Using Automated Speech Processing for Repeated Measurements in a Clinical Setting of the Behavioral Variability in the Stroop Task.

The Stroop interference task is indispensable to current neuropsychological practice. Despite this, it is limited in its potential for repeated administration, its sensitivity and its demands on professionals and their clients. We evaluated a digital Stroop deployed using a smart device. Spoken responses were timed using automated speech recognition. Participants included adult nonpatients (N = 113; k = 5 sessions over 5 days) and patients with psychiatric diagnoses (N = 85; k = 3-4 sessions per week over 4 weeks). Traditional interference (difference in response time between color incongruent words vs. color neutral words; M = 0.121 s) and facilitation (neutral vs. color congruent words; M = 0.085 s) effects were robust and temporally stable over testing sessions (ICCs 0.50-0.86). The performance showed little relation to clinical symptoms for a two-week window for either nonpatients or patients but was related to self-reported concentration at the time of testing for both groups. Performance was also related to treatment outcomes in patients. The duration of response word utterances was longer in patients than in nonpatients. Measures of intra-individual variability showed promise for understanding clinical state and treatment outcome but were less temporally stable than measures based solely on average response time latency. This framework of remote assessment using speech processing technology enables the fine-grained longitudinal charting of cognition and verbal behavior. However, at present, there is a problematic lower limit to the absolute size of the effects that can be examined when using voice in such a brief 'out-of-the-laboratory condition' given the temporal resolution of the speech-to-text detection system (in this case, 10 ms). This resolution will limit the parsing of meaningful effect sizes.

A Dynamic Method, Analysis, and Model of Short-Term Memory for Serial Order with Clinical Applications.

This study examined the robustness of a traditional memory task when moved out of controlled traditional settings. A letter recall task was designed to be self-administered via a smart-device which assessed recall by participants' writing their responses on the device. This enabled collection of both the letter recalled and the timing of this recall such that the temporal dynamics could be examined. Participants were patients with mental illness (n=71) and healthy volunteers (n=103). Temporal dynamics were examined using a new mechanism that measured memory retrieval time precisely. Data were analyzed for accuracy, time and their relationships. The classic memory phenomena and associated effects were replicated. In terms of temporal dynamics, this is the first demonstration of primacy and recency effects in time domain variables, as well as phonological similarity effects as evident by the inverted U-shaped curves in time. The speed of short-term memory processes affects accuracy, error types and timing. The robustness of these memory effects and new approach to temporal dynamics suggest this framework may be suitable for clinical applications, notably for the long-term monitoring of cognition in patients with mental illness.

Applying speech technologies to assess verbal memory in patients with serious mental illness.

Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial resources to deploy and score. Therefore, we developed a digital ambulatory verbal memory test with automated scoring, and repeated self-administration via smart devices. One hundred and four adults participated, comprising 25 patients with serious mental illness and 79 healthy volunteers. The study design was successful with high quality speech recordings produced to 92% of prompts (Patients: 86%, Healthy: 96%). The story recalls were both transcribed and scored by humans, and scores generated using natural language processing on transcriptions were comparable to human ratings (R = 0.83, within the range of human-to-human correlations of R = 0.73-0.89). A fully automated approach that scored transcripts generated by automatic speech recognition produced comparable and accurate scores (R = 0.82), with very high correlation to scores derived from human transcripts (R = 0.99). This study demonstrates the viability of leveraging speech technologies to facilitate the frequent assessment of verbal memory for clinical monitoring purposes in psychiatry.

Psychiatric Risk Assessment from the Clinician's Perspective: Lessons for the Future.

Accurate prediction of risk-states in Serious Mental Illnesses (SMIs) is critical for reducing their massive societal burden. Risk-state assessments are notably inaccurate. Recent innovations, including widely available and inexpensive mobile technologies for ambulatory "biobehavioral" data, can reshape risk assessment. To help understand and accelerate clinician involvement, we surveyed 90 multi-disciplinary clinicians serving SMI populations in various settings to evaluate how risk assessment is conducted and can improve. Clinicians reported considerable variability in conducting risk assessment, and few clinicians explicated their procedures beyond tying it to broader mental status examinations or interviews. Very few clinicians endorsed using currently-available standardized risk measures, and most reported low confidence in their utility. Clinicians also reported spending approximately half the time conducting individual risk assessments than optimally needed. When asked about improvement, virtually no clinicians acknowledged biobehavioral, objective technologies, or ambulatory recording. Overall, clinicians seemed unaware of meaningful ways to improve risk assessment.

Ambulatory vocal acoustics, temporal dynamics, and serious mental illness.

Acoustic analysis of vocal expression offers a potentially inexpensive, unobtrusive, and highly sensitive biobehavioral measure of serious mental illness (SMI)-related issues. Despite literature documenting its use for understanding SMI, prior studies have largely ignored that vocal expression is highly dynamic within individuals over time. We employed ambulatory vocal assessment from SMI outpatients to understand links between vocal expression, SMI symptoms, and affective states. Vocal samples were analyzed using a validated acoustic analysis protocol. Overall, vocal expression was not directly related to SMI symptoms but changed as a function of state and state by symptom interactions. The results suggest that (a) vocal expression fails to modulate across changing affective states in individuals with active SMI symptoms, (b) this lack of modulation may be commonly associated with many SMI symptoms, and (c) vocal analysis can accommodate temporal dynamics. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Moving psychological assessment out of the controlled laboratory setting: Practical challenges.

Behavioral assessment using smart devices affords novel methods, notably remote self-administration by the individuals themselves. However, this new approach requires navigating complex legal and technical terrain. Given the limited empirical data that currently exists, we provide and discuss anecdotes of the methodological, technical, legal, and cultural issues associated with an implementation in both U.S. and European settings of a mobile software application for regular psychological monitoring purposes. The tasks required participants to listen, watch, speak, and touch to interact with the smart device, thus assessing cognition, motor skill, and language. Four major findings merit mention: First, moving assessment out of the hands of a trained investigator necessitates excellent usability engineering, such that the tool is easily usable by the participant and the resulting data relevant to the investigator. Second, remote assessment requires that the data are transferred safely back to the investigator, and that risk of compromising participant confidentiality is minimized. Third, frequent data collection over long periods of time is associated with a possibility that participants may choose to withdraw consent for participation thus requiring data retraction. Fourth, data collection and analysis across international borders creates new challenges and new opportunities because of important cultural and language issues that may inform the underlying behavioral constructs of interest. In conclusion, the new technological frameworks provide unprecedented opportunities for remote self-administered behavioral assessments but will be most productive in multidisciplinary teams to ensure the highest level of user satisfaction and data quality, and to guarantee the highest level of data protection. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Updated U.S. Congressional Budget Office data reveal unprecedented increase in inequality.

The U.S. Congressional Budget Office recently updated its authoritative data series on household incomes, 1979-2005. The new data reveal a sharp increase in income inequality over the past few years. The increase in income inequality (both pre- and post-tax), as measured by the change in the shares of income going to different income classes, was greater from 2003 to 2005 than over any other two-year period covered by the CBO data.