Digital phenotyping of negative symptoms: the relationship to clinician ratings.
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ABSTRACT:
SUBMITTER: Cohen AS
PROVIDER: S-EPMC7825094 | 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 commonly assessed via clinical rating scales; however, these measures have several inherent limitations that impact validity and utility for their use in clinical trials. Objective digital phenotyping measures that overcome some of these limitations are now available. The current study evaluated the validity of accelerometry (ACL), a passive digital phenotyping method that involves collecting data on the presence, vigor, and variability of movement. Outpatients with schizophrenia (SZ: n = 50) and demographically matched healthy controls (CN: n = 70) had ACL continuously recorded from a smartphone and smartband for 6 days. Active digital phenotyping assessments, including surveys related to activity context, were also collected via 8 daily surveys throughout the 6 day period. SZ participants had lower scores on phone ACL variables reflecting vigor and variability of movement compared to CN. ACL variables demonstrated convergent validity as indicated by significant correlations with active digital phenotyping self-reports of time spent in goal-directed activities and clinical ratings of negative symptoms. The discriminant validity of ACL was demonstrated by low correlations with clinical rating scale measures of positive, disorganized, and total symptoms. Collectively, findings suggest that ACL is a valid objective measure of negative symptoms that may complement traditional approaches to assessing the construct using clinical rating scales.
| S-EPMC9261099 | biostudies-literature
Project description:BackgroundThe National Cancer Institute's Common Terminology Criteria for Adverse Events (NCI-CTCAE) reporting system is widely used by clinicians to measure patient symptoms in clinical trials. The European Organization for Research and Treatment of Cancer's Quality of Life core questionnaire (EORTC QLQ-C30) enables cancer patients to rate their symptoms related to their quality of life. We examined the extent to which patient and clinician symptom scoring and their agreement could contribute to the estimation of overall survival among cancer patients.MethodsWe analyzed baseline data regarding six cancer symptoms (pain, fatigue, vomiting, nausea, diarrhea, and constipation) from a total of 2279 cancer patients from 14 closed EORTC randomized controlled trials. In each trial that was selected for retrospective pooled analysis, both clinician and patient symptom scoring were reported simultaneously at study entry. We assessed the extent of agreement between clinician vs patient symptom scoring using the Spearman and kappa correlation statistics. After adjusting for age, sex, performance status, cancer severity, and cancer site, we used Harrell concordance index (C-index) to compare the potential for clinician-reported and/or patient-reported symptom scores to improve the accuracy of Cox models to predict overall survival. All P values are from two-sided tests.ResultsPatient-reported scores for some symptoms, particularly fatigue, did differ from clinician-reported scores. For each of the six symptoms that we assessed at baseline, both clinician and patient scorings contributed independently and positively to the predictive accuracy of survival prognostication. Cox models of overall survival that considered both patient and clinician scores gained more predictive accuracy than models that considered clinician scores alone for each of four symptoms: fatigue (C-index = .67 with both patient and clinician data vs C-index = .63 with clinician data only; P <.001), vomiting (C-index = .64 vs .62; P = .01), nausea (C-index = .65 vs .62; P < .001), and constipation (C-index = .62 vs .61; P = .01).ConclusionPatients provide a subjective measure of symptom severity that complements clinician scoring in predicting overall survival.
| S-EPMC3243678 | biostudies-literature
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.
| S-EPMC10529827 | biostudies-literature
Project description:BackgroundPlant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.ResultsHere, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.ConclusionsAraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.
| S-EPMC10943777 | biostudies-literature
Project description:Background and objectives:The underlying reasons why some women experience debilitating premenstrual symptoms and others do not are largely unknown. Here, we test the evolutionary ecological hypothesis that some negative premenstrual symptoms may be exacerbated by the presence of chronic sexually transmitted infections (STIs). Methodology:34 511 women were recruited through a digital period-tracker app. Participants were asked: (i) Have you ever been diagnosed with a STI? (ii) If yes, when was it, and were you given treatment? Those data were combined with longitudinal cycle data on menstrual bleeding patterns, the experience of pain and emotions and hormonal contraceptive use. Results:865 women had at least two complete menstrual cycle data and were eligible for analysis. Before diagnosis, the presence of an infection predicts a ca. 2-fold increase in the odds of reporting both headache, cramps and sadness during the late luteal phase and sensitive emotions during the wider luteal phase. After diagnosis, the odds of reporting negative symptoms pre-menstrually remain unchanged among STI negative individuals, but the odds of reporting sensitive emotions decrease among STI positive individuals receiving a treatment. No relationships between STIs, pain and emotions are observed among hormonal contraceptive users. Conclusions and implications:The results support the idea that a negative premenstrual experience might be aggravated by the presence of undiagnosed STIs, a leading cause of infertility worldwide. Caution is warranted in extrapolating the results as the data are self-reported, inflammatory levels are unknown and the tracker is biased towards recording negative premenstrual symptoms among Western individuals.
| S-EPMC6070031 | 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 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:Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17-36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.
| S-EPMC10579093 | biostudies-literature
Project description:The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for "digital phenotyping," the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.
| S-EPMC8322366 | biostudies-literature