Decision support training for advanced cancer family caregivers: Study protocol for the CASCADE factorial trial

BACKGROUND

Patients with advanced cancer face numerous decisions when diagnosed and often receive decision support from family caregivers. The CASCADE (CAre Supporters Coached to be Adept DEcision partners) factorial trial intervention aims to train caregivers in skills to provide effective decision support to patients and identify most effective intervention components.

METHODS

This is a 2-site, single-blind, 24 factorial trial to test components of the CASCADE decision support training intervention for family caregivers of patients with newly-diagnosed advanced cancer delivered by specially-trained, telehealth, palliative care lay coaches over 24 weeks. Family caregivers (target N = 352) are randomly assigned to one of 16 combinations of four components with two levels each: 1) psychoeducation on effective decision partnering principles (1 vs. 3 sessions); 2) decision support communication training (1 session vs. none); 3) Ottawa Decision Guide training (1 session vs. none) and 4) monthly follow-up (1 call vs. calls for 24 weeks). The primary outcome is patient-reported decisional conflict at 24 weeks. Secondary outcomes include patient distress, healthcare utilization, caregiver distress, and quality of life. Mediators and moderators (e.g., sociodemographics, decision self-efficacy, social support) will be explored between intervention components and outcomes. Results will be used to build two versions of CASCADE: one with only effective components (d ≥ 0.30) and another optimized for scalability and cost.

DISCUSSION

This protocol describes the first factorial trial, informed by the multiphase optimization strategy, of a palliative care decision-support intervention for advanced cancer family caregivers and will address the field's need to identify effective components that support serious illness decision-making.

TRIAL REGISTRATION

NCT04803604.

Study protocol for a factorial-randomized controlled trial evaluating the implementation, costs, effectiveness, and sustainment of digital therapeutics for substance use disorder in primary care (DIGITS Trial)

BACKGROUND

Experts recommend that treatment for substance use disorder (SUD) be integrated into primary care. The Digital Therapeutics for Opioids and Other SUD (DIGITS) Trial tests strategies for implementing reSET® and reSET-O®, which are prescription digital therapeutics for SUD and opioid use disorder, respectively, that include the community reinforcement approach, contingency management, and fluency training to reinforce concept mastery. This purpose of this trial is to test whether two implementation strategies improve implementation success (Aim 1) and achieve better population-level cost effectiveness (Aim 2) over a standard implementation approach.

METHODS/DESIGN

The DIGITS Trial is a hybrid type III cluster-randomized trial. It examines outcomes of implementation strategies, rather than studying clinical outcomes of a digital therapeutic. It includes 22 primary care clinics from a healthcare system in Washington State and patients with unhealthy substance use who visit clinics during an active implementation period (up to one year). Primary care clinics implemented reSET and reSET-O using a multifaceted implementation strategy previously used by clinical leaders to roll-out smartphone apps ("standard implementation" including discrete strategies such as clinician training, electronic health record tools). Clinics were randomized as 21 sites in a 2x2 factorial design to receive up to two added implementation strategies: (1) practice facilitation, and/or (2) health coaching. Outcome data are derived from electronic health records and logs of digital therapeutic usage. Aim 1's primary outcomes include reach of the digital therapeutics to patients and fidelity of patients' use of the digital therapeutics to clinical recommendations. Substance use and engagement in SUD care are additional outcomes. In Aim 2, population-level cost effectiveness analysis will inform the economic benefit of the implementation strategies compared to standard implementation. Implementation is monitored using formative evaluation, and sustainment will be studied for up to one year using qualitative and quantitative research methods.

DISCUSSION

The DIGITS Trial uses an experimental design to test whether implementation strategies increase and improve the delivery of digital therapeutics for SUDs when embedded in a large healthcare system. It will provide data on the potential benefits and cost-effectiveness of alternative implementation strategies.

CLINICALTRIALS

gov Identifier: NCT05160233 (Submitted 12/3/2021). https://clinicaltrials.gov/ct2/show/NCT05160233.

Working alliance and adherence mediate the effect of guidance in a web-based program for participants with mild to moderate depressive symptoms: A secondary mediation analysis

Guided web-based self-help programs for individuals with depressive symptoms have shown to be more efficacious than unguided programs. However, research has paid little attention to why guided interventions are superior. The present study investigated whether working alliance and adherence to the program mediated the effect of guidance on depressive symptom outcome. The study is a secondary analysis of a randomized factorial trial. In the trial, 302 adults with mild to moderate depressive symptoms (Patient Health Questionnaire-9 score: 5-14) were randomized to either a guided or an unguided group. All participants received access to a web-based self-help program based on problem-solving therapy. Working alliance with the treatment providers was assessed using an adapted version of the Working Alliance Inventory for Guided Internet Interventions two weeks (early-treatment) and eight weeks (post-treatment) after pre-treatment. The primary outcome was depressive symptoms at post-treatment. The total working alliance score was significantly higher for guided participants compared to unguided participants (at early-treatment: t _248.6 = -3.36, p < .001, d = 0.42, at post-treatment: t _194.9 = -4.77, p < .001, d = 0.66). The total working alliance score correlated significantly with the change in depressive symptoms for guided (rs = 0.16, 0.34) and unguided participants (rs = 0.26, 0.23). The WAI-I total score statistically mediated the relationship between guidance and outcome (at early-treatment: B = -0.028, at post-treatment: B = -0.053). Furthermore, the subscale tasks (at post-treatment: B = -0.051), the subscale goals (at early-treatment: B = -0.031 and at post-treatment: B = -0.052), and adherence to the program (B = -0.034) mediated the relationship between guidance and outcome. Finally, in a multiple mediation model both early-treatment working alliance and adherence to the program (B = -0.050) mediated the relationship between guidance and outcome. These findings indicate that guidance increases working alliance to treatment providers as early as two weeks after treatment beginning. The alliance predicts outcome and mediates the relationship between guidance and outcome. Participants' agreement with tasks and goals of a program seems to be more important than the bond with treatment providers. Treatment providers might therefore attune web-based programs to the preferences and expectations of participants. In addition to the working alliance, adherence to the program co-mediates the relationship between guidance and outcome.

Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention

BACKGROUND

E- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it's not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs.

METHODS

In a 2 (action planning: present vs absent) × 2 (coping planning: present vs absent) × 2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention 'MyPlan2.0' for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335, age = 35.8, 28.1% men) or SB (n = 138, age = 37.8, 37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB.

RESULTS

First, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735, p = 0.007) and reduced SB (t = - 2.573, p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302, p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x² = 8849, p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x² = 3.918, p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x² = 5.590, p = 0.014; x² = 17.722, p < 0.001; x² = 4.552, p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x² = 4.389, p = 0.031) and self-monitoring alone (x² = 8.858, p = 003), respectively.

CONCLUSIONS

This study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future.

TRIAL REGISTRATION

This study was preregistered as a clinical trial (ID number: NCT03274271 ). Release date: 20 October 2017.

Reporting of randomized factorial trials was frequently inadequate

OBJECTIVES

Factorial designs can allow efficient evaluation of multiple treatments within a single trial. We evaluated the design, analysis, and reporting in a sample of factorial trials.

STUDY DESIGN AND SETTING

Review of 2 × 2 factorial trials evaluating health-related interventions and outcomes in humans. Using Medline, we identified articles published between January 2015 and March 2018. We randomly selected 100 articles for inclusion.

RESULTS

Most trials (78%) did not provide a rationale for using a factorial design. Only 63 trials (63%) assessed the interaction for the primary outcome, and 39/63 (62%) made a further assessment for at least one secondary outcome. 12/63 trials (19%) identified a significant interaction for the primary outcome and 16/39 trials (41%) for at least one secondary outcome. Inappropriate methods of analysis to protect against potential negative effects from interactions were common, with 18 trials (18%) choosing the analysis method based on a preliminary test for interaction, and 13% (n = 10/75) of those conducting a factorial analysis including an interaction term in the model.

CONCLUSION

Reporting of factorial trials was often suboptimal, and assessment of interactions was poor. Investigators often used inappropriate methods of analysis to try to protect against adverse effects of interactions.

Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: an example of a neonatal cooling trial

BACKGROUND

Decisions to stop randomized trials are often based on traditional P value thresholds and are often unconvincing to clinicians. To familiarize clinical investigators with the application and advantages of Bayesian monitoring methods, we illustrate the steps of Bayesian interim analysis using a recent major trial that was stopped based on frequentist analysis of safety and futility.

METHODS

We conducted Bayesian reanalysis of a factorial trial in newborn infants with hypoxic-ischemic encephalopathy that was designed to investigate whether outcomes would be improved by deeper (32 °C) or longer cooling (120 h), as compared with those achieved by standard whole body cooling (33.5 °C for 72 h). Using prior trial data, we developed neutral and enthusiastic prior probabilities for the effect on predischarge mortality, defined stopping guidelines for a clinically meaningful effect, and derived posterior probabilities for predischarge mortality.

RESULTS

Bayesian relative risk estimates for predischarge mortality were closer to 1.0 than were frequentist estimates. Posterior probabilities suggested increased predischarge mortality (relative risk > 1.0) for the three intervention groups; two crossed the Bayesian futility threshold.

CONCLUSIONS

Bayesian analysis incorporating previous trial results and different pre-existing opinions can help interpret accruing data and facilitate informed stopping decisions that are likely to be meaningful and convincing to clinicians, meta-analysts, and guideline developers.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01192776 . Registered on 31 August 2010.