New opportunities for the early detection and treatment of cognitive decline: adherence challenges and the promise of smart and person-centered technologies

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Zhe He1, Michael Dieciuc2, Dawn Carr3, Sudipta Chakraborty4, Ankita Singh4, Ibukun E. Fowe2, Shenghao Zhang2, Mia Liza A. Lustria5, Antonio Terracciano6, Neil Charness2, Walter R. Boot2
1School of Information, Florida State University, 142 Collegiate Loop, Tallahassee, FL, 32306, USA
2Department of Psychology, Florida State University, Tallahassee, FL, USA
3Department of Sociology, Florida State University, Tallahassee, FL, USA
4Department of Computer Science, Florida State University, Tallahassee, FL, USA
5School of Behavioral Sciences and Social Medicine, Florida State University, Tallahassee, FL, USA
6Department of Geriatrics, Florida State University, Tallahassee, FL, USA

Tóm tắt

AbstractEarly detection of age-related cognitive decline has transformative potential to advance the scientific understanding of cognitive impairments and possible treatments by identifying relevant participants for clinical trials. Furthermore, early detection is also key to early intervention once effective treatments have been developed. Novel approaches to the early detection of cognitive decline, for example through assessments administered via mobile apps, may require frequent home testing which can present adherence challenges. And, once decline has been detected, treatment might require frequent engagement with behavioral and/or lifestyle interventions (e.g., cognitive training), which present their own challenges with respect to adherence. We discuss state-of-the-art approaches to the early detection and treatment of cognitive decline, adherence challenges associated with these approaches, and the promise of smart and person-centered technologies to tackle adherence challenges. Specifically, we highlight prior and ongoing work conducted as part of the Adherence Promotion with Person-centered Technology (APPT) project, and how completed work will contribute to the design and development of a just-in-time, tailored, smart reminder system that infers participants’ contexts and motivations, and how ongoing work might build toward a reminder system that incorporates dynamic machine learning algorithms capable of predicting and preventing adherence lapses before they happen. APPT activities and findings will have implications not just for cognitive assessment and training, but for technology-mediated adherence-support systems to facilitate physical exercise, nutrition, medication management, telehealth, and social connectivity, with the potential to broadly improve the engagement, health, and well-being of older adults.

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Tài liệu tham khảo

He W, Goodkind D, Kowal P. An Aging World: 2015. International Population Reports. Author: U.S. Census. Report No.: P95/16–1. Available from: https://www.census.gov/content/dam/Census/library/publications/2016/demo/p95-16-1.pdf. cited 2022 Oct 27

Projected Future Growth of Older Population. 2022. Available from: https://acl.gov/aging-and-disability-in-america/data-and-research/projected-future-growth-older-population. cited 2022 Nov 22

Allaire JC, Marsiske M. Everyday cognition: age and intellectual ability correlates. Psychol Aging. 1999;14(4):627–44.

Ball K, Owsley C, Sloane ME, Roenker DL, Bruni JR. Visual attention problems as a predictor of vehicle crashes in older drivers. Invest Ophthalmol Vis Sci. 1993;34(11):3110–23.

Hering A, Kliegel M, Rendell PG, Craik FIM, Rose NS. Prospective Memory Is a Key Predictor of Functional Independence in Older Adults. J Int Neuropsychol Soc JINS. 2018;24(6):640–5.

Diehl M, Willis SL, Schaie KW. Everyday problem solving in older adults: observational assessment and cognitive correlates. Psychol Aging. 1995;10(3):478–91.

Royall DR, Palmer R, Chiodo LK, Polk MJ. Declining executive control in normal aging predicts change in functional status: the Freedom House Study. J Am Geriatr Soc. 2004;52(3):346–52.

Seshadri S, Wolf PA, Beiser A, Au R, McNulty K, White R, et al. Lifetime risk of dementia and Alzheimer’s disease: The impact of mortality on risk estimates in the Framingham Study. Neurology. 1997;49(6):1498–504.

Brookmeyer R, Abdalla N. Lifetime Risks of Alzheimer’s Disease Dementia Using Biomarkers for Preclinical Disease. Alzheimers Dement J Alzheimers Assoc. 2018;14(8):981–8.

Dementia. 2022. Available from: https://www.who.int/news-room/fact-sheets/detail/dementia. cited 2022 Nov 28

Amjad H, Roth DL, Sheehan OC, Lyketsos CG, Wolff JL, Samus QM. Underdiagnosis of Dementia: an Observational Study of Patterns in Diagnosis and Awareness in US Older Adults. J Gen Intern Med. 2018;33(7):1131–8.

Fong TG, Inouye SK. The inter-relationship between delirium and dementia: the importance of delirium prevention. Nat Rev Neurol. 2022;18(10):579–96.

National Plan to Address Alzheimer’s Disease. ASPE. Available from: https://aspe.hhs.gov/collaborations-committees-advisory-groups/napa/napa-documents/napa-national-plan. cited 2022 Nov 22

Salthouse TA. Implications of Within-Person Variability in Cognitive and Neuropsychological Functioning for the Interpretation of Change. Neuropsychology. 2007;21(4):401–11. https://doi.org/10.1037/0894-4105.21.4.401.

Choi H, Jin KH, Alzheimer’s Disease Neuroimaging Initiative. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 2018;344:103–9.

Pérez-Grijalba V, Romero J, Pesini P, Sarasa L, Monleón I, San-José I, et al. Plasma Aβ42/40 Ratio Detects Early Stages of Alzheimer’s Disease and Correlates with CSF and Neuroimaging Biomarkers in the AB255 Study. J Prev Alzheimers Dis. 2019;6(1):34–41.

Hofer SM, Sliwinski MJ. Two - Design and Analysis of Longitudinal Studies on Aging. In: Birren JE, Schaie KW, Abeles RP, Gatz M, Salthouse TA, editors. Handbook of the Psychology of Aging. 6th ed. Burlington: Academic Press; 2006. p. 15–37. Available from: https://www.sciencedirect.com/science/article/pii/B9780121012649500057. cited 2022 Nov 4.

Sliwinski M, Hoffman L, Hofer SM. Evaluating Convergence of Within-Person Change and Between-Person Age Differences in Age-Heterogeneous Longitudinal Studies. Res Hum Dev. 2010;7(1):45–60.

Stawski RS, MacDonald SWS, Sliwinski MJ. Measurement Burst Design. In: The Encyclopedia of Adulthood and Aging. John Wiley & Sons, Ltd; 2015. p. 1–5. Available from: https://doi.org/10.1002/9781118521373.wbeaa313. cited 2022 Nov 4

Sliwinski MJ, Mogle JA, Hyun J, Munoz E, Smyth JM, Lipton RB. Reliability and Validity of Ambulatory Cognitive Assessments. Assessment. 2018;25(1):14–30.

Kalafatis C, Modarres MH, Apostolou P, Tabet N, Khaligh-Razavi SM. The Use of a Computerized Cognitive Assessment to Improve the Efficiency of Primary Care Referrals to Memory Services: Protocol for the Accelerating Dementia Pathway Technologies (ADePT) Study. JMIR Res Protoc. 2022;11(1):e34475.

Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1–32.

Brown MT, Bussell JK. Medication adherence: WHO cares? Mayo Clin Proc. 2011;86(4):304–14.

Middleton KR, Anton SD, Perri MG. Long-Term Adherence to Health Behavior Change. Am J Lifestyle Med. 2013;7(6):395–404.

Otsuki M, Tinsley BJ, Chao RK, Unger JB. An ecological perspective on smoking among Asian American college students: The roles of social smoking and smoking motives. Psychol Addict Behav. 2008;22:514–23.

Schüz N, Walters JAE, Frandsen M, Bower J, Ferguson SG. Compliance with an EMA monitoring protocol and its relationship with participant and smoking characteristics. Nicotine Tob Res Off J Soc Res Nicotine Tob. 2014;16(Suppl 2):S88-92.

Jongstra S, Wijsman LW, Cachucho R, Hoevenaar-Blom MP, Mooijaart SP, Richard E. Cognitive Testing in People at Increased Risk of Dementia Using a Smartphone App: The iVitality Proof-of-Principle Study. JMIR MHealth UHealth. 2017;5(5):e6939.

Koo BM, Vizer LM. Mobile Technology for Cognitive Assessment of Older Adults: A Scoping Review. Innov Aging. 2019;3(1):igy038.

Willis SL, Caskie GIL. Reasoning training in the ACTIVE study: how much is needed and who benefits? J Aging Health. 2013;25(8 Suppl):43S-64S.

Bagwell DK, West RL. Assessing compliance: active versus inactive trainees in a memory intervention. Clin Interv Aging. 2008;3(2):371–82.

Owen AM, Hampshire A, Grahn JA, Stenton R, Dajani S, Burns AS, et al. Putting brain training to the test. Nature. 2010;465(7299):775–8.

Hardy JL, Nelson RA, Thomason ME, Sternberg DA, Katovich K, Farzin F, et al. Enhancing Cognitive Abilities with Comprehensive Training: A Large, Online, Randomized, Active-Controlled Trial. PLoS ONE. 2015;10(9):e0134467.

Corbett A, Owen A, Hampshire A, Grahn J, Stenton R, Dajani S, et al. The Effect of an Online Cognitive Training Package in Healthy Older Adults: An Online Randomized Controlled Trial. J Am Med Dir Assoc. 2015;16(11):990–7.

Boot W, Champion M, Blakely D, Wright T, Souders D, Charness N. Video Games as a Means to Reduce Age-Related Cognitive Decline: Attitudes, Compliance, and Effectiveness. Front Psychol. 2013;4. Available from: https://doi.org/10.3389/fpsyg.2013.00031 cited 2022 Nov 28

Mewborn CM, Lindbergh CA, Stephen ML. Cognitive Interventions for Cognitively Healthy, Mildly Impaired, and Mixed Samples of Older Adults: A Systematic Review and Meta-Analysis of Randomized-Controlled Trials. Neuropsychol Rev. 2017;27(4):403–39.

Nguyen L, Murphy K, Andrews G. Immediate and long-term efficacy of executive functions cognitive training in older adults: A systematic review and meta-analysis. Psychol Bull. 2019;145(7):698–733.

Nguyen L, Murphy K, Andrews G. A Game a Day Keeps Cognitive Decline Away? A Systematic Review and Meta-Analysis of Commercially-Available Brain Training Programs in Healthy and Cognitively Impaired Older Adults. Neuropsychol Rev. 2022;32(3):601–30.

Trenorden KI, Hull MJ, Lampit A, Greaves D, Keage HAD. Older adults’ experiences of a computerised cognitive training intervention: a mixed methods study. Aust J Psychol. 2022;74(1):2036581.

Wurst R, Maliezefski A, Ramsenthaler C, Brame J, Fuchs R. Effects of Incentives on Adherence to a Web-Based Intervention Promoting Physical Activity: Naturalistic Study. J Med Internet Res. 2020;22(7):e18338.

Losina E, Smith SR, Usiskin IM, Klara KM, Michl GL, Deshpande BR, et al. Implementation of a workplace intervention using financial rewards to promote adherence to physical activity guidelines: a feasibility study. BMC Public Health. 2017;17(1):921.

Mitchell MS, Goodman JM, Alter DA, John LK, Oh PI, Pakosh MT, et al. Financial Incentives for Exercise Adherence in Adults: Systematic Review and Meta-Analysis. Am J Prev Med. 2013;45(5):658–67.

DeKoekkoek T, Given B, Given CW, Ridenour K, Schueller M, Spoelstra SL. mHealth SMS text messaging interventions and to promote medication adherence: an integrative review. J Clin Nurs. 2015;24(19–20):2722–35.

Newton NC, Debenham J, Slade T, Smout A, Grummitt L, Sunderland M, et al. Effect of Selective Personality-Targeted Alcohol Use Prevention on 7-Year Alcohol-Related Outcomes Among High-risk Adolescents: A Secondary Analysis of a Cluster Randomized Clinical Trial. JAMA Netw Open. 2022;5(11):e2242544.

Lustria MLA, Noar SM, Cortese J, Van Stee SK, Glueckauf RL, Lee J. A Meta-Analysis of Web-Delivered Tailored Health Behavior Change Interventions. J Health Commun. 2013;18(9):1039–69.

Lustria MLA, Cortese J, Noar SM, Glueckauf RL. Computer-tailored health interventions delivered over the web: Review and analysis of key components. Patient Educ Couns. 2009;74(2):156–73.

Lustria MLA, Cortese J, Gerend MA, Schmitt K, Kung YM, McLaughlin C. A model of tailoring effects: A randomized controlled trial examining the mechanisms of tailoring in a web-based STD screening intervention. Health Psychol. 2016;35:1214–24.

Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med Publ Soc Behav Med. 2018;52(6):446–62.

Oikonomidi T, Ravaud P, LeBeau J, Tran VT. A systematic scoping review of just-in-time, adaptive interventions (JITAIs) finds limited automation and incomplete reporting. J Clin Epidemiol. 2022;S0895–4356(22):00324–9.

Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, et al. Activity sensing in the wild: a field trial of ubifit garden. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery; 2008. p. 1797–806. (CHI ‘08). Available from: https://doi.org/10.1145/1357054.1357335. cited 2023 Jan 12

Gustafson DH, McTavish FM, Chih MY, Atwood AK, Johnson RA, Boyle MG, et al. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiat. 2014;71(5):566–72.

Ben-Zeev D, Kaiser SM, Brenner CJ, Begale M, Duffecy J, Mohr DC. Development and usability testing of FOCUS: a smartphone system for self-management of schizophrenia. Psychiatr Rehabil J. 2013;36(4):289–96.

Riley W, Obermayer J, Jean-Mary J. Internet and mobile phone text messaging intervention for college smokers. J Am Coll Health J ACH. 2008;57(2):245–8.

Patrick K, Raab F, Adams MA, Dillon L, Zabinski M, Rock CL, et al. A text message-based intervention for weight loss: randomized controlled trial. J Med Internet Res. 2009;11(1):e1.

Charness N, Boot W, Carr D, Chakraborty S, He Z, Lustria M, et al. Aims of the Adherence Promotion With Person-Centered Technology (APPT) Project. Innov Aging. 2021;5(Suppl 1):551.

Carr DC, Tian S, He Z, Chakraborty S, Dieciuc M, Gray N, et al. Motivation to Engage in Aging Research: Are There Typologies and Predictors? Gerontologist. 2022;62(10):1466–76.

Fowe IE, Boot WR. Understanding Older Adults’ Attitudes toward Mobile and Wearable Technologies to Support Health and Cognition. Front Psychol. 2022;13:1036092.

Dieciuc M, Zhang S, Gray N, Dilanchian A, Carr D, Lustria M, et al. A Qualitative Understanding of Motivations, Preferences, and Attitudes Toward Adherence-Based Technology. Innov Aging. 2021;5(Supplement_1):552.

He Z, Tian S, Singh A, Chakraborty S, Zhang S, Lustria MLA, et al. A Machine-Learning Based Approach for Predicting Older Adults’ Adherence to Technology-Based Cognitive Training. Inf Process Manag. 2022;59(5):103034.

Singh A, Chakraborty S, He Z, Tian S, Zhang S, Lustria MLA, et al. Deep learning-based predictions of older adults’ adherence to cognitive training to support training efficacy. Front Psychol. 2022;13. Available from: https://doi.org/10.3389/fpsyg.2022.980778. cited 2022 Nov 21

Harrell ER, Roque NA, Boot WR, Charness N. Investigating message framing to improve adherence to technology-based cognitive interventions. Psychol Aging. 2021;36:974–82.

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