Advancing ambulatory biobehavioral technologies beyond “proof of concept”: Introduction to the special section.

Cohen, Alex S.

doi:10.1037/pas0000694

Cited by 17 publications

(14 citation statements)

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“…Given that BvA and alogia reflect overt behaviors that can be quantified, it has long been proposed that computerized acoustic analysis could be used to measure them [9][10][11][12] . Presumably, this type of "digital phenotyping" 13 could be automated to provide a relatively efficient and sensitive "state" measure of negative symptoms; with applications for improving diagnostic accuracy and for efficiently tracking symptom severity, relapse risk, treatment response, and pharmacological side effects [14][15][16] . Moreover, acoustic analysis is based on speech analysis technologies that are freely available, well-validated for a variety of applications, and can be collected using a wide range of unobtrusive and in situ remote technologies (e.g., smartphones, archived videos) 17 .…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, acoustic analysis is based on speech analysis technologies that are freely available, well-validated for a variety of applications, and can be collected using a wide range of unobtrusive and in situ remote technologies (e.g., smartphones, archived videos) 17 . Despite the existence of several dozen studies evaluating computerized acoustic analysis of natural speech to measure BvA and alogia, there is insufficient psychometric support to consider them appropriate for clinical applications 16 , as they have shown only modest convergence with clinical ratings. For example, in a study of 309 patients with schizophrenia, clinically rated negative symptoms were nonsignificantly and weakly associated with pause times, intonation, tongue movements, emphasis, and other acoustically derived aspects of natural speech (absolute value of r's, 0.03-0.14) 18 .…”

Section: Introductionmentioning

confidence: 99%

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Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia

Cohen

Cox

et al. 2020

npj Schizophr

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Negative symptoms are a transdiagnostic feature of serious mental illness (SMI) that can be potentially “digitally phenotyped” using objective vocal analysis. In prior studies, vocal measures show low convergence with clinical ratings, potentially because analysis has used small, constrained acoustic feature sets. We sought to evaluate (1) whether clinically rated blunted vocal affect (BvA)/alogia could be accurately modelled using machine learning (ML) with a large feature set from two separate tasks (i.e., a 20-s “picture” and a 60-s “free-recall” task), (2) whether “Predicted” BvA/alogia (computed from the ML model) are associated with demographics, diagnosis, psychiatric symptoms, and cognitive/social functioning, and (3) which key vocal features are central to BvA/Alogia ratings. Accuracy was high (>90%) and was improved when computed separately by speaking task. ML scores were associated with poor cognitive performance and social functioning and were higher in patients with schizophrenia versus depression or mania diagnoses. However, the features identified as most predictive of BvA/Alogia were generally not considered critical to their operational definitions. Implications for validating and implementing digital phenotyping to reduce SMI burden are discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia

Cohen

Cox

et al. 2020

npj Schizophr

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“…New measures need to be developed that cover multiple domains of mental health, are reported by different respondents (e.g., child, parent, clinician), and are very brief. Cohen ( 2019 ) provides an excellent overview of what he calls ambulatory biobehavioral technologies in a special section of Psychological Assessment . He notes that the development of mobile devices can have a major impact on psychological assessment.…”

Section: Solutions To the Problem Of Poorly Designed Measuresmentioning

confidence: 99%

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Bickman

2020

Adm Policy Ment Health

111

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This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions. Keywords Mental health services • Artificial intelligence • Machine learning • Precision mental health • Randomized clinical trials (RCTs) • Precision medicine "AI will bring many wonders. It may also destabilize everything from nuclear détente to human friendships. We need to think much harder about how to adapt... it is changing human knowledge, perception and reality-and, in so doing, changing the course of human history. We seek to understand it and its consequences and encourage others across the disciplines to do the same."-Kissinger et al. 2019, pp. 24-26 "AI is one of the most profound things we're working on as humanity. It's more profound than fire or electricity."-Alphabet CEO Sundar Pichai, in Thompson and Bodoni 2020. This article is part of a special issue, Festschrift for Leonard Bickman: The Future of Children's Mental Health Services.

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“…However, to form an accurate picture of a person’s functioning across contexts, a large volume of data is needed. Analysis of acquired mobile assessment data will need to take into account the high frequency of measurements within individuals and between groups to produce clinically useful normative comparison data [ 79 ]. This brings forth the challenge of localizing the driving factors of within-person variance while also quantifying practice effects.…”

Section: Introductionmentioning

confidence: 99%

“…Some have raised concerns about the use of computerized neuropsychological assessment due to the lack of standardization and normative data, which when applied to patient care could ultimately lead to poor clinical decisions and errors in diagnosis. There have been several calls to the field to address this concern by focusing on resources and investigating the psychometrics of mobile cognitive assessment [ 29 , 61 , 79 ] and centralizing the development of a toolbox of standardized mobile neuropsychological assessments that can be validated more efficiently through a network of clinical researchers across the field [ 80 ].…”

Section: Introductionmentioning

confidence: 99%

Cognition in Context: Understanding the Everyday Predictors of Cognitive Performance in a New Era of Measurement

Weizenbaum¹,

Torous²,

Fulford³

2020

JMIR Mhealth Uhealth

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Background Research suggests that variability in attention and working memory scores, as seen across time points, may be a sensitive indicator of impairment compared with a singular score at one point in time. Given that fluctuation in cognitive performance is a meaningful metric of real-world function and trajectory, it is valuable to understand the internal state-based and environmental factors that could be driving these fluctuations in performance. Objective In this viewpoint, we argue for the use of repeated mobile assessment as a way to better understand how context shapes moment-to-moment cognitive performance. To elucidate potential factors that give rise to intraindividual variability, we highlight existing literature that has linked both internal and external modifying variables to a number of cognitive domains. We identify ways in which these variables could be measured using mobile assessment to capture them in ecologically meaningful settings (ie, in daily life). Finally, we describe a number of studies that have already begun to use mobile assessment to measure changes in real time cognitive performance in people’s daily environments and the ways in which this burgeoning methodology may continue to advance the field. Methods This paper describes selected literature on contextual factors that examined how experimentally induced or self-reported contextual variables (ie, affect, motivation, time of day, environmental noise, physical activity, and social activity) related to tests of cognitive performance. We also selected papers that used mobile assessment of cognition; these papers were chosen for their use of high-frequency time-series measurement of cognition using a mobile device. Results Upon review of the relevant literature, it is evident that contextual factors have the potential to meaningfully impact cognitive performance when measured in laboratory and daily life environments. Although this research has shed light on the question of what gives rise to real-life variability in cognitive function (eg, affect and activity), many of the studies were limited by traditional methods of data collection (eg, involving retrospective recall). Furthermore, cognition has often been measured in one domain or in one age group, which does not allow us to extrapolate results to other cognitive domains and across the life span. On the basis of the literature reviewed, mobile assessment of cognition shows high levels of feasibility and validity and could be a useful method for capturing individual cognitive variability in real-world contexts via passive and active measures. Conclusions We propose that, through the use of mobile assessment, there is an opportunity to combine multiple sources of contextual and cognitive data. These data have the potential to provide individualized digital signatures that could improve diagnostic precision and lead to meaningful clinical outcomes in a wide range of psychiatric and neurological disorders.

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Advancing ambulatory biobehavioral technologies beyond “proof of concept”: Introduction to the special section.

Cited by 17 publications

References 72 publications

Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia

Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Cognition in Context: Understanding the Everyday Predictors of Cognitive Performance in a New Era of Measurement

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