Setting Goals and Accepting Challenges for Behavior Change—Analysis of Participants’ Interactions With a Digital Multiple Health Behavior Intervention: Mixed Methods Study
Bibliographic record
Abstract
Background: Digital interventions are effective in promoting healthy behaviors and are recognized as one of many strategies for achieving healthier populations. These interventions often include goal-setting, but the practical application and fidelity of goal setting, especially when targeting multiple health behaviors, remain underexplored. In a factorial randomized trial, we included goal-setting as one of six behavior change components in the digital intervention "Buddy," targeting university and college students' alcohol, diet, physical activity, and smoking behaviors. However, we found no strong and consistent evidence of an effect of goal-setting alone on any of the outcomes, highlighting the need to investigate how participants used this component. Objective: This case study of Buddy aimed to gain insight into participants' interactions with the goal-setting component. Specific objectives were to identify the characteristics of participants who used this component and to analyze participants' self-authored content. Methods: This study combined fidelity and effectiveness findings and involved 1704 participants from 18 universities and colleges in Sweden. Self-authored goals and challenges were analyzed using summative content analysis. Logistic and negative binomial regression analyses were conducted to estimate the odds of setting a goal, selecting or self-authoring a challenge, to estimate the odds of setting a goal with respect to a specific behavior, and to estimate the frequency of selecting or self-authoring different behavioral challenges. Results: Of the 850 participants given access to the goal setting and challenges component, 427 (50%) set at least one goal and 403 (47%) selected or self-authored at least one challenge. A total of 607 goals were set, with most participants setting one goal (336/427, 79%). Goals primarily targeted physical activity (n=302), dietary behavior (n=140), and multiple health behaviors (n=53), typically combining physical activity with diet, alcohol, smoking, or sleep. Other goals included study performance, mental health, sleep, and mobile phone use (n=73). Fewer goals concerned alcohol (n=19) or tobacco (n=17). Participants selected 1506 challenges from 41 premade challenges, with dietary behavior challenges being most popular (667/1506, 44%). An additional 170 challenges were self-authored. Participants' baseline characteristics were associated with the odds of setting goals targeting specific behaviors and the frequency of selecting or self-authoring challenges targeting specific behaviors. Conclusions: Our analyses suggest that, while goal-setting is theoretically grounded, and participants used Buddy in ways that suited their personal needs, this did not translate to measurable behavior change in the study population. The self-authored content showed how participants used the component and provided insights into how they articulate behavior change in terms of personal goals, challenges, strategies for action, motivation plans, and rewards. Future research should explore the conditions under which goal-setting may be more or less effective, to better understand its nuances and potential benefits.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".