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Record W4320075182 · doi:10.2196/39544

User Experiences in a Digital Intervention to Support Total Skin Self-examination by Melanoma Survivors: Nested Qualitative Evaluation Embedded in a Randomized Controlled Trial

2023· article· en· W4320075182 on OpenAlexvenueno aff
Felicity Reilly, Nuha Wani, Susan Hall, Heather Morgan, Julia Allan, Lynda Constable, Maria Ntessalen, Peter Murchie

Bibliographic record

VenueJMIR Dermatology · 2023
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsnot available
FundersUniversity of AberdeenCancer Research UK
KeywordsRandomized controlled trialPsychological interventionIntervention (counseling)MedicineSkin cancerQualitative researchFamily medicineNursingCancerSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Melanoma is a relatively common cancer type with a high survival rate, but survivors risk recurrences or second primaries. Consequently, patients receive regular hospital follow-up, but this can be burdensome to attend and not optimally timed to detect arising problems. Total skin self-examination (TSSE) supports improved clinical outcomes from melanoma via earlier detection of recurrences and second primaries, and digital technology has the potential to support TSSE. Recent research with app-based interventions aimed at improving the well-being of older adults has found that they can use the technology and benefit from it, supporting the use of digital health care in diverse demographic groups. Thus, the Achieving Self-directed Integrated Cancer Aftercare (ASICA) digital health care intervention was developed. The intervention provided melanoma survivors with a monthly prompt to perform a TSSE as well as access to a dermatology nurse who provided them with feedback on photographs and descriptions of their skin. OBJECTIVE: We aimed to explore participants' attitudes, beliefs, and experiences regarding TSSE practices. Furthermore, we explored how participants experienced technology and how it influenced their practice of TSSE. Finally, we explored the practical and technical experiences of ASICA users. METHODS: This was a nested qualitative evaluation within a dual-center randomized controlled trial of the ASICA intervention. We conducted semistructured telephone interviews with the participants during a randomized controlled trial. The participants were purposively sampled to achieve a representative sample with representative proportions by age, sex, and residential geography. Interviews were transcribed verbatim and analyzed using a framework analysis approach applied within NVivo 12. RESULTS: A total of 22 interviews were conducted with participants from both groups. In total, 40% (9/22) of the interviewed participants were from rural areas, and 60% (13/22) were from urban areas; 60% (13/22) were from the intervention group, and 40% (9/22) were from the control group. Themes evolved around skin-checking behavior, other people's input into skin checking, contribution of health care professionals outside ASICA and its value, ideas around technology, practical experiences, and potential improvements. ASICA appeared to change participants' perceptions of skin checking. Users were more likely to report routinely performing TSSE thoroughly. There was some variation in beliefs about skin checking and using technology for health care. Overall, ASICA was experienced positively by participants. Several practical suggestions were made for the improvement of ASICA. CONCLUSIONS: The ASICA intervention appeared to have positively influenced the attitudes and TSSE practices of melanoma survivors. This study provides important qualitative information about how a digital health care intervention is an effective means of prompting, recording, and responding to structured TSSE by melanoma survivors. Technical improvements are required, but the app offers promise for technologically enhanced melanoma follow-up in future. TRIAL REGISTRATION: ClinicalTrials.gov NCT03328247; https://clinicaltrials.gov/ct2/show/NCT03328247?term=ASICA&rank=1. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-019-3453-x.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.019
GPT teacher head0.355
Teacher spread0.337 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized 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

Citations2
Published2023
Admission routes1
Has abstractyes

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