A pilot sequential multiple assignment randomized trial (SMART) protocol for developing an adaptive coaching intervention around a mobile application for athletes to improve carbohydrate periodization behavior

Yan, Xiaoxi; Dunne, David M.; Impey, Samuel G.; Cunniffe, Brian; Lefevre, Carmen E.; Mazorra, Rodrigo; Morton, James P.; Tod, David; Close, Graeme L.; Murphy, Rebecca; Chakraborty, Bibhas

doi:10.1016/j.conctc.2022.100899

Cited by 3 publications

(5 citation statements)

References 34 publications

(39 reference statements)

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“…Our work has been motivated by the digital approach to improving carbohydrate periodization behaviors in athlete (SMART+) study conducted in the UK, via a menu planner mobile application, Hexis Performance (Figure 1). 28 F I G U R E 1 Key screens of the Hexis Performance mobile application used in the SMART+ study. The messaging screen (right-most) is only activated when a nutrition coach (NC) is given.…”

Section: Smart+ Study: Digital Approach To Improving Carbohydrate Per...mentioning

confidence: 99%

“…The full SMART design used is shown in Figure 2, wherein all athletes received the App and were randomized at stage 1 to either follow a relaxed or stringent response criteria for the rest of the trial. The full three-stage SMART design, with 16 embedded DTRs, is described by Yan et al 28 The current study aims to assess feasibility, as such the primary outcomes focused on app and coaching uptake rates, and estimating the carbohydrate periodization success rates; there is less concern in formally comparing the clinical outcomes among the embedded regimes in the design. In this study, the clinical outcomes are carbohydrate periodization behavior ranking, carbohydrate periodization self-efficacy and belief about consequences, which are of ordinal nature, for example, the carbohydrate periodization behavior ranking ranks athletes as 1 (not periodizing at all), 2 (periodizing energy/kcal only) or 3 (periodizing both energy/kcal and carbohydrates).…”

Section: Smart+ Study: Digital Approach To Improving Carbohydrate Per...mentioning

confidence: 99%

“…Our work has been motivated by the digital approach to improving carbohydrate periodization behaviors in athlete (SMART+) study conducted in the UK, via a menu planner mobile application, Hexis Performance (Figure 1). 28 Carbohydrate periodization, also known as fueling for the work required, is the carbohydrate availability adjusted in accordance with the demands of the specific training session to be completed, to optimize training performance 29 . Despite the knowledge on what and when to eat, athletes usually struggle to adhere to the behavior, and often needs highly personalized support from nutrition coaches 30 .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome

Ghosh

Yan

Chakraborty

2023

Statistics in Medicine

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Sequential multiple assignment randomized trials (SMARTs) are used to construct data‐driven optimal intervention strategies for subjects based on their intervention and covariate histories in different branches of health and behavioral sciences where a sequence of interventions is given to a participant. Sequential intervention strategies are often called dynamic treatment regimes (DTR). In the existing literature, the majority of the analysis methodologies for SMART data assume a continuous primary outcome. However, ordinal outcomes are also quite common in clinical practice. In this work, first, we introduce the notion of generalized odds ratio (GOR$$ GOR $$) to compare two DTRs embedded in a SMART with an ordinal outcome and discuss some combinatorial properties of this measure. Next, we propose a likelihood‐based approach to estimate GOR$$ GOR $$ from SMART data, and derive the asymptotic properties of its estimate. We discuss alternative ways to estimate GOR$$ GOR $$ using concordant‐discordant pairs and two‐sample U$$ U $$‐statistic. We derive the required sample size formula for designing SMARTs with ordinal outcomes based on GOR$$ GOR $$. A simulation study shows the performance of the estimated GOR$$ GOR $$ in terms of the estimated power corresponding to the derived sample size. The methodology is applied to analyze data from the SMART+ study, conducted in the UK, to improve carbohydrate periodization behavior in athletes using a menu planner mobile application, Hexis Performance. A freely available Shiny web app using R is provided to make the proposed methodology accessible to other researchers and practitioners.

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“…Our work has been motivated by the digital approach to improving carbohydrate periodization behaviors in athlete (SMART+) study conducted in the UK, via a menu planner mobile application, Hexis Performance (Figure 1). 28 F I G U R E 1 Key screens of the Hexis Performance mobile application used in the SMART+ study. The messaging screen (right-most) is only activated when a nutrition coach (NC) is given.…”

Section: Smart+ Study: Digital Approach To Improving Carbohydrate Per...mentioning

confidence: 99%

Section: Smart+ Study: Digital Approach To Improving Carbohydrate Per...mentioning

confidence: 99%

“…Our work has been motivated by the digital approach to improving carbohydrate periodization behaviors in athlete (SMART+) study conducted in the UK, via a menu planner mobile application, Hexis Performance (Figure 1). 28 Carbohydrate periodization, also known as fueling for the work required, is the carbohydrate availability adjusted in accordance with the demands of the specific training session to be completed, to optimize training performance 29 . Despite the knowledge on what and when to eat, athletes usually struggle to adhere to the behavior, and often needs highly personalized support from nutrition coaches 30 .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome

Ghosh

Yan

Chakraborty

2023

Statistics in Medicine

Self Cite

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“…Attractive learning makes it easier for students to understand the material comprehensively (Shangguan, Wang, Gong, Guo, & Xu, 2020). On the other hand, Android applications in the coaching sector are also often developed from year to year (Yan et al, 2022). The use of Android applications is centered on the support for performance analysis (Bortolan et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Android-Based Applications in Physical Education and Sports: Systematic Review

Al Ardha,

Nurhasan,

Nur

et al. 2024

Retos

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Android-based applications have always experienced rapid progress and development over time. This development has an impact on various fields or scopes that exist around it. This study aims to comprehensively review the implementation of android applications in physical education and sports. This study used a systematic review method by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 331 articles from the SCOPUS database were screened to determine 10 relevant articles. These articles were then subjected to a systematic review. The results of the systematic review presented the research that has been conducted covering several fields, such as learning media, training, health, and sport biomechanics. The presentation of research data from the review results contributed to the development of research in the field of Android-based applications in physical education and sports. On the other hand, research related to Android-based applications needs to be done intensely in physical education. Keywords: technological development, physical activity, literature review, performance analysis.

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The Sequential Multiple Assignment Randomized Trial for Controlling Infectious Diseases: A Review of Recent Developments

Wang

Chakraborty

2023

Am J Public Health

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Infectious diseases have posed severe threats to public health across the world. Effective prevention and control of infectious diseases in the long term requires adapting interventions based on epidemiological evidence. The sequential multiple assignment randomized trial (SMART) is a multistage randomized trial that can provide valid evidence of when and how to adapt interventions for controlling infectious diseases based on evolving epidemiological evidence. We review recent developments in SMARTs to bring wider attention to the potential benefits of employing SMARTs in constructing effective adaptive interventions for controlling infectious diseases and other threats to public health. We discuss 2 example SMARTs for infectious diseases and summarize recent developments in SMARTs from the varied aspects of design, analysis, cost, and ethics. Public health investigators are encouraged to familiarize themselves with the related materials we discuss and collaborate with experts in SMARTs to translate the methodological developments into preeminent public health research. (Am J Public Health. 2023;113(1):49–59. https://doi.org/10.2105/AJPH.2022.307135 )

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A pilot sequential multiple assignment randomized trial (SMART) protocol for developing an adaptive coaching intervention around a mobile application for athletes to improve carbohydrate periodization behavior

Cited by 3 publications

References 34 publications

A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome

A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome

Analysis of Android-Based Applications in Physical Education and Sports: Systematic Review

The Sequential Multiple Assignment Randomized Trial for Controlling Infectious Diseases: A Review of Recent Developments

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