Using the iPod Touch for Patient Health Behavior Assessment and Health Promotion in Primary Care
Bibliographic record
Abstract
BACKGROUND: There is a growing recognition of the importance of lifestyle behavior change for health promotion and disease prevention, as well as the concomitant influence of patient-physician communication on effective behavior change. Mobile technology is increasingly being recognized as an important and efficient tool to collect patients' health behavior data and facilitate patient-physician communication. OBJECTIVE: The aim of this study was to assess the feasibility of an iPod touch-based health behavior assessment (HBA) tool in enhancing patient-physician collaborative goal-setting for health promotion in primary care. METHODS: A total of 109 patients from three primary care clinics in central Texas completed a brief HBA, which was programmed on an iPod touch device. An instant feedback report was generated for the patient and their physician simultaneously to facilitate collaborative goal-setting. Within approximately 7 days of the HBA, the patients were phoned for a follow-up survey for their feedback on the iPod touch-based HBA and resultant patient-physician communication. RESULTS: Patients were able to complete an HBA on the iPod touch with ease. Among those who completed the follow-up survey (n=83), 30% (25/83) reported that their physicians discussed the HBA report with them, while 29% (24/83) established behavior change goals with them. More than 90% (75/83) of the patients reported positive experiences with the iPod touch-based HBA. CONCLUSIONS: It is feasible to use mobile tools for HBA in the primary care setting. The HBA also facilitated patient-physician communication on behavior change. However, more research is needed on the effectiveness of large scale dissemination of mobile-based HBA technology on health communication and behavior change for preventing or managing lifestyle-related chronic conditions, such as obesity, diabetes, cancer, or heart diseases.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".