Data-Driven Implications for Translating Evidence-Based Psychotherapies into Technology-Delivered Interventions.
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ABSTRACT:
SUBMITTER: Schroeder J
PROVIDER: S-EPMC8078869 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo report the interim results from the training of providers inevidence-based psychotherapies (EBPs) and use of mobile applications.Design and settingThe Partnerships in Research to Implement and Disseminate Sustainable and Scalable Evidence (PRIDE) study is a cluster-randomised hybrid effectiveness-implementation trial comparing three delivery pathways for integrating comprehensive mental healthcare into primary care in Mozambique. Innovations include the use of EBPs and scaling-up of task-shifted mental health services using mobile applications.Main outcome measuresWe examined EBP training attendance, certification, knowledge and intentions to deliver each component. We collected qualitative data through rapid ethnography and focus groups. We tracked the use of the mobile applications to investigate early reach of a valid screening tool (Electronic Mental Wellness Tool) and the roll out of the EBPs PARTICIPANTS: Psychiatric technicians and primary care providers trained in the EBPs.ResultsPRIDE has trained 110 EBP providers, supervisors and trainers and will train 279 community health workers in upcoming months. The trainings improved knowledge about the EBPs and trainees indicated strong intentions to deliver the EBP core components. Trained providers began using the mobile applications and appear to identify cases and provide appropriate treatment.ConclusionsThe future of EBPs requires implementation within existing systems of care with fidelity to their core evidence-based components. To sustainably address the vast mental health treatment gap globally, EBP implementation demands: expanding the mental health workforce by training existing human resources; sequential use of EBPs to comprehensively treat mental disorders and their comorbid presentations and leveraging digital screening and treatment applications.
| S-EPMC8025148 | biostudies-literature
Project description:Despite the increasing popularity of translation research, few studies have described the process and challenges involved in implementing a translation study. The main objective was to determine whether a multi-component group behavioral intervention could be successfully translated from an academic setting into the community health system of federally qualified health centers (FQHCs) funded by the Health Resources and Services Administration (HRSA) in Miami, NY, and NJ. Key challenges and "lessons learned" from the dissemination and implementation process for the SMART/EST (Stress Management And Relaxation Training/Emotional Supportive Therapy) Women's Project (SWP) III in low-resource primary care settings are described. The Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) model served as the theoretical framework for the translation of the study. This study outlines several essential factors related to Glasgow's RE-AIM model that need to be considered in order to accomplish successful translation of evidence-based interventions from traditional academia to "real-world" community health center settings.
| S-EPMC4444700 | biostudies-other
Project description:BackgroundEvidence on technology-based psychological interventions (TBIs) for the treatment of depression is rapidly growing and covers a broad scope of research. Despite extensive research in this field, guideline recommendations are still limited to the general effectiveness of TBIs.ObjectiveThis study aims to structure evidence on TBIs by considering different application areas (eg, TBIs for acute treatment and their implementation in health care, such as stand-alone interventions) and treatment characteristics (eg, therapeutic rationale of TBIs) to provide a comprehensive evidence base and to identify research gaps in TBIs for diagnosed depression. Moreover, the reporting of negative events in the included studies is investigated in this review to enable subsequent safety assessment of the TBIs.MethodsRandomized controlled trials on adults diagnosed with unipolar depression receiving any kind of psychotherapeutic treatment, which was at least partly delivered by a technical medium, were eligible for inclusion in our preregistered systematic review. We searched for trials in CENTRAL (Cochrane Central Register of Controlled Trials; until August 2020), MEDLINE, PsycINFO, PSYNDEX, CINAHL; until the end of January 2018), clinical trial registers, and sources of gray literature (until the end of January 2019). Study selection and data extraction were conducted by 2 review authors independently.ResultsDatabase searches resulted in 15,546 records, of which 241 publications were included, representing 83 completed studies and 60 studies awaiting classification (ie, preregistered studies, study protocols). Almost all completed studies (78/83, 94%) addressed the acute treatment phase, being largely either implemented as stand-alone interventions (66/83, 80%) or blended treatment approaches (12/83, 14%). Studies on TBIs for aftercare (4/83, 5%) and for bridging waiting periods (1/83, 1%) were scarce. Most TBI study arms (n=107) were guided (59/107, 55.1%), delivered via the internet (80/107, 74.8%), and based on cognitive behavioral treatment approaches (88/107, 79.4%). Almost all studies (77/83, 93%) reported information on negative events, considering dropouts from treatment as a negative event. However, reports on negative events were heterogeneous and largely unsystematic.ConclusionsResearch has given little attention to studies evaluating TBIs for aftercare and for bridging waiting periods in people with depression, even though TBIs are seen as highly promising in these application areas; thus, high quality studies are urgently needed. In addition, the variety of therapeutic rationales on TBIs has barely been represented by identified studies hindering the consideration of patient preferences when planning treatment. Finally, future studies should use specific guidelines to systematically assess and report negative events.Trial registrationInternational Prospective Register of Systematic Reviews (PROSPERO) CRD42016050413; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42016050413.International registered report identifier (irrid)RR2-10.1136/bmjopen-2018-028042.
| S-EPMC7904404 | biostudies-literature
Project description:BackgroundCognitive-behavioral therapy (CBT) has long-standing evidence for efficacy in the treatment of alcohol use, yet implementation in clinical practice has been challenging. Delivery of CBT through technology-based platforms, such as web-based programs and mobile applications, has the potential to provide widespread access to this evidence-based intervention. While there have been reviews indicating the efficacy of technology-based delivery of CBT for various psychiatric conditions, none have focused on efficacy for alcohol use. The current meta-analysis was conducted to fill this research gap.MethodsDescriptive data were used to characterize the nature of the literature on technology-delivered, CBT-based interventions for alcohol use ("CBT Tech"). Inverse-variance-weighted effect sizes were calculated, and random effects, effect sizes were pooled in 4 subgroups.ResultsFifteen published trials conducted primarily with at-risk or heavy drinkers were identified. Of these studies, 60% explicitly targeted alcohol use moderation. The content of CBT Tech programs varied, ranging from 4 to 62 sessions/exercises, with many programs combining elements of motivational interviewing (47%). With respect to efficacy, CBT Tech as a stand-alone treatment in contrast to a minimal treatment control showed a positive and statistically significant, albeit small effect (g = 0.20: 95% CI = 0.22, 0.38, kes = 5). When CBT Tech was compared to treatment as usual (TAU), effects were nonsignificant. However, when CBT Tech was tested as an addition to TAU, in contrast to TAU only, the effect size was positive, significant (g = 0.30: 95% CI = 0.10, 0.50, kes = 7), and stable over 12-month follow-up. Only 2 studies compared CBT Tech to in-person CBT, and this pooled effect size did not suggest superior efficacy.ConclusionsThese results show a benefit for technology-delivered, CBT-based interventions as a stand-alone therapy for heavy drinking or as an addition to usual care in specialty substance use settings.
| S-EPMC6824956 | biostudies-literature
Project description:BACKGROUND:Extensive efforts have been made to train mental health providers in evidence-based psychotherapies (EBPs); there is increasing attention focused on the methods through which providers are trained to deliver EBPs. Evaluating EBP training methods is an important step in determining which methods are most effective in increasing provider skill and improving client outcomes. METHODS:We searched MEDLINE (Ovid) and PsycINFO for randomized controlled trials published from 1990 through June 2019 that evaluated EBP training methods to determine the effectiveness of EBP training modalities on implementation (provider and cost) and client outcomes. Eligible studies (N = 28) were evaluated for risk of bias, and the overall strength of evidence was assessed for each outcome. Data was extracted by a single investigator and confirmed by a second; risk of bias and strength of evidence were independently rated by two investigators and determined by consensus. RESULTS:Overall, EBP training improved short-term provider satisfaction, EBP knowledge, and adherence compared to no training or self-study of training materials (low to moderate strength of evidence). Training in an EBP did not increase treatment adoption compared to no training or self-study. No specific active EBP training modality was found to consistently increase provider EBP knowledge, skill acquisition/adherence, competence, adoption, or satisfaction compared to another active training modality. Findings were mixed regarding the additive benefit of post-training consultation on these outcomes. No studies evaluated changes in provider outcomes with regards to training costs and few studies reported on client outcomes. LIMITATIONS:The majority of included studies had a moderate risk of bias and strength of evidence for the outcomes of interest was generally low or insufficient. Few studies reported effect sizes. The ability to identify the most effective EBP training methods was limited by low strength of evidence for the outcomes of interest and substantial heterogeneity among studies. CONCLUSIONS:EBP training may have increased short-term provider satisfaction, EBP knowledge, and adherence though not adoption. Evidence was insufficient on training costs and client outcomes. Future research is needed on EBP training methods, implementation, sustainability, client outcomes, and costs to ensure efforts to train providers in EBPs are effective, efficient, and durable. TRIAL REGISTRATION:The protocol for this review is registered in PROSPERO (CRD42018093381).
| S-EPMC7251851 | biostudies-literature
Project description:There has been a recent surge of interest in using machine learning across chemical space in order to predict properties of molecules or design molecules and materials with the desired properties. Most of this work relies on defining clever feature representations, in which the chemical graph structure is encoded in a uniform way such that predictions across chemical space can be made. In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures. Our model borrows ideas from neural machine translation: it translates between two semantically equivalent but syntactically different representations of molecular structures, compressing the meaningful information both representations have in common in a low-dimensional representation vector. Once the model is trained, this representation can be extracted for any new molecule and utilized as a descriptor. In fair benchmarks with respect to various human-engineered molecular fingerprints and graph-convolution models, our method shows competitive performance in modelling quantitative structure-activity relationships in all analysed datasets. Additionally, we show that our descriptor significantly outperforms all baseline molecular fingerprints in two ligand-based virtual screening tasks. Overall, our descriptors show the most consistent performances in all experiments. The continuity of the descriptor space and the existence of the decoder that permits deducing a chemical structure from an embedding vector allow for exploration of the space and open up new opportunities for compound optimization and idea generation.
| S-EPMC6368215 | biostudies-literature
Project description:BACKGROUND:Given the complex and progressive nature of Alzheimer's disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. METHODS:Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer's Disease Assessment Scale-cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups' distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression. RESULTS:The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels. CONCLUSIONS:Our results, 'learned' from clinical data, indicate the existence of at least three subgroups of Alzheimer's patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care.
| S-EPMC6389228 | biostudies-literature
Project description:Purpose of reviewEvidence is growing for the positive effects of technology-delivered diabetes self-care interventions on behavioral and clinical outcomes. However, our understanding of how to effectively implement these interventions into routine clinical practice is limited. This article provides an overview of the methods and results of studies examining the implementation of technology-delivered diabetes self-care interventions into clinical care. We focus specifically on patient-facing behavioral interventions delivered with technology (e.g., text messaging, apps, websites).Recent findingsEleven articles were included in the review. Most studies (n = 9) examined barriers and facilitators to implementation, while about half (n = 5) integrated the intervention into clinical care and evaluated implementation and/or effectiveness. Only six studies applied a theory or framework. The most common determinants of implementation were time constraints for clinic staff, familiarity with technology, knowledge of the intervention, and perceived value. We found substantial variation in implementation outcomes, including which were reported, how they were assessed, and the results. In the four studies that evaluated effectiveness, hemoglobin A1c improved. Successful implementation of technology-delivered interventions has the potential to transform healthcare delivery and improve diabetes health on a population level. Promising strategies to address common determinants of implementation include appointing a clinic champion, developing staff training and educational materials, and adapting intervention processes to the clinic context. Future research should evaluate these implementation strategies to understand when and how they impact outcomes. Frameworks such as Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) can help ensure outcomes are systematically reported and allow for comparison across studies.
| S-EPMC8188808 | biostudies-literature
Project description:BackgroundThe optimal treatment for gender dysphoria is medical intervention, but many transgender and nonbinary people face significant treatment barriers when seeking help for gender dysphoria. When untreated, gender dysphoria is associated with depression, anxiety, suicidality, and substance misuse. Technology-delivered interventions for transgender and nonbinary people can be used discretely, safely, and flexibly, thereby reducing treatment barriers and increasing access to psychological interventions to manage distress that accompanies gender dysphoria. Technology-delivered interventions are beginning to incorporate machine learning (ML) and natural language processing (NLP) to automate intervention components and tailor intervention content. A critical step in using ML and NLP in technology-delivered interventions is demonstrating how accurately these methods model clinical constructs.ObjectiveThis study aimed to determine the preliminary effectiveness of modeling gender dysphoria with ML and NLP, using transgender and nonbinary people's social media data.MethodsOverall, 6 ML models and 949 NLP-generated independent variables were used to model gender dysphoria from the text data of 1573 Reddit (Reddit Inc) posts created on transgender- and nonbinary-specific web-based forums. After developing a codebook grounded in clinical science, a research team of clinicians and students experienced in working with transgender and nonbinary clients used qualitative content analysis to determine whether gender dysphoria was present in each Reddit post (ie, the dependent variable). NLP (eg, n-grams, Linguistic Inquiry and Word Count, word embedding, sentiment, and transfer learning) was used to transform the linguistic content of each post into predictors for ML algorithms. A k-fold cross-validation was performed. Hyperparameters were tuned with random search. Feature selection was performed to demonstrate the relative importance of each NLP-generated independent variable in predicting gender dysphoria. Misclassified posts were analyzed to improve future modeling of gender dysphoria.ResultsResults indicated that a supervised ML algorithm (ie, optimized extreme gradient boosting [XGBoost]) modeled gender dysphoria with a high degree of accuracy (0.84), precision (0.83), and speed (1.23 seconds). Of the NLP-generated independent variables, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) clinical keywords (eg, dysphoria and disorder) were most predictive of gender dysphoria. Misclassifications of gender dysphoria were common in posts that expressed uncertainty, featured a stressful experience unrelated to gender dysphoria, were incorrectly coded, expressed insufficient linguistic markers of gender dysphoria, described past experiences of gender dysphoria, showed evidence of identity exploration, expressed aspects of human sexuality unrelated to gender dysphoria, described socially based gender dysphoria, expressed strong affective or cognitive reactions unrelated to gender dysphoria, or discussed body image.ConclusionsFindings suggest that ML- and NLP-based models of gender dysphoria have significant potential to be integrated into technology-delivered interventions. The results contribute to the growing evidence on the importance of incorporating ML and NLP designs in clinical science, especially when studying marginalized populations.
| S-EPMC10337393 | biostudies-literature
Project description:BackgroundCaregivers may promote the uptake of science into patient care and the practice of evidence-informed medicine. The purpose of this study was to determine whether caregiver-mediated (non-clinical caregiver-delivered) interventions are effective in improving patient, caregiver, provider, or health system outcomes.MethodsWe searched the MEDLINE, Embase, PsycINFO, Cumulative Index of Nursing and Allied Health, and Scopus databases from inception to February 27, 2017. Interventions (with a comparison group) reporting on a quality improvement intervention mediated by a caregiver and directed to a patient, in all ages and patient-care settings, were selected for inclusion. A three-category framework was developed to characterize caregiver-mediated interventions: inform (e.g., provide knowledge), activate (e.g., prompt action), and collaborate (e.g., lead to interaction between caregivers and other groups [e.g., care providers]).ResultsFifty-six studies met the inclusion criteria, and 64% were randomized controlled trials (RCTs). The most commonly assessed outcomes were patient- (n?=?40) and caregiver-oriented (n?=?33); few health system- (n?=?10) and provider-oriented (n?=?2) outcomes were reported. Patient outcomes (e.g., satisfaction) were most improved by caregiver-mediated interventions that provided condition and treatment education (e.g., symptom management information) and practical condition-management support (e.g., practicing medication protocol). Caregiver outcomes (e.g., stress-related/psychiatric outcomes) were most improved by interventions that activated caregiver roles (e.g., monitoring blood glucose) and provided information related to that action (e.g., why and how to monitor). The risk of bias was generally high, and the overall quality of the evidence was low-moderate, based on Grading of Recommendations Assessment Development and Evaluation ratings.ConclusionsThere is a large body of research, including many RCTs, to support the use of caregiver-mediated interventions that inform and activate caregivers to improve patient and caregiver outcomes. Select caregiver-mediated interventions improve patient (inform-activate) and caregiver (inform-activate-collaborate) outcomes and should be considered by all researchers implementing patient- and family-oriented research.Systematic reviewPROSPERO, CRD42016052509 .
| S-EPMC6042352 | biostudies-literature