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Record W4361988428 · doi:10.2196/41638

Gamifying Cognitive Behavioral Therapy Techniques on Smartphones for Bangkok’s Millennials With Depressive Symptoms: Interdisciplinary Game Development

2023· article· en· W4361988428 on OpenAlexvenueno aff
Poe Sriwatanathamma, Veerawat Sirivesmas, Sone Simatrang, Nobonita Himani Bhowmik

Bibliographic record

VenueJMIR Serious Games · 2023
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativePsychologyMental healthVideo gameCognitionApplied psychologyPsychotherapistMultimediaComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: There is serious concern over the annual increase in depressive symptoms among millennials in Bangkok, Thailand. Their daily routine revolves around the use of their smartphones for work and leisure. Although accessibility to mental health care is expanding, it cannot keep up with the demand for mental health treatment. Outside Thailand, multiple projects and studies have attempted to merge gamification mechanisms and cognitive behavioral therapy (CBT) to create mobile health intervention apps and serious games with positive feedback. This presents an opportunity to explore the same approach in Thailand. OBJECTIVE: This study investigated the development process of gamifying CBT techniques to support game mechanics in a visual narrative serious game, BlueLine. The primary target of this research is Bangkok's millennials. In the game, players play as Blue, a Bangkok millennial who struggles to live through societal norms that influence his digital life and relationships. Through in-game scenarios, players will learn and understand how to lessen the impact of depressive symptoms via gamified interactions on their smartphones. METHODS: First, this paper follows each development step of solidifying BlueLine's game structure by integrating the Activating Events, Beliefs, Consequences, Disputation of Beliefs and Effective New Approaches (ABCDE) model and narrative in games. Second, the approach to select CBT and related therapeutic elements for gamification is based on suitability to the game structure. Throughout the process, CBT experts in Thailand have reviewed these scenarios. The approach forms the base of the player's interactions throughout the scenarios in BlueLine, broken down into 4 types of gamified mechanisms: narrative, verbal interactions, physical interactions, and social media interactions. RESULTS: With the game structure based on the ABCDE model, BlueLine scenarios implement gamified mechanisms in conjunction with the following CBT and related therapeutic elements: behavioral activation, self-monitoring, interpersonal skills, positive psychology, relaxation and mindful activities, and problem-solving. In each scenario, players guide Blue to overcome his triggered dysfunctional beliefs. During this process, players can learn and understand how to lessen the impact of depressive symptoms through gamified interactions. CONCLUSIONS: This paper presents the development process of gamifying CBT and related therapeutic techniques in BlueLine game scenarios. A scenario can harbor multiple techniques, including behavioral activation, self-monitoring, interpersonal skills, positive psychology, relaxation and mindful activities, and problem-solving. BlueLine's game structure does not limit the fact that the same combination of CBT elements ties each gamified mechanism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.434
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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