This study examines the neural underpinnings of the intention to use cognitive training digital therapeutics among older adults with subjective memory complaints. A total of 23 older adults with a clinical dementia rating score of 0.5—commonly interpreted as indicative of very mild cognitive impairment—participated in cognitive training using a smartphone application. Leveraging functional near-infrared spectroscopy during cognitive training and the technology acceptance model, this research investigates the predictive role of neural activation on the intention to use. Results revealed that higher activation in the right medial prefrontal cortex and reduced activation in the right dorsolateral prefrontal cortex during cognitive training were significantly associated with increased intention to use. The interplay between neural mechanisms in the motivational system and the cognitive control system at the prefrontal cortex level plays a critical role in shaping the intention to use.
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References
1.
WHO. Ageing and Health. 2024.
2.
SkariaAP. The economic and societal burden of Alzheimer disease: Managed care considerations. Am J Manag Care, 2022; 28,(Suppl 10):S188–S196; doi: 10.37765/ajmc.2022.89236
3.
ParkH, HaJ. Effect of digital technology interventions for cognitive function improvement in mild cognitive impairment and dementia: A systematic review and meta-analysis. Res Nurs Health, 2024; 47(4):409–422; doi: 10.1002/nur.22383
4.
LeeU, JungG, MaE-Y, et al.Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions. 2022.
5.
DavisF. Technology Acceptance Model. Massachusetts Institute of Technology; 1985.
6.
NadalC, SasC, DohertyG. Technology acceptance in mobile health: Scoping review of definitions, models, and measurement. J Med Internet Res, 2020; 22(7):e17256; doi: 10.2196/17256
7.
DimokaA, DavisFD. Where Does TAM Reside in the Brain? The Neural Mechanisms Underlying Technology Adoption. In: ICIS 2008 Proceedings 2008; p. 1.
8.
NissenA, KrampeC, KenningP, et al.Utilizing Mobile FNIRS to Investigate Neural Correlates of the TAM in ECommerce. 2019.
9.
TolgayB, Dell’orcoS, TrojanoL. EEGs as Potential Predictors of Virtual Agents’ Acceptance. In: 10th IEEE International Conference on Cognitive Infocommunications IEEE; 2019; pp. 433–438.
10.
KokA. Cognitive control, motivation and fatigue: A cognitive neuroscience perspective. Brain Cogn, 2022; 160:105880; doi: 10.1016/j.bandc.2022.105880
11.
LeeJH, LeeKU, LeeDY, et al.Development of the Korean version of the consortium to establish a registry for Alzheimer’s disease assessment packet (CERAD-K): Clinical and neuropsychological assessment batteries. J Gerontol B Psychol Sci Soc Sci, 2002; 57(1):47–53; doi: 10.1016/S0197-4580(00)82818-3
12.
HughesCP, BergL, DanzigerWL, et al.A new clinical scale for the staging of dementia. Br J Psychiatry, 1982; 140(6):566–572.
13.
MorrisJC. The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 1993; 43(11):2412–2414.
14.
ChoiSH, NaDL, LeeBH, et al.Estimating the validity of the Korean version of expanded clinical dementia rating (CDR) scale. Journal of the Korean Neurological Association, 2001.
15.
DavisFD. Perceived usefulness, perceived ease of use, and user acceptance of information. Source: MIS Quarterly, 1989; 13(3):319–340.
16.
VenkateshV, DavisFD. A theoretical extension of the technology acceptance model: Four Longitudinal field studies. Manage Sci, 2000; 46(2):186–204.
17.
HeroldF, WiegelP, ScholkmannF, et al.Applications of functional near-infrared spectroscopy (FNIRS) neuroimaging in exercise–cognition science: A systematic, methodology-focused review. JCM, 2018; 7(12):466; doi: 10.3390/jcm7120466
18.
PollatsekA, WellAD. On the Use of Counterbalanced Designs in Cognitive Research: A Suggestion for a Better and More Powerful Analysis. 1995.
19.
Orihuela-EspinaF, LeffDR, JamesDRC, et al.Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation. Phys Med Biol, 2010; 55(13):3701–3724; doi: 10.1088/0031-9155/55/13/009
20.
PintiP, AichelburgC, GilbertS, et al.A review on the use of wearable functional near-infrared spectroscopy in naturalistic environments. Jpn Psychol Res, 2018; 60(4):347–373; doi: 10.1111/jpr.12206
21.
TongY, Frederick B deB. Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain. Neuroimage, 2010; 53(2):553–564; doi: 10.1016/j.neuroimage.2010.06.049
22.
FristonKJ, JosephsO, ReesG, et al.Nonlinear event-related responses in fMRI. Magn Reson Med, 1998; 39(1):41–52; doi: 10.1002/mrm.1910390109
23.
VillringerA, DirnaglU. OPTICAL IMAGING OF BRAIN FUNCTION AND METABOLISM 2 Physiological Basis and Comparison to Other Functional Neuroimaging Methods. Springer Science: New York; 1997.
24.
BadiolaI, BlazekV, KumarJ. A Monte Carlo Investigation of Optical Pathlength in Inhomogeneous Tissue and Its Application to Near-Infrared Spectroscopy You May Also like Accuracy Enhancement in Reflective Pulse Oximetry by Considering Wavelength-Dependent Pathlengths. 1993.
25.
OBELAB Inc. NIRSIT Channel Information Brochure. Seoul, Korea; 2022.
26.
HayesAF. PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling. 2012:1–39.
27.
Guisado-FernándezE, GiuntiG, MackeyLM, et al.Factors influencing the adoption of smart health technologies for people with dementia and their informal caregivers: Scoping review and design framework. JMIR Aging, 2019; 2(1):e12192; doi: 10.2196/12192
28.
WigfieldA, EcclesJ. Expectancy-value theory of achievement motivation. Contemp Educ Psychol, 2000; 25(1):68–81; doi: 10.1006/ceps.1999.1015
29.
JungK, KimY-M. Prediction of the elderly’s usage intention of wearable robots: Focusing on the technology acceptance model. KSW, 2022; 17(2):271–278; doi: 10.21097/ksw.2022.5.17.2.271
30.
YangC, HuY, TalishinskyAD, et al.Medial prefrontal cortex and anteromedial thalamus interaction regulates goal-directed behavior and dopaminergic neuron activity. Nat Commun, 2022; 13(1):1386; doi: 10.1038/s41467-022-28892-7
31.
SegalowitzSJ, van NoordtS. Performance monitoring and the medial prefrontal cortex: A review of individual differences and context effects as a window on self-regulation. Front Hum Neurosci, 2012; 6:197; doi: 10.3389/fnhum.2012.00197
32.
DomenechP, RheimsS, KoechlinE. Neural mechanisms resolving exploitation-exploration dilemmas in the medial prefrontal cortex. Science, 2020; 369(6507); doi: 10.1126/science.abb0184
33.
AlexanderWH, BrownJW. Medial prefrontal cortex as an action-outcome predictor. Nat Neurosci, 2011; 14(10):1338–1344; doi: 10.1038/nn.2921
34.
NejatiV, MajdiR, SalehinejadMA, et al.The role of dorsolateral and ventromedial prefrontal cortex in the processing of emotional dimensions. Sci Rep, 2021; 11(1):1971; doi: 10.1038/s41598-021-81454-7
35.
GbadeyanO, McMahonK, SteinhauserM, et al.Stimulation of dorsolateral prefrontal cortex enhances adaptive cognitive control: A high-definition transcranial direct current stimulation study. J Neurosci, 2016; 36(50):12530–12536; doi: 10.1523/JNEUROSCI.2450-16.2016
36.
GierNR, StrelowE, KrampeC. Measuring dlPFC signals to predict the success of merchandising elements at the point-of-sale—A fNIRS approach. Front Neurosci, 2020; 14:575494; doi: 10.3389/fnins.2020.575494
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