Participant Engagement with a Hyper-Personalized Activity Tracking Smartphone App
Bibliographic record
Abstract
Background: Many mobile apps have been designed to monitor physical activity. While they may have many downloads, most users eventually stop using the app and become disengaged. We created a hyper-personalized physical activity tracking app to promote engagement with physical activity (PA) among users. It is unknown if this increased engagement and how engagement level may affect measured outcomes. Objective: The purpose of this study was to determine how users engaged with a hyper personalized activity tracking app for 6 months and whether this engagement affected physical activity Methods: Participants with cardiometabolic risk (CMR) factors were given an activity watch to track their PA (step counts) and asked to use the study mobile app for 6 months. App features included step tracking, personalized educational and motivational messages, biometric tracking and connection to a portal where their clinician could monitor their activity. App usage data were collected at 3- and 6-month study visits to determine app usage metrics for the 6 months. App engagement was determined by app usage metrics such as overall page clicks (number of clicks per page), frequency of use of individual features (number of clicks) and session length (time spent on a page). Participants were grouped by level of engagement with app (high, medium, low, none) post hoc to determine engagement effects on steps. Results: Information was collected on 128 participants. Over the 6-month study, 60 participants (47%) engaged with the app. Among users, app usage decreased by over 50% with the highest app usage during month 1 followed by month 4. There was no difference in the average app session lengths at 0 and 6 months (12 vs 11 seconds, respectively). The most commonly viewed feature was the personalized daily messages (92% of participants used feature, 20,902 clicks, 58% of total views). At least 85% of app users engaged with all the features. Each additional day of app use was associated with a nonsignificant increase of 13 steps in overall average daily step count. Median days of app use were used to define groups with high, medium, and low engagement (median 89, 35 and 3 days of use respectively). The low engagement group had an average 1220 less steps per day than the high engagement group (P<.001). High engagement group’s session length remained steady through the study period, compared to medium engagement group’s session length that fluctuated widely. While steps decreased over the 6 months, those in the medium engagement group decreased in weekly step counts at a steeper slope then other engagement groups. Conclusions: While participants engaged with most app features, we observed a 50% decrease in engagement over the 6-month study. Despite this result, those with high engagement were able to achieve more physical activity than those with low engagement. This increase in physical activity may lead to improvements in CMR factors and better quality of life.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".