Chatbot features for anxiety and depression: A scoping review

Chatbots can provide valuable support to patients in assessing and guiding management of various health problems particularly when human resources are scarce. Chatbots can be affordable and efficient on-demand virtual assistants for mental health conditions, including anxiety and depression. We review features of chatbots available for anxiety or depression. Six bibliographic databases were searched including backward and forwards reference list checking. The initial search returned 1302 citations. Post-filtering, 42 studies remained forming the final dataset for this scoping review. Most of the studies were from conference proceedings (62%, 26/42), followed by journal articles (26%, 11/42), reports (7%, 3/42), or book chapters (5%, 2/42). About half of the reviewed chatbots had functionality targeting both anxiety and depression (60%, 25/42), whereas 38% (16/42) targeted only depression, 38% (16/42) anxiety and the remaining addressed other mental health issues along with anxiety and depression. Avatars or fictional characters were rarely used in these studies only 26% (11/42) despite their increasing popularity. Mental health chatbots could benefit in helping patients with anxiety and depression and provide valuable support to mental healthcare workers, particularly when resources are scarce. Real-time personal virtual assistance fills in this gap. Their role in mental health care is expected to increase.

Anxiety and Depression Chatbot Features: A Scoping Review (Preprint).. [DOI: 10.2196/preprints.26341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Apps and web-based chatbots can provide valuable and meaningful support to healthcare workers in assessing and guiding management of various health problems particularly when human resources are scarce. Despite poor adherence to such apps, chatbots can be cost-effective and efficient on-demand virtual assistants for various mental health conditions, including anxiety and depression.

OBJECTIVE

This study aims to review the features of chatbots currently available for individuals with suspected anxiety or depression.

METHODS

ACM digital library, IEEE, Google Scholar, Embase, Medline, and PsychINFO were the six bibliographic databases searched for conducting the review. We conducted backward and forward reference list checking of included studies. Study selection and data extraction were performed by two reviewers independently; two other individual reviewers justified cross-checking of extracted data. We utilized a narrative approach for synthesizing the data.

RESULTS

The initial search returned a total of 917 citations. A total of 32 studies remained on filtering the publications, which formed the final dataset for this scoping review. While most of the studies were from conference proceedings (69%, n=22), the remainder were either journal articles (16%, n=5), reports (9%, n=3), or book chapters (6%, n=2). Of the studies that developed an actual chatbot, 16% (n=7) were web based and 63% (n=20) stand-alone in the form of an app. The remainder were available on both platforms or were only conceptual ideas. About half of the reviewed chatbots had functionality targeting both anxiety and depression (56%, n=18), whereas 38% (n=12) targeted only depression, 3% (n=1) anxiety and the remaining addressed other mental health issues along with anxiety and depression like public speaking anxiety, stress, lack of motivation, negative emotion, nervousness. Input modality of most of the chatbots was written (84%, n=27), followed by spoken (25%, n=8) and visual imaging (9%, n=3). Despite the fact of increasing popularity of embodiment techniques in chatbots such as avatars were rarely used in these studies only 34% (n=11)

CONCLUSIONS

Recent research shows that mental health chatbots could be of benefit in helping patients with anxiety and depression and provide valuable support to mental healthcare workers, particularly when resources are scarce. They often provide virtual assistance where medical professionals are inaccessible or users need anonymous real-time personal virtual assistance. Their role in mental health care is expected to increase following the COVID-19 pandemic and its impact on mental health and wellbeing of the world population.

An Evolutionary Bootstrapping Development Approach for a Mental Health Conversational Agent

Conversational agents are being used to help in the screening, assessment, diagnosis, and treatment of common mental health disorders. In this paper, we propose a bootstrapping approach for the development of a digital mental health conversational agent (i.e., chatbot). We start from a basic rule-based expert system and iteratively move towards a more sophisticated platform composed of specialized chatbots each aiming to assess and pre-diagnose a specific mental health disorder using machine learning and natural language processing techniques. During each iteration, user feedback from psychiatrists and patients are incorporated into the iterative design process. A survival of the fittest approach is also used to assess the continuation or removal of a specialized mental health chatbot in each generational design. We anticipate that our unique and novel approach can be used for the development of conversational mental health agents with the ultimate goal of designing a smart chatbot that delivers evidence-based care and contributes to scaling up services while decreasing the pressure on mental health care providers.

New mutational variants of Neurospora NADP-specific glutamate dehydrogenase

The am locus of Neurospora codes for NADP-dependent glutamate dehydrogenase (GDH). Four new am mutants that produced mutationally altered GDH have been characterized. Mutant am119 is a CRM-negative, complementing mutant that maps between am2 and am1. The other three mutants are CRM formers that produce varieties of GDH that can be activated by glutamate or succinate. The GDH of am130 and am131 is similar in terms of activation properties to that of am3. The GDH of am122 requires very high concentrations of dicarboxylate for activity. The mutation in am130 maps between am14 and am2 and resulted in a replacement at residue 75 of the GDH (pro leads to ser). The mutation in am122 maps near am11 and apparently resulted in the replacement of the tryptophan residue at position 389 with an unknown amino acid. The mutation in am131 maps between am2 and am1.