Evidence-Based Reviews

Helping older adults overcome the challenges of technology

Current Psychiatry. 2020 September;19(9):13-15,20-23A | doi:10.12788/cp.0021

References

1. Harvey PD. Domains of cognition and their assessment. Dialogues Clin Neuro. 2019;21(3):227-237.
2. Czaja SJ, Loewenstein DA, Sabbag SA, et al. A novel method for direct assessment of everyday competence among older adults. J Alzheimers Dis. 2017;57(4):1229-1238.
3. Czaja SJ, Loewenstein DA, Lee CC, et al. Assessing functional performance using computer-based simulations of everyday activities. Schizophr Res. 2017;183:130-136.
4. Tsai HS, Shillair R, Cotten SR. Social support and “playing around”: an examination of how older adults acquire digital literacy with tablet computers. J Appl Gerontol. 2017;36(1):29-55.
5. Cabrita M, Tabak M, Vollenbroek-Hutten MM. Older adults’ attitudes toward ambulatory technology to support monitoring and coaching of healthy behaviors: qualitative study. JMIR Aging. 2019;2(1):e10476. doi: 10.2196/10476.
6. Lim KY, Chang KJ, Kim HJ, et al. P.5.a.010 association between memory age identity and cognition in the elderly. Eur Neuropsychopharmacol. 2010;20(suppl 3):S555.
7. Moraes C, Pinto JA Jr, Lopes MA, et al. Impact of sociodemographic and health variables on mini-mental state examination in a community-based sample of older people. Eur Arch Psychiatry Clin Neurosci. 2010;260(7):535-542.
8. Freitas S, Simões MR, Alves L, et al. The relevance of sociodemographic and health variables on MMSE normative data. Appl Neuropsychol Adult. 2015;22(4):311-319.
9. Han C, Jo SA, Jo I, et al. An adaptation of the Korean mini-mental state examination (K-MMSE) in elderly Koreans: demographic influence and population-based norms (the AGE study). Arch Gerontol Geriatr. 2008;47(3):302-310.
10. Yin S, Zhu X, Li R, et al. Intervention-induced enhancement in intrinsic brain activity in healthy older adults. Sci Rep. 2014;4:7309.
11. Bender AR, Prindle JJ, Brandmaier AM, et al. White matter and memory in healthy adults: coupled changes over two years. Neuroimage. 2016;131:193-204.
12. Guye S, von Bastian CC. Working memory training in older adults: Bayesian evidence supporting the absence of transfer. Psychol Aging. 2017;32(8):732-746.
13. Taki Y, Kinomura S, Sato K, et al. Correlation between gray/white matter volume and cognition in healthy elderly people. Brain Cogn. 2011;75(2):170-176.
14. Cassidy AR, White MT, DeMaso DR, et al. Processing speed, executive function, and academic achievement in children with dextro-transposition of the great arteries: Testing a longitudinal developmental cascade model. Neuropsychology. 2016;30(7):874-885.
15. Aichele S, Rabbitt P, Ghisletta P. Life span decrements in fluid intelligence and processing speed predict mortality risk. Psychol Aging. 2015;30(3):598-612.
16. Eich TS, Castel AD. The cognitive control of emotional versus value-based information in younger and older adults. Psychol Aging. 2016;31(5):503-512.
17. Rolle CE, Anguera JA, Skinner SN, et al. Enhancing spatial attention and working memory in younger and older adults. J Cogn Neurosci. 2017;29(9):1483-1497.
18. Yaffe K, Lindquist K, Vittinghoff E, et al. The effect of maintaining cognition on risk of disability and death. J Am Geriatr Soc. 2010;58(5):889-894.
19. Madore KP, Schacter DL. An episodic specificity induction enhances means-end problem solving in young and older adults. Psychol Aging. 2014;29(4):913-924.
20. Matthews BR. Memory dysfunction. Continuum (Minneap Minn). 2015;21(3 Behavioral Neurology and Neuropsychiatry):613-626.
21. Mather M. The emotion paradox in the aging brain. Ann N Y Acad Sci. 2012;1251(1):33-49.
22. Gurera JW, Isaacowitz DM. Emotion regulation and emotion perception in aging: A perspective on age-related differences and similarities. Prog Brain Res. 2019;247:329-351.
23. Cai L, Chan JS, Yan JH, et al. Brain plasticity and motor practice in cognitive aging. Front Aging Neurosci. 2014;6:31.
24. Cassetta BD, Tomfohr-Madsen LM, Goghari VM. A randomized controlled trial of working memory and processing speed training in schizophrenia. Psychol Med. 2019;49(12):2009-2019.
25. Ball K, Berch DB, Helmers KF, et al. Effects of cognitive training interventions with older adults: a randomized controlled trial. JAMA. 2002;288(18):2271-2281.
26. Rebok GW, Ball K, Guey LT, et al. Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. J Am Geriatr Soc. 2014;62(1):16-24.
27. Edwards JD, Delahunt PB, Mahncke HW. Cognitive speed of processing training delays driving cessation. J Gerontol A Biol Sci Med Sci. 2009;64(12):1262-1267.
28. Ball K, Edwards JD, Ross LA, et al. Cognitive training decreases motor vehicle collision involvement of older drivers. J Am Geriatr Soc. 2010;58(11):2107-2113.
29. Wolinsky FD, Unverzagt FW, Smith DM, et al. The effects of the ACTIVE cognitive training trial on clinically relevant declines in health-related quality of life. J Gerontol B Psychol Sci Soc Sci. 2006;61(5):S281-S287.
30. Wolinsky FD, Unverzagt FW, Smith DM, et al. The ACTIVE cognitive training trial and health-related quality of life: protection that lasts for 5 years. J Gerontol A Biol Sci Med Sci. 2006;61(12):1324-1329.
31. Wolinsky FD, Vander Weg MW, Martin R, et al. The effect of speed-of-processing training on depressive symptoms in ACTIVE. J Gerontol A Biol Sci Med Sci. 2009;64(4):468-472.
32. Wolinsky FD, Vander Weg MW, Martin R, et al. Does cognitive training improve internal locus of control among older adults? J Gerontol B Psychol Sci Soc Sci. 2010;65(5):591-598.
33. Wolinsky FD, Mahncke HW, Kosinski M, et al. The ACTIVE cognitive training trial and predicted medical expenditures. BMC Health Serv Res. 2009;9:109.
34. Edwards JD, Xu H, Clark DO, et al. Speed of processing training results in lower risk of dementia. Alzheimers Dement (N Y). 2017;3(4):603-611.
35. Harvey PD, Tibiriçá L, Kallestrup P, et al. A computerized functional skills assessment and training program targeting technology based everyday functional skills. J Vis Exp. 2020;156:e60330. doi: 10.3791/60330.

The Box^10-22 describes some of the effects of aging on the brain, and how these changes are reflected in cognitive abilities.

Box

The aging brain’s effects on cognitive function

The global baseline intensity of human brain activity, determined by indirectly measuring blood oxygenation, decreases with age.¹⁰ Multiple domains of fluid cognition decline with age; these cognitive abilities include processing speed,^11,12 working memory,¹¹ episodic memory,¹¹ and executive function.¹¹ Expected neuroanatomic changes of aging include a decrease in cerebral grey matter volume as well as decreased white matter integrity, which is associated with diminished executive function and impaired working memory.¹³ Processing speed is associated with increased white matter microstructure during neurodevelopment.¹⁴ Diminished processing speed in older adults also may predict increased mortality risk.¹⁵ Individuals with advanced age may have augmented difficulty with episodic memory, especially when they are required to integrate information from more than one source.¹¹ Diminished hippocampal volume¹³ and reduced activity of the middle frontal gyrus are associated with age-related decline in episodic memory retrieval.¹⁰ Working memory¹⁶ is known to share a neurocircuitry overlap with attention processes.¹⁷ Working memory capacity also is closely associated with other cognitive functions, such as shifting and inhibition.¹⁰ Enhanced cerebellar activity is related to working memory; increased cerebellar activity is likely due to compensatory recruitment of neurons due to reduced activity in the superior frontal gyrus.¹⁰ The superior frontal gyrus contributes to both working memory as well as executive processing.¹⁰

Although the cognitive decline associated with aging is inevitable, individuals who experience cognitive decline at an increased rate are predisposed to worse outcomes. One longitudinal cohort study found that adults in their 8th and 9th decades of life with preserved cognitive function had a lower risk of disability and death.¹⁸

On the other hand, crystallized cognitive functions such as semantic memory,¹³ shortterm memory,¹³ and emotion regulation¹⁶ remain largely intact throughout the aging process. Semantic memory, a subtype of episodic memory, is related to associated facts or interpretations of previous occurrences.¹⁹ This type of memory is detached from an individual’s personal experience.²⁰ Semantic memory loss classically presents with anomia and detectable lesions in the anterior and temporal lobes.²⁰ Emotion regulation deficits are not a part of normal aging; in fact, emotional well-being is known to either improve or remain consistent with age.²¹ Emotional experiences in patients of advanced age may be more complex and unique in comparison to other cognitive abilities.²²

The role of cognitive training

Existing interventions for helping older adults improve their technology proficiency generally focus on improving cognition, and not necessarily on addressing skills learning. Skills learning and cognition are related; however, the brain depends on neural plasticity for skills learning, whereas cognitive declines are a result of gradual and functional worsening of memory, processing speed, executive functioning, and attention.²³ Interventions such as cognitive strategy training are capable of altering brain neurocircuitry to improve attention and memory.^10,11 Other interventions known to improve cognition include exercise¹⁰ and processing speed training.²⁴ On the other hand, skills learning is more effectively targeted by interventions that focus on stimulating realistic environments to mimic activities of daily living that involve technology.

Studies have consistently demonstrated cognitive improvements associated with computerized cognitive training (CCT). The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study was designed to evaluate the efficacy of cognitive training in 2,832 healthy adults age >65 across 6 recruitment sites in the United States.²⁵ Participants were randomized to a control group (no treatment) or to 1 of 3 treatment groups:

memory strategy training (instructor-led, not computerized)
reasoning training (instructor-led, not computerized)
speed training (no instructor, adaptive computerized training).

Each treatment group received 10 sessions of classroom-based training (1 hour each, twice per week for 5 weeks). Following the intervention, participants who had completed ≥8 sessions were randomized to receive 4 booster sessions at 11 and 35 months after the initial training, or no booster sessions.

Each cognitive training program significantly improved performance on within-domain cognitive tests relative to the control group. Effect sizes were large immediately following training; they declined over time, but were still significant at 10-year follow-up. As hypothesized, training effects did not generalize to neuropsychological tests in other training domains. The booster subgroup of speed training showed improved performance on a separate functional speed measure at 2-year²⁶ and 5-year follow-up.²⁷ Each condition showed slower decline in instrumental activities of daily living relative to the control group.

Continue to: The Figure...

References

Mortality burden of dementia may be greater than estimated

Helping older adults overcome the challenges of technology

References

The role of cognitive training

Pages

Next Article: