Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 10 appraisals
Journal of pediatric endocrinology & metabolism : JPEM
Beyond A1c: a narrative review of mental health integration in pediatric type 1 diabetes care
Background Youth with type 1 diabetes (T1D) experience substantially higher rates of depression, anxiety, diabetes distress, attention-deficit/hyperactivity disorder (ADHD), and disordered eating behaviors (DEB) than peers. These conditions may impair self-management and contribute to worsened glycemic outcomes.ContentWe synthesize prevalence and consequences of common comorbidities; summarize International Society for Pediatric and Adolescent Diabetes (ISPAD) and American Diabetes Association (ADA) guidance on psychosocial assessment; and appraise integrated models: co-located behavioral health, stepped care, and telehealth integration. We map barriers (workforce, workflow, stigma, fragmented communication, digital inequities) to concrete actions.SummaryPsychological morbidity and glycemic outcomes may be connected through behavioral and neuroendocrine pathways and family dynamics. Programs embedding behavioral health in pediatric diabetes care facilitate routine psychosocial screening and are associated with improved engagement and quality of life, and in some models, improved glycemic outcomes.OutlookTo close the implementation gap, teams should standardize routine psychosocial screening, embed trained mental-health professionals, use stepped-care models matching interventions to symptom severity, track referral follow-through, and leverage hybrid telehealth. Policy levers include reimbursement for integrated visits and investment in the behavioral-health workforce. The consistent associations between psychological and glycemic outcomes in youth with T1D support integrating psychosocial care as a core component of comprehensive diabetes management.
25 July 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Large language models accurately identify decision reasons in verbal reports.
Understanding the reasons behind human choices under risk is a central goal of decision scientists, but traditional methods relying on behavioral data are limited by strict invariance assumptions. We introduce a scalable analytical framework using large language models (LLMs) to analyze verbal reports and identify articulated reasons for choice between monetary lotteries. A validated LLM accurately identified predefined decision reasons in participants' free-text reports, aligning with their actual choices in 95% of trials. Our analysis reveals that the reasons behind people's decisions vary systematically and are driven more by the structure of the choice problem than by individual differences. Crucially, reasons identified from verbal reports yield more parsimonious and informative representations of decision processes compared to those inferred from choices alone; furthermore, problem-specific reason profiles achieve out-of-sample prediction accuracy that is competitive with established computational models. This work demonstrates that verbal reports are a rich data source and our analytical framework can unlock their potential, delivering results that challenge the field's foundational invariance assumptions and pave the way for more context-sensitive and interpretable models of human decision making.
1 July 2026
Read appraisal →eLife
Disinformation elicits learning biases.
In open societies, disinformation is often considered a threat to the very fabric of democracy. However, we know little about how disinformation exerts its impact, especially its influence on individual learning processes. Guided by the notion that disinformation exerts its pernicious effects by capitalizing on learning biases, we ask which aspects of learning from potential disinformation align with ideal 'Bayesian' principles, and which exhibit biases deviating from these standards. To this end, we harnessed a reinforcement learning framework, offering computationally tractable models capable of estimating latent aspects of a learning process as well as identifying biases in learning. In two experiments, participants completed a two-armed bandit task, where they repeatedly chose between two lotteries and received outcome-feedback from sources of varying credibility, who occasionally disseminated disinformation by lying about true choice outcome (e.g., reporting non-reward when a reward was truly earned or vice versa). Computational modelling indicated that learning increased in tandem with source credibility, consistent with ideal-Bayesian principles. However, we also observed striking biases reflecting divergence from idealized Bayesian learning patterns. Notably, in one experiment individuals learned from sources that should have been ignored, as these were known to be fully unreliable. Additionally, the presence of disinformation elicited exaggerated learning from trustworthy information (akin to jumping to conclusions) and exacerbated a normalized measure of 'positivity bias' whereby individuals self-servingly boost their learning from positive, relative to negative, choice feedback. Thus, in the face of disinformation we identify specific cognitive mechanisms underlying learning biases, with potential implications for societal strategies aimed at mitigating its harmful impacts.
28 June 2026
Read appraisal →Scientific reports
Classifying mental stress from eye tracking data: deep learning approaches for out-of-the-lab conditions
Eye-tracking signals such as pupil diameter and gaze behavior have been widely used for stress detection, yet most approaches rely on task-specific features, controlled laboratory settings, or multimodal sensor combinations, limiting scalability in less controlled environments. This work investigates whether unimodal eye-tracking time-series data can support task-agnostic stress detection beyond static laboratory tasks. We analyze stress classification across two complementary datasets: a virtual reality goalkeeper task with moderate visuomotor activity and stable recording conditions, and a virtual job interview dataset reflecting less controlled settings with uncalibrated signals. The results show that these signals alone contain informative patterns related to stress-associated autonomic and oculomotor responses. Under favorable conditions, performance reaches up to [Formula: see text] macro-averaged F1-score. At the same time, performance varies substantially across datasets, indicating that effective learning depends strongly on data quality, calibration, signal characteristics, and task design. Overall, the findings demonstrate the potential of unimodal eye tracking as a lower-burden alternative to more complex multimodal systems, while highlighting that reliable stress detection is fundamentally conditioned by the interplay of data, signal representation, and modeling approach.
23 June 2026
Read appraisal →BMJ open
Self-management experiences and needs in patients with chronic obstructive pulmonary disease: a meta-synthesis of qualitative studies
OBJECTIVES: To systematically review qualitative studies on the self-management experiences and needs of patients with chronic obstructive pulmonary disease (COPD) and to provide evidence for developing needs-based self-management intervention strategies. DESIGN: A systematic review and meta-aggregation of qualitative studies conducted in accordance with the Enhancing Transparency in Reporting the Synthesis of Qualitative Research statement. DATA SOURCES: PubMed, Cochrane Library, Web of Science, Scopus, CINAHL, Embase, CNKI, Wanfang Database, VIP Database and CBM were searched from database inception to March 2025 for qualitative studies on COPD self-management experiences and needs. ELIGIBILITY CRITERIA: Qualitative studies involving adults (≥18 years) with a confirmed diagnosis of COPD that reported patients' real feelings, experiences and needs during the self-management process in any setting (hospital, community or home). Studies were excluded if the full text could not be obtained or data were incomplete, if they were duplicate publications or if they were not published in Chinese or English. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened titles, abstracts and full texts, extracted data using a standardised form and assessed methodological quality using the 2016 Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research. Findings (participants' quotations and authors' interpretive statements) were synthesised using a JBI meta-aggregation approach to develop categories and overarching synthesised findings. RESULTS: 15 qualitative studies were included, comprising 251 patients with COPD. A total of 52 findings were identified, grouped into 13 categories and synthesised into 4 overarching findings: (1) physical symptom burden strongly shapes psychological well-being during COPD self-management; (2) patients face multiple and interrelated barriers to effective self-management; (3) some patients develop active coping strategies and self-management resources; and (4) patients express multidimensional and continuing support needs. CONCLUSIONS: Patients with COPD experience substantial physical and psychological distress and face multiple challenges in the self-management process. Healthcare professionals should prioritise patients' lived experiences, establish comprehensive and coordinated support systems and develop diversified, needs-based intervention strategies to address patients' multidimensional needs, thereby enhancing self-management capacity and improving quality of life. PROSPERO REGISTRATION NUMBER: CRD42024607051.
29 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Mediators of the Effect of Opt-In Versus Opt-Out Treatment on Smoking Cessation in a Randomized Clinical Trial
INTRODUCTION: Smoking cessation interventions are effective. Most smokers do not receive them, in part because smokers are required to "opt-in". Changing the default (CTD) randomized clinical trial, conducted at a large academic medical center, demonstrated that CTD to providing treatment unless refused (opt-out) improved quit rates. The objective of this study was to evaluate if treatment participation and default-theory based variables explained the effect of opt-out on cessation. METHODS: We conducted a secondary analysis of CTD data (N = 739). Descriptive statistics and correlations of the baseline variables were provided. Structural equation models were employed to investigate whether the treatment assignment was causally mediated by a priori proposed mediators including perceived endorsement, counseling participation, and medication utilization. RESULTS: The mean age of the participants was 51.39 (SD = 14.71) years, among whom 349 (47.23%) were females, and the majority was White (428, 57.92%). The indirect effects from the opt-out treatment arm to counseling participation to smoking cessation (b = 0.201, 95% CI = 0.105 to 0.303), and from opt-out to perceived endorsement to counseling participation to smoking cessation (b = 0.010, 95% CI = 0.002 to 0.022) were significant. The indirect effect from opt-out to perceived endorsement to counseling participation to medication utilization to smoking cessation was marginally significant (b = .001, 95% CI = 0.000 to 0.003). CONCLUSIONS: The opt-out approach enhanced patients' sense that their counselor endorsed and/or recommended treatment, as well as counseling participation. The results partially support our hypothesis that the advantage of Opt-out care would increase treatment participation and would do so by psychological variables thought to underpin the efficacy of defaults. IMPLICATIONS: Our results strengthen evidence for the value of delivering smoking cessation as a default (opt-out rather than opt-in) by illuminating likely mechanisms of action. These include increasing participation in counseling and two default-based theoretical mechanisms, perceived endorsement and implied endorsement.
23 May 2026
Read appraisal →International journal of health care quality assurance
Understanding trust in mandatory telemedicine use during COVID-19: a modified health belief model approach
PURPOSE: This study explores the determinants of trust in telemedicine when its use was mandated during the COVID-19 pandemic in South Korea. It extends the health belief model by incorporating two service related constructs: service quality and perceived cost. DESIGN/METHODOLOGY/APPROACH: An online survey was conducted with 288 individuals who were confirmed COVID-19 patients and required to use telemedicine services during isolation. Partial least squares structural equation modeling was applied to examine the relationships between health beliefs including perceived severity, perceived susceptibility and self-efficacy, service perceptions such as service quality and perceived cost, trust in telemedicine and behavioral intention to use telemedicine services. FINDINGS: The results showed that self-efficacy, service quality and perceived cost positively influenced trust in telemedicine. In contrast, perceived severity and perceived susceptibility did not have a meaningful impact. Trust was found to be a key factor in shaping patients' intention to continue using telemedicine services. Overall, the proposed model was supported, highlighting the importance of confidence and service related perceptions in building trust under mandatory usage conditions. ORIGINALITY/VALUE: This study provides empirical evidence on trust formation under mandatory telemedicine use, a scenario rarely explored in the literature. It demonstrates that trust is primarily driven by users' self-efficacy and service perceptions rather than by health risk perceptions, offering practical guidance for enhancing remote care engagement during policy enforced use conditions.
11 May 2026
Read appraisal →JMIR human factors
Personalizing Mobile Apps for Health Behavioral Change According to Personality: Cross-Sectional Validation of a Preference Matrix
BACKGROUND: Mobile health (mHealth) apps are increasingly used to support healthy lifestyle behaviors through features such as health tracking and personalized reminders. Personalized messaging, tailored to users' profiles, has been shown to improve engagement and retention in health-related contexts. Prior research has linked personality traits, based on the Big Five model, to preferences for specific app mechanisms, leading to the development of a preference matrix for personalizing mHealth apps. This matrix comprises 15 mechanisms derived from behavior change techniques and gamification elements, intended to guide developers in optimizing engagement according to user profiles. OBJECTIVE: This study aimed to validate this preference matrix by examining whether the associations between mechanisms and Big Five personality traits reported in the literature align with user preferences observed in an experimental setting. METHODS: A cross-sectional study was conducted using an online survey that collected demographic data, mHealth app usage, and personality traits. Participants were presented with mockups illustrating 15 mechanisms and were asked to select their preferred options. Logistic regression and ordinal logistic regression analyses were performed to examine associations between personality traits, mechanism selection, and motivation scores. All analyses were adjusted using the Bonferroni correction to account for multiple comparisons. RESULTS: A total of 214 participants completed the survey (mean age 29.42, SD 10.41 y; n=118, 55.1% women; n=89, 41.6% men; n=5, 2% identifying as other; and n=2, 1% nonrespondents). Higher conscientiousness significantly increased the likelihood of selecting the collection mechanism (eg, collecting badges or points; odds ratio [OR] 1.87, 95% CI 1.27-2.75). For competition (eg, competing with other users), conscientiousness (OR 3.22, 95% CI 1.73-6.00) and agreeableness (OR 1.93, 95% CI 1.08-3.45) were significant predictors. Preferences for rewards (eg, virtual incentives such as points or virtual currency) were associated with conscientiousness (OR 2.36, 95% CI 1.53-3.63) and neuroticism (OR 1.97, 95% CI 1.36-2.86). Additionally, 4 mechanisms-self-monitoring, progression, challenge, and quest-were selected by more than half of the participants, independent of personality traits. CONCLUSIONS: The findings partially validate the proposed preference matrix. Conscientiousness consistently emerged as a key predictor of preference across multiple mechanisms, highlighting its central role in engagement with gamified mHealth features. While some mechanisms appear to have universal appeal, others show personality-specific preferences, underscoring the value of combining baseline mechanisms with targeted personalization strategies in mHealth app design.
25 Apr 2026
Read appraisal →JMIR formative research
Effects of a Modified Exposure Claim for an e-Cigarette on Claim Comprehension, Behavioral Intentions, and Risk Perceptions Among US Adult Tobacco Users and Nonusers: Randomized Experimental Study
BACKGROUND: Effective communication about the relative risks of cigarettes and e-cigarettes can help increase switching away from cigarettes while minimizing unintended use. OBJECTIVE: This study examined comprehension of a proposed modified exposure claim (MEC) about an e-cigarette (IQOS VEEV, the study product [SP]) and the effects of claim exposure on SP use intentions and risk perceptions among adult tobacco users and nonusers. METHODS: Adult smokers with no intention to quit smoking (S-NIQ, n=606), adult smokers with an intention to quit smoking (S-IQ, n=600), adult e-cigarette users (ECU, n=630), adult former smokers (FS, n=619), adult tobacco and nicotine products (TNP) never-users aged 18-24 years (n=648), and adult TNP never-users aged 25 years and older (n=749; total N=3852) participated in a randomized between-groups online experimental study. Participants viewed a marketing brochure for the SP with (test condition) or without (control condition) an embedded MEC. Outcome measures included claim comprehension, intention to use the SP regularly, and perceived health risk to self from using the SP or smoking cigarettes. RESULTS: Most participants were female (n=2110, 54.8%), had a mean age of 40.2 (SD 14.93) years, and were equally split across the 4 US regions. S-IQ and S-NIQ were long-term, frequent cigarette smokers, while 91.4% (566/619) of FS were long-term quitters. ECU on average used e-cigarettes ≥15.2 times per day, and the majority of them (552/630, 87.6%) had started using e-cigarettes more than 12 months before. Most participants correctly understood the key elements of the claim: the SP produces lower levels of harmful chemicals compared to cigarettes (1818/1926, 94.4%), and switching completely from cigarettes to the SP reduces exposure to harmful chemicals (1832/1926, 95.1%). In both conditions, positive intention to use the SP was high among ECU (control: 238/314, 75.8% vs test: 249/315, 79%; P=.33), moderate among S-IQ (control: 127/299, 42.5%; test: 166/299, 55.5%; P<.001) and S-NIQ (control: 140/299, 46.8%; test: 166/307, 54.1%; P=.07), low among FS (control: 28/306, 9.2%; test: 33/312, 10.6%; P=.55), and very low among adult TNP never-users aged 18-24 years (control: 3/330, 0.9%; test: 8/318, 2.5%; P=.11), and adult TNP never-users aged 25 years and older (control: 5/373, 1.3% vs test: 12/375, 3.2%; P=.09). All groups understood that the SP posed a lower health risk compared to cigarettes. In all groups, claim exposure was associated with significantly lower risk perception of the SP relative to cigarettes (all comparisons, P<.001). CONCLUSIONS: The tested MEC has the potential to benefit public health by simultaneously increasing already high levels of SP use intention and reducing SP risk perceptions relative to cigarettes among adult tobacco users while generating low levels of use intention among tobacco nonusers.
23 Apr 2026
Read appraisal →JMIR human factors
e-Mental Health Intervention Preferences Among Informal Caregivers of Adults Living with Chronic Kidney Disease: Semistructured Interview Study
BACKGROUND: Informal caregivers of people living with chronic kidney disease commonly experience mental health difficulties. However, there is currently limited access to scalable psychological interventions such as those using e-mental health platforms. Furthermore, there is an absence of such interventions tailored for this group of caregivers, which may limit acceptability. OBJECTIVE: To inform future e-mental health intervention development, this study aimed to explore preferences for e-mental health interventions among informal caregivers of people living with chronic kidney disease. METHODS: A total of 13 informal caregivers of people living with chronic kidney disease participated in semistructured interviews. Eligible informal caregivers lived in the United Kingdom and were recruited via nonprofit organizations and social media advertisements. Semistructured interviews followed a topic guide exploring caregivers' e-mental health intervention preferences. Data were analyzed using manifest content analysis. RESULTS: A total of 4 categories and 13 subcategories were generated. "Design preferences" illustrates key intervention design features that were important to caregivers, such as involving caregivers and other interest-holders in the design process. "Content preferences" describes the information caregivers valued in an intervention, such as information about chronic kidney disease and resources to support caregivers. "Support preferences" reflects the characteristics of support caregivers preferred if support was available as part of the intervention, such as peer support and providing support that accommodates individual preferences. Lastly, "Dissemination preferences" describes methods caregivers recommended to promote intervention uptake, such as engaging communication strategies and trusted and accessible sources of information. CONCLUSIONS: Caregivers identified a number of e-mental health intervention design and delivery preferences that should be considered when developing e-mental health interventions for caregivers of people living with chronic kidney disease. To ensure such interventions are acceptable, feasible, and relevant, co-design approaches should be adopted during the intervention development process.
8 Apr 2026
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