Using digital tools in clinical, health and social care research:a mixed-methods study of UK stakeholders

Objective: The COVID-19 pandemic accelerated changes to clinical research methodology, with clinical studies being carried out via online/remote means. This mixed-methods study aimed to identify which digital tools are currently used across all stages of clinical research by stakeholders in clinical, health and social care research and investigate their experience using digital tools.Design: Two online surveys followed by semistructured interviews were conducted. Interviews were audiorecorded, transcribed and analysed thematically.Setting, participants: To explore the digital tools used since the pandemic, survey participants (researchers and related staff (n=41), research and development staff (n=25)), needed to have worked on clinical, health or social care research studies over the past 2 years (2020–2022) in an employing organisation based in the West Midlands region of England (due to funding from a regional clinical research network (CRN)). Survey participants had the opportunity to participate in an online qualitative interview to explore their experiences of digital tools in greater depth (n=8).Results: Six themes were identified in the qualitative interviews: ‘definition of a digital tool in clinical research’; ‘impact of the COVID-19 pandemic’; ‘perceived benefits/drawbacks of digital tools’; ‘selection of a digital tool’; ‘barriers and overcoming barriers’ and ‘future digital tool use’. The context of each theme is discussed, based on the interview results.Conclusions: Findings demonstrate how digital tools are becoming embedded in clinical research, as well as the breadth of tools used across different research stages. The majority of participants viewed the tools positively, noting their ability to enhance research efficiency. Several considerations were highlighted; concerns about digital exclusion; need for collaboration with digital expertise/clinical staff, research on tool effectiveness and recommendations to aid future tool selection. There is a need for the development of resources to help optimise the selection and use of appropriate digital tools for clinical research staff and participants

Evidence for feasibility of implementing online brief cognitive‐behavioral therapy for eating disorder pathology in the workplace

Objective: CBT-T is a brief (10-week) cognitive-behavioral therapy for non-underweight eating disorders. This report describes the findings from a single center, single group, feasibility trial of online CBT-T in the workplace as an alternative to health service settings. Method: This trial was approved by the Biomedical and Scientific Research Ethics committee, University of Warwick, UK (reference 125/20-21) and was registered with ISRCTN (reference number: ISRCTN45943700). Recruitment was based on self-reported eating and weight concerns rather than diagnosis, potentially enabling access to treatment for employees who have not previously sought help and for those with sub-threshold eating disorder symptoms. Assessments took place at baseline, mid-treatment (week 4), post-treatment (week 10), and follow-up (1 and 3 months post-treatment). Participant experiences following treatment were assessed using quantitative and qualitative approaches. Results: For the primary outcomes, pre-determined benchmarks of high feasibility and acceptability were met, based on recruiting >40 participants (N = 47), low attrition (38%), and a high attendance rate (98%) over the course of the therapy. Participant experiences revealed low previous help-seeking for eating disorder concerns (21%). Qualitative findings indicated a wide range of positive impacts of the therapy and the workplace as the therapeutic setting. Analysis of secondary outcomes for participants with clinical and sub-threshold eating disorder symptoms showed strong effect sizes for eating pathology, anxiety and depression, and moderate effect sizes for work outcomes. Discussion: These pilot findings provide a strong rationale for a fully powered randomized controlled trial to determine the effectiveness of CBT-T in the workplace. Public Significance: This study demonstrates the feasibility of implementing an eating disorders intervention (online CBT-T) in the workplace as an alternative to traditional healthcare settings. Recruitment was based on self-reported eating and weight concerns rather than diagnosis, potentially enabling access to treatment for employees who had not previously sought help. The data also provide insights into recruitment, acceptability, effectiveness, and future viability of CBT-T in the workplace

A feasibility study of the delivery of online brief cognitive-behavioral therapy (CBT-T) for eating disorder pathology in the workplace

Objective CBT-T is a brief (10 sessions) version of cognitive behavioral therapy for non-underweight eating disorders. This report describes the protocol for a single center, single group, feasibility trial of online CBT-T in the workplace as an alternative to the health-service setting. By offering mental health services for eating disorders in the workplace, greater accessibility and increased help-seeking behaviors could be achieved. Method Treatment will be delivered online over 10 weeks and offered to employees based on self-referral rather than meeting diagnostic criteria, making treatment available to employees with sub-threshold eating disorder symptoms. Results Assessments will be conducted at baseline, mid-treatment (week 4), posttreatment (week 10) and at follow-up (1 month and 3 months posttreatment). For the primary outcome, measures will include recruitment, attrition and attendance data using pre-set benchmarks to determine high, medium or low feasibility and acceptability. Qualitative participant experiences data will be analyzed using thematic analysis. Impact on work engagement and effect sizes will be determined from secondary outcome measures; the latter enabling sample size calculations for future study. Discussion These pilot data will provide insights to recruitment, acceptability, effectiveness and viability of a future fully powered clinical trial of online CBT-T in the workplace. Public Significance Statement This study will present feasibility data from an eating disorders intervention (online CBT-T) using the workplace as an alternative to the healthcare setting to recruit and treat workers. Recruitment will be based on self-reported eating and weight concerns rather than diagnosis potentially enabling treatment to employees who have not previously sought help. The data will also provide insights to recruitment, acceptability, effectiveness, and future viability of CBT-T in the workplace

Predicting depression using electronic health records : a systematic review

Background Depression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning (ML) methods have been used in combination with Electronic Health Records (EHRs) for prediction of depression. Methods Systematic literature searches were conducted within arXiv, PubMed, PsycINFO, Science Direct, SCOPUS and Web of Science electronic databases. Searches were restricted to information published after 2010 (from 1st January 2011 onwards) and were updated prior to the final synthesis of data (27th January 2022). Results Following the PRISMA process, the initial 744 studies were reduced to 19 eligible for detailed evaluation. Data extraction identified machine learning methods used, types of predictors used, the definition of depression, classification performance achieved, sample size, and benchmarks used. Area Under the Curve (AUC) values more than 0.9 were claimed, though the average was around 0.8. Regression methods proved as effective as more developed machine learning techniques. Limitations The categorization, definition, and identification of the numbers of predictors used within models was sometimes difficult to establish, Studies were largely Western Educated Industrialised, Rich, Democratic (WEIRD) in demography. Conclusion This review supports the potential use of machine learning techniques with EHRs for the prediction of depression. All the selected studies used clinically based, though sometimes broad, definitions of depression as their classification criteria. The reported performance of the studies was comparable to or even better than that found in primary care. There are concerns over the generalizability and interpretability

The performance of artificial intelligence-driven technologies in diagnosing mental disorders : an umbrella review

Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. We included 15 systematic reviews of 852 citations identified. The included reviews assessed the performance of AI models in diagnosing Alzheimer's disease (n = 7), mild cognitive impairment (n = 6), schizophrenia (n = 3), bipolar disease (n = 2), autism spectrum disorder (n = 1), obsessive-compulsive disorder (n = 1), post-traumatic stress disorder (n = 1), and psychotic disorders (n = 1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. Healthcare professionals in the field should cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety

Do mental health symptoms during the pandemic predict university non-completion in a sample of UK students? A prospective study

Mental health symptoms are highly prevalent in university students and have been further exacerbated following the COVID-19 pandemic. The aim of this study was to examine the prospective prediction of five mental health symptoms (anxiety, depression, insomnia, suicidality, substance misuse risk) on university non-completion. Baseline data were collected between July and September 2020 following the first UK lockdown and prior to the 2020/2021 academic year. Univariate binary logistic regression analyses were performed using data from 147 participants who were due to graduate at the end of the 2020/2021 academic year. Only substance misuse risk was found to predict university non-completion, with students with a higher risk of substance misuse more likely to not complete their university course. There appears to be an association between substance misuse risk and university non-completion; however, this was attenuated once study characteristic covariates (study level, changes in study hours and study engagement) were included, indicating possible associations between these variables. Future research should further consider the role of substance use in this population and the relationship with study characteristics, engagement and university completion

Machine learning models to detect anxiety and depression through social media : a scoping review

Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019–2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic

Measurement of the dependence of transverse energy production at large pseudorapidity on the hard-scattering kinematics of proton-proton collisions at √s=2.76 TeV with ATLAS

The relationship between jet production in the central region and the underlying-event activity in a pseudorapidity-separated region is studied in 4.0 pb-1 of s=2.76 TeV pp collision data recorded with the ATLAS detector at the LHC. The underlying event is characterised through measurements of the average value of the sum of the transverse energy at large pseudorapidity downstream of one of the protons, which are reported here as a function of hard-scattering kinematic variables. The hard scattering is characterised by the average transverse momentum and pseudorapidity of the two highest transverse momentum jets in the event. The dijet kinematics are used to estimate, on an event-by-event basis, the scaled longitudinal momenta of the hard-scattered partons in the target and projectile beam-protons moving toward and away from the region measuring transverse energy, respectively. Transverse energy production at large pseudorapidity is observed to decrease with a linear dependence on the longitudinal momentum fraction in the target proton and to depend only weakly on that in the projectile proton. The results are compared to the predictions of various Monte Carlo event generators, which qualitatively reproduce the trends observed in data but generally underpredict the overall level of transverse energy at forward pseudorapidity

Measurements of the charge asymmetry in top-quark pair production in the dilepton final state at s √ =8 TeV with the ATLAS detector

Measurements of the top-antitop quark pair production charge asymmetry in the dilepton channel, characterized by two high-pT leptons (electrons or muons), are presented using data corresponding to an integrated luminosity of 20.3  fb−1 from pp collisions at a center-of-mass energy s√=8  TeV collected with the ATLAS detector at the Large Hadron Collider at CERN. Inclusive and differential measurements as a function of the invariant mass, transverse momentum, and longitudinal boost of the tt¯ system are performed both in the full phase space and in a fiducial phase space closely matching the detector acceptance. Two observables are studied: AℓℓC based on the selected leptons and Att¯C based on the reconstructed tt¯ final state. The inclusive asymmetries are measured in the full phase space to be AℓℓC=0.008±0.006 and Att¯C=0.021±0.016, which are in agreement with the Standard Model predictions of AℓℓC=0.0064±0.0003 and Att¯C=0.0111±0.0004

Study of the B-c(+) -> J/psi D-s(+) and Bc(+) -> J/psi D-s*(+) decays with the ATLAS detector

The decays B-c(+) -> J/psi D-s(+) and B-c(+) -> J/psi D-s*(+) are studied with the ATLAS detector at the LHC using a dataset corresponding to integrated luminosities of 4.9 and 20.6 fb(-1) of pp collisions collected at centre-of-mass energies root s = 7 TeV and 8 TeV, respectively. Signal candidates are identified through J/psi -> mu(+)mu(-) and D-s(()*()+) -> phi pi(+)(gamma/pi(0)) decays. With a two-dimensional likelihood fit involving the B-c(+) reconstructed invariant mass and an angle between the mu(+) and D-s(+) candidate momenta in the muon pair rest frame, the yields of B-c(+) -> J/psi D-s(+) and B-c(+) -> J/psi D-s*(+), and the transverse polarisation fraction in B-c(+) -> J/psi D-s*(+) decay are measured. The transverse polarisation fraction is determined to be Gamma +/-+/-(B-c(+) -> J/psi D-s*(+))/Gamma(B-c(+) -> J/psi D-s*(+)) = 0.38 +/- 0.23 +/- 0.07, and the derived ratio of the branching fractions of the two modes is B-Bc+ -> J/psi D-s*+/B-Bc+ -> J/psi D-s(+) = 2.8(-0.8)(+1.2) +/- 0.3, where the first error is statistical and the second is systematic. Finally, a sample of B-c(+) -> J/psi pi(+) decays is used to derive the ratios of branching fractions B-Bc+ -> J/psi D-s*+/B-Bc+ -> J/psi pi(+) = 3.8 +/- 1.1 +/- 0.4 +/- 0.2 and B-Bc+ -> J/psi D-s*+/B-Bc+ -> J/psi pi(+) = 10.4 +/- 3.1 +/- 1.5 +/- 0.6, where the third error corresponds to the uncertainty of the branching fraction of D-s(+) -> phi(K+ K-)pi(+) decay. The available theoretical predictions are generally consistent with the measurement