Alzheimer's disease (AD) is characterized by a progressive deterioration of cognitive functions with episodic memory loss and spatial disorientation (SD) as main features. Getting lost in community due to AD is associated with a wide range of negative consequences, such as a strong decrease in patients' quality of life. Episodes of SD in the elderly can increase the possibility of being recovered in a nursing home, caused by a loss of the sense of autonomy as well as an increase in potential injuries and, in the worst cases, even death. Additionally, caregiver burden and increased stress, as well as scarce community resources represent other significant problems related to patients' SD. New technological solutions, such as virtual reality (VR), represent promising means for AD assessment and intervention, especially when they can reveal poor ecological performances. In addition to the advanced age, the ε4 allele of Apolipoprotein-E (APO-E) represents the most important risk factor for AD, providing the opportunity to evaluate subclinical behavioral alterations in individuals with subjective cognitive decline (SCD), and Mild Cognitive Impairment (MCI) due to AD, which represents the prodromic phase of dementia. Deterioration of spatial navigation (SN) abilities is often present early in the course of AD. Therefore, a better understanding the neural mechanisms related to SN impairment in patients at high risk of developing AD can help timely diagnosis and intervention. The present study, adopting a technological apparatus for the detection and the rehabilitation of SN deficits, aims to: (i) investigate the performances obtained on SN tasks in a sample of community-dwelling older adults grouped into three levels (healthy controls, individuals with SCD and patients with MCI due to AD), undergoing virtual (The AppeGame) and naturalistic open-space tests (Detour Navigation Test-modified version); (ii) correct SN deficits by computer-based cognitive remediation sessions and VR sessions; (iii) educate participants at high risk of developing dementia about the opportunity offered by technology in supporting SN in exploring urban circuits.
We will analyze results of the virtual and ecological tasks of SN as a function of age, ApoE genotype and belonging of one the three groups, using a multiple linear regression model. The subgroups of participants at highest risk of developing AD will be administered the aforementioned combined cognitive rehabilitation sessions, with a test/retest analysis. Finally, through an online technological monitoring system, participants will be provided personalized feedbacks via smartphone digital health applications connected to a wearable equipped with sensors, in order to self-manage during their journeys alone in urban environments thanks to the use of threshold algorithms capable of supporting their SN.
