Does Internet-based guided-self-help for depression cause harm? An individual participant data meta-analysis on deterioration rates and its moderators in randomized controlled trials.

BACKGROUND: Almost nothing is known about the potential negative effects of Internet-based psychological treatments for depression. This study aims at investigating deterioration and its moderators within randomized trials on Internet-based guided self-help for adult depression, using an individual patient data meta-analyses (IPDMA) approach. METHOD: Studies were identified through systematic searches (PubMed, PsycINFO, EMBASE, Cochrane Library). Deterioration in participants was defined as a significant symptom increase according to the reliable change index (i.e. 7.68 points in the CES-D; 7.63 points in the BDI). Two-step IPDMA procedures, with a random-effects model were used to pool data. RESULTS: A total of 18 studies (21 comparisons, 2079 participants) contributed data to the analysis. The risk for a reliable deterioration from baseline to post-treatment was significantly lower in the intervention v. control conditions (3.36 v. 7.60; relative risk 0.47, 95% confidence interval 0.29-0.75). Education moderated effects on deterioration, with patients with low education displaying a higher risk for deterioration than patients with higher education. Deterioration rates for patients with low education did not differ statistically significantly between intervention and control groups. The benefit-risk ratio for patients with low education indicated that 9.38 patients achieve a treatment response for each patient experiencing a symptom deterioration. CONCLUSIONS: Internet-based guided self-help is associated with a mean reduced risk for a symptom deterioration compared to controls. Treatment and symptom progress of patients with low education should be closely monitored, as some patients might face an increased risk for symptom deterioration. Future studies should examine predictors of deterioration in patients with low education.

Polynomial time algorithm for minmax scheduling with common due-window and proportional-linear shortening processing times.

This article deals with common due-window assignment and single-machine scheduling with proportional-linear shortening processing times. Objective cost is a type of minmax, that is, the maximal cost among all processed jobs is minimized. Our goal is to determine an optimal schedule, the optimal starting time, and size of due-window that minimize the worst cost, which consist of four parts: earliness, tardiness, starting time and length of the due-window. Optimal properties of the problem are given, and then an optimal polynomial algorithm is proposed to solve the problem.

A maintenance activity scheduling with time-and-position dependent deteriorating effects.

We deal with a single-machine scheduling problem with an optional maintenance activity (denoted by $ ma $), where the actual processing time of a job is a function of its starting time and position. The optional $ ma $ means that the machine will perform a $ ma $, after $ ma $ is completed, the machine will return to the initial state. The objective is to determine an optimal job sequence and the location of the maintenance activity such that makespan is to be minimized. Based on some properties of an optimal sequence, we introduce a polynomial time algorithm to solve the problem, and the time complexity is $ O({n}^4) $, where $ {n} $ is the number of jobs.

Single machine resource allocation scheduling problems with deterioration effect and general positional effect.

This paper investigates single machine scheduling problems where the actual processing time of a job is dependent on its starting time, processing position and the amount of resource allocation. We present two unified models and provide a bicriteria analysis for the general scheduling criteria and the total weighted resource consumption. We consider two different versions for treating the two criteria and show that the unified models can be applied to solve scheduling problems under various due window assignment considerations. We prove that two different versions of the problems can be solved in polynomial time, respectively.