Category: Nevin Manimala

JMIR Hum Factors. 2026 Apr 2;13:e77033. doi: 10.2196/77033.

ABSTRACT

BACKGROUND: Sleep is essential for overall health and well-being, but assessing sleep architecture is often costly and time-consuming, relying primarily on polysomnography (PSG). While wrist-worn wearables offer alternatives, they face limitations regarding user compliance, such as battery charging and physical discomfort. Nearable devices address these burdens, but they regularly lack rigorous validation, especially in real-world settings.

OBJECTIVE: This study evaluates the accuracy and reliability of the Withings Sleep Analyzer (WSA), a contactless sleep monitoring device, compared to PSG in a home setting using a large and diverse cohort of healthy individuals.

METHODS: A total of 117 healthy volunteers (69 women; mean 39.9, SD 11.4 years), prospectively recruited from the general population, underwent home-based PSG and simultaneous WSA recording. The study was conducted under free-living conditions, without constraints on substance intake, prebedtime activity, or forced sleep schedules. The main outcomes were the device’s performance in sleep-wake distinction and sleep stage identification using accuracy, kappa, sensitivity, specificity, and the mean absolute error of sleep measures on the entire population and demographic, clinical, and environmental subgroups.

RESULTS: WSA demonstrates high sensitivity (93%, 95% CI 92%-94%) for sleep detection and moderate sensitivity (73%, 95% CI 69%-77%) for wakefulness, achieving an overall accuracy of 87% (95% CI 86%-87%) for sleep-wake distinction. The device showed consistent performance across various demographic subgroups, including different age, BMI, mattress, and sleep arrangements (with or without bed partner) categories. Challenges were noted in accurately classifying specific sleep stages, particularly in distinguishing between light and deep sleep, with a mean accuracy of 63% (95% CI 62%-65%) and a Cohen κ of 0.49 (95% CI 0.47-0.51). The WSA tended to overestimate total sleep time (20 min, 95% CI 10 min to 31 min) and light sleep (1 h 21 min, 95% CI 1 h 8 min to 1 h 36 min) while underestimating rapid eye movement (-15 min, 95% CI -23 min to -8 min) and deep sleep (-46 min, 95% CI -59 min to -34 min) durations. Disagreements between expert reviewers were mirrored in part by the WSA’s misclassifications. Participants reported altered perceived sleep quality during the night with the PSG, suggesting discomfort during sleep.

CONCLUSIONS: Being contactless and placed under the mattress, the WSA offers a promising approach to long-term sleep monitoring in natural home environments. It shows competitive performance in sleep-wake and sleep stage identification compared to other consumer devices. Progress in wearable and nearable devices is necessary to enhance their accuracy to better support the monitoring of populations with impaired sleep, although limited by an imperfect gold standard. This work also emphasizes the importance of using large, diverse, and challenging datasets, as well as the need for a standardized methodology for accurate sleep stage classification.

PMID:41926681 | DOI:10.2196/77033

Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2026 Apr 2:1-13. doi: 10.1080/19440049.2026.2644495. Online ahead of print.

ABSTRACT

Food dyes enhance natural colour or provide colour to foods. Artificial food dyes are synthetic organic dyes not found in natural sources. Exposure to dyes has been linked to cases of attention deficit hyperactivity disorder (ADHD), rhinitis, urticaria and angioedema. This study used a Food Frequency Questionnaire (FFQ) to assess the exposure to artificial food dyes among the population of Rio Grande do Sul – Brazil through the estimated consumption of foods containing tartrazine (INS 102), sunset yellow (INS 110), amaranth (INS 123), Ponceau 4 R (INS 124), allura red (INS 129) and brilliant blue (INS 133). Exposure estimates were obtained using the maximum limits of usage permitted by Brazilian regulations. Different subpopulations were compared through the Wilcoxon-Mann-Whitney or Kruskal-Wallis tests, followed by the Dunn test; significant differences were found in the latter. The exposure to all dyes was significantly higher among children and adolescents, as was the risk of an intake higher than the Acceptable Daily Intake (ADI). There was also a relationship between lower per capita income and higher exposure to artificial dyes. Amaranth (INS 123) showed a higher risk of intake greater than the ADI (1.56% of survey participants). The exposure to artificial dyes can be considered safe in Rio Grande do Sul, except for amaranth, but and children and adolescents demand special attention.

PMID:41926679 | DOI:10.1080/19440049.2026.2644495

Adv Sci (Weinh). 2026 Apr 2:e20738. doi: 10.1002/advs.202520738. Online ahead of print.

ABSTRACT

We demonstrate that the primary factor determining the external quantum efficiency (EQE) of InGaN-based micro-scale light-emitting diodes (µLEDs) depends on their internal state. A comparative photoluminescence (PL) study shows that the lateral diffusion length of carriers in InGaN red µLEDs is significantly shorter than in InGaN blue µLEDs, primarily due to inhomogeneity in the bulk material. This results in an insignificant change in PL intensity regardless of sidewall conditions. Additionally, examinations of EQE and peak wavelength across various epitaxial designs and sidewall conditions reveal that sidewall-surface recombination does not significantly impact EQE in InGaN red µLEDs. Meanwhile, the peak wavelength, which represents the radiative recombination rate given by the quantum well design of InGaN red µLEDs, is found to dominantly influence the EQE of InGaN red µLEDs. Furthermore, statistical analysis based on the relative standard deviation indicates that the peak wavelength is one of the primary determinants of EQE in InGaN red µLEDs. These findings suggest that addressing internal state is crucial for optimizing EQE of µLEDs.

PMID:41926672 | DOI:10.1002/advs.202520738

Int J Audiol. 2026 Apr 2:1-13. doi: 10.1080/14992027.2026.2650507. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to evaluate the effectiveness of the integrated digit-in-noise test (iDIN), which uses digit sequences of varying lengths as test materials, in assessing speech-in-noise recognition among non-native Cantonese speakers with normal hearing.

DESIGN: A cross-sectional observational study was conducted involving native Mandarin speakers with Cantonese as a second language. Participants completed Cantonese and Mandarin versions of iDIN and the Cantonese Speech in Noise Test (HINT). In the iDIN, only the 2-, 3-, and 5-digit forward recall, as well as the 3-digit backward recall conditions, were administered. Data on language experience, proficiency, and exposure were collected through questionnaires.

STUDY SAMPLE: Forty-seven participants (mean age 29.13 years) with normal hearing and Cantonese as a second language were recruited in Hong Kong. All had varying durations of Cantonese exposure, from <1 year to over 10 years.

RESULTS: Statistically significant differences were observed between Cantonese and Mandarin speech reception thresholds (SRTs) for the 3-digit, 5-digit forward recall, and 3-digit backward recall conditions, but not for the 2-digit forward recall. Longer Cantonese exposure correlated with improved (lower) SRTs in Cantonese iDIN, with performance becoming comparable to native speakers after ∼2 years of exposure.

CONCLUSIONS: iDIN may have potential as a tool for assessing speech-in-noise recognition in non-native Cantonese speakers with normal hearing, particularly after two years of exposure, though further validation in clinical populations is needed.

PMID:41926650 | DOI:10.1080/14992027.2026.2650507

J Med Internet Res. 2026 Apr 2;28:e76432. doi: 10.2196/76432.

ABSTRACT

BACKGROUND: Digital biomarkers are increasingly being used to support depression assessment by providing objective, continuous, and real-time physiological and behavioral data. However, most existing studies have focused on individual biomarkers, such as sleep or cardiac parameters, while integrative evaluations that capture the multidimensional nature of depression remain limited.

OBJECTIVE: This systematic review evaluated digital biomarkers for depression and synthesized evidence on differences between individuals with depression and controls.

METHODS: Eligible studies included observational or interventional studies examining digital biomarkers for depression with validated outcome measures. We searched major international and Korean databases, including MEDLINE, PsycINFO, CINAHL, IEEE Xplore, Web of Science, Cochrane Library, KISS, RISS, KMbase, and KoreaMed, from inception to December 28, 2025. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool and the Scottish Intercollegiate Guidelines Network checklist. Meta-analyses were conducted using random-effects models with the Hartung-Knapp-Sidik-Jonkman method, and other outcomes were narratively summarized.

RESULTS: The search yielded 39,617 records, of which 132 studies involving 57,852 participants met the inclusion criteria. These studies encompassed various digital biomarkers, including sleep, physical activity, cardiac measures, smartphone-derived data, speech, GPS data, and circadian rhythms. A meta-analysis of 22 studies (6947 participants) revealed that individuals with depression had significantly longer sleep onset latency (5 studies; n=292; +4.75 min, 95% CI 2.46-7.04; P=.005; 95% prediction interval [PI] 0.01-10.27) and time in bed (3 studies; n=236; +31.81 min, 95% CI 18.22-45.39; P=.01; 95% PI 2.28-55.16). Physical activity counts were also significantly lower (5 studies; n=462; standardized mean difference -0.71, 95% CI -1.33 to -0.09; P=.03; 95% PI -2.18 to 0.71). Although individuals with depression showed a lower sleep efficiency, higher mean heart rate, and lower SD of normal-to-normal intervals, these differences were not statistically significant. Other digital markers yielded inconsistent results. Overall, these findings indicate that no single digital biomarker sufficiently captures depression-related changes. Instead, the results support the superiority of personalized, multimodal approaches. However, the generalizability of these findings is limited by the lack of standardized data collection protocols and high clinical heterogeneity across studies, as reflected in wide PIs.

CONCLUSIONS: Certain digital biomarkers, particularly sleep onset latency and physical activity counts, showed consistent average differences between the depression and control groups. However, wide PIs indicate substantial variability across settings, suggesting that no single marker is sufficient for reliable detection. This study advances the field by providing a comprehensive meta-analysis of multidimensional digital biomarkers, establishing a quantitative foundation for objective depression screening and monitoring. These findings support the use of personalized, multimodal digital phenotyping approaches and highlight the need for standardized, clinically interpretable frameworks for real-world depression monitoring.

PMID:41926632 | DOI:10.2196/76432

J Wound Care. 2026 Apr;35(Sup4a):S28-S36. doi: 10.12968/jowc.2025.0340. Epub 2026 Apr 2.

ABSTRACT

OBJECTIVE: To determine the composite cut-off value and diagnostic accuracy of C-reactive protein (CRP) for diabetic foot osteomyelitis (DFO).

METHOD: A retrospective study of patients was combined with a meta-analysis. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Pooled sensitivity, specificity and area under the curve (AUC) were calculated via forest plots and summary receiver operating characteristic curves. A composite cut-off model was developed using R 4.4.3 (R Foundation for Statistical Computing, Austria).

RESULTS: The experimental cohort comprised 265 patients (204 with DFO and 61 without). The meta-analysis comprised 12 studies, including a total of 1828 patients. The retrospective cohort demonstrated that CRP achieved an AUC of 0.63 (95% confidence interval (CI): 0.55, 0.71) for diagnosing DFO, with an optimal cut-off value of 9.57mg/l (sensitivity 74%, specificity 53%). Meta-analysis revealed pooled sensitivity of 74% (63-83%) and specificity of 73% (65-79%) (AUC=0.79; 95%CI: 0.75, 0.82). The composite model suggested a CRP cut-off of 15.20mg/l (sensitivity 80%, specificity 53%).

CONCLUSION: In this study, CRP demonstrated moderate diagnostic utility for DFO and could function as a screening adjunct.

PMID:41926570 | DOI:10.12968/jowc.2025.0340

J Cardiovasc Pharmacol. 2026 Apr 1;87(4):209-218. doi: 10.1097/FJC.0000000000001801.

ABSTRACT

Aspirin (ASA) is the cornerstone of the acute coronary syndrome primary and secondary prevention. Still, its role is debated in some high bleeding risk patients or cases that underwent second-generation drug-eluting stents with a better scaffold. This study compared the efficacy and safety of aspirin-free versus aspirin-based strategies in patients with ACS undergoing PCI. We systematically searched PubMed, Embase, Scopus, and ScienceDirect for studies comparing aspirin-based versus aspirin-free strategies in patients with ACS undergoing PCI. Pooled relative risk (RR) with 95% CI was calculated using a fixed effects model or a random effects model if heterogeneity was present. Significance was set at P < 0.05. Thirty studies including 207,938 patients (N = 104,062 in the ASA arm, and 103,876 in the ASA-free arm) were included in this study. There was a statistically significant reduction in risk of all-cause mortality [RR 0.93, 95% CI, 0.87-0.99, P-value = 0.024, I2 = 0%], BARC 2-5 [RR = 0.68, 95% CI, 0.58-0.81, P-value = <0.01, I2 = 0%], BARC 3 or 5 [RR= 0.71, 95% CI, 0.60-0.82, P-value= <0.01, I2 = 0%], TIMI major bleeding [RR = 0.66, 95% CI, 0.50-0.86, P-value= 0.02, I2 = 0%], TIMI minor or major bleeding [RR= 0.61, 95% CI, 0.52-0.72, P-value= <0.01, I2 = 0%], and ISTH major bleeding with aspirin-free strategy [RR= 0.52, 95% CI, 0.42-0.64, P-value= <0.001, I2= 0%]. Other secondary outcomes showed statistically nonsignificant results. The aspirin-free strategy showed lower all-cause mortality and bleeding, supporting its efficacy and safety in high bleeding risk patients.

PMID:41926558 | DOI:10.1097/FJC.0000000000001801

Crit Care Explor. 2026 Apr 2;8(4):e1393. doi: 10.1097/CCE.0000000000001393. eCollection 2026 Apr 1.

ABSTRACT

IMPORTANCE: Accurate prediction of intubation in critically ill patients could enable interventions that improve patient outcomes. However, the performance of intensive care physicians compared with machine learning (ML) models remains unknown.

OBJECTIVES: To investigate intensive care physicians’ ability to predict the need for intubation within 24 hours and compare their performance against an established ML model, Vent.io.

DESIGN, SETTING, AND PARTICIPANTS: This prospective observational study in two ICUs surveyed intensivists to test their ability to predict the need for intubation of adult patients under their care. Physician predictions of intubation were then compared with predictions from Vent.io.

MAIN OUTCOMES AND MEASURES: Primary metrics include prediction sensitivity, specificity, and descriptive statistics for both physicians and ML model. Generalized linear mixed models investigated the fixed effect of the predictor (physician vs. Vent.io) on both sensitivity and specificity while accounting for the random effects from different physicians. Similar modeling was used to investigate the relationship between physician confidence and correctness.

RESULTS: Overall, physicians were quite confident in their predictions of intubation with a median score of 8 (on a 0-10 point scale, with 0 being not at all confident and 10 being extremely confident) out of the 302 surveys administered. Sensitivity was 0.190 and 0.714 for physicians and Vent.io, respectively. Specificity was 0.960 and 0.673 for physicians and Vent.io, respectively. Generalized linear mixed modeling showed that physician confidence was associated with greater odds of correctly predicting intubation outcome (odds ratio [OR] 1.49; 95% CI, 1.22-1.84; p < 0.001). Vent.io had significantly greater odds of being correct when patients required intubation compared with physicians (OR 18.68; 95% CI, 1.87-186.31; p = 0.013). However, intensive care physicians outperformed Vent.io at correctly predicting when patients did not require intubation (OR 24.80; 95% CI, 13.22-46.52; p < 0.001).

CONCLUSIONS AND RELEVANCE: Although predictive performance compared with human experts is promising, Vent.io needs real-time testing in a randomized clinical trial to determine if its deployment can improve clinical outcomes.

PMID:41926168 | DOI:10.1097/CCE.0000000000001393

J Glob Health. 2026 Apr 3;16:04099. doi: 10.7189/jogh.16.04099.

ABSTRACT

BACKGROUND: The incidence and overuse of caesarean section, an adverse pregnancy outcome closely associated with pregnancy complications, is markedly high globally. However, previous research has predominantly examined individual complications in isolation, leaving a need for a comprehensive evaluation of multimorbidity patterns. We aimed to explore the associations between caesarean sections and adverse pregnancy outcomes in a Chinese population.

METHODS: We retrieved data from the National Maternal Near Miss Surveillance System in Jilin Province in China from 2021 to 2023. We summarised them using descriptive statistics and used the Rao-Scott χ² test to compare the differences between spontaneous labour and caesarean section. Then, we used latent class analysis (LCA) to cluster pregnancy complications and logistic regression to examine their association with modes of delivery.

RESULTS: We included 85 446 pregnant women, of whom 53 916 (63.1%) had undergone caesarean sections and 31 530 (36.9%) had experience spontaneous labour. There were significant differences in terms of pregnancy complications between pregnant women who underwent spontaneous labour and those who had caesarean sections. We then clustered pregnancy complication symptoms into six classes using LCA and fitted three models. After adjusting for potential confounders, the incidence of caesarean sections was significantly higher in pregnant women with diabetes and hypothyroidism (odds ratio (OR) = 1.177; 95% confidence interval (CI) = 1.105-1.253), hyperthyroidism and kidney disease (OR = 2.078; 95% CI = 1.391-3.106), with hypertension and hypothyroidism (OR = 3.613; 95% CI = 3.217-4.058), and hypertension, diabetes, and anaemia (OR = 3.365; 95% CI = 2.997-3.779) when compared to pregnant women with a lower incidence of pregnancy complications.

CONCLUSIONS: Caesarean sections occur frequently among pregnant women in China and are significantly associated with specific pregnancy complication clusters, particularly those involving hypertension, diabetes, anaemia, and thyroid dysfunction. These findings suggest that women with multimorbidity profiles should receive enhanced antenatal surveillance and individualised delivery planning to optimise maternal outcomes.

PMID:41926167 | DOI:10.7189/jogh.16.04099

J Glob Health. 2026 Apr 3;16:04080. doi: 10.7189/jogh.16.04080.

ABSTRACT

BACKGROUND: Malaria is a preventable disease that causes serious illness and death. In 2022, it remained the leading cause of death among children under five years of age in Burkina Faso, despite significant intervention efforts over the past two decades. Research on the effects of interventions and climatic factors on malaria morbidity has expanded, but their effects on malaria mortality remain unclear. We aimed to estimate the effects of interventions and lagged climatic factors on malaria mortality among children under five years of age in northwest Burkina Faso. We further evaluated the role of climatic seasonality in patterns of malaria mortality.

METHODS: We investigated the seasonal patterns of malaria mortality among children under five years of age and their association with climatic factors, such as rainfall and land surface temperature (LST), using wavelet analysis on mortality data from the Nouna Health Demographic Surveillance System spanning 2002-2021. Furthermore, we assessed the effects of interventions, including coverage of insecticide-treated nets (ITNs) and artemisinin-based combination therapies (ACTs), on malaria mortality alongside climate effects using Bayesian negative binomial temporal models for the period 2013-2021.

RESULTS: The lag time in the effects of climatic factors varied over time. Malaria mortality, rainfall, and LST showed a 12-month seasonal cycle throughout the years, while LST also had a six-month cycle in specific years. Rainfall lagged by 1.5 to 2 months and LST by 1 to 1.5 months, depending on the seasonal cycle and year. Rainfall was positively associated with malaria mortality (mortality rate ratio (MRR) = 1.59; 95% Bayesian credible interval (BCI) = 1.18, 1.95), LST showed a decrease in mortality (MRR = 0.68; 95% BCI = 0.52, 0.86), and ITN was associated with a reduction in mortality (MRR = 0.59; 95% BCI = 0.42, 0.79); however, ACT was not statistically important.

CONCLUSIONS: We found that ITN was more effective in reducing malaria mortality than temperature, but rainfall had a greater opposing impact on increasing malaria mortality. The seasonal mortality pattern was more influenced by rainfall than by temperature. Varying climatic lag times highlight the need for adaptive strategies. Policymakers should focus on climate-informed planning, sustained ITN coverage, and reassessment of ACT strategies to further reduce malaria mortality.

PMID:41926164 | DOI:10.7189/jogh.16.04080