Gamified Text Messaging Contingent on Device-Measured Steps: Randomized Feasibility Study of a Physical Activity Intervention for Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Physical activity can confer diverse benefits on cancer survivors. Unfortunately, many cancer survivors are not sufficiently active. The efficacy of physical activity interventions for this population may be increased by grounding them in Self-Determination Theory (SDT). Combining game design elements with wearable technologies may be a useful and scalable approach to targeting SDT constructs to promote cancer survivors' physical activity. OBJECTIVE: The primary aim of this study is to evaluate the feasibility and acceptability of Steps2Health, a physical activity intervention for cancer survivors. It also aims to investigate the effects of the intervention on motivation, physical activity, and step count. METHODS: We randomized 78 insufficiently active cancer survivors to an experimental or comparison group. All participants received a physical activity tracker. The experimental group participants also received a set sequence of multimedia messaging service messages that were triggered in real time by meeting predetermined cumulative step count totals. Messages presented information about a virtual journey and included photographs and vivid descriptions of locations to increase autonomous motivation. Additional messages targeted perceptions of relatedness (eg, role modeling) and competence (eg, facilitating mastery experiences). We administered pre- and postintervention surveys and conducted 15 individual interviews to evaluate the intervention. We performed directed content analysis of qualitative data and conducted mixed effects linear modeling to investigate participants' changes in motivation, self-reported physical activity, and device-measured step counts. RESULTS: There was minimal loss to follow-up (3/78, 4%), the device wear rate was high (2548/3044, 83.71% of days), and technical problems with messaging based on real-time step counts were limited. Our qualitative data analysis revealed 3 overarching themes: accessibility, autonomous motivation, and relatedness. Participants successfully navigated the technological aspects and game design elements of the intervention. Participants found messages targeting autonomous motivation and competence or self-efficacy to be enjoyable and compelling, but one feasibility criterion for participant engagement (response rate to text messages) was not met. Messages targeting relatedness were less highly rated than the messages targeting autonomous motivation and competence or self-efficacy. During the intervention, both groups increased their motivation for physical activity (B=0.16; 95% CI 0.01 to 0.30; P=.04; d=0.49), and assignment to the experimental group was associated with increased self-reported leisure activity score (B=10.78; 95% CI 3.54 to 18.02; P=.005; d=0.64). The experimental group had greater increases in daily step counts over time (B=322.08; 95% CI 54.01 to 590.15; P=.02; d=0.28). CONCLUSIONS: This study supports the feasibility of using real-time game design elements to target SDT constructs and increase cancer survivors' physical activity. Overall, our findings support the acceptability of the Steps2Health intervention, but fostering active participant engagement and targeting relatedness may present additional challenges. Steps2Health may help cancer survivors increase their physical activity levels.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".