ArticleBMJ open2023
Text messaging intervention to support patients with chronic pain during prescription opioid tapering: protocol for a double-blind randomised controlled trial.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Opioid Message Fatigue in the United States: Prevalence and Associations with Stigma and Policy Support.Substance use & misuse · 2026Article
- A digital health intervention to support patients with chronic pain during prescription opioid tapering: a pilot randomised controlled trial.Pain reports · 2024Article
- Telehealth and Virtual Reality Technologies in Chronic Pain Management: A Narrative Review.Current pain and headache reports · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
Abstract
introductionIncreases in pain and interference with quality of life is a common concern among people with chronic non-cancer pain (CNCP) who are tapering opioid medications. Research indicates that access to social and psychological support for pain self-management may help people to reduce their opioid dose without increasing pain and interference. This study evaluates the efficacy of a text messaging intervention designed to provide people with CNCP with social and psychological support for pain self-management while tapering long-term opioid therapy (LTOT) under the guidance of their prescriber. METHODS AND ANALYSIS: A double-blind randomised controlled trial will be conducted. Patients with CNCP (n=74) who are tapering LTOT will be enrolled from across Australia. Participants will continue with their usual care while tapering LTOT under the supervision of their prescribing physician. They will randomly receive either a psychoeducational video and supportive text messaging (two Short Message Service (SMS) per day) for 12 weeks or the video only. The primary outcome is the pain intensity and interference assessed by the Pain, Enjoyment of Life and General Activity scale. Secondary outcomes include mood, self-efficacy, pain cognitions, opioid dose reduction, withdrawal symptoms, and acceptability, feasibility, and safety of the intervention. Participants will complete questionnaires at baseline and then every 4 weeks for 12 weeks and will be interviewed at week 12. This trial will provide evidence for the efficacy of a text messaging intervention to support patients with CNCP who are tapering LTOT. If proven to be efficacious and safe, this low-cost intervention can be implemented at scale. ETHICS AND DISSEMINATION: The study protocol was reviewed and approved by the Northern Sydney Local Health District (Australia). Study results will be published in peer-reviewed journals and presented at scientific and professional meetings. TRIAL REGISTRATION NUMBER: ACTRN12622001423707.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.