive impairment in Cuban older adults. Journal of Alzheimer’s Disease, 79(1), 85–94. https://doi.org/10.3233/JAD-200985

    Article  PubMed  Google Scholar 

  • Romano, M. F., Shih, L. C., Paschalidis, I. C., Au, R., & Kolachalama, V. B. (2023). Large language models in neurology research and future practice. Neurology, 101(23), 1058–1067. https://doi.org/10.1212/WNL.0000000000207967

    Article  PubMed  PubMed Central  Google Scholar 

  • Silverstein, S. M., Harms, M. P., Carter, C. S., Gold, J. M., Keane, B. P., MacDonald, A., Daniel Ragland, J., & Barch, D. M. (2015). Cortical contributions to impaired contour integration in schizophrenia. Neuropsychologia, 75, 469–480. https://doi.org/10.1016/j.neuropsychologia.2015.07.003

    Article  PubMed  PubMed Central  Google Scholar 

  • Valladares-Rodriguez, S., Fernández-Iglesias, M. J., Anido-Rifón, L., Facal, D., Rivas-Costa, C., & Pérez-Rodríguez, R. (2019). Touchscreen games to detect cognitive impairment in senior adults. A user-interaction pilot study. International Journal of Medical Informatics, 127, 52–62. https://doi.org/10.1016/j.ijmedinf.2019.04.012

    Article  PubMed  Google Scholar 

  • Veneziani, I., Marra, A., Formica, C., Grimaldi, A., Marino, S., Quartarone, A., & Maresca, G. (2024). Applications of artificial intelligence in the neuropsychological assessment of dementia: A systematic review. Journal of Personalized Medicine, 14(1), Article 1. https://doi.org/10.3390/jpm14010113

  • Verghese, J., Noone, M. L., Johnson, B., Ambrose, A. F., Wang, C., Buschke, H., Pradeep, V. G., Salam, K. A., Shaji, K. S., & Mathuranath, P. S. (2012). Picture-based memory impairment screen for dementia. Journal of the American Geriatrics Society, 60(11), 2116–2120. https://doi.org/10.1111/j.1532-5415.2012.04191.x

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang, L., Zhang, Z., McArdle, J. J., & Salthouse, T. A. (2008). Investigating ceiling effects in longitudinal data analysis. Multivariate Behavioral Research, 43(3), 476–496. https://doi.org/10.1080/00273170802285941

    Article  CAS  Google Scholar 

  • Ward, A., Tardiff, S., Dye, C., & Arrighi, H. M. (2013). Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia: A systematic review of the literature. Dementia and Geriatric Cognitive Disorders Extra, 3(1), 320–332. https://doi.org/10.1159/000354370

    Article  PubMed  PubMed Central  Google Scholar 

  • Wolf, A., Tripanpitak, K., Umeda, S., & Otake-Matsuura, M. (2023). Eye-tracking paradigms for the assessment of mild cognitive impairment: A systematic review. Frontiers in Psychology, 14, 1197567. https://doi.org/10.3389/fpsyg.2023.1197567

    Article  PubMed  PubMed Central  Google Scholar 

  • Wouters, H., Zwinderman, A. H., Van Gool, W. A., Schmand, B., & Lindeboom, R. (2009). Adaptive cognitive testing in dementia. International Journal of Methods in Psychiatric Research, 18(2), 118–127. https://doi.org/10.1002/mpr.283

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported by the Canadian Institutes of Health Research (CIHR) Postdoctoral Fellowship (#176576; Canada), the Fonds de recherche du Québec – Santé (FRQS) Postdoctoral Fellowship (#305855; Canada) awarded to DT. The other authors have no conflict of interest to disclose.

Author information

Authors and Affiliations

  1. Boston, MA, USA

    Quentin Coppola, Marc Yangüez, Domenico Tullo, Susanne Jaeggi & Aaron Seitz

Authors
  1. Quentin Coppola
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marc Yangüez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Domenico Tullo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Susanne Jaeggi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aaron Seitz
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Coppola, Q., Yangüez, M., Tullo, D. et al. Advancing Cognitive Health in Aging Populations by Leveraging Digital Assessment. J Health Serv Psychol 50, 47–58 (2024). https://doi.org/10.1007/s42843-024-00102-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42843-024-00102-6

Keywords

Search

Navigation