Abstract
The presence of technology in neuropsychology and health care is now ubiquitous. If we are to provide the highest quality of care to our aging population, we must consider technological advances in our future planning and implementation. Technology has the potential to offer innovations for home-based prevention, early detection, compensation, independent living, safety and security, behavioral change, social support, and caregiver aid. Neuropsychologists are well positioned to play an important role in the development, evaluation, and dissemination of these technologies. In this chapter, we describe research demonstrating how both simple and more complex technology can be used in the real-world environment to proactively promote healthy lifestyle behaviors and improve autonomy, functional health, and quality of life. We illustrate how technology can provide opportunities for collecting continuous real-time assessment data, developing more ecologically valid assessment techniques, providing context-aware in-the-moment interventions, and monitoring real-world responses to therapy or rehabilitation techniques. We also discuss challenges that remain to be addressed in relation to technology-enabled real-world assessment and intervention.
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References
Ahmad, M., Eckert, C., & Teredesai, A. (2018). Interpretable machine learning in healthcare. In Proceedings of the 2018 Association for Computing Machinery International Conference on Bioinformatics, Computational Biology, and Health Informatics, New York, NY, USA, 559–560. https://doi.org/10.1145/3233547.3233667
Alami, H., Lehoux, P., Auclair, Y., de Guise, M., Gagnon, M. P., Shaw, J., Roy, D., Fleet, R., Ahmed, M. A. A., & Fortin, J. P. (2020). Artificial intelligence and health technology assessment: Anticipating a new level of complexity. Journal of Medical Internet Research, 22(7), Article e17707. https://doi.org/10.2196/17707
Alberdi, A. A., Weakley, A., Goenaga, A. A., Schmitter-Edgecombe, M., & Cook, D. J. (2018). Automatic assessment of functional health decline in older adults: A smart-home approach. Journal of Biomedical Informatics, 81, 119–130. https://doi.org/10.1016/j.jbi.2018.03.009
American Psychological Association. (2020, May 1). Guidance on psychological tele-assessment during the COVID-19 crisis. http://www.apaservices.org/practice/reimbursement/health-codes/testing/tele-assessment-covid-19
Aminikhanghahi, S., Schmitter-Edgecombe, M., & Cook, D. J. (2020). Context-aware delivery of ecological momentary assessment. IEEE Journal of Biomedical and Health Informatics, 24(4), 1206–1214. https://doi.org/10.1109/JBHI.2019.2937116
Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388. https://doi.org/10.1080/10705511.2017.1406803
Balaskas, A., Schueller, S. M., Cox, A. L., & Doherty, G. (2021). Ecological momentary interventions for mental health: A scoping review. PLoS ONE, 16(3), Article e0248152. https://doi.org/10.1371/journal.pone.0248152
Bent, B., Goldstein, B. A., Kibbe, W. A., & Dunn, J. P. (2020). Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digital Medicine, 3(18). https://doi.org/10.1038/s41746-020-0226-6
Beringer, R., Sixsmith, A., Campo, M., Brown, J., & McCloskey, R. (2011). The “acceptance” of ambient assisted living: Developing an alternate methodology to this limited research lens. In B. Abdulrazak, S. Giroux, B. Bouchard, H. Pigot, & M. Mokhtari (Eds.), Toward useful services for elderly and people with disabilities. ICOST 2011 (Lecture notes in computer science) (Vol. 6719). Springer. https://doi.org/10.1007/978-3-642-21535-3_21
Binette, J., & Vasold, K. (2018, August). 2018 home and community preferences: A national survey of adults age 18-plus. AARP Research. https://www.aarp.org/research/topics/community/info-2018/2018-home-community-preference.html
Blanson Henkemans, O. A., Caine, K. E., Rogers, W. A., Fisk, A. D., Neerincx, M. A., & de Ruyter, B. (2007). Medical monitoring for independent living: User-centered design of smart home technologies for older adults. Med-e-Tel.
Boman, I. L., Persson, A. C., & Bartfai, A. (2016). First steps in designing an all-in-one ICT-based device for persons with cognitive impairment: Evaluation of the first mock-up. BMC Geriatrics, 16, Article 61. https://doi.org/10.1186/s12877-016-0238-x
Braley, R., Fritz, R., van Son, C. R., & Schmitter-Edgecombe, M. (2019). Prompting technology and persons with dementia: The significance of context and communication. The Gerontologist, 59(1), 101–111. https://doi.org/10.1093/geront/gny071
Brault, N., & Saxena, M. (2020). For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth. Journal of Evaluation in Clinical Practice, 27(3), 513–519. https://doi.org/10.1111/jep.13528
Caprani, N., Doherty, A. R., Lee, H., Smeaton, A. F., O’Connor, N. E., & Gurrin, C. (2010, April 10). Designing a touch-screen sensecam browser to support an aging population.10th Conference on Human Factors in Computing Systems Extended Abstracts, 4291–4296. https://doi.org/10.1145/1753846.1754141
Carpenter, R. W., Wycoff, A. M., & Trull, T. J. (2016). Ambulatory assessment: New adventures in characterizing dynamic processes. Assessment, 23(4), 414–424. https://doi.org/10.1177/1073191116632341
Cella, M., Okruszek, U., Lawrence, M., Zarlenga, V., He, Z., & Wykes, T. (2018). Using wearable technology to detect the autonomic signature of illness severity in schizophrenia. Schizophrenia Research, 195, 537–542. https://doi.org/10.1016/j.schres.2017.09.028
Chandler, M. J., Locke, D. E. C., Duncan, N. L., Hanna, S. M., Cuc, A. V., Fields, J. A., Hoffman Snyder, C. R., Lunde, A. M., & Smith, G. E. (2017). Computer versus compensatory calendar training in individuals with mild cognitive impairment: Functional impact in a pilot study. Brain Sciences, 7(12), Article 112. https://doi.org/10.3390/brainsci7090112
Chaytor, N. S., & Fonseca, L. M. (2022). Ambulatory assessment. In T. D. Marcotte, M. Schmitter-Edgecombe, & I. Grant (Eds.), Neuropsychology of everyday functioning (2nd ed.). Guilford Press.
Ching-Show, L. (2008). Technology for care. Gerontechnology, 7(4), 349–350. https://doi.org/10.4017/gt.2008.07.04.008.00
Chudoba, L. A., Church, A. S., Dahmen, J. B., Brown, K. D., & Schmitter-Edgecombe, M. (2019). The development of a manual-based digital memory notebook intervention with case study illustrations. Neuropsychological Rehabilitation, 30(9), 1829–1851. https://doi.org/10.1080/09602011.2019.1611606
Cohen-Mansfield, J., & Biddison, J. (2007). The scope and future trends of gerontechnology: Consumers’ opinions and literature survey. Journal of Technology in Human Services, 25(3), 1–19. https://doi.org/10.1300/J017v25n03_01
Comeau, D. E. (2005, Spring). Proposal for a course of instruction in Gerontechnology within the University of North Texas Gerontology Program: “Gerontechnology: A class on aging with technology” [Capstone]. University of North Texas. https://nanopdf.com/download/gerontechnology-engenharia-de-reabilitaao_pdf
Cook, D. J., & Schmitter-Edgecombe, M. (2021). Fusing ambient and mobile sensor features into a behaviorome for predicting clinical health scores. IEEE Access, 9, 65033–65043. https://doi.org/10.1109/ACCESS.2021.3076362
Cook, D. J., Schmitter-Edgecombe, M., Jonsson, L., & Morant, A. V. (2019). Technology-enabled assessment of functional health. IEEE Reviews in Biomedical Engineering, 12, 319–332. https://doi.org/10.1109/RBME.2018.2851500
Courtney, K. L., Demiris, G., & Hensel, B. K. (2007). Obtrusiveness of information-based assistive technologies as perceived by older adults in residential care facilities: A secondary analysis. Medical Informatics and the Internet in Medicine, 32(3), 241–249. https://doi.org/10.1080/14639230701447735
Czaja, S. J., Sharit, J., Hernandez, M. A., Nair, S. N., & Loewenstein, D. (2010). Variability among older adults in internet health information-seeking performance. Gerontechnology, 9(1), 46–55. https://doi.org/10.4017/gt.2010.09.01.004.00
Czarnuch, S., Cohen, S., Parameswaran, V., & Mihailidis, A. (2013). A real-world deployment of the COACH prompting system. Journal of Ambient Intelligence and Smart Environments, 5(5), 463–478. https://doi.org/10.3233/AIS-130221
Dahmen, J., & Cook, D. J. (2021). Indirectly supervised anomaly detection of clinically meaningful health events from smart home data. ACM Transactions on Intelligent Systems and Technology, 12(2), 1–18. https://doi.org/10.1145/3439870
Dahmen, J., Minor, B., Cook, D., Vo, T., & Edgecombe, M. (2018). Smart home-driven digital memory notebook support of activity self-management for older adults. Gerontechnology, 17(2), 113–125. https://doi.org/10.4017/gt.2018.17.2.005.00
Daly, M., Delaney, L., Doran, P. P., Harmon, C., & MacLachlan, M. (2010). Naturalistic monitoring of the affect-heart rate relationship: A day reconstruction study. Health Psychology, 29(2), 186–195. https://doi.org/10.1037/a0017626
Dassel, K. B., & Schmitt, F. A. (2008). The impact of caregiver executive skills on reports of patient functioning. Gerontologist, 48(6), 781–792. https://doi.org/10.1093/geront/48.6.781
Dawadi, P., Cook, D., & Schmitter-Edgecombe, M. (2013a). Automated cognitive health assessment using smart home monitoring of complex tasks. IEEE Transactions on Systems, Man and Cybernetics, 43, 1302–1313. https://doi.org/10.1109/TSMC.2013.2252338
Dawadi, P., Cook, D., Schmitter-Edgecombe, M., & Parsey, C. (2013b). Automated assessment of cognitive health using smart home technologies. Technology and Health Care, 21, 323–343. https://doi.org/10.3233/THC-130734
Dawadi, P. N., Cook, D. J., & Schmitter-Edgecombe, M. (2016). Modeling patterns of activities using activity curves. Pervasive and Mobile Computing, Special Issue on Big Data Analytics for Smarter Health Care, 28, 51–69. https://doi.org/10.1016/j.pmcj.2015.09.007
De Wit, L., Chandler, M., Amofa, P., DeFeis, B., Mejia, A., O’Shea, D., Locke, D. E. C., Fields, J. A., & Smith, G. E. (2021). Memory support system training in mild cognitive impairment: Predictors of learning and adherence. Neuropsychological Rehabilitation, 31(1), 92–104. https://doi.org/10.1080/09602011.2019.1667833
Devanand, D. P., Liu, X., & Brown, P. J. (2017). Impact of functional deficits in instrumental activities of daily living in mild cognitive impairment: A clinical algorithm to predict progression to dementia. Alzheimer Disease and Associated Disorders, 31(1), 55–61. https://doi.org/10.1097/WAD.0000000000000160
Diehl, M., Hooker, K., & Sliwinski, M. J. (Eds.). (2014). Handbook of intraindividual variability across the life span (1st ed.). Routledge.
Dunn, J., Runge, R., & Snyder, M. (2018). Wearables and the medical revolution. Personalized Medicine, 15(5), 429–448. https://doi.org/10.2217/pme-2018-0044
Fan, D., Ruiz, L. L., Gong, J., & Lach, J. (2018). EHDC: An energy harvesting modeling and profiling platform for body sensor networks. IEEE Journal of Biomedical and Health Informatics, 22(1), 33–39. https://doi.org/10.1109/JBHI.2017.2733549
Fellger, A., Sprint, G., Weeks, D., Crooks, E., & Cook, D. J. (2020). Wearable device-independent next day activity and next night sleep prediction for rehabilitation populations. IEEE Journal of Translational Engineering in Health and Medicine, 8, 1–9. https://doi.org/10.1109/JTEHM.2020.3014564
Feuz, K., & Cook, D. (2017, February 4–9). Modeling skewed class distributions by reshaping the concept space. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Fransisco, CA, USA. https://ojs.aaai.org/index.php/AAAI/article/view/10903
Filkins, B. L., Young Kim, J., Roberts, B., Armstrong, W., Miller, M. A., Hultner, M. L., Castillo, A. P., Ducom, J.-C., Topol, E. J., & Steinhubl, S. R. (2016). Privacy and security in the era of digital health: What should translational researchers know and do about it? American Journal of Translational Research, 8(3), 1560–1580. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4859641/pdf/ajtr0008-1560.pdf
Fox, B. I., & Felkey, B. G. (2016). Keeping up with advancements in health information technology. Hospital Pharmacy, 51(4), 345–346. https://doi.org/10.1310/hpj5104-345
Freedman, M. J., Lester, K. M., McNamara, C., Milby, J. B., & Schumacher, J. E. (2006). Cell phones for ecological momentary assessment with cocaine-addicted homeless patients in treatment. Journal of Substance Abuse Treatment, 30(2), 105–111. https://doi.org/10.1016/j.jsat.2005.10.005
Frings, L., Wagner, K., Maiwald, T., Carius, A., Schinkel, A., Lehmann, C., & Schulze-Bonhage, A. (2008). Early detection of behavioral side effects of antiepileptic treatment using handheld computers. Epilepsy & Behavior, 13(2), 402–406. https://doi.org/10.1016/j.yebeh.2008.04.022
Gates, K. M., & Molenaar, P. C. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. Neuroimage, 63(1), 310–319. https://doi.org/10.1016/j.neuroimage.2012.06.026
Ghods, A., & Cook, D. (2022). PIP: Pictorial interpretable prototype learning for time series classification. IEEE Computational Intelligence, 17(1), 34–45. https://doi.org/10.1109/MCI.2021.3129957
Ghods, A., Caffrey, K., Lin, B., Fraga, K., Fritz, R., Schmitter-Edgecombe, M., Hundhausen, C., & Cook, D. J. (2019). Iterative design of visual analytics for a clinician-in-the-loop smart home. IEEE Journal of Biomedical and Health Informatics, 23(4), 1742–1748. https://doi.org/10.1109/JBHI.2018.2864287
Ghods, A., Shahrokni, A., Ghasemzadeh, H., & Cook, D. (2021). Remote monitoring of the performance status and burden of symptoms of patients with gastrointestinal cancer via a consumer-based activity tracker: Quantitative cohort study. JMIR. Cancer, 7(4), Article e22931. https://doi.org/10.2196/22931
Gietzelt, M., Nemitz, G., Wolf, K., Zu Schwabediseen, H. M., Haux, R., & Marschollek, M. (2009). A clinical study to assess fall risk using a single waist accelerometer. Informatics for Health and Social Care, 34(4), 181–188. https://doi.org/10.3109/17538150903356275
Gordon, A. M., & Mendes, W. B. (2021). A large-scale study of stress, emotions, and blood pressure in daily life using a digital platform. Proceedings of the National Academy of Sciences of the United States of America, 118(31), Article e2105573118. https://doi.org/10.1073/pnas.2105573118
Greenaway, M. C., Duncan, N. L., & Smith, G. E. (2013). The memory support system for mild cognitive impairment: Randomized trial of a cognitive rehabilitation intervention. International Journal of Geriatric Psychiatry, 28(4), 402–409. https://doi.org/10.1002/gps.3838
Guisado-Fernández, E., Giunti, G., Mackey, L. M., Blake, C., & Caulfield, B. M. (2019). Factors influencing the adoption of smart health technologies for people with dementia and their informal caregivers: Scoping review and design framework. JMIR. Aging, 2(1), Article e12192. https://doi.org/10.2196/12192
Ha, T., Tran, J., Liu, S., Jang, H., Jeong, H., Mitbander, R., Huh, H., Qiu, Y., Duong, J., Wang, R. L., Wang, P., Tandon, A., Sirohi, J., & Lu, N. (2019). A chest-laminated ultrathin and stretchable e-tattoo for the measurement of electrocardiogram, seismocardiogram, and cardiac time intervals. Advanced. Science, 6(14), Article 1900290. https://doi.org/10.1002/advs.201900290
Hackett, K., Lehman, S., Divers, R., Ambrogi, M., Gomes, L., Tan, C. C., & Giovannetti, T. (2020). Remind me to remember: A pilot study of a novel smartphone reminder application for older adults with dementia and mild cognitive impairment. Neuropsychological Rehabilitation, 1–29. https://doi.org/10.1080/09602011.2020.1794909
Hayes, T. L., Cobbinah, K., Dishongh, T., Kaye, J. A., Kimel, J., Labhard, M., Leen, T., Lundell, J., Ozertem, U., Pavel, M., Philipose, M., Rhodes, K., & Vurgun, S. (2009). A study of medication-taking and unobtrusive, intelligent reminding. Telemedicine and E-Health, 15(8), 770–776. https://doi.org/10.1089/tmj.2009.0033
Hobert, M. A., Maetzler, W., Aminian, K., & Chiari, L. (2014). Technical and clinical view on ambulatory assessment in Parkinson’s disease. Acta Neurologica Scandinavica, 130(3), 139–147. https://doi.org/10.1111/ane.12248
Hu, Y., Tilke, D., Adams, T., Crandall, A. S., Cook, D. J., & Schmitter-Edgecombe, M. (2016). Smart home in a box: Study of large-scale in-home smart environments deployment. Journal of Reliable Intelligent Environments, 2, 93–106. https://doi.org/10.1007/s40860-016-0021-y
Jones, A., Remmerswaal, D., Verveer, I., Robinson, E., Franken, I. H. A., Wen, C. K. F., & Field, M. (2019). Compliance with ecological momentary assessment protocols in substance users: A meta-analysis. Addiction, 114(4), 609–619. https://doi.org/10.1111/add.14503
Kang, H. G., Mahoney, D. F., Hoening, H., Hirth, V. A., Bonato, P., Hajjar, I., & Lipsitz, L. A. (2010). In situ monitoring of health in older adults: Technologies and issues. Journal of American Geriatrics Society, 58(8), 1579–1586. https://doi.org/10.1111/j.1532-5415.2010.02959.x
Kaushik, P., Intille, S., & Larson, K. (2008). User-adaptive reminders for home-based medical tasks: A case study. Methods of Information in Medicine, 47(3), 203–207.
Kaye, J. (2008). Home based technologies: A new paradigm for conducting dementia prevention trials. Alzheimer’s and Dementia, 4, S60–S66. https://doi.org/10.1016/j.jalz.2007.10.003
King, A. C., & Dwan, C. (2019). Electronic memory aids for people with dementia experiencing prospective memory loss: A review of empirical studies. Dementia, 18(6), 1994–2007. https://doi.org/10.1177/1471301217735180
Kleiman, E. M., Turner, B. J., Fedor, S., Beale, E. E., Huffman, J. C., & Nock, M. K. (2017). Examination of real-time fluctuations in suicidal ideation and its risk factors: Results from two ecological momentary assessment studies. Journal of Abnormal Psychology, 126(6), 726–738. https://doi.org/10.1037/abn0000273
Kraskowsky, L. H., & Finlayson, M. (2001). Factors affecting older adults’ use of adaptive equipment: Review of the literature. American Journal of Occupational Therapy, 55(3), 303–310. https://doi.org/10.5014/ajot.55.3.303
LaMonica, H. M., Davenport, T. A., Roberts, A. E., & Hickie, I. B. (2021). Understanding technology preferences and requirements for health information technologies designed to improve and maintain the mental health and well-being of older adults: Participatory design study. JMIR Aging, 4(1), Article e21461. https://doi.org/10.2196/21461
Liu, X., Fromm, J., Patel, S., & McDuff, D. (2020). Multi-task temporal shift attention networks for on-device contactless vitals measurement (presentation). In Conference on Neural Information Processing Systems, Vancouver, Canada. https://arxiv.org/abs/2006.03790
Low, Z. W. K., Li, Z., Owh, C., Chee, P. L., Ye, E., Dan, K., Chan, S. Y., Young, D. J., & Loh, X. J. (2020). Recent innovations in artificial skin. Biomaterials Science, 8(3), 776–797. https://doi.org/10.1039/C9BM01445D
Lundell, J., Hayes, T. L., Vurgun, S., Ozertem, U., Kimel, J., Kaye, J., Guilak, F., & Pavel, M. (2007). Continuous activity monitoring and intelligent contextual prompting to improve medication adherence (presentation). International Conference of the IEEE Engineering in Medicine and Biology Society, 6286–6289. https://doi.org/10.1109/IEMBS.2007.4353792
Mahadevan, N., Demanuele, C., Zhang, H., Volfson, D., Ho, B., Erb, M. K., & Patel, S. (2020). Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. NPJ Digital Medicine, 3, Article 5. https://doi.org/10.1038/s41746-019-0217-7
Maher, J. P., Rebar, A. L., & Dunton, G. F. (2018). Ecological momentary assessment is a feasible and valid methodological tool to measure older adults’ physical activity and sedentary behavior. Frontiers in Psychology, 9, Article 1485. https://doi.org/10.3389/fpsyg.2018.01485
Mancini, M., Schlueter, H., El-Gohary, M., Mattek, N., Duncan, C., Kaye, J., & Horak, F. B. (2016). Continuous monitoring of turning mobility and its association to falls and cognitive function: A pilot study. Journals of Gerontology: Series A, 71(8), 1102–1108. https://doi.org/10.1093/gerona/glw019
Marasinghe, K. M. (2016). Assistive technologies in reducing caregiver burden among informal caregivers of older adults: A systematic review. Disability and Rehabilitation: Assistive Technology, 11(5), 353–360. https://doi.org/10.3109/17483107.2015.1087061
Marchetti, G. F., Bellanca, J., Whitney, S. L., Lin, J. C., Musolino, M. C., Furman, G. R., & Redfern, M. S. (2013). The development of an accelerometer-based measure of human upright static anterior- posterior postural sway under various sensory conditions: Test-retest reliability, scoring and preliminary validity of the Balance Accelerometry Measure (BAM). Journal of Vestibular Research: Equilibrium and Orientation, 23(4-5), 227–235. https://doi.org/10.3233/VES-130490
Marino, M., Li, Y., Rueschman, M. N., Winkelman, J. W., Ellenbogen, J. M., Solet, J. M., Dulin, H., Berkman, L. F., & Buxton, O. M. (2013). Measuring sleep: Accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. Sleep, 36(11), 1747–1755. https://doi.org/10.5665/sleep.3142
Marschollek, M., Gietzelt, M., Schulze, M., Kohlmann, M., Song, B., & Wolf, K. (2012). Wearable sensors in healthcare and sensor-enhanced health information systems: All our tomorrows? Healthcare Informatics Research, 18(2), 97–104. https://doi.org/10.4258/hir.2012.18.2.97
Menai, M., van Hees, V. T., Elbaz, A., Kivimaki, M., Singh-Manoux, A., & Sabia, S. (2017). Accelerometer assessed moderate-to-vigorous physical activity and successful ageing: Results from the Whitehall II study. Scientific Reports, 7, Article 45772. https://doi.org/10.1038/srep45772
Milani, R. V., Bober, R. M., & Lavie, C. J. (2016). The role of technology in chronic disease care. Progress in Cardiovascular Diseases, 58(6), 579–583. https://doi.org/10.1016/j.pcad.2016.01.001
Misra, V., Bozkurt, A., Calhoun, B. H., Datta, S., Dickey, M., Kiani, M., Lach, J., Lee, B., Jur, J., Oralkan, O., Ozturk, M., Rajagopalan, R., Roundy, S., Strohmaier, J., Trolier-McKinstry, S., Vashaee, D., Wentzloff, D., & Werner, D. (2019). Optimizing the energy balance to achieve autonomous self-powering for vigilant health and IoT applications. Journal of Physics: Conference Series, 1407, Article 012001. https://doi.org/10.1088/1742-6596/1407/1/012001
Moore, R. C., Depp, C. A., Wetherell, J. L., & Lenze, E. J. (2016). Ecological momentary assessment versus standard assessment instruments for measuring mindfulness, depressed mood, and anxiety among older adults. Journal of Psychiatric Research, 75, 116–123. https://doi.org/10.1016/j.jpsychires.2016.01.011
Moore, R. C., Paolillo, E. W., Sundermann, E. E., Campbell, L. M., Delgadillo, J., Heaton, A., Swendsen, J., & Depp, C. A. (2021). Validation of the mobile verbal learning test: Illustration of its use for age and disease-related cognitive deficits. International Journal of Methods in Psychiatric Research, 30(1), Article e1859. https://doi.org/10.1002/mpr.1859
Morrell, L., Camic, P. M., & Genis, M. (2019). Factors associated with informant-reported cognitive decline in older adults: A systemised literature review. Dementia, 18(7-8), 2760–2784. https://doi.org/10.1177/1471301218759836
Mulvaney, S. A., Rothman, R. L., Dietrich, M. S., Wallston, K. A., Grove, E., Elasy, T. A., & Johnson, K. B. (2012). Using mobile phones to measure adolescent diabetes adherence. Health Psychology, 31(1), 43–50. https://doi.org/10.1037/a0025543
Nweke, H. F., Teh, Y. W., Mujtaba, G., & Al-Garadi, M. A. (2019). Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions. Information Fusion, 46, 147–170. https://doi.org/10.1016/j.inffus.2018.06.002
Ono, L. M., Confortin, S. C., Figueiró, T. H., Rech, C. R., & d’Orsi, E. (2020). Influence of instrumental activities of daily living on the cognitive impairment: EpiFloripa study. Aging & Mental Health, 24(3), 382–386. https://doi.org/10.1080/13607863.2018.1534079
Paire-Ficout, L., Lafont, S., Conte, F., Coquillat, A., Fabrigoule, C., Ankri, J., Blanc, F., Gabel, C., Novella, J. L., Morrone, I., & Mahmoudi, R. (2018). Naturalistic driving study investigating self-regulation behavior in early Alzheimer’s disease: A pilot study. Journal of Alzheimer’s Disease, 63(4), 1499–1508. https://doi.org/10.3233/JAD-171031
Paraschiakos, S., Cachucho, R., Moed, M., van Heemst, D., Mooijaart, S., Slagboom, E. P., Knobbe, A., & Beekman, M. (2020). Activity recognition using wearable sensors for tracking the elderly. User Modeling and User-Adapted Interaction, 30, 567–605. https://doi.org/10.1007/s11257-020-09268-2
Parise, M., Tartaglione, L., Cutruzzolà, A., Maiorino, M. I., Esposito, K., Pitocco, D., Gnasso, A., & Irace, C. (2021). Teleassistance for patients with type 1 diabetes during the COVID-19 pandemic: Results of a pilot study. Journal of Medical Internet Research, 23(4), Article e24552. https://doi.org/10.2196/24552
Parsons, T. D., Kane, R., & Duffield, T. C. (2022). Virtual reality-based neuropsychological assessments of everyday functioning. In T. D. Marcotte, M. Schmitter-Edgecombe, & I. Grant (Eds.), Neuropsychology of everyday functioning (2nd ed.). Guilford Press.
Penhale, B., & Manthorpe, J. (2001). Using electronic aids to assist people with dementia. Nursing and Resident Care, 3(12), 586–589. https://doi.org/10.12968/nrec.2001.3.12.9257
Pew Research Center. (2021, April 7). Mobile fact sheet. https://www.pewresearch.org/internet/fact-sheet/mobile/
Phillips, M. M., Phillips, K. T., Lalonde, T. L., & Dykema, K. R. (2014). Feasibility of text messaging for ecological momentary assessment of marijuana use in college students. Psychological Assessment, 26(3), 947–957. https://doi.org/10.1037/a0036612
Ploetz, T., & Guan, Y. (2018). Deep learning for human activity recognition in mobile computing. Computer, 51(5), 50–59. https://doi.org/10.1109/MC.2018.2381112
Raghunath, N., Dahmen, J., Brown, K., Cook, D., & Schmitter-Edgecombe, M. (2019). Creating a digital memory notebook application for individuals with mild cognitive impairment to support everyday functioning. Disability and Rehabilitation: Assistive Technology, 15(4), 421–431. https://doi.org/10.1080/17483107.2019.1587017
Rajan, K. B., Weuve, J., Barnes, L. L., McAninch, E. A., Wilson, R. S., & Evans, D. A. (2021). Population estimate of people with clinical AD and mild cognitive impairment in the United States (2020–2060). Alzheimer’s & Dementia, 17(12), 1966–1975. https://doi.org/10.1002/alz.12362
Ramirez-Bautista, J. A., Huerta-Ruelas, J. A., Chaparro-Cardenas, S. L., & Hernandez-Zavala, A. (2017). A review in detection and monitoring gait disorders using in-shoe plantar measurement systems. IEEE Reviews in Biomedical Engineering, 10, 299–309. https://doi.org/10.1109/RBME.2017.2747402
Rantz, M. J., Skubic, M., Popescu, M., Galambos, C., Koopman, R. J., Alexander, G. L., Phillips, L. J., Musterman, K., Back, J., & Miller, S. J. (2015). A new paradigm of technology-enabled ‘Vital Signs’ for early detection of health change for older adults. Gerontology, 61(3), 281–290. https://doi.org/10.1159/000366518
Rawtaer, I., Mahendran, R., Kua, E. H., Tan, H. P., Tan, H. X., Lee, T. S., & Ng, T. P. (2020). Early detection of mild cognitive impairment with in-home sensors to monitor behavior patterns in community-dwelling senior citizens in Singapore: Cross-sectional feasibility study. Journal of Medical Internet Research, 22(5), Article e16854. https://doi.org/10.2196/16854
Riediger, M., Wrzus, C., Klipker, K., Müller, V., Schmiedek, F., & Wagner, G. G. (2014). Outside of the laboratory: Associations of working-memory performance with psychological and physiological arousal vary with age. Psychology and Aging, 29(1), 103–114. https://doi.org/10.1037/a0035766
Robertson, K., Rosasco, C., Feuz, K., Schmitter-Edgecombe, M., & Cook, D. (2015). Prompting technologies: A comparison of time-based and context-aware transition-based prompting. Technology and Health Care, 23(6), 745–756. https://doi.org/10.3233/THC-151033
Ross, J. S., Bates, J., Parzynski, C., Akar, J., Curtis, J., Desai, N., Freeman, J., Gamble, G., Kuntz, R., Li, S. X., Marinac-Dabic, D., Masoudi, F., Normand, S. L., Ranasinghe, I., Shaw, R., & Krumholz, H. (2017). Can machine learning complement traditional medical device surveillance? A case-study of dual-chamber implantable cardioverter–defibrillators. Medical Devices: Evidence and Research, 10, 165–188. https://doi.org/10.2147/MDER.S138158
Rothman, A. J., Baldwin, A. S., & Hertel, A. W. (2004). Self-regulation and behavior change: Disentangling behavioral initiation and behavioral maintenance. In R. F. Baumeister & K. D. Vohs (Eds.), Handbook of self-regulation: Research, Theory, and applications (pp. 130–148). Guilford Press.
Rovini, E., Maremmani, C., & Cavallo, F. (2017). How wearable sensors can support Parkinson’s disease diagnosis and treatment: A systematic review. Frontiers in Neuroscience, 11, Article 555. https://doi.org/10.3389/fnins.2017.00555
Salazar, A. J., Silva, A. S., Silva, C., Borges, C. M., Correia, M. V., Santos, R. S., & Vilas-Boas, J. P. (2014). Low-cost wearable data acquisition for stroke rehabilitation: A proof-of-concept study on accelerometry for functional task assessment. Topics in Stroke Rehabilitation, 21(1), 12–22. https://doi.org/10.1310/tsr2101-12
Schmitter-Edgecombe, M., & Dyck, D. G. (2014). Cognitive rehabilitation multi-family group intervention for individuals with mild cognitive impairment and their care-partners. Journal of the International Neuropsychological Society, 20(9), 897–908. https://doi.org/10.1017/S1355617714000782
Schmitter-Edgecombe, M., & Giovannetti, T. (in press). Measures of activities of daily living. In G. J. Boyle, Y. Stern, D. J. Stein, & B. Sahakian (Eds.), The SAGE handbook of clinical neuropsychology (Vol. 2). Sage.
Schmitter-Edgecombe, M., Parsey, C., & Cook, D. (2011). Cognitive correlates of functional performance in older adults: Comparison of self-report, direct observation and performance-based measures. Journal of the International Neuropsychological Society, 17(5), 853–864. https://doi.org/10.1017/S1355617711000865
Schmitter-Edgecombe, M., McAlister, C., & Weakley, A. (2012). Naturalistic assessment of everyday functioning in individuals with mild cognitive impairment: The day out task. Neuropsychology, 26, 631–641. https://doi.org/10.1037/a0029352
Schmitter-Edgecombe, M., Sumida, C., & Cook, D. J. (2020). Bridging the gap between performance-based assessment and self-reported everyday functioning: An ecological momentary assessment approach. The Clinical Neuropsychologist, 34(4), 678–699. https://doi.org/10.1080/13854046.2020.1733097
Schmitter-Edgecombe, M., Brown, K., Luna, C., Chilton, R., Sumida, C. A., Holder, L., & Cook, D. (2022). Partnering a compensatory application with activity-aware prompting to improve use in individuals with amnestic mild cognitive impairment: A randomized controlled pilot clinical trial. Journal of Alzheimer’s Disease, 85(1), 73–90. https://doi.org/10.3233/JAD-215022
Sedano-Capdevila, A., Porras-Segovia, A., Bello, H. J., Baca-García, E., & Barrigon, M. L. (2021). Use of ecological momentary assessment to study suicidal thoughts and behavior: A systematic review. Current Psychiatry Reports, 23(7). https://doi.org/10.1007/s11920-021-01255-7
Seelye, A. M., Schmitter-Edgecombe, M., Das, B., & Cook, D. (2012). Application of cognitive rehabilitation theory to the development of smart prompting technology. IEEE Reviews in Biomedical Engineering, 5, 29–44. https://doi.org/10.1109/RBME.2012.2196691 PMID 23231987.
Seelye, A. M., Schmitter-Edgecombe, M., Cook, D. J., & Crandall, A. (2013). Naturalistic assessment of everyday activities and prompting technologies in mild cognitive impairment. Journal of the International Neuropsychological Society, 19(4), 442–452. https://doi.org/10.1017/S135561771200149X
Seelye, A., Mattek, N., Sharma, N., Witter, P., Brenner, A., Wild, K., Dodge, H., & Kaye, J. (2017). Passive assessment of routine driving with unobtrusive sensors: A new approach for identifying and monitoring functional level in normal aging and mild cognitive impairment. Journal of Alzheimer’s Disease, 59(4), 1427–1437. https://doi.org/10.3233/JAD-170116
Slamon, N. B., Penfil, S. H., Nadkarni, V. M., & Parker, R. M. (2018). A prospective pilot study of the biometrics of critical care practitioners during live patient care using a wearable “Smart Shirt.”. Journal of Intensive and Critical Care, 4(2), Article 10. https://doi.org/10.21767/2471-8505.100112
Small, B. J., Jim, H. S., Eisel, S. L., Jacobsen, P. B., & Scott, S. B. (2019). Cognitive performance of breast cancer survivors in daily life: Role of fatigue and depressed mood. Psycho-Oncology, 28(11), 2174–2180. https://doi.org/10.1002/pon.5203
Sohlberg, M. M., & Mateer, C. A. (2001). Cognitive rehabilitation: An integrative neuropsychological approach. Guilford Press.
Sprint, G., Cook, D. J., Weeks, D. L., & Borisov, V. (2015). Predicting functional independence measure scores during rehabilitation with wearable inertial sensors. IEEE Access, 3, 1350–1366. https://doi.org/10.1109/ACCESS.2015.2468213
Sprint, G., Cook, D., Fritz, R., & Schmitter-Edgecombe, M. (2016). Using smart homes to detect and analyze health events. IEEE Computer, 49(11), 29–37. https://doi.org/10.1109/MC.2016.338
Sprint, G., Cook, D., Weeks, D., Dahmen, J., & La Fleur, A. (2017). Analyzing sensor-based time series data to track changes in physical activity during inpatient rehabilitation. Sensors, 17(10), Article 2219. https://doi.org/10.3390/s17102219
St. John, P. D., & Montgomery, P. R. (2010). Cognitive impairment and life satisfaction in older adults. International Journal of Geriatric Psychiatry, 25, 814–821. https://doi.org/10.1002/gps.2422
Stanley, K. G., & Osgood, N. D. (2011). The potential of sensor-based monitoring as a tool for health care, health promotion, and research. Annals of Family Medicine, 9(4), 296–298. https://doi.org/10.1370/afm.1292
Sumner, J., Chong, L., Bundele, A., & Wei Lim, Y. (2021). Co-designing technology for aging in place: A systematic review. The Gerontologist, 61(7), e395–e409. https://doi.org/10.1093/geront/gnaa064
Swaminathan, S., Qirko, K., Smith, T., Corcoran, E., Wysham, N. G., Bazaz, G., Kappel, G., & Gerber, A. N. (2017). A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLoS ONE, 12(11), Article e0188532. https://doi.org/10.1371/journal.pone.0188532
Tam, J., & Schmitter-Edgecombe, M. (2017). Factors affecting aging services technology use in the aging population. In T. Parsons & R. Kane (Eds.), The role of technology in clinical neuropsychology (pp. 263–292). Oxford University Press.
Tam, J. W., & Schmitter-Edgecombe, M. (2019). A caregiver educational program: A video program to promote aging services technologies awareness. Geriatric Nursing, 40(1), 78–83. https://doi.org/10.1016/j.gerinurse.2018.06.015
Triantafyllidis, A. K., & Tsanas, A. (2019). Applications of machine learning in real-life digital health interventions: Review of the literature. Journal of Medical Internet Research, 21(4), Article e12286. https://doi.org/10.2196/12286
Tsai, C. F., Hwang, W. S., Lee, J. J., Wang, W. F., Huang, L. C., Huang, L. K., Lee, W. J., Sung, P. S., Liu, Y. C., Hsu, C. C., & Fuh, J. L. (2021). Predictors of caregiver burden in aged caregivers of demented older patients. BMC Geriatrics, 21, Article 59. https://doi.org/10.1186/s12877-021-02007-1
Tsui, K. M., & Yanco, H. A. (2010). Prompting devices: A survey of memory aids for task sequencing. Quality of Life Technologies International Symposium: Intelligent Systems for Better Living. http://robotics.cs.uml.edu/fileadmin/content/publications/2010/2010-06-qolt-ktsui-hyanco.pdf
United Nations, Department of Economic and Social Affairs, Population Division. (2017). World Population Ageing 2017 - Highlights. https://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2017_Highlights.pdf
Van Etten, E., Weakley, A., Schmitter-Edgecombe, M., & Cook, D. (2016). Subjective cognitive complaints and objective memory performance influence prompt preference for instrumental activities of daily living. Gerontechnology, 14(3). https://doi.org/10.4017/gt.2016.14.3.011.00
Wang, T., & Cook, D. J. (2021a). Multi-person activity recognition in continuously monitored smart homes. IEEE Transactions on Emerging Topics in Computing. https://doi.org/10.1109/TETC.2021.3072980
Wang, T., & Cook, D. J. (2021b). sMRT: Multi-resident tracking in smart homes with sensor vectorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(8), 2809–2821. https://doi.org/10.1109/TPAMI.2020.2973571
Wang, E. J., Li, W., Hawkins, D., Gernsheimer, T., Norby-Slycord, C., & Patel, S. N. (2016). HemaApp: Noninvasive blood screening of hemoglobin using smartphone cameras. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. https://doi.org/10.1145/2971648.2971653
Wang, C., Kim, Y., Shin, H., & Min, S. D. (2019a). Preliminary clinical application of textile insole sensor for hemiparetic gait pattern analysis. Sensors, 19(18), 3950. https://doi.org/10.3390/s19183950
Wang, J., Mahajan, H. P., Toto, P. E., McCue, M. P., & Ding, D. (2019b). The feasibility of an automatic prompting system in assisting people with traumatic brain injury in cooking tasks. Disability and Rehabilitation: Assistive Technology, 14(8), 817–825. https://doi.org/10.1080/17483107.2018.1499144
Weakley, A., Tam, J. W., van Son, C., & Schmitter-Edgecombe, M. (2019). Effectiveness of a video-based aging services technology education program for health care professionals. Gerontology & Geriatrics Education, 40(3), 339–356. https://doi.org/10.1080/02701960.2017.1281805
Woods, S. P., Iudicello, J. E., Morgan, E. E., Cameron, M. V., Doyle, K. L., Smith, T. V., & Cushman, C. (2016). Health-related everyday functioning in the internet age: HIV-associated neurocognitive disorders disrupt online pharmacy and health chart navigation skills. Archives of Clinical Neuropsychology, 31(2), 176–185. https://doi.org/10.1093/arclin/acv090
World Health Organization (2008). The world health report 2008: Primary health care now more than ever. http://www.who.int/whr/2008/08_overview_en.pdf
Yaddaden, A., Couture, M., Gagnon-Roy, M., Belchior, P., Lussier, M., Bottari, C., Giroux, S., Pigot, H., & Bier, N. (2020). Using a cognitive orthosis to support older adults during meal preparation: Clinicians’ perspective on COOK technology. Journal of Rehabilitation and Assistive Technologies Engineering, 7, Article 205566832090907. https://doi.org/10.1177/2055668320909074
Zhang, X., Lin, D., Pforsich, H., & Lin, V. W. (2020). Physician workforce in the United States of America: Forecasting nationwide shortages. Human Resources for Health, 18(1). https://doi.org/10.1186/s12960-020-0448-3
Acknowledgments
This work was partially supported by grants from the National Institute on Aging (#R35 AG071451) and the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Alzheimer’s Research Program (#AZ190055). Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the Department of Defense.
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Schmitter-Edgecombe, M., Luna, C., Cook, D.J. (2022). Technologies for Health Assessment, Promotion, and Intervention: Focus on Aging and Functional Health. In: Randolph, J.J. (eds) Positive Neuropsychology. Springer, Cham. https://doi.org/10.1007/978-3-031-11389-5_4
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