Reducing opioid use for chronic pain with mobile health interventions: a systematic scoping review

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Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Reducing opioid use for chronic pain with mobile health interventions: a systematic scoping review Michael R Magee , Ali Gholamrezaei , Amy G McNeilage , Alison Sim , Paul Glare , Claire E Ashton-James doi: https://doi.org/10.1101/2025.08.07.25333246 Michael R Magee 1 Pain Management Research Institute, Kolling Institute, Faculty of Medicine and Health, The University of Sydney , Australia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ali Gholamrezaei 1 Pain Management Research Institute, Kolling Institute, Faculty of Medicine and Health, The University of Sydney , Australia MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ali.gholamrezaei{at}sydney.edu.au Amy G McNeilage 1 Pain Management Research Institute, Kolling Institute, Faculty of Medicine and Health, The University of Sydney , Australia BSc Hons Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alison Sim 1 Pain Management Research Institute, Kolling Institute, Faculty of Medicine and Health, The University of Sydney , Australia MScMed, MOsteo Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul Glare 1 Pain Management Research Institute, Kolling Institute, Faculty of Medicine and Health, The University of Sydney , Australia MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Claire E Ashton-James 1 Pain Management Research Institute, Kolling Institute, Faculty of Medicine and Health, The University of Sydney , Australia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract This systematic scoping review explores the extent and characteristics of research on mobile health (mHealth) interventions for reducing opioid use in chronic pain management. A comprehensive search was run on eight major bibliographic databases (e.g., PubMed) as well as grey literature (e.g., clinical trials registries). Each record was screened by two independent reviewers. Studies were included if they investigated an mHealth intervention for adults with chronic pain and reported opioid use as an outcome (primary or secondary). Data of the study characteristics and results (if available) were extracted and analysed descriptively. Out of 3097 records, 25 (11 published studies, 14 protocols) were included. The studies were from five countries and conducted between 2009-2023. mHealth Interventions included mobile applications (20), text messaging (4), and interactive voice response (1). In about half of the studies (13), taking opioids was an eligibility criterion. In four studies, interventions were specifically designed to support tapering. Studies with published results concluded that the mHealth interventions are acceptable to participants (9) and generally feasible (6). This systematic scoping review shows that there is a growing interest in research investigating mHealth interventions to support people with chronic pain tapering opioids. Current research mostly investigated mobile applications designed for chronic pain management with opioids use as a secondary outcome. mHealth interventions specifically designed to support opioids tapering are emerging. Available results show that mHealth interventions are acceptable, feasible, and potentially efficacious for patients with chronic pain tapering opioids. Introduction Chronic pain, pain lasting longer than 3 months 1 , is the greatest cause of years lost to disability globally 2 , 3 and a huge financial drain on society 4 , 5 . Opioid medications are frequently prescribed for chronic pain but pose significant safety concerns, particularly with long-term use, including tolerance, dependence, cognitive impairment, and potentially fatal respiratory depression 6 - 8 . Patients currently taking prescribed opioid medications for whom the risks outweigh the benefits are advised to reduce or discontinue their use of opioid medications with their clinician, a process known as tapering 8 - 13 . However, tapering is challenging for many patients, who can experience negative impacts on pain, mood, and uncomfortable withdrawal symptoms 6 , 7 , 13 , 14 . Patients who have successfully tapered report that a combination of pain education, opioid tapering support, and social support (from clinicians and personal networks) may facilitate tapering success 15 - 19 . A systematic review and meta-analysis investigating the efficacy of interventions to reduce long-term opioid therapy for chronic pain concluded that the provision of multidisciplinary support such as psychology and physiotherapy pain self-management programs, is probably effective in reducing opioid doses 20 . However, multidisciplinary support is costly, and most pain treatment services cannot meet demand 21 . This inaccessibility adversely impacts those living with chronic pain and may perpetuate long-term opioid medication use when it is ill advised 13 , 22 . Digital health may provide a scalable, inexpensive solution to the lack of accessibility for opioid tapering support 23 . Digital platforms such as mobile phones and other portable technology provide patients with accessible support for many chronic health issues. Digital health supports may improve health equity and address the inequitable distribution of chronic pain treatment options across geographic and socioeconomic gradients 21 , 24 , 25 . A range of innovative digital interventions are either in use or in development to support chronic pain management 26 - 29 . For example, a systematic review of digital health interventions concluded there is good support for the use of internet-delivered interventions for chronic pain 30 . Flexible, at home digital solutions may be a particularly good fit within a chronic pain population, due to the levels of physical disability and associated challenges that some people living with chronic pain may experience 25 . Further, the stigma that some people with chronic pain report around opioid use may be mitigated through the anonymity of digital health supports 31 , 32 . Evidence regarding mobile health (mHealth) interventions to support patients with chronic pain tapering opioids is emerging. However, based on a preliminary search in March 2023 of MEDLINE, the Cochrane Database of Systematic Reviews, JBI Evidence Synthesis, and PROSPERO databases, there is currently no synthesised evidence (published or protocol) in this area. Therefore, this scoping review was conducted to address the primary research question ‘what is the scope of available evidence for mHealth in providing support to individuals with chronic pain during opioid tapering?’. Several review sub-questions were developed to align data extraction and reporting with the areas of interest for the review ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1: Research Questions Methods Protocol This review was informed by the Joanne Briggs Institute (JBI) Reviewer’s manual for the conduct and reporting of scoping reviews 34 as well as the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) 33 . A scoping review protocol was registered on Open Science Framework in February 2023 35 . A PRISMA-ScR checklist and summary of changes to the protocol after registration are included in the supplemental digital content. Population, Concept, Context (PCC) To define the scope of this review, a population, concept, context (PCC) framework was developed. The population considers studies of adults living with chronic pain. The two concepts are mHealth and opioids. The concept of mHealth is mobile phone-based digital health technologies for the purpose of delivering healthcare support or an intervention to patients 36 , 37 . Specifically, mHealth supports are asynchronous to clinician care (i.e. not digital telehealth services or didactic phone calls). The concept of opioids refers to any prescribed opioid medication for the management of chronic pain. Regarding context, studies were limited to research conducted since 1990. This timepoint should capture the inclusion of when mHealth technologies were increasingly accessible to the public. The inclusion and exclusion criteria are described in Table 2 . View this table: View inline View popup Download powerpoint Table 2: Inclusion and Exclusion Criteria Information Sources The scoping review considers both experimental and quasi-experimental study designs including randomised/non-randomised controlled trials, before and after studies, and interrupted time-series studies. In addition, analytical observational studies including prospective and retrospective cohort studies, case-control studies, and analytical cross-sectional studies are included. Descriptive observational study designs including case series, individual case reports, and descriptive cross-sectional studies and qualitative studies that met other inclusion criteria were included. Systematic and other reviews, as well as text and opinion papers, were included if they reported original data. Grey literature included conference presentations, theses, and clinical trial protocols. Search Strategy A database search strategy was developed by the researchers. Keywords linked to the PCC of chronic pain, opioids, and mHealth, were developed based on related review articles 20 , 30 , 38 . The search was run on PubMed (including MEDLINE), Scopus, Embase, CINAHL, APA PsychInfo, Global Health, JBI EBP Database, and the Cochrane Library. For grey literature, searches were run on ProQuest Dissertations and Theses, EBSCO Open Dissertations, The Conference Proceedings Citation Index (via Web of Science), and the World Health Organisation (WHO) Clinical Trials Registry. The search strategy, including all keywords, medical subject headings, and index terms were adapted for each included database or information source. One researcher carried out all database searches in April 2023. The search strategy is included in the supplemental digital content. A second search (in the same databases) was conducted in October 2024 to find any reported results of the clinical trials protocols included in the review. Authors of the clinical trials protocols were contacted if no published result was found. Study Selection Results were uploaded to Covidence 39 to manage screening. The title and abstract of each source (first pass) and then the full text (second pass) were screened by two independent reviewers. Disagreements were resolved through discussion or with a third reviewer. If there were multiple results for the same study, such as a trial protocol then the published findings, or the publication of the same data in more than one article/report, the earlier publications were excluded. Despite excluding earlier versions, any additional data that could contribute to the completeness of the review was also extracted. Pilot Search A pilot search was conducted to test the search, screening, and extraction procedure. A sample of 100 articles was selected from an initial search. The two reviewers had a high rate of agreement (above 75% agreement) considered acceptable in the protocol 34 . Eligible texts from the pilot search were extracted using the charting table from the protocol and changes were made to refine the extraction table template. Data Extraction and Data Analysis A descriptive analytic approach was utilised for data analysis 40 . Data relevant to the review questions and sub questions were extracted from each article and reported descriptively. One reviewer extracted the data from each study which was then verified by another member of the research team. In line with our aims and established guidelines for scoping reviews, we did not complete a quality assessment of the results of each study in this review 41 , 34 . Results What is the scope of available evidence for mHealth to support opioid tapering for individuals with chronic pain? The searches identified 3097 results from which 25 articles were included. See Figure 1 for the search flow including major reasons for exclusion. Eleven articles reported the results of original investigations 42 - 52 (hereafter referred to as research studies) and 14 reported protocols. Of the research studies, five were randomised clinical trials (RCTs) 44 , 45 , 49 , 50 , 52 , four were non-randomised published studies 42 , 43 , 46 , 51 , and two published abstracts from conference presentations (one RCT, one single-arm study) 47 , 48 . One published article 53 excluded at full text review (due to no opioid outcomes being reported) used the same sample and data as a conference abstract that was included 47 . The conference article reported opioid outcomes and was more recent, so was retained, though some data was drawn from the more comprehensive article 47 , 53 . The remaining 14 articles were trial protocols 54 , 55 , 56 , 57 , 58 , 59 , 51 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 . Five of the protocols were published in peer-reviewed journals 55 , 46 , 58 , 60 , 59 and the others were drawn from clinical trial registries 54 , 56 , 61 - 67 . If protocols were published both in articles and trial registries, the article was cited 59 , 68 . Of the 14 protocols, 11 were protocols for RCTs 55 , 56 , 57 , 59 , 51 , 60 , 61 , 63 , 64 , 65 , 66 , 67 while three 54 , 58 , 62 described single-arm open label trials. The extracted research studies and protocols were published between 2009 and 2023. There were a wide range of reported study durations, from two-weeks to up to a year. Sample sizes in the research studies ranged from 18 to 2245 participants. Of the 14 protocols, target sample sizes ranged from 40 to 350 participants and two did not report target sample sizes 54 , 62 . The results discussed below are presented in Table 3 . Download figure Open in new tab Figure 1: PRISMA Study Flow Chart View this table: View inline View popup Table 3. Intervention characteristics in the reviewed studies (n=25 studies) In what geographical and clinical contexts (research settings) have the mHealth interventions included in the review been delivered? The published studies were conducted in the USA 44 - 47 , 49 , 51 , 52 , Canada 43 , Germany 50 , Australia 42 , and Sweden 48 . The research protocols reported studies to be conducted in the USA 55 - 58 , 61 - 66 , Canada 67 , and Australia. 54 , 60 , 59 The most common recruitment setting was pain clinics, where five research studies 43 , 42 , 50 , 52 , 47 and five protocols 54 , 64 , 60 , 62 , 67 , 65 recruited participants. Three articles, two research studies 45 , 51 and one protocol 61 , recruited from other hospital clinics including: sickle cell disease (SCD) clinics, a surgical unit, and a buprenorphine treatment clinic for opioid use disorder (OUD) 61 . Five articles, two research studies 44 , 49 and three protocols 63 , 57 , 66 , recruited from primary care. Five articles, one research study 46 and four protocols 59 , 55 , 58 , 55 reported a hybrid strategy, recruiting from both medical clinics and through wider community advertising. Two articles, one conference abstract 48 and one protocol 56 , did not report their recruitment setting. What are the characteristics of the study sample populations? The sample populations were diverse/heterogeneous. Participants of the 25 articles had a range of chronic pain conditions. Twelve of the investigations, six research studies and six protocols, focussed on participants with any chronic non-cancer pain (CNCP) condition including several sub-populations. These subpopulations included patients with difficulty pacing 42 , patients who had recently completed an 11-week group Cognitive Behavioural Therapy (CBT) pain program 47 , participants living in regional areas 47 , 56 , individuals with sub-diagnostic depression symptoms and CNCP 50 , participants with CNCP and an upcoming surgery 67 , participants with CNCP and comorbid OUD 61 , and participants receiving buprenorphine and opioids 44 . Two studies reported high proportions of participants experiencing comorbid substance use disorder 44 , 46 . Other pain conditions included, chronic pain from arthritis 48 , 62 , chronic lower back pain 50 , 51 , 65 , chronic pain due to SCD 45 , 55 , 58 , and chronic musculoskeletal pain 47 . Four of the protocols did not specify the chronic pain condition 54 , 57 , 63 , 64 . Four studies (all from the USA), one research article 44 and three protocols 57 , 63 , 66 , recruited veterans. Demographics The data extraction table ( Table 3 ) lists demographic variables where reported. Participant ages across all studies had a mean of 49.4 years (SD= 10.7, Range= 26.9 to 63). All studies reported gender as a binary variable. The proportion split of gender or sex was variable, with the percentage of females ranging from 7.5% 44 to 96% 47 . Eight (73%) of the research studies reported some sample demographic information related to ethnicity, education, and employment status 36 , 44 , 45 , 49 , 50 , 52 , 41 , 46 . Ethnicity was an inclusion criteria in one SCD study 45 . Education level was reported in eight of the 25 articles 42 , 44 - 47 , 49 , 50 , 52 . Relationship status was reported in four studies 42 , 44 , 49 , 50 . Employment data was reported in six studies 42 - 44 , 46 , 49 , 52 . Only three research protocols specified what demographic or population statistics would be collected 47 , 60 , 59 . Whilst no research protocol planned to restrict recruitment based on gender, two of the 14 protocols had ethnicity requirements. One aimed to recruit mostly participants of African descent who live with SCD 58 . The other protocol only planned to recruit Black and Hispanic participants, identified as a marginalised population in the study catchment area 61 . View this table: View inline View popup Table 3. Population characteristics in the reviewed studies Opioids Taking prescribed opioids was an inclusion criteria in 13 articles, two research studies 44 , 52 and eleven protocols 57 , 54 , 55 , 58 , 60 , 59 , 61 , 62 , 63 , 67 , 66 . Four articles, one research study 45 and three protocols 54 , 60 , 59 , only recruited participants with chronic pain who were tapering opioids. Four articles (two research studies 44 , 52 , two protocols 60 , 56 ), had specific oral morphine equivalent dose (OME) requirements as part of their eligibility criteria. Of these, two were set at 20mg OME 56 , 41 . To increase recruitment one study abandoned their dose requirement 44 and a protocol was amended to reduce daily OME from 60mg to 30mg 60 . Some of the articles reported required minimum durations participants must take opioids, ranging from four weeks 59 to up to 90 days 55 . The proportion of the sample using opioids was the primary outcome in one 56 and opioid dose was the primary outcome in seven 49 , 52 , 57 , 61 - 63 , 67 (28% of total) articles that required participants to be using opioids. Three articles that required participants to use opioids did not have opioid dose or use as the key opioid outcome measure. Of these studies the primary outcomes were opioid tapering self-efficacy (two protocols) 60 , 59 and abnormalities in eye movement throughout the opioid withdrawal process (one protocol 54 ).One article required participants to take opioids, but the primary outcome was physical activity 33 . Of the one published research article requiring participants to take opioids, which reported OME as the primary outcome measure (alongside pain) the authors report 105 or 196 participants (53.6%) receiving their four-month mHealth intervention were able to reduce at least a 15 percent reduction in daily OME compared to 85 of 201 (42.3%) of treatment as usual participants, which the authors report as statistically significant 52 . Across the literature included in the review the most common ways of reporting opioid use was opioid dose, mostly reported as OME (five studies 42 - 44 , 47 , 46 , 10 protocols 55 , 57 , 58 , 60 , 59 , 62 , 56 , 64 , 63 , 66 ), and the proportion of the sample reporting using any opioids (five research articles 45 , 48 - 51 , one protocol 67 ). Some studies included both OME and sample proportions using opioids (two research articles 44 , 47 one protocol 56 ). One research study reported opioid outcomes within qualitative interviews only 46 . Self-report was the most common method of data collection, used by six research studies and five protocols 54 , 60 , 61 , 63 , 65 . Five articles, two research studies 49 , 52 and three protocols 55 , 67 , 59 used e-health records. One protocol used pharmacy dispensing to track opioid use, self-report, and a device which tracks real-time medication access 58 . Notably, one research study 48 and five protocols 54 , 56 , 66 , 67 , 62 (25% of results) did not report how they collected opioid data. Several investigations had additional opioid-related outcome measures including: opioid tapering self-efficacy 60 , 59 , the Current Opioid Misuse Measure (COMM) to monitor indicators of aberrant drug-related behaviours 55 , 66 , 64 ; the Screener and Opioid Assessment for Patients with Pain-Revised (SOAPP-R) to evaluate risk for developing problems on long-term opioid use 64 ; participant experiences of opioid withdrawal symptoms 60 , 59 , the Addiction Severity Index (ASI) an assessment of drug use in the past 30 days 61 ; opioid craving using the Medication Craving Scale 61 ; and momentary craving 66 . What are the characteristics of the mHealth interventions in the reviewed studies? Modes of delivery Mobile applications (apps) were used in 20 of the 25 articles (eight research studies, 12 protocols). Most of these studies only used an app, but some had additional features such as activity monitors 42 , 44 , supportive text messages 44 , posture monitoring sensors 65 , and an acupressure kit and video conference sessions with an acupressure clinician 46 . One protocol planned to compare the use of a mobile app with a virtual reality (VR) intervention for chronic pain 56 . One research study used Therapeutic Interactive Voice Response (TIVR) 47 , a system which uses touch tone features during a call to provide pre-recorded support. Four articles (one research study, three protocols) predominantly used text messaging for their mHealth intervention. These research studies included a website platform with text message support 50 , twice daily text messages with a 10-minute video at study commencement 60 , 59 , and one protocol provided text messages only 56 . Degree of Clinician Contact Clinician contact was varied. Five articles (three research studies 42 , 61 , 67 , two protocols 65 , 66 ) had clinician delivered components alongside their digital support. These included appointments with a regular clinician 61 , physician and clinical psychology sessions 67 , twice weekly posture training classes for 6-12 weeks 65 , and weekly psychology telehealth sessions for eight weeks 66 . Five articles (three research studies 46 , 48 , 50 , two protocols 55 , 57 ) reported clinician support embedded within their mHealth interventions. These included telehealth consultations and email support 46 , written feedback from clinicians delivered into app 50 , digital chat in app with request telephone call feature 48 , online peer groups and online health coach 55 , and on-request clinician or peer support 57 . Eight articles (four research studies 44 , 45 , 49 , 51 , four protocols 60 , 63 , 64 , 58 ) had clinician interaction at the start of the intervention including demonstrations and eligibility assessments 64 , 60 , and a single session of motivational interviewing 63 . Five articles (two research studies 43 , 47 , three protocols 54 , 52 , 59 ) reported no direct clinician contact. However, most of these studies specified they would provide technical support or follow up if needed. Two protocols 56 , 62 did not report the degree of clinician contact. Tailoring and engagement strategies A range of strategies were reported which can be considered tailoring and engagement. Sixteen of the 25 publications (nine research studies 42 , 43 , 44 , 47 , 48 , 49 , 50 , 51 , 52 , seven protocols 55 , 57 , 58 , 60 , 59 , 54 , 65 ) reported some degree of personalisation within their digital health interventions. For example, two studies specified use of participants’ names in text messages to increase engagement 60 , 59 . More sophisticated personalisation included using interactive charts and graphs to visually present data that participants input into their mHealth platform 42 , 43 , 54 , 65 . A range of articles described providing personalised feedback through their apps 50 , 51 , 57 or through pre-recorded voice messages from clinicians 47 . Some interventions allowed participants to customise times of days they would complete surveys or receive alerts 52 , 58 . An exercise-focused intervention provided personalised exercise programs 48 . Reminders, prompts, and push notifications were specifically mentioned as strategies to increase adherence and engagement in nine articles (six research studies 42 , 43 , 45 , 49 , 51 , 52 , three protocols 55 , 47 , 59 . Six articles (five research studies, one protocol) specified the use of two-way communication within their interventions. This included clinicians being able to see data from the app 43 , 65 and live clinician communication (either through the app or a request contact function) 46 , 48 , 51 . One protocol described multiple communication methods including a responsive chatbot and forums to connect users 55 . Three research articles included the use of rewards, one using a variable interval reward schedule with financial rewards 44 . The other two studies provided a device for the study period, which could be a potential incentive 42 , 45 . Three protocols reported providing peer testimonials in the form of videos 60 , 59 , 57 . Seven protocols 62 , 56 , 64 , 61 , 63 , 67 , 66 did not report any intervention components which were considered engagement strategies. What is the degree to which consumer involvement or co-design is included in the content development, intervention, and study design of the reviewed studies? There was a range in detail describing co-design. Fourteen of the 25 articles (six research studies 44 , 50 , 51 , 46 , 48 , 47 and eight protocols 58 , 54 , 62 , 64 , 63 , 67 , 65 , 66 ) did not report or specify any information on co-design. Eleven articles (five research studies 45 , 49 , 43 , 42 , 52 and six protocols 57 , 60 , 59 , 55 , 56 , 61 ) did specify some degree of co-design. Of these articles, ten (four research studies 45 , 49 , 42 , 52 and six protocols 60 , 59 , 57 , 55 , 56 , 61 ) described co-design with patients. This included soliciting feedback from patients about the intervention in design phases 45 . Seven of the articles (three research studies 43 , 42 and four protocols 57 , 60 , 59 , 51 ) described co-design with clinicians, such as surveying clinicians about the perceived acceptability and appropriateness of intervention content 60 , 59 . In another study, clinician co-design was used to integrate the digital health platform into clinical workflows within a hospital 43 . Three articles reported co-design with other stakeholders. One study considered their software engineer as a co-design member 42 . Two protocols reported co-designing their intervention with a representative of a consumer advocacy organisation 60 , 59 . Some protocols reported plans to follow up their studies with separate investigations of user/participant experiences through qualitative interviews with participants 43 , 46 , 51 , 67 , 60 , 59 . Five articles (two research studies 42 , 52 , three protocols 62 , 56 , 64 ) reported affiliations with a government service. Eight studies (two research studies 43 , 51 , four protocols 55 , 56 , 54 , 66 ) reported affiliation with an enterprise or business. These mostly included staff of the companies which owned the digital health interventions used in their study 43 , 54 , 66 . Four of the protocols reported affiliations with consumer representative organisations 55 , 64 , 60 , 59 . What is the degree to which theoretical frameworks and clinical interventions underpin the design and or content of mHealth interventions? Only one study, a conference abstract 48 , did not specify the theories, frameworks, or clinical interventions underpinning their mHealth intervention. One research article intervention primarily focussed on pharmacotherapy 49 . Most of the articles reported their intervention content drew upon at least one psychological approach including: CBT (eight total, three published studies 50 , 47 , 52 , five protocols 60 , 59 , 55 , 56 , 66 ), acceptance and commitment therapy (ACT; four total, one published study 43 , three protocols 55 , 61 , 67 ), mindfulness training (one protocol 66 ), behavioural incentives theory (one research study 44 ), motivational interviewing (three protocols 57 , 55 , 63 ), social cognitive theory and self-efficacy theory (three procotols 57 , 60 , 59 ), and pain self-management education (11 total, four published studies 45 , 42 , 47 , 52 , seven protocols 58 , 60 , 59 , 55 , 63 , 67 , 54 , 62 ), which was the most common. Physical approaches were also referenced including: yoga and pilates (one research study 51 ), physiotherapy and exercise (one research study 52 ), and Gokhale posture therapy (one protocol 65 ). Other physical approaches included massage, chiropractic and acupuncture approaches (one procotol 64 ) and auricular point acupressure (APA; one research study 46 ). What is the scope of evidence on acceptability and feasibility of interventions in the reviewed studies? Of the 25 included articles, eight (four research studies 42 , 45 , 46 , 51 , four protocols 58 , 57 , 63 , 60 ) reported acceptability or feasibility as the primary outcome of the study. Another eight (two research articles 47 , 48 , six protocols 54 , 62 , 61 , 63 , 66 , 67 ) reported neither acceptability nor feasibility outcomes in their reported or planned data. Seventeen articles (nine research studies 42 , 44 , 45 , 49 , 43 , 51 , 46 , 50 , 52 , eight protocols 58 , 57 , 55 , 64 , 56 , 65 , 60 , 59 ) reported acceptability outcomes. The variables used to evaluate acceptability included adherence/use (four research studies 43 , 42 , 51 , 52 ) one article reported 111 of 176 (63.4%) participants used the intervention for at least 30 days, indicating app acceptability 43 . Another study reported 52% of participants were compliant (using the app at least three times per week over 12-weeks), while 65% of the 75 participants rated their app experience as good or excellent 51 .Another adherence measure used a 0-6 scale with scores of two or more correlated to sufficient completion of educational content proven to provide benefits in previous research 52 . Of 200 participants, 136 (68%) of participants scored at least two (m=2.5, SD=2.0) 42 . Questionnaire responses (two research studies 45 , 50 , three protocols 58 , 46 , 60 ) included measures such as a six-item study acceptability scale, 13 item tablet (device) acceptability scale, and a six-item open ended questionnaire which was completed as an interview 45 . The results showed participants were satisfied using portable digital devices for data collection and accessing mHealth interventions 45 . Another study utilised the Client Satisfaction with online Interventions Questionnaire-8 (8-item, range 8-32) 69 where participants scored a mean of 22.87 (SD=4.92) 50 . End of- study interviews (three research studies 42 , 45 , 46 , eight protocols 58 , 57 , 55 , 64 , 56 , 65 , 60 , 59 ) generally reported reasonable or good acceptability 42 . One study used semi-structured interviews to establish acceptability and provided many quotes 46 . Dropout rates (two articles 44 , 49 ), indicated 95% (38 of 40) completion in one 44 , and 6% dropout rates (14 of 215) in another, with 5% (11 of 215) also not authorising follow-up access to medical records 49 . Feasibility, was included as an outcome in 14 studies (six research articles 42 , 45 , 50 , 51 , 46 , 52 , eight protocols 60 , 59 , 57 , 58 , 55 , 65 , 57 , 61 ) and not reported in four research studies 44 , 49 , 47 , 48 . Feasibility measures included enrolment success(protocol 58 ), dropouts (protocol 60 ), missing data (protocol 60 ), cost effectiveness (protocol 61 ), and intervention use during (two procotols 55 , 58 ) and after the study period (three protocols 58 , 57 , 65 ). One study assessed feasibility by clinic attendance, with 63% of participants rescheduling appointments, and four of 16 participants repeating the one-week monitoring period with an activity tracker 42 . Another study reported a 96% completion rate of baseline assessments, with all 27 participants completing 100% of the two week data collection despite recruitment challenges including some participants being to ill to complete their data collection 45 . Another study used interviews to evaluate feasibility 46 . Adverse events were the most common feasibility variable, in nine articles (six research studies 50 , 51 , 42 , 45 , 46 , 52 , three protocols 59 , 55 , 65 ). Most studies reported no adverse events 51 , 42 , 43 , 46 , 44 , 49 , 47 , 48 and collected adverse event data retrospectively, though some monitored for adverse events during study period 42 , 50 . In one study a significant increase in pain or psychological distress was considered an adverse event, occurring for 42.5% of mHealth and 47.0% of control participants, though no serious adverse events were reported 52 . One study terminated a face to face assessment (and the participants study participation) due to a patient becoming agitated and unresponsive to hospital staff 45 . In the largest study (n=9757), 1.3% (129 participants) reported adverse events 50 , with no differences between intervention and control groups, no adverse events attributed to the intervention, and no serious adverse events such as hospitalisation or suicide attempts 50 . Discussion To identify the scope of research on mobile phone digital health (mHealth) interventions to support opioid tapering, this study identified and described 25 research publications (research studies and protocols) recruiting adults with chronic pain which report changes in opioid use, before and after an mHealth intervention. Regarding the geographical context, participants were from five countries, with approximately half (13 of 25) conducted in the USA. Most articles recruited participants from health services (e.g. pain clinics and primary care). Most studies were published during or after 2021, potentially reflecting the increase in digital health interventions in recent years in response to technological advancement and health servicing needs during COVID-19 70 and the increased need for tapering support in chronic pain 13 . All participants had chronic pain and nearly half the research recruited participants with any chronic pain condition. Other demographic features were variable across studies. Some subgroups emerged including veterans (3 articles) and adults living with sickle cell disease (3 articles). Notably, where reported, the proportion of samples not working was between 40 to 60 percent. Opioids Most of the articles, required participants to use ongoing prescription opioid medications. However, only two of these articles were published research studies (i.e. not protocols). Of the other studies that reported data, only a subset of each sample took opioids, potentially limiting the conclusiveness of any results. Participant self-reported medication use was the most common form of collecting opioid data (typically reported as OME). Some articles used e-health records and pharmacy dispensing data to monitor medication use. Self-report is prone to error but is a feasible long-term method of data collection 59 particularly for tapering, often a long process 8 , 71 . The second most frequent variable after OME was the proportion of samples self-reporting using any opioids (binary variable). The proportion of a sample reporting opioid use is a useful statistic, particularly for samples where not all participants take opioids, or to evaluate tapering success in larger population interventions 13 . However, OME is more clinically relevant. Opioid risks are dose specific so reduction without cessation is a meaningful goal 13 and enables contrasting how dose may interact with sleep, pain and mood scores 13 , 15 , data regularly collected within many mHealth interventions in this review. Several included articles used evidence based measures for opioid related outcomes including drug-related behaviours 55 , 66 , 64 ; risk for developing problems on long-term opioids 64 ; withdrawal symptoms 60 , 59 , drug use 61 ; and opioid craving 61 , 66 . Finally, two interventions used opioid tapering self-efficacy as their primary opioid related outcome, hypothesising improved self-efficacy would facilitate tapering 60 , 59 . mHealth Apps were used in 20 of the 25 articles and there were five included articles which used text messaging (one of which was an app with optional texts). One study used interactive voice recordings within a call. There was high variability in the characteristics of the mHealth interventions. The design and features of the mHealth interventions were linked to the goals of each study. All interventions had some fully asynchronous mHealth support, but many interventions included group or individual meetings and online access to peers or clinicians. Resources required were variable and some required external devices including VR equipment and activity trackers. The mHealth interventions often embedded features to improve engagement including personalisation and interactive communication. Underpinning most mHealth interventions were theoretical frameworks and clinical interventions. Mostly these were psychological approaches, including CBT and pain education, or physical activity programs accompanying a psychological approach. Despite interventions being designed for specific subgroups this conceptual overlap may reflect that the pain management strategies most suitable for non-pharmacological approaches (i.e. they support tapering and can be delivered digitally) are pain self-management and psychologically oriented interventions 72 , 73 . However, none of the published studies reported that they tried to evaluate the hypothesised mediating mechanisms (e.g. self-efficacy and correlations with tapering outcomes). Further, consumer involvement is likely to enhance: consumer perceptions towards interventions (e.g. relevance), successful recruitment, and acceptability and feasibility of clinical trials 70 , 74 , 75 , 76 . In our research, the only articles specifically investigating tapering support with text message interventions 59 , 60 , extensive co-design research was conducted to suit the specific consumers 15 , 77 . Despite these benefits fourteen articles did not report any co-design information and many studies described co-design in a limited way. Limitations This review only included chronic pain studies and excluded similar mHealth studies for cancer, acute, and post-surgical pain. Further, the mHealth interventions must be deliverable via mobile phones. Therefore, we excluded studies that used computer based or website-based interventions. Digital interventions which may have worked on a smart phone but were described as computer-based may have been excluded. Despite these limits, we considered our design was the most appropriate and feasible for the focus of this investigation, the scope of mobile phone and chronic pain research with tapering outcomes.This scoping review did not evaluate the methodological quality of the studies or the quality of the data 34 . The methodological lack of data appraisal in scoping reviews should be considered in interpreting the results. The diversity in mHealth interventions, outcome measures, and reporting also necessitates caution when interpreting the overall results. Future directions Regarding the articles that were included in the review, eight were pilot studies. Not all participants in the published research took opioids, and those studies range in opioid dose, demographic characteristics, and the degree of care they received. With 14 protocols included in this study more data is in the pipeline. But may prove not to be completed, which may change the scope of these findings. At this stage the limited range of quantitative opioid data described in the results prevents accurately comparing the effectiveness of each interventions opioid data. Meaning systematic review of results is not yet necessary. But in time, may enable comparison of outcomes across the study populations and intervention types, which may help evaluate effectiveness. Dropouts occurred in most completed research, with some studies using tracking and follow-up methods for safety concerns, such as increased pain and psychological distress, treated as adverse events requiring clinician follow-up 50 , 52 . This demonstrates strong support in some studies. But such assertive care may not be feasible in larger studies or scalable mHealth programs, limiting mHealth’s ability to address clinician shortages and offer cost-effective, accessible support 22 - 24 . Most studies concluded their interventions were generally safe and acceptable. However, if distress and pain, expected in chronic pain and opioid tapering, should be classified as adverse events is debatable 32 . Future research should consider safety, monitoring, and outcomes for participants undergoing tapering whilst receiving digital support 32 . Researchers seeking to develop scalable mHealth supports for tapering amongst chronic pain populations could inform their future work with some proposals for consistency. Standardised opioid doses should be used to report opioid use 13 . Where it is not a study requirement to take opioids, the proportion of a sample using opioids can be reported in addition to OME 13 . Given the reported recruitment difficulties 44 , 60 of specific OME requirements, feasibility may improve if studies include any participant taking opioids for chronic pain regardless of their initial dose 13 . The mHealth interventions and their features were varied. Demonstrating the flexibility that digital health and mHealth supports can provide. Whilst there was some conceptual overlap, the degree of co-design, design features to improve engagement, and the level of clinician support are some of the variable intervention features that could be evaluated for their impact on pain and tapering outcomes. Particularly where these features could limit an mHealth support being acceptable, scalable, or affordable in another setting. Larger RCTs and implementation studies will provide more data regarding resource demands, implementation challenges, and to evaluate superiority of any approach 70 . Summary and Conclusions The rapid growth of digital tools for managing chronic pain and related opioid use has outpaced the availability of evidence-based interventions 78 . This scoping review examined the research investigating mHealth interventions supporting opioid tapering in chronic pain management, identifying 25 studies, including 14 protocols and 11 completed studies. Most interventions were app-based, though text messaging was also common. Psychological frameworks such as CBT, ACT, and self-efficacy theory underpinned most of the mHealth interventions. Early findings suggest that mHealth interventions are generally safe, with low rates of adverse events, and acceptable to those with chronic pain who may be reducing or seeking to reduce their opioid use. However, the limited scope of the completed studies, along with a wide range of mHealth designs, outcome measures, and few published articles specifically focussing on opioid tapering prevents clear conclusions about the most effective, if any, mHealth tools for opioid tapering. As more data from the 14 protocols and any other studies becomes available, it will be possible to better evaluate the feasibility, acceptability and efficacy of the mHealth interventions, particularly regarding their impact on supporting individuals living with chronic pain to reduce their reliance on opioid medications. These results demonstrate the breadth of options that are available to researchers, clinicians, and consumers to support tapering. Data Availability All data produced in the present study are available upon reasonable request to the authors Contributors MM wrote the first draft of this manuscript and protocol, led the search and extraction. AG, AM, and AS supported the search and screening. MM, CAJ, AG finalised the data extraction. All authors contributed to the study conceptualisation, development of the manuscript, and agreed to the final version of this manuscript to be published. Support and Funding This study was supported by a philanthropic gift to The University of Sydney from the Ernest Heine Family Foundation. MM receives an Australian Government Research Training Program (RTP) fee-offset postgraduate research scholarship and a scholarship from the Nock Family Foundation. 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OpenUrl PubMed 13. Badawy SM , Abebe KZ , Reichman CA , et al. Comparing the effectiveness of education versus digital cognitive behavioral therapy for adults with sickle cell disease: protocol for the cognitive behavioral therapy and real-time pain management intervention for sickle cell via mobile applications (CaRISMA) Study . JMIR research protocols . 2021 ; 10 ( 5 ): e29014 . OpenUrl PubMed 14. NCT04419168 . Cognitive Behavioral Therapy and Real-Time Pain Management Intervention for Sickle Cell Via Mobile Applications . 2020 . 15. Edmond SN , Wesolowicz DM , Moore BA , et al. Opioid tapering support using a web-based app: Development and protocol for a pilot randomized controlled trial . Contemp Clin Trials . 2022 ; 119 : 106857 . OpenUrl PubMed 16. Ezenwa MO , Yao Y , Mandernach MW , et al. A Stress and Pain Self-management mHealth App for Adult Outpatients With Sickle Cell Disease: Protocol for a Randomized Controlled Study . JMIR Research Protocols . 2022 ; 11 ( 7 ): e33818 . 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Randomized Trial for cLBP (Gokhale Project) . https://ClinicalTrials.gov/show/NCT05657964 ; 2023 . 28. Utah Uo . Treating Opioid Misuse Via Mindfulness-Based Just-in-Time Adaptive Intervention . https://ClinicalTrials.gov/show/NCT04567043 ; 2020 . References ACTRN12621001714875 2021 . Opioid weaning and eye markers . ACTRN12622001423707 2022 . Digital Support For People With Chronic Pain Who Are Reducing Prescription Opioids . Andrews , N. E. , Ireland , D. , Deen , M. & Varnfield , M . 2023 . Clinical utility of a mHealth assisted intervention for activity modulation in chronic pain: The pilot implementation of pain ROADMAP . European Journal of Pain . Badawy , S. M. , Abebe , K. Z. , Reichman , C. A. , Checo , G. , Hamm , M. E. , Stinson , J. , Lalloo , C. , Carroll , P. , Saraf , S. L. & Gordeuk , V. R . 2021 . Comparing the effectiveness of education versus digital cognitive behavioral therapy for adults with sickle cell disease: protocol for the cognitive behavioral therapy and real-time pain management intervention for sickle cell via mobile applications (CaRISMA) Study . JMIR research protocols , 10 , e29014 . OpenUrl PubMed Bhatia , A. , Kara , J. , Janmohamed , T. , Prabhu , A. , Lebovic , G. , Katz , J. & Clarke , H . 2021 . User engagement and clinical impact of the manage my pain app in patients with chronic pain: a real-world, multi-site trial . JMIR mHealth and uHealth , 9 , e26528 . OpenUrl Center, C.-S. M., University, N., University, S., Birmingham, U. O. A. A. & System, O. H. 2022 . Pragmatic Comparative Effectiveness Trial of Evidence-based, On-demand, Digital Behavioral Treatments for Chronic Pain . https://ClinicalTrials.gov/show/NCT04933474 . Compton , P. , Chaiyachati , K. H. , Dicks , T. , Medvedeva , E. & Chhabra , M . 2021 . A randomized controlled trial to evaluate a behavioral economic strategy for improving mobility in veterans with chronic pain . Plos one , 16 , e0257320 . OpenUrl PubMed Edmond , S. N. , Wesolowicz , D. M. , Moore , B. A. , Ibarra , J. , Chhabra , M. , Fraenkel , L. & Becker , W. C . 2022 . Opioid tapering support using a web-based app: Development and protocol for a pilot randomized controlled trial . Contemporary Clinical Trials , 119 , 106857 . OpenUrl PubMed Ezenwa , M. O. , Yao , Y. , Engeland , C. G. , Molokie , R. E. , Wang , Z. J. , Suarez , M. L. & Wilkie , D. J . 2016 . A randomized controlled pilot study feasibility of a tablet-based guided audio-visual relaxation intervention for reducing stress and pain in adults with sickle cell disease . Journal of Advanced Nursing , 72 , 1452 – 1463 . OpenUrl PubMed Ezenwa , M. O. , Yao , Y. , Mandernach , M. W. , Fedele , D. A. , Lucero , R. J. , Corless , I. , Dyal , B. W. , Belkin , M. H. , Rohatgi , A. & Wilkie , D. J . 2022 . A Stress and Pain Self-management mHealth App for Adult Outpatients With Sickle Cell Disease: Protocol for a Randomized Controlled Study . JMIR Research Protocols , 11 , e33818 . OpenUrl PubMed Gholamrezaei , A. , Magee , M. R. , Mcneilage , A. G. , Dwyer , L. , Jafari , H. , Sim , A. M. , Ferreira , M. L. , Darnall , B. D. , Glare , P. & Ashton-James , C. E . 2023 . Text messaging intervention to support patients with chronic pain during prescription opioid tapering: protocol for a double-blind randomised controlled trial . BMJ open , 13 , e073297 . OpenUrl Abstract / FREE Full Text Inflexxion, I., Institute, P.-C. O. R., Lifespan, Hospital, R. I. & Hospital, T. M. 2015 . Optimizing Patient Engagement in a Novel Pain Management Initiative (OPEN) . https://ClinicalTrials.gov/show/NCT02136108 . Kawi , J. , Yeh , C. H. , Lukkahatai , N. , Hardwicke , R. L. , Murphy , T. & Christo , P. J . 2022 . Exploring the Feasibility of Virtually Delivered Auricular Point Acupressure in Self-Managing Chronic Pain: Qualitative Study . Evidence-Based Complementary and Alternative Medicine , 2022 . Magee , M. , Gholamrezaei , A. , Mcneilage , A. G. , Dwyer , L. , Sim , A. , Ferreira , M. , Darnall , B. , Glare , P. & Ashton-James , C . 2022 . Evaluating acceptability and feasibility of a mobile health intervention to improve self-efficacy in prescription opioid tapering in patients with chronic pain: protocol for a pilot randomised, single-blind, controlled trial . BMJ open , 12 , e057174 . OpenUrl Abstract / FREE Full Text Medicine;, A. E. C. O. 2023 . Randomized Trial of ACT and a Care Management App in Primary Care-based Buprenorphine Treatment . https://ClinicalTrials.gov/show/NCT05039554 . Naylor , M. , Krauthamer , M. & Cloud , G . 2009 . 955 INTERACTIVE VOICE RESPONSE AS A THERAPEUTIC TOOL FOR CHRONIC PAIN AND OPIOID USE REDUCTION . European Journal of Pain , 13 , S269c – S270 . OpenUrl Naylor , M. R. , Keefe , F. J. , Brigidi , B. , Naud , S. & Helzer , J. E . 2008 . Therapeutic interactive voice response for chronic pain reduction and relapse prevention . Pain , 134 , 335 – 345 . OpenUrl CrossRef PubMed NCT04419168 2020 . Cognitive Behavioral Therapy and Real-Time Pain Management Intervention for Sickle Cell Via Mobile Applications . NCT05634291 2022 . Effects of the Nottingham Augmented Reality (AR) App for Arthritis Hand Joint Pain . Nero , H. , Lohmander , S. & Dahlberg , L . 2020 . Improved patient outcomes by a first-line osteoarthritis self-management program delivered digitally . Osteoarthritis and Cartilage , 28 , S164 – S165 . OpenUrl Odineal , D. D. , Marois , M. T. , Ward , D. , Schmid , C. H. , Cabrera , R. , Sim , I. , Wang , Y. , Wilsey , B. , Duan , N. & Henry , S. G . 2020 . Effect of mobile device-assisted N-of-1 trial participation on analgesic prescribing for chronic pain: randomized controlled trial . Journal of General Internal Medicine , 35 , 102 – 111 . OpenUrl CrossRef PubMed Research, V. O. O. & Development 2021 . Development/Testing of SUMMIT: a Tool to Help Patients Manage Pain While Tapering Opioids . https://ClinicalTrials.gov/show/NCT04746833 . Sander , L. B. , Paganini , S. , Terhorst , Y. , Schlicker , S. , Lin , J. , Spanhel , K. , Buntrock , C. , Ebert , D. D. & Baumeister , H . 2020 . Effectiveness of a guided web-based self-help intervention to prevent depression in patients with persistent back pain: the PROD-BP randomized clinical trial . JAMA psychiatry , 77 , 1001 – 1011 . OpenUrl PubMed University Health Network, T. 2018 . Reducing Opioid Use for Chronic Pain Patients Following Surgery . https://ClinicalTrials.gov/show/NCT03675386 . University, S. 2023 . Randomized Trial for cLBP (Gokhale Project) . https://ClinicalTrials.gov/show/NCT05657964 . Utah, U. O. 2020 . Treating Opioid Misuse Via Mindfulness-Based Just-in-Time Adaptive Intervention . https://ClinicalTrials.gov/show/NCT04567043 . Vad , V. B. , Madrazo-Ibarra , A. , Estrin , D. , Pollak , J. P. , Carroll , K. M. , Vojta , D. , Vad , A. & Trapness , C . 2022 . Back Rx, a personalized mobile phone application for discogenic chronic low back pain: a prospective pilot study . BMC Musculoskeletal Disorders , 23 , 923 . OpenUrl PubMed Wilson , M. , Dolor , R. J. , Lewis , D. , Regan , S. L. , Meulen , M. B. V. & Winhusen , T. J . 2023 . Opioid dose and pain effects of an online pain self-management program to augment usual care in adults with chronic pain: a multisite randomized clinical trial . Pain , 164 , 877 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted August 11, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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Share Reducing opioid use for chronic pain with mobile health interventions: a systematic scoping review Michael R Magee , Ali Gholamrezaei , Amy G McNeilage , Alison Sim , Paul Glare , Claire E Ashton-James medRxiv 2025.08.07.25333246; doi: https://doi.org/10.1101/2025.08.07.25333246 Share This Article: Copy Citation Tools Reducing opioid use for chronic pain with mobile health interventions: a systematic scoping review Michael R Magee , Ali Gholamrezaei , Amy G McNeilage , Alison Sim , Paul Glare , Claire E Ashton-James medRxiv 2025.08.07.25333246; doi: https://doi.org/10.1101/2025.08.07.25333246 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Pain Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15228) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6598) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9231) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00563d12ab9593a',t:'MTc3OTU1MzI0Ng=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();