GPS mobility as a digital biomarker of negative symptoms in schizophrenia: a case control study

Reprint of: A review of negative symptom assessment strategies in youth at clinical high-risk for psychosis

Studies attempting to deconstruct the heterogeneity of schizophrenia and the attenuated psychosis syndrome consistently find that negative symptoms are a core dimension that is distinct from other aspects of the illness (e.g., positive and disorganized symptoms). Negative symptoms are also highly predictive of poor community-based functional outcomes, suggesting they are a critical treatment target. Unfortunately, pharmacological and psychosocial treatments for negative symptoms have demonstrated limited effectiveness. To address this critical unmet therapeutic need, the NIMH sponsored a consensus development conference to delineate research priorities for the field and stimulate treatment development. A primary conclusion of this meeting was that next-generation negative symptom rating scales should be developed to address methodological and conceptual limitations of existing instruments. Although second-generation rating scales were developed for adults with schizophrenia, progress in this area has lagged behind for youth at clinical-high risk (CHR) for developing psychosis (i.e. those meeting criteria for a prodromal syndrome). Given that negative symptoms are highly predictive of the transition to diagnosable psychotic illness, enhancing our ability to detect negative symptoms in CHR youth is paramount. The current paper discusses conceptual and methodological limitations inherent to existing scales that assess negative symptoms in CHR youth. The theoretical and clinical implications of these limitations are evaluated. It is concluded that new scales specifically designed to assess negative symptoms in CHR youth are needed to accurately chart mental illness trajectories and determine when, where, and how to intervene. Recent efforts to develop next-generation measures designed specifically for CHR youth to meet this urgent need in the field are discussed. These new approaches offer significant progress for addressing issues inherent to earlier scales.

Capturing Clinical Symptoms with Ecological Momentary Assessment: Convergence of Momentary Reports of Psychotic and Mood Symptoms with Diagnoses and Standard Clinical Assessments

Objective: The development and deployment of technology-based assessments of clinical symptoms are increasing. This study used ecological momentary assessment (EMA) to examine clinical symptoms and relates these sampling results to structured clinical ratings. Methods: Three times a day for 30 days, participants with bipolar disorder (n=71; BPI) or schizophrenia (n=102; SCZ) completed surveys assessing five psychosis-related and five mood symptoms, in addition to reporting their location and who they were with at the time of survey completion. Participants also completed Positive and Negative Syndrome Scale (PANSS) interviews with trained raters. Mixed-model repeated-measures (MMRM) analyses examined diagnostic effects and the convergence between clinical ratings and EMA sampling. Results: In total, 12,406 EMA samples were collected, with 80-percent adherence to prompts. EMA-reported psychotic symptoms manifested substantial convergence with equivalent endpoint PANSS items. Patients with SCZ had more severe PANSS and EMA psychotic symptoms. There were no changes in symptom severity scores as a function of the number of previous assessments. Conclusions: EMA surveyed clinical symptoms converged substantially with commonly used clinical rating scales in a large sample, with high adherence. This suggested that remote assessment of clinical symptoms is valid and practical and was not associated with alterations in symptoms as a function of reassessment, with additional benefits of "in the moment" sampling, such as eliminating recall bias and the need for informant reports.

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.

The Path Forward for Digital Measures: Suppressing the Desire to Compare Apples and Pineapples

Digital measures are becoming more prevalent in clinical development. Methods for robust evaluation are increasingly well defined, yet the primary barrier for digital measures to transition beyond exploratory usage often relies on a comparison to the existing standards. This article focuses on how researchers should approach the complex issue of comparing across assessment modalities. We discuss comparisons of subjective versus objective assessments, or performance-based versus behavioral measures, and we pay particular attention to the situation where the expected association may be poor or nonlinear. We propose that, rather than seeking to replace the standard, research should focus on a structured understanding of how the new measure augments established assessments, with the ultimate goal of developing a more complete understanding of what is meaningful to patients.

Examining the relationship between community mobility and participation using GPS and self-report data

Higher levels of community mobility have been shown to be associated with better physical health, mental health, and quality of life. The ability to move about one's community is also expected to facilitate community participation, which is an aspect of health functioning. This study uses Global Positioning Systems (GPS) technology to track various dimensions of community mobility, such as destinations, time outside the home, and distance traveled, and examine the relationship between these variables and community participation in a sample of individuals with serious mental illnesses (SMI). This population was selected because they are known to have diminished health functioning in terms of their community participation, and the goal is to explore the extent to which mobility limitations may account for this. A total of 103 individuals with serious mental illnesses were recruited from mental health agencies and consented to having their mobility tracked using GPS for 13 days and answering questions about their community-based activities. Greater amount of participation was associated with having more destinations and spending more time out of the house, but not with traveling larger distances and having a greater activity space. None of the mobility variables were related to the number of important participation areas or sufficiency of participation. The findings support the hypothesis that greater mobility is related to more participation, although satisfaction with the degree to which one participates does not appear to be impacted, suggesting that other factors need to be accounted for. Health policymakers and providers should pay attention to community mobility as a factor that affects health outcomes such as participation, in individuals with serious mental illnesses, and other populations. In particular, attending to access to personal transport, public transportation, and other mobility options appears to be important, as well as interventions aimed at encouraging greater community mobility.

Can digital data diagnose mental health problems? A sociological exploration of 'digital phenotyping'

This paper critically explores the research and development of 'digital phenotyping', which broadly refers to the idea that digital data can measure and predict people's mental health as well as their potential risk for mental ill health. Despite increasing research and efforts to digitally track and predict ill mental health, there has been little sociological and critical engagement with this field. This paper aims to fill this gap by introducing digital phenotyping to the social sciences. We explore the origins of digital phenotyping, the concept of the digital phenotype and its potential for benefit, linking these to broader developments within the field of 'mental health sensing'. We then critically discuss the technology, offering three critiques. First, that there may be assumptions of normality and bias present in the use of algorithms; second, we critique the idea that digital data can act as a proxy for social life; and third that the often biological language employed in this field risks reifying mental health problems. Our aim is not to discredit the scientific work in this area, but rather to call for scientists to remain reflexive in their work, and for more social science engagement in this area.

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.

Emotional determinants of life-space through GPS and ecological momentary assessment in schizophrenia: What gets people out of the house?

BACKGROUND

Previous research employing global positioning satellite (GPS) data and ecological momentary assessment (EMA) has shown a smaller life-space (distance traveled from home) was associated with poorer community functioning and more severe negative symptoms in people with schizophrenia. Momentary emotional experiences may influence how much time is spent outside of the home. We evaluated the associations between emotional experiences in relation to life-space among people with schizophrenia compared to healthy controls (HCs).

METHODS

105 participants with schizophrenia and 76 HCs completed in-lab assessments of symptoms, cognition, and functioning. Participants completed EMA assessments of location and emotions seven times daily for seven days at stratified random intervals. GPS coordinates were collected 24 h a day over the 7-day study period. Analyses were performed at the momentary, day, and full week level using mixed effects models and Spearman correlations.

RESULTS

For HCs, greater happiness was associated with greater concurrent distance traveled away from home as measured by GPS. For participants with schizophrenia, greater anxiety was associated with greater distance traveled away from home and being outside of the home. Less happiness, but not anxiety, was also associated with greater negative symptoms, especially outside the home.

DISCUSSION

These findings suggest diminished positive emotion is associated with the experience of leaving the home in schizophrenia, but also suggest that anxiety may contribute to avoidance of out of home mobility. Interventions targeting both positive emotions and social anxiety may improve social functioning, and life-space may provide a useful outcome for functional rehabilitation interventions in schizophrenia.

Impact of dynamic greenspace exposure on symptomatology in individuals with schizophrenia

There are currently many tools available for capturing and defining the context of one's environment. Digital phenotyping, the use of technology and sensors to capture moment-to-moment behavior, has shown potential in quantifying the lived experience of mental illness and in the identification of individualized targets related to recovery. Environmental data suggests that greenspace may have a restorative capacity on mental health. In this paper, we explore the relationship of greenspace derived from geolocation with self-reported symptomatology from individuals with schizophrenia as well as healthy controls. Individuals with schizophrenia had less exposure to greenspace than controls, but their exposure demonstrated a dosage effect: high greenspace environments were associated with lower symptoms for anxiety (Cohen's d = -0.70), depression (d = -0.97), and psychosis (d = -0.94), whereas effect sizes for healthy controls were all negligible or small (d < 0.38). The notion that greenspace may have a more pronounced effect on individuals with mental illness presents both potential areas for recovery as well as implications for health care policy, especially in cities with a broad range of greenspace environments.

A review of negative symptom assessment strategies in youth at clinical high-risk for psychosis

Studies attempting to deconstruct the heterogeneity of schizophrenia and the attenuated psychosis syndrome consistently find that negative symptoms are a core dimension that is distinct from other aspects of the illness (e.g., positive and disorganized symptoms). Negative symptoms are also highly predictive of poor community-based functional outcomes, suggesting they are a critical treatment target. Unfortunately, pharmacological and psychosocial treatments for negative symptoms have demonstrated limited effectiveness. To address this critical unmet therapeutic need, the NIMH sponsored a consensus development conference to delineate research priorities for the field and stimulate treatment development. A primary conclusion of this meeting was that next-generation negative symptom rating scales should be developed to address methodological and conceptual limitations of existing instruments. Although second-generation rating scales were developed for adults with schizophrenia, progress in this area has lagged behind for youth at clinical-high risk (CHR) for developing psychosis (i.e. those meeting criteria for a prodromal syndrome). Given that negative symptoms are highly predictive of the transition to diagnosable psychotic illness, enhancing our ability to detect negative symptoms in CHR youth is paramount. The current paper discusses conceptual and methodological limitations inherent to existing scales that assess negative symptoms in CHR youth. The theoretical and clinical implications of these limitations are evaluated. It is concluded that new scales specifically designed to assess negative symptoms in CHR youth are needed to accurately chart mental illness trajectories and determine when, where, and how to intervene. Recent efforts to develop next-generation measures designed specifically for CHR youth to meet this urgent need in the field are discussed. These new approaches offer significant progress for addressing issues inherent to earlier scales.

A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data

The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data. This framework aims to generate context-enriched location data by identifying stationary, non-stationary, and recurrent stationary states in movement patterns. Subsequently, this context-enriched data is used to derive a series of behavioral phenotypes that are related to movement. By using smartphone-based location data collected from 245 subjects, including patients with schizophrenia, we show that the proposed framework is effective and accurate in generating context-enriched location data. This data was subsequently used to derive behavioral readouts that were sensitive in detecting behavioral nuances related to schizophrenia and aging, such as the time spent at home and the number of unique places visited. Overall, our results indicate that the proposed framework reliably preprocesses raw smartphone-based location data in such a manner that relevant behavioral phenotypes of interest can be derived.

Digital solutions for shaping mood and behavior among individuals with mood disorders

Mood disorders present on-going challenges to the medical field, with difficulties ranging from establishing effective treatments to understanding complexities of one's mood. One solution is the use of mobile apps and wearables for measuring physiological symptoms and real-time mood in order to shape mood and behavior. Current digital research is focused on increasing engagement in monitoring mood, uncovering mood dynamics, predicting mood, and providing digital microinterventions. This review discusses the importance and risks of user engagement, as well as barriers to improving it. Research on mood dynamics highlights the possibility to reveal data-driven computational phenotypes that could guide treatment. Mobile apps are being used to track voice patterns, GPS, and phone usage for predicting mood and treatment response. Future directions include utilizing mobile apps to deliver and evaluate microinterventions. To continue these advances, standardized reporting and study designs should be considered to improve digital solutions for mood disorders.

What Do People With Schizophrenia Do All Day? Ecological Momentary Assessment of Real-World Functioning in Schizophrenia

Schizophrenia is a major cause of disability worldwide. As new treatments for functioning are tested, the need grows to demonstrate real-world functioning gains. Ecological momentary assessment (EMA) may provide a more ecologically valid measure of functioning. In this study, smartphone-based EMA was used to signal participants with schizophrenia (N = 100) and controls (N = 71) 7 times a day for 7 days to respond to brief questionnaires about social interactions and functioning behaviors. Excellent adherence was found, with both groups completing an average of 85% of surveys and only 3% of participants with schizophrenia excluded for poor adherence. Four-week test-retest reliability was high (r = .83 for total productive behaviors). Relative to controls, participants with schizophrenia reported significantly less total productive activity (d = 1.2), fewer social interactions (d = 0.3), more nonproductive behaviors (d = 1.0; watching TV, resting), and more time at home (d = 0.8). Within the schizophrenia group, participants living independently showed better functioning on EMA relative to participants in supported housing (d = 0.8) and participants engaged in vocational activities showed better functioning than individuals not engaged in vocational activities (d = 0.55). Modest correlations were found between EMA and an in-lab self-report measure of functioning activities performed in the community, but not between EMA and measures of functional capacity or potential. This study demonstrated the feasibility, sensitivity reliability, and validity of EMA methods to assess functioning in schizophrenia. EMA provides a much-needed measure of what individuals with schizophrenia are actually doing in real-world contexts. These results also suggest that there may be important disjunctions between indices of abilities and actual real-world functioning.