Validation of the Fitbit Flex in an Acute Post–Cardiac Surgery Patient Population
Bibliographic record
Abstract
Purpose: This study examined the validity of the Fitbit Flex activity monitor for step count and distance walked among post–cardiac surgery patients. Method: Participants (n=20) from a major urban cardiac surgery centre were recruited 1–2 days before hospital discharge. The Fitbit Flex step count and distance walked outputs and video recording of each participant performing the 6-minute walk test were collected. Fitbit Flex output was compared with criterion measures of manual step count obtained from the video recording and manual measurement of distance walked. Statistical analysis compared the output and criterion measures using paired sample t-tests, Pearson correlation coefficients, Lin's concordance correlations, and Bland–Altman plots. Sub-analysis compared slower walking (<0.8 m/s; n=11) and faster walking (≥0.8 m/s; n=8) group speeds (1 participant was excluded from analysis). Results: Steps counted and distance walked were significantly different between the Fitbit Flex outputs and criterion measures (p<0.05). The Fitbit Flex steps counted and distance walked showed moderate association with manual measure steps counted (r=0.67) and distance walked (r=0.45). Lin's concordance coefficients revealed a lack of agreement between the Fitbit Flex and the criterion measurement of both steps counted (concordance correlation coefficient [CCC]=0.43) and distance walked (CCC=0.36). The percentage of relative error was −18.6 (SD 22.7) for steps counted and 25.4 (SD 45.8) for distance walked. Conclusions: The Fitbit Flex activity monitor was not a valid measure of step count and distance walked in this sample of post–cardiac surgery patients. The lack of agreement between outputs and criterion measures suggests the Fitbit Flex alone would not be an acceptable clinical outcome measure for monitoring walking progression in the early postoperative period.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".