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Combined impact of traditional and non-traditional health behaviors on mortality: a national prospective cohort study in Spanish older adults.

ABSTRACT: BACKGROUND: Data on the combined effect of lifestyles on mortality in older people have generally been collected from highly selected populations and have been limited to traditional health behaviors. In this study, we examined the combined impact of three traditional (smoking, physical activity and diet) and three non-traditional health behaviors (sleep duration, sedentary time and social interaction) on mortality among older adults. METHODS: A cohort of 3,465 individuals, representative of the Spanish population aged ?60 years, was established in 2000/2001 and followed-up prospectively through 2011. At baseline, the following positive behaviors were self-reported: never smoking or quitting tobacco >15 years, being very or moderately physically active, having a healthy diet score ? median in the cohort, sleeping 7 to 8 h/d, spending <8 h/d in sitting time, and seeing friends daily. Analyses were performed with Cox regression and adjusted for the main confounders. RESULTS: During an average nine-year follow-up, 1,244 persons died. Hazard ratios (95% confidence interval) for all-cause mortality among participants with two, three, four, five and six compared to those with zero to one positive behaviors were, respectively, 0.63 (0.46 to 0.85), 0.41 (0.31 to 0.55), 0.32 (0.24 to 0.42), 0.26 (0.20 to 0.35) and 0.20 (0.15 to 0.28) (P for trend <0.001). The results were similar regardless of age, sex and health status at baseline. Those with six vs. zero to one positive health behaviors had an all-cause mortality risk equivalent to being 14 years younger. Adding the three non-traditional to the four traditional behaviors improved the model fit (likelihood ratio test, P <0.001) and the accuracy of mortality prediction (c-statistic: + 0.0031, P = 0.040). CONCLUSIONS: Adherence to some traditional and non-traditional health behaviors may substantially reduce mortality risk in older adults.

SUBMITTER: Martinez-Gomez D

PROVIDER: S-EPMC3621845 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Combined impact of traditional and non-traditional health behaviors on mortality: a national prospective cohort study in Spanish older adults.

Martínez-Gómez David D Guallar-Castillón Pilar P León-Muñoz Luz M LM López-García Esther E Rodríguez-Artalejo Fernando F

BMC medicine 20130222

<h4>Background</h4>Data on the combined effect of lifestyles on mortality in older people have generally been collected from highly selected populations and have been limited to traditional health behaviors. In this study, we examined the combined impact of three traditional (smoking, physical activity and diet) and three non-traditional health behaviors (sleep duration, sedentary time and social interaction) on mortality among older adults.<h4>Methods</h4>A cohort of 3,465 individuals, represen ...[more]

PMID: 23433432

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Project description:BackgroundLifestyle risk behaviors are responsible for a large proportion of disease burden worldwide. Behavioral risk factors, such as smoking, poor diet, and physical inactivity, tend to cluster within populations and may have synergistic effects on health. As evidence continues to accumulate on emerging lifestyle risk factors, such as prolonged sitting and unhealthy sleep patterns, incorporating these new risk factors will provide clinically relevant information on combinations of lifestyle risk factors.Methods and findingsUsing data from a large Australian cohort of middle-aged and older adults, this is the first study to our knowledge to examine a lifestyle risk index incorporating sedentary behavior and sleep in relation to all-cause mortality. Baseline data (February 2006- April 2009) were linked to mortality registration data until June 15, 2014. Smoking, high alcohol intake, poor diet, physical inactivity, prolonged sitting, and unhealthy (short/long) sleep duration were measured by questionnaires and summed into an index score. Cox proportional hazards analysis was used with the index score and each unique risk combination as exposure variables, adjusted for socio-demographic characteristics. During 6 y of follow-up of 231,048 participants for 1,409,591 person-years, 15,635 deaths were registered. Of all participants, 31.2%, 36.9%, 21.4%, and 10.6% reported 0, 1, 2, and 3+ risk factors, respectively. There was a strong relationship between the lifestyle risk index score and all-cause mortality. The index score had good predictive validity (c index = 0.763), and the partial population attributable risk was 31.3%. Out of all 96 possible risk combinations, the 30 most commonly occurring combinations accounted for more than 90% of the participants. Among those, combinations involving physical inactivity, prolonged sitting, and/or long sleep duration and combinations involving smoking and high alcohol intake had the strongest associations with all-cause mortality. Limitations of the study include self-reported and under-specified measures, dichotomized risk scores, lack of long-term patterns of lifestyle behaviors, and lack of cause-specific mortality data.ConclusionsAdherence to healthy lifestyle behaviors could reduce the risk for death from all causes. Specific combinations of lifestyle risk behaviors may be more harmful than others, suggesting synergistic relationships among risk factors.

Project description:As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the "Healthy Ageing Initiative" study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.

Project description:BackgroundHospitalized older adults with preexisting dementia have increased risk of having delirium, but little is known regarding the effect of delirium superimposed on dementia (DSD) on the outcomes of these patients. Our aim was to investigate the association between DSD and hospital mortality and 12-mo mortality in hospitalized older adults.Methods and findingsThis was a prospective cohort study completed in the geriatric ward of a university hospital in São Paulo, Brazil. We included 1,409 hospitalizations of acutely ill patients aged 60 y and over from January 2009 to June 2015. Main variables and measures included dementia and dementia severity (Informant Questionnaire on Cognitive Decline in the Elderly, Clinical Dementia Rating) and delirium (Confusion Assessment Method). Primary outcomes were time to death in the hospital and time to death in 12 mo (for the discharged sample). Comprehensive geriatric assessment was performed at admission, and additional clinical data were documented upon death or discharge. Cases were categorized into four groups (no delirium or dementia, dementia alone, delirium alone, and DSD). The no delirium/dementia group was defined as the referent category for comparisons, and multivariate analyses were performed using Cox proportional hazards models adjusted for possible confounders (sociodemographic information, medical history and physical examination data, functional and nutritional status, polypharmacy, and laboratory covariates). Overall, 61% were women and 39% had dementia, with a mean age of 80 y. Dementia alone was observed in 13% of the cases, with delirium alone in 21% and DSD in 26% of the cases. In-hospital mortality was 8% for patients without delirium or dementia, 12% for patients with dementia alone, 29% for patients with delirium alone, and 32% for DSD patients (Pearson Chi-square = 112, p < 0.001). DSD and delirium alone were independently associated with in-hospital mortality, with respective hazard ratios (HRs) of 2.14 (95% CI = 1.33-3.45, p = 0.002) and 2.72 (95% CI = 1.77-4.18, p < 0.001). Dementia alone did not have a significant statistical association with in-hospital mortality (HR = 1.69, 95% CI = 0.72-2.30, p = 0.385). Finally, while 24% of the patients died after discharge, 12-mo mortality was not associated with dementia or delirium in any of the diagnostic groups (DSD: HR = 1.15, 95% CI = 0.79-1.68, p = 0.463; delirium alone: HR = 1.05, 95% CI = 0.71-1.54, p = 0.810; dementia alone: HR = 1.19, 95% CI = 0.79-1.78, p = 0.399). Limitations to this study include not exploring the effects of the duration and severity of delirium on the outcomes.ConclusionsDSD and delirium alone were independently associated with a worse prognosis in hospitalized older adults. Health care professionals should recognize the importance of delirium as a predictor of hospital mortality regardless of the coexistence with dementia.

Project description:Background:Excessive sedentary time is ubiquitous in Western societies. Previous studies have relied on self-reporting to evaluate the total volume of sedentary time as a prognostic risk factor for mortality and have not examined whether the manner in which sedentary time is accrued (in short or long bouts) carries prognostic relevance. Objective:To examine the association between objectively measured sedentary behavior (its total volume and accrual in prolonged, uninterrupted bouts) and all-cause mortality. Design:Prospective cohort study. Setting:Contiguous United States. Participants:7985 black and white adults aged 45 years or older. Measurements:Sedentary time was measured using a hip-mounted accelerometer. Prolonged, uninterrupted sedentariness was expressed as mean sedentary bout length. Hazard ratios (HRs) were calculated comparing quartiles 2 through 4 to quartile 1 for each exposure (quartile cut points: 689.7, 746.5, and 799.4 min/d for total sedentary time; 7.7, 9.6, and 12.4 min/bout for sedentary bout duration) in models that included moderate to vigorous physical activity. Results:Over a median follow-up of 4.0 years, 340 participants died. In multivariable-adjusted models, greater total sedentary time (HR, 1.22 [95% CI, 0.74 to 2.02]; HR, 1.61 [CI, 0.99 to 2.63]; and HR, 2.63 [CI, 1.60 to 4.30]; P for trend < 0.001) and longer sedentary bout duration (HR, 1.03 [CI, 0.67 to 1.60]; HR, 1.22 [CI, 0.80 to 1.85]; and HR, 1.96 [CI, 1.31 to 2.93]; P for trend < 0.001) were both associated with a higher risk for all-cause mortality. Evaluation of their joint association showed that participants classified as high for both sedentary characteristics (high sedentary time [?12.5 h/d] and high bout duration [?10 min/bout]) had the greatest risk for death. Limitation:Participants may not be representative of the general U.S. population. Conclusion:Both the total volume of sedentary time and its accrual in prolonged, uninterrupted bouts are associated with all-cause mortality, suggesting that physical activity guidelines should target reducing and interrupting sedentary time to reduce risk for death. Primary Funding Source:National Institutes of Health.

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Combined impact of traditional and non-traditional health behaviors on mortality: a national prospective cohort study in Spanish older adults.

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Combined impact of traditional and non-traditional health behaviors on mortality: a national prospective cohort study in Spanish older adults.

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