Natural Language Processing-Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study

Feasibility and user-experience of a virtual environment for social connection and education after stroke: A pilot study

OBJECTIVES

To evaluate the feasibility and usability of stroke survivor participation in an 8-week virtual environment intervention that provides opportunities for social support exchanges, social network interactions, and recovery education.

MATERIALS AND METHODS

A single-group, pre- and post-test measure design was used. Descriptive statistics were used to examine enrollment and retention rates, proportion of questionnaires completed, and virtual environment process data (e.g., number of log-ins) and usability scores. Changes in pre- and post-intervention questionnaire (e.g., usability, social support, depression, anxiety, loneliness, and self-efficacy) scores were explored using Wilcoxon signed-rank tests and paired t-test.

RESULTS

Fifteen (65 %) of the eligible stroke survivors enrolled (60 % white, 27 % black), 12 (80 %) had an ischemic stroke, ages ranged from 33 to 74 years (mean 44 years), and mean months since stroke was 33 ± 23. Retention and questionnaire completion rates were both 93 % (n = 14). Survivors logged into the virtual environment a total of 122 times, logged an average of 49 min/log-in, and 12 (80 %) attended support groups and social activities. Median usability score indicated lower than average usability. Improvement trends in social support, loneliness, and depressive symptoms were found, but significant changes in mean questionnaire scores were not found.

CONCLUSIONS

Overall, the results suggest that using a virtual environment to foster social support exchanges, social network interactions, and recovery education after stroke is feasible. Similar to other chronic disease populations, stroke survivor adoption of a virtual environment likely requires ongoing technical assistance, repetition of instructions, and opportunities for practice to reinforce engagement.

TRIAL REGISTRATION

NCT05487144.

Critical reflections on three popular computational linguistic approaches to examine Twitter discourses

Although computational linguistic methods-such as topic modelling, sentiment analysis and emotion detection-can provide social media researchers with insights into online public discourses, it is not inherent as to how these methods should be used, with a lack of transparent instructions on how to apply them in a critical way. There is a growing body of work focusing on the strengths and shortcomings of these methods. Through applying best practices for using these methods within the literature, we focus on setting expectations, presenting trajectories, examining with context and critically reflecting on the diachronic Twitter discourse of two case studies: the longitudinal discourse of the NHS Covid-19 digital contact-tracing app and the snapshot discourse of the Ofqual A Level grade calculation algorithm, both related to the UK. We identified difficulties in interpretation and potential application in all three of the approaches. Other shortcomings, such the detection of negation and sarcasm, were also found. We discuss the need for further transparency of these methods for diachronic social media researchers, including the potential for combining these approaches with qualitative ones-such as corpus linguistics and critical discourse analysis-in a more formal framework.

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.

Providing care beyond the therapy session — a natural language processing–based recommender system that identifies cancer patients who experience psychosocial challenges and provides self-care support (Preprint)

Background

The negative psychosocial impacts of cancer diagnoses and treatments are well documented. Virtual care has become an essential mode of care delivery during the COVID-19 pandemic, and online support groups (OSGs) have been shown to improve accessibility to psychosocial and supportive care. de Souza Institute offers CancerChatCanada, a therapist-led OSG service where sessions are monitored by an artificial intelligence–based co-facilitator (AICF). The AICF is equipped with a recommender system that uses natural language processing to tailor online resources to patients according to their psychosocial needs.

Objective

We aimed to outline the development protocol and evaluate the AICF on its precision and recall in recommending resources to cancer OSG members.

Methods

Human input informed the design and evaluation of the AICF on its ability to (1) appropriately identify keywords indicating a psychosocial concern and (2) recommend the most appropriate online resource to the OSG member expressing each concern. Three rounds of human evaluation and algorithm improvement were performed iteratively.

Results

We evaluated 7190 outputs and achieved a precision of 0.797, a recall of 0.981, and an F1 score of 0.880 by the third round of evaluation. Resources were recommended to 48 patients, and 25 (52%) accessed at least one resource. Of those who accessed the resources, 19 (75%) found them useful.

Conclusions

The preliminary findings suggest that the AICF can help provide tailored support for cancer OSG members with high precision, recall, and satisfaction. The AICF has undergone rigorous human evaluation, and the results provide much-needed evidence, while outlining potential strengths and weaknesses for future applications in supportive care.

Pathways to Acceptance in Participants of Advanced Cancer Online Support Groups

Background and Objectives: Individuals with cancer, especially advanced cancer, are faced with numerous difficulties associated with the disease, including an earlier death than expected. Those who are able to confront and accept the hardships associated with the disease in a way that aligns with their beliefs benefit from more positive psychological outcomes compared to those who are aware of their diagnosis but are unable to accept it. To date, there is limited research exploring factors contributing to illness and death acceptance in the context of advanced cancer in group therapy settings. Materials and Methods: The current study used a Directed Content Analysis approach on transcripts of online advanced cancer support groups to investigate if and how Yalom’s existential factors played a role in the emergence of acceptance. Results: The online support group platform, combined with the help of facilitators, offered supportive environments for individuals seeking help with cancer-related distress by helping patients move towards acceptance. Some participants had already begun the process of accepting their diagnosis before joining the group, others developed acceptance during the group process, while a few continued to be distressed. Our analysis revealed the emergence of four themes related to illness acceptance: (1) Facilitator-Initiated Discussion, including sub-themes of Mindfulness, Relaxation and Imagery, Changing Ways of Thinking, and Spirituality; (2) Personal attitudes, including sub-themes of Optimism and Letting Go of Control; (3) Supportive Environment, including the sub-themes of Providing Support to Others and Receiving Support from Others; and (4) Existential Experience, which included sub-themes of Living with the Diagnosis for an Extended Amount of Time, Legacy and Death Preparations, and Appreciating life. Conclusions: With a paradigm shift to online delivery of psychological services, recognizing factors that contribute to acceptance when dealing with advanced cancer may help inform clinical practices. Future studies should explore patient acceptance longitudinally to inform whether it emerges progressively, which has been suggested by Kübler-Ross.

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.