Effects Of Placement On Accuracy Of The Fitbit Force And One Accelerometers In Measuring Activity
Bibliographic record
Abstract
Fitbit TM activity monitors are relatively new physical activity monitoring devices. They are inexpensive units that provide immediate feedback to their users, making them a popular alternative for personal use. FitbitTM claims that wear location does not impact each devices’ ability to reliably measure physical activity, however to the best of our knowledge this has not been empirically confirmed PURPOSE: To evaluate the accuracy of the Fitbit Force TM and two different placement locations of the Fitbit One TM in measuring daily steps and time spent in different intensities of activity METHODS: Twenty-two females, aged 18-26yrs with a BMI of 23.24 ± 3.26kg/m2, wore two Fitbit Ones TM (Bra and Waist respectively) and a Fitbit Force TM (Wrist) for 7-consecutive days, only removing them for sleep and bathing time. A valid wear period was considered at least 4-6 days including 1 weekend day with a minimum of 12hrs of wear per day. A 3x4 factorial repeated measures analysis of variance (ANOVA) was performed comparing the Fitbit Force TM and the two Fitbit Ones TM SUMMARY OF RESULTS: See Table 1 for activity scores. No differences among the three devices (Pillai’s Trace= .161, F(2,20)=1.92, p>.05) on any measures or with the placement of the two OnesTM CONCLUSION: No differences were observed among the placement of the three devices in the measurement of daily steps or time spent in different intensities of activity. These data suggest that users should wear the device where it is most comfortable for them. The preference of wear placement may result in better long-term personal activity monitoring.Table: No title available.
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".