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Measurements of engagement in mobile behavioural interventions?

Measurements of engagement in mobile behavioural interventions?
Measurements of engagement in mobile behavioural interventions?
Mobile digital behaviour changes interventions (mDBCIs) are becoming increasingly useful and necessary within healthcare and wellbeing. Health interventions need to close the gap between intention to behave and the behaviour itself. If apps fail to engage users, the behavioural intervention material is never seen. This paper investigates the measurements of engagement using a health based quiz app. Quiz questions were created using the NHS website and fell into the following six categories: “healthy eating”, “losing weight”, “sleep”, “fitness”, “food” and “smoking”. Gamification features such as count down timers were used to encourage user participation. Notifications, with individual goals, were sent out to nudge the users to play the quiz. Engagement was measured in two ways. Firstly, a count of completed quiz questions to illustrate app engagement. Secondly, a participants learning was evaluated using a learning curve. This measured whether a participant understood and retained the health facts, illustrated by an improvement in their answers over time. A comparison between two participants using the first measurement (count of completed quiz questions) would have shown an identical rate of engagement, both answering 44 questions. However, the second measure (engagement with app content) showed a different rate of engagement. One participant improved producing the expected learning curve whilst the other consistently answered questions wrong, showing a lack of engagement with the intervention material.
Participants were allowed to select their preferred category of question and in this case chose different areas. This was to encourage them to choose topics of interest to further improve their commitment to the study and provide a more personal experience. Future research would include more participants within each question category for further analysis and direct comparison. However, this research illustrates that these two types of engagement, app and intervention material, separately paint different pictures. To improve the design and effectiveness of mDBCIs engagement analysis, studies need to utilise more than one method of measurement
Weston, Anna
96f3b126-bee2-45c5-82ff-8316a3c819cb
Morrison, Leanne
920a4eda-0f9d-4bd9-842d-6873b1afafef
Yardley, Lucy
64be42c4-511d-484d-abaa-f8813452a22e
Van Kleek, Max
4d869656-cd47-4cdf-9a4f-697fa9ba4105
Weal, Mark
e8fd30a6-c060-41c5-b388-ca52c81032a4
Weston, Anna
96f3b126-bee2-45c5-82ff-8316a3c819cb
Morrison, Leanne
920a4eda-0f9d-4bd9-842d-6873b1afafef
Yardley, Lucy
64be42c4-511d-484d-abaa-f8813452a22e
Van Kleek, Max
4d869656-cd47-4cdf-9a4f-697fa9ba4105
Weal, Mark
e8fd30a6-c060-41c5-b388-ca52c81032a4

Weston, Anna, Morrison, Leanne, Yardley, Lucy, Van Kleek, Max and Weal, Mark (2015) Measurements of engagement in mobile behavioural interventions? At Digital Health 2015 Digital Health 2015, Italy. 18 - 20 May 2015. 8 pp.

Record type: Conference or Workshop Item (Poster)

Abstract

Mobile digital behaviour changes interventions (mDBCIs) are becoming increasingly useful and necessary within healthcare and wellbeing. Health interventions need to close the gap between intention to behave and the behaviour itself. If apps fail to engage users, the behavioural intervention material is never seen. This paper investigates the measurements of engagement using a health based quiz app. Quiz questions were created using the NHS website and fell into the following six categories: “healthy eating”, “losing weight”, “sleep”, “fitness”, “food” and “smoking”. Gamification features such as count down timers were used to encourage user participation. Notifications, with individual goals, were sent out to nudge the users to play the quiz. Engagement was measured in two ways. Firstly, a count of completed quiz questions to illustrate app engagement. Secondly, a participants learning was evaluated using a learning curve. This measured whether a participant understood and retained the health facts, illustrated by an improvement in their answers over time. A comparison between two participants using the first measurement (count of completed quiz questions) would have shown an identical rate of engagement, both answering 44 questions. However, the second measure (engagement with app content) showed a different rate of engagement. One participant improved producing the expected learning curve whilst the other consistently answered questions wrong, showing a lack of engagement with the intervention material.
Participants were allowed to select their preferred category of question and in this case chose different areas. This was to encourage them to choose topics of interest to further improve their commitment to the study and provide a more personal experience. Future research would include more participants within each question category for further analysis and direct comparison. However, this research illustrates that these two types of engagement, app and intervention material, separately paint different pictures. To improve the design and effectiveness of mDBCIs engagement analysis, studies need to utilise more than one method of measurement

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More information

Accepted/In Press date: 26 March 2015
Published date: May 2015
Venue - Dates: Digital Health 2015, Italy, 2015-05-18 - 2015-05-20
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 377613
URI: https://eprints.soton.ac.uk/id/eprint/377613
PURE UUID: 60ecf0b2-984c-450f-b474-8ef2f281eea2
ORCID for Anna Weston: ORCID iD orcid.org/0000-0003-1675-0466
ORCID for Leanne Morrison: ORCID iD orcid.org/0000-0002-9961-551X
ORCID for Mark Weal: ORCID iD orcid.org/0000-0001-6251-8786

Catalogue record

Date deposited: 05 Oct 2015 08:21
Last modified: 06 Jun 2018 13:08

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