Validation of accelerometry as a digital phenotyping measure of negative symptoms in schizophrenia.
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ABSTRACT:
SUBMITTER: Strauss GP
PROVIDER: S-EPMC9261099 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveNegative 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.MethodParticipants 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.ResultsResults 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.ConclusionsThese 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.
| S-EPMC7751192 | biostudies-literature
Project description:Negative symptoms are a critical, but poorly understood, aspect of schizophrenia. Measurement of negative symptoms primarily relies on clinician ratings, an endeavor with established reliability and validity. There have been increasing attempts to digitally phenotype negative symptoms using objective biobehavioral technologies, eg, using computerized analysis of vocal, speech, facial, hand and other behaviors. Surprisingly, biobehavioral technologies and clinician ratings are only modestly inter-related, and findings from individual studies often do not replicate or are counterintuitive. In this article, we document and evaluate this lack of convergence in 4 case studies, in an archival dataset of 877 audio/video samples, and in the extant literature. We then explain this divergence in terms of "resolution"-a critical psychometric property in biomedical, engineering, and computational sciences defined as precision in distinguishing various aspects of a signal. We demonstrate how convergence between clinical ratings and biobehavioral data can be achieved by scaling data across various resolutions. Clinical ratings reflect an indispensable tool that integrates considerable information into actionable, yet "low resolution" ordinal ratings. This allows viewing of the "forest" of negative symptoms. Unfortunately, their resolution cannot be scaled or decomposed with sufficient precision to isolate the time, setting, and nature of negative symptoms for many purposes (ie, to see the "trees"). Biobehavioral measures afford precision for understanding when, where, and why negative symptoms emerge, though much work is needed to validate them. Digital phenotyping of negative symptoms can provide unprecedented opportunities for tracking, understanding, and treating them, but requires consideration of resolution.
| S-EPMC7825094 | biostudies-literature
Project description:Digital phenotyping has potential to quantify the lived experience of mental illness and generate real-time, actionable results related to recovery, such as the case of social rhythms in individuals with bipolar disorder. However, passive data features for social rhythm clinical targets in individuals with schizophrenia have yet to be studied. In this paper, we explore the relationship between active and passive data by focusing on temporal stability and variance at an individual level as well as large-scale associations on a population level to gain clinically actionable information regarding social rhythms. From individual data clustering, we found a 19% cluster overlap between specific active and passive data features for participants with schizophrenia. In the same clinical population, two passive data features in particular associated with social rhythms, "Circadian Routine" and "Weekend Day Routine," and were negatively associated with symptoms of anxiety, depression, psychosis, and poor sleep (Spearman ? ranged from -0.23 to -0.30, p?<?0.001). Conversely, in healthy controls, more stable social rhythms were positively correlated with symptomatology (Spearman ? ranged from 0.20 to 0.44, p?<?0.05). Our results suggest that digital phenotyping in schizophrenia may offer clinically relevant information for understanding how daily routines affect symptomatology. Specifically, negative correlations between smartphone reported anxiety, depression, psychosis, and poor sleep in individuals with schizophrenia, but not in healthy controls, offer an actionable clinical target and area for further investigation.
| S-EPMC7200667 | biostudies-literature
Project description:The negative symptoms have been described in association with schizophrenia since the early days of it being recognized as an entity. However, their elusive nature kept them unacknowledged until there was a revival of interest in them following the development of specific quantifying measures. Over the past three decades, there has been a tremendous surge in research on their types, measurements, status in the present classificatory system, and their implications. The developments in modern investigatory methods have provided the researchers with fresh insights into the underlying mechanisms, and a distributed functioning of the neuronal networks has emerged as the major abnormality. Accordingly, a variety of pharmacological and other treatment modalities have been developed which go beyond the traditional. Nevertheless, a lot remain unanswered. The present paper summarizes important concepts with regard to negative symptoms in schizophrenia.
| S-EPMC5479085 | biostudies-literature
Project description:ObjectiveNegative symptoms are currently viewed as having a 2-dimensional structure, with factors reflecting diminished expression (EXP) and motivation and pleasure (MAP). However, several factor-analytic studies suggest that the consensus around a 2-dimensional model is premature. The current study investigated and cross-culturally validated the factorial structure of BNSS-rated negative symptoms across a range of cultures and languages.MethodParticipants included individuals diagnosed with a psychotic disorder who had been rated on the Brief Negative Symptom Scale (BNSS) from 5 cross-cultural samples, with a total N = 1691. First, exploratory factor analysis was used to extract up to 6 factors from the data. Next, confirmatory factor analysis evaluated the fit of 5 models: (1) a 1-factor model, 2) a 2-factor model with factors of MAP and EXP, 3) a 3-factor model with inner world, external, and alogia factors; 4) a 5-factor model with separate factors for blunted affect, alogia, anhedonia, avolition, and asociality, and 5) a hierarchical model with 2 second-order factors reflecting EXP and MAP, as well as 5 first-order factors reflecting the 5 aforementioned domains.ResultsModels with 4 factors or less were mediocre fits to the data. The 5-factor, 6-factor, and the hierarchical second-order 5-factor models provided excellent fit with an edge to the 5-factor model. The 5-factor structure demonstrated invariance across study samples.ConclusionsFindings support the validity of the 5-factor structure of BNSS-rated negative symptoms across diverse cultures and languages. These findings have important implications for the diagnosis, assessment, and treatment of negative symptoms.
| S-EPMC6403061 | biostudies-literature
Project description:Mobility is an important correlate of physical, cognitive, and mental health in chronic illness, and can be measured passively with mobile phone global positional satellite (GPS) sensors. To date, GPS data have been reported in a few studies of schizophrenia, yet it is unclear whether these data correlate with concurrent momentary reports of location, vary by people with schizophrenia and healthy comparison subjects, or associate with symptom clusters in schizophrenia. A total of 142 participants with schizophrenia (n?=?86) or healthy comparison subjects (n?=?56) completed 7 days of ecological momentary assessment (EMA) reports of location and behavior, and simultaneous GPS locations were tracked every five minutes. We found that GPS-derived indicators of average distance travelled overall and distance from home, as well as percent of GPS samples at home were highly correlated with EMA reports of location at the day- and week-averaged level. GPS-based mobility indicators were lower in schizophrenia with medium to large effect sizes. Less GPS mobility was related to greater negative symptom severity, particularly diminished motivation, whereas greater GPS mobility was weakly associated with more community functioning. Neurocognition, depression, and positive symptoms were not associated with mobility indicators. Therefore, passive GPS sensing could provide a low-burden proxy measure of important outcomes in schizophrenia, including negative symptoms and possibly of functioning. As such, passive GPS sensing could be used for monitoring and timely interventions for negative symptoms in young persons at high risk for schizophrenia.
| S-EPMC6841669 | biostudies-literature
Project description: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.
| S-EPMC8192468 | biostudies-literature
Project description:Among individuals diagnosed, hospitalized, and treated for schizophrenia, up to 40% of those discharged may relapse within 1 year even with appropriate treatment. Passively collected smartphone behavioral data present a scalable and at present underutilized opportunity to monitor patients in order to identify possible warning signs of relapse. Seventeen patients with schizophrenia in active treatment at a state mental health clinic in Boston used the Beiwe app on their personal smartphone for up to 3 months. By testing for changes in mobility patterns and social behavior over time as measured through smartphone use, we were able to identify statistically significant anomalies in patient behavior in the days prior to relapse. We found that the rate of behavioral anomalies detected in the 2 weeks prior to relapse was 71% higher than the rate of anomalies during other time periods. Our findings show how passive smartphone data, data collected in the background during regular phone use without active input from the subjects, can provide an unprecedented and detailed view into patient behavior outside the clinic. Real-time detection of behavioral anomalies could signal the need for an intervention before an escalation of symptoms and relapse occur, therefore reducing patient suffering and reducing the cost of care.
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Project description:Wearable technology enables unobtrusive collection of longitudinally dense data, allowing for continuous monitoring of physiology and behavior. These digital phenotypes, or device-based indicators, are frequently leveraged to study depression. However, they are usually considered alongside questionnaire sum-scores which collapse the symptomatic gamut into a general representation of severity. To explore the contributions of passive sensing streams more precisely, associations of nine passive sensing-derived features with self-report responses to Center for Epidemiologic Studies Depression (CES-D) items were modeled. Using data from the NetHealth study on N=469 college students, this work generated mixed ordinal logistic regression models to summarize contributions of pulse, movement, and sleep data to depression symptom detection. Emphasizing the importance of the college context, wearable features displayed unique and complementary properties in their heterogeneously significant associations with CES-D items. This work provides conceptual and exploratory blueprints for a reductionist approach to modeling depression within passive sensing research.
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Project description:A growing body of research calibrating and validating accelerometers to classify physical activity intensities has led to a range of cut-points. However, the applicability of current calibration protocols to clinical populations remains to be addressed. The aim of this review was to evaluate the accuracy of the methods for calibrating and validating of accelerometers to estimate physical activity intensity thresholds for clinical populations. Six databases were searched between March and July to 2017 using text words and subject headings. Studies developing moderate-to-vigorous intensity physical activity cut-points for adult clinical populations were included. The risk of bias was assessed using the health measurement instruments and a specific checklist for calibration studies. A total of 543,741 titles were found and 323 articles were selected for full-text assessment, with 11 meeting the inclusion criteria. Twenty-three different methods for calibration were identified using different models of ActiGraph and Actical accelerometers. Disease-specific cut-points ranged from 591 to 2717 counts·min-1 and were identified for two main groups of clinical conditions: neuromusculoskeletal disorders and metabolic diseases. The heterogeneity in the available clinical protocols hinders the applicability and comparison of the developed cut-points. As such, a mixed protocol containing a controlled laboratory exercise test and activities of daily-life is suggested. It is recommended that this be combined with a statistical approach that allows for adjustments according to disease severity or the use of machine learning models. Finally, this review highlights the generalisation of cut-points developed on healthy populations to clinical populations is inappropriate.
| S-EPMC6931234 | biostudies-literature