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International Conference on Human-Computer Interaction

HCII 2021: HCI International 2021 - Late Breaking Posters pp 197–206Cite as

Rewards in Mental Health Applications for Aiding with Depression: A Meta-analysis

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1499))

Abstract

The combination of technology and therapeutic techniques has shown promise of providing people relief through mental health applications (MHapps). Previous research shows that computerized cognitive behavioral therapy can effectively reduce depressive symptoms. Anhedonia, a common symptom of depression, often leads to blunted sensitivity to reward. However, technology-based reward elements have been suggested as a way of increasing motivation and adherence towards treatment and app usage, yet it is unclear whether reward elements also help reduce depressive symptoms. We hypothesize that MHapps with reward elements will provide a greater reduction in depressive symptoms than MHapps without reward elements. Utilizing the PRISMA guidelines, a total of 5,597 articles were collected from 5 different databases. After duplicate removal, 2,741 articles remained to be manually screened by two independent researchers based on their titles and abstract. Once the screening phase concluded, 2,640 articles were excluded for failing to meet inclusion criteria or engaging in one or more of the exclusion criteria with an inter-rater reliability k-value of 0.85. Ultimately, 41 articles remained for data extraction. From these articles, 58 total comparisons between post-intervention MHapp interventions groups and control groups were included in the meta-analysis. We conducted three random effects models to compare the results of all studies (n = 58), the studies which included reward elements (n = 14), and the studies which did not include reward elements (n = 44). Results showed a small to moderate effect size across all MHapps in which the MHapp intervention effectively reduced depressive symptoms compared to controls (Hedge’s g = −.28). While reward-based MHapps (g = −.32) elicited a numerically larger effect size than MHapps without rewards (g = −.27), there was no significant difference in effectiveness between MHappps with and without rewards. This research has important clinical implications for understanding how reward elements influence the effectiveness of MHapps on depressive symptoms.

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Authors and Affiliations

  1. Clemson University, Clemson, SC, 29634, USA

    Stephanie Six, Maggie Harris, Emma Winterlind & Kaileigh Byrne

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  1. Stephanie Six
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  2. Maggie Harris
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  3. Emma Winterlind
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  4. Kaileigh Byrne
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Correspondence to Kaileigh Byrne .

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Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  3. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Six, S., Harris, M., Winterlind, E., Byrne, K. (2021). Rewards in Mental Health Applications for Aiding with Depression: A Meta-analysis. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Late Breaking Posters. HCII 2021. Communications in Computer and Information Science, vol 1499. Springer, Cham. https://doi.org/10.1007/978-3-030-90179-0_26

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  • DOI: https://doi.org/10.1007/978-3-030-90179-0_26

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