The effectiveness of transcutaneous electrical nerve stimulation (TENS) therapy on shoulder impingement syndrome: a systematic review
Bibliographic record
Abstract
Background Shoulder impingement syndrome (SIS) is a common condition that causes pain and functional impairment in the shoulder. There have been various studies assessing methods of pain management in SIS, however, the knowledge around the association between transcutaneous electrical nerve stimulation (TENS) and SIS management has been found to be limited. A systematic review of the literature will provide healthcare providers and the public with an evidence-based summary of evidence regarding the effectiveness of TENS in treating SIS pain. Objective To systematically assess and review the methodological rigour of all available studies that have used TENS to treat SIS in order to provide evidence-based knowledge to either support or refute its use in clinical practice. Methods A literature search was conducted on electronic databases Google, Google scholar and DUT Summons (Pubmed, MEDLine, Mantis, ScienceDirect). The relevant key search words used at this stage of the study included the term “TENS” and each of the following: shoulder pain, frozen shoulder, calcific tendonitis, shoulder impingement syndrome, shoulder myofascial pain syndrome, brachial dysfunction, shoulder dysfunction and shoulder bursitis. It was identified that SIS would be the focus of this study and therefore, full-text articles relevant to SIS and TENS were included, based on the inclusion and exclusion criteria outlined in the study design. The final list of included articles was reviewed by a total of seven reviewers using either the PEDro and/or Newcastle-Ottawa scale, in order to establish the methodological rigour of the studies. Results During the data collection process that took place between August 2019 to January 2020, 106 articles were screened for eligibility. Patients included in these studies were adults who were required to have been diagnosed with SIS. These articles included randomized controlled trials (RCTs), non-RCT’s, case studies and case reports. The 106 articles were reduced to 20 articles meeting the inclusion criteria, consisting of 19 RCTs and 1 non-RCT. Following the conclusion of the data collection process, the articles collectively were divided into three groups. Screening and review of the articles were conducted by the three reviewers allocated to each group. Each article was then ranked according to the criteria outlined in the PEDro and/or Newcastle-Ottawa scale, and the limitations of each article were stated. Following this process, the findings of all articles were collated, with the results indicating that there is limited evidence to support the use of TENS in the management of SIS. Conclusion Even though TENS has been found to be effective in inducing pain relief in a variety of conditions, clinicians should use TENS with caution as the limited and conflicting evidence available does not advocate for its use (alone or in combination with other therapies) in treating SIS. Thus, there is a demand for more high-quality studies pertaining to TENS and SIS clinical subcategories. It is however recognised that the findings of this study may be limited to a specific time period and could be influenced by more recently published studies not included. Key words Shoulder pain, Frozen shoulder, Calcific tendonitis, Shoulder impingement syndrome, shoulder myofascial pain syndrome, Brachial dysfunction, shoulder dysfunction, shoulder bursitis, transcutaneous electrical nerve stimulation
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".