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Record W4308596285 · doi:10.2196/35933

A Short Digital Food Frequency Questionnaire (DIGIKOST-FFQ) Assessing Dietary Intake and Other Lifestyle Factors Among Norwegians: Qualitative Evaluation With Focus Group Interviews and Usability Testing

2022· article· en· W4308596285 on OpenAlexvenueno aff
Hege Berg Henriksen, Markus Dines Knudsen, Monica Hauger Carlsen, Anette Hjartåker, Rune Blomhoff

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupUsabilityThematic analysisFood frequency questionnairePsychological interventionQualitative researchNorwegianApplied psychologyModerationMedicinePsychologyMedical educationComputer scienceEnvironmental healthNursingSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: In-person dietary counseling and interventions have shown promising results in changing habits toward healthier lifestyles, but they are costly to implement in large populations. Developing digital tools to assess individual dietary intake and lifestyle with integrated personalized feedback systems may help overcome this challenge. We developed a short digital food frequency questionnaire, known as the DIGIKOST-FFQ, to assess diet and other lifestyle factors based on the Norwegian Food-Based Dietary Guidelines. The DIGIKOST-FFQ includes a personalized feedback system, the DIGIKOST report, that benchmarks diet and lifestyle habits. We used qualitative focus group interviews and usability tests to test the feasibility and usability of the DIGIKOST application. OBJECTIVE: We aimed to explore attitudes, perceptions, and challenges in completing the DIGIKOST-FFQ. We also investigated perceptions and understanding of the personalized feedback in the DIGIKOST report and the technical flow and usability of the DIGIKOST-FFQ and the DIGIKOST report. METHODS: Healthy individuals and cancer survivors were invited to participate in the focus group interviews. The transcripts were analyzed using thematic analysis. Another group of healthy individuals completed the usability testing, which was administered individually by a moderator and 2 observers. The results were analyzed based on predefined assignments and discussion with the participants about the interpretation of the DIGIKOST report and technical flow of the DIGIKOST-FFQ. RESULTS: A total of 20 individuals participated in the focus group interviews, divided into 3 groups of healthy individuals and 3 groups of cancer survivors. Each group consisted of 3 to 4 individuals. Five main themes were investigated: (1) completion time (on average 19.1, SD 8.3, minutes, an acceptable duration), (2) layout (participants reported the DIGIKOST-FFQ was easy to navigate and had clear questions but presented challenges in reporting dietary intake, sedentary time, and physical activity in the last year), (3) questions (the introductory questions on habitual intake worked well), (4) pictures (the pictures were very helpful, but some portion sizes were difficult to differentiate and adding weight in grams would have been helpful), and (5) motivation (users were motivated to obtain personalized feedback). Four individuals participated in the usability testing. The results showed that the users could seamlessly log in, give consent, fill in the DIGIKOST-FFQ, and receive, print, and read the DIGIKOST report. However, parts of the report were perceived as difficult to interpret. CONCLUSIONS: The DIGIKOST-FFQ was overall well received by participants, who found it feasible to use; however, some adjustments with regard to reporting dietary intake and lifestyle habits were suggested. The DIGIKOST report with personalized feedback was the main motivation to complete the questionnaire. The results from the usability testing revealed a need for adjustments and updates to make the report easier to read.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.406
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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