Development and feasibility of an mHealth intervention for psychoeducational support of Nigerian women diagnosed with breast cancer undergoing chemotherapy: A pilot randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) remains a significant health burden globally, with high incidence and mortality rates, particularly in Nigeria. Chemotherapy, a common treatment modality for BC, often leads to various physical and psychological side effects, impacting patients' quality of life. Despite the growing use of mobile health (mHealth) interventions to provide psychoeducational support, there is a paucity of evidence regarding their feasibility and acceptability among Nigerian women with BC. OBJECTIVE: To develop and investigate the feasibility and acceptability of a mHealth psychoeducational intervention (mPEI) named the ChEmo Nurse Breast cancer Application (CENBA) programme. METHODS: A multi-centre, assessor-blinded, parallel-group pilot randomised controlled trial (RCT) was conducted at Lagos State University Teaching Hospital (LASUTH) and Lagos University Teaching Hospital (LUTH). Thirty women newly diagnosed with BC and undergoing chemotherapy were randomly assigned to an intervention or a control group. The intervention group received the CENBA programme, which included BC education, coping skills training, a discussion forum, and nurse-led consultations, delivered via a mobile application and phone calls over six weeks. The control group received standard care. Feasibility was assessed through consent, attrition, and completion rates, while acceptability was explored via qualitative interviews. RESULTS: The completion rate was 93.3%. Qualitative data indicated that participants found the intervention beneficial, particularly appreciating the educational content and the emotional support provided through the discussion forum and nurse consultations. CONCLUSION: The CENBA programme was perceived as a feasible and acceptable mHealth intervention for providing psychoeducational support to Nigerian women with BC undergoing chemotherapy. These findings suggest that the CENBA programme could be a valuable tool in addressing the psychoeducational needs of this population, warranting further investigation in a full-scale RCT. TRIAL REGISTRATION: This manuscript reports a feasibility study preceding the full trial, which was registered with the United States Clinical Trials registry (number NCT05489354).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".