Using a Human-Centered Design Process to Evaluate and Optimize User Experience of a Website (InPACT at Home) to Promote Youth Physical Activity: Case Study
Bibliographic record
Abstract
Background: Web-based physical activity interventions often fail to reach the anticipated public health impact due to insufficient use by the intended audiences. Objective: The purpose of this study was to use a human-centered design process to optimize the user experience of the Interrupting Prolonged sitting with ACTivity (InPACT) at Home website to promote youth physical activity participation. Methods: Qualitative interviews were conducted to assess engagement and pain points with the InPACT at Home website. Interview data were used to create affinity maps to identify themes of user responses, conduct a heuristic evaluation according to Nielsen's usability heuristics framework, and complete a competitive analysis to identify the strengths and weaknesses of competitors who offered similar products. Results: Key themes from end user interviews included liking the website design, finding the website difficult to navigate, and wanting additional features (eg, library of watched videos). The website usability issues identified were lack of labeling and categorization of exercise videos, hidden necessary actions and options hindering users from decision-making, error-prone conditions, and high cognitive load of the website. Competitive analysis results revealed that YouTube received the highest usability ratings followed by the Just Dance and Presidential Youth Fitness Program websites. Conclusions: Human-centered design approaches are useful for bringing end users and developers together to optimize user experience and impact public health. Future research is needed to examine the effectiveness of the InPACT at Home website redesign to attract new users and retain current users, with the end goal of increasing youth physical activity engagement.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".