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Record W4312463044 · doi:10.2196/34629

Mixed Reality Technology to Deliver Psychological Interventions to Adolescents With Asthma: Qualitative Study Using the Theoretical Framework of Acceptability

2022· article· en· W4312463044 on OpenAlexvenueno aff
Kelsey Sharrad, Caitlin Martini, Andrew Tai, Nicola Spurrier, Ross Smith, Adrian Esterman, Ian Gwilt, Debra Sandford, Kristin Carson‐Chahhoud

Bibliographic record

VenueJMIR Human Factors · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersCancer Research InstituteMedical Research CouncilNational Health and Medical Research CouncilLung Foundation AustraliaUniversity of South AustraliaChannel 7 Children's Research Foundation
KeywordsPsychological interventionQualitative researchAsthmaPsychologyApplied psychologyMedicineSociologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Interactive, mixed reality technologies such as augmented reality, virtual reality, and holographic technology may provide a novel solution to fast-track the translation of evidence into practice. They may also help overcome barriers to both mental health and asthma management service uptake, such as cost, availability of appointments, fear of judgment, and quality of care. OBJECTIVE: This study aimed to investigate if mixed reality technology is an acceptable mechanism for the delivery of a component of cognitive and behavioral therapies for the management of elevated psychological distress among young people with asthma. METHODS: To explore the perceived acceptability of these technologies, mixed reality tools were evaluated via qualitative, 1-on-1 interviews with young people with asthma and symptoms of psychological distress, parents/caregivers of young people with asthma and symptoms of psychological distress, and relevant health professionals. The Theoretical Framework of Acceptability was used for the deductive coding of the recorded interview transcripts. RESULTS: This study enrolled the following participants: (1) 3 adolescents with asthma and symptoms of psychological distress with a mean age of 14 (SD 1.7) years; (2) 4 parents/caregivers of adolescents with asthma with a mean age of 55 (SD 14.6) years; and (3) 6 health professionals with a mean age of 40.8 (SD 4.3) years. A total of 4 constructs-experienced affective attitude, experienced effectiveness, self-efficacy, and intervention coherence-were coded in all participant transcripts. The most frequently coded constructs were experienced affective attitude and intervention coherence, which were reported a total of 96 times. The least frequently coded construct was anticipated opportunity cost, which was reported a total of 5 times. Participants were mostly positive about the mixed reality resources. However, some concerns were raised regarding ethicality, particularly regarding privacy, accessibility, and messaging. Participants noted the need for technology to be used in conjunction with face-to-face engagement with health professionals and that some patients would respond to this type of delivery mechanism better than others. CONCLUSIONS: These results suggest that mixed reality technology to deliver psychological interventions may be an acceptable addition to current health care practices for young people with asthma and symptoms of psychological distress. TRIAL REGISTRATION: Australia and New Zealand Clinical Trials Registry ACTRN12620001109998; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380427.

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)0.021
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.152
GPT teacher head0.529
Teacher spread0.377 · 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 designQualitative
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".

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Citations3
Published2022
Admission routes1
Has abstractyes

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