Mechanical Percussion Devices: A Survey of Practice Patterns Among Healthcare Professionals
Bibliographic record
Abstract
Background: Mechanical percussion devices have become popular among sports medicine professionals. These devices provide a similar effect as manual percussion or tapotement used in therapeutic massage. To date, there are few published studies or evidence-based guidelines for these devices. There is a need to understand what professionals believe about this technology and how they use these devices in clinical practice. Purpose: To survey and document the knowledge, clinical application methods, and use of mechanical percussion devices among healthcare professionals in the United States. Design: Cross-sectional survey study. Methods: A 25 question online survey was emailed to members of the National Athletic Trainers Association, Academy of Orthopedic Physical Therapy, and American Academy of Sports Physical Therapy. Results: Four hundred twenty-five professionals completed the survey. Most professionals (92%, n=391) used devices from two manufacturers: Hyperice® and Theragun®. Seventy-seven percent directed clients to manufacturer and generic websites (n=329) to purchase devices. Most respondents used a medium and low device speed setting for pre- and post-exercise (62%, n=185), pain modulation (59%, n=253), and myofascial mobility (52%, n=222). A large proportion of respondents preferred a total treatment time between 30 seconds and three minutes (36-48%, n=153-204) or three to five minutes (18-22%, n=76-93). Most respondents (54-69%, n=229-293) believed that mechanical percussion increases local blood flow, modulates pain, enhances myofascial mobility, and reduces myofascial restrictions. Most respondents (72%, n=305) were influenced by other colleagues to use these devices. Sixty-six percent used patient reported outcomes (n=280) to document treatment efficacy. Live instruction was the most common mode of education (79%, n=334). Conclusion: These results are a starting point for future research and provide insight into how professionals use mechanical percussion devices. This survey also highlights the existing gap between research and practice. Future research should examine the efficacy of this technology and determine consensus-based guidelines. Level of Evidence: 3.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".