Evaluating the Acceptability and Utility of a Personalized Wellness App (Aspire2B) Using AI-Enabled Digital Biomarkers: Engagement Enhancement Pilot Study
Bibliographic record
Abstract
Background: There is significant global interest in promoting wellness, with digital solutions like mobile health apps being broadly downloaded; yet, there is a challenge in maintaining engagement for long-term behavior change. Developing a widely accepted mobile wellness app is imperative for advancing personalized wellness interventions. Objective: The primary objective of this study was to evaluate the Aspire2B wellness app (powered by Salus Optima), designed to exceed industry standards for participant engagement by incorporating evidence-based behavior change strategies and to assess its acceptability (eg, liking the face scan) and utility (eg, willing to use the face scan technology for other health insights) as a digital health solution. Methods: Participants aged 18-65 years, who were smartphone and fitness tracker users, were recruited in the United States during March-May 2022. Participants received US $5 compensation for downloading the app, with no further incentive for usage. Following completion of onboarding (ie, survey questions about lifestyle behaviors), participants were placed in either a nutrition, sleep, or fitness 4-week challenge. During the challenge, participants used various app features at their own will, such as a facial scan for wellness insights (eg, heart rate and biological age), recipes, and workout videos. These interactions with the app were cumulatively evaluated as engagement metrics. Participants were also asked to answer offboarding questions to evaluate any changes to lifestyle behaviors and experience using the app features (eg, acceptability of face scan experience). Results: Out of the 398 people who created an account, 85.9% (342/398) completed onboarding and a face scan. Following this, 74.9% (298/398) of users completed additional survey questions about current wellness behaviors. Notably, interaction with the app was relatively stable from week 2 to 4 (173/398, 43.5%), outperforming industry standards by roughly 3×. In addition, on average, participants completed 2.1-2.7 face scans per week, with approximately 7% (24/342) of participants maintaining regular use of face scan technology for 4 weeks. In users who completed offboarding questions, 88.8% (111/125) found Aspire2B credible, 64.8% (81/125) liked the face scan experience, 7.2% (9/125) disliked the face scan experience, and 83.2% (104/125) said they would use face scan technology for other insights into their health. Conclusions: These findings highlight strong initial engagement with Aspire2B, followed by significant sustained user engagement over a 4-week period. Furthermore, users indicated high levels of credibility and willingness to use face scan technology for wellness insights. These findings collectively demonstrate the capability of a personalized wellness app using AI-enabled digital biomarkers and evidence-supported behavior change techniques to yield positive user perception and provide long-term engagement.
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 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".