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Toward Understanding Users’ Interactions with a Mental Health App: An Association Rule Mining Approach

  • Conference paper
  • First Online:
Pervasive Computing Technologies for Healthcare (PH 2022)

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Abstract

Mental health apps are gaining increasing research attention. One reason for this is that many users find mental health apps a good alternative for self-management of mental conditions, especially in the last two years when access to physicians was limited because of the COVID-19 pandemic. Despite the existence of several mobile apps targeting mental health, studies show the need to explore and enhance existing mobile health (mHealth) apps to better serve patients and health practitioners. This work aims at analyzing data generated from users of a mobile app to enhance mHealth apps for improving mental health. Particularly, this paper aims to extract knowledge about the relationship between different activities (e.g., sport, home, school, etc.) that affect users’ moods. To achieve this goal, an association rule mining technique was applied on a dataset collected in the wild from 232 users of a mental health app called the FeelingMoodie app. They used the app from September 2021 to May 2022. Our results revealed interesting associations between various daily life activities. Based on these association rules, we provide insights and recommendations for building better mHealth apps and a more personalized user experience.

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Notes

  1. 1.

    AppAdvice: https://appadvice.com/app/feeling-moodie/1581336127.

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Author information

Authors and Affiliations

  1. Dalhousie University, Halifax, NS, Canada

    Alaa Alslaity, Gerry Chan & Rita Orji

  2. FeelingMoodie Inc, Moncton, NB, Canada

    Richard Wilson

Authors
  1. Alaa Alslaity
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  2. Gerry Chan
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  3. Richard Wilson
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  4. Rita Orji
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Corresponding author

Correspondence to Alaa Alslaity .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  2. Centre for Research and Technology Hellas, Thessaloniki, Greece

    Andreas Triantafyllidis

APPENDIX 1. Summary of the Association Rules

APPENDIX 1. Summary of the Association Rules

Rule

Mood

Antecedent

Consequent

{‘Romance’, ‘Sports’}

{‘Family’}

Overall

{‘Event’, ‘Friends’}

{‘Exams’}

Sad

{‘Event’, ‘Romance’}

{‘Exams’}

Sad

{‘Event’, ‘Sports’}

{‘Family’}

Sad

{‘Friends’}

Sad

{‘Exams’, ‘Family’}

{‘Music’}

Mad

{‘Exams’, ‘Hobbies’}

{‘Food’}

Good

{‘Music’}

Rad

{‘Sports’}

Rad

{‘Exams’, ‘Music’}

{‘Family’}

Mad

{‘Exams’, ‘Sleep’}

{‘Food’}

Good

{‘Exams’, ‘Sports’}

{‘Friends’}

Sad

{‘Exams’, ‘Weather’}

{‘Family’}

Rad

{‘Family’, ‘Romance’}

{‘Friends’}

Mad

{‘Family’, ‘Sports’}

{‘Friends’}

Sad

{‘Finance’, ‘Home’}

{‘Family’}

Mad

{‘Friends’, ‘Hobbies’}

{‘Relax’}

Mad

{‘Friends’, ‘Music’}

{‘Family’}

Mad

{‘Friends’, ‘Romance’}

{‘Family’}

Mad

{‘Friends’, ‘Sleep’}

{‘Food’}

Good

{‘Food’}

Overall

{‘Friends’, ‘Sports’}

{‘Family’}

Sad

{‘Friends’, ‘Weather’}

{‘Family’}

Neutral

{‘Hobbies’, ‘Sleep’}

{‘Food’}

Good

{‘Friends’}

Neutral

{‘Food’}

Overall

{‘Hobbies’, ‘Sports’}

{‘Sleep’}

Overall

{‘Friends’}

Neutral

{‘Music’, ‘Hobbies’}

{‘Food’}

Good

{‘Music’, ‘Hobbies’}

{‘Sleep’}

Overall

{‘Music’, ‘Romance’}

{‘Sleep’}

Overall

{‘Music’, ‘Work’}

{‘Family’}

Mad

 

{‘Sleep’}

Sad

{‘Other’, ‘Family’}

{‘Food’}

Good

{‘Other’, ‘Sleep’}

{‘Food’}

Good

{‘Romance’, ‘Hobbies’}

{‘Food’}

Good

{‘Romance’, ‘Sleep’}

{‘Friends’}

Neutral

 

{‘Music’}

Mad

{‘Romance’, ‘Sports’}

{‘Family’}

Sad

 

{‘Family’}

Rad

 

{‘Friends’}

Neutral

 

{‘Friends’}

Sad

{‘Romance’, ‘Work’}

{‘Friends’}

Neutral

{‘Shopping’, ‘Family’}

{‘Friends’}

Neutral

{‘Shopping’, ‘Food’}

{‘Sleep’}

Overall

{‘Shopping’, ‘Friends’}

{‘Family’}

Neutral

 

{‘Family’}

Rad

{‘Shopping’, ‘Home’}

{‘Sleep’}

Overall

 

{‘Food’}

Good

{‘Shopping’, ‘Music’}

{‘Family’}

Rad

 

{‘Weather’}

Rad

{‘Shopping’, ‘Relax’}

{‘Home’}

Rad

 

{‘Sleep’}

Overall

{‘Shopping’, ‘Sleep’}

{‘Food’}

Good

 

{‘Food’}

Overall

{‘Shopping’, ‘Sports’}

{‘Home’}

Rad

{‘Shopping’, ‘Weather’}

{‘Family’}

Rad

{‘Weather’, ‘Hobbies’}

{‘Romance’}

Neutral

{‘Weather’, ‘Romance’}

{‘Friends’}

Neutral

{‘Weather’, ‘Sleep’}

{‘Music’}

Mad

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Alslaity, A., Chan, G., Wilson, R., Orji, R. (2023). Toward Understanding Users’ Interactions with a Mental Health App: An Association Rule Mining Approach. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_32

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  • DOI: https://doi.org/10.1007/978-3-031-34586-9_32

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