Computer-assisted Therapy Research Articles

Internet interventions for parents of young children: A cross-sectional study of health, social, and welfare professionals’ attitudes to their use

BackgroundDespite the increasing popularity of internet-based parent support interventions (IPSI[s]), little is known about attitudes that might promote or prevent the uptake of such interventions among professionals working in prenatal, infant and child services for children under 5 years of age. ObjectiveThis cross-sectional study aimed to investigate attitudes towards IPSIs. We also wanted to identify predictors of attitudes to determine which professionals may be willing to use IPSIs in their work and to what extent attitudes related to efficacy and comfort predicted whether professionals would use IPSIs. MethodsBetween May and September 2018, 1 910 leaders and practitioners of prenatal, infant, and child services were recruited to a cross-sectional web-based survey either through: (a) a course database, (b) an official mailing list to infant and child health services, (c) social media or (d) other recruitment channels. Attitudes toward internet-based parenting interventions were measured by the Computer-Assisted Therapy Attitudes Scale. ResultsParticipants were generally positive about using IPSIs. Professionals’ belief in IPSIs efficacy and their comfort in using them, was associated with younger age, higher education, previous experience with IPSIs, and being a top-level manager. They were also more likely to believe IPSIs were efficacious if they had internet access at work and worked in well-baby clinics, and more comfortable using IPSIs if they were male. Higher scores on both the Efficacy and Comfort sub-scales were associated with more likely use of IPSIs, if such programs were available. ConclusionParticipants’ perceived efficacy and level of comfort with using IPSIs matters in terms of how likely they are to use IPSIs in their work. Prior experience with IPSIs predicted both efficacy and comfort, suggesting that exposure might be important to bridge the gap between expressed attitudes and actual use of IPSIs in practice.

ACL-Net: Semi-supervised Polyp Segmentation via Affinity Contrastive Learning

Automatic polyp segmentation from colonoscopy images is an essential prerequisite for the development of computer-assisted therapy. However, the complex semantic information and the blurred edges of polyps make segmentation extremely difficult. In this paper, we propose a novel semi-supervised polyp segmentation framework using affinity contrastive learning (ACL-Net), which is implemented between student and teacher networks to consistently refine the pseudo-labels for semi-supervised polyp segmentation. By aligning the affinity maps between the two branches, a better polyp region activation can be obtained to fully exploit the appearance-level context encoded in the feature maps, thereby improving the capability of capturing not only global localization and shape context, but also the local textural and boundary details. By utilizing the rich inter-image affinity context and establishing a global affinity context based on the memory bank, a cross-image affinity aggregation (CAA) module is also implemented to further refine the affinity aggregation between the two branches. By continuously and adaptively refining pseudo-labels with optimized affinity, we can improve the semi-supervised polyp segmentation based on the mutually reinforced knowledge interaction among contrastive learning and consistency learning iterations. Extensive experiments on five benchmark datasets, including Kvasir-SEG, CVC-ClinicDB, CVC-300, CVC-ColonDB and ETIS, demonstrate the effectiveness and superiority of our method. Codes are available at https://github.com/xiewende/ACL-Net.

Examining outcomes for service users accessing the Breaking Free Online computer-assisted therapy program for substance use disorders via a ‘telehealth’ approach: protocol for a two arm, parallel group randomized controlled trial

BackgroundBreaking Free Online (BFO), a computer-assisted therapy (CAT) program for substance use disorders (SUD), has been available across UK treatment services for the past decade and has demonstrated efficacy. The Covid-19 pandemic has contributed to digital and ‘telehealth’ approaches to healthcare delivery becoming more common and accepted, and has in parallel, increased numbers of referrals to SUD services because of the impact pandemic-related stress has had on substance using habits in the general population. Digital and telehealth approaches, such as BFO, have the potential to support the treatment system to meet this increased demand for SUD services.MethodsParallel-group randomized controlled trial of eight-week BFO as an adjunct to standard treatment for SUD, in comparison to standard treatment only, at a National Health Service (NHS) Mental Health Trust in North-West England. Participants will be service users aged 18 years and over with demonstrable SUD for at least 12-months. Interventional and control groups will be compared on multiple measures from baseline to post-treatment assessment at eight-weeks, and then three and six-months follow-up. Primary outcome will be self-reported substance use, with secondary outcomes being standardized assessments of substance dependence, mental health, biopsychosocial functioning and quality of life.DiscussionThis study will examine whether BFO and telehealth support, when delivered as an adjunct to standard SUD interventions, improves outcomes for services users receiving NHS SUD treatment. Findings from the study will be used to inform both developments to the BFO program and guidance around augmenting the delivery of CAT programs via telehealth.Trial registration registered with ISRCTN on 25th May 2021—registration number: 13694016. Protocol version: 3.0 05th April 2022. Trial status: This trial is currently open to recruitment—estimated to be completed in May 2023.

Computer-assisted training of executive functions in adult patients with non-progressive acquired brain damage – a pilot study on efficacy of a new therapeutic application

Executive dysfunction is most often caused by post-traumatic or post-stroke damage to the prefrontal regions of the brain. The aim of this study was to compare the efficacy of two computer-assisted therapy programs for executive dysfunctions in patients with acquired brain injury. Patients were trained using either a newly developed application ExeSystem (designed to help improve the ability to manage and control one’s own behavior by performing tasks imitating natural, everyday situations) or a combination of two commercial applications RehaCom and CogniPlus. Data collected after a three-week period of therapy conducted in two 15-person groups of participants indicated comparable efficacy of both therapy programs in improving quality of daily functioning, executive attention, as well as planning and problem-solving but not memory. The improvement in social competence (p = .028) was the only advantage of therapy with the ExeSystem. Therapeutic interactions using computer programs were shown to be positively evaluated by patients (p < .01). This study confirmed at least equal efficacy of computer-based executive function therapy using ExeSystem compared to RehaCom and CogniPlus. However, despite the implementation of a more ecological and comprehensive approach to the content of a new application, the benefits of this approach were limited.

Unguided Computer-Assisted Self-Help Interventions Without Human Contact in Patients With Obsessive-Compulsive Disorder: Systematic Review and Meta-analysis.

BackgroundComputer-assisted treatment may reduce therapist contact and costs and promote client participation. This meta-analysis examined the efficacy and acceptability of an unguided computer-assisted therapy in patients with obsessive-compulsive disorder (OCD) compared with a waiting list or attention placebo.ObjectiveThis study aimed to evaluate the effectiveness and adherence of computer-assisted self-help treatment without human contact in patients with OCD using a systematic review and meta-analysis approach.MethodsRandomized controlled trials with participants primarily diagnosed with OCD by health professionals with clinically significant OCD symptoms as measured with validated scales were included. The interventions included self-help treatment through the internet, computers, and smartphones. We excluded interventions that used human contact. We conducted a search on PubMed, Cochrane Central Register of Controlled Trials, EMBASE, World Health Organization International Clinical Trials Registry Platform, and ClinicalTrials.gov, as well as the reference lists of the included studies. The risk of bias was evaluated using version 2 of the Cochrane risk-of-bias tool for randomized trials. We calculated the standardized mean differences for continuous outcomes and risk ratios for dichotomous outcomes. The primary outcomes were short-term improvement of OCD symptoms measured by validated scales and dropout for any reason.ResultsWe included 11 randomized controlled trials with a total of 983 participants. The results indicated that unguided computer-assisted self-help therapy was significantly more effective than a waiting list or psychological placebo (standard mean difference −0.47, 95% CI −0.73 to −0.22). Unguided computer-assisted self-help therapy had more dropouts for any reason than waiting list or psychological placebo (risk ratio 1.98, 95% CI 1.21 to 3.23). However, the quality of evidence was very low because of the risk of bias and inconsistent results among the included studies. The subgroup analysis showed that exposure response and prevention and an intervention duration of more than 4 weeks strengthen the efficacy without worsening acceptability. Only a few studies have examined the interaction between participants and systems, and no study has used gamification. Most researchers only used text-based interventions, and no study has used a mobile device. The overall risk of bias of the included studies was high and the heterogeneity of results was moderate to considerable.ConclusionsUnguided computer-assisted self-help therapy for OCD is effective compared with waiting lists or psychological placebo. An exposure response and prevention component and intervention duration of more than 4 weeks may strengthen the efficacy without worsening the acceptability of the therapy.Trial RegistrationPROSPERO (International Prospective Register of Systematic Reviews) CRD42021264644; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=264644

Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing

Background and Objectives: Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by these works, a coarse-to-fine segmentation framework is proposed to obtain a complete airway tree.Methods: Our framework segments the overall airway and small branches via the multi-information fusion convolution neural network (Mif-CNN) and the CNN-based region growing, respectively. In Mif-CNN, atrous spatial pyramid pooling (ASPP) is integrated into a u-shaped network, and it can expend the receptive field and capture multi-scale information. Meanwhile, boundary and location information are incorporated into semantic information. These information are fused to help Mif-CNN utilize additional context knowledge and useful features. To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches. A voxel classification network (VCN), which can entirely capture the rich information around each voxel, is applied to classify the voxels into airway and non-airway. In addition, a shape reconstruction method is used to refine the airway tree.Results: We evaluate our method on a private dataset and a public dataset from EXACT09. Compared with the segmentation results from other methods, our method demonstrated promising accuracy in complete airway tree segmentation. In the private dataset, the Dice similarity coefficient (DSC), Intersection over Union (IoU), false positive rate (FPR), and sensitivity are 93.5%, 87.8%, 0.015%, and 90.8%, respectively. In the public dataset, the DSC, IoU, FPR, and sensitivity are 95.8%, 91.9%, 0.053% and 96.6%, respectively.Conclusion: The proposed Mif-CNN and CNN-based region growing method segment the airway tree accurately and efficiently in CT scans. Experimental results also demonstrate that the framework is ready for application in computer-aided diagnosis systems for lung disease and other related works.

Associations between baseline opioid use disorder severity, mental health and biopsychosocial functioning, with clinical responses to computer-assisted therapy treatment

ABSTRACT Background: Increasing rates of opioid-related overdose have been identified globally. Treatment for opioid use disorders (OUD) includes medications for opioid use disorder (MOUD) alongside behavioral support. Novel approaches to behavioral support should be explored, including computer-assisted therapy (CAT) programs. Objectives: Examine differences between baseline and post-treatment measures of opioid use and biopsychosocial functioning for individuals with OUD engaging with the CAT program ‘Breaking Free Online,’ and the extent to which participant characteristics may be associated with post-treatment measures. Methods: 1107 individuals engaged with CAT and provided baseline and post-treatment data – 724 (65.4%) were male, 383 (34.6%) were female. Results: Significant differences between baseline and post-treatment measures were identified (all p <.0001, effect sizes range:15 –.50). Participant characteristics were associated with post-treatment measures of opioid use, opioid dependence, mental health issues, quality of life, and biopsychosocial impairment (all p <.0001). An aggregated consensus measure of clinical impairment was found to be associated with changes in opioid use and post-treatment biopsychosocial functioning measures, with those participants with greater baseline clinical impairment demonstrating a greater magnitude of improvement from baseline to post-treatment than those with lower clinical impairment. Conclusion: CAT may reduce opioid use and improve biopsychosocial functioning in individuals with OUD. CAT could therefore provide a solution to the global opioid crisis if delivered as combination behavioral support alongside MOUD. Findings also indicate that it may be important for treatment systems to identify individuals with psychosocial complexity who might require behavioral support and MOUD.

A qualitative study of patients’ experiences and acceptance of computerised cognitive behavioural therapy in primary care, Scotland

AbstractComputerised cognitive behavioural therapy (CCBT) has been made available within the National Health Service (NHS) across Scotland as an alternative treatment for mild to moderate anxiety and depression. However, the provision of CCBT services is still limited in the NHS, possibly affecting delivery of this computer-aided therapy to patients and inhibiting acceptance and uptake of this intervention. This paper reports on the qualitative exploration of patients’ experiences and acceptance of one CCBT programme delivering computer-assisted therapy (Beating the Blues: BTB), examining particularly the point of referral, access to treatment, and support. Thematic analysis was conducted on semi-structured face-to-face and email interviews with 33 patients at different NHS organisations across Scotland. Data analysis generated six key themes which illustrated patients’ experiences relating to referral and access to the treatment, and the challenges they faced: (1) information dissemination; (2) expectations and the impact of waiting for BTB; (3) impact of locations on experience of BTB; (4) preference for home access; (5) desire for better human support; and (6) desire for additional application support features. The findings highlighted that better methods of implementing and delivering such CCBT services together with the design of the technological interventions are vital to the success of these services.Key learning aims(1)To understand the service models and methods of implementing and delivering one CCBT programme (BTB) in routine care;(2)To learn about user experiences of accessing and using BTB; and(3)To learn about the implications and factors that might have influenced uptake and understand the implications.

Associations between participant characteristics, digital intervention engagement and recovery outcomes for participants accessing ‘Breaking Free Online’ at ‘Change Grow Live’

ABSTRACT Background: Digital interventions can be tailored to the individual and allow automated collection of data. Computer-assisted therapy program ‘Breaking Free Online’ (BFO) is delivered within ‘Change Grow Live’ (CGL) services to support people in recovery from substance misuse. This paper reports baseline data from 5792 participants engaging with BFO at CGL, and post-treatment outcomes from 1489 (26%), and describes how baseline characteristics and BFO engagement were associated with outcomes. Methods: Participants completed a baseline assessment, engaged with BFO and completed a post-treatment assessment. Wilcoxon tests examined changes from baseline to post-treatment and linear regression examined associations between baseline service user characteristics, BFO engagement and outcomes. Results: Participants who did not provide post-treatment data had more severe mental health and biopsychosocial impairment at baseline. Outcomes were associated with baseline characteristics including substance use and dependence, biopsychosocial impairment, age and BFO engagement. Outcomes were significantly positively associated with the number of techniques in BFO completed, indicating a ‘dose-response’. Conclusions: Findings indicate positive outcomes for CGL service users who engaged with BFO through to post-treatment outcomes assessment stage. Stratifying BFO to those most likely to engage may improve treatment outcomes. Some groups may benefit from receiving BFO as ‘computer-assisted therapy’ with Practitioner support.

Research Progress of Internet-based Cognitive Behavioral Therapy in Post-traumatic Stress Disorder

Depression; Computer-assisted therapy; Cognitive-behavior therapy

GC-Net: Global context network for medical image segmentation

Background and ObjectiveMedical image segmentation plays an important role in many clinical applications such as disease diagnosis, surgery planning, and computer-assisted therapy. However, it is a very challenging task due to variant images qualities, complex shapes of objects, and the existence of outliers. Recently, researchers have presented deep learning methods to segment medical images. However, these methods often use the high-level features of the convolutional neural network directly or the high-level features combined with the shallow features, thus ignoring the role of the global context features for the segmentation task. Consequently, they have limited capability on extensive medical segmentation tasks. The purpose of this work is to devise a neural network with global context feature information for accomplishing medical image segmentation of different tasks. MethodsThe proposed global context network (GC-Net) consists of two components; feature encoding and decoding modules. We use multiple convolutions and batch normalization layers in the encoding module. On the other hand, the decoding module is formed by a proposed global context attention (GCA) block and squeeze and excitation pyramid pooling (SEPP) block. The GCA module connects low-level and high-level features to produce more representative features, while the SEPP module increases the size of the receptive field and the ability of multi-scale feature fusion. Moreover, a weighted cross entropy loss is designed to better balance the segmented and non-segmented regions. ResultsThe proposed GC-Net is validated on three publicly available datasets and one local dataset. The tested medical segmentation tasks include segmentation of intracranial blood vessel, retinal vessels, cell contours, and lung. Experiments demonstrate that, our network outperforms state-of-the-art methods concerning several commonly used evaluation metrics. ConclusionMedical segmentation of different tasks can be accurately and effectively achieved by devising a deep convolutional neural network with a global context attention mechanism.

Clinical and demographic patient characteristics, alcohol treatment goal preference and goal attainment during computer-assisted therapy with Breaking Free Online

ABSTRACTBackground: Few alcohol misuse interventions can be tailored to the individual and allow individuals to set personalized treatment goals, including reduction/moderation or abstinence. This study examined outcomes from a tailorable computer-assisted therapy program, Breaking Free Online (BFO), which prompts users to set personalized goals. Methods: Participants completed baseline and post-treatment assessments of alcohol dependence and use and biopsychosocial functioning. Whether baseline clinical and demographic participant characteristics predicted preferred treatment goal, and whether these characteristics and preferred treatment goal then predicted treatment goal attainment, were examined. Results: Participants who prioritized abstinence were more likely to be abstinent at baseline and to have received previous treatment, than participants who prioritized reduction/moderation. Participants who preferred reduction/moderation demonstrated better biopsychosocial functioning and lower alcohol dependence at baseline than those who preferred abstinence. Lower biopsychosocial impairment, lower alcohol dependence, and lower alcohol consumption were associated with a higher likelihood of achieving treatment goals, as was having abstinence as a goal. There were significant interactions between baseline alcohol consumption/severity of alcohol dependence and treatment goal preference. Conclusions: Findings may inform guidance around recommended treatment goals for individuals engaging with treatment for alcohol misuse.

Selected balance parameters of young patients with neurological problems before and after computer-assisted therapy (prototype Virtual Balance Clinic VB-Clinic).

Selected balance parameters of young patients with neurological problems before and after computer-assisted therapy (prototype Virtual Balance Clinic VB-Clinic).

The Advantages and Disadvantages of Online and Blended Therapy: Survey Study Amongst Licensed Psychotherapists in Austria.

BackgroundWeb-based and blended (face-to-face plus Web-based) interventions for mental health disorders are gaining significance. However, many licensed psychotherapists still have guarded attitudes toward computer-assisted therapy, hindering dissemination efforts.ObjectiveThe objective of this study was to provide a therapist-oriented evaluation of Web-based and blended therapies and identify commonalities and differences in attitudes toward both formats. Furthermore, it aimed to test the impact of an information clip on expressed attitudes.MethodsIn total, 95 Austrian psychotherapists were contacted and surveyed via their listed occupational email address. An 8-minute information video was shown to half of the therapists before 19 advantages and 13 disadvantages had to be rated on a 6-point Likert scale.ResultsThe sample resembled all assessed properties of Austrian psychotherapists (age, theoretical orientation, and region). Therapists did not hold a uniform overall preference. Instead, perceived advantages of both interventions were rated as neutral (t94=1.89, P=.06; d=0.11), whereas Web-based interventions were associated with more disadvantages and risks (t94=9.86, P<.001; d=0.81). The information clip did not excerpt any detectable effect on therapists’ attitudes (r95=−.109, P=.30). The application of modern technologies in the own therapeutic practice and cognitive behavioral orientation were positively related to the given ratings.ConclusionsThis study is the first to directly compare therapists’ attitudes toward Web-based and blended therapies. Positive attitudes play a pivotal role in the dissemination of new technologies, but unexperienced therapists seem to lack knowledge on how to benefit from technology-aided treatments. To speed up implementation, these aspects need to be addressed in the development of new interventions. Furthermore, the preference of blended treatments over Web-based interventions seems to relate to avoidance of risks. Although this study is likely to represent therapists’ attitudes in countries with less advanced electronic health services, therapists’ attitudes in more advanced countries might present differently.

Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial.

BackgroundStudents in need of mental health care face many barriers including cost, location, availability, and stigma. Studies show that computer-assisted therapy and 1 conversational chatbot delivering cognitive behavioral therapy (CBT) offer a less-intensive and more cost-effective alternative for treating depression and anxiety. Although CBT is one of the most effective treatment methods, applying an integrative approach has been linked to equally effective posttreatment improvement. Integrative psychological artificial intelligence (AI) offers a scalable solution as the demand for affordable, convenient, lasting, and secure support grows.ObjectiveThis study aimed to assess the feasibility and efficacy of using an integrative psychological AI, Tess, to reduce self-identified symptoms of depression and anxiety in college students.MethodsIn this randomized controlled trial, 75 participants were recruited from 15 universities across the United States. All participants completed Web-based surveys, including the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Positive and Negative Affect Scale (PANAS) at baseline and 2 to 4 weeks later (T2). The 2 test groups consisted of 50 participants in total and were randomized to receive unlimited access to Tess for either 2 weeks (n=24) or 4 weeks (n=26). The information-only control group participants (n=24) received an electronic link to the National Institute of Mental Health’s (NIMH) eBook on depression among college students and were only granted access to Tess after completion of the study.ResultsA sample of 74 participants completed this study with 0% attrition from the test group and less than 1% attrition from the control group (1/24). The average age of participants was 22.9 years, with 70% of participants being female (52/74), mostly Asian (37/74, 51%), and white (32/74, 41%). Group 1 received unlimited access to Tess, with daily check-ins for 2 weeks. Group 2 received unlimited access to Tess with biweekly check-ins for 4 weeks. The information-only control group was provided with an electronic link to the NIMH’s eBook. Multivariate analysis of covariance was conducted. We used an alpha level of .05 for all statistical tests. Results revealed a statistically significant difference between the control group and group 1, such that group 1 reported a significant reduction in symptoms of depression as measured by the PHQ-9 (P=.03), whereas those in the control group did not. A statistically significant difference was found between the control group and both test groups 1 and 2 for symptoms of anxiety as measured by the GAD-7. Group 1 (P=.045) and group 2 (P=.02) reported a significant reduction in symptoms of anxiety, whereas the control group did not. A statistically significant difference was found on the PANAS between the control group and group 1 (P=.03) and suggests that Tess did impact scores.ConclusionsThis study offers evidence that AI can serve as a cost-effective and accessible therapeutic agent. Although not designed to appropriate the role of a trained therapist, integrative psychological AI emerges as a feasible option for delivering support.Trial RegistrationInternational Standard Randomized Controlled Trial Number: ISRCTN61214172; https://doi.org/10.1186/ISRCTN61214172.

Protocol for a randomized controlled trial of the Breaking Free Online Health and Justice program for substance misuse in prison settings

BackgroundSubstance misuse, including problematic drug and alcohol use, are significant issues in society that can have multiple detrimental effects. Many people access support for their substance misuse during prison sentences, due to the associations between substance misuse and offending, and the high proportion of the prison population who have drug and alcohol issues. Breaking Free Online Health and Justice is a computer-assisted therapy program that has been developed to support substance-involved offenders to address their substance misuse and associated offending within prison settings.MethodsThis will be a parallel-group randomized controlled trial of 4-week Breaking Free Online Health and Justice program as an adjunct to standard treatment for substance misuse, in comparison to standard treatment only, in a male Category D open prison. Interventional and control groups will be compared in terms of the changes in their scores on multiple measures from baseline to post-treatment assessment at 4-weeks, and then 3- and 6-months follow-up. Participants will be adult male offenders serving sentences in prison in England who have demonstrable difficulties with drugs and/or alcohol for at least the past 12-months. The primary outcome measure will be self-reported substance misuse, with secondary outcomes being standardized psychometric assessments of substance dependence, mental health, biopsychosocial functioning, quality of life and post-release offending. Other secondary measures will include frequency of completion of specific intervention strategies in the program.DiscussionThis study will examine whether Breaking Free Online Health and Justice as an adjunct to standard substance misuse interventions in prisons, improves outcomes for substance-involved offenders receiving interventions in custodial settings. Findings from the study will be used to inform further developments of the program and potential improvements to custodial treatment.Trials registrationISRCTN09846981.

Understanding AVATAR therapy: who, or what, is changing?

Understanding AVATAR therapy: who, or what, is changing?

Examining effectiveness of tailorable computer-assisted therapy programmes for substance misuse: Programme usage and clinical outcomes data from Breaking Free Online

RationaleWhen evaluating complex, tailorable digital behavioural interventions, additional approaches may be required alongside established methodologies such as randomised controlled trials (RCTs). Research evaluating a computer-assisted therapy (CAT) programme for substance misuse, Breaking Free Online (BFO), is informed by Medical Research Council (MRC) guidance recommending examination of ‘mechanisms of action’ of individual intervention strategies, which is relevant when evaluating digital interventions with content that may evolve over time. AimsTo report findings from examination of mechanisms of action of tailoring advice within the BFO programme and outcomes from specific intervention strategies. MethodologyAnalysis of covariance and linear regressions were used to assess intervention completion data, and psychometric and clinical outcomes, for 2311 service users accessing drug and alcohol treatment services across the UK. ResultsTailoring advice provided to users appeared to prompt them to prioritise completion of intervention strategies associated with their areas of highest biopsychosocial impairment. Completion of specific intervention strategies within BFO were associated with specific clinical outcomes, with a dose response also being found. Mechanisms of action analyses revealed the primacy of cognitions, with cognitive restructuring strategies being associated with improvements in mental health, severity of substance dependence, quality of life and global biopsychosocial functioning. ConclusionsThe MRC framework provides an evolved research paradigm within the field of digital behavioural change. By assessing baseline profiles of need, BFO can target the most appropriate clinical content for individual users. Mechanisms of action research can be used to inform modifications to BFO to continually update clinical content and the technology platform.

Sparse feature competition and shapes similarity based ultrasound image sequence segmentation method

Segmentation of the object region in ultrasound image sequences plays a crucial role in many clinical applications. Active contour models are widely used in medical image segmentation. Yet, the inherent limitations of ultrasound imaging, such as its low signal-to-noise ratio or local fuzzy boundary caused by inhomogeneous distribution of intensity, have prevented the classical active contours from yielding satisfactory results. To cope with these limitations, many methods have been proposed for modeling image features or shapes prior to constraining active contours. However, most features are limited to constructing the object regions image features, and the object shapes priors usually are learnt from a large set of examples. This paper proposes a sparse feature complete and shapes similarity based method for segmentation of ultrasound image sequences. In this method, a novel contour searching strategy, named sparse feature competition, is proposed by exploiting the reconstruction errors based on both the object and the background feature dictionaries, which is used to alleviate the defects in ultrasound images. By proving that the variation in the object shape has the low-rank property in a linear space, the similarity between the object shapes in the image sequence is also exploited as a shapes prior, which can be interpreted as an unsupervised approach to the shapes prior modeling. To validate the performance of our method, sequences of clinical images were used as the training and test set. The proposed method was compared with three well-known methods on the same test set. The results demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against image defects; consequently, it is likely to improve the efficiency of computer-assisted therapy.

Pregnancy and the Acceptability of Computer-Based Versus Traditional Mental Health Treatments.

Recent recommendations urge increased depression screening in pregnant and postpartum women, potentially increasing demand for treatment. Computer-based psychotherapy treatments may address some of perinatal women's unique mental health treatment needs and barriers. We conducted a quantitative survey of pregnant women (≥12 weeks of gestation) on preferences regarding computer-based therapies compared with traditional therapies (psychotherapy and medication). Nonpregnant women and men served as comparison groups. Participants were provided descriptions of three computer-based therapies: video telehealth therapy (VTT), computer-assisted therapy (CAT), and self-guided online therapy (SGO). Participants were asked to select all options that they would consider for treatment as well as first choice preference. The Patient Health Questionnaire-9 (PHQ-9) assessed current depressive symptomatology, and the Mini International Neuropsychiatric Interview (MINI) assessed psychiatric history. Participants included pregnant females (n = 111), nonpregnant females (n = 147), and males (n = 54). Among pregnant women, 77.5% (n = 86) indicated that they would consider some form of computer-based therapy for mental health treatment during pregnancy; VTT was the most commonly considered, followed by CAT and SGO. When asked to select their preferred intervention, traditional talk therapy was the first choice among all three groups, controlling for treatment history and PHQ-9 score. About one-third of pregnant women chose some form of computer-based therapy as their top choice. While computer-based therapies were acceptable to most pregnant women in this sample, traditional talk therapy was the preferred option. Future research should consider how to tailor computer-based therapies to the unique needs of perinatal women.