An Autonomy-Supportive Online Decision Aid to Assist Smokers in Choosing Evidence-Based Cessation Assistance: Development Process and Protocol of a Randomized Controlled Trial

Autonomy support in telehealth: an evolutionary concept analysis

Aims

Autonomy support plays a critical role in safeguarding patients' fundamental rights and promoting health behaviors. The context of autonomy support is transitioning from traditional face-to-face healthcare settings to telehealth, leading to an evolution in the connotation of autonomy support. This study aimed to clarify the connotation of autonomy support in telehealth and to develop a conceptual framework to guide innovations in clinical practice and the advancement of related theories.

Methods

Rodgers' evolutionary method was used to clarified attributes, antecedents, and consequences of autonomy support in telehealth. The integrative review strategy of Whittemore and Knafl was used as the methodology for searching relevant literature.

Results

Twenty-five articles were included. The attributes were identified as: (i) technical feedback; (ii) virtual agent; (iii) choice; (iv) rationale; (v) empathy; (vi) collaboration; and (vii) strengths. The antecedents were: (i) telehealth service system; (ii) change in awareness toward autonomy support; and (iii) patient preference of needs for autonomy. The consequences were: (i) multidimensional perception; (ii) emotional experience; (iii) health behavior; (iv) social relation; and (v) technological dependence.

Conclusion

This study clarified the attributes, antecedents, and consequences of autonomy support in telehealth, developing and improving a conceptual framework for autonomy support. These findings will provide a theoretical foundation for developing technology-enabled autonomy support strategies in telehealth practice, better adapting to the emerging needs of patients in the context of the digital age.

Effects of an Explicit Value Clarification Method With Computer-Tailored Advice on the Effectiveness of a Web-Based Smoking Cessation Decision Aid: Findings From a Randomized Controlled Trial

BACKGROUND

Smoking continues to be a driver of mortality. Various forms of evidence-based cessation assistance exist; however, their use is limited. The choice between them may also induce decisional conflict. Offering decision aids (DAs) may be beneficial; however, insights into their effective elements are lacking.

OBJECTIVE

This study tested the added value of an effective element (ie, an "explicit value clarification method" paired with computer-tailored advice indicating the most fitting cessation assistance) of a web-based smoking cessation DA.

METHODS

A web-based randomized controlled trial was conducted among smokers motivated to stop smoking within 6 months. The intervention group received a DA with the aforementioned elements, and the control group received the same DA without these elements. The primary outcome measure was 7-day point prevalence abstinence 6 months after baseline (time point 3 [t=3]). Secondary outcome measures were 7-day point prevalence of abstinence 1 month after baseline (time point 2 [t=2]), evidence-based cessation assistance use (t=2 and t=3), and decisional conflict (immediately after DA; time point 1). Logistic and linear regression analyses were performed to assess the outcomes. Analyses were conducted following 2 (decisional conflict) and 3 (smoking cessation) outcome scenarios: complete cases, worst-case scenario (assuming that dropouts still smoked), and multiple imputations. A priori sample size calculation indicated that 796 participants were needed. The participants were mainly recruited on the web (eg, social media). All the data were self-reported.

RESULTS

Overall, 2375 participants were randomized (intervention n=1164, 49.01%), of whom 599 (25.22%; intervention n=275, 45.91%) completed the DAs, and 276 (11.62%; intervention n=143, 51.81%), 97 (4.08%; intervention n=54, 55.67%), and 103 (4.34%; intervention n=56, 54.37%) completed time point 1, t=2, and t=3, respectively. More participants stopped smoking in the intervention group (23/63, 37%) than in the control group (14/52, 27%) after 6 months; however, this was only statistically significant in the worst-case scenario (crude P=.02; adjusted P=.04). Effects on the secondary outcomes were only observed for smoking abstinence after 1 month (15/55, 27%, compared with 7/46, 15%, in the crude and adjusted models, respectively; P=.02) and for cessation assistance uptake after 1 month (26/56, 46% compared with 18/47, 38% only in the crude model; P=.04) and 6 months (38/61, 62% compared with 26/50, 52%; crude P=.01; adjusted P=.02) but only in the worst-case scenario. Nonuse attrition was 34.19% higher in the intervention group than in the control group (P<.001).

CONCLUSIONS

Currently, we cannot confidently recommend the inclusion of explicit value clarification methods and computer-tailored advice. However, they might result in higher nonuse attrition rates, thereby limiting their potential. As a lack of statistical power may have influenced the outcomes, we recommend replicating this study with some adaptations based on the lessons learned.

TRIAL REGISTRATION

Netherlands Trial Register NL8270; https://www.trialregister.nl/trial/8270.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/21772.

Integrating behaviour change interventions and patient decision aids: How to accomplish synergistic effects?

People make numerous health-related choices each day: For example, deciding to brush one's teeth or to eat well and healthy - or not to do these activities. To support complex decisions and subsequent behaviour change, both Behaviour Change Interventions (BCIs) and Patient Decision Aids (PtDAs) have been developed and evolved independently to support people in health-related decision making. In this paper, we critically review BCIs and PtDAs, examine their similarities and differences, and identify potential for integration of expertise to increase the benefits for people engaging with healthcare and health behaviours. The two approaches appear to mainly differ in terms of their (1) goals and foci, (2) theoretical basis, (3) development frameworks, (4) active ingredients and (5) effect evaluation. To facilitate the integration of scientific insights from these two fields, we recommend to (1) bring both fields together and promote interprofessional discussions, (2) train (health) professionals to recognise strengths of both approaches, (3) investigate the synergy of the two fields, (4) be prepared for and try to mitigate a culture shock when the fields start to interact. Knowledge generated by researching PtDAs could be used to facilitate decisional processes that enable patients to choose goals that are in line with their values and preferences, while insights from researching BCIs could be used to facilitate engagement with, and implementation of those goals. This integration could allow researchers and intervention providers to increase the benefits for people engaging with healthcare and health behaviours.

The Usability of an Online Tool to Promote the Use of Evidence-Based Smoking Cessation Interventions

To increase usage of evidence-based smoking cessation interventions (EBSCIs) among smokers, an online decision aid (DA) was developed. The aims of this study were (1) to conduct a usability evaluation; (2) to conduct a program evaluation and evaluate decisional conflict after using the DA and (3) to determine the possible change in the intention to use EBSCIs before and directly after reviewing the DA. A cross-sectional study was carried out in September 2020 by recruiting smokers via the Internet (n = 497). Chi-squared tests and t-tests were conducted to test the differences between smokers who differed in the perceived usability of the DA on the program evaluation and in decisional conflict. The possible changes in intention to use EBSCIs during a cessation attempt before and after reviewing the DA were tested using t-tests, McNemar's test and χ2 analysis. The participants evaluated the usability of the DA as moderate (MU; n = 393, 79.1%) or good (GU; n = 104, 20.9%). GU smokers rated higher on all the elements of the program evaluation and experienced less decisional conflict, but also displayed a higher intention to quit. After reviewing the DA, the participants on average had a significantly higher intention to use more EBSCIs, in particular in the form of eHealth. Recommendations to make the DA more usable could include tailoring, using video-based information and including value clarification methods. Furthermore, a hybrid variant in which smokers can use the DA independently and with the guidance of a primary care professional could aid both groups in choosing a fitting EBSCI option.