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Using personal characteristics and usage patterns for the prediction of adherence and effectiveness in a web-based intervention for the treatment of depression.

Liebing, M. (2014) Using personal characteristics and usage patterns for the prediction of adherence and effectiveness in a web-based intervention for the treatment of depression.

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Abstract:Background: Web-based interventions have been shown to be effective and efficient means of providing eHealth. It has been established, however, that high rates of non-adherence (ie. users not following the intervention protocol) have a detrimental effect on their success. At this point there are numerous theories surrounding the various factors that determine effectiveness and adherence in web-based interventions, but none that have been able to do so satisfactorily. Personal factors as well as use of the intervention seem to play an important role. Objective: The aims of this study were to (1) find out to what degree a combination of adherence, personal factors and usage patterns of the intervention could determine its effectiveness, (2) examine to what degree personal factors and usage patterns can predict adherence, (3) determine if there is any added value in using data from the first two weeks as opposed to the first week alone and (4) evaluate, whether there is any advantage to using usage patterns instead of separate intervention features for predicting effectiveness and adherence. Methods: Data were used from 195 participants that used the Web-based intervention Living to the full, a Web-based intervention for the prevention of depression, that had previously been shown to be effective. Log data of the original study were analyzed in quantitative analyses. Outcome measures were mean improvement scores on the Center for Epidemiologic Studies Depression Scale (CES-D) and Hospital Anxiety and Depression Scale (HADS-A), as well as the lesson a participant reached. Results: There were two variables that proved significant for predicting improvement scores for depression: adherence and being divorced. Use of certain intervention features was also significant. Regarding anxiety, no consistent significant predictors could be found. The most predictive power was found in determining adherence with R2 –values of up to .30. The only personal factor of significance was the average time spent online per day, but a number of intervention features were also indicated as predictors for adherence when used together. Conclusions: Valuable lessons for future research were learned by including usage patterns as well as personal characteristics in the research model for predicting effectiveness and adherence. Although the predictive value for effectiveness was small, results regarding adherence are more promising. In future, this may help in taking preventative action to keep users engaged with web-based interventions so they can derive as much effect from them as possible.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:77 psychology
Programme:Psychology MSc (66604)
Link to this item:http://purl.utwente.nl/essays/66247
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