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Record W4406842844 · doi:10.2196/63512

Development of a Mobile App Game for Practicing Lung Exercises: Feasibility Study

2025· article· en· W4406842844 on OpenAlexvenueno aff
Chatkhane Pearkao, Korakot Apiratwarakul, Lerkiat Wicharit, Wiphawadee Potisopha, Arunnee Jaitieng, Sukuman Homvisetvongsa, Puthachad Namwaing, Peerapon Pudtuan

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Chest injuries are a leading cause of death and disability, accounting for 10% of hospital admissions and 25% of injury-related deaths. About two-thirds of patients with thoracic injuries experience complications such as blood or air in the pleural space, causing lung deflation and poor gas exchange. Proper breathing management, using tools like incentive spirometers, improves lung function and recovery. However, there is a gap in mobile-based gaming apps designed for lung exercise, which could benefit both the general population and patients recovering from lung injuries. Objective: This research aimed to develop and evaluate a mobile app game for practicing lung exercises, accompanied by a prototype device called the Pulmo device. Methods: The study involved a sample group of 110 participants from the general public. It followed a research and development methodology comprising 4 steps. The research instruments included a mobile app game, a prototype lung exercise device, and questionnaires to assess users' satisfaction and the feasibility of both the app and the device. Results: The findings revealed that the participants demonstrated a high level of overall satisfaction with both the mobile app game and the prototype lung exercise device (mean 4.4, SD 0.4). The feasibility for the mobile app game and the prototype lung exercise device connected to the game was evaluated. The results indicated that the sample group perceived the overall feasibility to be at a high level (mean 4.4, SD 0.5). Conclusions: The research results reflected that the sample group believed the mobile app game for practicing lung exercises and the prototype device developed in this project have a high potential for practical application in promoting lung rehabilitation through gameplay. The mobile app game and the Pulmo device prototype received positive user feedback, indicating potential practical use; however, further validation is required among patients in need of pulmonary rehabilitation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.474
Teacher spread0.426 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations4
Published2025
Admission routes1
Has abstractyes

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