An Experimental Investigation of Human Presence and Mobile Technologies on College Students’ Sun Protection Intentions: Between-Subjects Study
Bibliographic record
Abstract
BACKGROUND: Health promotion and education programs are increasingly being adapted and developed for delivery through digital technologies. With this shift toward digital health approaches, it is important to identify design strategies in health education and promotion programs that enhance participant engagement and promote behavior change. OBJECTIVE: This study aimed to examine the impact of an experiment testing various mobile health (mHealth) skin cancer prevention messages on sun protection intentions and message perceptions among American college students. METHODS: A sample of 134 college students aged 18 years or older participated in a 2×2×2 between-subjects experimental study, designed to examine the individual and combinatory effects of multiple dimensions (human presence, screen size, and interactivity) of digital technologies. The primary study outcome was intention to use sun protection; secondary outcomes included attitudes toward the information, two dimensions of trust, and information processing. RESULTS: Generally, intention to use sun protection was positively associated with the presence of human characters in the health educational messages (P<.001), delivering educational health messages on a large screen (ie, iPad; P<.001), and higher interactivity (P<.001). Only human presence produced more favorable attitudes (P=.02). Affective trust was positively associated with human presence (P=.006) and large screen size (P<.001), whereas cognitive trust was positively associated with human presence (P<.001) and small screen size (P=.007). Moreover, large screen size led to more heuristic processing (P=.03), whereas small screen size led to more systematic processing (P=.04). CONCLUSIONS: This experimental study demonstrates that the impact of mHealth skin cancer prevention messages differs based on platform and delivery design features. Effects on behavioral intentions, attitudes, and trust were found for conditions with human presence, highlighting the importance of including this feature in mHealth programs. Results from this experimental study can be used to optimize the design of mHealth educational interventions that promote sun protection.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".