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Efficacy of music listening as a postoperative pain management intervention in adult patients: a systematic review

2010· review· en· W4245723572 on OpenAlexaboutno aff
Abigail Kusi-Amponsah, Nick Allcock, Wendy Stanton, Fiona Bath‐Hextall

Bibliographic record

VenueThe JBI Database of Systematic Reviews and Implementation Reports · 2010
Typereview
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningMedicineNursingIntervention (counseling)Pain managementOfficerFamily medicinePsychologyPhysical therapy

Abstract

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Centre conducting review University of Nottingham Centre for Evidence Based Nursing and Midwifery: A Collaborating Centre of the Joanna Briggs Institute. Primary Reviewer Abigail Kusi-Amponsah Master of Science in Advanced Nursing, School of Nursing, Midwifery and Physiotherapy University of Nottingham, UK Nursing Officer, Komfo Anokye Teaching Hospital Kumasi, Ghana Mobile: +447556015208/ 7538903209 Email: [email protected] Secondary Reviewer Dr. Nick Allcock Co-Director, University of Nottingham Centre for Evidence Based Nursing and Midwifery Associate Professor, School of Nursing, Midwifery and Physiotherapy Faculty of Medicine and Health Sciences University of Nottingham, UK Telephone: 0115 8493287 Fax: 0115 9709955 Mobile: 07977924133 Email: [email protected] Co-authors 1. Wendy Stanton Senior Librarian and Team Leader, Faculty of Medicine and Health Sciences University of Nottingham, UK Telephone: 01158230545 Email: [email protected] 2. Dr. Fiona Bath-Hextall Associate Professor and Reader in Evidence-Based Health Care Honorary Associate Professor in CEBD, School of Nursing, Midwifery and Physiotherapy Faculty of Medicine and Health Sciences University of Nottingham, UK Telephone: 01158230884 Email: [email protected] Commencement date: November 2010 Expected completion date: April, 2011 Review Objective The aim of this review is to critically analyse and synthesise the best available evidence on the impact of music listening on postoperative pain. Background Postoperative pain remains a major clinical problem in spite of recent advances in its management.1, 2, 3 According to Fine and Portenoy, 4 postoperative pain is an acute form of pain which is experienced after surgery. A survey conducted by Apfelbaum and colleagues revealed that about 69% of patients experience moderate to severe pain after surgery. 5 Even though postoperative pain is often expected by patients, it is an undesirable experience. 6 Postoperative pain may impair an individual's physical, psychological and social well being if poorly managed. 7 In addition, it may result in prolonged hospitalisation, 8 readmissions, 9 and increased cost of healthcare. 10 These consequences eventually have an enormous impact on the patient's family, society and the nation at large. 7 Analgesics are the mainstay in postoperative pain management. 11-13 However, these are associated with undesirable side effects including constipation, respiratory depression, allergic reactions, 14 altered cognitive function and drug dependence. 15 Thus, the use of complementary therapies that may decrease pain with lower risk of side effects may be of immense benefit. 9, 16 Music, as a nonpharmacologic therapy can be used during the perioperative period to reduce pain. Music therapy is the systematic process of using musical sounds to restore, maintain and improve the physical, psychological and social health needs of an individual. 17 It usually involves a music therapist and a client, and may take the form of composing, singing, dancing or listening to music. 18 This review will focus on the listening aspect of music therapy. Music listening is the act of hearing orderly arranged sounds that usually have rhythm, pitch, melody, harmony and intensity. 16, 19 Listening to pleasant, 20 familiar, 21 relaxing, 22 and preferred, 23 music distracts the prefrontal cortex from painful stimuli. 24, 25 Consequently, the transmission of painful impulses is inhibited through the activation of endogenous opiates, descending nerve impulses, and neuropeptides in the central nervous system. 26, 27 A number of primary research studies have evaluated the impact of music listening as a complementary therapy in postoperative pain management. 9 However these studies have produced conflicting findings. 11, 28-31 Over the last eight years, a number of systematic reviews on the efficacy of music as a pain management intervention have also been published. 20, 32-35 While some of these reviews claim that music possesses analgesic properties, 20 and can be used as an adjunct for pain relief; 34 others posit that the margin of this effect is slight and thus, have vague clinical significance. 32 Also, a systematic review conducted by Dunn 33 was inconclusive on the analgesic properties of music, while that of Evans posits that music has no pain reducing effect. 35 Some of these reviews did not focus on postoperative pain, 32, 35 while others included studies that used combined nonpharmacological therapies, 34 and only those that were conducted in developed countries. 33 Moreover, many of these reviews are outdated, 32, 33, 35 while the more recent reviews searched for studies up to 2007. 20, 34 A number of new studies have been published since 2007, 11, 23, 28, 36-39 although, they report conflicting findings. While some of these primary studies found a statistically significant pain reduction with the music group, 36-38 others found no significant statistical difference among study groups. 11, 23, 28, 39 For these reasons, we propose to undertake a systematic review to assess the efficacy of music listening as a postoperative pain management intervention. Inclusion Criteria Types of Participants Adults (18 years or older) who have undergone any elective major or minor surgery irrespective of the severity of underlying condition. Types of Interventions Any type of recorded music (patient preferred or investigator chosen) of any duration, that is delivered immediately after surgery to the 3rd postoperative day, through any medium (audio or video CD/ tape player, music pillows, e.t.c.), in addition to usual care. Comparator Usual care (use of analgesics and other routine postoperative care). Types of Outcome Measures Primary outcome Postoperative pain measured before and after the intervention by any validated pain assessment tool (such as the Visual Analogue Scale, Verbal Rating Scale, McGill Pain Questionnaire). Secondary outcomes Analgesic consumption measured by patient-controlled analgesia (PCA) pumps or patient records, and expressed as morphine equivalents (intramuscular/ subcutaneous/ intravenous). Number of adverse events reported in the individual papers included in the review. Types of Studies All randomised controlled trials (RCTs) that compare the efficacy of music listening and usual care to usual care alone in reducing postoperative pain will be included in this review. In the absence of RCTs, comparative studies without randomisation, cohort and case-control studies will be included if they report patient's experiences of pain intensity measured by any validated pain assessment tool. Exclusion Criteria This review will exclude studies in which music intervention was delivered by a music therapist. Studies that include infants and children, and those that evaluate the combined use of music and other nonpharmacologic interventions (such as guided imagery, therapeutic suggestions, and jaw relaxation) will be excluded. As this review focuses on postoperative pain, studies where participants have pre-existing chronic cancer or non-malignant pain, or trauma related pain will be excluded. Search Strategy A three-step search strategy will be utilised in the review. An initial search using keywords such as “music”, “music therapy”, “postoperative”, “surgery”, “analgesia” and “pain” will be entered into CINAHL and MEDLINE since they are the largest healthcare databases. Following this, the title, abstract and index terms of each article will be analysed. A second search using all the identified keywords and index terms will then be undertaken in the following databases: Symbol Allied and Complementary Medicine (AMED) (1985-2010) Symbol British Nursing Index (BNI) (1985-2010) Symbol Cochrane Central Register of Controlled Trials (CENTRAL) (1992-2010) Symbol Cumulative Index to Nursing and Allied Health Literature (CINAHL) (1982-2010) Symbol Embase (1980-2010) Symbol Medline (1950-2010) Symbol Mednar Symbol PsycINFO (1806-2010) Symbol Web of Science (1900-2010) Symbol Proquest Dissertations and Theses (2001-2010) Figure: No Caption available.Thirdly, additional studies will be searched from the reference lists of all identified articles. The search strategy will not be limited to any time period. Individual search strategies will be developed for each database due to different styles of indexing. Appendix I illustrates the search strategy for RCTs using Medline, CINAHL and Embase. Websites such as www.controlledtrials.com, www.clinicaltrials.gov, www.anzctr.org.au and www.who.int/trialsearch will be searched for ongoing trials. We will not impose any language restrictions and we shall seek to translate where necessary. Selection of Studies Two authors (AKA and NA) will independently check the titles and abstract identified from the searches for relevance to the inclusion criteria. If it is clear that the study does not refer to listening to music as defined or is not an RCT then it will be excluded. If it is unclear, then the full text will be obtained for independent assessment by the two authors (AKA and NA). Disagreements between reviewers will be resolved through discussion. However, a third reviewer (FB-H) will be consulted in case an agreement cannot be reached. Reasons for excluding papers will be given in the excluded studies table. Assessment of the Methodological Quality Studies will be assessed by two independent reviewers (AKA and NA) for methodological quality using the standardised critical appraisal checklist from the Joanna Briggs Institute Meta Analysis of Statistics Assessment and Review Instrument - JBI-MAStARI (Appendix II) prior to inclusion in the review. Disagreements between reviewers will be resolved through discussion. However, a third reviewer (FB-H) will be consulted in case an agreement cannot be reached. Data Extraction Data from each eligible study will be extracted by 2 independent review authors (AKA and NA) using JBI-MAStARI data extraction tool (Appendix III). The data extracted will include participant numbers and characteristics, place of study, patient's demographics, type of surgery, type of anaesthesia, and type of anaesthetic agent(s), nature of the interventions, drop-out rates and rationale. Discrepancies will be resolved between the same two authors. The third author (FB-H) will be involved if necessary. Missing data will be obtained from trial authors where possible. Review authors will not be blinded to the names of trial authors, journals or institutions, specific information on the study methods, populations, interventions and outcome measures. Data Synthesis Results, where appropriate, will be combined statistically using the JBI-MAStARI. Where it is not possible to perform a meta-analysis we will provide a narrative summary. Heterogeneity will be assessed using the I2 statistic. Where substantial heterogeneity (I2 >50%) exists between trial for any of the outcomes, the rationale for this will be explored. If necessary, we will perform sensitivity analysis to examine the effects of excluding subgroups such as low methodological quality studies. Conflicts of Interest All the reviewers have no potential conflicts of interest. Acknowledgements I would like to express my profound and sincere gratitude to Drs. Nick Allcock and Fiona Bath-Hextall for their unremitting energy, constructive criticisms and encouragement. Special thanks to Wendy Stanton for her valuable advice and assistance in developing the search strategy. My heartfelt appreciation also goes to Dr. Linda East and her team for their continuous assistance during the MSc. Advanced Nursing program. Also, I am heavily indebted to the Vice Chancellor, Pro-vice Chancellor, Provost of the College of Health Sciences, Dean of the Faculty of Allied Health Sciences, The Head of the Nursing Department, the members of the finance and administrative team of the Kwame Nkrumah University of Science and Technology, Ghana for giving me the needed support to develop personally and professionally. Note This review has been updated and revised from the review submitted for the award of MSc. Advanced Nursing.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.073
GPT teacher head0.441
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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