BACKGROUND
Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation.


OBJECTIVE
This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care.


METHODS
A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework.


RESULTS
Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues.


CONCLUSIONS
Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered.


TRIAL REGISTRATION
PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698.

Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis.

Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples.The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics.From the 29 topics identified, we identified 6 risk topics for depression: 'No Expectations', 'Sleep', 'Mental Therapy', 'Haircut', 'Studying', and 'Coursework'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings.Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets.This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings. 

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

Patients, providers, and trainees should understand the current types of asynchronous technologies that can be used to enhance the delivery and accessibility of mental health care. Asynchronous telepsychiatry (ATP) removes the need for real time communication between the clinician and patient, which improves efficiency and enables quality specialty care. ATP can be applied as distinct consultative and supervisory models in clinician-to-clinician, clinician-to-patient, and patient-to-mobile health settings.This review is based on research literature and the authors' clinical and medical training, using experiences with asynchronous telepsychiatry from before, during, and after the COVID-19 pandemic. Our studies demonstrate that ATP provides positive outcomes in the clinician-to-patient model with demonstrated feasibility, outcomes and patient satisfaction. One author's medical education experience in the Philippines during COVID-19 highlights the potential to utilize asynchronous technology in areas with limitations to online learning. We emphasize the need to teach media skills literacy around mental health to students, coaches, therapists, and clinicians when advocating for mental well-being. Several studies have demonstrated the feasibility of incorporating asynchronous e-tools such as self-guided multimedia and artificial intelligence for data collection at the clinician-to-clinician and patient-to-mobile health level. In addition, we offer fresh perspectives on recent trends in asynchronous telehealth in wellness, applying concepts such as "tele-exercise" and "tele-yoga."Asynchronous technologies continue to be integrated into mental health care services and research. Future research must ensure that the design and the usability of this technology puts the patient and provider first. 

/pdf/asynchronous-technologies-in-mental-health-care-and-wgex4z6f.pdf

Asynchronous Technologies in Mental Health Care and Education

Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processing has been employed on social media to predict depression, but limitations remain-lack of validated labels, biased user samples, and no context. Our study identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants using the Whisper tool and BERTopic model. Six topics with a median PHQ-8 greater than or equal to 10 were regarded as risk topics for depression: No Expectations, Sleep, Mental Therapy, Haircut, Studying, and Coursework. To elucidate the topic emergence and associations with depression, we compared behavioral (from wearables) and linguistic characteristics across identified topics. The correlation between topic shifts and changes in depression severity over time was also investigated, indicating the importance of longitudinally monitoring language use. We also tested the BERTopic model on a similar smaller dataset (356 speech recordings from 57 participants), obtaining some consistent results. In summary, our findings demonstrate specific speech topics may indicate depression severity. The presented data-driven workflow provides a practical approach to collecting and analyzing large-scale speech data from real-world settings for digital health research.

pdf/identifying-depression-related-topics-in-smartphone-2bfa6078e9.pdf

Background Psychosis causes a significant burden globally, including in China, where limited mental health resources hinder access to care. Smartphone-based remote monitoring offers a promising solution. This study aimed to assess the validity, feasibility, acceptability, and safety of a symptom self-monitoring smartphone app, YouXin, for people with psychosis in China. Methods A pre-registered non-randomised validity and feasibility study with a mixed-methods design. Participants with psychosis were recruited from a major tertiary psychiatric hospital in Beijing, China. Participants utilised the YouXin app to self-monitor psychosis and mood symptoms for four weeks. Feasibility outcomes were recruitment, retention and outcome measures completeness. Active symptom monitoring (ASM) validity was tested against corresponding clinical assessments (PANSS and CDS) using Spearman correlation. Ten participants completed qualitative interviews at study end to explore acceptability of the app and trial procedures. Results Feasibility parameters were met. The target recruitment sample of 40 participants was met, with 82.5% completing outcome measures, 60% achieving acceptable ASM engagement (completing >33% of all prompts), and 33% recording sufficient passive monitoring data to extract mobility indicators. Five ASM domains (hallucinations, suspiciousness, guilt feelings, delusions, grandiosity) achieved moderate correlation with clinical assessment. Both quantitative and qualitative evaluation showed high acceptability of YouXin. Clinical measurements indicated no symptom and functional deterioration. No adverse events were reported, suggesting YouXin is safe to use in this clinical population. Conclusions The trial feasibility, acceptability and safety parameters were met and a powered efficacy study is indicated. However, refinements are needed to improve ASM validity and increase passive monitoring data completeness.

Evaluating a smartphone-based symptom self-monitoring app for psychosis in China (YouXin): A non-randomised validity and feasibility study with a mixed-methods design

Background Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. Objective A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. Methods A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. Results The overall retention rate was 60%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=−2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=−0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list. Conclusions Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.

/pdf/the-feasibility-of-implementing-remote-measurement-9j6qz2ok.pdf

The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Fadekemi Adeleye

Author Tools

Chat about Author

Papers

The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement