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• What is the primary question addressed by this study? Does regular opioid use associated with increased dementia risk and poor brain health in chronic pain patients? • What is the main finding of this study? Regular opioid use was associated with higher risk of dementia and poorer neuroimaging outcomes in chronic pain patients than non-opioid analgesics. Number of opioid prescriptions was associated with dose-dependent risk of dementia. • What is the meaning of the finding? These findings imply a need for re-evaluation of opioid prescription practices for chronic pain patients and, if further evidence supports causality, provide insights into strategies to mitigate the burden of dementia. We aimed to investigate the association of regular opioid use, compared with non-opioid analgesics, with incident dementia and neuroimaging outcomes among chronic pain patients. The primary design is a prospective cohort study. To triangulate evidence, we also conducted a nested case-control study analyzing opioid prescriptions and a cross-sectional study analyzing neuroimaging outcomes. Dementia-free UK Biobank participants with chronic pain and regular analgesic use. Chronic pain status and regular analgesic use were captured using self-reported questionnaires and verbal interviews. Opioid prescription data were obtained from primary care records. Dementia cases were ascertained using primary care, hospital, and death registry records. Propensity score-matched Cox proportional hazards analysis, conditional logistic regression, and linear regression were applied to the data in the prospective cohort, nested case-control, and cross-sectional studies, respectively. Prospective analyses revealed that regular opioid use, compared with non-opioid analgesics, was associated with an increased dementia risk over the 15-year follow-up (Hazard ratio [HR], 1.18 [95% confidence interval (CI): 1.08–1.30]; Absolute rate difference [ARD], 0.44 [95% CI: 0.19–0.71] per 1000 person-years; Wald χ2 = 3.65; df = 1; p <0.001). The nested case-control study suggested that a higher number of opioid prescriptions was associated with an increased risk of dementia (1 to 5 prescriptions: OR = 1.21, 95% CI: 1.07–1.37, Wald χ2 = 3.02, df = 1, p = 0.003; 6 to 20: OR = 1.27, 95% CI: 1.08–1.50, Wald χ2 = 2.93, df = 1, p = 0.003; more than 20: OR = 1.43, 95% CI: 1.23–1.67, Wald χ2 = 4.57, df = 1, p < 0.001). Finally, neuroimaging analyses revealed that regular opioid use was associated with lower total grey matter and hippocampal volumes, and higher white matter hyperintensities volumes. Regular opioid use in chronic pain patients was associated with an increased risk of dementia and poorer brain health when compared to non-opioid analgesic use. These findings imply a need for re-evaluation of opioid prescription practices for chronic pain patients and, if further evidence supports causality, provide insights into strategies to mitigate the burden of dementia. [ABSTRACT FROM AUTHOR]

Background: Although COVID‐19 patients were suggested to experience worse cognitive outcomes, there is a paucity of evidence on time‐varying risk of dementia, especially the subtypes, as well as among critical subpopulations. Method: Out of over 50,000 individuals from a general population in the UK Biobank, SARS‐COV‐2 infected patients between March 1, 2020, and July 31, 2021 and maximally 5:1 propensity score matched contemporary non‐infected individuals were selected, with baseline dementia cases excluded. Matching was done on demographic characteristics, lifestyle, and comorbidities. Dementia was captured according to primary care, inpatient records, and death registry, with the follow‐up ending at the earliest of outcome occurrence, death, or August 31, 2021. Associations were evaluated using time‐varying hazard ratios (HRs) and odds ratios (ORs). Result: With a mean age of 65.0 years for 18,032 COVID‐19 patients and 83,008 controls, participants were followed for a median of 247 (IQR: 204‐305) days and 255 dementia cases occurred, including 90 Alzheimer's disease (AD) cases and 42 vascular dementia cases. Compared with matched controls, COVID‐19 infection was associated with increased risks of dementia during the acute phase (first 30 days), with HRs (95% CIs) being 12.77 (6.77, 24.08) for all‐cause dementia, 9.21 (2.77, 30.59) for AD, 5.53 (1.69, 18.11) for vascular dementia, and 25.35 (8.74, 73.56) for other dementia. Dementia risk declined drastically after COVID‐19 infection and sustained for all‐cause dementia, vascular dementia, and other dementia, while became non‐significant for AD. Among those not hospitalized during the acute phase, elevated dementia risk in the follow‐up remained for all‐cause dementia, vascular dementia, and other dementia, with ORs being 1.82, 4.55, and 1.64, respectively. Among most of the subpopulations classified by demographic characteristics, APOE genotype, and comorbidities (except for those with baseline chronic obstructive pulmonary diseases), COVID‐19 infection was associated with an elevated all‐cause dementia risk and no modification effect was detected. Conclusion: Declined yet sustained elevated dementia risk since COVID‐19 infection was found, with differential trends for dementia subtypes. Increased dementia risk from COVID‐19 infection also applied for the non‐hospitalized during the acute phase and most subpopulations. The potential dementia risk associated with Omicron variants warrants further evaluation. [ABSTRACT FROM AUTHOR]

Background: Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. Objective: The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. Methods: A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). Results: Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). Conclusions: At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states. [ABSTRACT FROM AUTHOR]