Mobile Technology Use in Clinical Research Examining Challenges and Implications for Health Promotion in South Africa: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The use of mobile technologies in fostering health promotion and healthy behaviors is becoming an increasingly common phenomenon in global health programs. Although mobile technologies have been effective in health promotion initiatives and follow-up research in higher-income countries and concerns have been raised within clinical practice and research in low- and middle-income settings, there is a lack of literature that has qualitatively explored the challenges that participants experience in terms of being contactable through mobile technologies. OBJECTIVE: This study aims to explore the challenges that participants experience in terms of being contactable through mobile technologies in a trial conducted in Soweto, South Africa. METHODS: A convergent parallel mixed methods research design was used. In the quantitative phase, 363 young women in the age cohorts 18 to 28 years were contacted telephonically between August 2019 and January 2022 to have a session delivered to them or to be booked for a session. Call attempts initiated by the study team were restricted to only 1 call attempt, and participants who were reached at the first call attempt were classified as contactable (189/363, 52.1%), whereas those whom the study team failed to contact were classified as hard to reach (174/363, 47.9%). Two outcomes of interest in the quantitative phase were "contactability of the participants" and "participants' mobile number changes," and these outcomes were analyzed at a univariate and bivariate level using descriptive statistics and a 2-way contingency table. In the qualitative phase, a subsample of young women (20 who were part of the trial for ≥12 months) participated in in-depth interviews and were recruited using a convenience sampling method. A reflexive thematic analysis approach was used to analyze the data using MAXQDA software (version 20; VERBI GmbH). RESULTS: Of the 363 trial participants, 174 (47.9%) were hard to reach telephonically, whereas approximately 189 (52.1%) were easy to reach telephonically. Most participants (133/243, 54.7%) who were contactable did not change their mobile number. The highest percentage of mobile number changes was observed among participants who were hard to reach, with three-quarters of the participants (12/16, 75%) being reported to have changed their mobile number ≥2 times. Eight themes were generated following the analysis of the transcripts, which provided an in-depth account of the reasons why some participants were hard to reach. These included mobile technical issues, coverage issues, lack of ownership of personal cell phones, and unregistered number. CONCLUSIONS: Remote data collection remains an important tool in public health research. It could, thus, serve as a hugely beneficial mechanism in connecting with participants while actively leveraging the established relationships with participants or community-based organizations to deliver health promotion and practice.
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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 | 0.051 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".