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Using the TWEETS for expected engagement assessment to personalize mental health apps A Mixed-Methods Approach.

Berden, N.K. (2020) Using the TWEETS for expected engagement assessment to personalize mental health apps A Mixed-Methods Approach.

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Abstract:Background: Besides the public availability and continuously increasing uptake rates of mobile mental health (mMH) interventions, potential users rarely continue interacting with those apps after their download. One reason for this seems to be the failure of those apps to engage the users and motivate further interaction with the interventions. In this context, engagement is seen as a subjective experience with an app which is created by the expectation that a certain app could satisfy certain mental health needs as well as the individual’s intention to direct one’s thoughts, emotions and behaviour towards the interaction with that app. Though research in this area is still scarce at this point, findings of previous studies point at a connection between engagement and personalization of mMH apps. Here, the users’ engagement with mMH apps was suggested to improve through personalization, which in turn related to an increased effectiveness of such apps. This leads to the current study's assumption that by assessing people’s expected engagement with different features of mMH apps, individual feature preferences could be detected to compile a personalized app version. To do so, the present study tested the applicability of the TWente Engagement with Ehealth and Technologies Scale (TWEETS) as a personalization tool for mMH apps. Methods: The mixed-methods design of this study included two consecutive online surveys and one voluntary follow-up interview (N= 11). During the first survey (N= 62), the TWEETS was used to assess participants’ expected engagement regarding different mMH app features. In the second survey (N= 58), participants were confronted with four different app versions that entailed personalized app feature combinations based on the scores of the first survey. Regarding these different app versions, participants again had to indicate their expected engagement using the TWEETS. Additional voluntary interviews were conducted to obtain information about the participants’ experiences with the TWEETS and feedback on the personalization procedure and possible improvement suggestions for both. Results: As it was hypothesised; (1) the TWEETS scores of the single app features from the first survey predicted how the suggested app versions in the second survey would be ranked; and (2) the TWEETS helped to discriminate different degrees of expected engagement between different features and app versions. Thus, the individual combination of the single features that received the highest, medium or lowest expected engagement scores in the first round were also the individually highest, medium, or lowest scored feature combinations in the second round, respectively, with significant scoring differences between those ranks. During the interviews the participants emphasized that they appreciate the opportunity to design the mMH app according to their preferences. It was also pointed out that they supported their preferences in scoring of the features and app versions and, thus, that they adjusted their scoring patterns according to the preferences they had formed before they completed the TWEETS. Nevertheless, repetitively completing the TWEETS was perceived as too time consuming and participants would prefer a quicker technique to personalize their apps. Conclusion: The current findings complement previous research about personalization and engagement by showing that the TWEETS was successful in detecting the participants' feature preferences based on their expected engagement. However, before using it as a personalization tool in real life scenarios, it is recommended to adjust its length and wording and to test its added value compared to a simpler personalization procedure. Due to the participants’ use of the TWEETS to explain their preference choices, the TWEETS seems to be useful in evaluating app features regarding their engagement potential. Further results and implications for future studies are discussed.
Item Type:Essay (Master)
Clients:
Unknown organization, Enschede
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:77 psychology
Programme:Psychology MSc (66604)
Link to this item:http://purl.utwente.nl/essays/83115
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