A Randomized Evaluation of MoodFX, a Patient-Centred e-Health Tool to Support Outcome Measurement for Depression: Une évaluation randomisée de MoodFX, un outil de santé en ligne centré sur le patient pour soutenir la mesure du résultat dans la dépression

BACKGROUND

e-Health tools using validated questionnaires to assess outcomes may facilitate measurement-based care for psychiatric disorders. MoodFX was created as a free online symptom tracker to support patients for outcome measurement in their depression treatment. We conducted a pilot randomized evaluation to examine its usability, and clinical utility.

METHODS

Patients presenting with a major depressive episode (within a major depressive or bipolar disorder) were randomly assigned to receive either MoodFX or a health information website as the intervention and control condition, respectively, with follow-up assessment surveys conducted online at baseline, 8 weeks and 6 months. The primary usability outcomes included the percentage of patients with self-reported use of MoodFX 3 or more times during follow up (indicating minimally adequate usage) and usability measures based on the System Usability Scale (SUS). Secondary clinical outcomes included the Quick Inventory of Depressive Symptomatology, Self-Rated (QIDS-SR) and Patient Health Questionnaire (PHQ-9).

RESULTS

Forty-nine participants were randomized (24 to MoodFX and 25 to the control condition). Of the 23 participants randomized to MoodFX who completed the user survey, 18 (78%) used MoodFX 3 or more times over the 6 months of the study. The mean SUS score of 72.7 (65th-69th percentile) represents good usability. Compared to the control group, the MoodFX group had significantly better improvement on QIDS-SR and PHQ-9 scores, with large effect sizes and higher response rates at 6 months. There were no differences between conditions on other secondary outcomes such as functioning and quality of life.

CONCLUSION

MoodFX demonstrated good usability and was associated with reduction in depressive symptoms. This pilot study supports the use of digital tools in depression treatment.

Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and P11 Gene DNA Methylation: Prédiction de l'efficacité précoce d'un antidépresseur chez des patients souffrant du trouble dépressif majeur d'après les caractéristiques multidimensionnelles de la méthylation de l'ADN du gène P11 et de la IRMf-rs

OBJECTIVE

This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD).

METHODS

A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening.

RESULTS

The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models.

CONCLUSION

The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD.

Factors Associated With Initiation of Antidepressant Medication in Adults With Type 1 and Type 2 Diabetes: A Primary Care Retrospective Cohort Study in Ontario, Canada

OBJECTIVES

Depression in patients with diabetes mellitus is common and associated with poorer outcomes. This study aims to identify demographic, socioeconomic and medical factors associated with the initiation of antidepressant medication after a diagnosis of diabetes in adult patients without a previous prescription for antidepressants. We also examined frequency of primary care visits in the year after antidepressant initiation compared with the year before treatment began.

METHODS

This was a retrospective cohort study using routinely collected electronic medical record data spanning January 2011 to December 2019 from the University of Toronto Practice-based Research Network (UTOPIAN) Data Safe Haven. Our primary outcome was a first prescription for an antidepressant in patients with diabetes. We used a mixed-effects logistic regression model to identify sociodemographic and medical factors associated with this event.

RESULTS

Among 22,750 patients with diabetes mellitus, 3,055 patients (13.4%) began taking an antidepressant medication. Increased odds of antidepressant initiation were observed in younger patients (odds ratio [OR], 1.77; 95% confidence interval [CI], 1.39 to 2.26), females (OR, 1.60; 95% CI, 1.46 to 1.7), those receiving insulin treatment (OR, 1.59; 95% CI, 1.43 to 1.78) and cases of polypharmacy (OR, 3.67; 95% CI, 3.29 to 4.11). There was an increase in the mean number of primary care visits from 4.6 to 5.9 per year after antidepressant initiation.

CONCLUSIONS

In patients with diabetes, age, sex and medical characteristics were associated with the initiation of antidepressants. These patients accessed primary care more frequently. Screening and prevention of depression, particularly in these subgroups, could reduce its personal and systemic burdens.

Association Between Antidepressant Use and Adherence to Anti-hyperglycemic Medications in Adults With Type 2 Diabetes and Depression: A Retrospective Cohort Study

OBJECTIVES

Depression is a known risk factor for poor medication adherence, but it is unclear whether depression treatment affects adherence rates. In this study, we examined the association between pharmacologic treatment of a new depressive episode and subsequent adherence to oral anti-hyperglycemic medications.

METHODS

In this retrospective cohort study we used administrative health data to follow adult new metformin users in Alberta, Canada, between 2008 and 2018. Depressive episodes starting ≥1 year after metformin initiation were identified and individuals starting antidepressant treatment within the first 90 days were compared with those who did not. The proportion of days covered (PDC) with oral anti-hyperglycemic medications in the subsequent year (days 91 to 455) was used to estimate adherence. The association between antidepressant treatment and poor adherence (PDC<0.8) was examined using multivariate logistic regression models.

RESULTS

A new depressive episode occurred in 6,201 people, with a mean age of 56.0 (standard deviation [SD], 15.4) years. Of this cohort, 3,303 (53.2%) were women. Mean PDC was 0.55 (SD, 0.41); 924 (57.0%) of 1,621 people who started antidepressant treatment and 2,709 (59.2%) of 4,580 controls had poor adherence (p=0.13). After adjusting for baseline comorbidities and other characteristics, antidepressant treatment was associated with a lower likelihood of poor adherence (adjusted odds ratio, 0.85; 95% confidence interval, 0.75 to 0.96; p=0.007).

CONCLUSIONS

Although overall adherence to anti-hyperglycemic medications was low after onset of a depressive episode, antidepressant treatment was associated with a lower likelihood of poor adherence.

[Psychotropic drugs in general practice]

The article presents a user-friendly overview of psychotropic drugs which are helpful for the prescription in a primary care practice. The author recommends to get familiar with just a small selection of drugs first and second line. This means to know well about their effectiveness, short-and long-term side effects, interactions with other drugs and the necessary monitoring that should be done.