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Depression : a very short introduction
What is depression? What is bipolar disorder? How are they diagnosed and how are they treated? Can a small child be diagnosed with depression and treated with antidepressants - and should they be? Covering depression, manic depression, and bipolar disorder, this Very Short Introduction gives a brief account of the history of these concepts, before focussing on the descriptions and understanding of these disorders today.
Book
Mode of delivery of Cognitive Behavioral Therapy for Insomnia: a randomized controlled non-inferiority trial of digital and face-to-face therapy
Abstract
Study Objectives
Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) has demonstrated efficacy in reducing insomnia severity in self-referred and community samples. It is unknown, however, how dCBT-I compares to individual face-to-face (FtF) CBT-I for individuals referred to clinical secondary services. We undertook a randomized controlled trial to test whether fully automated dCBT-I is non-inferior to individual FtF CBT-I in reducing insomnia severity.
Methods
Eligible participants were adult patients with a diagnosis of insomnia disorder recruited from a sleep clinic provided via public mental health services in Norway. The Insomnia Severity Index (ISI) was the primary outcome measure. The non-inferiority margin was defined a priori as 2.0 points on the ISI at week 33.
Results
Individuals were randomized to FtF CBT-I (n = 52) or dCBT-I (n = 49); mean baseline ISI scores were 18.4 (SD 3.7) and 19.4 (SD 4.1), respectively. At week 33, the mean scores were 8.9 (SD 6.0) and 12.3 (SD 6.9), respectively. There was a significant time effect for both interventions (p < 0.001); and the mean difference in ISI at week 33 was −2.8 (95% CI: −4.8 to −0.8; p = 0.007, Cohen’s d = 0.7), and −4.6 at week 9 (95% CI −6.6 to −2.7; p < 0.001), Cohen’s d = 1.2.
Conclusions
At the primary endpoint at week 33, the 95% CI of the estimated treatment difference included the non-inferiority margin and was wholly to the left of zero. Thus, this result is inconclusive regarding the possible inferiority or non-inferiority of dCBT-I over FtF CBT-I, but dCBT-I performed significantly worse than FtF CBT-I. At week 9, dCBT-I was inferior to FtF CBT-I as the 95% CI was fully outside the non-inferiority margin. These findings highlight the need for more clinical research to clarify the optimal application, dissemination, and implementation of dCBT-I.
Clinicaltrials.gov: NCT02044263: Cognitive Behavioral Therapy for Insomnia Delivered by a Therapist or on the Internet: a Randomized Controlled Non-inferiority Trial.
Journal Article
Nonadherence with antipsychotic medication in schizophrenia: challenges and management strategies
Nonadherence with medication occurs in all chronic medical disorders. It is a particular challenge in schizophrenia due to the illness's association with social isolation, stigma, and comorbid substance misuse, plus the effect of symptom domains on adherence, including positive and negative symptoms, lack of insight, depression, and cognitive impairment. Nonadherence lies on a spectrum, is often covert, and is underestimated by clinicians, but affects more than one third of patients with schizophrenia per annum. It increases the risk of relapse, rehospitalization, and self-harm, increases inpatient costs, and lowers quality of life. It results from multiple patient, clinician, illness, medication, and service factors, but a useful distinction is between intentional and unintentional nonadherence. There is no gold standard approach to the measurement of adherence as all methods have pros and cons. Interventions to improve adherence include psychoeducation and other psychosocial interventions, antipsychotic long-acting injections, electronic reminders, service-based interventions, and financial incentives. These overlap, all have some evidence of effectiveness, and the intervention adopted should be tailored to the individual. Psychosocial interventions that utilize combined approaches seem more effective than unidimensional approaches. There is increasing interest in electronic reminders and monitoring systems to enhance adherence, eg, Short Message Service text messaging and real-time medication monitoring linked to smart pill containers or an electronic ingestible event marker. Financial incentives to enhance antipsychotic adherence raise ethical issues, and their place in practice remains unclear. Simple pragmatic strategies to improve medication adherence include shared decision-making, regular assessment of adherence, simplification of the medication regimen, ensuring that treatment is effective and that side effects are managed, and promoting a positive therapeutic alliance and good communication between the clinician and patient. These elements remain essential for all patients, not least for the small minority where vulnerability and risk issue dictate that compulsory treatment is necessary to ensure adherence.
Journal Article
20,000 leagues under the sea
A retelling of the classic Jules Verne story, 20,000 Leagues Under the Sea, in illustrated format.
Book
Preventing Depression in Final Year Secondary Students: School-Based Randomized Controlled Trial
Depression often emerges for the first time during adolescence. There is accumulating evidence that universal depression prevention programs may have the capacity to reduce the impact of depression when delivered in the school environment.
This trial investigated the effectiveness of SPARX-R, a gamified online cognitive behavior therapy intervention for the prevention of depression relative to an attention-matched control intervention delivered to students prior to facing a significant stressor-final secondary school exams. It was hypothesized that delivering a prevention intervention in advance of a stressor would reduce depressive symptoms relative to the control group.
A cluster randomized controlled trial was conducted in 10 government schools in Sydney, Australia. Participants were 540 final year secondary students (mean 16.7 [SD 0.51] years), and clusters at the school level were randomly allocated to SPARX-R or the control intervention. Interventions were delivered weekly in 7 modules, each taking approximately 20 to 30 minutes to complete. The primary outcome was symptoms of depression as measured by the Major Depression Inventory. Intention-to-treat analyses were performed.
Compared to controls, participants in the SPARX-R condition (n=242) showed significantly reduced depression symptoms relative to the control (n=298) at post-intervention (Cohen d=0.29) and 6 months post-baseline (d=0.21) but not at 18 months post-baseline (d=0.33).
This is the first trial to demonstrate a preventive effect on depressive symptoms prior to a significant and universal stressor in adolescents. It demonstrates that an online intervention delivered in advance of a stressful experience can reduce the impact of such an event on the potential development or exacerbation of depression.
Australian New Zealand Clinical Trials Registry ACTRN12614000316606; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=365986 (Archived by WebCite at http://www.webcitation.org/ 6u7ou1aI9).
Journal Article
A prognostic model for predicting functional impairment in youth mental health services
Functional impairment is a major concern among those presenting to youth mental health services and can have a profound impact on long-term outcomes. Early recognition and prevention for those at risk of functional impairment is essential to guide effective youth mental health care. Yet, identifying those at risk is challenging and impacts the appropriate allocation of indicated prevention and early intervention strategies.
We developed a prognostic model to predict a young person's social and occupational functional impairment trajectory over 3 months. The sample included 718 young people (12-25 years) engaged in youth mental health care. A Bayesian random effects model was designed using demographic and clinical factors and model performance was evaluated on held-out test data via 5-fold cross-validation.
Eight factors were identified as the optimal set for prediction: employment, education, or training status; self-harm; psychotic-like experiences; physical health comorbidity; childhood-onset syndrome; illness type; clinical stage; and circadian disturbances. The model had an acceptable area under the curve (AUC) of 0.70 (95% CI, 0.56-0.81) overall, indicating its utility for predicting functional impairment over 3 months. For those with good baseline functioning, it showed excellent performance (AUC = 0.80, 0.67-0.79) for identifying individuals at risk of deterioration.
We developed and validated a prognostic model for youth mental health services to predict functional impairment trajectories over a 3-month period. This model serves as a foundation for further tool development and demonstrates its potential to guide indicated prevention and early intervention for enhancing functional outcomes or preventing functional decline.
Journal Article
Clinical staging and the differential risks for clinical and functional outcomes in young people presenting for youth mental health care
Background
Clinical staging proposes that youth-onset mental disorders develop progressively, and that active treatment of earlier stages should prevent progression to more severe disorders. This retrospective cohort study examined the longitudinal relationships between clinical stages and multiple clinical and functional outcomes within the first 12 months of care.
Methods
Demographic and clinical information of 2901 young people who accessed mental health care at age 12–25 years was collected at predetermined timepoints (baseline, 3 months, 6 months, 12 months). Initial clinical stage was used to define three fixed groups for analyses (stage 1a: ‘non-specific anxious or depressive symptoms’, 1b: ‘attenuated mood or psychotic syndromes’, 2+: ‘full-threshold mood or psychotic syndromes’). Logistic regression models, which controlled for age and follow-up time, were used to compare clinical and functional outcomes (role and social function, suicidal ideation, alcohol and substance misuse, physical health comorbidity, circadian disturbances) between staging groups within the initial 12 months of care.
Results
Of the entire cohort, 2093 young people aged 12–25 years were followed up at least once over the first 12 months of care, with 60.4% female and a baseline mean age of 18.16 years. Longitudinally, young people at stage 2+ were more likely to develop circadian disturbances (odds ratio [OR]=2.58; CI 1.60–4.17), compared with individuals at stage 1b. Additionally, stage 1b individuals were more likely to become disengaged from education/employment (OR=2.11, CI 1.36–3.28), develop suicidal ideations (OR=1.92; CI 1.30–2.84) and circadian disturbances (OR=1.94, CI 1.31–2.86), compared to stage 1a. By contrast, we found no relationship between clinical stage and the emergence of alcohol or substance misuse and physical comorbidity.
Conclusions
The differential rates of emergence of poor clinical and functional outcomes between early versus late clinical stages support the clinical staging model's assumptions about illness trajectories for mood and psychotic syndromes. The greater risk of progression to poor outcomes in those who present with more severe syndromes may be used to guide specific intervention packages.
Journal Article
Are circadian rhythms more favorable with lithium than with other mood stabilizers? An exploratory actigraphy study in euthymic bipolar disorder type 1
Bipolar Disorder (BD) is associated with alterations of circadian rhythms of activity (CRA). Experimental research suggests that lithium (Li) modifies CRA, but this has been rarely explored in BD using actigraphy.
The sample comprised 88 euthymic BD-I cases with 3 weeks of actigraphy. We used a Principal Component Analysis (PCA) to generate CRA dimensions. We then used linear regression analyses to compare these dimensions between groups of individuals defined according to prescribed mood stabilizers: Li monotherapy (“Li” group, n = 28), anticonvulsant or atypical antipsychotic monotherapy (“AC or AAP” group, n = 27) or combined treatments (“Li+AC or Li+AAP” group, n = 33). Analyses were adjusted for potential confounders (gender, age, body mass index, depressive symptoms, co-prescribed benzodiazepines and antidepressants, smoking status and past alcohol use disorder).
The PCA identified two dimensions: “robust CRA” (high amplitude and interdaily stability, with low intradaily variability) and “late chronotype”. Univariate analyses showed higher scores for “robust CRA” in the “Li” versus the “AC or AAP” (p = 0.021) or “Li+AC or Li+AAP” groups (p = 0.047). These findings remained significant after adjustments (respectively p = 0.010 and p = 0.019). Post-hoc analyses suggested lower variability, higher stability and higher amplitude of CRA in the “Li” group. Medication groups were similar for the “late chronotype” dimension (p = 0.92).
This actigraphy study is the first to show more favorable CRA in BD-I individuals receiving a Li monotherapy when compared with those receiving other classes or combinations of mood stabilizers. Replications in larger samples are required. Prospective studies are also warranted to elucidate whether the introduction of Li or other mood stabilizers might influence CRA in BD-I.
Journal Article
Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis
Digital mental health tools promise to enhance the reach and quality of care. Current tools often recommend content to individuals, typically using generic knowledge-based systems or predictive artificial intelligence (AI). However, predictive AI is problematic for interventional recommendations as cause-effect relationships can be confounded in observed data. Therefore, causal AI is required to compare future outcomes under different interventions.
We aimed to develop a causal AI recommendation system that uses an individual's current presentation, their preferences, and the learned dynamics between domains to rank interventions.
We frame the recommendation problem within a Bayesian decision-theoretic framework, whereby a preference ordering of decisions is estimated using the expected utility of outcomes under interventions. The causal processes are assumed to follow a structural causal model, where the posterior distribution of structural causal models is estimated using a Markov chain Monte Carlo method. Expected utilities under interventions are estimated using a do-operation, which estimates the effects of changing a variable on outcomes, while accounting for confounders. We apply our approach to rank domains relating to mental health and well-being as intervention targets for adults (n=619) who used the Innowell Fitness app between September 2021 to September 2023 and completed a questionnaire at 2 time points (1 wk-6 mo from baseline).
The causal AI recommendation system recommends intervention targets as a function of a user's baseline presentation, the causal effects of the intervention on itself and other domains, and the utility function. In our example, psychological distress was typically the optimal intervention target in complex cases where multiple domains were unhealthy at baseline, due to it affecting multiple domains with paths to personal functioning (probability [p] of path; ppath=86%), social support (ppath=92%), sleep (ppath=88%), and physical activity (ppath=86%). The probability of being the optimal intervention target was personal functioning (popt=30%), psychological distress (popt=29%), social support (popt=18%), nutrition (popt=9.6%), substance use (popt=6.7%), sleep (popt=4.5%), and physical activity (popt=2.2%).
This work illustrates the use of causality and decision-theoretic principles to personalize interventions in digital mental health tools.
Journal Article