A randomized controlled trial of a therapeutic relational agent for reducing substance misuse during the COVID-19 pandemic

Prochaska, Judith J.; Vogel, Erin A.; Chieng, Amy; Baiocchi, Michael; Maglalang, Dale Dagar; Pajarito, Sarah; Weingardt, Kenneth R.; Darcy, Alison M.; Robinson, Athena

doi:10.1016/j.drugalcdep.2021.108986

Cited by 57 publications

(55 citation statements)

References 33 publications

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“…However, two studies showed that users could potentially build a bond with chatbot apps. A study of the app Woebot (a therapeutic chatbot that delivers CBT), which was used to reduce substance misuse during the COVID-19, found that the bond subscale rating was higher than task and goal subscales ratings ( 50 ). Similarly, another study with Woebot found that the bond subscale rating was high and comparable to face-to-face therapy ( 51 ).…”

Section: Resultsmentioning

confidence: 99%

“…However, the types of usage data in these two studies were different. Clarke et al ( 14 ) measured number of program interactions (i.e., logins), number of modules completed, and frequency of self-monitoring, while Prochaska et al ( 50 ) measured days used, in-app text messages, and completed modules. In addition, Goldberg et al ( 49 ) adopted self-report usage frequency as engagement in study 1, and adopted usage data in study 2.…”

Section: Resultsmentioning

confidence: 99%

“…Only one study analyzed correlations between engagement and outcomes. A study of the chatbot app Woebot, used to help reduce substance misuse, found that greater frequency of use (numbers of in-app text) was not significantly associated with a reduction in substance use occasions (r = 0.23) ( 50 ).…”

Section: Resultsmentioning

confidence: 99%

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Digital Therapeutic Alliance With Fully Automated Mental Health Smartphone Apps: A Narrative Review

et al. 2022

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Fully automated mental health smartphone apps show strong promise in increasing access to psychological support. Therefore, it is crucial to understand how to make these apps effective. The therapeutic alliance (TA), or the relationship between healthcare professionals and clients, is considered fundamental to successful treatment outcomes in face-to-face therapy. Thus, understanding the TA in the context of fully automated apps would bring us insights into building effective smartphone apps which engage users. However, the concept of a digital therapeutic alliance (DTA) in the context of fully automated mental health smartphone apps is nascent and under-researched, and only a handful of studies have been published in this area. In particular, no published review paper examined the DTA in the context of fully automated apps. The objective of this review was to integrate the extant literature to identify research gaps and future directions in the investigation of DTA in relation to fully automated mental health smartphone apps. Our findings suggest that the DTA in relation to fully automated smartphone apps needs to be conceptualized differently to traditional face-to-face TA. First, the role of bond in the context of fully automated apps is unclear. Second, human components of face-to-face TA, such as empathy, are hard to achieve in the digital context. Third, some users may perceive apps as more non-judgmental and flexible, which may further influence DTA formation. Subdisciplines of computer science, such as affective computing and positive computing, and some human-computer interaction (HCI) theories, such as those of persuasive technology and human-app attachment, can potentially help to foster a sense of empathy, build tasks and goals and develop bond or an attachment between users and apps, which may further contribute to DTA formation in fully automated smartphone apps. Whilst the review produced a relatively limited quantity of literature, this reflects the novelty of the topic and the need for further research.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Digital Therapeutic Alliance With Fully Automated Mental Health Smartphone Apps: A Narrative Review

et al. 2022

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“…It can cause the body to quickly produce a large number of cytokines, such as IL-6, IL-12, IL-8, and IFN-α; this abnormal increase in the number of cytokines can cause aggregation of eosinophils and other infectious lesions. The organs and tissues are also severely damaged in the process of effectively eradicating the infection ( 44 , 45 ).…”

Section: Discussionmentioning

confidence: 99%

An Explainable AI Approach for the Rapid Diagnosis of COVID-19 Using Ensemble Learning Algorithms

Gong

Wang

Zhang

et al. 2022

Front. Public Health

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BackgroundArtificial intelligence-based disease prediction models have a greater potential to screen COVID-19 patients than conventional methods. However, their application has been restricted because of their underlying black-box nature.ObjectiveTo addressed this issue, an explainable artificial intelligence (XAI) approach was developed to screen patients for COVID-19.MethodsA retrospective study consisting of 1,737 participants (759 COVID-19 patients and 978 controls) admitted to San Raphael Hospital (OSR) from February to May 2020 was used to construct a diagnosis model. Finally, 32 key blood test indices from 1,374 participants were used for screening patients for COVID-19. Four ensemble learning algorithms were used: random forest (RF), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). Feature importance from the perspective of the clinical domain and visualized interpretations were illustrated by using local interpretable model-agnostic explanations (LIME) plots.ResultsThe GBDT model [area under the curve (AUC): 86.4%; 95% confidence interval (CI) 0.821–0.907] outperformed the RF model (AUC: 85.7%; 95% CI 0.813–0.902), AdaBoost model (AUC: 85.4%; 95% CI 0.810–0.899), and XGBoost model (AUC: 84.9%; 95% CI 0.803–0.894) in distinguishing patients with COVID-19 from those without. The cumulative feature importance of lactate dehydrogenase, white blood cells, and eosinophil counts was 0.145, 0.130, and 0.128, respectively.ConclusionsEnsemble machining learning (ML) approaches, mainly GBDT and LIME plots, are efficient for screening patients with COVID-19 and might serve as a potential tool in the auxiliary diagnosis of COVID-19. Patients with higher WBC count, higher LDH level, or higher EOT count, were more likely to have COVID-19.

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“…Finally, the objective of the study by Prochaska et al [17 ▪ ] was to tackle the disrupted access to treatments for SUDs during the COVID-19 pandemic and add to the growing body of literature on the use of automated chatbots. For this reason, the authors assessed the effectiveness of an automated self-help coach that delivers CBT in the format of brief, daily text-based conversations via a mobile application.…”

Section: Add-on Interventionsmentioning

confidence: 99%

Recent advances in digital health interventions for substance use disorders

Boumparis

Schaub²

2022

Current Opinion in Psychiatry

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Purpose of reviewThe purpose of this review is to provide an update of the recently published randomized controlled trials in the field of digital health interventions for substance use disorders.Recent findingsOver the past 2 years, five cannabis-specific and seven polysubstance-focused randomized controlled trials were published. No studies were found that focused on opioid or psychostimulant use disorders. Most studies examined feasibility but were underpowered to assess effectiveness. Given the optimistic results of the studies in regards to feasibility more fully powered trials should be conducted. In addition, the literature is in need for an increased focus on comorbidity and outcome standardization.SummaryAlthough the number of studies targeting new target groups, technologies and new delivery settings has increased – future studies should consider the identified gaps and suggestions to further strengthen the evidence of digital interventions targeting substance use disorders.

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A randomized controlled trial of a therapeutic relational agent for reducing substance misuse during the COVID-19 pandemic

Cited by 57 publications

References 33 publications

Digital Therapeutic Alliance With Fully Automated Mental Health Smartphone Apps: A Narrative Review

Digital Therapeutic Alliance With Fully Automated Mental Health Smartphone Apps: A Narrative Review

An Explainable AI Approach for the Rapid Diagnosis of COVID-19 Using Ensemble Learning Algorithms

Recent advances in digital health interventions for substance use disorders

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