Dementia or major neurocognitive disorder (MNCD) refers to multi-domain cognitive deficits resulting in a significant interference with independence in everyday functioning (American Psychiatric Association, 2013). The transitional phase between normal aging and dementia is instead defined as Mild Neurocognitive Disorder (mNCD) and it is characterized by both subjective complaints and objective cognitive impairments not interfering with basic activities of daily life (American Psychiatric Association, 2013). Given that pathological changes in MNCD could have occurred years ahead of the manifestation of mNCD (Jessen, 2010), there should be a “pre-mNCD” phase before its manifestation, that is the so called subjective cognitive decline (SCD; Jessen et al., 2014). SCD is indeed an intermediate state between normal cognition and mNCD that may predict the development of objective cognitive decline (Reisberg and Shulman, 2009; Jr et al., 2011). Thus, SCD and mNCD represent critical stages for early diagnosis and intervention of MNCD, also at the light of limited effects of pharmacological therapies on slowing cognitive symptoms (Thoene-Otto et al., 2012). Consequently, non-pharmacological intervention strategies, such as cognitive training (CT), gained increasing attention in these populations (Li et al., 2011; Bahar-Fuchs et al., 2013; Rojas et al., 2013; Andrieu et al., 2015; Smart et al., 2017).

The recent progress of information and communication technologies (ICT) has fostered an increasing interest in the use of technological tools also for CT (Hill et al., 2017). In this field, computer-based CT emerges as a treatment solution with many advantages for both the therapist and the patients, besides overcoming many limitations of traditional interventions (Irazoki et al., 2020). More recently, ICT-based reliable rehabilitation services have been implemented on a large scale, at distance, and directly at home, configuring what is called telerehabilitation (TR) (Zampolini et al., 2008; Cotelli et al., 2019; Maresca et al., 2020). TR can guarantee a continuum of care with the possibility to perform rehabilitation in an ecologic environment, independently or with the help of a caregiver. As a result, TR promotes equitable health care regardless of patients’ geographically distant living areas or disability, potentially reducing the overall cost of care compared with in-person rehabilitation (Peretti et al., 2017). In this field, most scientific efforts have been focused to devise user friendly systems than can be accessed and used without the direct intervention of the therapist (Castilla et al., 2020; De Cola et al., 2020). Another important feature of the ICT-supported TR modalities is the possibility to tailor duration and frequencies of treatment sessions according to patients’ characteristics (Bernini et al., 2021b).

Currently, a growing number of studies have explored the comparability of TR and in-person (in-P) CT in terms of cognitive outcomes (Jelcic et al., 2014; Realdon et al., 2016; Bernini et al., 2021b). There is however a lack of analysis of the characteristics of end users that make them more likely to prefer a TR modality over the other. Elderly generations are indeed familiar with classic technologies, such as television, radio, and telephone, but may be less inclined to use advanced ICT modalities (Fadzil et al., 2022). Beside the diagnostic category of patients, it would be important for clinicians to consider what are the factors underlying the interest in receiving this technological service, inclination to use the program, biases and expectations toward different program options, before offering digital CT solutions (e.g., TR, in-P CT). For instance, it has been shown in the field of Parkinson’s Disease (PD) that some socio-demographic variables, such as age and education, could be moderator of differences in the efficacy of an in-P CT (Sanchez-Luengos et al., 2021). Also cognitive functioning prior to receive CT may predict the outcome of the intervention: lower cognitive profiles resulted associated to more marked (but short-lasting) responsiveness to a computer-based CT (Bernini et al., 2023). Moreover, an active lifestyle may be a determining factor in cognitive rehabilitation given that it can help in increasing attendance and participation in sessions and achieving positive results, as demonstrated in older adults at risk of dementia receiving CT (Küster et al., 2016). Unfortunately, a consensus about the ideal candidate for either type of CT is still lacking (Di Tella et al., 2021) and, to the best of our knowledge, no study has been devoted to investigate what and how individual factors could influence patients’ preference for receiving CT (Isernia et al., 2019). Thus, further investigation on these topics is needed.

In the last years, our group has devised and implemented a cognitive rehabilitation (CoRe) software for an in-person computer-based CT (Alloni et al., 2017, 2018). CoRe was effective in restoring lost brain function and slowing degenerative diseases in early cognitive decline, compared with traditional interventions (Bernini et al., 2019, 2021a, 2023; Rodella et al., 2021). With a view to starting/continuing the CoRe program remotely (Quaglini et al., 2019), we have recently developed a “home” version (called HomeCoRe), able to provide a cognitive intervention directly at home (Bernini et al., 2021b,c).

In the present study, we explored the aspects associated to the preference for CT delivered with the HomeCoRe system (TR) or with the CoRe tool (in-P) for CT in patients with SCD or NCD. We focused on four profiles: lifestyle, socio-demographic, cognitive, and mental-health. In particular, we expected lifestyle to emerge as relevant among the others. This consideration takes its origin from the assumption that the adoption of protective behaviors in everyday life (e.g., cognitive engagement, leisure activities, nutrition, physical activity, health status, etc.) would make patients more open to new and emerging technological opportunities offered to support and facilitate successful aging (Dogra et al., 2022).

The present study aimed to explore the characteristics of patients at risk of dementia or with early cognitive impairment that are associated to preference for the TR or the in-P modality of CT. We hypothesized that patients’ lifestyle profile could be the main factor driving the preference, given that an active lifestyle may render patients more open to new and emerging technological opportunities offered to support and facilitate aging (Dogra et al., 2022). Our findings seem to support this hypothesis. In fact, the best performances of the logistic regression models were obtained in the sociodemographic and lifestyle areas, compared to mental health and cognitive.

Participants preferring TR were characterized by higher levels of cognitive reserve, in particular in what concerns leisure cognitively demanding activities carried out during the entire life span. Engaging in cognitive, social, and physical activities throughout life is known to contribute to high levels of cognitive reserve, which protects individuals from sequelae of neural damage, reduces the risk of developing dementia, and slows the rate of cognitive decline due to normal aging (Stern, 2009; Clare et al., 2017). Hence, high levels of cognitive reserve can prevent cognitive decline and stimulate intellectual curiosity and may potentially contribute to increase subjects’ openness to use innovative technological solutions, such as HomeCore-delivered TR in our case, which would not only simplify their daily activities, but also help them lead a more active life (Ranieri et al., 2021). A very recent consensus work (Stern et al., 2023) considered cognitive reserve as influenced by multiple genetic and environmental factors operating at various points or continuously across the lifespan. For example, proxies for cognitive reserve in human studies have included characteristics associated with both endowment and experience, including Intelligence Quotient at an early age, exposures to cognitive stimuli across the age span, education, occupational exposures, leisure activities, social networks, and other factors. In the context of the present study, we measured this construct through the CRIq, according to those considering education, occupation and leisure activities as the most frequently used proxies of cognitive reserve (Valenzuela and Sachdev, 2007; Solé-Padullés et al., 2009; Jones et al., 2010). Future studies should explore this topic further using more comprehensive proxies.

Moreover, we also found that participants who preferred HomeCoRe had lower LIBRA index, suggesting that they were more prone to adopt a “protective” lifestyle that may reduce their individual risk of dementia in later life (Linardakis et al., 2013; Schiepers et al., 2018). Specifically, an increase in the global LIBRA score by one point is related to a 19% higher risk for developing dementia (Schiepers et al., 2018). It is important to consider that cognitive reserve is not a static construct but rather it evolves throughout the entire life course (Valenzuela and Sachdev, 2006), and lifestyle habits can help protect older people from cognitive decline and dementia by supporting the development, connectivity, and maintenance of brain networks (Clare et al., 2017). In this regard, the results of the present study suggest that greater cognitive reserve and adoption of healthy lifestyles characterize those individuals more likely to adopt new technologies (Kalache and Gatti, 2003). Actually, the regression model for the lifestyle area kept variable associated with a healthy behavior, with an overall good performance. The association between Mediterranean diet and CRIq leisure time score was a corroboration of this.

By contrast, participants preferring in-P CT were characterized by age-stereotypic attitudes (Felt et al., 2016), being less interested in adopting new technologies. If we consider that technologies are becoming even more integrated into everyday life, people with less openness to innovation are more likely to become more disenfranchised and disadvantaged (Czaja et al., 2006) and are therefore more at risk to enter a vicious cycle that may negatively influence their cognitive abilities. Other factors were associated with the preference for TR or in-P CT. In particular, we found that the two preference groups differed in some variables, such as age, technological skills, and the distance from the clinic. These findings are not particularly surprising given that these factors are known to influence technology adoption (Czaja et al., 2006; Guzman-Parra et al., 2020; Dequanter et al., 2022). The logistic model for the socio-demographic area was the best model among all. Actually, the RF global model returned socio-demographic and lifestyle features as the most important. The lack of statistical significance in logistic model for some selected features (see age or Mediterranean diet) was to be intended as they were however important in predicting people preference for the rehabilitation delivery modality, either as confounders or as explanatory variables.

Contrary to expectations, cognitive profiles as well as diagnostic categories were not associated to participants’ preferences. This observation seems to be broadly confirmed both by the RF model and by the better performance (in terms of AUC) of the logistic models for each area. We are aware that cognitive abilities such as memory and speed of processing are important to successful performance of technology-based tasks (Charness et al., 2001; Czaja et al., 2001; Sharit et al., 2003) and represent important predictor of computer use (Umemuro, 2004) in the field of normal aging. It should be noted that our study has been carried out on participants at risk of dementia or with early cognitive impairment. One could expect that TR should be perceived as more cognitively demanding, being performed without the therapist support. If we match this result with the different lifestyle profile characterizing the two groups, with TR participants more inclined to active habits in daily life, it could be suggested that other factors instead of cognitive profile may be driving this preference. We also did not find any impact of the mental health profile on participants’ preference for TR vs. in-P CT. Again, others (Guzman-Parra et al., 2020) have shown that depression and health status were associated to a better attitude toward new technologies in early stages of dementia. However, our findings can be related to the fact that our sample of older adults was relatively mentally healthy and they had a fairly stable mood. Future studies with a larger sample might further shed light on the role of cognitive and mental health factors in determining participants’ preferences for technologies.

Overall, our findings suggest that the preference to receive CT delivered by TR or in person is a complex issue influenced by a variety of factors, mostly related to lifestyle habits and sociodemographic characteristics. The relationships among these variables are complex, indicating that people’s choices about preferring a particular CT cannot be explained solely by their clinical condition, including their diagnostic category and cognitive functioning. Several studies have shown that attitudes toward technology, in our case toward CT, are an important predictor of the acceptance of these tools (Al-Gahtani and King, 1999; Kelley et al., 1999; Czaja et al., 2006).

The present study aimed to identify, among each defined areas, the factors most likely to influence the preference in CT in delivery modalities. Based on our results, we believe that clinicians, when recommending cognitive rehabilitation to patients at risk for dementia, should not only consider their level of impairment, but also sociodemographic (i.e., level of technological skills, age, and distance from the clinic) and lifestyle items (i.e., cognitive reserve, regular physical activity, Mediterranean diet), because they could influence their preference and thus acceptance.

Understanding additional factors influencing preference and acceptance for a particular deliver modality is crucial to select the most suitable candidates for each intervention modality, as this can potentially influence treatment adherence and success.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/record/8159236.

The studies involving humans were approved by San Matteo Hospital ethics committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

SBe: Conceptualization, Investigation, Methodology, Writing – original draft. EB: Formal analysis, Data curation, Writing – review & editing. FF: Formal analysis, Data curation, Writing – review & editing. SP: Software, Writing – review & editing. SQ: Software, Writing – review & editing. CR: Data curation, Writing – original draft. AC: Resources, Writing – review & editing. SC: Funding acquisition, Resources, Supervision, Writing – review & editing. CT: Supervision, Writing – review & editing. TV: Supervision, Writing – review & editing. SBo: Conceptualization, Investigation, Methodology, Writing – original draft.