A Digital Box and Block Test for Hand Dexterity Measurement: Instrument Validation Study
Bibliographic record
Abstract
BACKGROUND: The Box and Block Test (BBT) measures unilateral gross manual dexterity and is widely used in clinical settings with a wide range of populations, including older people and clients with neurological disorders. OBJECTIVE: In this study, we present a newly developed digitized version of the BBT, called the digital BBT (dBBT). The physical design is similar to the original BBT, but the dBBT contains digital electronics that automate the test procedure, timing, and score measurement. The aim of this study is to investigate the validity and reliability of the dBBT. METHODS: We performed measurements at 2 time points for 29 healthy participants. BBT and dBBT were used at the first measurement time point, and dBBT was used again at the second measurement time point. Concurrent validity was assessed using the correlation between BBT and dBBT, the paired t test, and the Bland-Altman analysis. Test-retest reliability and interrater reliability were examined using the interclass correlation coefficient (ICC) by repeated measures with the dBBT within an interval of 10 days. RESULTS: Our results showed moderate concurrent validity (r=0.48, P=.008), moderate test-retest reliability (ICC 0.72, P<.001), a standard error of measurement of 3.1 blocks, and the smallest detectable change at a 95% CI of 8.5 blocks. Interrater reliability was moderate with an ICC of 0.67 (P=.02). The Bland-Altman analysis showed sufficient accuracy of the dBBT in comparison with the conventional BBT. CONCLUSIONS: The dBBT can contribute to objectifying the measurement of gross hand dexterity without losing its important characteristics and is simple to implement.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".