SNapp, a Tailored Smartphone App Intervention to Promote Walking in Adults of Low Socioeconomic Position: Development and Qualitative Pilot Study
Bibliographic record
Abstract
BACKGROUND: Adults of low socioeconomic position (SEP) are generally less physically active than those who are more socioeconomically advantaged, which increases their cardiovascular disease incidence risk. Moreover, individuals of low SEP are often less easily reached with physical activity (PA) interventions than individuals of higher SEP. Smartphone apps have been presented as a promising platform for delivering PA interventions to difficult-to-reach individuals of low SEP. Although PA apps are widely available, they are rarely based on health behavior theories and most predominantly offer generic PA advice. Consequently, it is unlikely that available apps are the most effective PA intervention tools. OBJECTIVE: To respond to these areas for improvement, we developed SNapp, an app-based intervention encouraging adults of low SEP to increase PA by providing tailored coaching messages targeting walking behavior. This study aimed to describe SNapp's stepwise development and pilot evaluation process. METHODS: We applied a stepwise approach: analyzing the health problem, developing a program framework, developing tailoring assessments, writing tailored messages, automating the tailoring process, and implementing and evaluating the program in a qualitative pilot study (11 participants). RESULTS: SNapp consisted of several elements. First, an app was developed to collect step count and geolocation data using smartphone sensor functionalities. In addition, a survey measure was created to assess users' behavior change technique (BCT) preferences. These 3 data types were used to tailor SNapp's coaching messages to stimulate walking. This allows SNapp to offer feedback on performance levels, contextually tailored prompts when users are near green spaces, and coaching content that aligns with individual BCT preferences. Finally, a server-based Python program that interacts with databases containing user data and tailored messages was built using Microsoft Azure to select and automatically send messages to users through Telegram messenger. Pilot study findings indicated that SNapp was rated positively, with participants reporting that its design, technical functioning, and message content were acceptable. Participants suggested additional functionalities that are worth considering for future updates. CONCLUSIONS: SNapp is an app-based intervention that aims to promote walking in adults of low SEP by offering tailored coaching messages. Its development is theory based, and it is among the first to incorporate contextualized feedback and content tailored to individual BCT preferences. The effectiveness of SNapp will be evaluated in a 12-month real-life parallel cluster-randomized controlled trial.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".