“Please Don’t Send Us Spam!” A Participative, Theory-Based Methodology for Developing an mHealth Intervention
Bibliographic record
Abstract
BACKGROUND: Mobile health solutions have the potential of reducing burdens on health systems and empowering patients with important information. However, there is a lack of theory-based mHealth interventions. OBJECTIVE: The purpose of our study was to develop a participative, theory-based, mobile phone, audio messaging intervention attractive to recently circumcised men at voluntary medical male circumcision (VMMC) clinics in the Cape Town area in South Africa. We aimed to shift some of the tasks related to postoperative counselling on wound management and goal setting on safe sex. We place an emphasis on describing the full method of message generation to allow for replication. METHODS: We developed an mHealth intervention using a staggered qualitative methodology: (1) focus group discussions with 52 recently circumcised men and their partners to develop initial voice messages they felt were relevant and appropriate, (2) thematic analysis and expert consultation to select the final messages for pilot testing, and (3) cognitive interviews with 12 recent VMMC patients to judge message comprehension and rank the messages. Message content and phasing were guided by the theory of planned behavior and the health action process approach. RESULTS: Patients and their partners came up with 245 messages they thought would help men during the wound-healing period. Thematic analysis revealed 42 different themes. Expert review and cognitive interviews with more patients resulted in 42 messages with a clear division in terms of needs and expectations between the initial wound-healing recovery phase (weeks 1-3) and the adjustment phase (weeks 4-6). Discussions with patients also revealed potential barriers to voice messaging, such as lack of technical knowledge of mobile phones and concerns about the invasive nature of the intervention. Patients' own suggested messages confirmed Ajzen's theory of planned behavior that if a health promotion intervention can build trust and be relevant to the recipient's needs in the first contacts, then the same recipients will perceive subsequent motivational messages more favorably. The health action process approach was also a useful tool for guiding the phasing of the messages. Participants were more positive and salutogenic than public health experts. CONCLUSIONS: The system showed how a process of consultation can work with a set of potential recipients of an mHealth service to ensure that their needs are included. Classic behavioral theories can and should be used to design modern mHealth interventions. We also believe that patients are the best source of messaging, ensuring that messages are culturally relevant and interesting to the recipient.
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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 | 0.092 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".