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Background. The AMBI-RA study aimed to assess the influence of clinical and demographic factors on the choice between JAK inhibitors (JAKis), TNF inhibitors (TNFis), and non-TNFi bDMARDs in patients with rheumatoid arthritis (RA), before and after the publication of the EMA safety recommendations on JAKis (January 2023). Six-month outcomes were analyzed to determine whether these recommendations affected the achievement of therapeutic targets.   Materials and Methods. In this single-center, ambidirectional cohort study, clinical, laboratory, and clinimetric data were collected at baseline and after 6 months of follow-up following the initiation of a new therapeutic line with JAK inhibitors (JAKis), TNF inhibitors (TNFis), or non-TNFi bDMARDs in patients with rheumatoid arthritis (RA). Patients were divided into two cohorts: retrospective (pre-EMA) and prospective (post-EMA). The association between clinical and demographic factors and treatment selection was assessed using univariate and multivariate logistic regression analyses. Additionally, we evaluated the relationship between these factors and non-response at 6 months, defined as failure to achieve remission or low disease activity (REM/LDA) according to DAS28-CRP, or early discontinuation of the bDMARD due to lack of efficacy.   Results. A total of 539 treatment courses were analyzed: 300 in the retrospective cohort (pre-EMA: 2019-2022) and 239 in the prospective cohort (post-EMA: 2023-09/2024). Following the EMA safety recommendations, the use of TNFis increased from 32% to 41.4%, while JAKi prescriptions declined from 26% to 16.3%. TNF is also became the most frequently prescribed first-line agents, rising from 51.6% to 74%, whereas JAKis and non-TNFi bDMARDs decreased from 18% to 4.1% and from 30.5% to 21.9%, respectively (Table 1). Except for older age, the EMA-identified risk factors did not significantly influence JAKi prescriptions in the prospective cohort. TNFis were consistently associated with first-line use in both cohorts, while non-TNFi bDMARDs were positively associated with older age and negatively associated with first-line use in the prospective cohort (Tables 2-3). Regarding treatment outcomes at 6 months, remission (REM) rates decreased from 45.1% to 33.1%, low disease activity (LDA) from 58.6% to 49.3%, and non-responder rates increased from 41.8% to 50.6%. Moreover, first-line treatment was the only factor significantly and negatively associated with non-response at 6 months in the retrospective cohort, but not in the prospective one, suggesting a possible loss of the clinical benefit historically linked to first-line therapy (Table 4).   Conclusions. The AMBI-RA study represents the first attempt, through an ambidirectional design, to compare the association between clinical and demographic variables and the prescription of b/tsDMARDs before and after the EMA safety recommendations on JAK inhibitors. The analysis of their impact on the examined cohort suggests a lower effectiveness of first-line treatment strategies, which have shifted predominantly toward TNFis in the post-EMA period. Future multicenter studies are warranted to further explore how regulatory decisions by international agencies may influence therapeutic target achievement in clinical practice.

Abstract Two-dimensional (2D) transition metal carbide MXene-based materials hold great potentials applied for new electromagnetic wave (EMW) absorbers. However, the application of MXenes in the field of electromagnetic wave absorption (EMA) is limited by the disadvantages of poor impedance matching, single loss mechanism, and easy oxidation. In this work, MoO3/TiO2/Mo2TiC2T x hybrids were prepared by the annealing-treated Mo2TiC2T x MXene and uniform MoO3 and TiO2 oxides in-situ grew on Mo2TiC2T x layers. At the annealing temperature of 300 °C, the minimum reflection loss (RLmin) value of MoO3/TiO2/Mo2TiC2T x reaches −30.76 dB (2.3 mm) at 10.18 GHz with a significantly broadening effective absorption bandwidth (EAB) of 8.6 GHz (1.8 mm). The in-situ generated oxides creating numerous defects and heterogeneous interfaces enhance dipolar and interfacial polarizations and optimize the impedance matching of Mo2TiC2T x . Considering the excellent overall performance, the MoO3/TiO2/Mo2TiC2T x hybrids can be a promising candidate for EMA.

Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown. Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events - the reveal of midterm exam grades - to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs. Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes - that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding. Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.

Accurately detecting student classroom behaviors in classroom videos is beneficial for analyzing students’ classroom performance and consequently enhancing teaching effectiveness. To address challenges such as object density, occlusion, and multi-scale scenarios in classroom video images, this paper introduces an improved YOLOv8 classroom detection model. Firstly, by combining modules from the Res2Net and YOLOv8 network models, a novel C2f_Res2block module is proposed. This module, along with MHSA and EMA, is integrated into the YOLOv8 model. Experimental results on a classroom detection dataset demonstrate that the improved model in this paper exhibits better detection performance compared to the original YOLOv8, with an average precision (mAP@0.5) increase of 4.2%.

Feelings of well-being and happiness fluctuate over time and contexts. Ecological Momentary Assessment (EMA) studies can capture fluctuations in momentary behavior, and experiences by assessing these multiple times per day. Traditionally, EMA was performed using pen and paper. Recently, due to technological advances EMA studies can be conducted more easily with smartphones, a device ubiquitous in our society. The goal of this review was to evaluate the literature on smartphone-based EMA in well-being research in healthy subjects. The systematic review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Searching PubMed and Web of Science, we identified 53 studies using smartphone-based EMA of well-being. Studies were heterogeneous in designs, context, and measures. The average study duration was 12.8 days, with well-being assessed 2–12 times per day. Half of the studies included objective data (e.g. location). Only 47.2% reported compliance, indicating a mean of 71.6%. Well-being fluctuated daily and weekly, with higher well-being in evenings and weekends. These fluctuations disappeared when location and activity were accounted for. On average, being in nature and physical activity relates to higher well-being. Working relates to lower well-being, but workplace and company do influence well-being. The important advantages of using smartphones instead of other devices to collect EMAs are the easier data collection and flexible designs. Smartphone-based EMA reach far larger maximum sample sizes and more easily add objective data to their designs than palm-top/PDA studies. Smartphone-based EMA research is feasible to gain insight in well-being fluctuations and its determinants and offers the opportunity for parallel objective data collection. Most studies currently focus on group comparisons, while studies on individual differences in well-being patterns and fluctuations are lacking. We provide recommendations for future smartphone-based EMA research regarding measures, objective data and analyses.

Cigarette smoking is the leading cause of preventable mortality in the United States. Cessation interventions delivered through smartphone apps can reach large populations of individuals who smoke. Ecological momentary assessment (EMA), a feature often included in existing cessation apps, can be used to track behaviors and other important constructs and to inform just-in-time interventions. However, the isolated influence of EMA engagement on smoking cessation is unknown. In addition, the implications of incentivizing the use of EMA for cessation outcomes are currently unknown. The National Cancer Institute's publicly available smoking cessation app, quitSTART, includes a 2-week voluntary EMA protocol (42 total EMA prompts), which provides an opportunity to explore the impact of EMA incentivization on smoking cessation. This study aimed to examine the influence of app-based EMA participation on smoking cessation for people who are incentivized to use EMA compared with those who are not incentivized (representing the current implementation of EMA within quitSTART). In total, 152 US adults were recruited from web, social media, and SMS text message sources into a randomized controlled trial. All eligible participants were randomized to either nonincentivized EMA or incentivized EMA. Participants completed baseline, 2-week, and 4-week assessments. The primary outcome of interest was 7-day point prevalence abstinence measured at 2 and 4 weeks after app download. Average EMAs completed by arm were compared using a t test. Firth logistic regression modeling was used to determine the association between arm and smoking abstinence at 2 and 4 weeks, adjusted for smoking frequency and concurrent use of other tobacco products. The mean number of EMAs completed was 13.3 (range 0-40, SD 11.2) in the incentivized arm and 4.7 (range 0-28, SD 5.8) in the nonincentivized arm (P<.001). Cessation rates were 9% and 20.3% at 2 weeks (P=.06), and 17.5% and 36.6% at 4 weeks (P=.01) in the incentivized arm and nonincentivized arm, respectively. Study arm was not associated with cessation in the adjusted models (adjusted odds ratio [OR] at 2 weeks 0.60, 95% CI 0.21-1.73; adjusted OR at 4 weeks 0.51, 95% CI 0.22-1.19). This study attempted to isolate and examine the effect of incentivizing EMA engagement on smoking cessation success for adults using a smartphone app to quit. While participants randomized to incentivization of EMA showed higher engagement with this feature, our findings suggest that there was no additional short-term cessation benefit from this engagement. Crude analyses found a potential benefit for allowing autonomy over the use of app features, despite the ability of EMA completion to provide real-time, tailored cessation support.

Stress is often associated with pathophysiologic responses, like blood pressure (BP) reactivity, which when experienced repeatedly may be one pathway through which stress leads to poor physical health. Previous laboratory and field studies linking stress to physiological measures are limited by small samples, narrow demographics, and artificial stress manipulations, whereas large-scale studies often do not capture measures like BP reactivity in daily life. We examined perceived stress, emotions, heart rate, and BP during daily life using a 3-wk app-based study. We confirmed the validity of a smartphone-based optic sensor to measure BP and then analyzed data from more than 330,000 daily responses from over 20,000 people. Stress was conceptualized as the ratio of situational demands relative to individual resources to cope. We found that greater demands were associated with higher BP reactivity, but critically, the ratio of demands relative to resources improved prediction of BP changes. When demands were higher and resources were lower, there was higher BP reactivity. Additionally, older adults showed greater concordance between self-reported stress and physiologic responses than younger adults. We also observed that physiologic reactivity was associated with current emotional state, and both valence and arousal mattered. For example, BP increased with high-arousal negative emotions (e.g., anger) and decreased with low-arousal positive emotions (e.g., contentment). Taken together, this work underscores the potential for expanding stress science and public health data using handheld phones to reliably and validly measure physiologic responses linked to stress, emotion, and physical health.

Background/Hypothesis Digital interventions targeting transdiagnostic mechanisms in daily life may be a promising translational strategy for prevention and early intervention of psychotic and other severe mental disorders. We aimed to investigate the feasibility and initial signals of efficacy of a transdiagnostic, compassion-focused, hybrid ecological momentary intervention for improving resilience (ie, EMIcompass) in youth with early mental health problems. Study Design In an exploratory, assessor-blind randomized controlled trial, youth aged 14–25 with current distress, broad at-risk mental state, or first episode of severe mental disorder were randomly allocated to experimental (EMIcompass+treatment as usual [TAU]) or control condition (TAU). Data on primary (stress reactivity) and secondary candidate mechanisms as well as candidate primary (psychological distress) and secondary outcomes were collected. Study Results Criteria for the feasibility of trial methodology and intervention delivery were met (n = 92 randomized participants). No serious adverse events were observed. Initial outcome signals were evident for reduced momentary stress reactivity (stress×time×condition, B = −0.10 95%CI −0.16–−0.03, d = −0.10), aberrant salience (condition, B = −0.38, 95%CI −0.57–−0.18, d = −0.56) as well as enhanced momentary resilience (condition, B = 0.55, 95%CI 0.18–0.92, d = 0.33) and quality of life (condition, B = 0.82, 95%CI 0.10–1.55, d = 0.60) across post-intervention and 4-week follow-up. No outcome signals were observed for self-reported psychological distress (condition, B = 0.57, 95%CI −1.59–2.72, d = 0.09), but there was suggestive evidence of reduced observer-rated symptoms at the 4-week follow-up (B = −1.41, 95%CI −2.85–0.02, d = −0.41). Conclusions Our findings provide evidence of feasibility and initial signals that EMIcompass may reduce stress reactivity and improve quality of life. A definitive trial is now warranted.

It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices’ sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76).

Smartphone app-based ecological momentary assessment (EMA) without face-to-face contact between researcher and participant (app-based noncontact EMA) potentially provides a valuable data collection tool when geographic, time, and situational factors (eg, COVID-19 restrictions) place constraints on in-person research. Nevertheless, little is known about the feasibility of this method, particularly in older and naïve EMA participants. This study aims to assess the feasibility of app-based noncontact EMA as a function of previous EMA experience, by recruiting and comparing a group of participants who had never participated in EMA before against a group of participants who had been part of an earlier in-person EMA study, and age, by recruiting middle-aged to older adults. Overall, 151 potential participants were invited via email; 46.4% (70/151) enrolled in the study by completing the baseline questionnaire set and were emailed instructions for the EMA phase. Of these participants, 67% (47/70) downloaded an EMA app and ran the survey sequence for 1 week. In total, 5 daytime surveys and 1 evening survey, each day, assessed participants' listening environment, social activity, and conversational engagement. A semistructured exit telephone interview probed the acceptability of the method. As markers of feasibility, we assessed the enrollment rate, study completion rate, reason for noncompletion, EMA survey response rate, and likelihood of reporting an issue with survey alerts and requested assistance from researchers, family, or friends. Enrollment rates among invitees (63.3% vs 38.2%; P=.004) and completion rates among enrollees (83.9% vs 53.8%; P<.001) were higher in the experienced than in the naïve EMA group. On average, experienced participants responded to 64.1% (SD 30.2%) of the daytime EMA surveys, and naïve participants responded to 54.3% (SD 29.5%) of the daytime EMA surveys (P=.27). Among participants who retrospectively reported issues with survey alerts, only 19% (3/16) requested researcher assistance during data collection. Older participants were more likely to report not being alerted to EMA surveys (P=.008), but age was unrelated to all other markers of feasibility. Post hoc analyses of the effect of the phone operating system on markers of feasibility revealed that response rates were higher among iOS users (mean 74.8%, SD 20.25%) than among Android users (mean 48.5%, SD 31.35%; P=.002). Smartphone app-based noncontact EMA appears to be feasible, although participants with previous EMA experience, younger participants, and iOS users performed better on certain markers of feasibility. Measures to increase feasibility may include extensive testing of the app with different phone types, encouraging participants to seek timely assistance for any issues experienced, and recruiting participants who have some previous EMA experience where possible. The limitations of this study include participants' varying levels of existing relationship with the researcher and the implications of collecting data during the COVID-19 social restrictions.