Research Methods in Medicine & Health Sciences

Silicone bands offer a non-invasive method for measuring environmental chemicals; however, their feasibility with young children is uncertain. This mixed-methods study examines toddler compliance with silicone bands, identifies predictors of compliance, and offers recommendations for research.

Children wore silicone wrist and ankle bands for 1 week; parents completed daily diaries capturing wear time of each band (hours) to indicate compliance (focal outcome). Mothers completed questionnaires capturing family characteristics and child cognitive and behavioral characteristics. Compliance and its associations with family and child characteristics were examined using descriptive, comparative, and associative statistics, and predictive modeling. Comments provided by parents in diaries and research assistants’ feedback were summarized to inform recommendations for future studies.

Children (n = 115, 28–39 months) tended to be highly compliant with wearing both bands (56%) or the ankle band only (23%), as reflected by a median of 11–12 hours/day of wear time over the week. Compliance was higher for ankle than wrist bands (p-value <0.001). Wear time was not associated with child age, sex, associated clothing when wearing the band, or socioeconomic status (p-values >0.05). Child language and anxiety were positively associated with wristband compliance (p-values ≤0.038), whereas higher behavioral inhibition and lower effortful control were associated with higher ankle band compliance (p-values ≤0.016). Qualitative data suggest improving band appeal and resolving sizing issues could improve compliance.

Results support the feasibility of deploying silicone bands with toddler-aged children. Studies using child-worn bands should account for hourly wear time, as compliance may predict both chemical exposure levels and child characteristics.

To synthesize the self-reported prevalence of scientific misconduct in health-related research through a systematic review and meta-analysis, including a broader range of unethical practices than previous investigations.

A systematic search was conducted across six electronic databases and three sources of gray literature. Observational studies reporting the prevalence of scientific misconduct among researchers, students, or health professionals were included. The methodological quality was assessed using the Joanna Briggs Institute checklist, and a random-effects meta-analysis was performed using the Freeman-Tukey double arcsine transformation.

Twenty-nine studies were included, totaling 17,919 analyzed responses. The most prevalent form of misconduct was honorary authorship (32%; 95% CI: 21%–44%), followed by plagiarism (28%; 95% CI: 12%–47%), data falsification (12%; 95% CI: 6%–19%), data fabrication (11%; 95% CI: 6%–19%), and methodological manipulation (4%; 95% CI: 2%–7%). High heterogeneity was observed for most outcomes (I² ≥ 95%), except for methodological manipulation (I² = 62%). No evidence of publication bias was found (p > 0.05).

Scientific misconduct remains a widespread issue in health research. The findings underscore the need for institutional policies that promote research integrity, transparent authorship criteria, and ethical training. Despite the potential for underreporting due to self-reported data, the results provide an important overview of the extent and types of misconduct present in the current scientific practice.

Hierarchical Bayesian modelling using groupings of adverse events (AEs) into system organ classes (SOC) are a set of approaches that have been proposed for analysing safety signals in clinical trials. However AEs may be the expression of more than one clinical pathology and the classification of an AE into a single SOC may not always be clear. Further, medical dictionaries may assign AEs which are difficult to classify into a generic disorders SOC. When modelling AE data using SOCs, the misclassification of an AE may lead to either a potential safety signal being missed, or a safety signal being incorrectly flagged.

We investigate the use of mixed membership models as one approach to handling this issue.

Results indicate that this type of approach does have a real effect on model results, and the implications are discussed.

N-of-1 designs involve randomising an individual between treatments and recording outcomes over several timepoints. They are rarely used in mental health research. This research aims to explore the reasons behind this, identifying both opportunities and barriers to their implementation.

A cross-sectional qualitative study was conducted to explore healthcare professionals’ and trial app developers’ views on N-of-1 trials in mental health. Data were collected through semi-structured interviews and analysed using framework analysis, informed by the Theoretical Domains Framework.

We conducted interviews with nine participants, including three psychiatrists, two mental health nurses, two other health professionals, and two people who were involved in developing apps for research in mental health to explore perspectives on N-of-1 trials in mental health. We generated three key themes: (1) N-of-1 trial designs are appropriate for very specific combinations of patient populations, interventions and settings in mental health. (2) If used appropriately, N-of-1 trials have the potential to inform treatment choice, improve outcomes and empower patients. (3) Improved knowledge and understanding of N-of-1 trials is needed among clinicians, patients and the wider research system to facilitate the use of this trial design in mental health.

Our findings suggest a potential role for N-of-1 trials in mental health, specifically for patients with stable chronic conditions and for interventions that can be safely discontinued and re-started. N-of-1 trials could inform treatment choices, improve outcomes, and empower patients. However, significant barriers to implementing this trial design exist within the current UK health and research system.

Platform trials have gained prominence, particularly during the COVID-19 pandemic, due to their flexibility and efficiency in evaluating multiple interventions. However, their complex design introduces methodological and regulatory challenges, especially in randomisation when adding new treatment arms.

This study uses simulations based on a real platform trial to assess the performance of different randomisation methods: simple randomisation, stratified block randomisation, stratified block urn design, and minimisation. We evaluate these methods under various allocation ratios, focusing on covariate balance, allocation predictability, and allocation accuracy, particularly for newly added arms. We also examine the trade-off between covariate balance and allocation predictability.

This study highlights the inherent trade-off between achieving good covariate balance and maintaining adequate allocation unpredictability. SBUD slightly outperforms SBR for certain stage 2 allocation ratios, though not consistently across all scenarios. Minimisation achieves the best covariate balance among all methods but at the cost of reduced randomness. Allocating more patients exclusively to the newly added arm does not effectively reduce covariate imbalance; however, allocating more patients to both the control and new arms yields better balance. Additionally, incorporating non-concurrent data in the minimisation process improves covariate balance compared to using only concurrent data.

The findings suggest that when the primary goal is to achieve covariate balance, especially for new arms, trialists should consider using minimisation with non-concurrent data and allocate patients proportionately to control and new arms. Balancing covariate distribution while maintaining sufficient randomness is crucial to ensuring the methodological robustness and reliability of platform trials.