Frontiers in Psychiatry is proud to present our Psychometrics series. This series of Article Collections will deal with the definition and measurement of clinical constructs. The Psychometrics in Computational Psychiatry Article Collection aims to collect original research and review articles on Predicting Human Psychometric Properties Using Computational Language Models. Potential psychometric models include structural equation models, item response models and multilevel models. Articles can include, but are not limited to, the following topics: -Clinical psychometrics -The challenge of quantitative psychiatry and measurement -Application, validity, and reliability of psychiatric scales -Measuring and modeling

Department of Psychology, University of Turin

Hanyang University

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Frontiers in Psychiatry

Computational Psychiatry

Psychometrics in Psychiatry 2022: Computational Psychiatry

The Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) measures the full spectrum of attention and activity symptoms, not just the negative end of the distribution. Previous studies revealed strong psychometric properties of the parent and teacher report versions; however, there is little research on the new self-report form of the SWAN. Therefore, our research aimed to explore the psychometric characteristics of the SWAN self-report. A non-clinical sample of young women (N = 664, mean age: 20.01 years, SD: 3.08 years) completed the SWAN self-report, the Strengths and Difficulties Questionnaire (SDQ) and the Mental Health Continuum Short Form (MHC-SF). We tested several models using confirmatory factor analyses to assess the factorial validity of the SWAN self-report. Distributional characteristics, convergent, and predictive validity were assessed. A bifactor model with a general factor and a specific inattention factor (bifactor-1) provided the best fit in our data (CFI = 0.977, TLI/NFI = 0.972, RMSEA = 0.053 [90% CI: 0.047 – 0.059], SRMR = 0.061, ω = 0.90). The reliability of the general ADHD factor was good (ωh = 0.87), and the specific inattention factor was acceptable (ωh = 0.73). The distribution of the SWAN self-report scores did not differ from the normal distribution. A strong correlation between the SWAN and the SDQ Hyperactivity subscale was found. The analyses revealed good predictive validity. Our results suggest that the SWAN self-report is a valuable tool for assessing symptoms of ADHD in adolescents and young adults.

The Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) measures the full spectrum of attention and activity symptoms, not just the negative end of the distribution. Previous studies revealed strong psychometric properties of the parent and teacher report versions; however, there is little research on the new self-report form of the SWAN. Therefore, our research aimed to explore the psychometric characteristics of the SWAN self-report. A non-clinical sample of young women (N = 664, mean age: 20.01 years, SD: 3.08 years) completed the SWAN self-report, the Strengths and Difficulties Questionnaire (SDQ) and the Mental Health Continuum Short Form (MHC-SF). We tested several models using confirmatory factor analyses to assess the factorial validity of the SWAN self-report. Distributional characteristics, convergent, and predictive validity were assessed. A bifactor model with a general factor and a specific inattention factor (bifactor-1) provided the best fit in our data (CFI = 0.977, TLI/NFI = 0.972, RMSEA = 0.053 [90% CI: 0.047 – 0.059], SRMR = 0.061, ω = 0.90). The reliability of the general ADHD factor was good (ωh&nbsp;= 0.87), and the specific inattention factor was acceptable (ωh = 0.73). The distribution of the SWAN self-report scores did not differ from the normal distribution. A strong correlation between the SWAN and the SDQ Hyperactivity subscale was found. The analyses revealed good predictive validity. Our results suggest that the SWAN self-report is a valuable tool for assessing symptoms of ADHD in adolescents and young adults.

Psychometric properties of the self-report version of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Scale in a sample of Hungarian adolescents and young adults

IntroductionMental health services have transitioned from treating symptoms to emphasizing personal recovery. Despite its importance, integrating personal recovery into clinical practice remains work in progress. This study evaluates the psychometric qualities of the Brief INSPIRE-O, a five-item patient-reported outcome measure assessing personal recovery.</sec>MethodThe study collected data from 2018 to 2020 at the Mental Health Services, Capital Region of Denmark, using an internet-based system examining 8,192 non-psychotic patients – receiving outpatient treatment.</sec>MaterialsThis study evaluated the Brief INSPIRE-O and used measures of symptomatology (SCL-10), well-being (WHO-5), and social functioning (modified SDS).</sec>ResultsThe study population comprised 76.8% females with a mean age of 32.9 years, and diagnoses included anxiety (28%), depression (34%), and personality disorder (19%). The mean Brief INSPIRE-O score (39.9) was lower than the general population norm (71.1). The Brief INSPIRE-O showed acceptable test–retest reliability (0.75), scalability (0.39), and internal consistency (0.73). Correlations with other mental health criteria were in the expected direction for symptomatology (−0.46), well-being (0.60), and social functioning (−0.43) and remained consistent across diagnoses.</sec>DiscussionThe Brief INSPIRE-O demonstrated strong psychometric qualities and could be recommended as a measure of personal recovery for use in both research and clinical practice. Its strong theoretical basis and short completion time make it suitable for use for research. Incorporating Brief INSPIRE-O into clinical assessment will further support the process of mental health systems re-orientating towards personal recovery.</sec>

Introduction: Mental health services have transitioned from treating symptoms to emphasizing personal recovery. Despite its importance, integrating personal recovery into clinical practice remains work in progress. This study evaluates the psychometric qualities of the Brief INSPIRE-O, a five-item patient-reported outcome measure assessing personal recovery.Method: The study collected data from 2018 to 2020 at the Mental Health Services, Capital Region of Denmark, using an internet-based system examining 8,192 non-psychotic patients – receiving outpatient treatment.Materials: This study evaluated the Brief INSPIRE-O and used measures of symptomatology (SCL-10), well-being (WHO-5), and social functioning (modified SDS).Results: The study population comprised 76.8% females with a mean age of 32.9 years, and diagnoses included anxiety (28%), depression (34%), and personality disorder (19%). The mean Brief INSPIRE-O score (39.9) was lower than the general population norm (71.1). The Brief INSPIRE-O showed acceptable test–retest reliability (0.75), scalability (0.39), and internal consistency (0.73). Correlations with other mental health criteria were in the expected direction for symptomatology (−0.46), well-being (0.60), and social functioning (−0.43) and remained consistent across diagnoses.Discussion: The Brief INSPIRE-O demonstrated strong psychometric qualities and could be recommended as a measure of personal recovery for use in both research and clinical practice. Its strong theoretical basis and short completion time make it suitable for use for research. Incorporating Brief INSPIRE-O into clinical assessment will further support the process of mental health systems re-orientating towards personal recovery.

Scalability, test–retest reliability and validity of the Brief INSPIRE-O measure of personal recovery in psychiatric services

An interesting recent development in emotion research and clinical psychology is the discovery that affective states can be modeled as a network of temporally interacting moods or emotions. Additionally, external factors like stressors or treatments can influence the mood network by amplifying or dampening the activation of specific moods. Researchers have turned to multilevel autoregressive models to fit these affective networks using intensive longitudinal data gathered through ecological momentary assessment. Nonetheless, a more comprehensive examination of the performance of such models is warranted. In our study, we focus on simple directed intraindividual networks consisting of two interconnected mood nodes that mutually enhance or dampen each other. We also introduce a node representing external factors that affect both mood nodes unidirectionally. Importantly, we disregard the potential effects of a current mood/emotion on the perception of external factors. We then formalize the mathematical representation of such networks by exogenous linear autoregressive mixed-effects models. In this representation, the autoregressive coefficients signify the interactions between moods, while external factors are incorporated as exogenous covariates. We let the autoregressive and exogenous coefficients in the model have fixed and random components. Depending on the analysis, this leads to networks with variable structures over reasonable time units, such as days or weeks, which are captured by the variability of random effects. Furthermore, the fixed-effects parameters encapsulate a subject-specific network structure. Leveraging the well-established theoretical and computational foundation of linear mixed-effects models, we transform the autoregressive formulation to a classical one and utilize the existing methods and tools. To validate our approach, we perform simulations assuming our model as the true data-generating process. By manipulating a predefined set of parameters, we investigate the reliability and feasibility of our approach across varying numbers of observations, levels of noise intensity, compliance rates, and scalability to higher dimensions. Our findings underscore the challenges associated with estimating individualized parameters in the context of common longitudinal designs, where the required number of observations may often be unattainable. Moreover, our study highlights the sensitivity of autoregressive mixed-effect models to noise levels and the difficulty of scaling due to the substantial number of parameters.

An interesting recent development in emotion research and clinical psychology is the discovery that affective states can be modeled as a network of temporally interacting moods or emotions. Additionally, external factors like stressors or treatments can influence the mood network by amplifying or dampening the activation of specific moods. Researchers have turned to multilevel autoregressive models to fit these affective networks using intensive longitudinal data gathered through ecological momentary assessment. Nonetheless, a more comprehensive examination of the performance of such models is warranted. In our study, we focus on simple directed intraindividual networks consisting of two interconnected mood nodes that mutually enhance or dampen each other. We also introduce a node representing external factors that affect both mood nodes unidirectionally. Importantly, we disregard the potential effects of a current mood/emotion on the perception of external factors. We then formalize the mathematical representation of such networks by exogenous linear autoregressive mixed-effects models. In this representation, the autoregressive coefficients signify the interactions between moods, while external factors are incorporated as exogenous covariates. We let the autoregressive and exogenous coefficients in the model have fixed and random components. Depending on the analysis, this leads to networks with variable structures over reasonable time units, such as days or weeks, which are captured by the variability of random effects. Furthermore, the fixed-effects parameters encapsulate a subject-specific network structure. Leveraging the well-established theoretical and computational foundation of linear mixed-effects models, we transform the autoregressive formulation to a classical one and utilize the existing methods and tools. To validate our approach, we perform simulations assuming our model as the true data-generating process. By manipulating a predefined set of parameters, we investigate the reliability and feasibility of our approach across varying numbers of observations, levels of noise intensity, compliance rates, and scalability to higher dimensions. Our findings underscore the challenges associated with estimating individualized parameters in the context of common longitudinal designs, where the required number of observations may often be unattainable. Moreover, our study highlights the sensitivity of autoregressive mixed-effect models to noise levels and the difficulty of scaling due to the substantial number of parameters.

Intraindividual time-varying dynamic network of affects: linear autoregressive mixed-effects models for ecological momentary assessment

ObjectiveThe aim of this study is to investigate the psychometric characteristics of outpatients diagnosed with somatic symptom disorder (SSD) in biomedical, Traditional Chinese Medicine (TCM) and psychosomatic settings.</sec>Materials and methodsA total of 697 participants who completed SCID-5 and questionnaires were presented in our former study, as 3 of them had missed questionnaire data, a total of 694 participants are presented in this study. A secondary analysis of the psychometric characteristics of Somatic Symptom Disorder–B Criteria Scale (SSD-12), Somatic Symptom Severity Scale of the Patient-Health Questionnaire (PHQ-15), Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) is done to compare differences among outpatients from the three settings of medical specialties.</sec>ResultsBased on the DSM-5 criteria, 90 out of 224 (40.2%) participants enrolled in biomedical departments (represented by neurology and gastroenterology departments), 44/231 (19.0%) in TCM departments, and 101/239 (42.3%) in the psychosomatic medicine departments were diagnosed with SSD. The scores of PHQ-15 in the biomedical, TCM and psychosomatic settings were 11.08 (± 4.54), 11.02 (± 5.27) and 13.26 (± 6.20); PHQ-9 were 10.43 (± 6.42), 11.20 (± 5.46) and 13.42 (± 7.32); GAD-7 were 8.52 (± 6.22), 9.57 (± 5.06) and 10.83 (± 6.24); SSD-12 were 22.26 (± 11.53), 22.98 (± 10.96) and 25.03 (± 11.54) respectively. The scores of PHQ-15, PHQ-9 and GAD-7 in SSD patients were significantly higher in psychosomatic departments than that in biomedical settings (p < 0.05). The cutoff point for SSD-12 was ≥16 in total patients; 16, 16, 17 in biomedical, TCM and psychosomatic settings, respectively. The cutoff point for PHQ-15 was found to be ≥8 in total patients; 8, 9, 11 in biomedical, TCM and psychosomatic settings, respectively.</sec>ConclusionSSD patients from psychosomatic departments had higher level of somatic symptom severity, depression and anxiety than from TCM and biomedical settings. In our specific sample, a cutoff point of ≥16 for SSD-12 could be recommended in all three settings. But the cutoff point of PHQ-15 differs much between different settings, which was ≥8, 9, and 11 in biomedical, TCM, and psychosomatic settings, respectively.</sec>

Objective: The aim of this study is to investigate the psychometric characteristics of outpatients diagnosed with somatic symptom disorder (SSD) in biomedical, Traditional Chinese Medicine (TCM) and psychosomatic settings.Materials and methods: A total of 697 participants who completed SCID-5 and questionnaires were presented in our former study, as 3 of them had missed questionnaire data, a total of 694 participants are presented in this study. A secondary analysis of the psychometric characteristics of Somatic Symptom Disorder–B Criteria Scale (SSD-12), Somatic Symptom Severity Scale of the Patient-Health Questionnaire (PHQ-15), Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) is done to compare differences among outpatients from the three settings of medical specialties.Results: Based on the DSM-5 criteria, 90 out of 224 (40.2%) participants enrolled in biomedical departments (represented by neurology and gastroenterology departments), 44/231 (19.0%) in TCM departments, and 101/239 (42.3%) in the psychosomatic medicine departments were diagnosed with SSD. The scores of PHQ-15 in the biomedical, TCM and psychosomatic settings were 11.08 (± 4.54), 11.02 (± 5.27) and 13.26 (± 6.20); PHQ-9 were 10.43 (± 6.42), 11.20 (± 5.46) and 13.42 (± 7.32); GAD-7 were 8.52 (± 6.22), 9.57 (± 5.06) and 10.83 (± 6.24); SSD-12 were 22.26 (± 11.53), 22.98 (± 10.96) and 25.03 (± 11.54) respectively. The scores of PHQ-15, PHQ-9 and GAD-7 in SSD patients were significantly higher in psychosomatic departments than that in biomedical settings (p &lt; 0.05). The cutoff point for SSD-12 was ≥16 in total patients; 16, 16, 17 in biomedical, TCM and psychosomatic settings, respectively. The cutoff point for PHQ-15 was found to be ≥8 in total patients; 8, 9, 11 in biomedical, TCM and psychosomatic settings, respectively.Conclusion: SSD patients from psychosomatic departments had higher level of somatic symptom severity, depression and anxiety than from TCM and biomedical settings. In our specific sample, a cutoff point of ≥16 for SSD-12 could be recommended in all three settings. But the cutoff point of PHQ-15 differs much between different settings, which was ≥8, 9, and 11 in biomedical, TCM, and psychosomatic settings, respectively.

Differences in psychometric characteristics of outpatients with somatic symptom disorder from general hospital biomedical (neurology/gastroenterology), traditional Chinese medicine, and psychosomatic settings