Residential Environment Assessment by Older Adults in Nursing Homes during COVID-19 Outbreak.

The most vulnerable residential settings during the COVID-19 pandemic were older adult's nursing homes, which experienced high rates of incidence and death from this cause. This paper aims to ascertain how institutionalized older people assessed their residential environment during the pandemic and to examine the differences according to personal and contextual characteristics. The COVID-19 Nursing Homes Survey (Madrid region, Spain) was used. The residential environment assessment scale (EVAER) and personal and contextual characteristics were selected. Descriptive and multivariate statistical analysis were applied. The sample consisted of 447 people (mean age = 83.8, 63.1% = women, 50.8% = widowed, 40% = less than primary studies). Four residential assessment subscales (relationships, mobility, residential aspects, privacy space) and three clusters according to residential rating (medium-high with everything = 71.5% of cases, low with mobility = 15.4%, low with everything = 13.1%) were obtained. The logistic regression models for each cluster category showed to be statistically significant. Showing a positive affect (OR = 1.08), fear of COVID-19 (OR = 1.06), high quality of life (OR = 1.05), not having suspicion of depression (OR = 0.75) and performing volunteer activities (OR = 3.67) were associated with the largest cluster. It is concluded that a better residential evaluation was related to more favourable personal and contextual conditions. These results can help in the design of nursing homes for older adults in need of accommodation and care to facilitate an age-friendly environment.

The Impact of COVID-19 on Nursing Homes: Study Design and Population Description.

Nursing homes for the elderly in Spain have experienced high rates of infection and mortality from COVID-19, although rates have varied from one region to another. Madrid is the region where most institutionalized older adults have died from the coronavirus. However, there is little known about the psychosocial and environmental factors involved in the high incidence of COVID-19 among the institutionalised population in this region. This article describes the protocol of a study on nursing homes during the SARS-CoV-2 pandemic in the Autonomous Community of Madrid (hereafter: Region of Madrid or Madrid Region) and provides information on the study design, measures used, and characteristics of the population studied. A questionnaire about life in nursing homes during the COVID-19 pandemic was designed and a total of 447 persons over 60 years of age without cognitive impairment-220 in private nursing homes and 227 in public nursing homes-participated by answering questions about different topics: personal situations during the pandemic, feelings and methods of coping, residential environment, health, quality of life, ageism, and self-perception of ageing. The institutionalised person profile discussed in this study was an old woman, widowed, without children, with a low level of education, with multimorbidity, and who perceived her health and quality of life positively. Most of the participants were very concerned about COVID-19 and its effects. In fact, 38% had been diagnosed with COVID-19, of whom 20% were admitted to hospital and 20% had suffered negative impacts, such as pain and neurological problems. In addition, 70% of the residents remained confined to their rooms, which increased their perceptions of loneliness and social isolation. The worst-rated aspects of the nursing home resulted from the restrictive measures imposed on nursing homes during the pandemic. This research offers useful material for understanding the pandemic and its consequences from the perspective of the older institutionalised population, which could provide insights for designing public policies.

Active ageing profiles among older adults in Spain: A Multivariate analysis based on SHARE study.

BACKGROUND: Following the active ageing model based on the Health, Lifelong Learning, Participation and Security pillars, this research has a twofold objective: i) to classify older adults according to active ageing profiles, taking into account the four pillars, and ii) to ascertain the relationship between the profiles and personal and contextual factors, as well as well-being and quality of life in old age. METHODS: A study sample of 5,566 Spanish older adults who participated in wave 6 of the Survey of Health, Ageing and Retirement in Europe (SHARE) was included. Data were analysed in different steps applying several statistical analyses (Principal Component, Cluster, Discriminant, Multiple Correspondence and bivariate analysis with Pearson chi-square and ANOVA). RESULTS: Five older adult profiles were obtained (I: with moderate activity; II: quasi-dependents; III: with active ageing-limiting conditions; IV: with diverse and balanced activity; V: with excellent active ageing conditions). The first three profiles were characterised by subjects with a high average age, low educational level, who were retired or housewives, and who perceived a moderate level of loneliness, satisfaction with the social network and quality of life, as well as having a larger family network, but living in small households or alone. In contrast, the latter two profiles showed better personal and contextual conditions, well-being and quality of life. DISCUSSION AND CONCLUSIONS: The multidimensional approach to active ageing followed in this article has revealed the presence of several older adult profiles, which are confined to groups with better or worse active ageing conditions. In this context, if ageing is a process that reflects the previous way of life, intervention priorities will have to consider actions that promote better conditions during the life cycle.

Big Data in metagenomics: Apache Spark vs MPI.

The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when dealing with increasing problems sizes, making in this way the usage of High Performance Computing (HPC) technologies such as the message passing interface (MPI) a promising alternative. Recently, MetaCacheSpark, an Apache Spark based software for detection and quantification of species composition in food samples has been proposed. This tool can be used to analyze high throughput sequencing data sets of metagenomic DNA and allows for dealing with large-scale collections of complex eukaryotic and bacterial reference genome. In this work, we propose MetaCache-MPI, a fast and memory efficient solution for computing clusters which is based on MPI instead of Apache Spark. In order to evaluate its performance a comparison is performed between the original single CPU version of MetaCache, the Spark version and the MPI version we are introducing. Results show that for 32 processes, MetaCache-MPI is 1.65× faster while consuming 48.12% of the RAM memory used by Spark for building a metagenomics database. For querying this database, also with 32 processes, the MPI version is 3.11× faster, while using 55.56% of the memory used by Spark. We conclude that the new MetaCache-MPI version is faster in both building and querying the database and uses less RAM memory, when compared with MetaCacheSpark, while keeping the accuracy of the original implementation.

Very Fast Tree: speeding up the estimation of phylogenies for large alignments through parallelization and vectorization strategies.

MOTIVATION: FastTree-2 is one of the most successful tools for inferring large phylogenies. With speed at the core of its design, there are still important issues in the FastTree-2 implementation that harm its performance and scalability. To deal with these limitations, we introduce VeryFastTree, a highly tuned implementation of the FastTree-2 tool that takes advantage of parallelization and vectorization strategies to boost performance. RESULTS: VeryFastTree is able to construct a tree on a standard server using double-precision arithmetic from an ultra-large 330k alignment in only 4.5 h, which is 7.8× and 3.5× faster than the sequential and best parallel FastTree-2 times, respectively. AVAILABILITY AND IMPLEMENTATION: VeryFastTree is available at the GitHub repository: https://github.com/citiususc/veryfasttree. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

New Metrics to Assess Type 2 Diabetes After Bariatric Surgery: The "Time-Within-Remission Range".

Almost one third of patients do not achieve type 2 diabetes remission after bariatric surgery or are unable to sustain this effect long term. Our objective was to delve further into the dynamic responses of diabetes after bariatric surgery and to evaluate the "time-within-remission range" as a variable of metabolic control. A descriptive cohort study was done using a computerised multicentre and multidisciplinary registry. All data were adjusted by propensity score. A total of 1186 subjects with a follow-up of 4.5 ± 2.5 years were included. Type of surgery, diabetes remission, recurrence of diabetes, "time-within-remission range" and key predictors of diabetes outcomes were assessed. All patients (70% women, 51.4 ± 9.2 years old, body mass index (BMI) 46.3 ± 6.9 kg/m2) underwent primary bariatric procedures. "Time-within-remission range" were 83.3% (33.3-91.6) after gastric bypass, 68.7% (7.1-87.5) after sleeve gastrectomy and 90% (83.3-92.8) after malabsorptive techniques (p < 0.001 for all). Duration of diabetes, baseline HbA1c and insulin treatment were significantly negatively correlated with the "time-within-remission range". The association of bariatric techniques with "time-within-remission range", using gastric bypass as a reference, were: odds ratio (OR) 3.70 (2.34-5.84), p < 0.001 for malabsorptive techniques and OR 0.55 (0.40-0.75), p < 0.001 for sleeve gastrectomy. Characteristics of type 2 diabetes powerfully influence the outcomes of bariatric surgery. The "time-within-remission range" unveils a superiority of gastric bypass compared to sleeve gastrectomy.

RISK FACTORS FOR HYPOGLYCEMIA IN INPATIENTS WITH TOTAL PARENTERAL NUTRITION AND TYPE 2 DIABETES: A POST HOC ANALYSIS OF THE INSUPAR STUDY.

Objective: Treatment of hyperglycemia with insulin is associated with increased risk of hypoglycemia in type 2 diabetes mellitus (T2DM) patients receiving total parenteral nutrition (TPN). The aim of this study was to determine the predictors of hypoglycemia in hospitalized T2DM patients receiving TPN. Methods: Post hoc analysis of the INSUPAR study, which is a prospective, open-label, multicenter clinical trial of adult inpatients with T2DM in a noncritical setting with indication for TPN. Results: The study included 161 patients; 31 patients (19.3%) had hypoglycemic events, but none of them was severe. In univariate analysis, hypoglycemia was significantly associated with the presence of diabetes with end-organ damage, duration of diabetes, use of insulin prior to admission, glycemic variability (GV), belonging to the glargine insulin group in the INSUPAR trial, mean daily grams of lipids in TPN, mean insulin per 10 grams of carbohydrates, duration of TPN, and increase in urea during TPN. Multiple logistic regression analysis showed that the presence of diabetes with end-organ damage, GV, use of glargine insulin, and TPN duration were risk factors for hypoglycemia. Conclusion: The presence of T2DM with end-organ damage complications, longer TPN duration, belonging to the glargine insulin group, and greater GV are factors associated with the risk of hypoglycemia in diabetic noncritically ill inpatients with parenteral nutrition. Abbreviations: ADA = American Diabetes Association; BMI = body mass index; CV% = coefficient of variation; DM = diabetes mellitus; GI = glargine insulin; GV = glycemic variability; ICU = intensive care unit; RI = regular insulin; T2DM = type 2 diabetes mellitus; TPN = total parenteral nutrition.

A big data approach to metagenomics for all-food-sequencing.

BACKGROUND: All-Food-Sequencing (AFS) is an untargeted metagenomic sequencing method that allows for the detection and quantification of food ingredients including animals, plants, and microbiota. While this approach avoids some of the shortcomings of targeted PCR-based methods, it requires the comparison of sequence reads to large collections of reference genomes. The steadily increasing amount of available reference genomes establishes the need for efficient big data approaches. RESULTS: We introduce an alignment-free k-mer based method for detection and quantification of species composition in food and other complex biological matters. It is orders-of-magnitude faster than our previous alignment-based AFS pipeline. In comparison to the established tools CLARK, Kraken2, and Kraken2+Bracken it is superior in terms of false-positive rate and quantification accuracy. Furthermore, the usage of an efficient database partitioning scheme allows for the processing of massive collections of reference genomes with reduced memory requirements on a workstation (AFS-MetaCache) or on a Spark-based compute cluster (MetaCacheSpark). CONCLUSIONS: We present a fast yet accurate screening method for whole genome shotgun sequencing-based biosurveillance applications such as food testing. By relying on a big data approach it can scale efficiently towards large-scale collections of complex eukaryotic and bacterial reference genomes. AFS-MetaCache and MetaCacheSpark are suitable tools for broad-scale metagenomic screening applications. They are available at https://muellan.github.io/metacache/afs.html (C++ version for a workstation) and https://github.com/jmabuin/MetaCacheSpark (Spark version for big data clusters).

Factor Analysis and Clustering of the Movement Disorder Society-Non-Motor Rating Scale.

BACKGROUND: The primary validation of the Movement Disorder Society Non-Motor Rating Scale was recently published, but 2 important structural analyses were not included. The objective of this study was to examine the structural characteristics of the Movement Disorder Society Non-Motor Rating Scale by factor and cluster analyses. METHODS: Data came from the validation study, an international multicenter cross-sectional study of 402 Parkinson's disease patients. Demographic and clinical data, the Movement Disorder Society Non-Motor Rating Scale, and Hoehn and Yahr staging were used. Exploratory and confirmatory factor analysis and nonhierarchical cluster analysis were performed. RESULTS: The exploratory factor analysis showed that all 13 domains of the Movement Disorder Society Non-Motor Rating Scale, except 1, and the Non-Motor Fluctuations subscale performed as unidimensional (variance explained: 0.36, sleep and wakefulness; -0.82, orthostatic hypotension). The confirmatory factor analysis could be carried out in 9 domains and showed that 6 of them and the Non-Motor Fluctuations subscale adjusted to the model satisfactorily according to the root mean square error of approximation. Furthermore, all domains had comparative fit index values >0.95, except depression and pain (both, 0.94) and sleep and wakefulness (0.90). The Non-Motor Fluctuations subscale showed satisfactory root mean square error of approximation (0.07), but a low comparative fit index value (0.91). A 5-cluster solution, correctly classifying 96% of the cases, was found. CONCLUSIONS: Overall, most subscales of the Movement Disorder Society Non-Motor Rating Scale are unidimensional, and although each subscale is distinct in terms of content covered, factors and clusters that are of clinical relevance are discernible and contribute to our understanding of possible nonmotor subtypes in Parkinson's disease. © 2020 International Parkinson and Movement Disorder Society.

Regular insulin added to total parenteral nutrition vs subcutaneous glargine in non-critically ill diabetic inpatients, a multicenter randomized clinical trial: INSUPAR trial.

BACKGROUND: There is no established insulin regimen in T2DM patients receiving parenteral nutrition. AIMS: To compare the effectiveness (metabolic control) and safety of two insulin regimens in patients with diabetes receiving TPN. DESIGN: Prospective, open-label, multicenter, clinical trial on adult inpatients with type 2 diabetes on a non-critical setting with indication for TPN. Patients were randomized on one of these two regimens: 100% of RI on TPN or 50% of Regular insulin added to TPN bag and 50% subcutaneous GI. Data were analyzed according to intention-to-treat principle. RESULTS: 81 patients were on RI and 80 on GI. No differences were observed in neither average total daily dose of insulin, programmed or correction, nor in capillary mean blood glucose during TPN infusion (165.3 ± 35.4 in RI vs 172.5 ± 43.6 mg/dL in GI; p = 0.25). Mean capillary glucose was significantly lower in the GI group within two days after TPN interruption (160.3 ± 45.1 in RI vs 141.7 ± 43.8 mg/dL in GI; p = 0.024). The percentage of capillary glucose above 180 mg/dL was similar in both groups. The rate of capillary glucose ≤70 mg/dL, the number of hypoglycemic episodes per 100 days of TPN, and the percentage of patients with non-severe hypoglycemia were significantly higher on GI group. No severe hypoglycemia was detected. No differences were observed in length of stay, infectious complications, or hospital mortality. CONCLUSION: Effectiveness of both regimens was similar. GI group achieved better metabolic control after TPN interruption but non-severe hypoglycemia rate was higher in the GI group. CLINICAL TRIAL REGISTRY: This trial is registered at clinicaltrials.gov as NCT02706119.

Parkinson's Disease Severity at 3 Years Can Be Predicted from Non-Motor Symptoms at Baseline.

OBJECTIVE: The aim of this study is to present a predictive model of Parkinson's disease (PD) global severity, measured with the Clinical Impression of Severity Index for Parkinson's Disease (CISI-PD). METHODS: This is an observational, longitudinal study with annual follow-up assessments over 3 years (four time points). A multilevel analysis and multiple imputation techniques were performed to generate a predictive model that estimates changes in the CISI-PD at 1, 2, and 3 years. RESULTS: The clinical state of patients (CISI-PD) significantly worsened in the 3-year follow-up. However, this change was of small magnitude (effect size: 0.44). The following baseline variables were significant predictors of the global severity change: baseline global severity of disease, levodopa equivalent dose, depression and anxiety symptoms, autonomic dysfunction, and cognitive state. The goodness-of-fit of the model was adequate, and the sensitive analysis showed that the data imputation method applied was suitable. CONCLUSION: Disease progression depends more on the individual's baseline characteristics than on the 3-year time period. Results may contribute to a better understanding of the evolution of PD including the non-motor manifestations of the disease.

Distribution and impact on quality of life of the pain modalities assessed by the King's Parkinson's disease pain scale.

In Parkinson's disease, pain is a prevalent and complex symptom of diverse origin. King's Parkinson's disease pain scale, assesses different pain syndromes, thus allowing exploration of its differential prevalence and influence on the health-related quality of life of patients. Post hoc study 178 patients and 83 matched controls participating in the King's Parkinson's disease pain scale validation study were used. For determining the respective distribution, King's Parkinson's disease pain scale items and domains scores = 0 meant absence and ≥1 presence of the symptom. The regular scores were used for the other analyses. Health-related quality of lifewas evaluated with EQ-5D-3L and PDQ-8 questionnaires. Parkinson's disease patients experienced more pain modalities than controls. In patients, Pain around joints (King's Parkinson's disease pain scale item 1) and Pain while turning in bed (item 8) were the most prevalent types of pain, whereas Burning mouth syndrome (item 11) and Pain due to grinding teeth (item 10) showed the lowest frequency. The total number of experienced pain modalities closely correlated with the PDQ-8 index, but not with other variables. For all pain types except Pain around joints (item 1) and pain related to Periodic leg movements/RLS (item 7), patients with pain had significantly worse health-related quality of life. The influence of pain, as a whole, on the health-related quality of life was not remarkable after adjustment by other variables. When the particular types of pain were considered, adjusted by sex, age, and Parkinson's disease duration, pain determinants were different for EQ-5D-3L and PDQ-8. King's Parkinson's disease pain scale allows exploring the distribution of the diverse syndromic pain occurring in Parkinson's disease and its association with health-related quality of life.

PASTASpark: multiple sequence alignment meets Big Data.

MOTIVATION: One basic step in many bioinformatics analyses is the multiple sequence alignment. One of the state-of-the-art tools to perform multiple sequence alignment is PASTA (Practical Alignments using SATé and TrAnsitivity). PASTA supports multithreading but it is limited to process datasets on shared memory systems. In this work we introduce PASTASpark, a tool that uses the Big Data engine Apache Spark to boost the performance of the alignment phase of PASTA, which is the most expensive task in terms of time consumption. RESULTS: Speedups up to 10× with respect to single-threaded PASTA were observed, which allows to process an ultra-large dataset of 200 000 sequences within the 24-h limit. AVAILABILITY AND IMPLEMENTATION: PASTASpark is an Open Source tool available at https://github.com/citiususc/pastaspark. CONTACT: josemanuel.abuin@usc.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Self-Assessment of Disability in Parkinson's Disease: The MDS-UPDRS Part II Versus Clinician-Based Ratings.

BACKGROUND: Parkinson's disease (PD) is characterized by motor and nonmotor symptoms that progress with time, causing disability. The performance of a disease-specific, self-applied tool for assessing disability, the MDS-UPDRS Part II, is tested against generic and rater-based rating scales. METHODS: An international, cross-sectional, observational study was performed. Patients were assessed with the Hoehn and Yahr (HY) and five disability measures: MDS-UPDRS Part II, Schwab and England Scale (S&E), Clinical Impression of Severity Index-PD (CISI-PD) Disability item, Barthel Index (BI), and Rapid Assessment of Disability Scale (RADS). Data analysis included correlation coefficients, Mann-Whitney and Kruskal-Wallis tests, and intraclass-correlation coefficient for concordance. RESULTS: The sample was composed of 451 patients, 55.2% men, with a mean age of 65.06 years (SD = 10.71). Disability rating scales correlated from |0.75| (CISI-PD Disability with BI) to 0.87 (MDS-UPDRS Part II with RADS). In general, MDS-UPDRS Part II showed high correlation coefficients with clinical variables and satisfactory concordance with the rest of disability measures, with ICC ranging from 0.83 (with BI) to 0.93 (with RADS). All disability rating scales showed statistical significant differences in the sample grouped by sex, age, disease duration, and severity level. CONCLUSIONS: The MDS-UPDRS Part II showed an appropriate performance to assess disability in PD, even better than some rater-based, generic or specific, scales applied in this study.

SparkBWA: Speeding Up the Alignment of High-Throughput DNA Sequencing Data.

Next-generation sequencing (NGS) technologies have led to a huge amount of genomic data that need to be analyzed and interpreted. This fact has a huge impact on the DNA sequence alignment process, which nowadays requires the mapping of billions of small DNA sequences onto a reference genome. In this way, sequence alignment remains the most time-consuming stage in the sequence analysis workflow. To deal with this issue, state of the art aligners take advantage of parallelization strategies. However, the existent solutions show limited scalability and have a complex implementation. In this work we introduce SparkBWA, a new tool that exploits the capabilities of a big data technology as Spark to boost the performance of one of the most widely adopted aligner, the Burrows-Wheeler Aligner (BWA). The design of SparkBWA uses two independent software layers in such a way that no modifications to the original BWA source code are required, which assures its compatibility with any BWA version (future or legacy). SparkBWA is evaluated in different scenarios showing noticeable results in terms of performance and scalability. A comparison to other parallel BWA-based aligners validates the benefits of our approach. Finally, an intuitive and flexible API is provided to NGS professionals in order to facilitate the acceptance and adoption of the new tool. The source code of the software described in this paper is publicly available at https://github.com/citiususc/SparkBWA, with a GPL3 license.

Analysis of four scales for global severity evaluation in Parkinson's disease.

Global evaluations of Parkinson's disease (PD) severity are available, but their concordance and accuracy have not been previously tested. The present international, cross-sectional study was aimed at determining the agreement level among four global scales for PD (Hoehn and Yahr, HY; Clinical Global Impression of Severity, CGIS; Clinical Impression of Severity Index, CISI-PD; and Patient Global Impression of Severity, PGIS) and identifying which of them better correlates with itemized PD assessments. Assessments included additional scales for evaluation of the movement impairment, disability, affective disorders, and quality of life. Spearman correlation coefficients, weighted and generalized kappa, and Kendall's concordance coefficient were used. Four hundred thirty three PD patients, 66% in HY stages 2 or 3, mean disease duration 8.8 years, were analyzed. Correlation between the global scales ranged from 0.60 (HY with PGIS) to 0.91 (CGIS with CISI-PD). Kendall's coefficient of concordance resulted 0.76 (P<0.0001). HY and CISI-PD showed the highest association with age, disease duration, and levodopa-equivalent daily dose, and CISI-PD with measures of PD manifestations, disability, and quality of life. PGIS and CISI-PD correlated similarly with anxiety and depression scores. The lowest agreement in classifying patients as mild, moderate, or severe was observed between PGIS and HY or CISI-PD (58%) and the highest between CGIS and CISI-PD (84.3%). The four PD global severity scales agree moderately to strongly among them; clinician-based ratings estimate PD severity, as established by other measures, better than PGIS; and the CISI-PD showed the highest association with measures of impairment, disability, and quality of life.

BigBWA: approaching the Burrows-Wheeler aligner to Big Data technologies.

UNLABELLED: BigBWA is a new tool that uses the Big Data technology Hadoop to boost the performance of the Burrows-Wheeler aligner (BWA). Important reductions in the execution times were observed when using this tool. In addition, BigBWA is fault tolerant and it does not require any modification of the original BWA source code. AVAILABILITY AND IMPLEMENTATION: BigBWA is available at the project GitHub repository: https://github.com/citiususc/BigBWA.

Is a practice-based rural research network feasible in Europe?

Research in family medicine is a well-established entity nationally and internationally, covering all aspects of primary care including remote and isolated practices. However, due to limited capacity and resources in rural family medicine, its potential is not fully exploited yet. An idea to foster European rural primary care research by establishing a practice-based research network has been recently put forward by several members of the European Rural and Isolated Practitioners Association (EURIPA) and the European General Practice Research Network (EGPRN). Two workshops on why, and how to design a practice-based research network among rural family practices in Europe were conducted at two international meetings. This paper revisits the definition of practice-based research in family medicine, reflects on the current situation in Europe regarding the research in rural family practice, and discusses a rationale for practice-based research in rural family medicine. A SWOT analysis was used as the main tool to analyse the current situation in Europe regarding the research in rural family practice at both meetings. The key messages gained from these meetings may be employed by the Wonca Working Party on research, the International Federation of Primary Care Research Network and the EGPRN that seek to introduce a practice-based research approach. The cooperation and collaboration between EURIPA and EGPRN creates a fertile ground to discuss further the prospect of a European practice-based rural family medicine research network, and to draw on the joint experience.

Active ageing and quality of life: factors associated with participation in leisure activities among institutionalized older adults, with and without dementia.

OBJECTIVES: Active ageing, considered from the perspective of participation in leisure activities, promotes life satisfaction and personal well-being. The aims of this work are to define and explain leisure activity profiles among institutionalized older adults, considering their sociodemographic characteristics and objective and subjective conditions in relation to their quality of life. METHODS: Two samples of institutionalized people aged 60 and over were analysed together: 234 older adults without dementia and 525 with dementia. Sociodemographic, economic, family and social network, and health and functioning variables were selected. Cluster analysis was applied to obtain activity profiles according to the leisure activities, and ordinal regression models were performed to analyse factors associated to activity level. RESULTS: The sample was clustered into three groups of people: active (27%), moderately active (35%) and inactive people (38%). In the final regression model (Nagelkerke pseudo R(2) = 0.500), a higher level of activity was associated with better cognitive function (Pfeiffer scale), self-perceived health status and functional ability, as well as with a higher frequency of gathering with family and friends, and higher educational level. CONCLUSION: The decline in physical and mental health, the loss of functional capabilities and the weakening of family and social ties represent a significant barrier to active ageing in a context of institutionalization.