Teenagers and Texting: Use of a Youth Ecological Momentary Assessment System in Trajectory Health Research With Latina Adolescents
Bibliographic record
Abstract
BACKGROUND: Adolescent females send and receive more text messages than any others, with an average of 4050 texts a month. Despite this technological inroad among adolescents, few researchers are utilizing text messaging technology to collect real time, contextualized data. Temporal variables (ie, mood) collected regularly over a period of time could yield useful insights, particularly for evaluating health intervention outcomes. Use of text messaging technology has multiple benefits, including capacity of researchers to immediately act in response to texted information. OBJECTIVE: The objective of our study was to custom build a short messaging service (SMS) or text messaging assessment delivery system for use with adolescents. The Youth Ecological Momentary Assessment System (YEMAS) was developed to collect automated texted reports of daily activities, behaviors, and attitudes among adolescents, and to examine the feasibility of YEMAS. This system was created to collect and transfer real time data about individual- and social-level factors that influence physical, mental, emotional, and social well-being. METHODS: YEMAS is a custom designed system that interfaces with a cloud-based communication system to automate scheduled delivery of survey questions via text messaging; we designed this university-based system to meet data security and management standards. This was a two-phase study that included development of YEMAS and a feasibility pilot with Latino adolescent females. Relative homogeneity of participants was desired for the feasibility pilot study; adolescent Latina youth were sought because they represent the largest and fastest growing ethnic minority group in the United States. Females were targeted because they demonstrate the highest rate of text messaging and were expected to be interested in participating. Phase I involved development of YEMAS and Phase II involved piloting of the system with Latina adolescents. Girls were eligible to participate if they were attending one of the participating high schools and self-identified as Latina. We contacted 96 adolescents; of these, 24 returned written parental consent forms, completed assent processes, and enrolled in the study. RESULTS: YEMAS was collaboratively developed and implemented. Feasibility was established with Latina adolescents (N=24), who responded to four surveys daily for two two-week periods (four weeks total). Each survey had between 12 and 17 questions, with responses including yes/no, Likert scale, and open-ended options. Retention and compliance rates were high, with nearly 18,000 texts provided by the girls over the course of the pilot period. CONCLUSIONS: Pilot results support the feasibility and value of YEMAS, an automated SMS-based text messaging data collection system positioned within a secure university environment. This approach capitalizes on immediate data transfer protocols and enables the documentation of participants' thoughts, feelings, and behaviors in real time. Data are collected using mobile devices that are familiar to participants and nearly ubiquitous in developed countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".