Step away from depression-results from a multicenter randomized clinical trial with a pedometer intervention during and after inpatient treatment of depression.

Evidence for the effectiveness of physical activity (PA) in the treatment of depression prevails for outpatients with mild and moderate symptom levels. For inpatient treatment of severe depression, evidence-based effectiveness exists only for structured and supervised group PA interventions. The Step Away from Depression (SAD) study investigated the effectiveness of an individual pedometer intervention (PI) combined with an activity diary added to inpatient treatment as usual (TAU). In this multicenter randomized controlled trial, 192 patients were randomized to TAU or TAU plus PI. The two primary outcomes at discharge were depression-blindly rated with the Montgomery-Åsberg Depression Rating Scale (MADRS)-and average number of daily steps measured by accelerometers. Secondary outcomes were self-rated depression and PA, anxiety, remission and response rates. Multivariate analysis of variance (MANOVA) revealed no significant difference between both groups for depression and daily steps. Mean MADRS scores at baseline were 29.5 (SD = 8.3) for PI + TAU and 28.8 (SD = 8.1) for TAU and 16.4 (SD = 10.3) and 17.2 (SD = 9.9) at discharge, respectively. Daily steps rose from 6285 (SD = 2321) for PI + TAU and 6182 (SD = 2290) for TAU to 7248 (SD = 2939) and 7325 (SD = 3357). No differences emerged between groups in secondary outcomes. For severely depressed inpatients, a PI without supervision or further psychological interventions is not effective. Monitoring, social reinforcement and motivational strategies should be incorporated in PA interventions for this population to reach effectiveness.

Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men.

Machine learning is increasingly introduced into medical fields, yet there is limited evidence for its benefit over more commonly used statistical methods in epidemiological studies. We introduce an unsupervised machine learning framework for longitudinal features and evaluate it using sexual behaviour data from the last 20 years from over 3'700 participants in the Swiss HIV Cohort Study (SHCS). We use hierarchical clustering to find subgroups of men who have sex with men in the SHCS with similar sexual behaviour up to May 2017, and apply regression to test whether these clusters enhance predictions of sexual behaviour or sexually transmitted diseases (STIs) after May 2017 beyond what can be predicted with conventional parameters. We find that behavioural clusters enhance model performance according to likelihood ratio test, Akaike information criterion and area under the receiver operator characteristic curve for all outcomes studied, and according to Bayesian information criterion for five out of ten outcomes, with particularly good performance for predicting future sexual behaviour and recurrent STIs. We thus assess a methodology that can be used as an alternative means for creating exposure categories from longitudinal data in epidemiological models, and can contribute to the understanding of time-varying risk factors.

Assessing the potential impact of transmission during prolonged viral shedding on the effect of lockdown relaxation on COVID-19.

A key parameter in epidemiological modeling which characterizes the spread of an infectious disease is the generation time, or more generally the distribution of infectiousness as a function of time since infection. There is increasing evidence supporting a prolonged viral shedding window for COVID-19, but the transmissibility in this phase is unclear. Based on this, we develop a generalized Susceptible-Exposed-Infected-Resistant (SEIR) model including an additional compartment of chronically infected individuals who can stay infectious for a longer duration than the reported generation time, but with infectivity reduced to varying degrees. Using the incidence and fatality data from different countries, we first show that such an assumption also yields a plausible model in explaining the data observed prior to the easing of the lockdown measures (relaxation). We then test the predictive power of this model for different durations and levels of prolonged infectiousness using the incidence data after the introduction of relaxation in Switzerland, and compare it with a model without the chronically infected population to represent the models conventionally used. We show that in case of a gradual easing on the lockdown measures, the predictions of the model including the chronically infected population vary considerably from those obtained under a model in which prolonged infectiousness is not taken into account. Although the existence of a chronically infected population still remains largely hypothetical, we believe that our results provide tentative evidence to consider a chronically infected population as an alternative modeling approach to better interpret the transmission dynamics of COVID-19.