Digital phenotyping of negative symptoms: the relationship to clinician ratings

Towards clinical subtypes in schizophrenia: integrating cognitive, functional, and digital phenotyping assessments

Heterogeneity in the clinical presentation of schizophrenia impairs both proper and preventative care. The digital phenotyping data gathered from an international multi-site cohort study in people with schizophrenia (SZ) offers a novel opportunity to explore clinically meaningful subtypes in the context of clinical, functional, and cognitive data. Using a set of behavioral features derived from smartphone digital phenotyping, clinical assessment of symptoms including PANSS, clinical assessment of cognition with BACS, and clinical assessment of functioning with the social functioning assessments over the target period of twelve months, we found that the international cohort of 74 patients were categorized into three well-defined clusters that suggest clinically actionable targets from differential correlations in each. Namely, the identified clusters seemed to share phenotypic traits with the affective psychosis with more severe symptomatic presentation, a non-affective SZ with functional impairment, and a higher functioning non-affective SZ cluster. Partial correlation analysis further highlighted the emergence of different features per cluster, where anxiety symptoms were most notable for one group, whereas psychotic symptoms were most notable for the other two. Importantly, we showcase an analysis pipeline that transparently addresses challenges of missing data and potential skew so that this research methodology can be applied to future prospective validation studies. This study hopes to build a foundation for future digital phenotyping clustering work by scaling up to new sites, and populations to uncover the nature and extent of heterogeneity in schizophrenia.

Current limitations in technology-based cognitive assessment for severe mental illnesses: a focus on feasibility, reliability, and ecological validity

Cognitive impairments are frequently observed in subjects with severe mental illnesses (SMI), leading to a remarkable impact in their real-world functioning. Well-validated and gold standard instruments are available for the assessment of cognitive deficits, but different limitations should be considered, such as the need for specific training, lengthy administration times, practice effects, or reliance on subjective reports. Recent advances in digital technologies, such as ecological momentary assessments (EMA), virtual reality (VR), and passive digital phenotyping (DP), offer promising complementary approaches for capturing real-world cognitive functioning. In the current mini-review, we examine current research gaps that limit the application of these technologies, with a specific focus on feasibility, reliability and ecological validity. EMA may capture real-world functioning by increasing the number of evaluations throughout the day, but its use might be hindered by high participant burden and missing data. Furthermore, to achieve an accurate interpretation of EMA, studies should account for sampling and moment selection biases and the presence of several confounding factors. DP faces significant ethical and logistical challenges, including privacy and informed consent concerns, as well as challenges in data interpretation. VR could serve as a platform for both more ecologically valid cognitive assessments and rehabilitation interventions, but current barriers include technological and psychometric limitations, underdeveloped theoretical frameworks, and ethical considerations. Addressing these issues is crucial for ensuring that these novel technologies can effectively serve as valuable complements to traditional neuropsychological cognitive batteries.

Assessment of Negative Symptoms in Schizophrenia: From the Consensus Conference-Derived Scales to Remote Digital Phenotyping

The assessment of negative symptoms in schizophrenia has advanced since the 2006 NIMH-MATRICS Consensus Statement, leading to the development of second-generation rating scales like the Brief Negative Symptom Scale and the Clinical Assessment Interview for Negative Symptoms. These scales address the limitations of first-generation tools, such as the inclusion of aspects that are not negative symptoms and the lack of assessment of the subject's internal experience. However, psychometric validation of these scales is still in progress, and they are not yet recommended by regulatory agencies, thus limiting their use in clinical trials and settings. Complementing these traditional methods, remote digital phenotyping offers a novel approach by leveraging smartphones and wearable technology to capture real-time, high-resolution clinical data. Despite the potential to overcome traditional assessment barriers, challenges remain in aligning these digital measures with clinical ratings and ensuring data security. Equally important is patient acceptance, as the success of remote digital phenotyping relies on the willingness of patients to use these technologies. This review provides a critical overview of both second-generation scales and remote digital phenotyping for assessing negative symptoms, highlighting future research needs.

Social network reductions are associated with negative symptoms in schizophrenia

BACKGROUND

A recent environmental systems theory of negative symptoms in schizophrenia (SZ) proposes a role for reductions in social networks that exist within microsystems (i.e., the contexts in which social interactions occur). However, it is unclear which aspects of social networks are most impacted in SZ and whether these are differentially associated with specific domains of negative symptoms. The current study aimed to address these gaps in the literature using a novel social network tool in combination with Ecological Momentary Assessment (EMA) and clinical ratings of negative symptoms.

METHODS

Participants included 40 outpatients diagnosed with SZ and 35 demographically matched healthy controls (CN) who completed the sociogram, Brief Negative Symptom Scale (BNSS), and 7 days of EMA surveys assessing anhedonia, avolition, and asociality. ANOVAs examined group differences in social network characteristics. Correlations examined associations between social network characteristics and negative symptoms measured via the BNSS and EMA.

RESULTS

Results indicated that: (1) SZ had greater social network reductions than CN, including lower: network density, number of microsystems, people in microsystems, connections across and within microsystems (p's < 0.05, d-value range 0.58 to 0.74); (2) these social network reductions were associated with greater severity of negative symptoms on the BNSS (r range - 0.28-0.34, p < .05) and asociality measured via EMA surveys (r's = - 0.24 to - 0.26, p's < 0.05).

CONCLUSIONS

Findings clarified the nature of social network dysfunction in SZ and identify novel targets for psychosocial interventions focused on modifying the number of social microsystems and the connections within/across these microsystems.

The Role of Defeatist Performance Beliefs in State Fluctuations of Negative Symptoms in Schizophrenia Measured in Daily Life via Ecological Momentary Assessment

BACKGROUND AND HYPOTHESIS

The Cognitive Model of Negative Symptoms is a prominent model that posits that defeatist performance beliefs (DPB) are a key psychological mechanism underlying negative symptoms in those with schizophrenia (SZ). However, the ecological validity of the model has not been established, and temporally specific evaluations of the model's hypotheses have not been conducted. This study tested the model's key hypotheses in real-world environments using ecological momentary assessment (EMA).

STUDY DESIGN

Fifty-two outpatients with SZ and 55 healthy controls (CN) completed 6 days of EMA. Multilevel models examined concurrent and time-lagged associations between DPB and negative symptoms in daily life.

STUDY RESULTS

SZ displayed greater DPB in daily life than CN. Furthermore, greater DPB were associated with greater concurrently assessed negative symptoms (anhedonia, avolition, and asociality) in daily life. Time-lagged analyses indicated that in both groups, greater DPB at time t led to elevations in negative symptoms (anhedonia, avolition, or asociality) at t + 1 above and beyond the effects of negative symptoms at time t.

CONCLUSIONS

Results support the ecological validity of the Cognitive Model of Negative Symptoms and identify a temporally specific association between DPB and subsequent negative symptoms that is consistent with the model's hypotheses and a putative mechanistic pathway in Cognitive Behavioral Therapy for negative symptoms. Findings suggest that DPB are a psychological factor contributing to negative symptoms in real-world environments. Implications for measuring DPB in daily life and providing just-in-time mobile health-based interventions to target this mechanism are discussed.

Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures

Automatically extracted measures of speech constitute a promising marker of psychosis as disorganized speech is associated with psychotic symptoms and predictive of psychosis-onset. The potential of speech markers is, however, hampered by (i) lengthy assessments in laboratory settings and (ii) manual transcriptions. We investigated whether a short, scalable data collection (online) and processing (automated transcription) procedure would provide data of sufficient quality to extract previously validated speech measures. To evaluate the fit of our approach for purpose, we assessed speech in relation to psychotic-like experiences in the general population. Participants completed an 8-minute-long speech task online. Sample 1 included measures of psychometric schizotypy and delusional ideation (N = 446). Sample 2 included a low and high psychometric schizotypy group (N = 144). Recordings were transcribed both automatically and manually, and connectivity, semantic, and syntactic speech measures were extracted for both types of transcripts. 73%/86% participants in sample 1/2 completed the experiment. Nineteen out of 25 speech measures were strongly (r > 0.7) and significantly correlated between automated and manual transcripts in both samples. Amongst the 14 connectivity measures, 11 showed a significant relationship with delusional ideation. For the semantic and syntactic measures, On Topic score and the Frequency of personal pronouns were negatively correlated with both schizotypy and delusional ideation. Combined with demographic information, the speech markers could explain 11-14% of the variation of delusional ideation and schizotypy in Sample 1 and could discriminate between high-low schizotypy with high accuracy (0.72-0.70, AUC = 0.78-0.79) in Sample 2. The moderate to high retention rate, strong correlation of speech measures across manual and automated transcripts and sensitivity to psychotic-like experiences provides initial evidence that online collected speech in combination with automatic transcription is a feasible approach to increase accessibility and scalability of speech-based assessment of psychosis.

Automated analysis of speech as a marker of sub-clinical psychotic experiences

Automated speech analysis techniques, when combined with artificial intelligence and machine learning, show potential in capturing and predicting a wide range of psychosis symptoms, garnering attention from researchers. These techniques hold promise in predicting the transition to clinical psychosis from at-risk states, as well as relapse or treatment response in individuals with clinical-level psychosis. However, challenges in scientific validation hinder the translation of these techniques into practical applications. Although sub-clinical research could aid to tackle most of these challenges, there have been only few studies conducted in speech and psychosis research in non-clinical populations. This work aims to facilitate this work by summarizing automated speech analytical concepts and the intersection of this field with psychosis research. We review psychosis continuum and sub-clinical psychotic experiences, and the benefits of researching them. Then, we discuss the connection between speech and psychotic symptoms. Thirdly, we overview current and state-of-the art approaches to the automated analysis of speech both in terms of language use (text-based analysis) and vocal features (audio-based analysis). Then, we review techniques applied in subclinical population and findings in these samples. Finally, we discuss research challenges in the field, recommend future research endeavors and outline how research in subclinical populations can tackle the listed challenges.

Targeted Treatment of Schizophrenia Symptoms as They Manifest, or Continuous Treatment to Reduce the Risk of Psychosis Recurrence

Current pharmacological treatment of schizophrenia employs drugs that interfere with dopamine neurotransmission, aiming to suppress acute exacerbation of psychosis and maintenance treatment to reduce the risk of psychosis recurrence. According to this treatment scheme, available psychotropic drugs intended to treat negative symptoms, cognitive impairment, or anxiety are administered as add-ons to treatment with antipsychotics. However, an alternative treatment scheme proposes a targeted or intermittent treatment approach, by which antipsychotic drugs are administered upon psychosis exacerbation and discontinued upon remission or stabilization, while negative symptoms, cognitive impairment, or anxiety are treated with specific psychotropics as monotherapy. Along these lines, antipsychotics are renewed only in the event of recurrence of psychotic symptoms. This 50-year-old debate between targeted and continuous treatment schemes arises from disagreements about interpreting scientific evidence and discordant views regarding benefit/risk assessment. Among the debate's questions are: (1) what is the percentage of individuals who can maintain stability without antipsychotic maintenance treatment, and what is the percentage of those who exacerbate despite antipsychotic treatment? (2) how to interpret results of placebo-controlled 9- to 18-month-long maintenance trials in a life-long chronic disorder, and how to interpret results of the targeted trials, some of which are open label or not randomized; (3) how to weigh the decreased risk for psychotic recurrence vs the almost certainty of adverse effects on patient's quality of life. Patients' profiles, preferences, and circumstances of the care provision should be considered as the targeted vs continuous treatment options are considered.

Rationale and Challenges for a New Instrument for Remote Measurement of Negative Symptoms

There is a broad consensus that the commonly used clinician-administered rating scales for assessment of negative symptoms share significant limitations, including (1) reliance upon accurate self-report and recall from the patient and caregiver; (2) potential for sampling bias and thus being unrepresentative of daily-life experiences; (3) subjectivity of the symptom scoring process and limited sensitivity to change. These limitations led a work group from the International Society of CNS Clinical Trials and Methodology (ISCTM) to initiate the development of a multimodal negative symptom instrument. Experts from academia and industry reviewed the current methods of assessing the domains of negative symptoms including diminished (1) affect; (2) sociality; (3) verbal communication; (4) goal-directed behavior; and (5) Hedonic drives. For each domain, they documented the limitations of the current methods and recommended new approaches that could potentially be included in a multimodal instrument. The recommended methods for assessing negative symptoms included ecological momentary assessment (EMA), in which the patient self-reports their condition upon receipt of periodic prompts from a smartphone or other device during their daily routine; and direct inference of negative symptoms through detection and analysis of the patient's voice, appearance or activity from audio/visual or sensor-based (eg, global positioning systems, actigraphy) recordings captured by the patient's smartphone or other device. The process for developing an instrument could resemble the NIMH MATRICS process that was used to develop a battery for measuring cognition in schizophrenia. Although the EMA and other digital measures for negative symptoms are at relatively early stages of development/maturity and development of such an instrument faces substantial challenges, none of them are insurmountable.

The role of defeatist performance beliefs on cognitive effort-cost decision-making in schizophrenia

Impairments in effort-cost decision-making have been consistently observed in people with schizophrenia (SZ) and may be an important mechanism of negative symptoms. However, the processes that give rise to impairments in effort-cost decision-making are unclear, leading to limited progress in identifying the most relevant treatment targets. Drawing from cognitive models of negative symptoms and goal-directed behavior, this study aimed to examine how and under what type of task conditions defeatist performance beliefs contribute to these decision-making processes. Outpatients with SZ (n = 30) and healthy controls (CN; n = 28) completed a cognitive effort allocation task, the Cognitive Effort-Discounting (COGED) task, which assesses participants' willingness to exert cognitive effort for monetary rewards based on parametrically varied working memory demands (completing N-back levels). Results showed that although participants with SZ demonstrated reduced willingness to work for rewards across N-back levels compared to CN participants, they showed less choice modulation across different N-back conditions. However, among SZ participants with greater defeatist performance beliefs, there was a reduced willingness to choose the high effort option at higher N-back levels (N-back levels 3, 4, and 5 versus 2-back). Results suggest that compared to CN, the SZ group's subjective willingness to expend effort largely did not dynamically adjust as cognitive load increased. However, defeatist beliefs may undermine willingness to expend cognitive effort, especially when cognitive task demands are high. These beliefs may be a viable treatment target to improve effort-cost decision-making impairments in people with SZ.

Digital Phenotyping in Adults with Schizophrenia: A Narrative Review

PURPOSE OF REVIEW

As care for older adult patients with schizophrenia lacks innovation, technology can help advance the field. Specifically, digital phenotyping, the real-time monitoring of patients' behaviors through smartphone sensors and symptoms through surveys, holds promise as the method can capture the dynamicity and environmental correlates of disease.

RECENT FINDINGS

Few studies have used digital phenotyping to elucidate adult patients' experiences with schizophrenia. In this narrative review, we summarized the literature using digital phenotyping on adults with schizophrenia. No study focused solely on older adult patients. Studies including all adult patients were heterogeneous in measures used, duration, and outcomes. Despite limited research, digital phenotyping shows potential for monitoring outcomes such as negative, positive, and functional symptoms, as well as predicting relapse. Future research should work to target the symptomology persistent in chronic schizophrenia and ensure all patients have the digital literacy required to benefit from digital interventions and homogenize datasets to allow for more robust conclusions.

Identifying Medications Underlying Communication Atypicalities in Psychotic and Affective Disorders: A Pharmacovigilance Study Within the FDA Adverse Event Reporting System

PURPOSE

Communication atypicalities are considered promising markers of a broad range of clinical conditions. However, little is known about the mechanisms and confounders underlying them. Medications might have a crucial, relatively unknown role both as potential confounders and offering an insight on the mechanisms at work. The integration of regulatory documents with disproportionality analyses provides a more comprehensive picture to account for in future investigations of communication-related markers. The aim of this study was to identify a list of drugs potentially associated with communicative atypicalities within psychotic and affective disorders.

METHOD

We developed a query using the Medical Dictionary for Regulatory Activities to search for communicative atypicalities within the FDA Adverse Event Reporting System (updated June 2021). A Bonferroni-corrected disproportionality analysis (reporting odds ratio) was separately performed on spontaneous reports involving psychotic, affective, and non-neuropsychiatric disorders, to account for the confounding role of different underlying conditions. Drug-adverse event associations not already reported in the Side Effect Resource database of labeled adverse drug reactions (unexpected) were subjected to further robustness analyses to account for expected biases.

RESULTS

A list of 291 expected and 91 unexpected potential confounding medications was identified, including drugs that may irritate (inhalants) or desiccate (anticholinergics) the larynx, impair speech motor control (antipsychotics), or induce nodules (acitretin) or necrosis (vascular endothelial growth factor receptor inhibitors) on vocal cords; sedatives and stimulants; neurotoxic agents (anti-infectives); and agents acting on neurotransmitter pathways (dopamine agonists).

CONCLUSIONS

We provide a list of medications to account for in future studies of communication-related markers in affective and psychotic disorders. The current test case illustrates rigorous procedures for digital phenotyping, and the methodological tools implemented for large-scale disproportionality analyses can be considered a road map for investigations of communication-related markers in other clinical populations.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.23721345.

Online speech assessment of the psychotic spectrum: Exploring the relationship between overlapping acoustic markers of schizotypy, depression and anxiety

BACKGROUND

Remote assessment of acoustic alterations in speech holds promise to increase scalability and validity in research across the psychosis spectrum. A feasible first step in establishing a procedure for online assessments is to assess acoustic alterations in psychometric schizotypy. However, to date, the complex relationship between alterations in speech related to schizotypy and those related to comorbid conditions such as symptoms of depression and anxiety has not been investigated. This study tested whether (1) depression, generalized anxiety and high psychometric schizotypy have similar voice characteristics, (2) which acoustic markers of online collected speech are the strongest predictors of psychometric schizotypy, (3) whether including generalized anxiety and depression symptoms in the model can improve the prediction of schizotypy.

METHODS

We collected cross-sectional, online-recorded speech data from 441 participants, assessing demographics, symptoms of depression, generalized anxiety and psychometric schizotypy.

RESULTS

Speech samples collected online could predict psychometric schizotypy, depression, and anxiety symptoms with weak to moderate predictive power, and with moderate and good predictive power when basic demographic variables were added to the models. Most influential features of these models largely overlapped. The predictive power of speech marker-based models of schizotypy significantly improved after including symptom scores of depression and generalized anxiety in the models (from R2 = 0.296 to R2 = 0. 436).

CONCLUSIONS

Acoustic features of online collected speech are predictive of psychometric schizotypy as well as generalized anxiety and depression symptoms. The acoustic characteristics of schizotypy, depression and anxiety symptoms significantly overlap. Speech models that are designed to predict schizotypy or symptoms of the schizophrenia spectrum might therefore benefit from controlling for symptoms of depression and anxiety.

Speech disturbances in schizophrenia: Assessing cross-linguistic generalizability of NLP automated measures of coherence

INTRODUCTION

Language disorders - disorganized and incoherent speech in particular - are distinctive features of schizophrenia. Natural language processing (NLP) offers automated measures of incoherent speech as promising markers for schizophrenia. However, the scientific and clinical impact of NLP markers depends on their generalizability across contexts, samples, and languages, which we systematically assessed in the present study relying on a large, novel, cross-linguistic corpus.

METHODS

We collected a Danish (DK), German (GE), and Chinese (CH) cross-linguistic dataset involving transcripts from 187 participants with schizophrenia (111DK, 25GE, 51CH) and 200 matched controls (129DK, 29GE, 42CH) performing the Animated Triangles Task. Fourteen previously published NLP coherence measures were calculated, and between-groups differences and association with symptoms were tested for cross-linguistic generalizability.

RESULTS

One coherence measure, i.e. second-order coherence, robustly generalized across samples and languages. We found several language-specific effects, some of which partially replicated previous findings (lower coherence in German and Chinese patients), while others did not (higher coherence in Danish patients). We found several associations between symptoms and measures of coherence, but the effects were generally inconsistent across languages and rating scales.

CONCLUSIONS

Using a cumulative approach, we have shown that NLP findings of reduced semantic coherence in schizophrenia have limited generalizability across different languages, samples, and measures. We argue that several factors such as sociodemographic and clinical heterogeneity, cross-linguistic variation, and the different NLP measures reflecting different clinical aspects may be responsible for this variability. Future studies should take this variability into account in order to develop effective clinical applications targeting different patient populations.

Ecological Momentary Assessment Of State Fluctuations In Mindfulness And Symptoms In Psychotic Disorders

Mindfulness skills are a component of many modern cognitive-behavioral therapies that are used to treat a wide range of disorders, including psychotic disorders. While habitual (i.e., trait) mindfulness is associated with clinical outcomes, the effects of momentary (i.e., state) mindfulness are unclear. This is due in part to previous studies using cross-sectional designs relying on trait self-report questionnaires. Although such approaches are invaluable, they lack temporal specificity to evaluate momentary changes and effects of mindfulness. To address these limitations, the current study used ecological momentary assessment (EMA) to evaluate state levels of two mindfulness skills, acceptance and monitoring, and their association with state fluctuations in symptoms. Participants included individuals with affective and non-affective psychotic disorders (PD; n = 49) and healthy controls (CN; n = 53) who completed six days of EMA. Results indicated that the PD group endorsed lower state acceptance than CN; however, the groups did not significantly differ in monitoring. Further, greater state mindfulness skills in both acceptance and monitoring were associated with greater positive affect, reduced negative affect, and reduced negative symptoms. However, participants with a predominantly affective psychosis presentation showed differential effects compared to those with non-affective presentations. These findings suggest that mindfulness training for people with psychotic disorders may benefit from focusing on improving acceptance in order to improve emotional experience and build on existing monitoring skills. Further, mindfulness based psychosocial interventions may offer a novel means of treating negative symptoms in people with PD, which are currently stalled and largely unresponsive to other treatments.

Negative Symptom Inventory-Self-Report (NSI-SR): Initial development and validation

Negative symptoms (i.e., anhedonia, avolition, asociality, blunted affect, alogia) are frequently observed in the schizophrenia-spectrum (SZ) and associated with functional disability. While semi-structured interviews of negative symptoms represent a gold-standard approach, they require specialized training and may be vulnerable to rater biases. Thus, brief self-report questionnaires measuring negative symptoms may be useful. Existing negative symptom questionnaires demonstrate that this approach may be promising in schizophrenia, but no measure has been devised for use across stages of psychotic illness. The present study reports initial psychometric validation of the Negative Symptom Inventory-Self-Report (NSI-SR), the self-report counterpart of the Negative Symptom Inventory-Psychosis Risk clinical interview. The NSI-SR is a novel transphasic negative symptoms measure assessing the domains of anhedonia, avolition, and asociality. The NSI-SR and related measures were administered to two samples: 1) undergraduates (n = 335), 2) community participants, including: SZ (n = 32), clinical-high risk for psychosis (CHR, n = 25), and healthy controls matched to SZ (n = 31) and CHR (n = 30). The psychometrically trimmed 11-item NSI-SR showed good internal consistency and a three-factor solution reflecting avolition, asociality, and anhedonia. The NSI-SR demonstrated convergent validity via moderate to large correlations with clinician-rated negative symptoms and related constructs in both samples. Discriminant validity was supported by lower correlations with positive symptoms in both samples; however, correlations with positive symptoms were still significant. These initial psychometric findings suggest that the NSI-SR is a reliable and valid brief questionnaire capable of measuring negative symptoms across phases of psychotic illness.

Negative symptoms in schizophrenia differ across environmental contexts in daily life

A recent environmental theory of negative symptoms posits that environmental contexts (e.g., location, social partner) play a significant-yet often unaccounted for-role in negative symptoms of schizophrenia (SZ). "Gold-standard" clinical rating scales offer limited precision for evaluating how contexts impact symptoms. To overcome some of these limitations, Ecological Momentary Assessment (EMA) was used to determine whether there were state fluctuations in experiential negative symptoms (anhedonia, avolition, and asociality) in SZ across contexts (locations, activities, social interaction partner, social interaction method). Outpatients with SZ (n = 52) and healthy controls (CN: n = 55) completed 8 daily EMA surveys for 6 days assessing negative symptom domains (anhedonia, avolition, and asociality) and contexts. Multilevel modeling demonstrated that negative symptoms varied across location, activity, social interaction partner, and social interaction method. For the majority of contexts, SZ and CN did not report significantly different levels of negative symptoms, with SZ only reporting higher negative symptoms than CN while eating, resting, interacting with a significant other, or being at home. Further, there were several contexts where negative symptoms were similarly reduced (e.g., recreation, most social interactions) or elevated (e.g., using the computer, working, running errands) in each group. Results demonstrate that experiential negative symptoms dynamically change across contexts in SZ. Some contexts may "normalize" experiential negative symptoms in SZ, while other contexts, notably some used to promote functional recovery, may increase experiential negative symptoms.

Voice Patterns as Markers of Schizophrenia: Building a Cumulative Generalizable Approach Via a Cross-Linguistic and Meta-analysis Based Investigation

BACKGROUND AND HYPOTHESIS

Voice atypicalities are potential markers of clinical features of schizophrenia (eg, negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages.

STUDY DESIGN

We provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling, contrasting meta-analytically informed and skeptical inferences.

STUDY RESULTS

We found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages.

CONCLUSIONS

The findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.

Remote Assessment of Negative Symptoms of Schizophrenia

In contrast to the validated scales for face-to-face assessment of negative symptoms, no widely accepted tools currently exist for remote monitoring of negative symptoms. Remote assessment of negative symptoms can be broadly divided into 3 categories: (1) remote administration of an existing negative-symptom scale by a clinician, in real time, using videoconference technology to communicate with the patient; (2) direct inference of negative symptoms through detection and analysis of the patient's voice, appearance, or activity by way of the patient's smartphone or other device; and (3) ecological momentary assessment, in which the patient self-reports their condition upon receipt of periodic prompts from a smartphone or other device during their daily routine. These modalities vary in cost, technological complexity, and applicability to the different negative-symptom domains. Each modality has unique strengths, weaknesses, and issues with validation. As a result, an optimal solution may be more likely to employ several techniques than to use a single tool. For remote assessment of negative symptoms to be adopted as primary or secondary endpoints in regulated clinical trials, appropriate psychometric standards will need to be met. Standards for substituting 1 set of measures for another, as well as what constitutes a "gold" reference standard, will need to be precisely defined and a process for defining them developed. Despite over 4 decades of progress toward this goal, significant work remains to be done before clinical trials addressing negative symptoms can utilize remotely assessed secondary or primary outcome measures.

Thirty years of research on negative symptoms of schizophrenia: A scientometric analysis of hotspots, bursts, and research trends

Research on negative symptoms of schizophrenia has received renewed interest since the 1980s. A scientometric analysis that objectively maps scientific knowledge, with changes in recent trends, is currently lacking. We searched the Web of Science Core Collection (WOSCC) on December 17, 2021 using relevant keywords. R-bibliometrix and CiteSpace were used to perform the analysis. We retrieved 27,568 references published between 1966 and 2022. An exponential rise in scientific interest was observed, with an average annual growth rate in publications of 16.56% from 1990 to 2010. The co-cited reference network that was retrieved presented 24 different clusters with a well-structured network (Q=0.7921; S=0.9016). Two distinct major research trends were identified: research on the conceptualization and treatment of negative symptoms. The latest trends in research on negative symptoms include evidence synthesis, nonpharmacological treatments, and computational psychiatry. Scientometric analyses provide a useful summary of changes in negative symptom research across time by identifying intellectual turning point papers and emerging trends. These results will be informative for systematic reviews, meta-analyses, and generating novel hypotheses.

Aberrant reward processing to positive versus negative outcomes across psychotic disorders

Several studies of reward processing in schizophrenia have shown reduced sensitivity to positive, but not negative, outcomes although inconsistencies have been reported. In addition, few studies have investigated whether patients show a relative deficit to social versus nonsocial rewards, whether deficits occur across the spectrum of psychosis, or whether deficits relate to negative symptoms and functioning. This study examined probabilistic implicit learning via two visually distinctive slot machines for social and nonsocial rewards in 101 outpatients with diverse psychotic disorders and 48 community controls. The task consisted of two trial types: positive (optimal to choose a positive vs. neutral machine) and negative (optimal to choose a neutral vs. negative machine), with two reward conditions: social (faces) and nonsocial (money) reward conditions. A significant group X trial type interaction indicated that controls performed better on positive than negative trials, whereas patients showed the opposite pattern of better performance on negative than positive trials. In addition, both groups performed better for social than nonsocial stimuli, despite lower overall task performance in patients. Within patients, worse performance on negative trials showed significant, small-to-moderate correlations with motivation and pleasure-related negative symptoms and social functioning. The current findings suggest reward processing disturbances, particularly decreased sensitivity to positive outcomes, extend beyond schizophrenia to a broader spectrum of psychotic disorders and relate to important clinical outcomes.

Smartphone-based mobility metrics capture daily social motivation and behavior in schizophrenia

Impaired social functioning contributes to reduced quality of life and is associated with poor physical and psychological well-being in schizophrenia, and thus is a key psychosocial treatment target. Low social motivation contributes to impaired social functioning, but is typically examined using self-report or clinical ratings, which are prone to recall biases and do not adequately capture the dynamic nature of social motivation in daily life. In the current study, we examined the utility of global positioning system (GPS)-based mobility data for capturing social motivation and behavior in people with schizophrenia. Thirty-one participants with schizophrenia engaged in a 60-day mobile intervention designed to increase social motivation and functioning. We examined associations between twice daily self-reports of social motivation and behavior (e.g., number of social interactions) collected via Ecological Momentary Assessment (EMA) and passively collected daily GPS mobility metrics (e.g., number of hours spent at home) in 26 of these participants. Findings suggested that greater mobility on a given day was associated with more EMA-reported social interactions on that day for four out of five examined mobility metrics: number of hours spent at home, number of locations visited, probability of being stationary, and likelihood of following one's typical routine. In addition, greater baseline social functioning was associated with less daily time spent at home and lower probability of following a daily routine during the intervention. GPS-based mobility thus corresponds with social behavior in daily life, suggesting that more social interactions may occur at times of greater mobility in people with schizophrenia, while subjective reports of social interest and motivation are less associated with mobility for this population.

Technology and Mental Health: State of the Art for Assessment and Treatment

Technology is ubiquitous in society and is now being extensively used in mental health applications. Both assessment and treatment strategies are being developed and deployed at a rapid pace. The authors review the current domains of technology utilization, describe standards for quality evaluation, and forecast future developments. This review examines technology-based assessments of cognition, emotion, functional capacity and everyday functioning, virtual reality approaches to assessment and treatment, ecological momentary assessment, passive measurement strategies including geolocation, movement, and physiological parameters, and technology-based cognitive and functional skills training. There are many technology-based approaches that are evidence based and are supported through the results of systematic reviews and meta-analyses. Other strategies are less well supported by high-quality evidence at present, but there are evaluation standards that are well articulated at this time. There are some clear challenges in selection of applications for specific conditions, but in several areas, including cognitive training, randomized clinical trials are available to support these interventions. Some of these technology-based interventions have been approved by the U.S. Food and Drug administration, which has clear standards for which types of applications, and which claims about them, need to be reviewed by the agency and which are exempt.

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Schizophrenia (SCZ) and depression (MDD) are two chronic mental disorders that seriously affect the quality of life of millions of people worldwide. We aim to develop machine-learning methods with objective linguistic, speech, facial, and motor behavioral cues to reliably predict the severity of psychopathology or cognitive function, and distinguish diagnosis groups. We collected and analyzed the speech, facial expressions, and body movement recordings of 228 participants (103 SCZ, 50 MDD, and 75 healthy controls) from two separate studies. We created an ensemble machine-learning pipeline and achieved a balanced accuracy of 75.3% for classifying the total score of negative symptoms, 75.6% for the composite score of cognitive deficits, and 73.6% for the total score of general psychiatric symptoms in the mixed sample containing all three diagnostic groups. The proposed system is also able to differentiate between MDD and SCZ with a balanced accuracy of 84.7% and differentiate patients with SCZ or MDD from healthy controls with a balanced accuracy of 82.3%. These results suggest that machine-learning models leveraging audio-visual characteristics can help diagnose, assess, and monitor patients with schizophrenia and depression.

Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung?

Zusammenfassung. Digitale Phänotypisierung stellt einen neuen, leistungsstarken Ansatz zur Realisierung psychodiagnostischer Aufgaben in vielen Bereichen der Psychologie und Medizin dar. Die Grundidee besteht aus der Nutzung digitaler Spuren aus dem Alltag, um deren Vorhersagekraft für verschiedenste Anwendungsmöglichkeiten zu überprüfen und zu nutzen. Voraussetzungen für eine erfolgreiche Umsetzung sind elaborierte Smart Sensing Ansätze sowie Big Data-basierte Extraktions- (Data Mining) und Machine Learning-basierte Analyseverfahren. Erste empirische Studien verdeutlichen das hohe Potential, aber auch die forschungsmethodischen sowie ethischen und rechtlichen Herausforderungen, um über korrelative Zufallsbefunde hinaus belastbare Befunde zu gewinnen. Hierbei müssen rechtliche und ethische Richtlinien sicherstellen, dass die Erkenntnisse in einer für Einzelne und die Gesellschaft als Ganzes wünschenswerten Weise genutzt werden. Für die Psychologie als Lehr- und Forschungsdomäne bieten sich durch Digitale Phänotypisierung vielfältige Möglichkeiten, die zum einen eine gelebte Zusammenarbeit verschiedener Fachbereiche und zum anderen auch curriculare Erweiterungen erfordern. Die vorliegende narrative Übersicht bietet eine theoretische, nicht-technische Einführung in das Forschungsfeld der Digitalen Phänotypisierung, mit ersten empirischen Befunden sowie einer Diskussion der Möglichkeiten und Grenzen sowie notwendigen Handlungsfeldern.

Revisiting how People with Schizophrenia Spend Their Days: Associations of lifetime milestone Achievements with Daily Activities examined with Ecological Momentary Assessment

Milestone achievements are reduced in people with schizophrenia and are lower in comparison to people with bipolar disorder. However, it is not clear what the implications are for engagement in momentary activities based on milestone achievements. Further, some recent research has suggested that psychotic symptoms are associated with challenges in self-assessment of activities, but there is less information about the correlations of milestone achievements and ongoing psychotic symptoms. We examined momentary activities and symptoms as a function of lifetime milestone achievement in 102 individuals with schizophrenia and 71 with bipolar disorder. Ecological Momentary Assessment (EMA) was used to sample daily activities and concurrent symptoms 3 times per day for 30 days. Each survey asked the participant where they were, who they were with, and what they were doing, as well as sampling the concurrent presence of psychotic symptoms. Not being financially responsible for their residence was associated with engaging in fewer productive activities. Participants who never had a relationship were more commonly home and alone and engaged in fewer social interactions. A lifetime history of employment was correlated with engaging in more productive activities, including at home. More common momentary psychosis was seen in participants who failed to achieve each of the functional milestones. Lifetime milestone achievements were associated with greater frequencies of productive behaviors and with fewer momentary experiences of psychosis, suggesting that psychotic symptoms may have importance for sustaining disability that would be challenging to detect without momentary information.

Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation

BACKGROUND AND HYPOTHESIS

Despite decades of "proof of concept" findings supporting the use of Natural Language Processing (NLP) in psychosis research, clinical implementation has been slow. One obstacle reflects the lack of comprehensive psychometric evaluation of these measures. There is overwhelming evidence that criterion and content validity can be achieved for many purposes, particularly using machine learning procedures. However, there has been very little evaluation of test-retest reliability, divergent validity (sufficient to address concerns of a "generalized deficit"), and potential biases from demographics and other individual differences.

STUDY DESIGN

This article highlights these concerns in development of an NLP measure for tracking clinically rated paranoia from video "selfies" recorded from smartphone devices. Patients with schizophrenia or bipolar disorder were recruited and tracked over a week-long epoch. A small NLP-based feature set from 499 language samples were modeled on clinically rated paranoia using regularized regression.

STUDY RESULTS

While test-retest reliability was high, criterion, and convergent/divergent validity were only achieved when considering moderating variables, notably whether a patient was away from home, around strangers, or alone at the time of the recording. Moreover, there were systematic racial and sex biases in the model, in part, reflecting whether patients submitted videos when they were away from home, around strangers, or alone.

CONCLUSIONS

Advancing NLP measures for psychosis will require deliberate consideration of test-retest reliability, divergent validity, systematic biases and the potential role of moderators. In our example, a comprehensive psychometric evaluation revealed clear strengths and weaknesses that can be systematically addressed in future research.

Efficacy and Safety of Roluperidone for the Treatment of Negative Symptoms of Schizophrenia

BACKGROUND

This is a placebo-controlled multi-national trial of roluperidone, a compound with antagonist properties for 5-HT2A, sigma2, and α1A-adrenergic receptors, targeting negative symptoms in patients with schizophrenia. This trial follows a previous trial that demonstrated roluperidone superiority over placebo in a similar patient population.

METHODS

Roluperidone 32 mg/day, roluperidone 64 mg/day, or placebo was administered for 12 weeks to 513 patients with schizophrenia with moderate to severe negative symptoms. The primary endpoint was the PANSS-derived Negative Symptom Factor Score (NSFS) and the key secondary endpoint was Personal and Social Performance scale (PSP) total score.

RESULTS

NSFS scores were lower (improved) for roluperidone 64 mg compared to placebo and marginally missing statistical significance for the intent-to-treat (ITT) analysis data set (P ≤ .064), but reached nominal significance (P ≤ .044) for the modified-ITT (m-ITT) data set. Changes in PSP total score were statistically significantly better on roluperidone 64 mg compared to placebo for both ITT and m-ITT (P ≤ .021 and P ≤ .017, respectively).

CONCLUSIONS

Results of this trial confirm the potential of roluperidone as a treatment of negative symptoms and improving everyday functioning in patients with schizophrenia. Study registration: Eudra-CT: 2017-003333-29; NCT03397134.

Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials

BACKGROUND

Digital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this purpose.

AIMS

Machine learning was used to address this gap in the literature and determine whether: (1) diagnostic status could be classified from digital phenotyping measures relevant to negative symptoms and (2) the 5 negative symptom domains (anhedonia, avolition, asociality, alogia, and blunted affect) were differentially classified by active and passive digital phenotyping variables.

METHODS

Participants included 52 outpatients with a PD and 55 healthy controls (CN) who completed 6 days of active (ecological momentary assessment surveys) and passive (geolocation, accelerometry) digital phenotyping data along with clinical ratings of negative symptoms.

RESULTS

Machine learning algorithms classifying the presence of a PD diagnosis yielded 80% accuracy for cross-validation in H2O AutoML and 79% test accuracy in the Recursive Feature Elimination with Cross Validation feature selection model. Models classifying the presence vs absence of clinically significant elevations on each of the 5 negative symptom domains ranged in test accuracy from 73% to 91%. A few active and passive features were highly predictive of all 5 negative symptom domains; however, there were also unique predictors for each domain.

CONCLUSIONS

These findings suggest that negative symptoms can be modeled from digital phenotyping data recorded in situ. Implications for selecting the most appropriate digital phenotyping variables for use as outcome measures in clinical trials targeting negative symptoms are discussed.

High Predictive Accuracy of Negative Schizotypy With Acoustic Measures

Negative schizotypal traits potentially can be digitally phenotyped using objective vocal analysis. Prior attempts have shown mixed success in this regard, potentially because acoustic analysis has relied on small, constrained feature sets. We employed machine learning to (a) optimize and cross-validate predictive models of self-reported negative schizotypy using a large acoustic feature set, (b) evaluate model performance as a function of sex and speaking task, (c) understand potential mechanisms underlying negative schizotypal traits by evaluating the key acoustic features within these models, and (d) examine model performance in its convergence with clinical symptoms and cognitive functioning. Accuracy was good (> 80%) and was improved by considering speaking task and sex. However, the features identified as most predictive of negative schizotypal traits were generally not considered critical to their conceptual definitions. Implications for validating and implementing digital phenotyping to understand and quantify negative schizotypy are discussed.

Digital phenotyping in psychiatry: A scoping review

BACKGROUND

Digital phenotyping has been defined as the moment-by-moment assessment of an illness state through digital means, promising objective, quantifiable data on psychiatric patients' conditions, and could potentially improve diagnosis and management of mental illness. As it is a rapidly growing field, it is to be expected that new literature is being published frequently.

OBJECTIVE

We conducted this scoping review to assess the current state of literature on digital phenotyping and offer some discussion on the current trends and future direction of this area of research.

METHODS

We searched four databases, PubMed, Ovid MEDLINE, PsycINFO and Web of Science, from inception to August 25th, 2021. We included studies written in English that 1) investigated or applied their findings to diagnose psychiatric disorders and 2) utilized passive sensing for management or diagnosis. Protocols were excluded. A narrative synthesis approach was used, due to the heterogeneity and variability in outcomes and outcome types reported.

RESULTS

Of 10506 unique records identified, we included a total of 107 articles. The number of published studies has increased over tenfold from 2 in 2014 to 28 in 2020, illustrating the field's rapid growth. However, a significant proportion of these (49% of all studies and 87% of primary studies) were proof of concept, pilot or correlational studies examining digital phenotyping's potential. Most (62%) of the primary studies published evaluated individuals with depression (21%), BD (18%) and SZ (23%) (Appendix 1).

CONCLUSION

There is promise shown in certain domains of data and their clinical relevance, which have yet to be fully elucidated. A consensus has yet to be reached on the best methods of data collection and processing, and more multidisciplinary collaboration between physicians and other fields is needed to unlock the full potential of digital phenotyping and allow for statistically powerful clinical trials to prove clinical utility.

Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia

Digital phenotyping has potential for use as an objective and ecologically valid form of symptom assessment in clinical trials for schizophrenia. However, there are critical methodological factors that must be addressed before digital phenotyping can be used for this purpose. The current study evaluated levels of adherence, feasibility, and tolerability for active (i.e., signal and event contingent ecological momentary assessment surveys) and passive (i.e., geolocation, accelerometry, and ambulatory psychophysiology) digital phenotyping methods recorded from smartphone and smartband devices. Participants included outpatients diagnosed with schizophrenia (SZ: n = 54) and demographically matched healthy controls (CN: n = 55), who completed 6 days of digital phenotyping. Adherence was significantly lower in SZ than CN for active recordings, but not markedly different for passive recordings. Some forms of passive recordings had lower adherence (ambulatory psychophysiology) than others (accelerometry and geolocation). Active digital phenotyping adherence was predicted by higher psychosocial functioning, whereas passive digital phenotyping adherence was predicted by education, positive symptoms, negative symptoms, and psychosocial functioning in people with SZ. Both groups found digital phenotyping methods tolerable and feasibility was supported by low frequency of invalid responding, brief survey completion times, and similar impediments to study completion. Digital phenotyping methods can be completed by individuals with SZ with good adherence, feasibility, and tolerability. Recommendations are provided for using digital phenotyping methods in clinical trials for SZ.

Predicting self-injurious thoughts in daily life using ambulatory assessment of state cognition

Self-injurious thoughts (SITs) fluctuate considerably from moment to moment. As such, "static" and temporally stable predictors (e.g., demographic variables, prior history) are suboptimal in predicting imminent SITs. This concern is particularly true for "online" cognitive abilities, which are important for understanding SITs, but are typically measured using tests selected for temporal stability. Advances in ambulatory assessments (i.e., real-time assessment in a naturalistic environment) allow for measuring cognition with improved temporal resolution. The present study measured relationships between "state" cognitive performance, measured using an ambulatory-based Trail Making Test, and SITs. Self-reported state hope and social connectedness was also measured. Data were collected using a specially designed mobile application (administered 4x/week up to 28 days) in substance use inpatients (N = 99). Consistent with prior literature, state hope and social connectedness was significantly associated with state SITs. Importantly, poorer state cognitive performance also significantly predicted state SITs, independent of hallmark static and state self-report risk variables. These findings highlight the potential importance of "online" cognition to predict SITs. Ambulatory recording reflects an efficient, sensitive, and ecological valid methodology for evaluating subjective and objectives predictors of imminent SITs.

Avolition as the core negative symptom in schizophrenia: relevance to pharmacological treatment development

Negative symptoms have long been considered a core component of schizophrenia. Modern conceptualizations of the structure of negative symptoms posit that there are at least two broad dimensions (motivation and pleasure and diminished expression) or perhaps five separable domains (avolition, anhedonia, asociality, blunted affect, alogia). The current review synthesizes a body of emerging research indicating that avolition may have a special place among these dimensions, as it is generally associated with poorer outcomes and may have distinct neurobiological mechanisms. Network analytic findings also indicate that avolition is highly central and interconnected with the other negative symptom domains in schizophrenia, and successfully remediating avolition results in global improvement in the entire constellation of negative symptoms. Avolition may therefore reflect the most critical treatment target within the negative symptom construct. Implications for targeted treatment development and clinical trial design are discussed.

Validating Biobehavioral Technologies for Use in Clinical Psychiatry

The last decade has witnessed the development of sophisticated biobehavioral and genetic, ambulatory, and other measures that promise unprecedented insight into psychiatric disorders. As yet, clinical sciences have struggled with implementing these objective measures and they have yet to move beyond "proof of concept." In part, this struggle reflects a traditional, and conceptually flawed, application of traditional psychometrics (i.e., reliability and validity) for evaluating them. This paper focuses on "resolution," concerning the degree to which changes in a signal can be detected and quantified, which is central to measurement evaluation in informatics, engineering, computational and biomedical sciences. We define and discuss resolution in terms of traditional reliability and validity evaluation for psychiatric measures, then highlight its importance in a study using acoustic features to predict self-injurious thoughts/behaviors (SITB). This study involved tracking natural language and self-reported symptoms in 124 psychiatric patients: (a) over 5-14 recording sessions, collected using a smart phone application, and (b) during a clinical interview. Importantly, the scope of these measures varied as a function of time (minutes, weeks) and spatial setting (i.e., smart phone vs. interview). Regarding reliability, acoustic features were temporally unstable until we specified the level of temporal/spatial resolution. Regarding validity, accuracy based on machine learning of acoustic features predicting SITB varied as a function of resolution. High accuracy was achieved (i.e., ~87%), but only when the acoustic and SITB measures were "temporally-matched" in resolution was the model generalizable to new data. Unlocking the potential of biobehavioral technologies for clinical psychiatry will require careful consideration of resolution.

Geolocation as a Digital Phenotyping Measure of Negative Symptoms and Functional Outcome

OBJECTIVE

Negative symptoms and functional outcome have traditionally been assessed using clinical rating scales, which rely on retrospective self-reports and have several inherent limitations that impact validity. These issues may be addressed with more objective digital phenotyping measures. In the current study, we evaluated the psychometric properties of a novel "passive" digital phenotyping method: geolocation.

METHOD

Participants included outpatients with schizophrenia or schizoaffective disorder (SZ: n = 44), outpatients with bipolar disorder (BD: n =19), and demographically matched healthy controls (CN: n = 42) who completed 6 days of "active" digital phenotyping assessments (eg, surveys) while geolocation was recorded.

RESULTS

Results indicated that SZ patients show less activity than CN and BD, particularly, in their travel from home. Geolocation variables demonstrated convergent validity by small to medium correlations with negative symptoms and functional outcome measured via clinical rating scales, as well as active digital phenotyping behavioral indices of avolition, asociality, and anhedonia. Discriminant validity was supported by low correlations with positive symptoms, depression, and anxiety. Reliability was supported by good internal consistency and moderate stability across days.

CONCLUSIONS

These findings provide preliminary support for the reliability and validity of geolocation as an objective measure of negative symptoms and functional outcome. Geolocation offers enhanced precision and the ability to take a "big data" approach that facilitates sophisticated computational models. Near-continuous recordings and large numbers of samples may make geolocation a novel outcome measure for clinical trials due to enhanced power to detect treatment effects.