Duration and quality of sleep and risk of physical function impairment and disability in older adults: Results from the ENRICA and ELSA Cohorts

Sleep duration and quality have been associated with poor physical function, but both the temporality of the association and the independence of sleep duration and quality are unclear. We examined the prospective association of sleep duration and quality with physical function impairment and disability in older adults. Data were taken from participants in the Seniors-ENRICA (2012-2015, n= 1,773) and in the ELSA cohort (waves 4 and 6, n=4,885) aged ≥60 years. Sleep duration and quality were self-reported. Physical function impairment and disability was obtained either from self-reports (ENRICA and ELSA) or from performance assessment (ENRICA). Logistic regression models were adjusted for potential confounders. After a follow-up of 2.0-2.8 years, no association was found between changes in sleep duration and physical function impairment or disability. However, in both studies, poor general sleep quality was linked to higher risk of impaired agility [OR: 1.93 (95% CI: 1.30-2.86) in Seniors-ENRICA and 1.65 (1.24-2.18) in ELSA study] and mobility [1.46 (0.98-2.17) in Seniors-ENRICA and 1.59 (1.18-2.15) in ELSA study]. Poor general sleep quality was also associated with decreased physical component summary (PCS) [1.39 (1.05-1.83)], disability in instrumental activities of daily living [1.59 (0.97-2.59)] and in basic activities of daily living [1.73 (1.14-2.64)] in Seniors-ENRICA. In addition, compared to those with no sleep complaints, participants with 2 or more sleep complaints had greater risk of impaired agility, impaired mobility, decreased PCS and impaired lower extremity function in both cohorts. Poor sleep quality was associated with higher risk of physical impairment and disability in older adults from Spain and from EnglandThe Seniors-ENRICA study was supported by FIS grants 13/0288, 16/609 and 16/1512 (Instituto de Salud Carlos III, State Secretary of R+D+I, and FEDER/FSE), the FRAILOMIC Initiative (FP7-HEALTH-2012-Proposal no. 305483-2), the ATHLOS project (EU H2020- Project ID: 635316) and the JPI HDHL (SALAMANDER project

Frequency, intensity and localization of pain as risk factors for frailty in older adults

Background: the association between pain characteristics and frailty risk is uncertain. Objective: to investigate the separate impact of the frequency, intensity and location of pain on frailty risk and its possible mechanisms. Methods: prospective cohort of 1505 individuals ≥63 years followed between 2012 and 2015 in Spain. In 2012, pain was classified into: lowest pain (Score 0), middle pain (Score 1-4) and highest pain (Score 5-6). Incident frailty was assessed in 2015 as having ≥3 Fried criteria or a Frailty Index (FI) ≥0.30. Results: in multivariate analyses, the risk of frailty (measured with the Fried criteria or the FI) increased progressively with the frequency of pain, its intensity and the number of pain locations. Compared with those having the lowest pain score, the odds ratio (95% confidence interval) of Fried-based frailty was 1.24 (0.56-2.75) in the middle score and 2.39 (1.34-4.27; P-trend <0.01) in the highest score. Corresponding values for frailty as FI ≥0.30 were 1.39 (0.80-2.42) and 2.77 (1.81-4.24; P-trend <0.01). Odds ratios did not change after adjustment for alcohol intake, Mediterranean diet adherence or sedentary time, but were reduced with adjustment for pain-associated chronic diseases (cardiovascular disease, diabetes, chronic lung disease, osteomuscular disease and depression). A higher pain score was linked to higher risk of exhaustion and low physical activity (two out of five Fried criteria) and to a worse score in all FI domains. Conclusion: frequency, intensity and location of pain were associated with higher risk of frailty. Study associations were partly explained by pain-associated morbidity

Obesity-related eating behaviors are associated with higher food energy density and higher consumption of sugary and alcoholic beverages: a cross-sectional study.

Obesity-related eating behaviors (OREB) are associated with higher energy intake. Total energy intake can be decomposed into the following constituents: food portion size, food energy density, the number of eating occasions, and the energy intake from energy-rich beverages. To our knowledge this is the first study to examine the association between the OREB and these energy components.Data were taken from a cross-sectional study conducted in 2008-2010 among 11,546 individuals representative of the Spanish population aged ≥ 18 years. Information was obtained on the following 8 self-reported OREB: not planning how much to eat before sitting down, eating precooked/canned food or snacks bought at vending machines or at fast-food restaurants, not choosing low-energy foods, not removing visible fat from meat or skin from chicken, and eating while watching TV. Usual diet was assessed with a validated diet history. Analyses were performed with linear regression with adjustment for main confounders.Compared to individuals with ≤ 1 OREB, those with ≥ 5 OREB had a higher food energy density (β 0.10; 95% CI 0.08, 0.12 kcal/g/day; p-trend<0.001) and a higher consumption of sugary drinks (β 7; 95% CI -7, 20 ml/day; p-trend<0.05) and of alcoholic beverages (β 24; 95% CI 10, 38 ml/day; p-trend<0.001). Specifically, a higher number of OREB was associated with higher intake of dairy products and red meat, and with lower consumption of fresh fruit, oily fish and white meat. No association was found between the number of OREB and food portion size or the number of eating occasions.OREB were associated with higher food energy density and higher consumption of sugary and alcoholic beverages. Avoiding OREB may prove difficult because they are firmly socially rooted, but these results may nevertheless serve to palliate the undesirable effects of OREB by reducing the associated energy intake

Symptoms awareness, emergency medical service utilization and hospital transfer delay in myocardial infarction

Abstract Background The length of time between symptom onset and reperfusion therapy in patients with ST-segment elevation acute myocardial infarction (STEMI) is a key determinant of mortality. Information on this delay is scarce, particularly for developing countries. The objective of the study is to prospectively evaluate the individual components of reperfusion time (RT) in patients with STEMI treated at a University Hospital in 2012. Methods Medical records were reviewed to determine RT, its main (patient delay time [PDT] and system delay time [SDT]) and secondary components and hospital access variables. Cognitive responses were evaluated using a semi-structured questionnaire. Results A total of 50 patients with a mean age of 59 years (SD = 10.5) were included, 64% of whom were male. The median RT was 430 min, with an interquartile range of 315–750 min. Regarding the composition of RT in the sample, PDT corresponded to 18.9% and SDT to 81.1%. Emergency medical services were used in 23.5% of cases. Patients treated in intermediate care units showed a significant increase in SDT (p = 0.008). Regarding cognitive variables, PDT was approximately 40 min longer among those who answered “I didn’t think it was serious” (p = 0.024). Conclusions In a Brazilian tertiary public hospital, RT was higher than that recommended by international guidelines, mainly because of long SDT, which was negatively affected by time spent in intermediate care units. Emergency Medical Services underutilization was noted. A patient’s low perception of severity increased PDT

Top positive and negative Pearson correlations coefficients between food groups and total energy density from solid food^a.

<p>N = 11,546.</p>a<p>Results are shown only for food groups with Pearson correlation coefficient >0.05.</p>b<p>Jam, chocolate pudding, chocolate truffles, chocolate-hazelnut creams, nougats, marzipan, cakes, sponge cakes, croissants, donuts, pastries and cookies.</p>c<p>White bread, wholemeal bread, breadsticks, hamburger and hotdog buns.</p>d<p>Pork sausages, veal sausages, and poultry sausages.</p>e<p>Unripened cheese, ripened cheese, processed cheese, yogurt, custard, mousse, and ice cream.</p>f<p>Unstuffed pasta, stuffed pasta, and pizza.</p>g<p>Baked potatoes, boiled potatoes, mashed potatoes, French fries, and potato chips.</p>h<p>Veal, beef, pork, wild boar, horse, lamb and goat..</p>i<p>Berries, custard apple, apple, pear, plum, pomegranate, passion fruit, fig, kiwi, lychee, lime, lemon, tangerine, orange, mango, peach, nectarine, apricot, loquat, persimmon, watermelon, papaya, and pineapple.</p>j<p>Chard, celery, watercress, collard green, borage, spinach, cabbage, endive, lettuce, thistle, scallion, fennel, onion, leek, garlic, asparagus, palm heart, turnip, parsnip, radishes, beets, soy, carrot, artichoke, eggplant, broccoli, cauliflower, zucchini, pumpkin, green been, corn, pepper, tomato, champignon, and mushroom.</p>k<p>Pollack, weever, blue whiting, cod, sea bream, red scorpionfish, dogfish, black seabream, pouting, megrim, halibut, common sole, seabass, whiting, hake, grouper, flathead mullet, common pandora, young hake, catshark, plaice, angler, blonde ray, turbot, red mullet, and white seabream.</p>l<p>Chickpeas, beans, and lentils.</p>m<p>Anchovy, sardine, eel, herring, tuna, albacore, Atlantic horse mackerel, Atlantic mackerel, transparent goby, conger, swordfish, pomfret, and salmon.</p>n<p>Chicken, quail, pheasant, goose, duck, turkey, pigeon, partridge and rabbit.</p

Association of the number of obesity-related eating behaviors with individual foods groups associated with lower energy density (ED).

<p>N = 11,546.</p>*<p>p<0.05;</p><p>** p<0.001;</p><p>EO: Eating occasion; SD: Standard deviation.</p>a<p>Obesity-related eating behaviors are as follows: not planning how much to eat before sitting down, consuming precooked and/or canned foods ≥1 time/wk, buying snacks at vending machines ≥1 time/wk, eating at fast-food restaurants ≥1 time/wk, never or almost never choosing low-energy foods, never or almost never removing visible fat from meat, never or almost never removing skin from chicken, and eating while watching TV >2 times/wk.</p>b<p>Adjusted for sex, age, educational level, smoking, social class, leisure time physical activity, time spent watching TV, body mass index (<25, 25–29.9, ≥30 kg/m²), coronary disease, stroke, asthma, cancer, osteomuscular disease, portion size of solid food, energy density of solid food, number of EO of solid food, consumption of sugary beverages, and consumption of alcoholic beverages, when appropriate.</p

Portion size, energy density, number of eating occasions, and consumption of sugary and alcoholic beverages, according to the characteristics of the study participants^a.

<p>EO: Eating occasion; SD: Standard deviation</p>a<p>Values are means (standard deviation).</p>b<p>Eating occasions are breakfast, mid-morning snack, lunch, afternoon snacks, dinner and eating between these meals (in most cases after dinner).</p