Dropout in 3 online interventions: role of attitudes, usability, and acceptability
Method Stream: Other
ECs: Only 14 EC (standard, no or limited own data collection. Applicable in case of a clinical internship)
Description:
Evidence supporting self-applied online interventions to increase well-being and reduce symptoms of anxiety and depression, among other benefits is robust. However, it is common knowledge that the dropout rate of the participants is very high. Reasons for dropout vary, with variables such as attitudes, usability, and acceptability being of particularly high relevance. However, most of the available literature researches dropout in the context of a single intervention, and not analyzing several interventions at once.
In this study, we will analyze the data available from participants who dropped out on three online interventions implemented during the COVID-19 pandemic, this data includes Attitudes towards psychological online interventions, usability, and five open questions that allowed the participants to explain in more detail their reasons for dropping out. Along with this data each intervention implemented a series of psychometric instruments that evaluated depression, anxiety, and sleep quality (among others).
The results of this study will help understand how to overcome the barriers of participants for using online interventions and how to reduce the probability of dropping out, allowing participants to benefit more from these online interventions.
Who are we looking for?
Students with an interest in researching quantitative data and interest in analyzing dropout rates and increase of adherence. Interest in (advanced) R programming is beneficial.
What do we offer?
Available data previously collected from 3 studies.