Maternal health behaviours: the development and feasibility evaluation of a mindfulness-based behaviour change intervention for pregnant women

Background: Maternal health behaviours are associated with the likelihood of pregnancy complications, and with infants’ immediate and lifetime outcomes. Aim: The overarching aim was to investigate whether mindfulness training might have any potential as the basis of a maternal behaviour change intervention. Method: The project employed mixed methods. The intervention development was guided by the Behaviour Change Wheel handbook. A cross-section survey investigated relationships between pregnant women’s trait mindfulness and: health behaviours: physical activity, taking Vitamin D supplements, BMI at conception, drinking alcohol, smoking; subjective wellbeing and perceived stress; and health behaviour motivation (n = 318). The feasibility of a novel 17-week maternal mindfulness-based behaviour change intervention, “Mind the Bump”, was evaluated in an uncontrolled repeated measures and feedback feasibility study (n = 32). Results: Trait mindfulness was not related to maternal health behaviours. Trait mindfulness was positively related to positive affect and wellbeing, health behaviour motivation, and negatively related to perceived stress and negative affect. Non-adherence to UK recommendations for exercise, Vitamin D, alcohol, and smoking was related to: poorer subjective wellbeing and lower health behaviour motivation. Concurrent risks were more common in women with lower wellbeing and higher negative affect. The intervention was feasible in terms of recruitment, acceptability, and retention. Adherence was moderate in the contact period (week 1 to 8), and reduced in the self-led period (week 9 to 16). There were no significant changes in health behaviours: physical activity, fruit and vegetable intake, Vitamin D supplementation, or alcohol consumption. There were significant improvements in positive aspects of mental health: mindfulness, positive affect, and wellbeing. There were no significant changes in negative aspects of mental health: perceived stress, negative affect, general anxiety, antenatal depression, and pregnancy distress. There may be more potential to improve health behaviours prior to pregnancy

"I am quite mellow but I wouldn't say everyone else is”: how UK students compare their drinking behaviour to their peers’

Background: Excessive drinking is commonplace at UK Universities. Individuals may misperceive how much they drink compared to others and are less likely to think that they will suffer adverse consequences. Young people often distance themselves and their friends from ‘problem drinkers’. Objectives: The aim of the study was to explore how student drinkers compared their own drinking behaviours to the drinking behaviours of others. Methods: An online survey was completed by 416 students aged 18-30 (68.5% female). They were asked ‘how do you think your drinking compares with other people like you?’ and ‘how do you think your behaviour when you drink compares with other people like you?’ Answers were subjected to thematic analysis. Results: The first main theme was about ‘identification as a ‘good’ drinker’. Participants suggested their own behaviour when drinking was similar to their sober behaviour. Further, they viewed themselves as more able to maintain a balance between staying in control and having fun while drinking. The second main theme was about ‘distancing from being a ‘bad’ drinker. Participants distanced themselves from negative prototypical drinkers, such compulsive or anti-social drinkers. They also attributed their own drinking behaviours to situational factors, but described other people as intentionally violent or aggressive. Conclusions/Importance: These findings may explain the failure of some health messages to change drinking behaviours. If drinkers perceive that their behaviour when they drink is better than other people’s then they may discount intervention messages. Targeting these biases could be incorporated into future interventions

Personalized digital interventions showed no impact on risky drinking in young adults

Aim: To assess the effectiveness of two personalized digital interventions (OneTooMany and Drinks Meter) compared to controls. Method: Randomized controlled trial (AEARCTR-0,001,082). Volunteers for the study, aged 18–30, were randomly allocated to one of two interventions or one of two control groups and were followed up 4 weeks later. Primary outcomes were AUDIT-C, drinking harms and pre-loading. Drinks Meter provided participants with brief screening and advice for alcohol in addition to normative feedback, information on calories consumed and money spent. OneTooMany presented a series of socially embarrassing scenarios that may occur when drinking, and participants were scored according to if/how recently they had been experienced. Results: The study failed to recruit and obtain sufficient follow-up data to reach a prior estimated power for detecting a difference between groups and there was no indication in the analysable sample of 402 subjects of a difference on the primary outcome measures (Drinks Meter; AUDIT-C IRR = 0.98 (0.89–1.09); Pre-loading IRR = 1.01 (0.95–1.07); Harms IRR = 0.97 (0.79–1.20); OneTooMany; AUDIT-C IRR = 0.96 (0.86–1.07); Pre-loading IRR = 0.99 (0.93–1.06); Harms IRR = 1.16 (0.94–1.43). Conclusion: Further research is needed on the efficacy of such instruments and their ingredients. However, recruitment and follow-up are a challenge

University student attitudes to prosocial bystander behaviours

Purpose: Half of British university students experience assault and harassment behaviours; few report them. Bystander intervention training has been recommended as a means of reducing these behaviours, but there is little evidence about their potential effectiveness in UK contexts. This study sought to understand UK students’ attitudes towards reporting and intervening in sexual assault, harassment, and hate crimes. Design: A mixed methods cross sectional survey (N=201; 75.6% women) was conducted in one British university. Open text data were analysed using thematic analysis. Findings: Students considered harassment and assault unacceptable, and were confident to intervene in and likely to report incidents. However, fear of backlash was a barrier to intervening and reporting, and they felt that victims should decide whether to report incidents. Students perceived perpetrators as being ignorant about what constitutes consent, harassment, and assault. They identified a need for university community education about this and how to report incidents and support peers. Research limitations/implications: This cross sectional survey was conducted at one UK University. The data might not reflect other students’ attitudes, and may be subject to response bias. Practical implications: University community bystander training should be acceptable, report and support systems might be utilised by students. This may have potential to reduce prevalence and increase reporting. Originality: This is the first study to investigate UK student attitudes to prosocial bystander behaviours

Evaluation of an artificial intelligence enhanced application for student wellbeing: pilot randomised trial of the mind tutor

There has been an increase in the number of UK university students disclosing mental health conditions in recent years. This paper describes the evaluation of the Mind Tutor app, an artificial intelligence based wellbeing app specifically designed for first year undergraduate students, which included a chatbot function that guided students to relevant wellbeing content. The content of the app was developed based on data about mental health and wellbeing issues reported by students and focussed on anxiety, low mood, academic study, transition to university and relationships. Two randomised controlled evaluation studies were conducted with N = 177 and N = 240 first year undergraduate students from two UK universities (the second due to delays in development work and difficulties with recruitment in the first trial). The Mind Tutor had no significant impact on student wellbeing. The study suffered from poor recruitment and retention rates. However, further research is warranted to understand factors that may increase engagement and acceptability of app based tools to increase student wellbeing

Dynamic Energy Landscapes of Riboswitches Help Interpret Conformational Rearrangements and Function

Riboswitches are RNAs that modulate gene expression by ligand-induced conformational changes. However, the way in which sequence dictates alternative folding pathways of gene regulation remains unclear. In this study, we compute energy landscapes, which describe the accessible secondary structures for a range of sequence lengths, to analyze the transcriptional process as a given sequence elongates to full length. In line with experimental evidence, we find that most riboswitch landscapes can be characterized by three broad classes as a function of sequence length in terms of the distribution and barrier type of the conformational clusters: low-barrier landscape with an ensemble of different conformations in equilibrium before encountering a substrate; barrier-free landscape in which a direct, dominant “downhill” pathway to the minimum free energy structure is apparent; and a barrier-dominated landscape with two isolated conformational states, each associated with a different biological function. Sharing concepts with the “new view” of protein folding energy landscapes, we term the three sequence ranges above as the sensing, downhill folding, and functional windows, respectively. We find that these energy landscape patterns are conserved in various riboswitch classes, though the order of the windows may vary. In fact, the order of the three windows suggests either kinetic or thermodynamic control of ligand binding. These findings help understand riboswitch structure/function relationships and open new avenues to riboswitch design

Roadmap on holography

From its inception holography has proven an extremely productive and attractive area of research. While specific technical applications give rise to 'hot topics', and three-dimensional (3D) visualisation comes in and out of fashion, the core principals involved continue to lead to exciting innovations in a wide range of areas. We humbly submit that it is impossible, in any journal document of this type, to fully reflect current and potential activity; however, our valiant contributors have produced a series of documents that go no small way to neatly capture progress across a wide range of core activities. As editors we have attempted to spread our net wide in order to illustrate the breadth of international activity. In relation to this we believe we have been at least partially successful

Algorithm for optimal denoising of Raman spectra

Raman spectroscopy has been demonstrated to have diagnostic potential in areas such as urine and cervical cytology, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra using various multi-variate statistical classification tools. However, Raman scattering is an inherently weak process, which often results in low signal to noise ratios, thus limiting the method’s diagnostic capabilities under certain conditions. A common approach for reducing the experimental noise is Savitzky-Golay smoothing. While this method is effective in reducing the noise signal, it has the undesirable effect of smoothing the underlying Raman features, compromising their discriminative utility. Maximum Likelihood Estimation is a method for estimating the parameters of a statistical model given an available dataset and a priori knowledge of the model type. In this paper, we demonstrate how Savitzky-Golay smoothing may be enhanced with Maximum Likelihood Estimation in order to prevent significant deviation from the ‘true’ Raman signal yet retain the robust smoothing properties of the Savitzky-Golay filter. The algorithm presented here is demonstrated to have a lower impact on Raman spectral features at known spectral peaks while providing superior denoising capabilities, when compared with established smoothing algorithms; artificially noised databases and experimental data are used to evaluate and compare the performance of the algorithms in terms of the signal to noise ratio. The proposed method is demonstrated to typically provide at least a 50% increase in the signal to noise ratio when compared to the raw data, and consistently out-performs two alternative smoothing filters