Ethical dilemmas posed by mobile health and machine learning in psychiatry research

Jacobson, Nicholas C.; Bentley, Kate H.; Walton, Ashley; Wang, Shirley B.; Fortgang, Rebecca; Millner, Alexander J.; Coombs, Garth; Rodman, Alexandra M.; Coppersmith, Daniel D L

doi:10.2471/blt.19.237107

Cited by 51 publications

(64 citation statements)

References 34 publications

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“…This line of research is still in the proof of concept phase and will face a number of challenges before it can be clinically implemented (Dukart, Weis, Genon, & Eickhoff, 2021). For example, biases in machine learning (Schnack, 2020), privacy issues, generalizability and technical pitfalls should be dealt with in the first place (Jacobson et al, 2020;Starke, De Clercq, Borgwardt, & Elger, 2020). After these critical hurdles are taken, we envision acoustic analysis to have several valuable clinical implementations.…”

Section: Discussionmentioning

confidence: 99%

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

Boer

Voppel²,

Brederoo³

et al. 2021

Psychol. Med.

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Background Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. Methods Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. Results The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. Conclusions Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

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Section: Discussionmentioning

confidence: 99%

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

Boer

Voppel²,

Brederoo³

et al. 2021

Psychol. Med.

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“…The referral initiation process may have been potentially overwhelming for some persons, thereby triggering decompensation. More research is needed to further address these issues (Jacobson et al, 2020a). We also found that providing suggestive diagnoses decreases the likelihood of mental health self-references searches by 22% for any given later time point, and decreases the likelihood of searching for psychoactive medications by 8% at any given later time point.…”

Section: Discussionmentioning

confidence: 72%

Impact of Online Mental Health Screening Tools on Help-Seeking, Care Receipt, and Suicidal Ideation and Suicidal Intent: Evidence from Internet Search Behavior in a Large U.S. Cohort

Jacobson¹,

Yom‐Tov²,

Lekkas³

et al. 2020

Preprint

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Introduction: Most people with psychiatric illnesses do not receive treatment for almost a decade after disorder onset. Online mental health screens reflect one mechanism designed to shorten this lag in help-seeking, yet there has been limited research on the effectiveness of screening tools in naturalistic settings. Material and methods: We examined a cohort of persons directed to a mental health screening tool via the Bing search engine (n=126,060). We evaluated the impact of tool content on later searches for mental health self-references, self-diagnosis, care seeking, psychoactive medications, suicidal ideation, and suicidal intent. Website characteristics were evaluated by pairs of independent raters to ascertain screen type and content. These included the presence/absence of a suggestive diagnosis, a message on interpretability, as well as referrals to digital treatments, in-person treatments, and crisis services. Results: Using machine learning models, the results suggested that screen content predicted later searches with mental health self-references (AUC =0·73), mental health self-diagnosis (AUC = 0·69), mental health care seeking (AUC = 0·61), psychoactive medications (AUC = 0·55), suicidal ideation (AUC = 0·58), and suicidal intent (AUC = 0·60). Cox-proportional hazards models suggested individuals utilizing tools with in-person care referral were significantly more likely to subsequently search for methods to actively end their life (HR = 1·727, p = 0·007). Discussion: Online screens may influence help-seeking behavior, suicidal ideation, and suicidal intent. Websites with referrals to in-person treatments could put persons at greater risk of active suicidal intent. Further evaluation using large-scale randomized controlled trials is needed.

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“…While the current study was not designed to examine these questions, future work should attempt to determine whether phone communication is with parents or peers and, taking a step further, introduce content analysis (e.g., from text messages) to extract the nature of the communication. While informative, we have restricted our analyses to frequency of communication, as content analysis pushes the boundaries of ethics and protection of privacy (Jacobson et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

A year in the social life of a teenager: Within-person fluctuations in stress, phone communication, and anxiety and depression

Rodman¹,

Bustamante²,

Dennison³

et al. 2020

Preprint

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Stressful life events (SLEs) are strongly associated with the emergence of adolescent anxiety and depression, but the underlying mechanisms remain poorly understood, especially at the within-person level. We investigated how adolescent social communication (i.e., frequency of calls and texts) following SLEs relates to changes in internalizing symptoms in a multi-timescale intensive year-long study (N=30; n=355 monthly observations; n=~5,000 experience-sampling observations). Within-person increases in SLEs were associated with receiving more calls than usual at both monthly- and momentary-levels, and making more calls at the monthly-level. Increased calls were prospectively associated with worsening internalizing symptoms at the monthly-level only, suggesting that SLEs rapidly influences phone communication patterns, but these communication changes may have a more protracted, cumulative influence on internalizing symptoms. Finally, increased incoming calls prospectively mediated the association between SLEs and anxiety at the monthly-level. We identify adolescent social communication fluctuations as a potential mechanism conferring risk for stress-related internalizing psychopathology.

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Ethical dilemmas posed by mobile health and machine learning in psychiatry research

Cited by 51 publications

References 34 publications

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

Impact of Online Mental Health Screening Tools on Help-Seeking, Care Receipt, and Suicidal Ideation and Suicidal Intent: Evidence from Internet Search Behavior in a Large U.S. Cohort

A year in the social life of a teenager: Within-person fluctuations in stress, phone communication, and anxiety and depression

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