A Review of Accelerometer-based Activity Monitoring in Cancer Survivorship Research
Bibliographic record
Abstract
BACKGROUND: In the cancer survivorship context, physical activity and sedentary behavior have been measured using different methods. PURPOSE: To conduct a narrative review of published research in cancer survivor populations to summarize the quality and identify gaps in reporting on accelerometer data collection, data processing, and outcome measures in cancer survivors. METHODS: An initial PubMed® search of articles published in English was conducted in January 2017, and a final search was conducted in May 2017. Variables extracted included study characteristics, methods for accelerometry data collection (e.g., device used), data processing (e.g., cut points used), and data reporting (e.g., time spent in different activity intensities). RESULTS: A total of 46 articles were eligible for inclusion in the review. The majority of studies (34 of 46) targeted a single cancer group and 18 of these 34 studies were in survivors of breast cancer. Half (54%) of the studies used an ActiGraph® accelerometer. Methods of accelerometer data processing varied across studies. Definitions of non-wear time, vectors used during processing, and filters applied during processing were reported by 51%, 60%, and 8% of studies, respectively. Most studies reported moderate and vigorous physical activity (78%), 50% reported sedentary time, and 43% reported light-intensity activity. Cut points to categorize these activities varied between studies. CONCLUSIONS: This narrative review highlights inconsistency in the methods used to collect, process, and report accelerometry data across cancer survivor studies. Accelerometry has potential to add detailed knowledge of the levels and patterns of physical activities and sedentary behaviors across the cancer spectrum. Recommendations are made to improve data processing and reporting methods to maximize the scientific validity of future accelerometer research in this field.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".