Designing Health Apps to Support Dietetic Professional Practice and Their Patients: Qualitative Results From an International Survey
Bibliographic record
Abstract
BACKGROUND: Dietitians are engaging with mobile health (mHealth) technologies, particularly with diet and nutrition apps in their patient care. Despite the plethora of apps available, the majority are not designed with a dietitian's input. OBJECTIVE: The aim of this study was to identify the user preferences of dietitians in relation to tools, resources, and design features for smartphone health apps that would support their dietetic professional practice and their patients. METHODS: As part of a larger international Web-based survey of health-app use among dietitians, three open-ended responses were included for specific exploration of app design features and additional resources or tools that could guide the development of apps for use in dietetic practice and patient care. Inductive thematic analysis of responses was conducted using the qualitative data analysis program, NVivo version 11 (QSR International Pty Ltd), to understand the design preferences and features valued by dietitians. RESULTS: The responses from 381 dietitian respondents were analyzed. Five key themes were identified. Dietitians wanted access to credible apps, suggesting that dietetic associations should have greater involvement in reviewing and endorsing evidence-based apps for use in dietary counseling. Improvements to the usability of apps, relating to their ease of use and design, were also raised, as self-monitoring of dietary behaviors using existing nutrition apps was deemed to be burdensome. Furthermore, apps providing dietitian-oriented support were favored, for example, those with the ability to streamline the dietary assessment process, so that dietitians could spend more time on dietary counseling and negotiating patient goals for dietary and lifestyle behavior change. Provision of patient-oriented support, such as functionality to tailor apps to patient-specific needs, was also considered important. Finally, respondents valued apps that could integrate into their work systems to enhance the quality of the dietitian-patient relationship. CONCLUSIONS: App developers should draw upon the features and characteristics valued by dietitians to guide their development of apps that support dietetic practice and enhance patient care. Moreover, to achieve better dietitian and patient-centered app design, it is imperative that app developers take a collaborative approach with dietitians, their professional associations, and their patients.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".