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. 2022 Jun;2(6):475-483.
doi: 10.1038/s43587-022-00237-5. Epub 2022 Jun 17.

Leveraging technology to personalize cognitive enhancement methods in aging

David A Ziegler  1   2   3 , Joaquin A Anguera  1   2   3   4 , Courtney L Gallen  1   2   3 , Wan-Yu Hsu  1   3 , Peter E Wais  1   2   3 , Adam Gazzaley  1   2   3   4   5
Affiliations

Leveraging technology to personalize cognitive enhancement methods in aging

David A Ziegler et al. Nat Aging. 2022 Jun.

Abstract

As population aging advances at an increasing rate, efforts to help people maintain or improve cognitive function late in life are critical. Although some studies have shown promise, the question of whether cognitive training is an effective tool for improving general cognitive ability remains incompletely explored, and study results to date have been inconsistent. Most approaches to cognitive enhancement in older adults have taken a 'one size fits all' tack, as opposed to tailoring interventions to the specific needs of individuals. In this Perspective, we argue that modern technology has the potential to enable large-scale trials of public health interventions to enhance cognition in older adults in a personalized manner. Technology-based cognitive interventions that rely on closed-loop systems can be tailored to individuals in real time and have the potential for global testing, extending their reach to large and diverse populations of older adults. We propose that the future of cognitive enhancement in older adults will rely on harnessing new technologies in scientifically informed ways.

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Conflict of interest statement

Competing interests A. G. is co-founder, shareholder, BOD member, and advisor for Akili Interactive, a company that produces therapeutic video games. A. G. is shareholder and advisor for Neuroelectrics, a company that produces non-invasive brain stimulation devices and for Empatica, a company that produces biophysiological recording devices. The other authors declare no potential competing interests.

Figures

Fig. 1 |
Fig. 1 |. Elements that define a closed-loop system.
As a participant interacts with a cognitive assessment or intervention, data are collected about the person’s state (for example, task performance, arousal, neural activity). These data are analyzed in real time, either on the participant’s device or using advanced cloud-based analytics to characterize the person’s state in that moment, and adaptive algorithms are used to adjust the challenge, rewards, and stimuli, thus tailoring the intervention to the individual’s current state.
Fig. 2 |
Fig. 2 |. Examples of technologies for personalized cognitive enhancement in aging.
a, Combining fitness and cognitive challenges in a meaningful way can lead to synergistic effects of an intervention. b, Digital forms of meditation make the practice more accessible to large, diverse populations. c, Head-mounted VR displays enable highly immersive environments that provide the context and engagement needed to bolster long-term memory functions. d, Neurostimulation applied in conjunction with a cognitive intervention can help accelerate learning and enhance gains in cognitive abilities.
Fig. 3 |
Fig. 3 |. Our vision for the future of mobile RCTs.
First, recruitment is conducted entirely remotely through social-media outreach, online advertisements, agreements with senior living centers, and direct mail campaigns. Next, OA are sent a link to visit a mobile RCT portal where they complete informed consent and eligibility forms on their computers or mobile devices. They then complete baseline cognitive assessments, demographic questionnaires, and surveys of real-life behaviors and conditions, and those data are input into machine-learning algorithms that predict which digital intervention is most appropriate for each person, and randomization is done in a stratified manner. Once assigned to an arm, OA complete the digital intervention at home while data are collected remotely. Finally, advanced device or cloud-based analytics allow for rapid analysis of results in real time, accelerating the pace of research and discovery. These results can then be interpreted by researchers or visualized and presented to the participants as a performance dashboard, enabling them to monitor their own progress.
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