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Record W3131700179 · doi:10.2196/23302

Tabletop Board Game Elements and Gamification Interventions for Health Behavior Change: Realist Review and Proposal of a Game Design Framework

2021· review· en· W3131700179 on OpenAlexvenueno aff
Daniel Epstein, Adam J. Zemski, Joanne Enticott, Christopher Barton

Bibliographic record

VenueJMIR Serious Games · 2021
Typereview
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Game mechanicsBehavior changeGame designComputer scienceAppealMeaning (existential)PsychologyHuman–computer interactionSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Games, when used as interventional tools, can influence behavior change by incentivizing, reinforcing, educating, providing feedback loops, prompting, persuading, or providing meaning, fun, and community. However, not all game elements will appeal to all consumers equally, and different elements might work for different people and in different contexts. OBJECTIVE: The aim of this study was to conduct a realist review of tabletop games targeting behavior change and to propose a framework for designing effective behavior change games. METHODS: A realist review was conducted to inform program theory in the development of tabletop games for health behavior change. The context, mechanisms used to change behavior, and outcomes of included studies were reviewed through a realist lens. RESULTS: Thirty-one papers met the eligibility criteria and were included in the review. Several design methods were identified that enhanced the efficacy of the games to change behavior. These included design by local teams, pilot testing, clearly defined targets of behavior change, conscious attention to all aspects of game design, including game mechanics, dynamics, aesthetics, and the elicitation of emotions. Delivery with other mediums, leveraging behavioral insights, prior training for delivery, and repeated play were also important. Some design elements that were found to reduce efficacy included limited replayability or lack of fun for immersive engagement. CONCLUSIONS: Game designers need to consider all aspects of the context and the mechanisms to achieve the desired behavior change outcomes. Careful design thinking should include consideration of the game mechanics, dynamics, aesthetics, emotions, and contexts of the game and the players. People who know the players and the contexts well should design the games or have significant input. Testing in real-world settings is likely to lead to better outcomes. Careful selection and purposeful design of the behavior change mechanisms at play is essential. Fun and enjoyment of the player should be considered, as without engagement, there will be no desired intervention effect.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.181
GPT teacher head0.493
Teacher spread0.312 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations46
Published2021
Admission routes1
Has abstractyes

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