Understanding User Experience: Exploring Participants' Messages With a Web-Based Behavioral Health Intervention for Adolescents With Chronic Pain

Machine and cognitive intelligence for human health: systematic review

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

Online multidisciplinary interventions for paediatric chronic pain: A content analysis

BACKGROUND

Many online interventions for paediatric chronic pain have been developed and evaluated. In accordance with the biopsychosocial model, the recommended treatment approach for chronic pain is multidisciplinary. Despite this, multidisciplinary components within existing online interventions have not been examined. The objective of the present review was to summarise and evaluate the content of existing online interventions for paediatric chronic pain by mapping intervention content to evidence-based guidelines for chronic pain management.

METHODS

Interventions were identified using an updated systematic review. Nine chronic pain management strategies that reflect evidence-based guidance for multidisciplinary chronic pain management were defined by the authors, examples of which include 'pain education', 'activity pacing' and 'physiotherapy'. Identified interventions were then coded against the target strategies. These codes were compiled descriptively to provide an overview of how well each chronic pain management strategy was represented across the dataset, and which interventions represented the most strategies.

RESULTS

Thirty-five articles, relating to 13 unique interventions for paediatric chronic pain management were identified; few encompassed a complete multidisciplinary approach. Many CBT-based interventions included multidisciplinary elements. Across interventions, physiotherapy and non-pharmacological physical therapies were the least represented chronic pain management strategies.

CONCLUSIONS

The content analysis revealed a lack of online interventions encompassing complete multidisciplinary pain management. It is important that new interventions for paediatric chronic pain management are evidence-based and reflect current best practice guidelines. Established intervention development approaches should be utilised and include a process evaluation to help identify which intervention components are effective in which contexts.

SIGNIFICANCE

This content analysis of online interventions for paediatric chronic pain highlights the need for multidisciplinary practices in pain management to be translated into online interventions. Improving the availability of pain management resources is essential for many families who cannot attend specialist pain clinics, particularly in the context of the COVID-19 pandemic. There is potential for new resources, as well as for established resources, to be further developed to deliver a broader range of pain management content.

Suitability of Text-Based Communications for the Delivery of Psychological Therapeutic Services to Rural and Remote Communities: Scoping Review

BACKGROUND

People living in rural and remote areas have poorer access to mental health services than those living in cities. They are also less likely to seek help because of self-stigma and entrenched stoic beliefs about help seeking as a sign of weakness. E-mental health services can span great distances to reach those in need and offer a degree of privacy and anonymity exceeding that of traditional face-to-face counseling and open up possibilities for identifying at-risk individuals for targeted intervention.

OBJECTIVE

This scoping review maps the research that has explored text-based e-mental health counseling services and studies that have used language use patterns to predict mental health status. In doing so, one of the aims was to determine whether text-based counseling services have the potential to circumvent the barriers faced by clients in rural and remote communities using technology and whether text-based communications, in particular, can be used to identify individuals at risk of psychological distress or self-harm.

METHODS

We conducted a comprehensive electronic literature search of PsycINFO, PubMed, ERIC, and Web of Science databases for articles published in English through November 2020.

RESULTS

Of the 9134 articles screened, 70 met the eligibility criteria and were included in the review. There is preliminary evidence to suggest that text-based, real-time communication with a qualified therapist is an effective form of e-mental health service delivery, particularly for individuals concerned with stigma and confidentiality. There is also converging evidence that text-based communications that have been analyzed using computational linguistic techniques can be used to accurately predict progress during treatment and identify individuals at risk of serious mental health conditions and suicide.

CONCLUSIONS

This review reveals a clear need for intensified research into the extent to which text-based counseling (and predictive models using modern computational linguistics tools) may help deliver mental health treatments to underserved groups such as regional communities, identify at-risk individuals for targeted intervention, and predict progress during treatment. Such approaches have implications for policy development to improve intervention accessibility in at-risk and underserved populations.