Digital Mental Health: How to Engage With Innovation

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https://www.psychiatrictimes.com/telepsychiatry/digital-mental-health-how-engage-innovation-part-1?rememberme=1&elq_mid=7181&elq_cid=1671665&GUID=7A434E9D-C737-4234-9614-E04C33A5717C

Digital Mental Health: How to Engage With Innovation, Part 1
By John Torous, MD, MBI, Bridianne O’Dea, Ph.D., and Mark Larsen, PhD

June 3, 2019

Digital mental health was a popular topic at this year’s American Psychiatric Association (APA) Annual Meeting. Over a dozen sessions focused on new technologies ranging from smartphone apps to voice analytics, virtual reality to social media. While it was impossible to attend every session, we asked the presenters from one session to share some in-depth comments and thoughts with the readers of Psychiatric Times. Their session was entitled “Revitalizing Psychiatry Through Engaging with Innovation to Increase Access and Inclusion with Care.”

In the first article of this two-part series, the presenters discuss social media use for youth, and app privacy and efficacy claims that clinicians and patients must evaluate daily. Topics that will be covered in Part 2 are voice analytics for detecting and monitoring mood, and smartphone and web-based passive data as a digital biomarker for mental health disorders.

Dr. Bridianne O’Dea addresses “Does Social Media Impact Mental Health in Youth?”

Social media has become ubiquitous among young people, with up to 85% of those aged 12 to 17 years using one or more platforms.1 YouTube, Instagram, Snapchat, and Facebook are the most popular social media platforms among Western teens. As young people have quickly adapted to sharing their life online, parents have become increasingly concerned about problematic use. Teens who report that social media has a positive impact on their lives favor its ability to connect and stay in contact with others, to provide entertainment, and to increase the ease of finding news and information. However, many teens admit to concerns about bullying, relationship damage, unrealistic nature of online sharing, and time-wasting. These concerns are echoed by parents who report being more concerned about their child’s technology use than drugs and alcohol.2

While many studies have examined the associations between mental health outcomes and social media use, followed by systematic reviews and meta-analyses, results remain inconclusive. Most studies are cross-sectional, with only small effect sizes found. Despite an unclear causal link between social media and poor mental health, clinicians and parents have found themselves needing to address problematic use among youth. While there are no clear diagnostic frameworks, clinicians are encouraged to examine the domains of excessive use, withdrawal symptoms, tolerance, and negative repercussions. Identifying any underlying mental illness is key. A range of self-report measures has been developed to assist in the measurement of problematic Internet and technology use.

Although no gold-standard treatments exist, cognitive-behavioral therapy adapted for Internet use has shown some promise among adults.3 Ultimately, clinicians and parents are encouraged to implement strategies that include behaviour modification (ie, non-screen time), cognitive restructuring (ie, challenging negative thoughts associated with use), and harm reduction to address co-morbidities. Parents are encouraged to promote self-regulation and autonomy, model the behaviors they wish to see in their children and create regular time for open and honest discussions about online activities with their children. Learn more at https://research.unsw.edu.au/people/dr-bridianne-odea

"Is That Mental Health App Safe and Effective?" >>

Dr. Mark Larsen addresses “Is That Mental Health App Safe and Effective?”

More than 10,000 mental health apps may be available for immediate download today, but what do we know about their safety and effectiveness? In a recent study, we intercepted the traffic from mental health smartphone apps and found that over 50% are sending data to destinations not disclosed in the privacy policy.4 In essence, an app may promise not to send or share data but it seems the majority are not keeping their word. This does not mean we should not use mental health apps but rather that caution should be exercised, especially if the app comes from a source or developer you do not recognize and trust.

Many apps are making claims on the app store that tout how effective their app is. We explored the veracity of these claims by comparing what the app is telling consumers and what has been studied and published in the peer-reviewed literature. We found that while many apps make claims, fewer than 2% can back up those claims with actual evidence using their app.5 One point to be aware of is that many apps say they are designed with “evidence-based CBT” but fail to show how well that evidence-based, derived from face-to-face interaction, translates directly into the app in question.

The takeaway lesson is that there are good apps out there, but if you rely on chance, you may not find a great one. One useful tool to consider that takes into account these concerns about privacy and evidence is the APA app evaluation model, which you can access here: https://www.psychiatry.org/psychiatrists/practice/mental-health-apps/app-evaluation-model. To learn more about Dr. Mark Larsen’s work, please visit https://research.unsw.edu.au/people/dr-mark-larsen

Digital Mental Health: How to Engage With Innovation, Part 2

By John Torous, MD, MBI, Julien Epps, Ph.D., and Abhishek Pratap

June 4, 2019

A popular topic at the 2019 American Psychiatric Association (APA) Annual Meeting was digital mental health. In this two-part series, we asked presenters at the session “Revitalizing Psychiatry Through Engaging with Innovation to Increase Access and Inclusion with Care” to share some in-depth comments and thoughts with Psychiatric Times.

Part 1 of this series covered social media use for youth, and app privacy and efficacy claims. In this article, the presenters discuss voice analytics for detecting and monitoring mood, and smartphone and web-based passive data as a digital biomarker for mental health disorders.

Professor Julien Epps addresses “Can We Diagnose Mental Illnesses Using Voice Data Collected From Phones?”

Speech production is not only the most complex coordination of neuromuscular activity in the entire body, but is sensitive to a vast range of influencing factors, including cognitive function, affective state, motor function, fatigue, and social context. Speech can also be conveniently collected non-invasively and non-intrusively at low cost via smartphone and has attracted research attention as a behavioral signal that is indicative of many psychiatric disorders.

Speech comprises two main components: linguistic what is said and acoustic how it is said. To date, most research has focused on acoustic approaches. For example, insights about depressed speech include reductions in prosodic variation, spectral variability, vowel space area and speech rate, and increases in phone duration and motor incoordination. However, even features as simple as the duration of speech detected by a smartphone over a fixed period, as a proxy for social interaction, have been shown to be indicative of depression level.

Automatic speech-based assessment systems are likely to be applied to the screening of the general population or to the monitoring of mental state within an individual (eg, in response to treatment), rather than to diagnosis. Because of the richness of information conveyed by speech, it contains many forms of unwanted variability: due to speaker identity, spoken content, and other factors unrelated to the disorder. Mitigation of these sources of variability is an active research area.

Many speech feature extraction approaches can run in a fraction of real-time on a smartphone processor, or extracted features can be efficiently sent via the network to a server to process. Similarly, many machine learning approaches are computationally feasible for smartphone platforms. Smartphones offer a unique opportunity to either elicit speech (via app) or process speech passively during everyday use. Elicitation methods include sustained vowels, read a speech, diadochokinetic stimuli (eg, “pa-ta-ka”), interview, virtual human interview, and mood induction. However, tasks controlled for effect, articulatory effort, and linguistic complexity have been shown to help discriminate disorders more effectively based on speech.

Opportunities abound, including the possibility for regular minimal-cost assessment, longitudinal within-individual analysis, and integration with smartphone-based interventions or automatically cued therapy (eg, social rhythm therapy). Smartphones also offer the opportunity to crowdsource very large databases and hence develop a stronger evidence base. For more information, please contact j.epps@unsw.edu.au.

“Can the Digital Data From Smartphone and Online Web Searches Indicate Early Signs of Severe Mental Health Issues, Including Suicide?” >>

Abhishek Pratap addresses “Can the Digital Data From Smartphone and Online Web Searches Indicate Early Signs of Severe Mental Health Issues, Including Suicide?”

The large-scale adoption of smartphones and online search engines (>5 billion queries a day) offers a unique opportunity for researchers to engage, assess, and monitor social, emotional, and behavioral states at the population level, in real-time, with limited user burden, and at low cost. Estimates show that adults in the US are spending more than 150 minutes on their phones daily, with more than 2500 average screen touches. There is growing evidence for utilizing this high-volume, high-velocity smartphone usage data (passive sensing) to assess mood disorders at the population level and on an individualized (N-of-1) level.

Continuous passive data streams (eg, GPS) could offer the means to assess, contextualize, and trigger need-based survey assessments objectively without requiring the user to share sensitive location data. Geospatial contextual analysis pipeline (gSCAP) is a recent effort that is piloting ways to process and generate geospatial contextual features (eg, visiting a park, having coffee, spending time at home) onboard the user’s device without knowing the exact location.

Online information-seeking behavior (web searches) is another rich source of passively collected data that can potentially uncover health-related behavior based on the proximity and type of information sought by the user. In an on-going pilot study as part of the Aftercare Focus Study (AFS) at the University of Washington, Seattle, search data with true labels (prior suicide attempts) show distinct trends before and after a confirmed suicide attempt event. This could offer an unparalleled opportunity to understand proximal risk factors for adults who are severely distressed or thinking of killing themselves and potentially provide an early intervention opportunity. You can find more work by Abhishek Pratap at https://scholar.google.com/citations?user=qt2AE_4AAAAJ&hl=en

Dr. Torous is Director of the Digital Psychiatry Division, Department of Psychiatry at Beth Israel Deaconess Medical Center, Boston; Editor in Chief of JMIR Mental Health; Web Editor of JAMA Psychiatry; and Digital Psychiatry Editor for Psychiatric Times. Twitter: @JohnTorousMD. Dr. Epps is Professor in Signal Processing and Deputy Head of School (Education), School of Electrical Engineering and Telecommunications, at UNSW Sydney (University of New South Wales) in Australia. Abhishek Pratap is a Principal Scientist, Digital Health, at Sage Bionetworks in Seattle, WA.