1. Investigación

The increase in life expectancy has also been accompanied by an increase in the use of medication to treat chronic diseases. Polypharmacy is associated with medication-related problems such as the increase in the anticholinergic burden. Older people are more susceptible to anticholinergic effects on the central nervous system and this, in turn, may be related to cognitive impairment. In this paper, we develop an updated anticholinergic burden scale, the CRIDECO Anticholinergic Load Scale (CALS) via a systematic review of the literature and compare it with the currently most used Anticholinergic Burden Scale (ACB). Our new scale includes 217 different drugs with anticholinergic properties, 129 more than the ACB. Given the effect that anticholinergic medications have on cognitive performance, we then used both scales to investigate the relationship between anticholinergic burden and cognitive impairment in adult Spanish subjects with subjective memory complaint. In our population, we observed an association between cognitive impairment and the anticholinergic burden when measured by the new CALS, but not when the ACB was applied. The use of a more comprehensive and upgraded scale will allow better discrimination of the risk associated with the use of anticholinergic medications on cognitive impairment. CALS can help raise awareness among clinicians of the problems associated with the use of medications, or combinations of them, with large anticholinergic effect, and promote a better personalized pharmacological approach for each patient.

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further. This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.

Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. To do that the electroencephalogram (EEG) signal is usually studied through spectral power band filtering, but this paper proposes an alternative novel method of preprocessing the EEG signal based on supervised filters. Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. The proposed solution extends with this novel approach an algorithm that was submitted to win the third prize of an international Data Science challenge promoted by Kaggle contest platform and the American Epilepsy Society, the Epilepsy Foundation, National Institutes of Health (NIH) and Mayo Clinic. A formal description of these preprocessing methods is presented and a detailed analysis in terms of Receiver Operating Characteristics (ROC) curve and Area Under ROC curve is performed. The obtained results show statistical significant improvements when compared with the spectral power band filtering (PBF) typical baseline. A trend between performance and the dataset size is observed, suggesting that the supervised filters bring better information, compared to the conventional PBF filters, as the dataset grows in terms of monitored variables (sensors) and time length. The paper demonstrates a better accuracy in forecasting when new filters are employed and its main contribution is in the field of machine learning algorithms to develop more accurate predictive systems.

Triathlon has become increasingly popular in recent years. In this discipline, maximum oxygen consumption (VO2max) is considered the gold standard for determining competition cardiovascular capacity. However, the emergence of wearable sensors (as Stryd) has drastically changed training and races, allowing for the more precise evaluation of athletes and study of many more potential determining variables. Thus, in order to discover factors associated with improved running efficiency, we studied which variables are correlated with increased speed. We then developed a methodology to identify associated running patterns that could allow each individual athlete to improve their performance. To achieve this, we developed a correlation matrix, implemented regression models, and created a heat map using hierarchical cluster analysis. This highlighted relationships between running patterns in groups of young triathlon athletes and several different variables. Among the most important conclusions, we found that high VO2max did not seem to be significantly correlated with faster speed. However, faster individuals did have higher power per kg, horizontal power, stride length, and running effectiveness, and lower ground contact time and form power ratio. VO2max appeared to strongly correlate with power per kg and this seemed to indicate that to run faster, athletes must also correctly manage their power.

The increased pressure on primary care makes it important for other health care providers, such as community pharmacists, to collaborate with general practitioners in activities related to chronic disease care. Therefore, the objective of the present project was to develop a protocol of action that allows close pharmacist-physician collaboration to carry out a coordinated action for very early detection of cognitive impairment (CI). Methods: A comparative study to promote early detection of CI was conducted in 19 community pharmacies divided into two groups: one group with interprofessional collaboration (IPC) and one group without interprofessional collaboration (NonIPC). IPC was defined as an interactive procedure involving all pharmacists, general practitioners and neurologists. A total of 281 subjects with subjective memory complaints were recruited. Three tests were used in the community pharmacies to detect possible CI: Memory Impairment Screening, Short Portable Mental State Questionnaire, and Semantic Verbal Fluency. Individuals with at least one positive cognitive test compatible with CI, were referred to primary care, and when appropriate, to the neurology service. Finally, we evaluated the differences in clinical and diagnostic follow-up in both groups after six months. Results: The NonIPC study group included 38 subjects compatible with CI referred to primary care (27.54%). Ten were further referred to a neurology department (7.25%) and four of them (2.90%) obtained a confirmed clinical diagnosis of CI. In contrast, in the IPC group, 46 subjects (32.17%) showed results compatible with CI and were referred to primary care. Of these, 21 (14.68%) were subsequently referred to a neurology service, while the remaining 25 were followed up by primary care. Nineteen individuals out of those referred to a neurology service obtained a confirmed clinical diagnosis of CI (13.29%). The percentage of subjects in the NonIPC group referred to neurology and the percentage of subjects diagnosed with CI, was significantly lower in comparison to the IPC group (p-value 0.0233; p-value 0.0007, respectively). Conclusions: The creation of IPC teams involving community pharmacists, general practitioners, and neurologists allow for increased detection of patients with CI or undiagnosed dementia and facilitates their clinical follow-up. This opens the possibility of diagnosis in patients in the very early stages of dementia, which can have positive implications to improve the prognosis and delay the evolution of the disease.

Each year, 50% of runners su er from injuries. Consequently, more studies are being published about running biomechanics; these studies identify factors that can help prevent injuries. Scientific evidence suggests that recreational runners should use personalized biomechanical training plans, not only to improve their performance, but also to prevent injuries caused by the inability of amateur athletes to tolerate increased loads, and/or because of poor form. This study provides an overview of the di erent normative patterns of lower limb muscle activation and articular ranges of the pelvis during running, at self-selected speeds, in men and women. Methods: 38 healthy runners aged 18 to 49 years were included in this work. We examined eight muscles by applying two wearable superficial electromyography sensors and an inertial sensor for three-dimensional (3D) pelvis kinematics. Results: the largest di erences were obtained for gluteus maximus activation in the first double float phase (p = 0.013) and second stance phase (p = 0.003), as well as in the gluteus medius in the second stance phase (p = 0.028). In both cases, the activation distribution was more homogeneous in men and presented significantly lower values than those obtained for women. In addition, there was a significantly higher percentage of total vastus medialis activation in women throughout the running cycle with the median (25th–75th percentile) for women being 12.50% (9.25–14) and 10% (9–12) for men. Women also had a greater range of pelvis rotation during running at self-selected speeds (p = 0.011). Conclusions: understanding the di erences between men and women, in terms of muscle activation and pelvic kinematic values, could be especially useful to allow health professionals detect athletes who may be at risk of injury.

Background: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running. Methods: After completing a seven-month gait retraining program, a questionnaire was used to assess 19 triathletes for the incidence of injuries. They were also biomechanically analyzed at the beginning and end of the program while running at a speed of 90% of their maximum aerobic speed (MAS) using surface sensor dynamic electromyography and kinematic analysis. We used classification tree (random forest) techniques from the field of artificial intelligence to identify linear and non-linear relationships between di erent biomechanical patterns and injuries to identify which styles best prevent injuries. Results: Fewer injuries occurred after completing the program, with athletes showing less pelvic fall and greater activation in gluteus medius during the first phase of the float phase, with increased trunk extension, knee flexion, and decreased ankle dorsiflexion during the initial contact with the ground. Conclusions: The triathletes who had su ered the most injuries ran with increased pelvic drop and less activation in gluteus medius during the first phase of the float phase. Contralateral pelvic drop seems to be an important variable in the incidence of injuries in young triathletes.

Community pharmacists and general practitioners have daily contact with patients with Alzheimer’s disease (AD) but the number of positive cases constantly increases every day. Thus, the aim of this research is to describe the level of AD knowledge among community pharmacists and general practitioners in Spain, in order to see where the biggest gaps in the knowledge are. Therefore, a cross-sectional study has been carried out, using the Alzheimer’s disease knowledge survey (ADKS), among members of the Spanish Society of Primary Care Physicians and the Spanish Society of Family and Community Pharmacy to report the differences in AD knowledge in both professional collectives. The ADKS has been responded by 578 community pharmacists and 104 general practitioners and consists of a battery of 30 questions, whose possible answers are true or false. It assesses the AD knowledge in seven areas (impact on the disease, risk factors, course of the disease, diagnosis, care, treatment and symptoms). Results indicate that Spanish pharmacists and general practitioners have a high personal knowledge of AD, nevertheless, it is not associated with greater awareness. Both scored above 80% at the categories: diagnostic, treatment and symptoms. However, lower knowledge level (60% of correct answers) was found in those related to risk factors, such as the ignorance about hypercholesterolemia or hypertension as risk factors for the disease. Community pharmacists are already acting to control cardiovascular risk factors, but a wider knowledge of the relationship of these factors to AD is needed to act against these silent risk factors. Thus, pharmacists may also be involved in the management of AD that includes recognizing early symptoms for early detection of cognitive impairment. Hence, knowledge about risk factors is very important in developing this expanding role.

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.