Creating a synergy effect: A cluster randomized controlled trial testing the effect of a tailored multimedia intervention on patient outcomes
Open accessAbstract
Objective Improving adherence is a challenge and multiple barriers are likely to explain non-adherence. These barriers differ per patient and over course of the regimen. Hence, personalized interventions tailored to the specific barriers are needed. In a theoretical and evidence-based Tailored Multimedia Intervention, technology (online preparatory assessment, text messaging) was used as an add-on to a tailored counseling session (learned during a communication skills training), with the expectation of synergistic effects.Methods A cluster randomized controlled trial was conducted in six hospitals, eight nurses and 160 chronic patients. Patient satisfaction with communication, beliefs about medication, self-efficacy and medication adherence were assessed at initiation of the treatment and after six months.Results Intervention effects were found for patient satisfaction with nurses' affective communication and self-efficacy at the initiation of treatment. The effect on self-efficacy remained after six months.Conclusion By combining tailored counseling with technology, this intervention resulted in positive changes in important prerequisites of medication adherence.Practical implications Technology can contribute significantly to health care providers' ability to tailor information to the patients' needs.
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.
Integrity signals
Registry
Cites
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.
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?
Personalizing treatment across multiple fields of healthcare leads to higher treatment adherence and improved satisfaction.
“Studies across multiple fields of healthcare have shown that personalizing treatment leads to higher treatment adherence as well as improved satisfaction [20–23].”
From Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study (2026) · cites this paper as [22]
In Results §3.2 (page unavailable), patients receiving the tailored intervention reported higher satisfaction with nurses’ affective communication at three weeks, but not higher satisfaction on the other measures or at six months. Results §3.4 found no significant increase in medication adherence. The study involved patients with inflammatory bowel disease, so it does not itself establish the claim across multiple healthcare fields. (LLM, conf=0.90)
Experimental — accuracy not yet measuredAll personalized intervention studies use predictive models based on multi-subject data.
“Yet, of note, all of these personalized intervention studies use predictive models based on multi-subject data.”
From Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study (2026) · cites this paper as [22]
The Introduction and Conclusion describe this study as testing a tailored intervention, and the Discussion says its content was tailored to patients’ self-reported adherence barriers. The paper does not say that its intervention used a predictive model based on multi-subject data, or establish that all personalized intervention studies do so. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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
Physician Communication and Patient Adherence to Treatment
Haskard‐Zolnierek, K. B., & DiMatteo, M. R. (2009) · Medical Care · doi:10.1097/mlr.0b013e31819a5acc · Cited by 2,522 · see citing works
Horne, R., Weinman, J. A., & Hankins, M. C. (1999) · Psychology and Health · doi:10.1080/08870449908407311 · Cited by 2,383 · see citing works
Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions
Noar, S. M., Benac, C. N., & Harris, M. S. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.4.673 · Cited by 2,136 · see citing works
Impact of Medication Adherence on Hospitalization Risk and Healthcare Cost
Sokol, M. C., McGuigan, K. A., Verbrugge, R. R., & Epstein, R. S. (2005) · Medical Care · doi:10.1097/01.mlr.0000163641.86870.af · Cited by 1,729 · see citing works
Patients' memory for medical information
Kessels, R. P. C. (2003) · Journal of the Royal Society of Medicine · doi:10.1258/jrsm.96.5.219 · Cited by 1,426 · see citing works
A meta-analysis of the association between adherence to drug therapy and mortality
Simpson, S. H., Eurich, D. T., Majumdar, S. R., Padwal, R. S., Tsuyuki, R. T., Varney, J., & Johnson, J. A. (2006) · BMJ · doi:10.1136/bmj.38875.675486.55 · Cited by 1,353 · see citing works
Reliability and validity of a new Medication Adherence Rating Scale (MARS) for the psychoses
Thompson, K. N., Kulkarni, J., & Sergejew, A. A. (2000) · Schizophrenia Research · doi:10.1016/s0920-9964(99)00130-9 · Cited by 997 · see citing works
Understanding tailoring in communicating about health
Hawkins, R. P., Kreuter, M., Resnicow, K., Fishbein, M., & Dijkstra, A. (2008) · Health Education Research · doi:10.1093/her/cyn004 · Cited by 934 · cited 1× · see citing works
“By identifying these specific adherence barriers, cognitive acceptance, perceived relevance, and message impact can be enhanced [10].”
BackgroundHorne, R., & Weinman, J. A. (2002) · Psychology and Health · doi:10.1080/08870440290001502 · Cited by 867 · see citing works
A meta-analysis of computer-tailored interventions for health behavior change
Krebs, P., Prochaska, J. O., & Rossi, J. S. (2010) · Preventive Medicine · doi:10.1016/j.ypmed.2010.06.004 · Cited by 854 · see citing works
Interventions to Enhance Medication Adherence in Chronic Medical Conditions: A Systematic Review
Kripalani, S., Yao, X., & Haynes, R. B. (2007) · Archives of Internal Medicine · doi:10.1001/archinte.167.6.540 · Cited by 810 · see citing works
Borrelli, B., Sepinwall, D., Ernst, D., Bellg, A. J., Czajkowski, S., Breger, R., DeFrancesco, C., Levesque, C., Sharp, D. L., Ogedegbe, G., et al. (2005) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.73.5.852 · Cited by 803 · see citing works
A meta-analysis of web-delivered tailored health behavior change interventions
Lustria, M. L. A., Noar, S. M., Cortese, J., Van Stee, S. K., Glueckauf, R. L., & Lee, J. (2013) · Journal of Health Communication · doi:10.1080/10810730.2013.768727 · Cited by 689 · see citing works
Understanding the Impact of Synergy in Multimedia Communications
Naik, P. A., & Raman, K. (2003) · Journal of Marketing Research · doi:10.1509/jmkr.40.4.375.19385 · Cited by 583 · see citing works
Medication nonadherence and the outcomes of patients with quiescent ulcerative colitis
Kane, S. V., Huo, D., Aikens, J. E., & Hanauer, S. B. (2003) · The American Journal of Medicine · doi:10.1016/s0002-9343(02)01383-9 · Cited by 568 · see citing works
Medication Adherence Leads To Lower Health Care Use And Costs Despite Increased Drug Spending
Roebuck, M. C., Liberman, J. N., Gemmill, M., & Brennan, T. A. (2011) · Health Affairs · doi:10.1377/hlthaff.2009.1087 · Cited by 464 · see citing works
Hugtenburg, J. G., Vervloet, M., van Dijk, L., Timmers, L., & Elders, P. J. M. (2013) · Patient Preference and Adherence · doi:10.2147/ppa.s29549 · Cited by 402 · see citing works
Clifford, S., Barber, N., & Horne, R. (2007) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2007.05.004 · Cited by 384 · see citing works
Beliefs about medicines predict refill adherence to inhaled corticosteroids
Menckeberg, T. T., Bouvy, M. L., Bracke, M., Kaptein, A. A., Leufkens, H. G. M., Raaijmakers, J. A. M., & Horne, R. (2007) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2007.07.016 · Cited by 345 · see citing works
Petrie, K. J., Perry, K., Broadbent, E., & Weinman, J. (2012) · British Journal of Health Psychology · doi:10.1111/j.2044-8287.2011.02033.x · Cited by 330 · see citing works
Patient Nonadherence To Medication in Inflammatory Bowel Disease
Sewitch, M. J., Abrahamowicz, M., Barkun, A. N., Bitton, A., Wild, G., Cohen, A., & Dobkin, P. L. (2003) · The American Journal of Gastroenterology · doi:10.1111/j.1572-0241.2003.07522.x · Cited by 327 · see citing works
Predictors of Medication Adherence in Inflammatory Bowel Disease
Ediger, J. P., Walker, J. R., Graff, L. A., Lix, L. M., Clara, I. P., Rawsthorne, P. E., Rogala, L., Miller, N., McPhail, C., Deering, K. L., et al. (2007) · The American Journal of Gastroenterology · doi:10.1111/j.1572-0241.2007.01212.x · Cited by 276 · see citing works
Horne, R., Parham, R., Driscoll, R., & Robinson, A. (2008) · Inflammatory Bowel Diseases · doi:10.1002/ibd.20846 · Cited by 267 · see citing works
A large-scale validation study of the Medication Adherence Rating Scale (MARS)
Fialko, L., Garety, P. A., Kuipers, E., Dunn, G., Bebbington, P., Fowler, D. G., & Freeman, D. H. (2007) · Schizophrenia Research · doi:10.1016/j.schres.2007.10.029 · Cited by 239 · see citing works
Patients’ Memory for Medical Information
Shankar, J. (2003) · Journal of the Royal Society of Medicine · doi:10.1177/014107680309601027 · Cited by 163 · see citing works
Measuring patients’ self-efficacy in understanding and using prescription medication
Cameron, K. A., Ross, E. L., Clayman, M. L., Bergeron, A. R., Federman, A. D., Bailey, S. C., Davis, T. C., & Wolf, M. S. (2010) · Patient Education and Counseling · doi:10.1016/j.pec.2010.06.029 · Cited by 103 · see citing works
Opening the black box: Understanding cross-media effects
Voorveld, H. A. M., Neijens, P. C., & Smit, E. G. (2010) · Journal of Marketing Communications · doi:10.1080/13527260903160460 · Cited by 102 · see citing works
Self-citationTechnology-mediated interventions for enhancing medication adherence
Mistry, N., Keepanasseril, A., Wilczynski, N., Nieuwlaat, R., Ravall, M., & Haynes, R. (2015) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocu047 · Cited by 102 · see citing works
Linn, A. J., van Dijk, L., Smit, E. G., Jansen, J., & van Weert, J. C. M. (2013) · Journal of Crohn s and Colitis · doi:10.1016/j.crohns.2013.04.001 · Cited by 85 · see citing works
Self-citationThe Implications of “Contamination” for Experimental Design in Education
Rhoads, C. H. (2010) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998610379133 · Cited by 71 · see citing works
van Weert, J. C. M., Jansen, J., Spreeuwenberg, P. M. M., van Dulmen, S., & Bensing, J. M. (2010) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2010.10.010 · Cited by 68 · see citing works
Lustria, M. L. A., Cortese, J., Gerend, M. A., Schmitt, K., Kung, Y. M., & McLaughlin, C. (2016) · Health Psychology · doi:10.1037/hea0000399 · Cited by 62 · see citing works
Adherence rates with infliximab therapy in Crohn's disease
Kane, S., & Dixon, L. B. (2006) · Alimentary Pharmacology & Therapeutics · doi:10.1111/j.1365-2036.2006.03092.x · Cited by 53 · see citing works
Nguyen, M. H., van Weert, J. C. M., Bol, N., Loos, E. F., Tytgat, K. M. A. J., van de Ven, A. W. H., & Smets, E. M. A. (2016) · Human Communication Research · doi:10.1111/hcre.12097 · Cited by 51 · see citing works
Improving Medication Adherence in Coronary Heart Disease
Zullig, L. L., Ramos, K., & Bosworth, H. B. (2017) · Current Cardiology Reports · doi:10.1007/s11886-017-0918-y · Cited by 47 · see citing works
Allemann, S. S., Nieuwlaat, R., Navarro, T., Haynes, B. R., Hersberger, K. E., & Arnet, I. (2017) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2017.07.011 · Cited by 46 · see citing works
Communication in medical encounters.
Bensing, J. M., & Verhaak, P. F. M. (2004) · Data Archiving and Networked Services (DANS) · Cited by 43 · see citing works
Kooij, M. J., van Geffen, E. C. G., Heerdink, E. R., van Dijk, L., & Bouvy, M. L. (2015) · Patient Education and Counseling · doi:10.1016/j.pec.2015.02.020 · Cited by 29 · see citing works
Linn, A. J., van Weert, J. C. M., Smit, E. G., Perry, K., & van Dijk, L. (2013) · Patient Education and Counseling · doi:10.1016/j.pec.2013.03.009 · Cited by 29 · see citing works
Self-citationMayberry, L. S., Mulvaney, S. A., Johnson, K. B., & Osborn, C. Y. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816668374 · Cited by 29 · see citing works
Foster, J. M., Smith, L. D., Usherwood, T. P., Sawyer, S. M., & Reddel, H. K. (2015) · Journal of Asthma · doi:10.3109/02770903.2015.1091473 · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals
Nan, J., Herbert, M. S., Purpura, S., Henneken, A. N., Ramanathan, D., & Mishra, J. (2024) · Sensors · doi:10.3390/s24082640 · Cited by 22 · see citing works
Nan, J., Purpura, S., Jaiswal, S., Afshar, H., Maric, V., Manchanda, J. K., Taylor, C. T., Ramanathan, D., & Mishra, J. (2026) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-026-00062-3 · Cited by 1 · see citing works
“The coaches received 8 h of training consisting of both didactic training as well as practical skills development with role play of realistic participant-coach interactions supervised with feedback from the study psychiatrist (co-author DR).”
- Authors
- Linn, A. J., van Dijk, L., van Weert, J. C. M., Gebeyehu, B. G., van Bodegraven, A. A., & Smit, E. G.
- Affiliations
- University of Amsterdam (NL)
- Corresponding author
- Linn, Annemiek J.
- Venue
- Patient Education and Counseling
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 101
- Issue
- 8
- Pages
- 1419-1426
- ISSN
- 0738-3991 (ISSN-L)
- OpenAlex
- W2792519785
- Cited by
- 31
- References
- 41
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 9 · 2023: 7 · 2022: 1 · 2021: 6 · 2020: 1
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Patient Education and Counseling (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (acceptedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Pure Amsterdam UMC (submittedVersion)
- Funders
- Amsterdam School of Communication Research/ASCoR
- Merck Sharp & Dohme B.V. and Teva Pharmaceutical Industries
- Topics
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacy and Medical Practices
- Keywords
- medication adherence, patient satisfaction, self-efficacy, nurse-patient communication, emotional communication, communication skills training, barriers to adherence, chronic disease patients, text messaging, individualized counseling
- MeSH terms
- Adult, Counseling, Female, Gastrointestinal Agents, Humans, Inservice Training, Male, Middle Aged, Netherlands, Nurse-Patient Relations, Nursing Staff, Hospital, Patient Education as Topic, Surveys and Questionnaires, Inflammatory Bowel Diseases, Treatment Outcome, Patient Satisfaction, Multimedia, Self Efficacy, Medication Adherence, Text Messaging
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