ORCID Profile
0000-0002-3731-4930
Current Organisation
KU Leuven
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Publisher: Springer Science and Business Media LLC
Date: 21-11-2016
DOI: 10.1038/NG.3725
Publisher: Springer Science and Business Media LLC
Date: 02-2016
DOI: 10.1038/NN.4228
Publisher: JMIR Publications Inc.
Date: 11-11-2021
Abstract: he mobility of an in idual measured by phone-collected location data has been found to be associated with depression however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. e aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. ata used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse–Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants’ location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. his study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-in idual level than the between-in idual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, i P /i =.01), Location Entropy (time distribution on different locations) (φ=−0.04, i P /i =.02), and Residential Location Count (reflecting traveling) (φ=0.05, i P /i =.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=−0.07, i P /i & .001) the subsequent periodicity of mobility. everal phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.
Publisher: Cambridge University Press (CUP)
Date: 12-07-2016
Publisher: Elsevier BV
Date: 02-2018
DOI: 10.1016/J.PSYCHRES.2017.11.067
Abstract: This study investigated cardio-metabolic risk factors among patients with severe mental illness who do or do not meet the recommendations of 150min per week of physical activity. A secondary aim was to assess whether those that do meet the recommendations report lower levels of mental health symptoms. 107 (60♀) Ugandan in- and outpatients (mean age=34.4 ± 9.7 years) with severe mental illness (depression=7, bipolar disorder=55, schizophrenia=45) completed the Physical Activity Vital Sign (PAVS) method and Brief Symptoms Inventory -18. Participants were also screened for abdominal obesity (waist circumference>90cm), overweight (body mass index≥25) and hypertension (systolic pressure≥140mmHg and/or diastolic pressure≥90mmHg).48.6% (n = 52) of patients met the physical activity recommendations as assessed by the PAVS method. 41.1% (n = 44) were overweight, 40.2% (n = 43) had abdominal obesity and 23.4% (n = 25) had hypertension. Those who did not meet the physical activity recommendations were significantly older, had a higher BSI-18 somatisation score, and had a higher risk of overweight [relative risk (RR) = 2.88, 95% confidence interval (CI) = 1.59-4.99], abdominal obesity (RR = 1.82, 95%CI = 1.13-2.93), and hypertension (RR = 2.16, 95%CI = 0.99-4.73). The PAVS is a feasible method of assessing physical activity among patients with severe mental illness in a low resource setting. The PAVS may have clinical utility for physical and mental health risk stratification.
Publisher: Elsevier BV
Date: 03-2013
Publisher: Elsevier BV
Date: 06-2018
Publisher: JMIR Publications Inc.
Date: 26-09-2020
Abstract: leep problems tend to vary according to the course of the disorder in in iduals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. he main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). aily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The i z /i score was used to evaluate the significance of the coefficient of each feature. e tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly ( i P /i & .05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage ( i z /i =5.45, i P /i & .001), awakening times (z=5.53, i P /i & .001), insomnia (z=4.55, i P /i & .001), mean sleep offset time (z=6.19, i P /i & .001), and hypersomnia (z=5.30, i P /i & .001) were the top 5 features ranked by i z /i score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. e demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Oxford University Press (OUP)
Date: 05-09-2018
Publisher: Springer Science and Business Media LLC
Date: 02-02-2015
DOI: 10.1038/NG.3211
Publisher: Elsevier BV
Date: 07-2013
DOI: 10.1016/J.SCHRES.2013.04.032
Abstract: Kurt Schneider defined 'first rank symptoms' (FRS) of psychosis. Previous research found two clusters of FRS: 'loss of ego bound' symptoms (e.g., delusions of external control) and auditory hallucinations (e.g, commenting voices). In patients with a psychosis we investigated whether FRS are a separate cluster within the group of positive symptoms, consisting of two underlying factors that are stable over time. We conducted a principal axis factor analysis (PAF) at baseline (n = 857) and a confirmative factor analysis (CFA) at three-year follow-up (n = 414) on (FRS) symptom score. Also, we investigated the stability of the two-factor structure of FRS over the interval. PAF on 16 items representing positive symptoms at baseline revealed two factors with eigenvalues > 1. FRS-delusional self experience (thought withdrawal, thought broadcasting, thought insertion, and beliefs that impulses and/or actions are controlled by an outside force) clustered in one factor and FRS-auditory hallucinations (auditory hallucinations, conversational voices, and voices commenting on one's actions) in the second factor. Furthermore, CFA on the FRS-items at follow-up confirmed the two-factor structure of FRS. FRS delusional self experience and FRS-auditory hallucinations at baseline were significantly associated with the same factors at three-year follow-up (FRS-delusional self experience: r = 0.38 FRS-auditory hallucinations r = 0.47). Hence, our findings confirm a two-factor structure of first rank symptoms, i.e. FRS-delusional self experience and FRS-auditory hallucinations, with a moderate to large internal coherence within each factor and relative stability over time. Future studies on self-processes may contribute to our understanding of the pathophysiology of first rank symptoms.
Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2017
DOI: 10.1101/222596
Abstract: Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.
Publisher: Elsevier BV
Date: 03-2013
DOI: 10.1016/J.PSYCHRES.2012.09.015
Abstract: This study investigated psychometric properties of two widely used instruments to measure subclinical levels of psychosis, the Community Assessment of Psychic Experiences (CAPE) and the Structured Interview for Schizotypy-Revised (SIS-R), and aimed to enhance measurements through the use of multidimensional measurement models. Data were collected in 747 siblings of schizophrenia patients and 341 healthy controls. Multidimensional Item-Response Theory, Mokken Scale and ordinal factor analyses were performed. Both instruments showed good psychometric properties and were measurement invariant across siblings and controls. The latent traits measured by the instruments show a correlation of 0.62 in siblings and 0.47 in controls. Multidimensional modeling resulted in smaller standard errors for SIS-R scores. By exploiting correlations among related traits through multidimensional models, scores from one diagnostic instrument can be estimated more reliably by making use of information from instruments that measure related traits.
Publisher: Oxford University Press (OUP)
Date: 02-2016
Publisher: JMIR Publications Inc.
Date: 14-08-2023
DOI: 10.2196/45233
Abstract: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-in idual longitudinal variation or screening in iduals at high risk and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. We aimed to address these 3 challenges to inform future work in stratified analyses. Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the in idual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. This work contributes to our understanding of how these mobile health–derived features are associated with depression symptom severity to inform future work in stratified analyses.
Publisher: Wiley
Date: 22-11-2017
DOI: 10.1111/EIP.12521
Abstract: The International Physical Activity Questionnaire (IPAQ) is a self-report tool commonly used in mental healthcare settings to assess physical activity. However, its validity has not yet been investigated in first-episode psychosis (FEP). The aim of this study was to examine the concurrent validity of the IPAQ compared with an objective real-life measure, the Sensewear Armband (SWA), in assessing moderate and vigorous physical activity (MVPA) in people with FEP. A secondary aim was to explore whether there are differences in correlates of the IPAQ vs SWA scores. In total, 19 outpatients with FEP (15 men 24.4 ± 5.1 years) wore an SWA for 5 full consecutive days, subsequently completed the IPAQ, performed a maximal cardiorespiratory fitness test and were assessed with the Positive and Negative Syndrome Scale (PANSS). There was no significant correlation between time spent in MVPA according to the IPAQ and SWA. In contrast with SWA scores, there were no significant associations between IPAQ scores and cardiorespiratory fitness levels. No correlations with PANSS scores were observed in both measures. The current results suggest that the IPAQ should be used with caution when assessing levels of MVPA in FEP. More accurate methods of measuring physical activity are needed in this population.
Publisher: Cold Spring Harbor Laboratory
Date: 11-09-2020
DOI: 10.1101/2020.09.10.20192310
Abstract: Polygenic scores (PGSs), which assess the genetic risk of in iduals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies (GWASs). PGS methods differ in which DNA variants are included and the weights assigned to them some require an independent tuning s le to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors. The Psychiatric Genomics Consortium working groups for schizophrenia (SCZ) and major depressive disorder (MDD) bring together many independently collected case- control cohorts. We used these resources (31K SCZ cases, 41K controls 248K MDD cases, 563K controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and nine methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) are compared. Compared to PC+T, the other nine methods give higher prediction statistics, MegaPRS, LDPred2 and SBayesR significantly so, up to 9.2% variance in liability for SCZ across 30 target cohorts, an increase of 44%. For MDD across 26 target cohorts these statistics were 3.5% and 59%, respectively. Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparison and are recommended in applications to psychiatric disorders.
Publisher: Springer Science and Business Media LLC
Date: 05-07-2018
DOI: 10.1038/S41598-018-28160-Z
Abstract: Previous studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is −0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank s le. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.
Publisher: Springer Science and Business Media LLC
Date: 25-03-2019
Publisher: Elsevier BV
Date: 06-2023
Publisher: JMIR Publications Inc.
Date: 08-05-2023
Abstract: lthough mental health problems occur in interaction with the natural environment, bringing this contextualized information into the therapy room is challenging. The experience s ling method (ESM) may facilitate this by assessing clients' thoughts, feelings, symptoms, and behavior as they are experienced in everyday life. However, ESM is still primarily used in research settings with little uptake in clinical practice. One aspect that may facilitate clinical implementation concerns the use of 'ESM protocols', which involves providing practitioners with ready-to-use ESM questionnaires, s ling schemes, visualizations, and training. his pilot study's objective was to evaluate the usability of an ESM protocol in clinical practice using a mixed-methods approach. n this pilot study, we created an ESM protocol and tested its usability in clinical practice. The ESM protocol was tailored to the m-Path software platform, consisting of a dashboard for practitioners and an app for clients. The dashboard was used to configure an ESM questionnaire template we designed. Additionally, the dashboard contained custom data visualizations that were made based on end-user feedback. The app was used for completing ESM assessments. A total of 8 practitioners and 17 clients used ESM in practice between December 2020 and July 2021. Usability was assessed using questionnaires, ESM compliance rates, and semi-structured interviews. he usability was overall rated reasonable to good by practitioners (Mean scores to usability items ranging from 5.33 [SD = 0.91] to 6.06 [SD = 0.73] on a cale from 1 to 7]). However, practitioners expressed difficulty personalizing the template and reported insufficient guidelines on how to use ESM in clinical practice. On average, clients completed 55% (SD=25%) of the ESM questionnaires. They rated the usability as reasonable to good but slightly lower and more variable than the practitioners (mean scores to usability items ranging from 4.18 [SD = 1.7] to 5.94 [SD = 1.5] on a cale from 1 to 7). Clients also voiced several concerns over the piloted ESM template, with some indicating no interest in the continued use of ESM. he findings suggest that using an ESM protocol may facilitate the implementation of ESM as a mobile health assessment tool in psychiatry. However, further adaptions should be made prior to further implementation. Adaptions include training on personalizing questionnaires, adding additional s ling scheme formats, and creating a dynamic data visualization interface. Future studies should also identify factors determining the suitability of ESM for specific treatment goals among different client populations.
Publisher: Elsevier BV
Date: 08-2022
DOI: 10.1016/J.JAD.2022.05.005
Abstract: Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics. The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables. A total of 547 participants (87.8% of the total s le) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance. Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions. These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.
Publisher: JMIR Publications Inc.
Date: 23-04-2021
Abstract: he Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture in iduals’ proximity information, such as the nearby Bluetooth devices count (NBDC). The continuous NBDC data can partially reflect in iduals’ behaviors and status, such as social connections and interactions, working status, mobility, and social isolation and loneliness, which were found to be significantly associated with depression by previous survey-based studies. his paper aims to explore the NBDC data’s value in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). he data used in this paper included 2,886 bi-weekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the UK as part of the EU RADAR-CNS study. From the NBDC data two weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring periodicity and regularity of in iduals’ life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with the depressive symptoms worsening, one or more of the following changes were found in the preceding two weeks’ NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics, R^2= 0.526, and root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R^2=0.338, RMSE = 4.547). ur statistical results indicate that the NBDC data has the potential to reflect changes in in iduals’ behaviors and status concurrent with the changes in the depressive state. The prediction results demonstrate the NBDC data has a significant value in predicting depressive symptom severity. These findings may have utility for mental health monitoring practice in real-world settings.
Publisher: Wiley
Date: 28-11-2012
DOI: 10.1002/AJMG.B.32120
Publisher: Elsevier BV
Date: 02-2013
Publisher: Wiley
Date: 11-12-2015
DOI: 10.1002/AJMG.B.32402
Publisher: Oxford University Press (OUP)
Date: 23-09-2010
Publisher: JMIR Publications Inc.
Date: 19-11-2021
DOI: 10.2196/30309
Abstract: Negative symptoms occur in in iduals at ultrahigh risk (UHR) for psychosis. Although there is evidence that observer ratings of negative symptoms are associated with level of functioning, the predictive value of subjective experience in daily life for in iduals at UHR has not been studied yet. This study therefore aims to investigate the predictive value of momentary manifestations of negative symptoms for clinical outcomes in in iduals at UHR. Experience s ling methodology was used to measure momentary manifestations of negative symptoms (blunted affective experience, lack of social drive, anhedonia, and social anhedonia) in the daily lives of 79 in iduals at UHR. Clinical outcomes (level of functioning, illness severity, UHR status, and transition status) were assessed at baseline and at 1- and 2-year follow-ups. Lack of social drive, operationalized as greater experienced pleasantness of being alone, was associated with poorer functioning at the 2-year follow-up (b=−4.62, P=.01). Higher levels of anhedonia were associated with poorer functioning at the 1-year follow-up (b=5.61, P=.02). Higher levels of social anhedonia were associated with poorer functioning (eg, disability subscale: b=6.36, P=.006) and greater illness severity (b=−0.38, P=.045) at the 1-year follow-up. In exploratory analyses, there was evidence that in iduals with greater variability of positive affect (used as a measure of blunted affective experience) experienced a shorter time to remission from UHR status at follow-up (hazard ratio=4.93, P=.005). Targeting negative symptoms in in iduals at UHR may help to predict clinical outcomes and may be a promising target for interventions in the early stages of psychosis.
Publisher: JMIR Publications Inc.
Date: 25-09-2020
DOI: 10.2196/19992
Abstract: In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed. We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)–base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19. We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background. We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P .001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P .001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P .001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P .001 for Italy and Spain P=.02 for Denmark), went to bed later (P .001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P .001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more. RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.
Publisher: Informa UK Limited
Date: 14-02-2023
Publisher: American Medical Association (AMA)
Date: 05-2016
Publisher: Wiley
Date: 15-03-2012
DOI: 10.1002/MPR.1352
Publisher: Springer Science and Business Media LLC
Date: 07-01-2021
DOI: 10.1038/S41380-020-00969-Z
Abstract: Important questions remain about the profile of cognitive impairment in psychotic disorders across adulthood and illness stages. The age-associated profile of familial impairments also remains unclear, as well as the effect of factors, such as symptoms, functioning, and medication. Using cross-sectional data from the EU-GEI and GROUP studies, comprising 8455 participants aged 18 to 65, we examined cognitive functioning across adulthood in patients with psychotic disorders (n = 2883), and their unaffected siblings (n = 2271), compared to controls (n = 3301). An abbreviated WAIS-III measured verbal knowledge, working memory, visuospatial processing, processing speed, and IQ. Patients showed medium to large deficits across all functions (ES range = -0.45 to -0.73, p < 0.001), while siblings showed small deficits on IQ, verbal knowledge, and working memory (ES = -0.14 to -0.33, p < 0.001). Magnitude of impairment was not associated with participant age, such that the size of impairment in older and younger patients did not significantly differ. However, first-episode patients performed worse than prodromal patients (ES range = -0.88 to -0.60, p < 0.001). Adjusting for cannabis use, symptom severity, and global functioning attenuated impairments in siblings, while deficits in patients remained statistically significant, albeit reduced by half (ES range = -0.13 to -0.38, p < 0.01). Antipsychotic medication also accounted for around half of the impairment in patients (ES range = -0.21 to -0.43, p < 0.01). Deficits in verbal knowledge, and working memory may specifically index familial, i.e., shared genetic and/or shared environmental, liability for psychotic disorders. Nevertheless, potentially modifiable illness-related factors account for a significant portion of the cognitive impairment in psychotic disorders.
Publisher: Public Library of Science (PLoS)
Date: 22-06-2012
Publisher: Informa UK Limited
Date: 11-06-2021
DOI: 10.1080/00221325.2021.1930995
Abstract: 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder caused by a microdeletion on the long arm of chromosome 22. Sleep problems have been reported in this population, and psychiatric disorders and affect dysregulation are common to the behavioral phenotype of 22q11DS. Sleep and affect have been consistently linked across multiple studies, yet despite this very little research has investigated sleep problems in 22q11DS, or the link between sleep and affect in this population. The Experience S ling Method was used to track daily reports of sleep quality and affect in a total of 29 in iduals with 22q11DS and 21 control subjects. Measurements were recorded during a 6-day period using an electronic device that prompted daily response with audio cues. Participants with 22q11DS were found to experience a longer sleep onset latency and a greater amount, and duration, of night wakings compared with control subjects. Despite this, no significant between-group difference was found for subjective sleep quality. 22q11DS participants reported more experiences of negative affect and less positive affect than control subjects. A bidirectional relationship was found between sleep measures and affect. Sleep problems can cause a wide range of negative health effects, and in iduals with 22q11DS are particularly vulnerable to deficits of sleep. To ensure high standards of care, healthcare providers should be aware of the possibility and impact of sleep problems in this population.
Publisher: Center for Open Science
Date: 14-04-2023
Abstract: Background: Non-suicidal self-injury (NSSI) is a major mental health concern. Despite increased research efforts on establishing the prevalence and correlates of the presence or severity of NSSI, we still lack basic knowledge of the course, predictors, and relationship of NSSI with other self-harming behaviors in daily life. Such information will be helpful for better informing mental health professionals and allocating treatment resources. The Detection of Acute rIsk of seLf-injurY (DAILY) project will address these gaps among treatment-seeking in iduals.Objectives: This protocol paper presents the DAILY project's aims, design, and materials. The objectives are to advance understanding of (1) the short-term course and contexts of elevated risk of NSSI thoughts, urges, and behavior, (2) the transition from NSSI thoughts/urges to NSSI behavior, (3) and the association of NSSI with disordered eating, substance use, and suicidal thoughts and behaviors. A secondary aim is to evaluate the perspectives of treatment-seeking in iduals and mental health professionals regarding the feasibility, scope, and utility of digital self-monitoring and interventions that target NSSI in daily life.Methods: The DAILY project is funded by the Research Foundation Flanders (Belgium). Data collection involves three phases, including a baseline assessment (Phase 1), 28 days of Ecological Momentary Assessment (EMA) followed by a clinical session and feedback survey (Phase 2), two follow-up surveys and an optional interview (Phase 3). The EMA protocol consists of regular EMA surveys (six times per day), additional burst EMA surveys spaced at a higher frequency when experiencing intense NSSI urges (three times within 30 minutes), and event registrations of NSSI behavior. Primary outcomes are NSSI thoughts, NSSI urges, self-efficacy to resist NSSI, and NSSI behavior, with disordered eating (restrictive eating, binge eating, purging), substance use (binge drinking, smoking cannabis), and suicidal thoughts and behaviors surveyed as secondary outcomes. Assessed predictors include emotions, cognitions, contextual information, and social appraisals.Results: We will recruit approximately 120 treatment-seeking in iduals aged 15-39 years from mental health services across the Flanders region of Belgium. Recruitment began in June 2021 and is anticipated to conclude in spring 2023.Conclusions: The findings of the DAILY project will provide a detailed characterization of the short-term course of NSSI and advance understanding of how, why, and when NSSI and other self-harming behaviors unfold among treatment-seeking in iduals. This will inform clinical practice and provide the scientific building blocks for novel intervention approaches outside the therapy room that support people who self-injure in real-time.
Publisher: Elsevier BV
Date: 06-2018
Publisher: Elsevier BV
Date: 11-2019
Publisher: Springer Science and Business Media LLC
Date: 02-11-2015
DOI: 10.1038/NG.3431
Publisher: Springer Science and Business Media LLC
Date: 07-2014
DOI: 10.1038/NATURE13595
Publisher: Public Library of Science (PLoS)
Date: 18-06-2012
Publisher: Public Library of Science (PLoS)
Date: 09-10-2013
Publisher: Cambridge University Press (CUP)
Date: 15-02-2013
DOI: 10.1017/S0033291713000196
Abstract: Schizophrenia is associated with lower pre-morbid intelligence (IQ) in addition to (pre-morbid) cognitive decline. Both schizophrenia and IQ are highly heritable traits. Therefore, we hypothesized that genetic variants associated with schizophrenia, including copy number variants (CNVs) and a polygenic schizophrenia (risk) score (PSS), may influence intelligence. IQ was estimated with the Wechsler Adult Intelligence Scale (WAIS). CNVs were determined from single nucleotide polymorphism (SNP) data using the QuantiSNP and PennCNV algorithms. For the PSS, odds ratios for genome-wide SNP data were calculated in a s le collected by the Psychiatric Genome-Wide Association Study (GWAS) Consortium (8690 schizophrenia patients and 11 831 controls). These were used to calculate in idual PSSs in our independent s le of 350 schizophrenia patients and 322 healthy controls. Although significantly more genes were disrupted by deletions in schizophrenia patients compared to controls ( p = 0.009), there was no effect of CNV measures on IQ. The PSS was associated with disease status ( R 2 = 0.055, p = 2.1 × 10 −7 ) and with IQ in the entire s le ( R 2 = 0.018, p = 0.0008) but the effect on IQ disappeared after correction for disease status. Our data suggest that rare and common schizophrenia-associated variants do not explain the variation in IQ in healthy subjects or in schizophrenia patients. Thus, reductions in IQ in schizophrenia patients may be secondary to other processes related to schizophrenia risk.
Publisher: Elsevier BV
Date: 05-2012
DOI: 10.1016/J.SCHRES.2012.02.017
Abstract: Schizophrenia is associated with poor quality of life (QOL). Whereas the effects of neurocognitive deficits and psychopathology on QOL of schizophrenia patients have recently been elucidated, little is known about social cognitive deficits in this regard. This study investigated the influence of social cognition on QOL in schizophrenia. A s le of 1032 patients, 1011 of their siblings, and 552 healthy controls was recruited from the Dutch Genetic Risk and Outcome in Psychosis (GROUP) study. Participants completed a battery of cognitive tests, including social cognitive tests on theory of mind and emotion perception. To assess QOL the World Health Organization QOL Assessment-BREF (WHOQOL-BREF) was used. Schizophrenia symptoms were assessed with the Positive and Negative Syndrome Scale (PANSS). Social cognitive performance was significantly worse in patients compared to siblings and healthy controls. Patients had the poorest QOL, while QOL in healthy controls was better than in siblings. Theory of mind but not emotion perception or neurocognition was associated with QOL in patients, whereas neurocognition was the only significant predictor of QOL in siblings and healthy controls. There was a significant interaction between theory of mind and symptom severity with respect to QOL. Our study indicates that social cognition is associated with QOL in schizophrenia. Theory of mind rather than emotion perception is associated with QOL, and this association is moderated by schizophrenia symptoms. In particular, patients with relatively unimpaired theory of mind and more severe schizophrenia symptoms have poor QOL and could therefore benefit from therapeutic intervention.
Publisher: Frontiers Media SA
Date: 20-03-2020
Publisher: Springer Science and Business Media LLC
Date: 07-03-2018
DOI: 10.1038/S41467-017-02769-6
Abstract: Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.
Publisher: Public Library of Science (PLoS)
Date: 22-01-2014
Publisher: Cold Spring Harbor Laboratory
Date: 10-05-2022
DOI: 10.1101/2022.05.10.22274890
Abstract: Changes in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDD) and Multiple Sclerosis (MS). Data were collected as a part of the RADAR-CNS (Remote Assessment of Disease and Relapse – Central Nervous System) programme. We analyzed the following data from long-term participants in a decentralized multinational study: symptoms of depression, heart rate (HR) during the day and night social activity sedentary state, steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender. Participants with MDD (N=255) and MS (N=214) were included in the analyses. Overall, depressive symptoms remained stable across the three periods in both groups. Lower mean HR and HR variation were observed between pre and during lockdown during the day for MDD and during the night for MS. HR variation during rest periods also decreased between pre-and post-lockdown in both clinical conditions. We observed a reduction of physical activity for MDD and MS upon the introduction of lockdowns. The group with MDD exhibited a net increase in social interaction via social network apps over the three periods. Behavioral response to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDD and MS.
Publisher: Springer Science and Business Media LLC
Date: 06-09-2019
DOI: 10.1038/S41380-019-0463-8
Abstract: Based on the discovery by the Resilience Project (Chen R. et al. Nat Biotechnol 34:531–538, 2016) of rare variants that confer resistance to Mendelian disease, and protective alleles for some complex diseases, we posited the existence of genetic variants that promote resilience to highly heritable polygenic disorders1,0 such as schizophrenia. Resilience has been traditionally viewed as a psychological construct, although our use of the term resilience refers to a different construct that directly relates to the Resilience Project, namely: heritable variation that promotes resistance to disease by reducing the penetrance of risk loci, wherein resilience and risk loci operate orthogonal to one another. In this study, we established a procedure to identify unaffected in iduals with relatively high polygenic risk for schizophrenia, and contrasted them with risk-matched schizophrenia cases to generate the first known “polygenic resilience score” that represents the additive contributions to SZ resistance by variants that are distinct from risk loci. The resilience score was derived from data compiled by the Psychiatric Genomics Consortium, and replicated in three independent s les. This work establishes a generalizable framework for finding resilience variants for any complex, heritable disorder.
Publisher: Elsevier BV
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 11-05-2020
Publisher: Elsevier BV
Date: 04-2010
Publisher: Cambridge University Press (CUP)
Date: 15-05-2023
DOI: 10.1017/S0033291723001034
Abstract: Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in in iduals with a history of recurrent major depressive disorder (MDD) explored the intra-in idual variations in HR parameters and their relationship with depression severity. Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.
Publisher: Elsevier BV
Date: 10-2015
Publisher: SAGE Publications
Date: 12-2011
DOI: 10.3109/00048674.2011.620562
Abstract: Objective: The schizophrenia and other non-affective disorders categories listed in the DSM-IV, are currently under revision for the development of the fifth edition. The aim of the present study is to demonstrate the validity of these categories by investigating possible differences between diagnostic patient subgroups on various measures. Methods: 1064 patients with a diagnosis of non-affective psychosis (schizophrenia N = 731 (paranoid type 82%), schizoaffective N = 63, schizophreniform N = 120, psychosis not otherwise specified/brief psychotic disorder N = 150) participated in this study. Dependent variables were demographic and clinical characteristics, severity of psychopathology, premorbid and current functioning, and indicators of quality of life. Results: Within the diagnostic group of schizophrenia, no significant differences were observed between paranoid schizophrenia, disorganized, and undifferentiated schizophrenia. Patients with schizophrenia experienced more severe psychopathology and had poorer levels of current functioning compared to patients with psychosis not otherwise specified or brief psychotic disorder. Differences between schizophrenia and schizoaffective disorder were less clear. Conclusion: Our results do not support the validity of schizophrenia subtypes. Schizophrenia can be distinguished from brief psychotic disorder and psychotic disorder not otherwise specified. These findings may fuel the actual DSM-V discussion.
Publisher: Cambridge University Press (CUP)
Date: 31-10-2012
Publisher: Cambridge University Press (CUP)
Date: 22-05-2020
Publisher: JMIR Publications Inc.
Date: 12-04-2021
DOI: 10.2196/24604
Abstract: Sleep problems tend to vary according to the course of the disorder in in iduals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P .05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P .001), awakening times (z=5.53, P .001), insomnia (z=4.55, P .001), mean sleep offset time (z=6.19, P .001), and hypersomnia (z=5.30, P .001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.
Publisher: Springer Science and Business Media LLC
Date: 18-09-2011
DOI: 10.1038/NG.940
Publisher: JMIR Publications Inc.
Date: 08-05-2023
DOI: 10.2196/48821
Publisher: JMIR Publications Inc.
Date: 15-06-2023
DOI: 10.2196/46244
Abstract: Nonsuicidal self-injury (NSSI) is a major mental health concern. Despite increased research efforts on establishing the prevalence and correlates of the presence and severity of NSSI, we still lack basic knowledge of the course, predictors, and relationship of NSSI with other self-damaging behaviors in daily life. Such information will be helpful for better informing mental health professionals and allocating treatment resources. The DAILY (Detection of Acute rIsk of seLf-injurY) project will address these gaps among in iduals seeking treatment. This protocol paper presents the DAILY project’s aims, design, and materials used. The primary objectives are to advance understanding of (1) the short-term course and contexts of elevated risk for NSSI thoughts, urges, and behavior (2) the transition from NSSI thoughts and urges to NSSI behavior and (3) the association of NSSI with disordered eating, substance use, and suicidal thoughts and behaviors. A secondary aim is to evaluate the perspectives of in iduals seeking treatment and mental health professionals regarding the feasibility, scope, and utility of digital self-monitoring and interventions that target NSSI in daily life. The DAILY project is funded by the Research Foundation Flanders (Belgium). Data collection involves 3 phases: a baseline assessment (phase 1), 28 days of ecological momentary assessment (EMA) followed by a clinical session and feedback survey (phase 2), and 2 follow-up surveys and an optional interview (phase 3). The EMA protocol consists of regular EMA surveys (6 times per day), additional burst EMA surveys spaced at a higher frequency when experiencing intense NSSI urges (3 surveys within 30 minutes), and event registrations of NSSI behavior. The primary outcomes are NSSI thoughts, NSSI urges, self-efficacy to resist NSSI, and NSSI behavior, with disordered eating (restrictive eating, binge eating, and purging), substance use (binge drinking and smoking cannabis), and suicidal thoughts and behaviors surveyed as secondary outcomes. The assessed predictors include emotions, cognitions, contextual information, and social appraisals. We will recruit approximately 120 in iduals seeking treatment aged 15 to 39 years from mental health services across the Flanders region of Belgium. Recruitment began in June 2021 and data collection is anticipated to conclude in August 2023. The findings of the DAILY project will provide a detailed characterization of the short-term course and patterns of risk for NSSI and advance understanding of how, why, and when NSSI and other self-damaging behaviors unfold among in iduals seeking treatment. This will inform clinical practice and provide the scientific building blocks for novel intervention approaches outside of the therapy room that support people who self-injure in real time. DERR1-10.2196/46244
Publisher: JMIR Publications Inc.
Date: 23-04-2021
DOI: 10.2196/29840
Publisher: Wiley
Date: 23-02-2019
DOI: 10.1002/AJMG.B.32716
Publisher: Springer Science and Business Media LLC
Date: 17-02-2023
DOI: 10.1038/S41746-023-00749-3
Abstract: Recent growth in digital technologies has enabled the recruitment and monitoring of large and erse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years. Majority of participants (67.6%) continued to remain engaged in the study after 43 weeks. Unsupervised clustering of participants’ study apps and Fitbit usage data showed 3 distinct engagement subgroups for each data stream. We found: (i) the least engaged group had the highest depression severity (4 PHQ8 points higher) across all data streams (ii) the least engaged group (completed 4 bi-weekly surveys) took significantly longer to respond to survey notifications (3.8 h more) and were 5 years younger compared to the most engaged group (completed 20 bi-weekly surveys) and (iii) a considerable proportion (44.6%) of the participants who stopped completing surveys after 8 weeks continued to share passive Fitbit data for significantly longer (average 42 weeks). Additionally, multivariate survival models showed participants’ age, ownership and brand of smartphones, and recruitment sites to be associated with retention in the study. Together these findings could inform the design of future digital health studies to enable equitable and balanced data collection from erse populations.
Publisher: JMIR Publications Inc.
Date: 04-10-2022
DOI: 10.2196/40667
Abstract: Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored. The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R2=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R2=0.06). This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings.
Publisher: JMIR Publications Inc.
Date: 10-05-2021
Abstract: egative symptoms occur in in iduals at ultrahigh risk (UHR) for psychosis. Although there is evidence that observer ratings of negative symptoms are associated with level of functioning, the predictive value of subjective experience in daily life for in iduals at UHR has not been studied yet. his study therefore aims to investigate the predictive value of momentary manifestations of negative symptoms for clinical outcomes in in iduals at UHR. xperience s ling methodology was used to measure momentary manifestations of negative symptoms (blunted affective experience, lack of social drive, anhedonia, and social anhedonia) in the daily lives of 79 in iduals at UHR. Clinical outcomes (level of functioning, illness severity, UHR status, and transition status) were assessed at baseline and at 1- and 2-year follow-ups. ack of social drive, operationalized as greater experienced pleasantness of being alone, was associated with poorer functioning at the 2-year follow-up ( i b /i =−4.62, i P /i =.01). Higher levels of anhedonia were associated with poorer functioning at the 1-year follow-up ( i b /i =5.61, i P /i =.02). Higher levels of social anhedonia were associated with poorer functioning (eg, disability subscale: i b /i =6.36, i P /i =.006) and greater illness severity ( i b /i =−0.38, i P /i =.045) at the 1-year follow-up. In exploratory analyses, there was evidence that in iduals with greater variability of positive affect (used as a measure of blunted affective experience) experienced a shorter time to remission from UHR status at follow-up (hazard ratio=4.93, i P /i =.005). argeting negative symptoms in in iduals at UHR may help to predict clinical outcomes and may be a promising target for interventions in the early stages of psychosis.
Publisher: Cambridge University Press (CUP)
Date: 06-2019
DOI: 10.1016/J.EURPSY.2019.04.002
Abstract: Despite increased awareness that non-suicidal self-injury (NSSI) poses a significant public health concern on college c uses worldwide, few studies have prospectively investigated the incidence of NSSI in college and considered targeting college entrants at high risk for onset of NSSI. Using data from the Leuven College Surveys (n = 4,565 56.8%female, M age = 18.3, SD = 1.1), students provided data on NSSI, sociodemographics, traumatic experiences, stressful events, perceived social support, and mental disorders. A total of 2,163 baseline responders provided data at a two-year annual follow-up assessment (63.2% conditional response rate). One-year incidence of first onset NSSI was 10.3% in year 1 and 6.0% in year 2, with a total of 8.6% reporting sporadic NSSI (1–4 times per year) and 7.0% reporting repetitive NSSI (≥ 5 times per year) during the first two years of college. Many hypothesized proximal and distal risk factors were associated with the subsequent onset of NSSI (ORs = 1.5–18.2). Dating violence prior to age 17 and severe role impairment in daily life were the strongest predictors. Multivariate prediction suggests that an intervention focused on the 10% at highest risk would reach 23.9% of students who report sporadic, and 36.1% of students who report repetitive NSSI during college (cross-validated AUCs =.70–.75). The college period carries high risk for the onset of NSSI. In idualized web-based screening may be a promising approach for detecting young adults at high risk for self-injury and offering timely intervention.
Publisher: Springer Science and Business Media LLC
Date: 21-03-2017
DOI: 10.1038/NCOMMS14774
Abstract: We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique in iduals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05–21.6 P =1 × 10 −4 ) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS ( P =8.4 × 10 −7 ). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08–1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.
Publisher: Springer Science and Business Media LLC
Date: 08-04-2022
Publisher: Oxford University Press (OUP)
Date: 24-05-2014
Publisher: Springer Science and Business Media LLC
Date: 28-01-2014
DOI: 10.1038/MP.2013.195
Publisher: Cold Spring Harbor Laboratory
Date: 08-08-2017
DOI: 10.1101/173435
Abstract: Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a significant proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention and treatment. In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was significantly higher than that based on the average genomic region (91 regions, p = 1.2×10 −6 ) while the inverse was not significant (19 regions, p=0.89). Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several significant correlations with: 1) SCZ subphenotypes: negative symptoms (SCZ, p=3.6×10 −6 ) and manic symptoms (BD, p=2×10 −5 ), 2) BD subphenotypes: psychotic features (SCZ p=1.2×10 −10 , BD p=5.3×10 −5 ) and age of onset (SCZ p=7.9×10 −4 ). Finally, we show that psychotic features in BD has significant SNP-heritability (h 2 snp =0.15, SE=0.06), and a significant genetic correlation with SCZ (r g =0.34) in addition there is a significant sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p=0.0038, one-side binomial test). For the first time, we have identified specific loci pointing to a potential role of 4 genes ( DARS2 , ARFGEF2 , DCAKD and GATAD2A ) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.
Publisher: Oxford University Press (OUP)
Date: 19-09-2011
Publisher: JMIR Publications Inc.
Date: 21-12-2022
Abstract: ajor depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-in idual longitudinal variation or screening in iduals at high risk and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. e aimed to address these 3 challenges to inform future work in stratified analyses. sing smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the in idual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. e demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. his work contributes to our understanding of how these mobile health–derived features are associated with depression symptom severity to inform future work in stratified analyses.
Publisher: Springer Science and Business Media LLC
Date: 11-08-2202
DOI: 10.1038/NG.2711
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.JAD.2018.06.033
Abstract: Theoretical and empirical literature suggests that non-suicidal self-injury (NSSI) is an important correlate of suicide risk. The present study was designed to evaluate: (a) whether NSSI is associated with increased odds of subsequent onsets of suicidal thoughts and behaviors (STB) independent of common mental disorders, (b) whether NSSI is associated with increased risk of transitioning from suicide ideation to attempt, and (c) which NSSI characteristics are associated with STB after NSSI. Using discrete-time survival models, based on retrospective age of onset reports from college students (n = 6,393, 56.8% female), we examined associations of temporally prior NSSI with subsequent STB (i.e., suicide ideation, plan, and attempt) controlling mental disorders (i.e., MDD, Broad Mania, GAD, Panic Disorder, and risk for Alcohol Dependence). NSSI characteristics associated with subsequent STB were examined using logistic regressions. NSSI was associated with increased odds of subsequent suicide ideation (OR = 2.8), plan (OR = 3.0), and attempt (OR = 5.5) in models that controlled for the distribution of mental disorders. Further analyses revealed that NSSI was associated with increased risk of transitioning to a plan among those with ideation, as well as attempt among those with a plan (ORs = 1.7-2.1). Several NSSI characteristics (e.g., automatic positive reinforcement, earlier onset NSSI) were associated with increased odds of experiencing STB. Surveys relied on self-report, and thus, there is the potential for recall bias. This study provides support for the conceptualization of NSSI as a risk factor for STB. Investigation of the underlying pathways accounting for these time-ordered associations is an important avenue for future research.
Publisher: Elsevier BV
Date: 12-2022
DOI: 10.1016/J.CMPB.2022.107204
Abstract: Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients' activity profiles has the potential to assess the level of MS-induced disability in free-living conditions. In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months' duration. We combined these features with participants' demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the in idual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS). The results showed promising estimation performance with R This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance.
Publisher: Elsevier BV
Date: 12-2021
Publisher: Springer Science and Business Media LLC
Date: 27-01-2016
DOI: 10.1038/NATURE16549
Publisher: Public Library of Science (PLoS)
Date: 12-04-2012
Publisher: JMIR Publications Inc.
Date: 08-05-2020
Abstract: n the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed. e aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)–base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19. e analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background. e were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods ( i P /i & .001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones ( i P /i & .001 for Italy, Spain, and the United Kingdom), spending more time using social media apps ( i P /i & .001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate ( i P /i & .001 for Italy and Spain i P /i =.02 for Denmark), went to bed later ( i P /i & .001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more ( i P /i & .001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more. ADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.
Publisher: Center for Open Science
Date: 08-09-2022
Abstract: Psychotic disorders are among the most burdensome mental health problems, requiring ongoing care and support. While the Experience s ling method (ESM), a structured self-monitoring technique, offers a promising approach to supporting person-centered care, there has been a general lack of user involvement in implementing these digital innovations in routine mental health care. The present study explored the perspective of people with a history of psychotic disorders using a user-centered design within focus groups. While people with lived experience recognize the potential of ESM to become more aware and increase control over their mental health through early detection of symptoms, concerns were voiced about the validity and burden of mental health self-monitoring. Participants indicated that ESM tools should allow for a high degree of personalization and enable assessing a broad range of daily-life experiences not limited to psychotic symptoms (i.e., including moods and emotions, social functioning, and general functioning). Future developments of clinical ESM tools for the management of psychosis should allow for a broad assessment of in iduals’ daily life experiences, identify solutions for easy personalization, and address potential barriers for use by identifying factors that influence the perceived burden of ESM and strategies to strengthen confidence in self-monitoring.
Publisher: Public Library of Science (PLoS)
Date: 28-10-2016
Publisher: Elsevier BV
Date: 07-2022
Publisher: Springer Science and Business Media LLC
Date: 09-02-2022
DOI: 10.3758/S13428-021-01777-1
Abstract: The experience s ling method (ESM) has revolutionized our ability to conduct psychological research in the natural environment. However, researchers have a large degree of freedom when preprocessing ESM data, which may hinder scientific progress. This study illustrates the use of multiverse analyses regarding preprocessing choices related to data exclusion (i.e., based on various levels of compliance and exclusion of the first assessment day) and the calculation of constructs (i.e., composite scores calculated as the mean, median, or mode) by reanalyzing established group differences in negative affect, stress reactivity, and emotional inertia between in iduals with and without psychosis. Data came from five studies and included 233 in iduals with psychosis and 223 healthy in iduals (in total, 26,892 longitudinal assessments). Preprocessing choices related to data exclusion did not affect conclusions. For both stress reactivity and emotional inertia of negative affect, group differences were affected when negative affect was calculated as the mean compared to the median or mode. Further analyses revealed that this could be attributed to considerable differences in the within- and between-factor structure of negative affect. While these findings show that observed differences in affective processes between in iduals with and without psychosis are robust to preprocessing choices related to data exclusion, we found disagreement in conclusions between different central tendency measures. Safeguarding the validity of future experience s ling research, scholars are advised to use multiverse analysis to evaluate the robustness of their conclusions across different preprocessing scenarios.
Publisher: American Medical Association (AMA)
Date: 07-2014
Publisher: Springer Science and Business Media LLC
Date: 18-02-2019
Publisher: Center for Open Science
Date: 25-11-2019
Abstract: Introduction: Although research over the past decade has resulted in significantly increased knowledge about distal risk factors for non-suicidal self-injury (NSSI), little is known about short-term (proximal) factors that predict NSSI thoughts and behaviors. Drawing on contemporaneous theories of NSSI, as well as the concept of ideation-to-action, the present study clarifies (a) real-time factors that predict NSSI thoughts and (b) the extent to which theoretically important momentary factors (i.e., negative affect, positive affect, and self-efficacy to resist NSSI) predict NSSI behavior in daily life, beyond NSSI thoughts.Methods: Using Experience S ling Methodology (ESM), intensive longitudinal data was obtained from 30 young adults with frequent NSSI episodes in the last year. Participants completed assessments up to eight times per day for 12 consecutive days (signal-contingent s ling). This resulted in the collection of 2,222 assessments (median compliance = 79.2%) during which 591 NSSI thoughts and 270 NSSI behaviors were recorded. Using the dynamic structural equation modeling framework, multilevel vector autoregressive models were constructed. Results: Within the same assessment, negative affect was positively associated with NSSI thoughts, whereas positive affect and self-efficacy to resist NSSI were each negatively associated with NSSI thoughts. Across assessments, higher-than-usual negative affect and self-efficacy to resist NSSI were predictive of short-term change in NSSI thoughts. While fluctuations in both negative affect and positive affect prospectively predicted NSSI behavior, these factors became non-significant in models that controlled for the predictive effect of NSSI thoughts. In contrast, self-efficacy to resist NSSI incrementally predicted a lower probability of engaging in NSSI, above and beyond NSSI thoughts. Discussion: This study provides preliminary evidence that affective fluctuations may uniquely predict NSSI thoughts but not NSSI behaviors, and point to the role of personal belief in the ability to resist NSSI in preventing NSSI behavior. These findings illustrate the need to differentiate between the development of NSSI thoughts and the progression from NSSI thoughts to behavior, as these are likely distinct processes, with different predictors.
Publisher: Oxford University Press (OUP)
Date: 18-08-2015
DOI: 10.1093/IJE/DYV136
Publisher: Cambridge University Press (CUP)
Date: 2021
DOI: 10.1017/S2045796021000251
Abstract: Childhood trauma is associated with an elevated risk for psychosis, but the psychological mechanisms involved remain largely unclear. This study aimed to investigate emotional and psychotic stress reactivity in daily life as a putative mechanism linking childhood trauma and clinical outcomes in in iduals at ultra-high-risk (UHR) for psychosis. Experience s ling methodology was used to measure momentary stress, affect and psychotic experiences in the daily life of N = 79 UHR in iduals in the EU-GEI High Risk Study. The Childhood Trauma Questionnaire was used to assess self-reported childhood trauma. Clinical outcomes were assessed at baseline, 1- and 2-year follow-up. The association of stress with positive ( β = −0.14, p = 0.010) and negative affect ( β = 0.11, p = 0.020) was modified by transition status such that stress reactivity was greater in in iduals who transitioned to psychosis. Moreover, the association of stress with negative affect ( β = 0.06, p = 0.019) and psychotic experiences ( β = 0.05, p = 0.037) was greater in in iduals exposed to high v . low levels of childhood trauma. We also found evidence that decreased positive affect in response to stress was associated with reduced functioning at 1-year follow-up ( B = 6.29, p = 0.034). In addition, there was evidence that the association of childhood trauma with poor functional outcomes was mediated by stress reactivity (e.g. indirect effect: B = −2.13, p = 0.026), but no evidence that stress reactivity mediated the association between childhood trauma and transition (e.g. indirect effect: B = 0.14, p = 0.506). Emotional and psychotic stress reactivity may be potential mechanisms linking childhood trauma with clinical outcomes in UHR in iduals.
Publisher: Oxford University Press (OUP)
Date: 02-05-2017
Publisher: JMIR Publications Inc.
Date: 11-03-2022
DOI: 10.2196/34898
Abstract: The mobility of an in idual measured by phone-collected location data has been found to be associated with depression however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse–Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants’ location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-in idual level than the between-in idual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=−0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=−0.07, P .001) the subsequent periodicity of mobility. Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.
Publisher: Elsevier BV
Date: 02-2015
DOI: 10.1016/J.SCHRES.2014.11.027
Abstract: Substantial evidence exists about emotion processing (EP) impairments in schizophrenia patients. However, whether these deficits are present primarily during psychosis (i.e., state dependent) or an integral part of the disorder (i.e., trait dependent) remains unclear. EP was assessed with the degraded facial affect recognition task in schizophrenia patients (N=521) and healthy controls (N=312) at baseline (T1) and after a three year follow-up (T2). In schizophrenia patients symptomatic remission was assessed with the Positive and Negative Syndrome Scale (PANSS) remission tool. Patients were ided into four groups: remission T1 and remission T2 (RR) remission T1 and non-remission T2 (RN) non-remission T1 and non-remission T2 (NN) and non-remission T1 and remission T2 (NR). Factorial repeated measures ANCOVA was used to compare EP performance over time between groups. Age, gender and general cognition were included as covariates. Schizophrenia patients performed worse than healthy controls on EP at T1 (p=0.001). The patients that were in symptomatic remission at both time points (the RR group) performed worse than the healthy controls at T2 (p<0.001). Significant group×time interactions were found between RR and RN (p=0.001), and between NR and RN (p=0.04), indicating a differential EP performance over time. No group×time interaction was found between NN and NR. The results show relatively poor EP performance in schizophrenia patients compared to healthy controls. EP performance in schizophrenia patients was associated with symptomatic remission. The results provide support for the hypothesis that EP deficits in schizophrenia are both state and trait dependent.
Publisher: Cambridge University Press (CUP)
Date: 03-11-2022
DOI: 10.1017/S0033291722003178
Abstract: Although non-suicidal self-injury (NSSI) is known typically to begin in adolescence, longitudinal information is lacking about patterns, predictors, and clinical outcomes of NSSI persistence among emerging adults. The present study was designed to (1) estimate NSSI persistence during the college period, (2) identify risk factors and high-risk students for NSSI persistence patterns, and (3) evaluate the association with future mental disorders and suicidal thoughts and behaviors (STB). Using prospective cohorts from the Leuven College Surveys ( n = 5915), part of the World Mental Health International College Student Initiative, web-based surveys assessed mental health and psychosocial problems at college entrance and three annual follow-up assessments. Approximately one in five (20.4%) students reported lifetime NSSI at college entrance. NSSI persistence was estimated at 56.4%, with 15.6% reporting a high-frequency repetitive pattern (≥five times yearly). Many hypothesized risk factors were associated with repetitive NSSI persistence, with the most potent effects observed for pre-college NSSI characteristics. Multivariate models suggest that an intervention focusing on the 10–20% at the highest predicted risk could effectively reach 34.9–56.7% of students with high-frequency repetitive NSSI persistence (PPV = 81.8–93.4, AUC = 0.88–0.91). Repetitive NSSI persistence during the first two college years predicted 12-month mental disorders, role impairment, and STB during the third college year, including suicide attempts. Most emerging adults with a history of NSSI report persistent self-injury during their college years. Web-based screening may be a promising approach for detecting students at risk for a highly persistent NSSI pattern characterized by subsequent adverse outcomes.
Publisher: Center for Open Science
Date: 11-11-2022
Abstract: Although the literature suggests trait-like differences in affective and cognitive vulnerabilities between in iduals with and without a history of non-suicidal self-injury (NSSI), little is known about how these dispositional differences are experienced in the natural environment. The present study compares the intensity, inertia, interaction, and variability of affective (negative and positive affect) and cognitive states (rumination, self-criticism) in the everyday lives of in iduals who do and do not engage in NSSI. Using experience s ling methodology (ESM), 60 emerging adults (ages=18-22 years) with and without past-year NSSI (equally distributed) completed a baseline battery of questionnaires and an ESM s ling protocol consisting of eight questionnaires per day for 12 days (in total, 96 questionnaires per participant), resulting in 4,587 assessments (median compliance=83.3% IQR=71.9-91.7). In a dynamic structural equation modeling framework, dynamic parameters (i.e., mean intensity, carryover effects, spillover effects, and within-person variability) were evaluated using multilevel vector autoregressive models. Emerging adults who engage in NSSI experience higher intensity and greater variability of negative affect, rumination, and self-criticism, whereas lower intensity and greater variability of positive affect. In addition, past-year NSSI predicted stronger affective-cognitive interactions over time, with stronger spillover effects of negative and positive affect on subsequent rumination and self-criticism in in iduals who engage in NSSI. Depressive symptoms and trait levels of emotion dysregulation and self-criticism partially negated these differences. Our findings provide evidence that emerging adults who self-injure experience more negative affective-cognitive states in daily life and point to the potential relevance of boosting positive emotions to buffer negative cognitions.
Publisher: Center for Open Science
Date: 20-11-2022
Abstract: Facilitating the uptake and making better use of technological advances will be pivotal for counseling and clinical psychology to respond to the rising call for more community-based and person-centered care. While the Experience S ling Method (ESM), a structured self-report digital diary, could help facilitate this transition, it is currently unclear how practitioners envision using ESM in clinical practice. Focus groups were organized with 36 mental health practitioners (Mage = 39.37, SDage = 12.18, 58.33% female) across Flanders (Belgium). Four broad topics were discussed: (1) how to use ESM, (2) how to visualize clinically relevant information, (3) the software requirements thereof, and (4) barriers and facilitators for implementing ESM in clinical practice. Thematic analysis was conducted and Cohen's Kappa was calculated to measure inter-rater reliability. Cohen's Kappa was .79, indicating good inter-rater reliability. Different clinical applications emerged (e.g., screening, evaluation of treatment). Practitioners expressed difficulty determining how to visualize ESM data, and novel features for use emerged (e.g., integration with electronic health records). Various barriers (e.g., lack of best-practice guidelines) and facilitators (e.g., simplicity) to clinical implementation were identified. Implications for clinical implementation and future software development work are discussed.
Publisher: Wiley
Date: 16-08-2011
DOI: 10.1111/J.1600-0447.2010.01596.X
Abstract: The experience s ling method (ESM) represents a valuable way of assessing clinical phenomena in real world settings and across time. Despite its theoretical advantages, using this methodology in psychiatric populations is challenging. This paper acts as a guide to researchers wishing to employ this approach when investigating mental illness. The contents represent the opinions of researchers around the United Kingdom and the Netherlands who are experienced at using the ESM. In ESM studies, participants are required to fill in questions about their current thoughts, feelings and experiences when prompted by an electronic device (e.g. a wristwatch, PDA). Entries are typically made at fixed or random intervals over 6 days. This article outlines how to design and validate an ESM diary. We then discuss which s ling procedure to use and how to increase compliance through effective briefing and telephone sessions. Debriefing, data management and analytical issues are considered, before suggestions for future clinical uses of the ESM are made. The last decade has seen an increase in the number of studies employing the ESM in clinical research. Further research is needed to examine the optimal equipment and procedure for different clinical groups.
No related grants have been discovered for Inez Myin-Germeys.