Smartphone Ownership and Usage Among Pregnant Women Living With HIV in South Africa: Secondary Analysis of CareConekta Trial Data
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) initiatives are increasingly common in low-resource settings, but the appropriateness of smartphone interventions in health care settings is uncertain. More research is needed to establish the appropriateness and feasibility of integrating new mHealth modalities (novel apps and social media apps) in the South African context. OBJECTIVE: In this study, to inform future mHealth interventions, we describe smartphone ownership, preferences, and usage patterns among pregnant women living with HIV in Gugulethu, South Africa. METHODS: We screened pregnant women living with HIV from December 2019 to February 2021 for the CareConekta trial. To be enrolled in the trial, respondents were required to be 18 years of age or older, living with HIV, ≥28 weeks pregnant, and own a smartphone that met the technical requirements of the CareConekta app. In this secondary analysis, we describe mobile phone ownership and sociodemographic characteristics of all women screened for eligibility (n=639), and smartphone use patterns among those enrolled in the trial (n=193). RESULTS: Overall, median age was 31 (IQR 27-35) years. Of the 582 women who owned smartphones, 580 responded to the question about whether or not it was a smartphone, 2 did not. Among those with smartphones, 92% (421/458) of them used the Android operating system of version 5.0 or above, 98% (497/506) of phones had a GPS, and 96% (485/506) of individuals charged their phones less than twice a day. Among women who were enrolled in the trial, nearly all (99%, 190/193) owned the smartphone themselves; however, 14% (26/193) shared their smartphone with someone. In this case, 96% (25/26) reported possessing the phone most of the day. Median duration of ownership of the smartphone was 12 (IQR 5-24) months, median duration with current phone number use was 25 (IQR 12-60) months, and median number of cell phone numbers owned 2 years prior to enrollment in the trial was 2 (IQR 1-2). Receiving (192/193, 99.5%) and making (190/193, 99%) phone calls were among the most common smartphone uses. The least used features were GPS (106/193, 55%) and email (91/193, 47%). WhatsApp was most frequently reported as a favorite app (181/193, 94%). CONCLUSIONS: Smartphone ownership is very common among pregnant women living with HIV in this low-resource, periurban setting. Phone sharing was uncommon, nearly all used the Android system, and phones retained sufficient battery life. These results are encouraging to the development of mHealth interventions. Existing messaging platforms-particularly WhatsApp-are exceedingly popular and could be leveraged for interventions. Findings of moderate smartphone ownership turnover and phone number turnover are considerations for mHealth interventions in similar settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT03836625; https://clinicaltrials.gov/ct2/show/NCT03836625?term=NCT03836625.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".