Dataset Information

Randomization procedures for multicomponent behavioral intervention factorial trials in the multiphase optimization strategy framework: challenges and recommendations.

ABSTRACT: The multiphase optimization strategy (MOST) is an increasingly popular framework to prepare, optimize, and evaluate multicomponent behavioral health interventions. Within this framework, it is common to use a factorial trial to assemble an optimized multicomponent intervention by simultaneously testing several intervention components. With the possibility of a large number of conditions (unique combinations of components) and a goal to balance conditions on both sample size (for statistical efficiency) and baseline covariates (for internal validity), such trials face additional randomization challenges compared to the standard two-arm trial. The purpose of the current paper is to compare and contrast potential randomization methods for factorial trials in the context of MOST and to provide guidance for the reporting of those methods. We describe the principles, advantages, and disadvantages of several randomization methods in the context of factorial trials. We then provide examples to examine current practice in the MOST-related literature and provide recommendations for reporting of randomization. We identify two key randomization decisions for MOST-related factorial trials: (i) whether to randomize to components or conditions and (ii) whether to use restricted randomization techniques, such as stratification, permuted blocks, and minimization. We also provide a checklist to assist researchers in ensuring complete reporting of randomization methods used. As more investigators use factorial trials within the MOST framework for assembling optimized multicomponent behavioral interventions, appropriate implementation and rigorous reporting of randomization procedures will be essential for ensuring the efficiency and validity of the results.

SUBMITTER: Gallis JA

PROVIDER: S-EPMC6875651 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Randomization procedures for multicomponent behavioral intervention factorial trials in the multiphase optimization strategy framework: challenges and recommendations.

Gallis John A JA Bennett Gary G GG Steinberg Dori M DM Askew Sandy S Turner Elizabeth L EL

Translational behavioral medicine 20191101 6

The multiphase optimization strategy (MOST) is an increasingly popular framework to prepare, optimize, and evaluate multicomponent behavioral health interventions. Within this framework, it is common to use a factorial trial to assemble an optimized multicomponent intervention by simultaneously testing several intervention components. With the possibility of a large number of conditions (unique combinations of components) and a goal to balance conditions on both sample size (for statistical effi ...[more]

PMID: 30590759

Similar Datasets

Project description:BackgroundFamily caregiver interventions are essential to support dementia caregiving. However, such interventions are typically complex and consist of multiple components. Existing evidence rarely delineates the effectiveness and interactions between individual components. To optimise intervention, we adopt the multiphase optimisation strategy (MOST) to test the implementation fidelity and determine the effect of each component and the interactions between each component and the corresponding outcome.MethodsA prospective, assessor-blinded, randomised clinical trial with fractional factorial design using the MOST principle. Two hundred fifty family dementia caregivers will be randomised to one of 16 experimental conditions in a fractional factorial design involving six intervention components: (1) dementia and caregiving education; (2) self-care skills; (3) behavioural symptom management; (4) behavioural activation; (5) modified mindfulness-based cognitive therapy; and (6) support group. The first one is the core component, and the five remaining will be examined. Physical health, caregiver burden, stress, psychological well-being, anxiety and depressive symptoms, and social support will be assessed over the 12-month study period. Following the intention-to-treat principle, linear mixed models and regression analyses will be used to examine the specific effect of the five components and their two-way interactions to propose the most effective combination.DiscussionThis is the first study adopting the multiphase optimisation strategy to identify the most active and engaging components of a psychological intervention for caregivers of patients with dementia. In view that dementia caregiver interventions are increasingly diversified and complex, such knowledge is important to maximise the intervention efficacy and allow the intervention to be implemented within an efficient timeframe and dosage. The optimisation of caregiver support interventions is critical to enhance the health outcomes of caregivers and care recipients, thereby, delaying possible institutionalisation and reducing the costs of long-term dementia care.Trial registrationThis study was retrospectively registered in the WHO Primary Registry - Chinese Clinical Trials Registry (ChiCTR2300071235). (Protocol date 30/10/2020; version identifier 2020-2021-0045). Registered on 9 May, 2023.Reporting methodSPIRIT guideline was followed.Patient or public contributionNo patient or public involvement.

Project description:BackgroundDelivery of behavioral interventions is complex, as the majority of interventions consist of multiple components used either simultaneously, sequentially, or both. The importance of clearly delineating delivery strategies within these complex interventions-and furthermore understanding the impact of each strategy on effectiveness-has recently emerged as an important facet of intervention research. Yet, few methodologies exist to prospectively test the effectiveness of delivery strategies and how they impact implementation. In the current paper, we describe a study protocol for a large randomized controlled trial in which we will use the Multiphase Optimization Strategy (MOST), a novel framework developed to optimize interventions, i.e., to test the effectiveness of intervention delivery strategies using a factorial design. We apply this framework to delivery of Family Navigation (FN), an evidence-based care management strategy designed to reduce disparities and improve access to behavioral health services, and test four components related to its implementation.Methods/designThe MOST framework contains three distinct phases: Preparation, Optimization, and Evaluation. The Preparation phase for this study occurred previously. The current study consists of the Optimization and Evaluation phases. Children aged 3-to-12 years old who are detected as "at-risk" for behavioral health disorders (n = 304) at a large, urban federally qualified community health center will be referred to a Family Partner-a bicultural, bilingual member of the community with training in behavioral health and systems navigation-who will perform FN. Families will then be randomized to one of 16 possible combinations of FN delivery strategies (2 × 2 × 2× 2 factorial design). The primary outcome measure will be achieving a family-centered goal related to behavioral health services within 90 days of randomization. Implementation data on the fidelity, acceptability, feasibility, and cost of each strategy will also be collected. Results from the primary and secondary outcomes will be reviewed by our team of stakeholders to optimize FN delivery for implementation and dissemination based on effectiveness, efficiency, and cost.DiscussionIn this protocol paper, we describe how the MOST framework can be used to improve intervention delivery. These methods will be useful for future studies testing intervention delivery strategies and their impact on implementation.Trial registrationClinicalTrials.gov, NCT03569449. Registered on 26 June 2018.

Project description:BackgroundAsthma causes substantial morbidity and mortality in the United States, particularly among African American emerging adults (AAEAs; aged 18-30 years), but very few asthma programs have targeted this population. Interventions that provide education and address underlying motivation for managing asthma may be the most effective. However, intensive face-to-face interventions are often difficult to implement in this population.ObjectiveThe purpose of this study is to develop an effective mobile asthma management intervention to improve control among AAEAs.MethodsWe will assess the ability of multiple technologic components to assist and improve traditional asthma education. The first component is the Motivational Enhancement System for asthma management. It is a mobile 4-session intervention using supported self-regulation and motivational interviewing. Personalized content is based on each participant's activity level, daily experiences, and goals. The second component is supportive accountability. It is administered by asthma nurses using targeted mobile support (Skype/voice calls) to provide education, promote self-efficacy, and overcome barriers through a motivational interviewing-based framework. The third component is SMS text messaging. It provides reminders for asthma education, medication adherence, and physical activity. The fourth component is physical activity tracking. It uses wearable technology to help meet user-defined physical activity goals. Using a multiphase optimization strategy (MOST) framework, we will test intervention components and combinations of components to identify the most effective mobile intervention. The MOST framework is an innovative, and cost- and time-effective framework that uses engineering principles to produce effective behavioral interventions. We will conduct a component selection experiment using a factorial research design to build an intervention that has been optimized for maximum efficacy, using a clinically significant improvement in asthma. Participants (N=180) will be randomized to 1 of 6 intervention arms. Participants will be recruited from multiple sites of the American Lung Association-Airway Clinical Research Centers network and ambulatory care clinics at the Detroit Medical Center. Data collections will occur at baseline, and 3, 6, and 12 months.ResultsAt study completion, we will have an empirically supported optimized mobile asthma management intervention to improve asthma control for AAEAs. We hypothesize that postintervention (3, 6, and 12 months), participants with uncontrolled asthma will show a clinically significant improvement in asthma control. We also hypothesize that improvements in asthma management behaviors (including physical activity), quality of life, symptoms, adherence, and exacerbation (secondary outcomes) will be observed.ConclusionsAAEAs are disproportionately impacted by asthma, but have been underrepresented in research. Mobile asthma management interventions may help improve asthma control and allow people to live healthier lives. During this project, we will use an innovative strategy to develop an optimized mobile asthma management intervention using the most effective combination of nurse-delivered asthma education, a smartphone app, and text messaging.International registered report identifier (irrid)PRR1-10.2196/37946.

Project description:BackgroundPretreatments are one of the main bottlenecks for the lignocellulose conversion process and the search for cheaper and effective pretreatment methodologies for each biomass is a complex but fundamental task. Here, we used a 2ν5-1 fractional factorial design (FFD) to optimize five pretreatment variables: milling time, temperature, double treatment, chemical concentration, and pretreatment time in acid-alkali (EA) and acid-organosolv (EO) pretreatments, applied to elephant grass leaves.ResultsFFD allowed optimization of the pretreatment conditions using a reduced number of experiments and allowed the identification of secondary interactions between the factors. FFD showed that the temperature can be kept at its lower level and that the first acid step can be eliminated in both pretreatments, without significant losses to enzymatic hydrolysis. EA resulted in the highest release of reducing sugars (maximum of 205 mg/g substrate in comparison to 152 mg/g in EO and 40 mg/g in the untreated sample), using the following conditions in the alkali step: [NaOH] = 4.5% w/v; 85 °C and 100 min after ball milling the sample. The factors statistically significant (P < 0.05) in EA pretreatment were NaOH concentration, which contributes to improved hydrolysis by lignin and silica removal, and the milling time, which has a mechanical effect. For EO samples, the statistically significant factors to improved hydrolysis were ethanol and catalyst concentrations, which are both correlated to higher cellulose amounts in the pretreated substrates. The catalyst is also correlated to lignin removal. The detailed characterization of the main hemicellulosic sugars in the solids after pretreatments revealed their distinct recalcitrance: glucose was typically more recalcitrant than xylose and arabinose, which could be almost completely removed under specific pretreatments. In EA samples, the removal of hemicellulose derivatives was very dependent on the acid step, especially arabinose removal.ConclusionThe results presented herewith contribute to the development of more efficient and viable pretreatments to produce cellulosic ethanol from grass biomasses, saving time, costs and energy. They also facilitate the design of enzymatic cocktails and a more appropriate use of the sugars contained in the pretreatment liquors, by establishing the key recalcitrant polymers in the solids resulting from each processing step.

Dataset Information

Randomization procedures for multicomponent behavioral intervention factorial trials in the multiphase optimization strategy framework: challenges and recommendations.

Publications

Randomization procedures for multicomponent behavioral intervention factorial trials in the multiphase optimization strategy framework: challenges and recommendations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets