1 / 26

Adherence to a blended smoking cessation treatment

Learn how adherence is measured in smoking cessation treatment, how patients adhere to treatment, and what predicts adherence. Explore a study on blended treatment and discover different measures and predictors of adherence.

versiej
Download Presentation

Adherence to a blended smoking cessation treatment

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. How do we measure adherence, how do patients adhere and what predicts adherence? Adherence to a blended smoking cessation treatment

  2. ConsortiumRookvrijLeven Technology, Health & Care Lectorate, Saxion University of Applied Sciences Department of Psychology, Health & Technology; University of Twente Department of Pulmonary Medicine; Medical Spectrum Twente Department Online Treatment and Prevention; Tactus Addiction Treatment

  3. Background: Blended Treatment Promising way to deliver behavioral change interventions Combining the strengths of face-to-face (F2F) treatment with the unique features of Web-based care ”Best of both worlds”

  4. Definition: Adherence • Problems in context of counselling: • premature termination of the treatment • failures to complete between session tasks and exercises

  5. Adherenceand Smoking Cessation Treatment • Primary determinant of treatment effectiveness (Dose-Response Relationship) • Measurement • F2F: completed tasks and/or attended sessions • Web: log-ins, module completion, time spend online, messages/emails, print requests … • Adherence rates (smoking cessation) • Widely vary between studies (5%-96%) • Adherencerapidlydeclines, resulting in rather low adherence rates (<40%)

  6. Knownpredictorstoadherence in smokingcessationtreatment • Higher age • Male gender • Higher internet skills • Negative attitude toward smoking and higher motivation to quit at baseline • Higher self-efficacy at baseline • Early success in quitting after the start of the treatment • Lower nicotine dependency at baseline and fewer withdrawal symptoms after quitting

  7. Questions How do we measure adherence? How do patients adhere? What predicts adherence?

  8. 1. How do we measure adherence?

  9. Studyparticipants • Subset (N=70) of an RCT on the effectiveness of “Blended Smoking Cessation Treatment” (BSCT) versus Face-to-face-treatment as usual • Outpatient smoking cessation clinic at the Medical Spectrum Twente hospital (Enschede/The Netherlands) • Inclusion criteria • being at least 18 years old, • currently smoking (at least one cigarette a day) • having access to email and internet • being able to read and write Dutch

  10. Studyintervention BSCT A combination of F2F-treatment and Web-based sessions blended into one integrated smoking cessation treatment delivered in routine care settings Consists of 5 F2F sessions at the outpatient clinic and 5 Web-based sessions (50-50 balance between F2F and Web) High-intensity treatment (6h total) derived from the Dutch Guideline Tobacco Addiction, fulfilling the requirements of the Dutch care module for smoking cessation Supports three quitting strategies

  11. BSCT

  12. Adherence data collection • Minutes-based measure (sum of minutes in treatment based on hospital administration) • Easy • Simple • Time-saving • Features-based measure (patients activities based on patient records and online dossiers) • More in-depth assessment • Identification of weaknesses • Improvement

  13. Agreement between the two measures of assessing adherence (N=70) Adherence based on a threshold (60%) • Dichotom: • Cohen's kappa test showed moderate agreement between the evaluation of adherence using the minutes-based and the features-based measure (κ = .438, p < .001) - agreement in the classification of 51 (72.9%) patients • Continuous: • Adherence assessed using the minutes-based measure was moderately correlated with adherence evaluated using the features-based measure (rho(70) = .529, p < .001)

  14. 2. How do patients adhere?

  15. Method • Same participantsandintervention • N=75 (5 patientsmore) • Adherencebased on feature-basedmeasurement • Self-reported smoking status • 3-month and 6-month follow-up (Web-based questionnaire)

  16. Adherenceto BSCT Adherence based on the threshold (60%)

  17. Adherenceoverthecourseofthetreatment (N=75)

  18. Adherence and quitting

  19. 3. What predicts adherence?

  20. Method • Same participantsandintervention • N=75 (5 patientsmore) • Adherencebased on feature-basedmeasurement • Patients’ Characteristics • 33 person-, smoking-, and health-related characteristics (intake measurement, Web-based questionnaire)

  21. Predictors in BSCT (online questionnaire) • Sex • Age • Marital status • Housing situation • Education status • Main income • Main day activity • Internet skills • Reason to start treatment • Dependency • Attitude neg • Attitude pos • Self-efficacy • Readiness to quit • Earlier quit attempts • Social support • Social modeling • Medication in general • Medication addiction • Medication psychic • Medication physical • Medication others • Health complaints • Health + smoking related complaints • Depression • Anxiety • Stress • DASS total • Quality of life • Quality of life (VAS)

  22. Univariate • Univariately associated based on the features-based measure (N=75)

  23. Multivariate Multivariate regression analyses • Marital status and social modeling—accounting for 25% of the variance (Nagelkerke R Square)—were independent predictors of whether patients were adherent to BSCT. • Patients having a partner had 11 times higher odds of being adherent (OR=11.3; CI: 1.33 to 98.99; P=.03). • For social modeling, graded from 0 (=partner and friends are not smoking) to 8 (=both partner and nearly all friends are smoking), each unit increase was associated with 28% lower odds of being adherent (OR=0.72; CI: 0.55 to 0.94; P=.02).

  24. Discussion • Both types of measurement agree/correlate with each other. • The features-based measure seems more valid, but is more complicated and time-consuming. • There is a dose response relationship between adherence and quitting (as to be expected). • Adherence to BSCT is rather low, especially for the Web-based parts. • Strength • Adequate measures (promising tools for future research) • Predictors identified (improvement) • Limitation • No comparison to eg. F2F treatment as usual • Indirect assessment of adherence • Low sample size • No “golden standard” to compare with

  25. Literature Siemer, L., Pieterse, M. E., Brusse-Keizer, M. G., Postel, M. G., Allouch, S. B., & Sanderman, R. (2016). Study protocolfor a non-inferioritytrialof a blended smokingcessationtreatment versus face-to-face treatment (LiveSmokefree-Study). BMC publichealth, 16(1), 1187. Siemer, L., Brusse-Keizer, M. G., Postel, M. G., Allouch, S. B., Bougioukas, A. P., Sanderman, R., & Pieterse, M. E. (2018). Blended Smoking Cessation Treatment: Exploring Measurement, Levels, andPredictorsofAdherence. Journal ofmedical Internet research, 20(8). Patrinopoulos Bougioukas, A. (2017). Adherence to blended smoking cessation treatment (Master's thesis, University of Twente).

  26. THE END

More Related