Feasibility, Acceptability, and Influence of mHealth-Supported N-of-1 Trials for Enhanced Cognitive and Emotional Well-Being in US Volunteers

Kravitz, Richard L.; Aguilera, Adrián; Chen, Elaine J.; Choi, Yong K.; Hekler, Eric B.; Karr, Chris; Kim, Katherine; Phatak, Sayali S.; Sarkar, Sayantani; Schueller, Stephen M.; Sim, Ida; Yang, Jiabei; Schmid, Christopher H.

doi:10.3389/fpubh.2020.00260

Cited by 12 publications

(14 citation statements)

References 35 publications

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“…Karkar et al have demonstrated how smart technology might be used by patients to experiment and test individual dietary triggers for irritable bowel disease [ 27 ]. Kravitz et al demonstrated it is possible to recruit a large (if somewhat biased) population (n = 447) to conduct their own cross-over N-of-1 trials to test treatments to enhance cognitive and emotional wellbeing [ 42 ]. Interestingly, those participants with the lowest expectations of the treatment experienced the greatest gains [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Nutrition-Related N-of-1 Studies Warrant Further Research to Provide Evidence for Dietitians to Practice Personalized (Precision) Medical Nutrition Therapy: A Systematic Review

et al. 2023

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N-of-1 trials provide a higher level of evidence than randomized controlled trials for determining which treatment works best for an individual, and the design readily accommodates testing of personalized nutrition. The aim of this systematic review was to synthesize nutrition-related studies using an N-of-1 design. The inclusion criterion was adult participants; the intervention/exposure was any nutrient, food, beverage, or dietary pattern; the comparators were baseline values, a control condition untreated or placebo, or an alternate treatment, alongside any outcomes such as changes in diet, body weight, biochemical outcomes, symptoms, quality of life, or a disease outcome resulting from differences in nutritional conditions. The information sources used were Medline, Embase, Scopus, Cochrane Central, and PsychInfo. The quality of study reporting was assessed using the Consort Extension for N-of-1 trials (CENT) statement or the STrengthening Reporting of OBservational Studies in Epidemiology (STROBE) guidelines, as appropriate. From 211 articles screened, a total of 7 studies were included and were conducted in 5 countries with a total of 83 participants. The conditions studied included prediabetes, diabetes, irritable bowel syndrome, weight management, and investigation of the effect of diet in healthy people. The quality of reporting was mostly adequate, and dietary assessment quality varied from poor to good. The evidence base is small, but served to illustrate the main characteristics of N-of-1 study designs and considerations for moving research forward in the era of personalized medical nutrition therapy.

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Section: Discussionmentioning

confidence: 99%

Nutrition-Related N-of-1 Studies Warrant Further Research to Provide Evidence for Dietitians to Practice Personalized (Precision) Medical Nutrition Therapy: A Systematic Review

et al. 2023

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“…Another way to reduce costs and barriers is to make it easier for individuals to find relevant trials (or to design their own), complete enrollment procedures, and provide meaningful informed consent. A number of electronic platforms, many created for use on mobile devices, have been used or are in development (Barr et al, 2015;Konigorski et al, 2020;(Kravitz et al, 2020). As point out, however, For clinicians interested in embedding N-of-1 trials in their clinical practice, a personalized trial platform needs to be developed that allows users to customize trial designs according to the use case.…”

Section: Reducing Costs and Burdensmentioning

confidence: 99%

Conduct and Implementation of Personalized Trials in Research and Practice

Kravitz

Duan

2022

Harvard Data Science Review

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The mainstay of evidence development in medicine is the parallel-group randomized controlled trial (RCT), which generates estimates of treatment efficacy or effectiveness for the average person in the trial. In contrast, personalized trials (sometimes referred to as 'single-person trials' or 'N-of-1 trials') assess the comparative effectiveness of two or more treatments in a single individual. These single-subject, randomized crossover trials have been used in a scattershot fashion in medicine for over 40 years but have not been widely adopted.An important barrier is the paucity of strong evidence that personalized trials improve outcomes. However, the principal impediment may have less to do with proof of efficacy than with practical aspects of design and implementation. These include decisions about treatment regimen flexibility, blinding, and washout periods as well as organizational, clinician, and patient-level challenges. After reviewing the essential elements of personalized trials, this article addresses these speed bumps and fundamentally asks, 'Why have personalized trials not been more widely adopted, and how can they be made more readily deployable and useful?' The article concludes by suggesting ways in which emerging technologies and approaches promise to overcome existing barriers and open promising vistas for the next generation of personalized-trial researchers and practitioners.

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“…Successful implementations of N-of-1 trials using mobile applications are becoming more common Kaplan et al, 2022;Kravitz et al, 2018Kravitz et al, , 2020Marcus et al, 2022). The display in Figure 3 was generated on a mobile phone using an application developed specially for the I-STOP-AFib study.…”

Section: Practical Issuesmentioning

confidence: 99%

Bayesian Models for N-of-1 Trials

Schmid

Yang

2022

Harvard Data Science Review

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We describe Bayesian models for data from N-of-1 trials, reviewing both the basics of Bayesian inference and applications to data from single trials and collections of trials sharing the same research questions and data structures. Bayesian inference is natural for drawing inferences from N-of-1 trials because it can incorporate external and subjective information to supplement trial data as well as give straightforward interpretations of posterior probabilities as an individual's state of knowledge about their own condition after their trial. Bayesian models are also easily augmented to incorporate specific characteristics of N-of-1 data such as trend, carryover, and autocorrelation and offer flexibility of implementation. Combining data from multiple N-of-1 trials using Bayesian multilevel models leads naturally to inferences about population and subgroup parameters such as average treatment effects and treatment effect heterogeneity and to improved inferences about individual parameters. Data from a trial comparing different diets for treating children with inflammatory bowel disease are used to illustrate the models and inferences that may be drawn. The analysis shows that certain diets were better on average at reducing pain, but that benefits were restricted to a subset of patients and that withdrawal from the study was a good marker for lack of benefit.

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Feasibility, Acceptability, and Influence of mHealth-Supported N-of-1 Trials for Enhanced Cognitive and Emotional Well-Being in US Volunteers

Cited by 12 publications

References 35 publications

Nutrition-Related N-of-1 Studies Warrant Further Research to Provide Evidence for Dietitians to Practice Personalized (Precision) Medical Nutrition Therapy: A Systematic Review

Nutrition-Related N-of-1 Studies Warrant Further Research to Provide Evidence for Dietitians to Practice Personalized (Precision) Medical Nutrition Therapy: A Systematic Review

Conduct and Implementation of Personalized Trials in Research and Practice

Bayesian Models for N-of-1 Trials

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