2. Universidad Cardenal Herrera-CEU

Cognitive impairment (CI), an intermediate phase between the decline in physiological cognition and dementia, is known to be mediated by a variety of risk and protective factors, with age being the most influential of these. The multifactorial nature of CI and the worldwide phenomenon of an aging population makes decoupling old age from disease through the concept of healthy aging (HA) a matter of major interest. Focusing on psychosocial variables and psychological constructs, here we designed and piloted a data collection booklet (DeCo-B) to assess CI and HA from a holistic perspective. The DeCo-B comprises six sections: sociodemographic factors, CI, meaning in life, psychosocial factors, health problems, and lifestyle. The estimated prevalence of CI and HA in our cohort were 24.4% and 6.6%, respectively. Spearman correlations mainly identified pairwise associations between the meaning in life domains and psychosocial variables. Moreover, age, marital status, purpose in life, resilience, chronic pain, cognitive reserve, and obstructive sleep apnea were significantly associated with an increased risk of CI. Our results showed that DeCo-B is a suitable tool for researching how modifiable risk and protective factors influence cognitive status. The complex interrelationships between variables should be further investigated and, for practical reasons, the questionnaire should be optimized in future work.

Purpose: The early detection of Mild Cognitive Impairment (MCI) is essential in aging societies where dementia is becoming a common manifestation among the elderly. Thus our aim is to develop a decision tree to discriminate individuals at risk of MCI among non-institutionalized elderly users of community pharmacy. A more clinically and patient-oriented role of the community pharmacist in primary care makes the dispensation of medication an adequate situation for an effective, rapid, easy, and reproducible screening of MCI. Methods: A cross-sectional study was conducted with 728 non-institutionalized participants older than 65. A total of 167 variables were collected such as age, gender, educational attainment, daily sleep duration, reading frequency, subjective memory complaint, and medication. Two screening tests were used to detect possible MCI: Short Portable Mental State Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE). Participants classified as positive were referred to clinical diagnosis. A decision tree and predictive models are presented as a result of applying techniques of machine learning for a more efficient enrollment. Results: One hundred and twenty-eight participants (17.4%) scored positive on MCI tests. A recursive partitioning algorithmwith themost significant variables determined that the most relevant for the decision tree are: female sex, sleeping more than 9 h daily, age higher than 79 years as risk factors, and reading frequency. Moreover, psychoanaleptics, nootropics, and antidepressants, and anti-inflammatory drugs achieve a high score of importance according to the predictive algorithms. Furthermore, results obtained from these algorithms agree with the current research on MCI. Conclusion: Lifestyle-related factors such as sleep duration and the lack of reading habits are associated with the presence of positive in MCI test. Moreover, we have depicted how machine learning provides a sound methodology to produce tools for early detection of MCI in community pharmacy. Impact of findings on practice: The community of pharmacists provided with adequate tools could develop a crucial task in the early detection of MCI to redirect them immediately to the specialists in neurology or psychiatry. Pharmacists are one of the most accessible and regularly visited health care professionals and they can play a vital role in early detection of MCI.