Patients’ User Experience of a Blended Face-to-Face and Web-Based Smoking Cessation Treatment: Qualitative Study (Preprint)

BACKGROUND Blended web-based and face-to-face (F2F) treatment is a promising electronic health service because the strengths of one mode of delivery should compensate for the weaknesses of the other. OBJECTIVE The aim of this study was to explore this compensation by examining patients’ user experience (UX) in a blended smoking cessation treatment (BSCT) in routine care. METHODS Data on patients’ UX were collected through in-depth interviews (n=10) at an outpatient smoking cessation clinic in the Netherlands. A content analysis of the semantic domains was used to analyze patients’ UX. To describe the UX, the Hassenzahl UX model was applied, examining 4 of the 5 key elements of UX from a user’s perspective: (1) patients’ standards and expectations, (2) apparent character (pragmatic and hedonic attributes), (3) usage situation, and (4) consequences (appeal, emotions, and behavior). RESULTS BSCT appeared to be a mostly positively experienced service. Patients had a positive-pragmatic standard and neutral-open expectation toward BSCT at the treatment start. The pragmatic attributes of the F2F sessions were mostly perceived as positive, whereas the pragmatic attributes of the web sessions were perceived as both positive and negative. For the hedonic attributes, there seemed to be a difference between the F2F and web sessions. Specifically, the hedonic attributes of the web sessions were experienced as mostly negative, whereas those of the F2F sessions were experienced as mostly positive. For the usage situation, the physical and social contexts were experienced positively, whereas the task and technical contexts were experienced negatively. Nevertheless, the consequential appeal of BSCT was positive. However, the consequential emotions and behavior varied, ultimately resulting in diverse combinations of consequential appeal, emotions, and behavior (positive, negative, and mixed). CONCLUSIONS This study provided insights into the UX of a blended treatment, and the results support the expectation that in a blended treatment, the strengths of one mode of delivery may compensate for the weaknesses of the other. However, in this certain setting, this is mainly achieved in only one way: F2F sessions compensated for the weaknesses of the web sessions. As a practical conclusion, this may mean that the web sessions, supported by the strengths of the F2F sessions, offer an interesting approach for further improving the blended treatment. Our theoretical findings reflect the relevance of the aspects of hedonism, such as fun, joy, or happiness in the UX, which were not mentioned in relation to the web sessions and were only scarcely mentioned in relation to the F2F sessions. Future research should further investigate the role of hedonistic aspects in a blended treatment and whether increased enjoyment of a blended treatment could increase treatment adherence and, ultimately, effectiveness.

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Patients’ User Experience of a Blended Face-to-Face and Web-Based Smoking Cessation Treatment: Qualitative Study

JMIR Formative Research ◽

10.2196/14550 ◽

2020 ◽

Vol 4 (6) ◽

pp. e14550 ◽

Cited By ~ 2

Author(s):

Lutz Siemer ◽

Somaya Ben Allouch ◽

Marcel E Pieterse ◽

Marjolein Brusse-Keizer ◽

Robbert Sanderman ◽

...

Keyword(s):

Smoking Cessation ◽

Mode Of Delivery ◽

Web Based ◽

Smoking Cessation Treatment ◽

Face To Face ◽

Cessation Treatment ◽

Usage Situation ◽

And Behavior ◽

Blended Treatment ◽

The Web

Background Blended web-based and face-to-face (F2F) treatment is a promising electronic health service because the strengths of one mode of delivery should compensate for the weaknesses of the other. Objective The aim of this study was to explore this compensation by examining patients’ user experience (UX) in a blended smoking cessation treatment (BSCT) in routine care. Methods Data on patients’ UX were collected through in-depth interviews (n=10) at an outpatient smoking cessation clinic in the Netherlands. A content analysis of the semantic domains was used to analyze patients’ UX. To describe the UX, the Hassenzahl UX model was applied, examining 4 of the 5 key elements of UX from a user’s perspective: (1) patients’ standards and expectations, (2) apparent character (pragmatic and hedonic attributes), (3) usage situation, and (4) consequences (appeal, emotions, and behavior). Results BSCT appeared to be a mostly positively experienced service. Patients had a positive-pragmatic standard and neutral-open expectation toward BSCT at the treatment start. The pragmatic attributes of the F2F sessions were mostly perceived as positive, whereas the pragmatic attributes of the web sessions were perceived as both positive and negative. For the hedonic attributes, there seemed to be a difference between the F2F and web sessions. Specifically, the hedonic attributes of the web sessions were experienced as mostly negative, whereas those of the F2F sessions were experienced as mostly positive. For the usage situation, the physical and social contexts were experienced positively, whereas the task and technical contexts were experienced negatively. Nevertheless, the consequential appeal of BSCT was positive. However, the consequential emotions and behavior varied, ultimately resulting in diverse combinations of consequential appeal, emotions, and behavior (positive, negative, and mixed). Conclusions This study provided insights into the UX of a blended treatment, and the results support the expectation that in a blended treatment, the strengths of one mode of delivery may compensate for the weaknesses of the other. However, in this certain setting, this is mainly achieved in only one way: F2F sessions compensated for the weaknesses of the web sessions. As a practical conclusion, this may mean that the web sessions, supported by the strengths of the F2F sessions, offer an interesting approach for further improving the blended treatment. Our theoretical findings reflect the relevance of the aspects of hedonism, such as fun, joy, or happiness in the UX, which were not mentioned in relation to the web sessions and were only scarcely mentioned in relation to the F2F sessions. Future research should further investigate the role of hedonistic aspects in a blended treatment and whether increased enjoyment of a blended treatment could increase treatment adherence and, ultimately, effectiveness.

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Adherence to Blended or Face-to-Face Smoking Cessation Treatment and Predictors of Adherence: Randomized Controlled Trial

Journal of Medical Internet Research ◽

10.2196/17207 ◽

2020 ◽

Vol 22 (7) ◽

pp. e17207

Author(s):

Lutz Siemer ◽

Marjolein G J Brusse-Keizer ◽

Marloes G Postel ◽

Somaya Ben Allouch ◽

Robbert Sanderman ◽

...

Keyword(s):

Smoking Cessation ◽

Treatment Time ◽

Patient Characteristics ◽

Smoking Cessation Treatment ◽

Face To Face ◽

Cessation Treatment ◽

The Face ◽

Blended Treatment ◽

Predictors Of Adherence ◽

The Web

Background Blended face-to-face and web-based treatment is a promising way to deliver smoking cessation treatment. Since adherence has been shown to be an indicator of treatment acceptability and a determinant for effectiveness, we explored and compared adherence and predictors of adherence to blended and face-to-face alone smoking cessation treatments with similar content and intensity. Objective The objectives of this study were (1) to compare adherence to a blended smoking cessation treatment with adherence to a face-to-face treatment; (2) to compare adherence within the blended treatment to its face-to-face mode and web mode; and (3) to determine baseline predictors of adherence to both treatments as well as (4) the predictors to both modes of the blended treatment. Methods We calculated the total duration of treatment exposure for patients (N=292) of a Dutch outpatient smoking cessation clinic who were randomly assigned either to the blended smoking cessation treatment (n=130) or to a face-to-face treatment with identical components (n=162). For both treatments (blended and face-to-face) and for the two modes of delivery within the blended treatment (face-to-face vs web mode), adherence levels (ie, treatment time) were compared and the predictors of adherence were identified within 33 demographic, smoking-related, and health-related patient characteristics. Results We found no significant difference in adherence between the blended and the face-to-face treatments. Participants in the blended treatment group spent an average of 246 minutes in treatment (median 106.7% of intended treatment time, IQR 150%-355%) and participants in the face-to-face group spent 238 minutes (median 103.3% of intended treatment time, IQR 150%-330%). Within the blended group, adherence to the face-to-face mode was twice as high as that to the web mode. Participants in the blended group spent an average of 198 minutes (SD 120) in face-to-face mode (152% of the intended treatment time) and 75 minutes (SD 53) in web mode (75% of the intended treatment time). Higher age was the only characteristic consistently found to uniquely predict higher adherence in both the blended and face-to-face groups. For the face-to-face group, more social support for smoking cessation was also predictive of higher adherence. The variability in adherence explained by these predictors was rather low (blended R2=0.049; face-to-face R2=0.076). Within the blended group, living without children predicted higher adherence to the face-to-face mode (R2=0.034), independent of age. Higher adherence to the web mode of the blended treatment was predicted by a combination of an extrinsic motivation to quit, a less negative attitude toward quitting, and less health complaints (R2=0.164). Conclusions This study represents one of the first attempts to thoroughly compare adherence and predictors of adherence of a blended smoking cessation treatment to an equivalent face-to-face treatment. Interestingly, although the overall adherence to both treatments appeared to be high, adherence within the blended treatment was much higher for the face-to-face mode than for the web mode. This supports the idea that in blended treatment, one mode of delivery can compensate for the weaknesses of the other. Higher age was found to be a common predictor of adherence to the treatments. The low variance in adherence predicted by the characteristics examined in this study suggests that other variables such as provider-related health system factors and time-varying patient characteristics should be explored in future research. Trial Registration Netherlands Trial Register NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113

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Adherence to Blended or Face-to-Face Smoking Cessation Treatment and Predictors of Adherence: Randomized Controlled Trial (Preprint)

10.2196/preprints.17207 ◽

2019 ◽

Author(s):

Lutz Siemer ◽

Marjolein G J Brusse-Keizer ◽

Marloes G Postel ◽

Somaya Ben Allouch ◽

Robbert Sanderman ◽

...

Keyword(s):

Smoking Cessation ◽

Treatment Time ◽

Patient Characteristics ◽

Smoking Cessation Treatment ◽

Face To Face ◽

Cessation Treatment ◽

The Face ◽

Blended Treatment ◽

Predictors Of Adherence ◽

The Web

BACKGROUND Blended face-to-face and web-based treatment is a promising way to deliver smoking cessation treatment. Since adherence has been shown to be an indicator of treatment acceptability and a determinant for effectiveness, we explored and compared adherence and predictors of adherence to blended and face-to-face alone smoking cessation treatments with similar content and intensity. OBJECTIVE The objectives of this study were (1) to compare adherence to a blended smoking cessation treatment with adherence to a face-to-face treatment; (2) to compare adherence within the blended treatment to its face-to-face mode and web mode; and (3) to determine baseline predictors of adherence to both treatments as well as (4) the predictors to both modes of the blended treatment. METHODS We calculated the total duration of treatment exposure for patients (N=292) of a Dutch outpatient smoking cessation clinic who were randomly assigned either to the blended smoking cessation treatment (n=130) or to a face-to-face treatment with identical components (n=162). For both treatments (blended and face-to-face) and for the two modes of delivery within the blended treatment (face-to-face vs web mode), adherence levels (ie, treatment time) were compared and the predictors of adherence were identified within 33 demographic, smoking-related, and health-related patient characteristics. RESULTS We found no significant difference in adherence between the blended and the face-to-face treatments. Participants in the blended treatment group spent an average of 246 minutes in treatment (median 106.7% of intended treatment time, IQR 150%-355%) and participants in the face-to-face group spent 238 minutes (median 103.3% of intended treatment time, IQR 150%-330%). Within the blended group, adherence to the face-to-face mode was twice as high as that to the web mode. Participants in the blended group spent an average of 198 minutes (SD 120) in face-to-face mode (152% of the intended treatment time) and 75 minutes (SD 53) in web mode (75% of the intended treatment time). Higher age was the only characteristic consistently found to uniquely predict higher adherence in both the blended and face-to-face groups. For the face-to-face group, more social support for smoking cessation was also predictive of higher adherence. The variability in adherence explained by these predictors was rather low (blended R2=0.049; face-to-face R2=0.076). Within the blended group, living without children predicted higher adherence to the face-to-face mode (R2=0.034), independent of age. Higher adherence to the web mode of the blended treatment was predicted by a combination of an extrinsic motivation to quit, a less negative attitude toward quitting, and less health complaints (R2=0.164). CONCLUSIONS This study represents one of the first attempts to thoroughly compare adherence and predictors of adherence of a blended smoking cessation treatment to an equivalent face-to-face treatment. Interestingly, although the overall adherence to both treatments appeared to be high, adherence within the blended treatment was much higher for the face-to-face mode than for the web mode. This supports the idea that in blended treatment, one mode of delivery can compensate for the weaknesses of the other. Higher age was found to be a common predictor of adherence to the treatments. The low variance in adherence predicted by the characteristics examined in this study suggests that other variables such as provider-related health system factors and time-varying patient characteristics should be explored in future research. CLINICALTRIAL Netherlands Trial Register NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113

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Blended Smoking Cessation Treatment: Exploring Measurement, Levels, and Predictors of Adherence (Preprint)

10.2196/preprints.9969 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lutz Siemer ◽

Marjolein GJ Brusse-Keizer ◽

Marloes G Postel ◽

Somaya Ben Allouch ◽

Angelos Patrinopoulos Bougioukas ◽

...

Keyword(s):

Smoking Cessation ◽

Marital Status ◽

Adherence Rate ◽

Social Modeling ◽

Web Based ◽

Smoking Cessation Treatment ◽

Face To Face ◽

Cessation Treatment ◽

Number Of Patients ◽

Predictors Of Adherence

BACKGROUND Blended face-to-face and Web-based treatment is a promising way to deliver cognitive behavioral therapy. Since adherence has been shown to be a measure for treatment’s acceptability and a determinant for treatment’s effectiveness, in this study, we explored adherence to a new blended smoking cessation treatment (BSCT). OBJECTIVE The objective of our study was to (1) develop an adequate method to measure adherence to BSCT; (2) define an adequate degree of adherence to be used as a threshold for being adherent; (3) estimate adherence to BSCT; and (4) explore the possible predictors of adherence to BSCT. METHODS The data of patients (N=75) were analyzed to trace adherence to BSCT delivered at an outpatient smoking cessation clinic. In total, 18 patient activities (eg, using a Web-based smoking diary tool or responding to counselors’ messages) were selected to measure adherence; the degree of adherence per patient was compared with quitting success. The minimum degree of adherence of patients who reported abstinence was examined to define a threshold for the detection of adherent patients. The number of adherent patients was calculated for each of the 18 selected activities; the degree of adherence over the course of the treatment was displayed; and the number of patients who were adherent was analyzed. The relationship between adherence and 33 person-, smoking-, and health-related characteristics was examined. RESULTS The method for measuring adherence was found to be adequate as adherence to BSCT correlated with self-reported abstinence (P=.03). Patients reporting abstinence adhered to at least 61% of BSCT. Adherence declined over the course of the treatment; the percentage of adherent patients per treatment activity ranged from 82% at the start of the treatment to 11%-19% at the final-third of BSCT; applying a 61% threshold, 18% of the patients were classified as adherent. Marital status and social modeling were the best independent predictors of adherence. Patients having a partner had 11-times higher odds of being adherent (OR [odds ratio]=11.3; CI: 1.33-98.99; 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: 0.55-0.94; P=.02). CONCLUSIONS The current study is the first to explore adherence to a blended face-to-face and Web-based treatment (BSCT) based on a substantial group of patients. It revealed a rather low adherence rate to BSCT. The method for measuring adherence to BSCT could be considered adequate because the expected dose-response relationship between adherence and quitting could be verified. Furthermore, this study revealed that marital status and social modeling were independent predictors of adherence. CLINICALTRIAL Netherlands Trial Registry NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113 (Archived by WebCite at http://www.webcitation.org/71BAPwER8).

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Blended face-to-face and web-based smoking cessation treatment (BSCT): a description of patients’ user experience (UX)

10.1183/13993003.congress-2019.pa1690 ◽

2019 ◽

Author(s):

Lutz Siemer ◽

Somaya Ben Allouch ◽

Marcel E. Pieterse ◽

Marjolein Brusse-Keizer ◽

Robbert Sanderman ◽

...

Keyword(s):

Smoking Cessation ◽

User Experience ◽

Web Based ◽

Smoking Cessation Treatment ◽

Face To Face ◽

Cessation Treatment

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Delivery of smoking cessation treatment via live chat: An analysis of client-centered coaching skills and behavior change techniques

Patient Education and Counseling ◽

10.1016/j.pec.2021.11.030 ◽

2021 ◽

Author(s):

Michael V Burke ◽

Sarah Cha ◽

Therese M Shumaker ◽

Margaret LaPlante ◽

Laura McConahey ◽

...

Keyword(s):

Smoking Cessation ◽

Behavior Change ◽

Behavior Change Techniques ◽

Smoking Cessation Treatment ◽

Live Chat ◽

Cessation Treatment ◽

Coaching Skills ◽

And Behavior

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Web-Based Acceptance and Commitment Therapy Smoking Cessation Treatment for Smokers With Depressive Symptoms

Journal of Dual Diagnosis ◽

10.1080/15504263.2014.992588 ◽

2015 ◽

Vol 11 (1) ◽

pp. 56-62 ◽

Cited By ~ 8

Author(s):

Helen A. Jones ◽

Jaimee L. Heffner ◽

Laina Mercer ◽

Christopher M. Wyszynski ◽

Roger Vilardaga ◽

...

Keyword(s):

Smoking Cessation ◽

Depressive Symptoms ◽

Acceptance And Commitment Therapy ◽

Web Based ◽

Smoking Cessation Treatment ◽

Commitment Therapy ◽

Acceptance And Commitment ◽

Cessation Treatment

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Comparison of mHealth and Face-to-Face Interventions for Smoking Cessation Among People Living With HIV: Meta-Analysis (Preprint)

10.2196/preprints.9329 ◽

2017 ◽

Author(s):

Olalekan A Uthman ◽

Chidozie U Nduka ◽

Mustapha Abba ◽

Rocio Enriquez ◽

Helena Nordenstedt ◽

...

Keyword(s):

Smoking Cessation ◽

Treatment Effects ◽

People Living With Hiv ◽

Short Term ◽

Smoking Cessation Treatment ◽

Face To Face ◽

Cessation Treatment ◽

Meta Analyses ◽

Living With Hiv

BACKGROUND The prevalence of smoking among people living with HIV (PLHIV) is higher than that reported in the general population, and it is a significant risk factor for noncommunicable diseases in this group. Mobile phone interventions to promote healthier behaviors (mobile health, mHealth) have the potential to reach a large number of people at a low cost. It has been hypothesized that mHealth interventions may not be as effective as face-to-face strategies in achieving smoking cessation, but there is no systematic evidence to support this, especially among PLHIV. OBJECTIVE This study aimed to compare two modes of intervention delivery (mHealth vs face-to-face) for smoking cessation among PLHIV. METHODS Literature on randomized controlled trials (RCTs) investigating effects of mHealth or face-to-face intervention strategies on short-term (4 weeks to <6 months) and long-term (≥6 months) smoking abstinence among PLHIV was sought. We systematically reviewed relevant RCTs and conducted pairwise meta-analyses to estimate relative treatment effects of mHealth and face-to-face interventions using standard care as comparison. Given the absence of head-to-head trials comparing mHealth with face-to-face interventions, we performed adjusted indirect comparison meta-analyses to compare these interventions. RESULTS A total of 10 studies involving 1772 PLHIV met the inclusion criteria. The average age of the study population was 45 years, and women comprised about 37%. In the short term, mHealth-delivered interventions were significantly more efficacious in increasing smoking cessation than no intervention control (risk ratio, RR, 2.81, 95% CI 1.44-5.49; n=726) and face-to-face interventions (RR 2.31, 95% CI 1.13-4.72; n=726). In the short term, face-to-face interventions were no more effective than no intervention in increasing smoking cessation (RR 1.22, 95% CI 0.94-1.58; n=1144). In terms of achieving long-term results among PLHIV, there was no significant difference in the rates of smoking cessation between those who received mHealth-delivered interventions, face-to-face interventions, or no intervention. Trial sequential analysis showed that only 15.16% (726/1304) and 5.56% (632/11,364) of the required information sizes were accrued to accept or reject a 25% relative risk reduction for short- and long-term smoking cessation treatment effects. In addition, sequential monitoring boundaries were not crossed, indicating that the cumulative evidence may be unreliable and inconclusive. CONCLUSIONS Compared with face-to-face interventions, mHealth-delivered interventions can better increase smoking cessation rate in the short term. The evidence that mHealth increases smoking cessation rate in the short term is encouraging but not sufficient to allow a definitive conclusion presently. Future research should focus on strategies for sustaining smoking cessation treatment effects among PLHIV in the long term.

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