Randomization procedures for multicomponent behavioral intervention factorial trials in the multiphase optimization strategy framework: challenges and recommendations.
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ABSTRACT:
SUBMITTER: Gallis JA
PROVIDER: S-EPMC6875651 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
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Project description:Spinal manipulative therapy (SMT) is a common nonpharmacological treatment for low back pain (LBP). Although generally supported by systematic reviews and practice guidelines, clinical trials evaluating SMT have been characterized by small effect sizes. This study adopts a Multiphase Optimization Strategy framework to examine individual components of an SMT delivery protocol using a single-blind trial with the goal of identifying and optimizing a multicomponent SMT protocol. We enrolled 241 participants with LBP. All participants received 2 SMT treatment sessions in the first week then were randomly assigned additional treatment based on a fully factorial design. The 3 randomized treatment components provided in twice weekly sessions over 3 weeks were multifidus activating exercise, spinal mobilizing exercise, and additional SMT dose. Primary outcomes included clinical (Oswestry Disability Index, numeric pain intensity rating) and mechanistic (spinal stiffness, multifidus muscle activation) measures assessed at baseline, 1, 4, and 12 weeks. Significant differences were found for the Oswestry index after 12 weeks for participants receiving multifidus activating exercise (mean difference = -3.62, 97.5% CI: -6.89, -0.35; P= .01). There were no additional significant main or interaction effects for other treatment components or different outcome measures. The optimized SMT protocol identified in this study included SMT sessions followed by multifidus activating exercises. PERSPECTIVE: Optimizing the effects of nonpharmacological treatments such as SMT for LBP is challenging due to uncertainty regarding mechanisms and the complexity of multicomponent protocols. This factorial randomized trial examined SMT protocols provided with differing co-interventions with mechanistic and patient-centered outcomes. Patient-centered outcomes were optimized by inclusion of lumbar multifidus strengthening exercises.
| S-EPMC8190177 | biostudies-literature
Project description:BackgroundBehavioral lifestyle intervention (BLI) is recommended as a first-line treatment for obesity. While BLI has been adapted for online delivery to improve potential for dissemination while reducing costs and barriers to access, weight losses are typically inferior to gold standard treatment delivered in-person. It is therefore important to refine and optimize online BLI in order to improve the proportion of individuals who achieve a minimum clinically significant weight loss and mean weight loss.Study designFive experimental intervention components will be tested as adjuncts to an established 12-month online BLI: virtual reality for BLI skills training, interactive video feedback, tailored intervention to promote physical activity, skills for dysregulated eating, and social support combined with friendly competition. Following the Multiphase Optimization Strategy (MOST) framework, the components will first be refined and finalized during Preparation Phase pilot testing and then evaluated in a factorial experiment with 384 adults with overweight or obesity. A priori optimization criteria that balance efficacy and efficiency will be used to create a finalized treatment package that produces the best weight loss outcomes with the fewest intervention components. Mediation analysis will be conducted to test hypothesized mechanisms of action and a moderator analysis will be conducted to understand for whom and under what circumstances the interventions are effective.ConclusionThis study will provide important information about intervention strategies that are useful for improving outcomes of online BLI. The finalized treatment package will be suitable for testing in a future randomized trial in the MOST Evaluation Phase.
| S-EPMC7887033 | biostudies-literature
Project description:BackgroundEffective weight loss interventions exist, yet few can be scaled up for wide dissemination. Further, none has been fully delivered via text message. We used the multiphase optimization strategy (MOST) to develop multicomponent interventions that consist only of active components, those that have been experimentally determined to impact the chosen outcome.ObjectiveThe goal of this study is to optimize a standalone text messaging obesity intervention, Charge, using the MOST framework to experimentally determine which text messaging components produce a meaningful contribution to weight change at 6 months.MethodsWe designed a 6-month, weight loss texting intervention based on our interactive obesity treatment approach (iOTA). Participants are randomized to one of 32 experimental conditions to test which standalone text messaging intervention components produce a meaningful contribution to weight change at 6 months.ResultsThe project was funded in February 2017; enrollment began in January 2018 and data collection was completed in June 2019. Data analysis is in progress and first results are expected to be submitted for publication in 2021.ConclusionsFull factorial trials are particularly efficient in terms of cost and logistics when leveraged for standalone digital treatments. Accordingly, MOST has the potential to promote the rapid advancement of digital health treatments. Subject to positive findings, the intervention will be low cost, immediately scalable, and ready for dissemination. This will be of great potential use to the millions of Americans with obesity and the providers who treat them.Trial registrationClinicalTrials.gov NCT03254940; https://clinicaltrials.gov/ct2/show/NCT03254940.International registered report identifier (irrid)RR1-10.2196/19506.
| S-EPMC7850907 | biostudies-literature
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.
| S-EPMC10696816 | biostudies-literature
Project description:Increasing numbers of economic evaluations are conducted alongside randomised controlled trials. Such studies include factorial trials, which randomise patients to different levels of two or more factors and can therefore evaluate the effect of multiple treatments alone and in combination. Factorial trials can provide increased statistical power or assess interactions between treatments, but raise additional challenges for trial-based economic evaluations: interactions may occur more commonly for costs and quality-adjusted life-years (QALYs) than for clinical endpoints; economic endpoints raise challenges for transformation and regression analysis; and both factors must be considered simultaneously to assess which treatment combination represents best value for money. This article aims to examine issues associated with factorial trials that include assessment of costs and/or cost-effectiveness, describe the methods that can be used to analyse such studies and make recommendations for health economists, statisticians and trialists. A hypothetical worked example is used to illustrate the challenges and demonstrate ways in which economic evaluations of factorial trials may be conducted, and how these methods affect the results and conclusions. Ignoring interactions introduces bias that could result in adopting a treatment that does not make best use of healthcare resources, while considering all interactions avoids bias but reduces statistical power. We also introduce the concept of the opportunity cost of ignoring interactions as a measure of the bias introduced by not taking account of all interactions. We conclude by offering recommendations for planning, analysing and reporting economic evaluations based on factorial trials, taking increased analysis costs into account. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
| S-EPMC5599939 | biostudies-literature
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.
| S-EPMC6915979 | biostudies-literature
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.
| S-EPMC9131162 | biostudies-literature
Project description:BackgroundFactorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 × 2 approach, using dichotomized genetic scores to divide the population into four subgroups as in a factorial randomized trial.MethodsWe describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure.ResultsSimulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between 4- and 10-fold improvement in efficiency is demonstrated over the 2 × 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal-sized groups.ConclusionsPrevious factorial Mendelian randomization analyses may have been underpowered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.
| S-EPMC7750987 | biostudies-literature
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.
| S-EPMC6058377 | biostudies-literature
Project description:Here, we report an engineering approach toward multicomponent self-assembly processes by developing a methodology to circumvent spurious, metastable assemblies. The formation of metastable aggregates often hampers self-assembly of molecular building blocks into the desired nanostructures. Strategies are explored to master the pathway complexity and avoid off-pathway aggregates by optimizing the rate of assembly along the correct pathway. We study as a model system the coassembly of two monomers, the R- and S-chiral enantiomers of a ?-conjugated oligo(p-phenylene vinylene) derivative. Coassembly kinetics are analyzed by developing a kinetic model, which reveals the initial assembly of metastable structures buffering free monomers and thereby slows the formation of thermodynamically stable assemblies. These metastable assemblies exert greater influence on the thermodynamically favored self-assembly pathway if the ratio between both monomers approaches 1:1, in agreement with experimental results. Moreover, competition by metastable assemblies is highly temperature dependent and hampers the assembly of equilibrium nanostructures most effectively at intermediate temperatures. We demonstrate that the rate of the assembly process may be optimized by tuning the cooling rate. Finally, it is shown by simulation that increasing the driving force for assembly stepwise by changing the solvent composition may circumvent metastable pathways and thereby force the assembly process directly into the correct pathway.
| S-EPMC3808594 | biostudies-literature