An Agile Development Cycle of an Online Memory Program for Healthy Older Adults
Bibliographic record
Abstract
Online interventions for older adults should be tailored to their unique needs to increase the efficacy of and adherence to the intervention. The agile development cycle is a dynamic model to solicit and incorporate feedback from older adults during the design process. We combined this approach with the framework of Harvard University's clinical and translational phases that provide a clear structure for evaluating new health programs before they are offered in the community. We based our online memory program on the empirically validated in-person Memory and Aging Program. The aim of the present study was to combine the agile development cycle with the clinical and translational phases framework to develop and pilot an online memory program tailored to the unique needs of older adults. Study 1 involved piloting individual program modules on site and integrating participant feedback into the program's design to optimize usability. Study 2 involved two sequential pilots of the program accessed remotely to evaluate preliminary clinical outcomes and obtain feedback for iterative modifications. Plans for further validation and limitations are discussed. The successful application of the agile development cycle implemented in this series of studies can be adapted by others seeking to offer online content for targeted end users.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".