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Journal of Medical Internet Research Jun 2024Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the...
BACKGROUND
Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings.
OBJECTIVE
This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts.
METHODS
Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score.
RESULTS
Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=-93.61, P<.001), increased sleep variability (β=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P=.001; sleep offset: β=1.12, P<.001; M10 onset: β=0.73, P=.003; HR acrophase: β=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = -31.51, P=.002) and summer (β of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer.
CONCLUSIONS
Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.
Topics: Humans; Seasons; Female; Circadian Rhythm; Male; Wearable Electronic Devices; Adult; Longitudinal Studies; Depression; Middle Aged; Retrospective Studies; Telemedicine
PubMed: 38941600
DOI: 10.2196/55302 -
PloS One 2024The reactor coolant pump is a key equipment in a nuclear power plant. If the leakage exceeds a certain threshold, it may cause reactor overheating and shutdown. The...
The reactor coolant pump is a key equipment in a nuclear power plant. If the leakage exceeds a certain threshold, it may cause reactor overheating and shutdown. The reactor coolant pump leakage fault usually has two problems: corrosion and scaling. Accurately and efficiently diagnosing the leakage fault mode as early as possible and predicting its remaining useful life (RUL) are important for taking timely maintenance measures. In this paper, an integrated method is proposed. First, the cross-sectional area of the first seal is extracted as a fault indicator. The motivation is that corrosion may enlarge the cross-sectional area, and scaling may reduce the cross-sectional area. Based on the fluid mechanics theory, an integrated model with several uncertain parameters is established among the cross-sectional area, temperature, and leakage at the inlet and outlet of the first seal. In the diagnosing process, a modified change-detection method is proposed to detect the starting point of degradation. Then, the unknown parameters in the previous relation are estimated, and the degrading data before the starting point of degradation are used to diagnose the leakage fault mode. Second, a time-series model of the autoregressive integrated moving average (ARIMA) is established to predict the remaining useful life based on the degrading data after the starting point of degradation. Finally, the leakage degrading data from six reactor coolant pumps of a nuclear power plant is used to perform the leakage fault mode diagnosis and life prediction with degradation point detection error rates not exceeding 4%, fault mode diagnosis correction rates 100% and practical RUL predicting results, which proves that the proposed integrated method is accurate and efficient. The proposed integrated method combines the advantages of both the physical model diagnosis and the data-driven model diagnosis and innovatively make use of the quantity of flow from the output side of the primary pump as the monitoring indicator and the cross-sectional area as the characteristic index together to diagnose the leakage fault mode happened to the seal and predict its RUL, which can meet the needs of actual operation and maintenance to ensure a healthy and stable operation of the pump and prevent unexpected shutdowns of nuclear power plants and serious accidents.
Topics: Nuclear Power Plants; Models, Theoretical; Nuclear Reactors; Equipment Failure; Equipment Failure Analysis
PubMed: 38941331
DOI: 10.1371/journal.pone.0304652 -
Reconciling Coulter Counter and laser diffraction particle size analysis for aquaculture monitoring.Environmental Monitoring and Assessment Jun 2024The disaggregated inorganic grain size (DIGS) of bottom sediment analyzed with a Coulter Counter (CC) has been used to show that the fraction of sediment deposited in...
The disaggregated inorganic grain size (DIGS) of bottom sediment analyzed with a Coulter Counter (CC) has been used to show that the fraction of sediment deposited in flocs (floc fraction) increased in both the near and far field after the introduction of open cage salmon aquaculture, altering benthic habitat and species composition. As a result, DIGS was identified as a potential indicator of regional environmental changes due to aquaculture. Laser diffraction is an attractive alternative to the CC because of its greater efficiency and larger size range. To determine if a laser diffraction instrument, Beckman-Coulter LS 13 320 (LS), could replace the CC within a Canadian national aquaculture monitoring program, the DIGS of 581 samples from five different regions in eastern Canada were analyzed with an LS and a CC. Results show that the LS could not be used to calculate floc fraction. Instead, % sortable silt and the volume % of inorganic particles < 16 µm were evaluated as alternative proxies for fine sediment properties. LS and CC values for these parameters were correlated, but they were significantly different and the relationship between the instruments was dependent on the area sampled. The LS did not capture variations between areas seen in the CC DIGS data. Where the DIGS from the CC found no sorting in the finest size classes, all the LS samples had similar size distributions characterized by smooth modal peaks. The LS and CC both return values that can be used to monitor changes in the deposition of fine-grained sediment, but the LS cannot determine changes in floc deposition and caution is required if comparing different sedimentary environments.
Topics: Aquaculture; Environmental Monitoring; Particle Size; Geologic Sediments; Canada; Animals; Lasers
PubMed: 38940996
DOI: 10.1007/s10661-024-12786-w -
Journal of Clinical Hypertension... Jun 2024Extreme cold exposure has been widely considered as a cardiac stress and may result in cardiac function decompensation. This study was to examine the risk factors that...
Extreme cold exposure has been widely considered as a cardiac stress and may result in cardiac function decompensation. This study was to examine the risk factors that contribute to changes in cardiovascular indicators of cardiac function following extreme cold exposure and to provide valuable insights into the preservation of cardiac function and the cardiac adaptation that occur in real-world cold environment. Seventy subjects were exposed to cold outside (Mohe, mean temperature -17 to -34°C) for one day, and were monitored by a 24-h ambulatory blood pressure device and underwent echocardiography examination before and after extreme cold exposure. After exposure to extreme cold, 41 subjects exhibited an increase in ejection fraction (EF), while 29 subjects experienced a decrease. Subjects with elevated EF had lower baseline coefficients of variation (CV) in blood pressure compared to those in the EF decrease group. Additionally, the average real variability (ARV) of blood pressure was also significantly lower in the EF increase group. Multivariate regression analysis indicated that both baseline CV and ARV of blood pressure were independent risk factors for EF decrease, and both indicators proved effective for prognostic evaluation. Correlation analysis revealed a correlation between baseline blood pressure CV and ARV, as well as EF variation after exposure to extreme cold environment. Our research clearly indicated that baseline cardiovascular indicators were closely associated with the changes in EF after extreme cold exposure. Furthermore, baseline blood pressure variability could effectively predict alterations in left cardiac functions when individuals were exposed to extreme cold environment.
PubMed: 38940288
DOI: 10.1111/jch.14862 -
Annals of Agricultural and... Jun 2024Correlations between the number of milk somatic cells (SCC), the number of microorganisms, and the content of basic components of milk were studied on five farms (F1-F5)...
INTRODUCTION AND OBJECTIVE
Correlations between the number of milk somatic cells (SCC), the number of microorganisms, and the content of basic components of milk were studied on five farms (F1-F5) with cows of the same breed, but with different milking systems.
MATERIAL AND METHODS
From each farm, 50 Holstein Friesien milk samples were collected once a month (250 samples/month; n=3,000) during March 2022 - February 2023. Samples from farms F1 and F5 were tested for fat, protein, lactose, no fat dry matter content (FTIR spectroscopy), for the SCC (Fossomatic 7), and for the differential cells (Vetscan DC-Q).
RESULTS
The highest fat content was confirmed on farm F5 (3.85 ± 1.70%) and F4 (3.82 ± 0.21%) with automatic milking system (AMS). However, from the point of view of protein content, these farms showed slightly lower values (<0.05). F1 did not meet the minimum required amount for fat content (2.84 ± 0.81%) set by the legislation of the Slovakia. The comparison shows that there is not much difference in cell size between healthy cells and mastitis cells. The average size of healthy cells was approximately 8.77 ± 0.49 μm. In the monitored period, the average values determined were at the level of 292,000/mL (5.46 ± 0.72 log10 SCC) in cow milk samples, while for the rest of the year, the values remained at 256,000/mL (5.40 ± 0.80 log10 SCC). F1 was categorized as a positive farm with a high TLC (total milk leucocyte count) concentration (5.58 log10 cells/mL, 406.65 ± 53.80 × 10 cells/mL) and a predominant NEU fraction (61%). Farms F2, F4, and F5 were classified as negative farms (TLC was 4.70 ± 0.26 log10 cells/ml).
CONCLUSIONS
According to the results, the size of SCCs in healthy milk does not differ from SCCs found in mastitis milk. From the results, it can be concluded that the transition to the latest generation of robotic milking method can positively affect milk production and its quality.
Topics: Animals; Milk; Dairying; Female; Cell Count; Cattle; Lactose; Slovakia; Milk Proteins; Lactation
PubMed: 38940103
DOI: 10.26444/aaem/187170 -
JACC. Advances Jan 2024Low-density lipoprotein cholesterol (LDL-C) is used to guide lipid-lowering therapy after a myocardial infarction (MI). Lack of LDL-C testing represents a missed...
BACKGROUND
Low-density lipoprotein cholesterol (LDL-C) is used to guide lipid-lowering therapy after a myocardial infarction (MI). Lack of LDL-C testing represents a missed opportunity for optimizing therapy and reducing cardiovascular risk.
OBJECTIVES
The purpose of this study was to estimate the proportion of Medicare beneficiaries who had their LDL-C measured within 90 days following MI hospital discharge.
METHODS
We conducted a retrospective cohort study of Medicare beneficiaries ≥66 years of age with an MI hospitalization between 2016 and 2020. The primary analysis used data from all beneficiaries with fee-for-service coverage and pharmacy benefits (532,767 MI hospitalizations). In secondary analyses, we used data from a 5% random sample of beneficiaries with fee-for-service coverage without pharmacy benefits (10,394 MI hospitalizations), and from beneficiaries with Medicare Advantage (176,268 MI hospitalizations). The proportion of beneficiaries who had their LDL-C measured following MI hospital discharge was estimated accounting for the competing risk of death.
RESULTS
In the primary analysis (mean age 76.9 years, 84.4% non-Hispanic White), 29.9% of beneficiaries had their LDL-C measured within 90 days following MI hospital discharge. Among Hispanic, Asian, non-Hispanic White, and non-Hispanic Black beneficiaries, the 90-day postdischarge LDL-C testing was 33.8%, 32.5%, 30.0%, and 26.0%, respectively. Postdischarge LDL-C testing within 90 days was highest in the Middle Atlantic (36.4%) and lowest in the West North Central (23.4%) U.S. regions. In secondary analyses, the 90-day postdischarge LDL-C testing was 26.9% among beneficiaries with fee-for-service coverage without pharmacy benefits, and 28.6% among beneficiaries with Medicare Advantage coverage.
CONCLUSIONS
LDL-C testing following MI hospital discharge among Medicare beneficiaries was low.
PubMed: 38939806
DOI: 10.1016/j.jacadv.2023.100753 -
JACC. Advances Jan 2024A simple ambulatory measure of cardiac function could be helpful for monitoring heart failure patients.
BACKGROUND
A simple ambulatory measure of cardiac function could be helpful for monitoring heart failure patients.
OBJECTIVES
The purpose of this paper was to determine whether a novel pulse waveform analysis using data obtained by our developed multisensor-ambulatory blood pressure monitoring (ABPM) device, the 'Sf/Am' ratio, is associated with echocardiographic left ventricular ejection fraction (LVEF).
METHODS
Multisensor-ABPM was conducted twice at baseline in 20 heart failure (HF) patients with HF-reduced LVEF or HF-preserved LVEF (median age 66 years, male 65%) and over a 6- to 12-month follow-up after patient-tailored treatment. We assessed the changes in the pulse waveform index Sf/Am and LVEF that occurred between the baseline and follow-up. The Sf/Am consists of the area of the ejection part in the square forward wave (Sf) and the amplitude of the measured wave (Am). We divided the patients into the recovered (n = 11) and not-recovered (n = 9) groups defined by a ≥10% increase in LVEF.
RESULTS
Although the ambulatory BP levels and variabilities did not change in either group, the Sf/Am increased significantly in the recovered group (baseline 21.4 ± 4.5; follow-up, 25.6 ± 3.7, = 0.004). The not-recovered group showed no difference between the baseline and follow-up. The follow-up/baseline Sf/Am ratio was significantly associated with the LVEF ratio ( = 0.469, = 0.037). The Sf/Am was significantly correlated with the LVEF in overall measurements (n = 40, = 0.491, = 0.001).
CONCLUSIONS
These results demonstrated that a novel noninvasive pulse waveform index, the Sf/Am measured by multisensor-ABPM is associated with LVEF. The Sf/Am may be useful for estimating cardiac function.
PubMed: 38939805
DOI: 10.1016/j.jacadv.2023.100737 -
Journal of Arrhythmia Jun 2024We explored the results of two tests of the novel HeartInsight algorithm for heart failure (HF) prediction, reconstructing trends from historical cases. Results suggest...
We explored the results of two tests of the novel HeartInsight algorithm for heart failure (HF) prediction, reconstructing trends from historical cases. Results suggest potential extension of HeartInsight to implantable cardioverter defibrillators patients without history of HF and illustrate the importance of the baseline clinical profile in enhancing algorithm specificity.
PubMed: 38939800
DOI: 10.1002/joa3.13032 -
Journal of Arrhythmia Jun 2024We report the behavior of OptiVol2 fluid index (OVFI2) and intrathoracic impedance on remote monitoring before the appearance of signs of infection. A sustained rise in...
We report the behavior of OptiVol2 fluid index (OVFI2) and intrathoracic impedance on remote monitoring before the appearance of signs of infection. A sustained rise in OVFI2 early after implantation reflects peri-device fluid retention.
PubMed: 38939798
DOI: 10.1002/joa3.13005 -
Journal of Arrhythmia Jun 2024Remote monitoring (RM) of cardiac implantable electrical devices (CIEDs) can detect various events early. However, the diagnostic ability of CIEDs has not been...
BACKGROUND
Remote monitoring (RM) of cardiac implantable electrical devices (CIEDs) can detect various events early. However, the diagnostic ability of CIEDs has not been sufficient, especially for lead failure. The first notification of lead failure was almost noise events, which were detected as arrhythmia by the CIED. A human must analyze the intracardiac electrogram to accurately detect lead failure. However, the number of arrhythmic events is too large for human analysis. Artificial intelligence (AI) seems to be helpful in the early and accurate detection of lead failure before human analysis.
OBJECTIVE
To test whether a neural network can be trained to precisely identify noise events in the intracardiac electrogram of RM data.
METHODS
We analyzed 21 918 RM data consisting of 12 925 and 1884 Medtronic and Boston Scientific data, respectively. Among these, 153 and 52 Medtronic and Boston Scientific data, respectively, were diagnosed as noise events by human analysis. In Medtronic, 306 events, including 153 noise events and randomly selected 153 out of 12 692 nonnoise events, were analyzed in a five-fold cross-validation with a convolutional neural network. The Boston Scientific data were analyzed similarly.
RESULTS
The precision rate, recall rate, F1 score, accuracy rate, and the area under the curve were 85.8 ± 4.0%, 91.6 ± 6.7%, 88.4 ± 2.0%, 88.0 ± 2.0%, and 0.958 ± 0.021 in Medtronic and 88.4 ± 12.8%, 81.0 ± 9.3%, 84.1 ± 8.3%, 84.2 ± 8.3% and 0.928 ± 0.041 in Boston Scientific. Five-fold cross-validation with a weighted loss function could increase the recall rate.
CONCLUSIONS
AI can accurately detect noise events. AI analysis may be helpful for detecting lead failure events early and accurately.
PubMed: 38939795
DOI: 10.1002/joa3.13037