BACKGROUND
International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics.


AIMS
To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity.


METHOD
Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results.


RESULTS
Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%.


CONCLUSIONS
The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder.

Abstract Background The nine-item Patient Health Questionnaire (PHQ-9) and the seven-item Generalised Anxiety Disorder scale (GAD-7) are routinely used in research and clinical practice. Whilst measurement invariance of these measures across gender has been demonstrated individually in general population studies and clinical samples, less is known about invariance of the distinct but correlated latent factors (‘depression’ and ‘anxiety’). The current study assessed measurement invariance of these constructs across males and females seeking treatment for common mental health disorders. Methods Data were provided from eight psychological treatment services in London, England. Data from initial assessments with the services where individual items on the PHQ-9 and GAD-7 were available were included in analyses. Measurement invariance was explored across self-identified genders, with ‘male’ and ‘female’ categories available in the dataset. Sensitivity analyses were conducted using propensity score matching on sociodemographic and clinical variables. Results Data were available for 165,872 patients (110,833 females, 55,039 males). There was evidence of measurement invariance between males and females in both the full sample and a propensity score matched sample (n = 46,249 in each group). Conclusions Measurement invariance of the correlated depression and anxiety factors of PHQ-9 and GAD-7 were indicated in this sample of individuals seeking psychological treatment for CMHDs. These results support the use of these measures in routine clinical practice for both males and females. This is of particular importance for assessing the prevalence of clinically significant levels of symptoms as well as comparing treatment outcomes across genders. 

/pdf/measurement-invariance-of-the-phq-9-and-gad-7-across-males-3c9wiib4.pdf

Measurement invariance of the PHQ-9 and GAD-7 across males and females seeking treatment for common mental health disorders

BackgroundMajor depressive disorder (MDD) is a heterogeneous mental disorder. International guidelines present overall symptom severity as the key dimension for clinical characterisation. However, additional layers of individual differences may reside within severity levels related to differences in how symptoms interact with one-another in a given patient, referred to as symptom dynamics. We investigate these individual differences by estimating the proportion of patients that display differences in their symptom relationship patterns while sharing the same overall symptom severity.MethodsPatients with MDD recruited at four centres in the Netherlands between 2016-2018 rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for symptoms were collected through Ecological Momentary Assessments scheduled to measure each patient five times per day for 28 days. Each patient’s symptom dynamics were estimated using dynamic networks based on the graphical vector autoregressive model. Individual differences in these symptom relationship patterns in groups of patients sharing the same symptom severity levels were estimated using Individual Network Invariance Tests, before the overall proportion of patients that displayed differential symptom dynamics while sharing the same symptom severity was calculated. To compute 95% bootstrapped confidence intervals around this proportion, 10,000 re-estimations following random draws with replacement of the symptom severity groups were performed. A supplementary simulation study was conducted to investigate the accuracy of our methodology by identifying its average false positive detection rate in a simulated scenario where no individual differences should be present.ResultsOut of 74 patients, 73 were analysed (Mage = 34.57 [SD 13.12]; 56.16% females; 63.01% employed) and 8,395 observations were conducted across the 28-day period (average completed assessments per person: 115; SD 16.81). 23 severity levels (IDS-SR values) were observed in the sample, enabling the investigation of individual differences in 23 groups of MDD patients (between 2 to 6 patients per group) sharing the same severity. Differential symptom dynamics were identified across 63.01% (95% bootstrapped CI 40.98, 82.05) of patients displaying the same severity. The simulation study revealed our method’s average false detection of individual differences in our scenario to be 2.22%.ConclusionsThe majority of MDD patients sharing the same symptom severity displayed differential symptom dynamics. Our findings were robust against false positive conclusions. Examining symptom dynamics provides information about the person-specific psychopathological expression of patients beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may serve as a promising dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression. 

pdf/towards-precision-in-the-diagnostic-profiling-of-patients-3zxbbp6476.pdf

Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics in the assessment of major depressive disorder

Background Cognitive–behavioural therapy (CBT) and counselling for depression (CfD) are recommended first-line treatments for depression. While they approach change differently, there is little understanding of the impact those approaches have on change during treatment. Objectives This study aimed to identify whether CBT and CfD target different symptoms and explore the implications of modelling choices when quantifying change during treatment. Methods Symptom-specific effects of treatment were identified using moderated network modelling. This was a retrospective cohort study of 12 756 individuals who received CBT or CfD for depression in primary/community care psychological therapy services in England. Change was modelled several ways within the whole sample and a propensity score matched sample (n=3446). Findings CBT for depression directly affected excessive worry, trouble relaxing and apprehensive expectation and had a stronger influence on changes between suicidal ideation and concentration. CfD had a stronger direct influence on thoughts of being a failure and on the associated change between being an easily annoyed and apprehensive of expectation. There were inconsistencies when modelling change using the first and second appointments as the baseline. Residual score models produced more conservative findings than models using difference scores. Conclusions CfD and CBT for depression have differential effects on symptoms demonstrating specific mechanisms of change. Clinical implications CBT was uniquely associated with changes in symptoms associated with anxiety and may be better suited to those with anxiety symptoms comorbid to their depression. When assessing change, the baseline should be the first therapy session, not the pretreatment assessment. Residual change scores should be preferred over difference score methods.

https://mentalhealth.bmj.com/content/ebmental/26/1/e300621.full.pdf

Symptom-specific effects of counselling for depression compared to cognitive–behavioural therapy

Routine outcome monitoring (ROM), including the use of feedback, has become a much vaunted method in psychological therapies but is little used in university/college counselling and mental health services, perhaps because its adoption raises questions for many practitioners and service leaders. There is a need for both clinical‐ and research‐based statements to clarify the reasoning and rationale for ROM. This paper aims to present and respond to common challenges of and reservations about using ROM in student counselling and/or mental health services.The article poses 15 questions and issues about the adoption of ROM drawn from the literature on this topic and further refined by practitioner‐ and researcher‐members of a consortium comprising service leads, practitioners, and researchers working in the field of student counselling in the UK. The questions address nine themes: (1) the purpose and yield of ROM; (2) the burden of measurement; (3) the impact on clients and process of therapy; (4) consistency with therapeutic theory; (5) client groups and settings; (6) concern from practitioners; (7) equality, diversity, and inclusion; (8) implementation; and (9) relationship with the paradigm of practice‐based evidence.Responses to each of the 15 questions are provided from a methodological, evidence‐based, and clinical perspective.The responses provide practitioners with the necessary information to enable them to make informed decisions as to the value, or otherwise, of adopting ROM, including feedback, in the delivery of counselling interventions, and generating evidence created from clinical practice.

Routine outcome monitoring (ROM) and feedback in university student counselling and mental health services: Considerations for practitioners and service leads

Psychotherapy is an effective treatment for many common mental health problems, but the mechanisms of action and processes of change are unclear, perhaps driven by the focus on a single diagnosis which does not reflect the heterogeneous symptom experiences of many patients. The objective of this study was to better understand therapeutic change, by illustrating how symptoms evolve and interact during psychotherapy. Data from 113,608 patients from psychological therapy services who completed depression and anxiety symptom measures across three to six therapy sessions were analysed. A panel graphical vector-autoregression model was estimated in a model development sample (N = 68,165) and generalizability was tested in a confirmatory model, fitted to a separate (hold-out) sample of patients (N = 45,443). The model displayed an excellent fit and replicated in the confirmatory holdout sample. First, we found that nearly all symptoms were statistically related to each other (i.e. dense connectivity), indicating that no one symptom or association drives change. Second, the structure of symptom interrelations which emerged did not change across sessions. These findings provide a dynamic view of the process of symptom change during psychotherapy and give rise to several causal hypotheses relating to structure, mechanism, and process. 

/pdf/transdiagnostic-symptom-dynamics-during-psychotherapy-2iyss3ct.pdf

Transdiagnostic symptom dynamics during psychotherapy

https://doi.org/10.1016/j.comppsych.2022.152348

Are students less likely to respond to routinely delivered psychological treatment? A retrospective cohort analysis

Assessment attendance and treatment engagement with talking and internet-enabled therapies of people with and without a long-term physical health condition: analysis of Talking Therapies service data.

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Transdiagnostic symptom dynamics during psychotherapy

Are students less likely to respond to routinely delivered psychological treatment? A retrospective cohort analysis

Assessment attendance and treatment engagement with talking and internet-enabled therapies of people with and without a long-term physical health condition: analysis of Talking Therapies service data.