2f:I[703058,["/_next/static/immutable/chunks/3bhq_5eh1d760.js","/_next/static/immutable/chunks/1855-ogyi8eh4.js","/_next/static/immutable/chunks/2pfsjpn6k6zlg.js","/_next/static/immutable/chunks/26c7v3_3uwnwu.js","/_next/static/immutable/chunks/25a3ozcrvi1ff.js","/_next/static/immutable/chunks/2r4wx50nvjh_4.js","/_next/static/immutable/chunks/23fsw06hsi5b1.js","/_next/static/immutable/chunks/06_yt270h41rg.js","/_next/static/immutable/chunks/3ezsiz-7shkna.js","/_next/static/immutable/chunks/20x2y96ldu5wx.js","/_next/static/immutable/chunks/2lxntcm3pd1_h.js","/_next/static/immutable/chunks/00dgco8irq3jw.js","/_next/static/immutable/chunks/00nwe-kq0rboc.js","/_next/static/immutable/chunks/2051ffx3xzio9.js"],"PaperListPageClient"] 30:T975,Daytime napping is common among older adults, but evidence linking objective daytime nap patterns to mortality is limited, as prior studies relied largely on self-report. Some daytime nap characteristics, such as timing and variability of nap duration across days, are also understudied. To test whether actigraphy-measured daytime nap duration, frequency, variability, and timing are associated with all-cause mortality. This prospective cohort study used data from the population-based Rush Memory and Aging Project, in which the analytic baseline was initiated in August 2005. Participants in this current study were community-dwelling adults aged 56 years or older in northern Illinois, with up to 19 years of follow-up. Data for this study were gathered until April 2025. Daytime napping, defined as sleeping between 9 am and 7 pm and assessed by up to 14 days of actigraphy data. Cox proportional hazards regression models were used to estimate associations between baseline daytime nap characteristics (duration, frequency, variability in duration across days, and timing) and subsequent all-cause mortality. Among 1338 participants over a mean (SD) follow-up of 8.30 (4.78) years (mean [SD] participant age, 81.40 [7.39] years; 1018 [76.0%] female), 926 (69.2%) died at a mean (SD) of 7.54 (4.52) years (range, 0.08-19.08 years) after analytic baseline. Participants wore the actigraphy device for a mean (SD) of 9.58 (1.22) days. After adjusting for all covariates, longer daytime nap duration (adjusted hazard ratio [AHR] per 1-hour increase, 1.13; 95% CI, 1.04-1.23; P = .005) and higher nap frequency (AHR per additional daily nap, 1.07; 95% CI, 1.02-1.13; P = .003) at baseline were associated with increased mortality. Morning nappers had higher mortality risk compared with early afternoon nappers (AHR, 1.30; 95% CI, 1.03-1.64; P = .03). Variability in daytime nap duration was not associated with mortality after covariate adjustment (AHR per 1-hour increase, 1.01; 95% CI, 0.89-1.14; P = .93). In this prospective cohort study of community-dwelling older adults, longer and more frequent daytime napping, especially in the morning, were associated with higher all-cause mortality. The findings suggest incorporating wearable device-based daytime nap assessments into clinical and public health practices may provide novel opportunities for early risk identification of health conditions.31:Tfc8, Importance Daytime napping is common among older adults, but evidence linking objective daytime nap patterns to mortality is limited, as prior studies relied largely on self-report. Some daytime nap characteristics, such as timing and variability of nap duration across days, are also understudied. Objective To test whether actigraphy-measured daytime nap duration, frequency, variability, and timing are associated with all-cause mortality. Design, Setting, and Participants This prospective cohort study used data from the population-based Rush Memory and Aging Project, in which the analytic baseline was initiated in August 2005. Participants in this current study were community-dwelling adults aged 56 years or older in northern Illinois, with up to 19 years of follow-up. Data for this study were gathered until April 2025. Main Outcome and Measures Daytime napping, defined as sleeping between 9 am and 7 pm and assessed by up to 14 days of actigraphy data. Cox proportional hazards regression models were used to estimate associations between baseline daytime nap characteristics (duration, frequency, variability in duration across days, and timing) and subsequent all-cause mortality. Results Among 1338 participants over a mean (SD) follow-up of 8.30 (4.78) years (mean [SD] participant age, 81.40 [7.39] years; 1018 [76.0%] female), 926 (69.2%) died at a mean (SD) of 7.54 (4.52) years (range, 0.08-19.08 years) after analytic baseline. Participants wore the actigraphy device for a mean (SD) of 9.58 (1.22) days. After adjusting for all covariates, longer daytime nap duration (adjusted hazard ratio [AHR] per 1-hour increase, 1.13; 95% CI, 1.04-1.23; P = .005) and higher nap frequency (AHR per additional daily nap, 1.07; 95% CI, 1.02-1.13; P = .003) at baseline were associated with increased mortality. Morning nappers had higher mortality risk compared with early afternoon nappers (AHR, 1.30; 95% CI, 1.03-1.64; P = .03). Variability in daytime nap duration was not associated with mortality after covariate adjustment (AHR per 1-hour increase, 1.01; 95% CI, 0.89-1.14; P = .93). Conclusions and Relevance In this prospective cohort study of community-dwelling older adults, longer and more frequent daytime napping, especially in the morning, were associated with higher all-cause mortality. The findings suggest incorporating wearable device–based daytime nap assessments into clinical and public health practices may provide novel opportunities for early risk identification of health conditions. 32:T6a0,The aim of this study was to examine whether the association between happiness and all-cause mortality persists after adjustment for health status and sociodemographic factors in Japanese adults. The study addressed the gap in understanding the relationship between happiness and mortality in Japanese populations. A total of 3,187 adults from Minami-Izu, Japan, were included in a prospective study tracking all-cause mortality from October 2016 to October 2023. Happiness was assessed using a single-item, self-report measure of state emotional well-being. Sociodemographic factors (age, sex, marital status, education, and economic status) and health status (body mass index and physical function) were considered as covariates. Binary logistic regression models were used to examine the association between happiness and mortality, adjusted for these factors. During the follow-up, 277 participants died. The risk of all-cause mortality was higher in those who reported being unhappy compared with those who reported being happy after adjustment for age and sex (OR: 2.69, 95% CI: 1.63-4.44) and also significantly higher in those who reported being unhappy compared with those who reported being happy after adjustment for age, sex, all socioeconomic factors and health status (OR: 1.85, 95% CI: 1.09-3.16). Sensitivity analyses excluding deaths within the first year showed consistent results. Happiness is independently associated with reduced mortality risk, even after adjustment for sociodemographic and health status factors. These findings highlight the importance of promoting positive well-being in Japanese populations. (PsycInfo Database Record (c) 2026 APA, all rights reserved).33:T815,The effects of small, realistic changes in physical activity and sedentary behaviour on population-level mortality are unclear. We aimed to estimate the proportion of deaths preventable by 5-min and 10-min incremental increases in moderate-to-vigorous intensity physical activity (MVPA) and 30-min and 60-min reductions in daily sedentary time. We did an individual participant data meta-analysis of prospective cohort studies. We included studies with device-measured physical activity and sedentary time. We estimated the proportion of deaths prevented (potential impact fractions; PIFs) by changes in (1) the approximately 20% least active participants (high-risk approach) and (2) all participants except the approximately 20% most active (population-based approach). We calculated PIFs from adjusted hazard ratios estimated for 5-min and 10-min increases in MVPA and 30-min and 60-min reductions in sedentary time from observed levels across the activity distribution. We included seven cohorts from Norway, Sweden, and the USA (n=40 327; 4895 deaths). Data from the UK Biobank (n=94 719; 3487 deaths) were analysed separately. A 5-min/day increase in MVPA in the least active participants might prevent 6·0% (95% CI 4·3-7·4) of all deaths. A similar increase in MVPA in all participants except the most active might prevent 10·0% (6·3-13·4) of all deaths. Reducing sedentary time by 30 min/day might prevent 3·0% (2·0-4·1) of all deaths in the high-risk approach and 7·3% (4·8-9·6) in the population-based approach. Results from the UK Biobank were of a smaller magnitude but still substantial-eg, reducing sedentary time by 30 min/day in all except the most active participants was associated with preventing 4·5% (2·8-6·1) of total deaths. Small and realistic increases in MVPA of 5 min/day might prevent up to 6% of all deaths in a high-risk approach and 10% of all deaths in population-based approach. Reducing sedentary time by 30 min/day might prevent a smaller, but still meaningful, proportion of deaths in the two risk scenarios. None.25:[[["$","script","0",{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"CollectionPage\",\"name\":\"All-Cause Mortality: condition Research & Clinical Takeaways\",\"description\":\"Explore peer-reviewed papers on All-Cause Mortality (condition). Clinician-written summaries and actionable practice insights. 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the meta-analysis component is based on only three cohorts, limiting its statistical power compared to larger formal meta-analyses.","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.30964","tag_type":"warning","value":"The meta-analysis pools three cohorts with different enrollment ages, follow-up periods, and covariate sets, which may introduce heterogeneity not fully captured in aggregate HR estimates.","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.30964","tag_type":"warning","value":"The stroke finding was inconsistent across cohorts (significant in LonGenity and NECS but not UK Biobank), and the cancer null result should be interpreted cautiously given potential follow-up length limitations.","value_numeric":null}],"summaries":{"what_studied":"Three independent longitudinal cohorts (LonGenity, NECS, UK Biobank; combined n=4,030; follow-up up to 16–29 years) compared offspring of centenarians (at least one parent ≥100 years) with offspring of shorter-lived parents on age at death and onset of CVD, cancer, hypertension, and stroke, using Cox proportional hazards regression and meta-analysis.","key_result":"Centenarians' offspring had significantly lower hazards for death (HR 0.58; 95% CI, 0.41–0.81), CVD (HR 0.67; 95% CI, 0.46–0.97), and hypertension (HR 0.68; 95% CI, 0.62–0.75), with death delayed by ~3.1 years and hypertension onset delayed by ~5.2 years; stroke benefit was seen only in LonGenity and NECS; no cancer association was found.","limitations":"- Cohorts differ in enrollment criteria, follow-up duration, and covariate availability, limiting direct comparability.\n- Observational design cannot establish causality; residual confounding by lifestyle and socioeconomic factors is possible.\n- Stroke and cancer results were inconsistent across cohorts, reducing confidence in those specific findings.","practice_takeaway":"Having a centenarian parent is associated with meaningfully later onset of cardiovascular disease and hypertension (by ~3–5 years) and lower mortality risk — supporting parental longevity as a useful criterion when recruiting participants for healthy-aging research. Clinicians can reassure patients with centenarian parents that their cardiovascular and longevity outlook may be meaningfully better than average."}},{"doi":"10.1007/s40256-026-00811-x","pmid":"42587225","title":"Evaluating the Impact of Stair Climbing on Cardiovascular Risk Reduction: A Systematic Review and Meta-analysis","journal_title":"American Journal of Cardiovascular Drugs","journal_issn":"1175-3277","published_at":"2026-08-13T00:00:00.000Z","url":"https://doi.org/10.1007/s40256-026-00811-x","source_score":1795,"is_retracted":false,"is_open_access":false,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-08-19T06:28:39.961Z","updated_at":"2026-08-19T06:28:39.961Z","tags":[{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"category","value":"Cardiac & Cardiovascular Systems","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"condition","value":"All-Cause Mortality","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"condition","value":"Cardiovascular Disease","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"condition","value":"Cardiovascular Mortality","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"impact_flag","value":"confirms_prior","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"level_of_evidence","value":"2","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"modality","value":"Physical Activity Intervention","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"sample_size","value":"n=480479","value_numeric":480479},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"study_design","value":"Systematic Review and Meta-Analysis","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"warning","value":"All 9 included studies were observational (cohort design); no randomized controlled trials met inclusion criteria despite RCTs being listed as eligible — causality cannot be inferred.","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"warning","value":"Cardiovascular morbidity outcomes (MI, ischaemic stroke, heart failure) are mentioned as associated with stair climbing but no pooled effect sizes are reported in the abstract or results — quantitative data for morbidity are absent.","value_numeric":null},{"paper_doi":"10.1007/s40256-026-00811-x","tag_type":"warning","value":"The cardiovascular mortality pooled analysis included only 5 of the 9 studies (n=455,619); the remaining 4 studies contributed to other outcomes but details are not provided in the available text.","value_numeric":null}],"summaries":{"what_studied":"This systematic review and meta-analysis evaluated the association between stair climbing and cardiovascular outcomes — including cardiovascular mortality, all-cause mortality, and cardiovascular disease incidence (MI, ischaemic stroke, heart failure) — across 9 observational studies with 480,479 patients identified from PubMed and Embase up to April 2025.","key_result":"Stair climbing was associated with a 39% reduction in cardiovascular mortality (RR 0.61, 95% CI 0.48–0.79, p = 0.0002) and a 24% reduction in all-cause mortality (RR 0.76, 95% CI 0.62–0.94, p = 0.01) in pooled analysis of 5 studies (n = 455,619).","limitations":"- All included studies were observational (cohort design), so causality cannot be established.\n- Three studies rated only \"Fair\" quality on the Newcastle-Ottawa Scale, introducing residual confounding risk.\n- Only 9 studies met inclusion criteria; cardiovascular morbidity outcomes (MI, stroke, heart failure) were not pooled quantitatively.","practice_takeaway":"Encourage patients — especially sedentary ones — to incorporate stair climbing into daily routines at work and home, as even this modest activity is linked to meaningfully lower cardiovascular and all-cause mortality. No special equipment or gym access is required."}},{"doi":"10.1001/jamanetworkopen.2026.28227","pmid":"42574013","title":"Physical Fitness and All-Cause Mortality in Older Adults","journal_title":"JAMA Network Open","journal_issn":"2574-3805","published_at":"2026-08-10T00:00:00.000Z","url":"https://doi.org/10.1001/jamanetworkopen.2026.28227","source_score":122,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-08-17T13:33:02.619Z","updated_at":"2026-08-17T13:33:02.619Z","tags":[{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"age_group","value":"older_adult","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"category","value":"Medicine, General & Internal","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"condition","value":"All-Cause Mortality","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"condition","value":"Physical Deconditioning","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"impact_flag","value":"practice_changing","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"level_of_evidence","value":"2","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"modality","value":"Balance and Agility Testing","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"modality","value":"Cardiorespiratory Fitness Testing","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"modality","value":"Physical Fitness Assessment","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"sample_size","value":"n=13423","value_numeric":13423},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"study_design","value":"Prospective Cohort Study","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"warning","value":"Fitness assessments were performed at a single time point (2015–2016); no longitudinal fitness trajectory data were available, which may underestimate the true association.","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"warning","value":"The cohort is 62.5% female, which may affect generalizability to male-predominant populations.","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.28227","tag_type":"warning","value":"The composite fitness index is a study-specific construct and not yet validated for clinical use outside this cohort.","value_numeric":null}],"summaries":{"what_studied":"This nationwide cohort study in Taiwan evaluated associations between objectively measured physical fitness across 4 domains (cardiorespiratory fitness, muscular strength, flexibility, balance/agility) and all-cause mortality in 13,423 community-dwelling adults aged ≥65 years, with up to ~7 years of follow-up (2015–2022).","key_result":"12.2% of participants died over median 7.0 years. Compared to the lowest quintile, the highest quintile had significantly lower mortality: 8-foot up-and-go (AHR 0.41), 1-leg stance (AHR 0.50), 30-second chair stand (AHR 0.55), and 2-minute step test (AHR 0.58). A composite fitness index showed the strongest protection (AHR 0.39, 95% CI 0.32–0.48).","limitations":"- Single-country cohort (Taiwan) limits generalizability to other populations.\n- Self-reported physical activity was used as a covariate, introducing potential recall bias.\n- Fitness was measured only at baseline; changes in fitness over time were not captured.","practice_takeaway":"Routine objective fitness testing — especially balance, agility, and lower-body strength — can meaningfully stratify mortality risk in older adults beyond comorbidity burden alone. Clinicians should consider integrating simple tests like the 8-foot up-and-go and 1-leg stance into geriatric assessments to guide function-oriented interventions."}},{"doi":"10.1080/02813432.2026.2712981","pmid":"42568036","title":"Link between alcohol consumption and myocardial infarction, stroke, and all-cause mortality among Swedish male automotive workers over 30 years","journal_title":"Scandinavian Journal of Primary Health Care","journal_issn":"0281-3432","published_at":"2026-08-07T00:00:00.000Z","url":"https://doi.org/10.1080/02813432.2026.2712981","source_score":19,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-08-17T13:33:20.978Z","updated_at":"2026-08-17T13:33:20.978Z","tags":[{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"age_group","value":"adult","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"category","value":"Primary Health Care","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"condition","value":"All-Cause Mortality","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"condition","value":"Myocardial Infarction","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"condition","value":"Stroke","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"impact_flag","value":"limited_evidence","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"level_of_evidence","value":"2","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"modality","value":"Alcohol Consumption","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"sample_size","value":"n=973","value_numeric":973},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"sex","value":"male","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"study_design","value":"Prospective Cohort Study","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"warning","value":"Alcohol intake was measured only at baseline (1993); no data on changes in consumption patterns over the 30-year follow-up were available, which may introduce misclassification bias.","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"warning","value":"Socioeconomic confounding is substantial: alcohol and wine drinkers were more often married and in clerical roles, which are independently protective. Multivariate adjustment attenuates several associations, so causal interpretation of beverage-specific effects is uncertain.","value_numeric":null},{"paper_doi":"10.1080/02813432.2026.2712981","tag_type":"warning","value":"The OR for all-cause mortality and MI for total alcohol drinkers are both reported as 0.57 in the abstract — this appears to be a copy/paste error in the source text; the MI OR refers to a different comparison than mortality and readers should consult Table 5 for full detail.","value_numeric":null}],"summaries":{"what_studied":"This 30-year prospective cohort study followed 973 Swedish men aged 45–50 at baseline (1993–1995), employed at a Volvo automotive plant, to examine associations between self-reported alcohol consumption (by beverage type and quantity) and first-time MI, first-time stroke, and all-cause mortality through December 2023.","key_result":"Alcohol drinkers had lower odds of all-cause mortality (OR 0.57, 95% CI 0.41–0.79) and MI (OR 0.57, 95% CI 0.41–0.79) vs. non-drinkers, with a 1.3-year median survival advantage. Beer drinkers had higher odds of MI (OR 1.70, 95% CI 1.05–2.75) and earlier MI onset by ~0.6 years. Wine drinking was protective for mortality (OR 0.55, 95% CI 0.41–0.75) but not independently for MI in multivariate analysis. No significant association was found between any alcohol type and stroke (p > 0.05).","limitations":"- All-male, single-occupation Swedish cohort limits generalizability to women and other populations.\n- Alcohol intake was self-reported at baseline only; consumption may have changed substantially over 30 years.\n- High-risk drinkers were underrepresented (n=87), and no per-occasion intake data were available, limiting dose-response precision.","practice_takeaway":"The apparent survival benefit of moderate alcohol and the MI risk linked to beer likely reflect socioeconomic confounding (drinkers were more often married and in clerical roles) rather than a direct causal effect — clinicians should not use these findings to endorse alcohol use. Beer-drinking patients may warrant closer MI risk monitoring, but beverage-specific advice should await larger, more diverse studies."}},{"doi":"10.1016/j.annemergmed.2026.06.017","pmid":"42412031","title":"Association Between Time Spent in the Emergency Department and 30-Day Mortality: A Population-Level Observational Study in England","journal_title":"Annals of Emergency Medicine","journal_issn":"0196-0644","published_at":"2026-07-07T00:00:00.000Z","url":"https://doi.org/10.1016/j.annemergmed.2026.06.017","source_score":4,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-07-19T13:39:55.822Z","updated_at":"2026-07-19T13:39:55.822Z","tags":[{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"category","value":"Emergency Medicine","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"condition","value":"All-Cause Mortality","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"condition","value":"Emergency Department Crowding","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"impact_flag","value":"safety_signal","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"level_of_evidence","value":"3","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"sample_size","value":"n=6721179","value_numeric":6721179},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"study_design","value":"Retrospective Observational Study","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"warning","value":"Logistic regression was used with ED time as a continuous predictor compared to a 2-hour reference; the choice of reference point and model specification may influence interpretation of odds ratios.","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"warning","value":"The population is restricted to England's Type 1 EDs and non-immediate acuity patients; results may not apply to other health systems or higher-acuity presentations.","value_numeric":null},{"paper_doi":"10.1016/j.annemergmed.2026.06.017","tag_type":"warning","value":"The study is cross-sectional and observational; the authors explicitly caution that residual confounding — not a causal effect of ED time — may explain the mortality association.","value_numeric":null}],"summaries":{"what_studied":"This population-level retrospective observational study examined the association between time spent in Type 1 EDs in England and all-cause 30-day mortality among 6.7 million non-immediate acuity patients who survived to discharge or admission (March 2021–March 2022).","key_result":"1.3% of patients died within 30 days. Compared to a 2-hour ED stay, adjusted odds of 30-day death rose progressively: aOR 1.1 (95% CI 1.07–1.14) at 3 hours, 1.6 (1.48–1.68) at 6 hours, 1.9 (1.80–2.03) at 9 hours, and 2.1 (2.02–2.28) at 12 hours.","limitations":"- Cross-sectional, observational design cannot establish causality; residual confounding is a major concern.\n- Study restricted to non-immediate acuity patients in England's Type 1 EDs, limiting generalizability to other settings or higher-acuity presentations.\n- Reasons for longer stays (e.g., clinical complexity vs. system crowding) were not disentangled, making it unclear whether ED time itself drives mortality.","practice_takeaway":"Prolonged ED stays are linked to meaningfully higher 30-day mortality risk, even in non-immediate patients — but causality is unproven. Clinicians and administrators should treat ED length-of-stay as a potential patient safety signal worth monitoring, while awaiting causal research."}},{"doi":"10.1111/dom.70857","pmid":"42225278","title":"Associations of Changes in Weight and Relative Fat Mass With All‐Cause and Cardiovascular Disease Mortality Among Older Community‐Dwellers in Guangzhou, China","journal_title":"Diabetes, Obesity and Metabolism","journal_issn":"1462-8902","published_at":"2026-06-01T00:00:00.000Z","url":"https://doi.org/10.1111/dom.70857","source_score":2,"is_retracted":false,"is_open_access":false,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-06-09T13:50:33.109Z","updated_at":"2026-06-09T13:50:33.109Z","tags":[{"paper_doi":"10.1111/dom.70857","tag_type":"age_group","value":"older_adult","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"category","value":"Endocrinology & Metabolism","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"condition","value":"All-Cause Mortality","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"condition","value":"Cardiovascular Disease Mortality","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"condition","value":"Obesity","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"impact_flag","value":"practice_changing","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"level_of_evidence","value":"2","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"modality","value":"Anthropometric Monitoring","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"sample_size","value":"n=203962","value_numeric":203962},{"paper_doi":"10.1111/dom.70857","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"study_design","value":"Prospective Cohort Study","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"warning","value":"Relative fat mass (RFM) is a less commonly used metric than BMI or waist circumference; clinicians may need to calculate it (uses height and waist circumference) rather than rely on standard EHR fields.","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"warning","value":"The 'impact_flag' of practice_changing was applied cautiously — the study is observational and cannot confirm causality; reverse causation (illness driving weight loss) is a key confounder not fully addressed.","value_numeric":null},{"paper_doi":"10.1111/dom.70857","tag_type":"warning","value":"The paradoxical finding of highest CVD risk with >10% weight decrease AND >10% RFM increase (e.g., sarcopenic obesity pattern) is notable but based on a subgroup and warrants cautious interpretation.","value_numeric":null}],"summaries":{"what_studied":"This prospective cohort study followed 203,962 community-dwelling adults aged ≥65 years in Guangzhou, China, examining how changes in weight and relative fat mass (RFM) between 2018 and 2019–2020 jointly and independently associated with all-cause and CVD mortality through December 31, 2023 (median follow-up 5.6 years).","key_result":"Both weight and RFM changes showed U-shaped relationships with mortality. vs. stable weight, all-cause mortality risk was highest with >10% weight decrease (HR 1.89, 95% CI 1.76–2.03) and also elevated with >10% weight increase (HR 1.50, 95% CI 1.39–1.63). Jointly, >10% decrease in both weight and RFM carried the highest all-cause mortality risk (HR 2.09, 95% CI 1.82–2.41), while the highest CVD mortality risk occurred with >10% weight decrease plus >10% RFM increase (HR 2.02, 95% CI 1.53–2.67).","limitations":"- Observational design limits causal inference; unmeasured confounders (e.g., illness-driven weight loss) may explain part of the elevated risk with weight decrease.\n- Weight and RFM were measured at only two time points; intermediate fluctuations are not captured.\n- Findings from a single Chinese city may not generalize to other ethnic or geographic populations.","practice_takeaway":"Monitor older adults for weight changes exceeding 10% in either direction, as both large losses and large gains raise mortality risk — losses more so. When weight drops >10%, also assess RFM change, since combined large losses confer the greatest all-cause mortality risk and a paradoxical RFM rise with weight loss signals the highest CVD risk."}},{"doi":"10.1001/jamanetworkopen.2026.7938","pmid":"42008265","title":"Objectively Measured Daytime Napping Patterns and All-Cause Mortality in Older Adults","journal_title":"JAMA Network Open","journal_issn":"2574-3805","published_at":"2026-04-21T00:00:00.000Z","url":"https://doi.org/10.1001/jamanetworkopen.2026.7938","source_score":943,"is_retracted":false,"is_open_access":true,"raw_metadata":{"ooir":{"day":"2026-04-25","doi":"10.1001/jamanetworkopen.2026.7938","days":"30","issn":"2574-3805","rank":"3","field":"Clinical Medicine","score":"943","category":"Medicine, General & Internal","categories":["Medicine, General & Internal"],"_categories":["Medicine, General & Internal"]},"pubmed":{"doi":"10.1001/jamanetworkopen.2026.7938","title":"Objectively Measured Daytime Napping Patterns and All-Cause Mortality in Older Adults.","journal":"JAMA network open","methods":"","results":"Among 1338 participants over a mean (SD) follow-up of 8.30 (4.78) years (mean [SD] participant age, 81.40 [7.39] years; 1018 [76.0%] female), 926 (69.2%) died at a mean (SD) of 7.54 (4.52) years (range, 0.08-19.08 years) after analytic baseline. Participants wore the actigraphy device for a mean (SD) of 9.58 (1.22) days. After adjusting for all covariates, longer daytime nap duration (adjusted hazard ratio [AHR] per 1-hour increase, 1.13; 95% CI, 1.04-1.23; P = .005) and higher nap frequency (AHR per additional daily nap, 1.07; 95% CI, 1.02-1.13; P = .003) at baseline were associated with increased mortality. Morning nappers had higher mortality risk compared with early afternoon nappers (AHR, 1.30; 95% CI, 1.03-1.64; P = .03). Variability in daytime nap duration was not associated with mortality after covariate adjustment (AHR per 1-hour increase, 1.01; 95% CI, 0.89-1.14; P = .93).","abstract":"$30","keywords":[],"pubmed_id":"42008265","conclusions":"","publication_date":"2026-04-21T00:00:00"},"crossref":{"DOI":"10.1001/jamanetworkopen.2026.7938","URL":"https://doi.org/10.1001/jamanetworkopen.2026.7938","ISSN":["2574-3805"],"link":[{"URL":"https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2847953/gao_2026_oi_260258_1776101084.56712.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"page":"e267938","type":"journal-article","issue":"4","score":1,"title":["Objectively Measured Daytime Napping Patterns and All-Cause Mortality in Older Adults"],"author":[{"given":"Chenlu","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts"},{"name":"Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts"}]},{"given":"Ruixue","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts"}]},{"given":"Xi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"}]},{"given":"Arlen","family":"Gaba","sequence":"additional","affiliation":[{"name":"Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts"},{"name":"Tufts Medical Center, Boston, Massachusetts"}]},{"given":"Lei","family":"Yu","sequence":"additional","affiliation":[{"name":"Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois"}]},{"given":"Aron S.","family":"Buchman","sequence":"additional","affiliation":[{"name":"Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois"}]},{"given":"David A.","family":"Bennett","sequence":"additional","affiliation":[{"name":"Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois"}]},{"given":"Lei","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts"},{"name":"Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts"},{"name":"Broad Institute of MIT and Harvard, Cambridge, Massachusetts"}]},{"given":"Kun","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts"},{"name":"Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts"},{"name":"Broad Institute of MIT and Harvard, Cambridge, Massachusetts"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts"},{"name":"Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts"},{"name":"Broad Institute of MIT and Harvard, Cambridge, 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Disorders","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.7938","tag_type":"impact_flag","value":"practice_changing","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.7938","tag_type":"level_of_evidence","value":"2","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.7938","tag_type":"modality","value":"Actigraphy","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.7938","tag_type":"sample_size","value":"n=1338","value_numeric":1338},{"paper_doi":"10.1001/jamanetworkopen.2026.7938","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.7938","tag_type":"study_design","value":"Prospective Cohort Study","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.7938","tag_type":"warning","value":"Reverse causation is a key concern — increased napping may reflect underlying illness rather than independently driving mortality risk.","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.7938","tag_type":"warning","value":"The cohort skews heavily female (76%) and very old (mean 81 years), which may limit applicability to younger or male-predominant populations.","value_numeric":null},{"paper_doi":"10.1001/jamanetworkopen.2026.7938","tag_type":"warning","value":"The impact_flag 'practice_changing' is applied cautiously; findings are observational and require replication before formal clinical guideline changes.","value_numeric":null}],"summaries":{"what_studied":"This prospective cohort study examined whether actigraphy-measured daytime nap duration, frequency, variability, and timing were associated with all-cause mortality in 1,338 community-dwelling adults aged ≥56 years (mean age 81.4 years), with up to 19 years of follow-up from the Rush Memory and Aging Project.","key_result":"Longer nap duration (AHR 1.13 per 1-hour increase; 95% CI, 1.04–1.23) and higher nap frequency (AHR 1.07 per additional daily nap; 95% CI, 1.02–1.13) were each independently associated with greater all-cause mortality; morning nappers had 30% higher mortality risk vs. early afternoon nappers (AHR 1.30; 95% CI, 1.03–1.64). Nap duration variability was not significant after adjustment.","limitations":"- Single baseline actigraphy assessment (~9.6 days) may not capture long-term napping habits or changes over time.\n- Cohort is predominantly female (76%) and older (mean age 81), limiting generalizability to younger or more balanced-sex populations.\n- Observational design cannot establish causality; reverse causation (napping driven by underlying illness) is possible.","practice_takeaway":"Clinicians should consider asking older patients about daytime napping patterns — especially frequent or morning napping — as a potential early signal of declining health warranting further workup. Wearable actigraphy may offer a practical, objective tool for flagging at-risk older adults in clinical or public health settings."}},{"doi":"10.1037/hea0001571","pmid":"41556935","title":"Association of state happiness with mortality: Evidence from a prospective cohort study in Japan.","journal_title":"Health Psychology","journal_issn":"1930-7810","published_at":"2026-01-20T00:00:00.000Z","url":"https://doi.org/10.1037/hea0001571","source_score":122,"is_retracted":false,"is_open_access":false,"raw_metadata":{"ooir":{"day":"2026-05-08","doi":"10.1037/hea0001571","days":"7","issn":"0278-6133","rank":"2","field":"Clinical Medicine","score":"122","category":"Psychology, Clinical","categories":["Psychology, Clinical"],"_categories":["Psychology, Clinical"]},"pubmed":{"doi":"10.1037/hea0001571","title":"Association of state happiness with mortality: Evidence from a prospective cohort study in Japan.","journal":"Health psychology : official journal of the Division of Health Psychology, American Psychological Association","methods":"A total of 3,187 adults from Minami-Izu, Japan, were included in a prospective study tracking all-cause mortality from October 2016 to October 2023. Happiness was assessed using a single-item, self-report measure of state emotional well-being. Sociodemographic factors (age, sex, marital status, education, and economic status) and health status (body mass index and physical function) were considered as covariates. Binary logistic regression models were used to examine the association between happiness and mortality, adjusted for these factors.","results":"During the follow-up, 277 participants died. The risk of all-cause mortality was higher in those who reported being unhappy compared with those who reported being happy after adjustment for age and sex (OR: 2.69, 95% CI: 1.63-4.44) and also significantly higher in those who reported being unhappy compared with those who reported being happy after adjustment for age, sex, all socioeconomic factors and health status (OR: 1.85, 95% CI: 1.09-3.16). Sensitivity analyses excluding deaths within the first year showed consistent results.","abstract":"$32","keywords":[],"pubmed_id":"41556935","conclusions":"","publication_date":"2026-01-20T00:00:00"},"crossref":{"DOI":"10.1037/hea0001571","URL":"https://doi.org/10.1037/hea0001571","ISSN":["1930-7810","0278-6133"],"link":[{"URL":"https://psycnet.apa.org/journals/hea/45/5/560.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"page":"560-565","type":"journal-article","issue":"5","score":1,"title":["Association of state happiness with mortality: Evidence from a prospective cohort study in Japan."],"author":[{"given":"Akitomo","family":"Yasunaga","sequence":"first","affiliation":[]},{"given":"Ai","family":"Shibata","sequence":"additional","affiliation":[]},{"given":"Yoshino","family":"Hosokawa","sequence":"additional","affiliation":[]},{"given":"Mohammad Javad","family":"Koohsari","sequence":"additional","affiliation":[]},{"given":"Rina","family":"Miyawaki","sequence":"additional","affiliation":[]},{"given":"Kuniko","family":"Araki","sequence":"additional","affiliation":[]},{"given":"Kaori","family":"Ishii","sequence":"additional","affiliation":[]},{"given":"Koichiro","family":"Oka","sequence":"additional","affiliation":[]}],"funder":[{"id":[{"id":"10.13039/501100003478","id-type":"DOI","asserted-by":"publisher"}],"DOI":"10.13039/501100003478","name":"MHLW","award":["JPMH22FA1004"],"award-info":[{"award-number":["JPMH22FA1004"]}],"doi-asserted-by":"publisher"},{"id":[{"id":"10.13039/501100001691","id-type":"DOI","asserted-by":"crossref"}],"DOI":"10.13039/501100001691","name":"Japan Society for the Promotion of Science","award":["JP23K09701"],"award-info":[{"award-number":["JP23K09701"]}],"doi-asserted-by":"crossref"}],"issued":{"date-parts":[[2026,5]]},"member":"15","prefix":"10.1037","source":"Crossref","volume":"45","created":{"date-time":"2026-01-20T14:18:11Z","timestamp":1768918691000,"date-parts":[[2026,1,20]]},"indexed":{"date-time":"2026-05-05T03:41:32Z","timestamp":1777952492050,"date-parts":[[2026,5,5]]},"license":[{"URL":"https://creativecommons.org/licenses/by-nc-nd/4.0","start":{"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000,"date-parts":[[2026,1,19]]},"delay-in-days":0,"content-version":"vor"}],"subject":[],"abstract":null,"language":"en","relation":{},"resource":{"primary":{"URL":"https://doi.apa.org/doi/10.1037/hea0001571"}},"subtitle":[],"deposited":{"date-time":"2026-05-04T12:55:59Z","timestamp":1777899359000,"date-parts":[[2026,5,4]]},"issn-type":[{"type":"electronic","value":"1930-7810"},{"type":"print","value":"0278-6133"}],"published":{"date-parts":[[2026,5]]},"publisher":"American Psychological Association (APA)","reference":null,"short-title":[],"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"update-policy":"https://doi.org/10.1037/hea0001571","alternative-id":["2027-16336-001","41556935"],"content-domain":{"domain":[],"crossmark-restriction":false},"original-title":[],"container-title":["Health Psychology"],"reference-count":0,"published-online":{"date-time":null,"timestamp":null,"date-parts":[[2026,5]]},"references-count":0,"short-container-title":["Health Psychology"],"is-referenced-by-count":0},"unpaywall":{"doi":"10.1037/hea0001571","is_oa":false,"title":"Association of state happiness with mortality: Evidence from a prospective cohort study in Japan.","updated":"2026-05-06T13:23:02+00:00","published_date":"2026-01-20","best_oa_location":null}},"created_at":"2026-05-09T13:22:27.293Z","updated_at":"2026-05-14T15:13:26.918Z","tags":[{"paper_doi":"10.1037/hea0001571","tag_type":"age_group","value":"adult","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"category","value":"Psychology, Clinical","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"condition","value":"All-Cause Mortality","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"condition","value":"Psychological Well-Being","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"impact_flag","value":"confirms_prior","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"level_of_evidence","value":"2","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"sample_size","value":"n=3187","value_numeric":3187},{"paper_doi":"10.1037/hea0001571","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"study_design","value":"Prospective Cohort Study","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"warning","value":"Binary logistic regression was used instead of time-to-event analysis; this is a methodological limitation worth noting when interpreting effect sizes.","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"warning","value":"Happiness was measured using a single self-report item; the specific wording or validated scale is not described in the abstract, making replication or comparison with other studies difficult.","value_numeric":null},{"paper_doi":"10.1037/hea0001571","tag_type":"warning","value":"The study population is from a single rural Japanese town, so findings may not generalize broadly across cultures or urban settings.","value_numeric":null}],"summaries":{"what_studied":"This prospective cohort study examined whether self-reported happiness (single-item state well-being measure) is independently associated with all-cause mortality in 3,187 Japanese adults from Minami-Izu, followed from October 2016 to October 2023 (~7 years), after adjusting for sociodemographic and health status factors.","key_result":"277 participants died during follow-up. Unhappy individuals had nearly 3× higher mortality risk vs. happy individuals after adjusting for age and sex (OR 2.69, 95% CI 1.63–4.44), and still 85% higher risk after full adjustment for socioeconomic and health factors (OR 1.85, 95% CI 1.09–3.16).","limitations":"- Happiness was measured with a single self-report item, which may lack precision and reliability compared to validated multi-item scales.\n- The study was conducted in one rural town (Minami-Izu), limiting generalizability to urban or non-Japanese populations.\n- Binary logistic regression was used rather than survival analysis (e.g., Cox regression), which does not account for time-to-event variation across the follow-up period.","practice_takeaway":"Clinicians should consider screening for emotional well-being as part of routine health assessments, since low happiness is independently linked to higher mortality risk even after accounting for health status. Promoting positive well-being may be a meaningful target in preventive care for older Japanese adults."}},{"doi":"10.1016/s0140-6736(25)02219-6","pmid":"41544645","title":"Deaths potentially averted by small changes in physical activity and sedentary time: an individual participant data meta-analysis of prospective cohort studies","journal_title":"The Lancet","journal_issn":"0140-6736","published_at":"2026-01-17T00:00:00.000Z","url":"https://doi.org/10.1016/s0140-6736(25)02219-6","source_score":988,"is_retracted":false,"is_open_access":false,"raw_metadata":{"ooir":{"day":"2026-01-18","doi":"10.1016/s0140-6736(25)02219-6","days":"7","issn":"0140-6736","rank":"3","field":"Clinical Medicine","score":"988","category":"Medicine, General & Internal","categories":["Medicine, General & Internal"],"_categories":["Medicine, General & Internal"]},"pubmed":{"doi":"10.1016/S0140-6736(25)02219-6","title":"Deaths potentially averted by small changes in physical activity and sedentary time: an 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cohort studies","updated":"2026-01-15T02:17:35+00:00","published_date":"2026-01-01","best_oa_location":null}},"created_at":"2026-01-19T09:31:36.873Z","updated_at":"2026-01-21T12:05:26.220Z","tags":[{"paper_doi":"10.1016/s0140-6736(25)02219-6","tag_type":"category","value":"Medicine, General & Internal","value_numeric":null},{"paper_doi":"10.1016/s0140-6736(25)02219-6","tag_type":"condition","value":"All-Cause Mortality","value_numeric":null},{"paper_doi":"10.1016/s0140-6736(25)02219-6","tag_type":"modality","value":"Lifestyle Intervention","value_numeric":null},{"paper_doi":"10.1016/s0140-6736(25)02219-6","tag_type":"sample_size","value":"n=40,327","value_numeric":40327},{"paper_doi":"10.1016/s0140-6736(25)02219-6","tag_type":"study_design","value":"Individual Participant Data Meta-analysis","value_numeric":null},{"paper_doi":"10.1016/s0140-6736(25)02219-6","tag_type":"warning","value":"Abstract provides estimates for 5-min MVPA and 30-min sedentary reductions; 10-min/60-min results are not detailed here.","value_numeric":null},{"paper_doi":"10.1016/s0140-6736(25)02219-6","tag_type":"warning","value":"Design label approximated; this is an IPD meta-analysis of prospective cohorts.","value_numeric":null},{"paper_doi":"10.1016/s0140-6736(25)02219-6","tag_type":"warning","value":"Potential impact fractions are modeled from adjusted hazard ratios rather than trial outcomes.","value_numeric":null}],"summaries":{"what_studied":"An individual participant data meta-analysis of device-measured activity from seven prospective cohorts in Norway, Sweden, and the USA (n=40,327; 4,895 deaths), with the UK Biobank analysed separately (n=94,719; 3,487 deaths), estimating the proportion of deaths preventable by small daily increases in MVPA (5–10 min) and reductions in sedentary time (30–60 min) using high-risk (~20% least active) and population-based (all except ~20% most active) approaches.","key_result":"In the main cohorts, adding 5 min/day of MVPA might prevent 6.0% (95% CI 4.3–7.4) of deaths in the least active and 10.0% (6.3–13.4) in all except the most active; reducing sedentary time by 30 min/day might prevent 3.0% (2.0–4.1) and 7.3% (4.8–9.6) of deaths, respectively. In UK Biobank, reducing sedentary time by 30 min/day in all except the most active was associated with preventing 4.5% (2.8–6.1) of deaths.","practice_takeaway":"Encourage small, realistic changes: add about 5 minutes of MVPA per day and cut sitting by about 30 minutes per day; these shifts were associated with preventing roughly 3–10% of deaths across risk-targeted and population-wide scenarios in device-measured cohorts."}}],"initialOffset":9,"fetchConfig":{"kind":"tags","tags":[{"tag_type":"condition","value":"All-Cause Mortality"}]},"breadcrumbItems":[{"name":"Home","href":"/"},{"name":"Research Hub","href":"/hub"},{"name":"Conditions","href":"/hub/tag/condition"},{"name":"All-Cause Mortality"}],"hrefConfig":{"basePath":"/hub/papers","webViewQueryParams":{"hub_ctx":"tag","hub_tag_type":"condition","hub_tag_value":"All-Cause Mortality"}}}]]

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