CoChat
Sign Up
Cited by 97Digital Mental Health InterventionsMobile Health and mHealth ApplicationsMusculoskeletal pain and rehabilitation

A web-based acceptance-facilitating intervention for identifying patients’ acceptance, uptake, and adherence of internet- and mobile-based pain interventions: randomized controlled trial

Open access
Lin, Jiaxi, Faust, Bianca, Ebert, David Daniel, Krämer, Lena, Baumeister, Harald·2018·Journal of Medical Internet Research
DOI 10.2196/jmir.9925PMC PMC6123541

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Internet- and mobile-based interventions are effective for the treatment of chronic pain. However, little is known about patients' willingness to engage with these types of interventions and how the uptake of such interventions can be improved. OBJECTIVE: The aim of this study was to identify people's acceptance, uptake, and adherence (primary outcomes) with regard to an internet- and mobile-based intervention for chronic pain and the influence of an information video as an acceptance-facilitating intervention (AFI). METHODS: In this randomized controlled trial with a parallel design, we invited 489 individuals with chronic pain to participate in a Web-based survey assessing the acceptance of internet- and mobile-based interventions with the offer to receive an unguided internet- and mobile-based intervention for chronic pain after completion. Two versions of the Web-based survey (with and without AFI) were randomly sent to two groups: one with AFI (n=245) and one without AFI (n=244). Participants who completed the Web-based survey with or without AFI entered the intervention group or the control group, respectively. In the survey, the individuals' acceptance of pain interventions, measured with a 4-item scale (sum score ranging from 4 to 20), predictors of acceptance, sociodemographic and pain-related variables, and physical and emotional functioning were assessed. Uptake rates (log in to the intervention) and adherence (number of completed modules) to the intervention was assessed 4 months after intervention access. To examine which factors influence acceptance, uptake rate, and adherence in the internet- and mobile-based interventions, we conducted additional exploratory subgroup analyses. RESULTS: In total, 57 (intervention group) and 58 (control group) participants in each group completed the survey and were included in the analyses. The groups did not differ with regard to acceptance, uptake rate, or adherence (P=.64, P=.56, P=.75, respectively). Most participants reported moderate (68/115, 59.1%) to high (36/115, 31.3%) acceptance, with 9.6% (11/115) showing low acceptance (intervention group: mean 13.91, SD 3.47; control group: mean 13.61, SD 3.50). Further, 67% (38/57, intervention group) and 62% (36/58, control group) had logged into the intervention. In both groups, an average of 1.04 (SD 1.51) and 1.14 (SD 1.90) modules were completed, respectively. CONCLUSIONS: The informational video was not effective with regard to acceptance, uptake rate, or adherence. Despite the high acceptance, the uptake rate was only moderate and adherence was remarkably low. This study shows that acceptance can be much higher in a sample participating in an internet- and mobile-based intervention efficacy trial than in the target population in routine health care settings. Thus, future research should focus not only on acceptance and uptake facilitating interventions but also on ways to influence adherence. Further research should be conducted within routine health care settings with more representative samples of the target population. TRIAL REGISTRATION: German Clinical Trial Registration DRKS00006183; http://www.drks.de/drks_web/navigate.do ?navigationId=trial.HTML&TRIAL_ID=DRKS00006183 (Archived by WebCite at http://www.webcitation.org/70ebHDhne).

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/jmir.9925

Cites

89 of 89 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • There has been limited research on the impact of AFI in the context of smart sensing as well as in a diabetes population.

    “Although, AFI have been effectively used in numerous studies (55–58), there has been limited research on the impact in the context of smart sensing as well as in a diabetes population.”

    From Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial (2024) · cites this paper as [58]

    The Introduction (page unavailable) identifies three prior AFI trials, including one in a diabetes population, which supports describing diabetes-specific research as limited. Neither the Introduction nor the searched passages addresses AFIs in smart sensing, so that part of the claim is not supported by this paper. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Studies on the acceptance of IMIs show that willingness to be treated via IMIs applies to only a small part of the target population regarding both patients and therapists.

    “Studies on the acceptance of IMIs show that this only applies to a small part of the target population regarding both patients (13–16) and therapists (17, 18).”

    From Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders (2021) · cites this paper as [16]

    The Introduction reports low baseline acceptance in earlier studies of patient groups, and the Discussion > Conclusions says acceptance in an efficacy-trial sample can be much higher than in the routine-care target population. But this paper’s own Results > Primary Outcomes found moderate or high acceptance in most participants, so it does not establish that willingness is limited to only a small part of patients generally. The separate therapist claim is assigned to other citations in the citing sentence. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Targeting only acceptance of internet-based programs may not lead to an improved uptake rate or adherence.

    “Nonetheless, targeting only acceptance of internet-based programs may not lead to an improved uptake rate or adherence (Lin et al., 2018).”

    From Predictors of acceptability and engagement in a self-guided online program for depression and anxiety (2021) · cites this paper as Lin et al., 2018

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. [66]Baumeister H Nowoczin L Lin J Seifferth H Seufert J Laubner K Ebert DD Impact of an acceptance facilitating intervention on diabetes patients' acceptance of Internet-based interventions for depression: a randomized controlled trial Diabetes Res Clin Pract 2014 7 105 1 30 9 10.1016/j.diabres.2014.04.031 24862240 24862240 · DOI · Paper page
  2. [67]Taiwo A Downe A The theory of user acceptance and use of technology (UTAUT): A meta-analytic review of empirical findings J Theor Appl Inf Technol. ? 2013 49 1 48 58

Cited by

Sorted by citations

Related

Authors
Lin, J., Faust, B., Ebert, D. D., Krämer, L., & Baumeister, H.
Affiliations
  • University of Freiburg (DE)
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
  • Universität Ulm (DE)
Corresponding author
Baumeister, Harald
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2018-08-21
Volume
20
Issue
8
Pages
e244
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/jmir.9925
PMC
PMC6123541
OpenAlex
W2799881981
Cited by
97
References
89
Citations by year
2026: 5 · 2025: 8 · 2024: 14 · 2023: 17 · 2022: 18 · 2021: 17 · 2020: 8 · 2019: 8 · 2018: 1
FWCI
10.72
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
Funders
Deutscher Akademischer Austauschdienst
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Pharmacology › Musculoskeletal pain and rehabilitation
Keywords
chronic pain, online intervention, mobile-based interventions, intervention uptake, intervention adherence, patient acceptance, information video, physical function, emotional functioning, digital health
MeSH terms
Adult, Female, Humans, Male, Middle Aged, Surveys and Questionnaires, Telemedicine, Internet, Chronic Pain, Mobile Applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review