The use of smartphone health apps and other <scp>mobile h</scp>ealth (mHealth) technologies in dietetic practice: a three country study
Bibliographic record
Abstract
Abstract Background Smartphone health applications (apps) and other mobile health (mHealth) technologies may assist dietitians in improving the efficiency of patient care. The present study investigated the use of health apps and text messaging in dietetic practice and formulated intervention recommendations for supporting app uptake by dietitians based on the behavioural ‘COM‐B’ system, where interactions between capability, opportunity and motivation influence behaviour. Methods A 52‐item online survey tool, taking 20 min to complete, was developed and piloted, with questions exploring the use of health apps and text messaging in dietetic practice, types of apps dietitians recommended and that patients used, and barriers and enablers to app use in dietetic practice. The Australian, New Zealand and British dietetic associations distributed the survey to their members. Results A 5% response rate was achieved internationally, with 570 completed responses included for further analysis. Health apps, namely nutrition apps, were used by 62% of dietitians in their practice, primarily as an information resource (74%) and for patient self‐monitoring (60%). The top two nutrition apps recommended were MyFitnessPal® (62%) and the Monash University Low FODMAP Diet® (44%). Text messaging was used by 51% of respondents, mainly for appointment‐related purposes (84%). Conclusions Although the reported use of smartphone health apps in dietetic practice is high, health apps and other mHealth technologies are not currently being used for behaviour change, nor are they an integral part of the nutrition care process. Dietetic associations should provide training, education and advocacy to enable the profession to more effectively engage with and implement apps into their practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".