Although digital health solutions are increasingly popular in clinical psychiatry, one application that has not been fully explored is the utilization of survey technology to monitor patients outside of the clinic. Supplementing routine care with digital information collected in the “clinical whitespace” between visits could improve care for patients with severe mental illness. This study evaluated the feasibility and validity of using online self-report questionnaires to supplement in-person clinical evaluations in persons with and without psychiatric diagnoses. We performed a rigorous in-person clinical diagnostic and assessment battery in 54 participants with schizophrenia (N = 23), depressive disorder (N = 14), and healthy controls (N = 17) using standard assessments for depressive and psychotic symptomatology. Participants were then asked to complete brief online assessments of depressive (Quick Inventory of Depressive Symptomatology) and psychotic (Community Assessment of Psychic Experiences) symptoms outside of the clinic for comparison with the ground-truth in-person assessments. We found that online self-report ratings of severity were significantly correlated with the clinical assessments for depression (two assessments used: R = 0.63, p < 0.001; R = 0.73, p < 0.001) and psychosis (R = 0.62, p < 0.001). Our results demonstrate the feasibility and validity of collecting psychiatric symptom ratings through online surveys. Surveillance of this kind may be especially useful in detecting acute mental health crises between patient visits and can generally contribute to more comprehensive psychiatric treatment.
- Award ID(s):
- 2047296
- NSF-PAR ID:
- 10401377
- Date Published:
- Journal Name:
- Frontiers in Digital Health
- Volume:
- 4
- ISSN:
- 2673-253X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Objective To compare individuals who have experienced binge‐eating disorder (BED) and anorexia nervosa (AN) (BED AN+) to those who have experienced BED and not AN (BED AN–).
Method Participants (
N = 898) met criteria for lifetime BED and reported current binge eating. Approximately 14% had a lifetime diagnosis of AN. Analyses compared BED AN+ and BED AN– on sociodemographic variables and clinical history.Results The presence of lifetime
AN was associated with more severe eating disorder symptoms, including earlier onset, more frequent, more chronic, and more types of eating disorder behaviors over the lifetime, as well as a higher lifetime prevalence of bulimia nervosa (BN ). Participants with lifetimeAN reported being more likely to have received treatments forBED orBN , had significantly lower minimum, current, and maximumBMIs , had more severe general anxiety, and were significantly more likely to be younger and female. In the full sample, the lifetime prevalence of unhealthy weight control behaviors was high and treatment utilization was low, despite an average 15‐year history since symptom onset. Gastrointestinal disorders and comorbid anxiety, depression, and attention‐deficit/hyperactivity disorder symptoms were prevalent.Discussion Individuals fared poorly on a wide array of domains, yet those with lifetime
AN fared considerably more poorly. All patients withBED should be screened for mental health and gastrointestinal comorbidities and offered referral and treatment options.Public Significance Individuals experiencing binge‐eating disorder have severe symptomology, but those who have experienced binge‐eating disorder and anorexia nervosa fare even more poorly. Our study emphasizes that patients with binge‐eating disorder would benefit from being screened for mental health and gastrointestinal comorbidities, and clinicians should consider history of unhealthy weight control behaviors to inform treatment and relapse prevention.
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Bipolar disorder, a severe chronic mental illness characterized by pathological mood swings from depression to mania, requires ongoing symptom severity tracking to both guide and measure treatments that are critical for maintaining long-term health. Mental health professionals assess symptom severity through semi-structured clinical interviews. During these interviews, they observe their patients’ spoken behaviors, including both what the patients say and how they say it. In this work, we move beyond acoustic and lexical information, investigating how higher-level interactive patterns also change during mood episodes. We then perform a secondary analysis, asking if these interactive patterns, measured through dialogue features, can be used in conjunction with acoustic features to automatically recognize mood episodes. Our results show that it is beneficial to consider dialogue features when analyzing and building automated systems for predicting and monitoring mood.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fdgth.2022.916810
