Poor Separation of Clinical Symptom Profiles by DSM-5 Disorder Criteria

A Psychometric Evaluation of the Expanded Version of the Inventory of Depression and Anxiety Symptoms (IDAS-II) in Children and Adolescents

The expanded version of the Inventory of Depression and Anxiety Symptoms (IDAS-II) is a self-report measure of 18 empirically derived internalizing symptom dimensions. The measure has shown good psychometric properties in adults but has never been evaluated in children and adolescents. A Swedish version of the IDAS-II was administered to 633 children and adolescents (Mage =16.6 [SD = 2.0]) and 203 adults (Mage = 35.4 [SD = 12.1]). The model/data fit of the 18-factor structure was excellent in both samples and measurement invariance across age groups was supported. All scales showed good to excellent internal consistency and psychometric properties replicated in the younger youth sample (< 16 years). Among youth, good convergent validity was established for all scales and divergent validity for most scales. The IDAS-II was better at identifying youth with current mental health problems than an internationally recommended scale of internalizing symptoms. In conclusion, the IDAS-II shows promise as a measure of internalizing symptoms in youth.

Independent and Joint Associations of Physical Activity and Sleep on Mental Health Among a Global Sample of 200,743 Adults

BACKGROUND

Previous research has demonstrated that both sleep and physical activity (PA) are independently associated with various indicators of mental health among adults. However, their joint contribution to mental health has received limited attention. The present study used cross-sectional data from the Mental Health Million Project to examine the independent and joint effects of sleep and PA on mental health among a global sample of adults, and whether these effects differ among individuals receiving mental health treatment.

METHOD

The sample included 200,743 participants (33.1% young adults, 45.6% middle-aged adults, 21.3% older adults; 57.6% females, 0.9% other) from 213 countries, territories, and archipelagos worldwide that completed a comprehensive 47-item assessment of mental health including both problems (i.e., ill-being) and assets (i.e., well-being): the Mental Health Quotient. Participants also reported their weekly frequency of PA and adequate sleep, and mental health treatment status. A series of generalized linear mixed models were computed.

RESULTS

Independent dose-response associations were observed, whereby greater amounts of PA and adequate sleep were each associated with better mental health. In addition, a synergistic interaction was observed in which the positive correlation of PA with mental health was strengthened with greater frequency of adequate sleep. These benefits were less pronounced among adults receiving mental health treatment.

CONCLUSION

While findings suggest sleep can help to offset the negative influence of a physically inactive lifestyle (and vice versa), our results point to a "more is better" approach for both behaviors when it comes to promoting mental health.

Leveraging big data for causal understanding in mental health: a research framework

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

Endophenotype trait domains for advancing gene discovery in autism spectrum disorder

Autism spectrum disorder (ASD) is associated with a diverse range of etiological processes, including both genetic and non-genetic causes. For a plurality of individuals with ASD, it is likely that the primary causes involve multiple common inherited variants that individually account for only small levels of variation in phenotypic outcomes. This genetic landscape creates a major challenge for detecting small but important pathogenic effects associated with ASD. To address similar challenges, separate fields of medicine have identified endophenotypes, or discrete, quantitative traits that reflect genetic likelihood for a particular clinical condition and leveraged the study of these traits to map polygenic mechanisms and advance more personalized therapeutic strategies for complex diseases. Endophenotypes represent a distinct class of biomarkers useful for understanding genetic contributions to psychiatric and developmental disorders because they are embedded within the causal chain between genotype and clinical phenotype, and they are more proximal to the action of the gene(s) than behavioral traits. Despite their demonstrated power for guiding new understanding of complex genetic structures of clinical conditions, few endophenotypes associated with ASD have been identified and integrated into family genetic studies. In this review, we argue that advancing knowledge of the complex pathogenic processes that contribute to ASD can be accelerated by refocusing attention toward identifying endophenotypic traits reflective of inherited mechanisms. This pivot requires renewed emphasis on study designs with measurement of familial co-variation including infant sibling studies, family trio and quad designs, and analysis of monozygotic and dizygotic twin concordance for select trait dimensions. We also emphasize that clarification of endophenotypic traits necessarily will involve integration of transdiagnostic approaches as candidate traits likely reflect liability for multiple clinical conditions and often are agnostic to diagnostic boundaries. Multiple candidate endophenotypes associated with ASD likelihood are described, and we propose a new focus on the analysis of "endophenotype trait domains" (ETDs), or traits measured across multiple levels (e.g., molecular, cellular, neural system, neuropsychological) along the causal pathway from genes to behavior. To inform our central argument for research efforts toward ETD discovery, we first provide a brief review of the concept of endophenotypes and their application to psychiatry. Next, we highlight key criteria for determining the value of candidate endophenotypes, including unique considerations for the study of ASD. Descriptions of different study designs for assessing endophenotypes in ASD research then are offered, including analysis of how select patterns of results may help prioritize candidate traits in future research. We also present multiple candidate ETDs that collectively cover a breadth of clinical phenomena associated with ASD, including social, language/communication, cognitive control, and sensorimotor processes. These ETDs are described because they represent promising targets for gene discovery related to clinical autistic traits, and they serve as models for analysis of separate candidate domains that may inform understanding of inherited etiological processes associated with ASD as well as overlapping neurodevelopmental disorders.

Profiles of depressive symptoms in Peru: An 8-year analysis in population-based surveys

Background Profiles of depressive symptoms have been described due to heterogeneity in symptomatology and presentation. In our study, we estimate depressive symptom profiles and relate these symptom profiles to risk factors in the Peruvian population. Methods We carried out an observational study based on the Peruvian Demographic and Health Survey (2014-2022). Men and women aged 15 years and older living in urban and rural areas in all regions of Peru were included. The Patient Health Questionnaire-9 was used to define depressive symptom profiles. We estimated latent class models to define the profiles and performed a Poisson regression analysis to determine the associated factors. Results A total of 259,655 participants were included. The three-class model was found to be the most appropriate, and the classes were defined according to the severity of depressive symptoms (moderate-severe symptoms, mild symptoms, and without depressive symptoms). Also, it was found that the three classes identified have not changed during the years of evaluations, presenting very similar prevalence over the years. In addition, women are more likely than men to belong to a class with more severe depressive symptoms; and the older the age, the higher the probability of belonging to a class with greater severity of depressive symptoms. Conclusions Our study found that at the population level in Peru, depressive symptoms are grouped into three classes according to the intensity of the symptomatology present (no symptoms, mild symptoms and moderate-severe symptoms).

A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study

BACKGROUND

Anxiety is one of the leading causes of mental health disability around the world. Currently, a majority of the population who experience anxiety go undiagnosed or untreated. New and innovative ways of diagnosing and monitoring anxiety have emerged using smartphone sensor-based monitoring as a metric for the management of anxiety. This is a novel study as it adds to the field of research through the use of nonidentifiable smartphone usage to help detect and monitor anxiety remotely and in a continuous and passive manner.

OBJECTIVE

This study aims to evaluate the accuracy of a novel mental behavioral profiling metric derived from smartphone usage for the identification and tracking of generalized anxiety disorder (GAD).

METHODS

Smartphone data and self-reported 7-item GAD anxiety assessments were collected from 229 participants using an Android operating system smartphone in an observational study over an average of 14 days (SD 29.8). A total of 34 features were mined to be constructed as a potential digital phenotyping marker from continuous smartphone usage data. We further analyzed the correlation of these digital behavioral markers against each item of the 7-item Generalized Anxiety Disorder Scale (GAD-7) and its influence on the predictions of machine learning algorithms.

RESULTS

A total of 229 participants were recruited in this study who had completed the GAD-7 assessment and had at least one set of passive digital data collected within a 24-hour period. The mean GAD-7 score was 11.8 (SD 5.7). Regression modeling was tested against classification modeling and the highest prediction accuracy was achieved from a binary XGBoost classification model (precision of 73%-81%; recall of 68%-87%; F₁-score of 71%-79%; accuracy of 76%; area under the curve of 80%). Nonparametric permutation testing with Pearson correlation results indicated that the proposed metric (Mental Health Similarity Score [MHSS]) had a colinear relationship between GAD-7 Items 1, 3 and 7.

CONCLUSIONS

The proposed MHSS metric demonstrates the feasibility of using passively collected nonintrusive smartphone data and machine learning-based data mining techniques to track an individuals' daily anxiety levels with a 76% accuracy that directly relates to the GAD-7 scale.

Cautionary Observations Concerning the Introduction of Psychophysiological Biomarkers into Neuropsychiatric Practice

The combination of statistical learning technologies with large databases of psychophysiological data has appropriately generated enthusiastic interest in future clinical applicability. It is argued here that this enthusiasm should be tempered with the understanding that significant obstacles must be overcome before the systematic introduction of psychophysiological measures into neuropsychiatric practice becomes possible. The objective of this study is to identify challenges to this effort. The nonspecificity of psychophysiological measures complicates their use in diagnosis. Low test-retest reliability complicates use in longitudinal assessment, and quantitative psychophysiological measures can normalize in response to placebo intervention. Ten cautionary observations are introduced and, in some instances, possible directions for remediation are suggested.

Assessment of Population Well-being With the Mental Health Quotient: Validation Study

BACKGROUND

The Mental Health Quotient (MHQ) is an anonymous web-based assessment of mental health and well-being that comprehensively covers symptoms across 10 major psychiatric disorders, as well as positive elements of mental function. It uses a novel life impact scale and provides a score to the individual that places them on a spectrum from Distressed to Thriving along with a personal report that offers self-care recommendations. Since April 2020, the MHQ has been freely deployed as part of the Mental Health Million Project.

OBJECTIVE

This paper demonstrates the reliability and validity of the MHQ, including the construct validity of the life impact scale, sample and test-retest reliability of the assessment, and criterion validation of the MHQ with respect to clinical burden and productivity loss.

METHODS

Data were taken from the Mental Health Million open-access database (N=179,238) and included responses from English-speaking adults (aged≥18 years) from the United States, Canada, the United Kingdom, Ireland, Australia, New Zealand, South Africa, Singapore, India, and Nigeria collected during 2021. To assess sample reliability, random demographically matched samples (each 11,033/179,238, 6.16%) were compared within the same 6-month period. Test-retest reliability was determined using the subset of individuals who had taken the assessment twice ≥3 days apart (1907/179,238, 1.06%). To assess the construct validity of the life impact scale, additional questions were asked about the frequency and severity of an example symptom (feelings of sadness, distress, or hopelessness; 4247/179,238, 2.37%). To assess criterion validity, elements rated as having a highly negative life impact by a respondent (equivalent to experiencing the symptom ≥5 days a week) were mapped to clinical diagnostic criteria to calculate the clinical burden (174,618/179,238, 97.42%). In addition, MHQ scores were compared with the number of workdays missed or with reduced productivity in the past month (7625/179,238, 4.25%).

RESULTS

Distinct samples collected during the same period had indistinguishable MHQ distributions and MHQ scores were correlated with r=0.84 between retakes within an 8- to 120-day period. Life impact ratings were correlated with frequency and severity of symptoms, with a clear linear relationship (R²>0.99). Furthermore, the aggregate MHQ scores were systematically related to both clinical burden and productivity. At one end of the scale, 89.08% (8986/10,087) of those in the Distressed category mapped to one or more disorders and had an average productivity loss of 15.2 (SD 11.2; SEM [standard error of measurement] 0.5) days per month. In contrast, at the other end of the scale, 0% (1/24,365) of those in the Thriving category mapped to any of the 10 disorders and had an average productivity loss of 1.3 (SD 3.6; SEM 0.1) days per month.

CONCLUSIONS

The MHQ is a valid and reliable assessment of mental health and well-being when delivered anonymously on the web.

A Machine Learning Approach Detecting Digital Behavioural Patterns of Depression Using Non-intrusive Smartphone Data - A Complementary Path to PHQ-9 Assessment: A Prospective Observational Study

BACKGROUND

Depression is a major global cause of morbidity, an economic burden and the greatest health challenge leading to chronic disability. Mobile monitoring of mental conditions has long been a sought-after metric to overcome the problems associated with the screening, diagnosis and monitoring of depression and its heterogeneous presentation. The widespread availability of smartphones has made it possible to use its data to generate digital behavioral models which can be used for both clinical and remote screening and monitoring purposes, providing a tentative and scalable solution to the pressing global need for early and effective solutions. This study is novel because it adds to the field by conducting a trial using private and non-intrusive sensors that can help detect and monitor depression in a continuous passive manner.

OBJECTIVE

This study demonstrates a novel mental behavioral profiling metric (Mental Health Similarity Score) derived from analyzing passively monitored, private and non-intrusive smartphone usage data, to identify and track depressive behavior and its progression. The analysis is performed using machine learning models trained on different levels of depression severity measured through the PHQ-9 (Patient Health Questionnaire-9) questionnaire.

METHODS

Smartphone data sets and self-reported 9-item PHQ depression assessments were collected from 558 smartphone users on the Android operating system in an observational study over an average of 10.7 days (SD=23.7). We quantified 37 digital behavioral markers from the passive smartphone data set and explored the relationship between the digital behavioral markers and depression using correlation coefficients and random forest models. We leveraged 4 supervised machine learning (ML) classification algorithms with hyperparameter optimization, fifteen-fold cross-validation, bootstrapping and imbalanced data handling to predict depression and its severity using PHQ-9 scores as the ground truth. We also quantified an additional 3 digital markers from gyroscope sensors and explored its feasibility in improving the model's accuracy in detecting depression.

RESULTS

Of the 558 participants, 254 (46%) were males and 286 (51%) were females and 18 (3%) preferred not to say. Participants age distribution is as follows: 474 (85%) users between the ages of 18-25, 29 (5%) aged between 26-35, 42 (7%) aged between 36-55, 10 (2%) were aged between 56-64 and 3 (<1%) above 64 years of age. Of the 558 reported PHQ-9 assessments, 63 responses were non-depressed (scored <5), 124 responses indicated mild depression (scored 5-9), 162 indicated moderate depression (scored 10-14), 131 indicated moderately severe (scored 15-19) and 78 indicated severe depression (scored 20-27), as identified by the PHQ-9 cut off points. Gender imbalance was present within each of the 5 severity groups, with a male majority in the non-depressed and mild groups and female majority in the moderate, moderately severe, and severe groups. Of the 469 individuals that reported having 'No Diagnosis' as their current diagnostic status in their demographic's questionnaire, 307 (65%) scored moderate to severe depression (PHQ-9 scores >=10). The PHQ-9 two class (none vs. severe) model achieved the following metrics: precision 85-89%; recall 85-89%; F1 87%, and overall accuracy is 87%. The PHQ-9 three class (none vs. mild vs. severe) model achieved the following metrics: precision 74-86%; recall 76-83%; F1 75-84%, and overall accuracy is 78%. A significant positive Pearson correlation was found between PHQ-9 questions 2, 6 and 9 within the severely depressed users and the mental behavioral profiling metric (r=0.73). The PHQ-9 question specific (questions 2,6, and 9) model achieved the following metrics: precision 76-80%; recall 75-81%; F1 78-89%, and overall accuracy is 78%. When adding a gyroscope sensor as a feature, the Pearson correlation between 2,6 and 9 dropped from r= 0.73 to r=0.46. Mean activity (P=3.08e-4) and average gap activity (P=1.69e-4) features from the gyroscope sensors had statistically significant differences between none and severe individuals. The PHQ-9 two class model + gyro features achieved the following metrics: precision 74-78%; recall 67-83%; F1 72-78%, and overall accuracy is 76%.

CONCLUSIONS

Our results demonstrate that the Mental Health Similarity Score can be used to identify and track depressive behavior and its progression with high accuracy. Therefore, the current and traditional methods of assessing depression can be coupled with digital behavioral markers to have a significant impact in mitigating depression and its far-reaching consequences.

CLINICALTRIAL