Measurement properties of remotely or self-administered physical performance measures to assess mobility: a systematic review protocol
Bibliographic record
Abstract
Background Physical performance measures assessing mobility are important tools for assessing current functional status, predicting future functional status, and monitoring change. To respond to the increased necessity to conduct research and care remotely and self-monitor one’s health status, there is a need for reliable, valid, and responsive measures that can be self or remotely administered.Objectives To evaluate (i) the test procedures and population suitability of remotely or self-administered lower extremity performance measures and (ii) the measurement properties of scores for these measures.Methods This review will include quantitative studies with adult participants (≥ 18 years) who are living independently in the community. For the purposes of this review, mobility is defined as the ability to move by changing body position or location or transferring from one place to another as per the International Classification of Functioning, Disability and Health (ICF) framework. Five databases; MEDLINE, EMBASE, CINAHL, AMED and Cochrane CENTRAL will be searched to identify relevant studies. Reference lists of relevant studies will be hand-searched to identify additional eligible studies. Title and abstracts screening, full text screening and data extraction will be completed independently by two reviewers. Results will be compared against COnsensus-based Standards for the selection of health Measurement Instruments’ (COSMIN) criteria for measurement properties which provide a sufficient, insufficient, or indeterminate rating based on whether a previously defined hypothesis (set by research team or by COSMIN). The quality of each study will be assessed by two independent reviewers using COSMIN’s Risk of Bias tool.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".