A Mixed Methods Evaluation of Wearable Technology: Findings from the Vivo Play Scientist (VPS) Program
Bibliographic record
Abstract
During the COVID-19 pandemic, a Canadian recreation centre launched a community-based intervention to increase physical activity (PA) and reduce sedentary behaviour (SB). The Vivo Play Scientist (VPS) program provided a free wearable device (Garmin Vivofit4) that synchronized with a customized eHealth dashboard. Aim: The study investigated the feasibility and effectiveness of the VPS program through the participants’ use and experiences of the device and dashboard using the Technology Acceptance Model (TAM). Method: We employed a concurrent mixed-methods approach of online surveys and semi-structured telephone interviews and estimated the device and dashboard’s perceived usefulness and ease of use with TAM. Results: Of the 318 participants (mean age 39.8) 87 enrolled and completed the survey at baseline-T0, 4 wks-T1, and 8 wks-T2. Maximal-variation sampling was used to select 23 participants (78%, F) for interviews. We compared frequency of use, perceived usefulness and ease of use of the device and dashboard across all surveys using non-parametric statistical tests. A thematic analysis was used to analyze data. Participants had some experience using a wearable device (46%) or eHealth application (49%). A high use (≥4 d/wk.) of Vivofit4 at T1 (93%) and T2 (87%) occurred, but dashboard use was less frequent (≥1 d/wk. T1 54.0% and T2 47.1%). Average levels of perceived usefulness and ease of use for the Vivofit4 and dashboard remained constant from T1 to T2. Average daily PA scores decreased from T1 to T2 (4.9 to 4.5; p = 0.017). Conclusion: Participants were guarded about the value of the dashboard use and reported several challenges associated with the VPS program, but the free device and dashboard did provide PA support during the pandemic.
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 | 0.050 | 0.043 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".