Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research

Tailoring the treatment of inflammatory rheumatic diseases by a better stratification and characterization of the clinical patient heterogeneity. Findings from a systematic literature review and experts' consensus

Inflammatory rheumatic diseases are different pathologic conditions associated with a deregulated immune response, codified along a spectrum of disorders, with autoinflammatory and autoimmune diseases as two-end phenotypes of this continuum. Despite pathogenic differences, inflammatory rheumatic diseases are commonly managed with a limited number of immunosuppressive drugs, sometimes with partial evidence or transferring physicians' knowledge in different patients. In addition, several randomized clinical trials, enrolling these patients, did not meet the primary pre-established outcomes and these findings could be linked to the underlying molecular diversities along the spectrum of inflammatory rheumatic disorders. In fact, the resulting patient heterogeneity may be driven by differences in underlying molecular pathology also resulting in variable responses to immunosuppressive drugs. Thus, the identification of different clinical subsets may possibly overcome the major obstacles that limit the development more effective therapeutic strategies for these patients with inflammatory rheumatic diseases. This clinical heterogeneity could require a diverse therapeutic management to improve patient outcomes and increase the frequency of clinical remission. Therefore, the importance of better patient stratification and characterization is increasingly pointed out according to the precision medicine principles, also suggesting a new approach for disease treatment. In fact, based on a better proposed patient profiling, clinicians could more appropriately balance the therapeutic management. On these bases, we synthetized and discussed the available literature about the patient profiling in regard to therapy in the context of inflammatory rheumatic diseases, mainly focusing on randomized clinical trials. We provided an overview of the importance of a better stratification and characterization of the clinical heterogeneity of patients with inflammatory rheumatic diseases identifying this point as crucial in improving the management of these patients.

Leveraging Single-Case Experimental Designs to Promote Personalized Psychological Treatment: Step-by-Step Implementation Protocol with Stakeholder Involvement of an Outpatient Clinic for Personalized Psychotherapy

Our objective is to implement a single-case experimental design (SCED) infrastructure in combination with experience-sampling methods (ESM) into the standard diagnostic procedure of a German outpatient research and training clinic. Building on the idea of routine outcome monitoring, the SCED infrastructure introduces intensive longitudinal data collection, individual effectiveness measures, and the opportunity for systematic manipulation to push personalization efforts further. It aims to empower psychotherapists and patients to evaluate their own treatment (idiographic perspective) and to enable researchers to analyze open questions of personalized psychotherapy (nomothetic perspective). Organized around the principles of agile research, we plan to develop, implement, and evaluate the SCED infrastructure in six successive studies with continuous stakeholder involvement: In the project development phase, the business model for the SCED infrastructure is developed that describes its vision in consideration of the context (Study 1). Also, the infrastructure's prototype is specified, encompassing the SCED procedure, ESM protocol, and ESM survey (Study 2 and 3). During the optimization phase, feasibility and acceptability are tested and the infrastructure is adapted accordingly (Study 4). The evaluation phase includes a pilot implementation study to assess implementation outcomes (Study 5), followed by actual implementation using a within-institution A-B design (Study 6). The sustainability phase involves continuous monitoring and improvement. We discuss to what extent the generated data could be used to address current questions of personalized psychotherapy research. Anticipated barriers and limitations during the implementation processes are outlined.

Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine

Power analysis informs a priori planning of behavioral and medical research, including for randomized clinical trials that are nomothetic (i.e., studies designed to infer results to the general population based on interindividual variabilities). Far fewer investigations and resources are available for power analysis of clinical trials that follow an idiographic approach, which emphasizes intraindividual variabilities between baseline (control) phase versus one or more treatment phases. We tested the impact on statistical power to detect treatment outcomes of four idiographic trial design factors that are under researchers' control, assuming a multiple baseline design: sample size, number of observations per participant, proportion of observations in the baseline phase, and competing statistical models (i.e., hierarchical modeling versus piecewise regression). We also tested the impact of four factors that are largely outside of researchers' control: population size, proportion of intraindividual variability due to residual error, treatment effect size, and form of outcomes during the treatment phase (phase jump versus gradual change). Monte Carlo simulations using all combinations of the factors were sampled with replacement from finite populations of 200, 1750, and 3500 participants. Analyses characterized the unique relative impact of each factor individually and all two-factor combinations, holding all others constant. Each factor impacted power, with the greatest impact being from larger treatment effect sizes, followed respectively by more observations per participant, larger samples, less residual variance, and the unexpected improvement in power associated with assigning closer to 50% of observations to the baseline phase. This study's techniques and R package better enable a priori rigorous design of idiographic clinical trials for rare diseases, precision medicine, and other small-sample studies.

Baduanjin exercise for chronic non-specific low back pain: protocol for a series of N-of-1 trials

INTRODUCTION

Chronic non-specific low back pain (CNLBP) is one of the most common health problems worldwide. According to the clinical guideline released by the American College of Physicians, exercise has been recommended for the treatment of chronic LBP. In recent years, traditional Chinese medicine (TCM) is becoming increasingly popular for the management of chronic LBP. Baduanjin exercise is one of the exercise therapies in TCM. N-of-1 trial is a randomised cross-over self-controlled trial suitable for patients with this chronic disease. A series of similar N-of-1 trials can be pooled to estimate the overall and individual therapeutic effects synchronously by hierarchical Bayesian analysis. And N-of-1 trials are considered as a good tool for evaluating the therapeutic effect of TCM. Therefore, this study aims to conduct a series of N-of-1 trials with hierarchical Bayesian analysis for assessing whether Baduanjin exercise is effective and safe for CNLBP.

METHODS AND ANALYSIS

This study conducts a series of N-of-1 trials on Baduanjin exercise for the management of CNLBP. Fifty participants will receive 1-3 treatment cycles. They will be randomised into a Baduanjin exercise or waiting list group for a week during the two periods of each treatment cycle. The primary outcome is the 10-point Visual Analogue Scale. The secondary outcomes include the Oswestry Disability Index, the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire and the Short Form Health Survey 12. Statistical analysis will be conducted with WinBUGS V.1.4.3 software. Overall and individual therapeutic effects will be estimated synchronously by hierarchical Bayesian analysis.

ETHICS AND DISSEMINATION

This study is approved by the Medical Ethics Committee of Tianjin University of TCM (reference number TJUTCM-EC20220005). Our findings will be published in a peer-reviewed journal or international conference.

TRIAL REGISTRATION NUMBER

ChiCTR2200063307.

Protocol for randomized personalized trial for stress management compared to standard of care

Stress is a significant public health burden in the United States, with most Americans reporting unhealthy levels of stress. Stress management techniques include various evidence-based treatments shown to be effective but with heterogeneous treatment responses, indicating a lack of uniform benefits for all individuals. Designed to assess a participant's response to a specific intervention, personalized (N-of-1) trials provide guidance for which treatment (s) work (s) best for the individual. Prior studies examining the effects of mindfulness meditation, yoga, and walking for stress reduction found all three interventions to be associated with significant reductions in self-reported measures of stress. Delivering these treatments using a personalized trial approach has the potential to assist clinicians in identifying the best stress management techniques for individuals with persistently high stress while fostering treatment decisions that consider their personal condition/barriers. This trial will evaluate a personalized approach compared to standard of care for three interventions (guided mindfulness meditation; guided yoga; and guided brisk walking) to manage perceived stress. Participants will respond to daily surveys and wear a Fitbit device for 18 weeks. After a 2-week baseline period, participants in the personalized trial groups will receive 12 weeks of interventions in randomized order, while participants in the standard-of-care group will have access to all interventions for self-directed stress management. After intervention, all participants will undergo 2 weeks of observation, followed by two additional weeks of the stress management intervention of their choosing while continuing outcome measurement. At study completion, all participants will be sent a satisfaction survey. The primary analysis will compare perceived stress levels between the personalized and standard of care arms. The results of this trial will provide further support for the use of personalized designs for managing stress. Clinical Trial Registration: clinicaltrials.gov, NCT05408832. Protocol version: 9/14/2022, 21-0968-MRB.

A Series of Personalized Virtual Light Therapy Interventions for Fatigue: Feasibility Randomized Crossover Trial for N-of-1 Treatment

BACKGROUND

Fatigue is one of the most common symptoms treated in primary care and can lead to deficits in mental health and functioning. Light therapy can be an effective treatment for symptoms of fatigue; however, the feasibility, scalability, and individual-level heterogeneity of light therapy for fatigue are unknown.

OBJECTIVE

This study aimed to evaluate the feasibility, acceptability, and effectiveness of a series of personalized (N-of-1) interventions for the virtual delivery of bright light (BL) therapy and dim light (DL) therapy versus usual care (UC) treatment for fatigue in 60 participants.

METHODS

Participants completed satisfaction surveys comprising the System Usability Scale (SUS) and items assessing satisfaction with the components of the personalized trial. Symptoms of fatigue were measured using the Patient-Reported Outcomes Measurement Information System (PROMIS) daily, PROMIS weekly, and ecological momentary assessment (EMA) questionnaires delivered 3 times daily. Comparisons of fatigue between the BL, DL, and UC treatment periods were conducted using generalized linear mixed model analyses between participants and generalized least squares analyses within individual participants.

RESULTS

Participants rated the usability of the personalized trial as acceptable (average SUS score=78.9, SD 15.6), and 92% (49/53) of those who completed satisfaction surveys stated that they would recommend the trial to others. The levels of fatigue symptoms measured using the PROMIS daily fatigue measure were lower or improved in the BL (B=-1.63, 95% CI -2.63 to -0.63) and DL (B=-1.44, 95% CI -2.50 to -0.38) periods relative to UC. The treatment effects of BL and DL on the PROMIS daily measure varied among participants. Similar findings were demonstrated for the PROMIS weekly and EMA measures of fatigue symptoms.

CONCLUSIONS

The participant scores on the SUS and satisfaction surveys suggest that personalized N-of-1 trials of light therapy for fatigue symptoms are both feasible and acceptable. Both interventions produced significant (P<.05) reductions in participant-reported PROMIS and EMA fatigue symptoms relative to UC. However, the heterogeneity of these treatment effects across participants indicated that the effect of light therapy was not uniform. This heterogeneity along with high ratings of usability and satisfaction support the use of personalized N-of-1 research designs in evaluating the effect of light therapy on fatigue for each patient. Furthermore, the results of this trial provide additional support for the use of a series of personalized N-of-1 research trials.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04707846; https://clinicaltrials.gov/ct2/show/NCT04707846.

A Series of Remote Melatonin Supplement Interventions for Poor Sleep: Protocol for a Feasibility Pilot Study for a Series of Personalized (N-of-1) Trials

BACKGROUND

Poor sleep, defined as short-duration or poor-quality sleep, is a frequently reported condition with many deleterious effects including poorer cognitive functioning, increased accidents, and poorer health. Melatonin has been shown to be an efficacious treatment to manage symptoms of poor sleep. However, the treatment effects of melatonin on sleep can vary greatly between participants. Personalized, or N-of-1, trial designs represent a method for identifying the best treatment for individual participants. Although using N-of-1 trials of melatonin to treat poor sleep is possible, the feasibility, acceptability, and effectiveness of N-of-1 trials using melatonin are unknown. Using the National Institutes of Health Stage Model for Behavioral Intervention Development, a stage IB (intervention refinement, modification, and adaptation and pilot testing) design appeared to be needed to address these feasibility questions.

OBJECTIVE

This trial series evaluates the feasibility, acceptability, and effectiveness of a series of personalized interventions for remote delivery of melatonin dose (3 and 0.5 mg) versus placebo supplements for self-reported poor sleep among 60 participants. The goal of this study is to provide valuable information about implementing remote N-of-1 randomized controlled trials to improve poor sleep.

METHODS

Participants will complete a 2-week baseline followed by six 2-week alternating intervention periods of 3 mg of melatonin, 0.5 mg of melatonin, and placebo. Participants will be randomly assigned to 2 intervention orders. The feasibility and acceptability of the personalized trial approach will be determined with participants' ratings of usability and satisfaction with the remote, personalized intervention delivery system. The effectiveness of the intervention will be measured using participants' self-reported sleep quality and duration and Fitbit tracker-measured sleep duration and efficiency. Additional measures will include ecological momentary assessment measures of fatigue, stress, pain, mood, concentration, and confidence as well as measures of participant adherence to the intervention, use of the Fitbit tracker, and survey data collection.

RESULTS

As of the submission of this protocol, recruitment for this National Institutes of Health stage IB personalized trial series is approximately 78.3% complete (47/60). We expect recruitment and data collection to be finalized by June 2023.

CONCLUSIONS

Evaluating the feasibility, acceptability, and effectiveness of a series of personalized interventions of melatonin will address the longer term aim of this program of research-is integrating N-of-1 trials useful patient care? The personalized trial series results will be published in a peer-reviewed journal and will follow the CONSORT (Consolidated Standards of Reporting Trials) extension for N-of-1 trials (CENT 2015) reporting guidelines. This trial series was approved by the Northwell Health institutional review board.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05349188; https://www.clinicaltrials.gov/study/NCT05349188.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45313.

Protocol of a feasibility study of a virtual personalized (N-of-1) trial for increasing low-intensity physical activity in older adults via habit formation

Background

Personalized interventions that can be delivered remotely are needed to increase physical activity (PA) in older adults to reduce risk of CV disease and mortality. Prior research indicates that Behavioral Change Techniques (BCTs) (e.g., goal setting, self-monitoring, behavioral repetition) can instill a habit for increasing daily walking. However, past interventions relied on between-subject randomized clinical trials, which can only only be informative about response of the hypothetical average person. Personalized trial designs can identify the benefits of an intervention for a specific individual although extended periods are required for collecting frequent measurements within-subject. Advances in remote, virtual technologies (e.g., text messaging, activity trackers), integrated with automatic platforms, can meet these requirements because they capacitate delivery of BCT interventions, and collection of data during daily life without personal contact. This Stage I-b trial is designed test whether a virtual, personalized intervention is feasible and acceptable to older adults, can elicit participant adherence and exhibit preliminary evidence for efficacy.

Methods

A series of up to 60 single-arm, personalized trials, involving no personal contact, will recruit adults, 45-75 years of age, to wear an activity tracker during a 2-week baseline and a 10-week intervention. Five BCT prompts to execute a walking plan will be delivered on a daily basis during the intervention phase. Participants will rate satisfaction with personalized trial components and whether automaticity of the walking plan can be achieved. Step-counts, adherence to the walking plan and self-monitoring of step-count will also be recorded.

Exploring human biology with N-of-1 clinical trials

Studies on humans that exploit contemporary data-intensive, high-throughput 'omic' assay technologies, such as genomics, transcriptomics, proteomics and metabolomics, have unequivocally revealed that humans differ greatly at the molecular level. These differences, which are compounded by each individual's distinct behavioral and environmental exposures, impact individual responses to health interventions such as diet and drugs. Questions about the best way to tailor health interventions to individuals based on their nuanced genomic, physiologic, behavioral, etc. profiles have motivated the current emphasis on 'precision' medicine. This review's purpose is to describe how the design and execution of N-of-1 (or personalized) multivariate clinical trials can advance the field. Such trials focus on individual responses to health interventions from a whole-person perspective, leverage emerging health monitoring technologies, and can be used to address the most relevant questions in the precision medicine era. This includes how to validate biomarkers that may indicate appropriate activity of an intervention as well as how to identify likely beneficial interventions for an individual. We also argue that multivariate N-of-1 and aggregated N-of-1 trials are ideal vehicles for advancing biomedical and translational science in the precision medicine era since the insights gained from them can not only shed light on how to treat or prevent diseases generally, but also provide insight into how to provide real-time care to the very individuals who are seeking attention for their health concerns in the first place.

Footwear and insole design parameters to prevent occurrence and recurrence of neuropathic plantar forefoot ulcers in patients with diabetes: a series of N-of-1 trial study protocol

BACKGROUND

Foot complications occur in conjunction with poorly controlled diabetes. Plantar forefoot ulceration contributes to partial amputation in unstable diabetics, and the risk increases with concomitant neuropathy. Reducing peak plantar forefoot pressure reduces ulcer occurrence and recurrence. Footwear and insoles are used to offload the neuropathic foot, but the success of offloading is dependent on patient adherence. This study aims to determine which design and modification features of footwear and insoles improve forefoot plantar pressure offloading and adherence in people with diabetes and neuropathy.

METHODS

This study, involving a series of N-of-1 trials, included 21 participants who had a history of neuropathic plantar forefoot ulcers. Participants were recruited from two public hospitals and one private podiatry clinic in Sydney, New South Wales, Australia. This trial is non-randomised and unblinded. Participants will be recruited from three sites, including two high-risk foot services and a private podiatry clinic in Sydney, Australia. Mobilemat™ and F-Scan® plantar pressure mapping systems by TekScan® (Boston, USA) will be used to measure barefoot and in-shoe plantar pressures. Participants' self-reports will be used to quantify the wearing period over a certain period of between 2 and 4 weeks during the trial. Participant preference toward footwear, insole design and quality-of-life-related information will be collected and analysed. The descriptive and inferential statistical analyses will be performed using IBM SPSS Statistics (version 27). And the software NVivo (version 12) will be utilised for the qualitative data analysis.

DISCUSSION

This is the first trial assessing footwear and insole interventions in people with diabetes by using a series of N-of-1 trials. Reporting self-declared wearing periods and participants' preferences on footwear style and aesthetics are the important approaches for this trial. Patient-centric device designs are the key to therapeutic outcomes, and this study is designed with that strategy in mind.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000699965p. Registered on June 23, 2020.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Series of virtual light therapy interventions for fatigue: a feasibility pilot study protocol for a series of personalised (N-of-1) trials

INTRODUCTION

Fatigue is one of the most commonly recorded patient symptoms that can result in deficits in aspects of psychomotor functioning, cognition, work performance and mood. Research shows that bright light and dim light therapy may be an efficacious way to reduce symptoms of fatigue. Still, the feasibility, scalability, individual treatment effects and adverse event heterogeneity of these treatments are unknown.

METHODS AND ANALYSIS

The current study evaluates the feasibility, acceptability and effectiveness of a series of personalised (N-of-1) interventions for virtual delivery of bright light therapy and dim light therapy versus usual care treatment for fatigue in 60 participants. We hypothesise that this study will provide valuable information about implementing virtual, N-of-1 randomised controlled trials (RCTs) for fatigue. It will also offer results about determining participants' ratings of usability and satisfaction with the virtual, personalised intervention delivery system; evaluating participants' improvement of fatigue symptoms; and, in the long term, identify ways to integrate N-of-1 light therapy trials into patient care.

ETHICS AND DISSEMINATION

This trial was approved by the Northwell Health Institutional Review Board. The trial results will be published in a peer-reviewed journal. All publications resulting from this series of personalised trials will follow the Consolidated Standards of Reporting Trials extension for N-of-1 trials CENT 2015 reporting guidelines.

REGISTRATION DETAILS

This trial is registered in www.

CLINICALTRIALS

gov (number NCT04707846).

TRIAL REGISTRATION NUMBER

NCT04707846.

N-of-1 trials to facilitate evidence-based deprescribing: Rationale and case study

Deprescribing has emerged as an important aspect of patient-centred medication management but is vastly underutilized in clinical practice. The current narrative review will describe an innovative patient-centred approach to deprescribing-N-of-1 trials. N-of-1 trials involve multiple-period crossover design experiments conducted within individual patients. They enable patients to compare the effects of two or more treatments or, in the case of deprescribing N-of-1 trials, continuation with a current treatment versus no treatment or placebo. N-of-1 trials are distinct from traditional between-patient studies such as parallel-group or crossover designs which provide an average effect across a group of patients and obscure differences between individuals. By generating data on the effect of an intervention for the individual rather than the population, N-of-1 trials can promote therapeutic precision. N-of-1 trials are a particularly appealing strategy to inform deprescribing because they can generate individual-level evidence for deprescribing when evidence is uncertain, and can thus allay patient and physician concerns about discontinuing medications. To illustrate the use of deprescribing N-of-1 trials, we share a case example of an ongoing series of N-of-1 trials that compare maintenance versus deprescribing of beta-blockers in patients with heart failure with preserved ejection fraction. By providing quantifiable data on patient-reported outcomes, promoting personalized pharmacotherapy, and facilitating shared decision making, N-of-1 trials represent a potentially transformative strategy to address polypharmacy.

Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care

The term 'data science' usually refers to the process of extracting value from big data obtained from a large group of individuals. An alternative rendition, which we call personalized data science (Per-DS), aims to collect, analyze, and interpret personal data to inform personal decisions. This article describes the main features of Per-DS, and reviews its current state and future outlook. A Per-DS investigation is of, by, and for an individual, the Per-DS investigator, acting simultaneously as her own investigator, study participant, and beneficiary, and making personalized decisions for study design and implementation. The scope of Per-DS studies may include systematic monitoring of physiological or behavioral patterns, case-crossover studies for symptom triggers, pre-post trials for exposure-outcome relationships, and personalized (N-of-1) trials for effectiveness. Per-DS studies produce personal knowledge generalizable to the individual's future self (thus benefiting herself) rather than knowledge generalizable to an external population (thus benefiting others). This endeavor requires a pivot from data mining or extraction to data gardening, analogous to home gardeners producing food for home consumption-the Per-DS investigator needs to 'cultivate the field' by setting goals, specifying study design, identifying necessary data elements, and assembling instruments and tools for data collection. Then, she can implement the study protocol, harvest her personal data, and mine the data to extract personal knowledge. To facilitate Per-DS studies, Per-DS investigators need support from community-based, scientific, philanthropic, business, and government entities, to develop and deploy resources such as peer forums, mobile apps, 'virtual field guides,' and scientific and regulatory guidance.

The Family of Single-Case Experimental Designs

Single-case experimental designs (SCEDs) represent a family of research designs that use experimental methods to study the effects of treatments on outcomes. The fundamental unit of analysis is the single case-which can be an individual, clinic, or community-ideally with replications of effects within and/or between cases. These designs are flexible and cost-effective and can be used for treatment development, translational research, personalized interventions, and the study of rare diseases and disorders. This article provides a broad overview of the family of single-case experimental designs with corresponding examples, including reversal designs, multiple baseline designs, combined multiple baseline/reversal designs, and integration of single-case designs to identify optimal treatments for individuals into larger randomized controlled trials (RCTs). Personalized N-of-1 trials can be considered a subcategory of SCEDs that overlaps with reversal designs. Relevant issues for each type of design-including comparisons of treatments, design issues such as randomization and blinding, standards for designs, and statistical approaches to complement visual inspection of single-case experimental designs-are also discussed.

Bayesian Models for N-of-1 Trials

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.

Conduct and Implementation of Personalized Trials in Research and Practice

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.

Personalized Feedback for Personalized Trials: Construction of Summary Reports for Participants in a Series of Personalized Trials for Chronic Lower Back Pain

Personalized (N-of-1) trials offer a patient-centered research approach that can provide important clinical information for patients when selecting which treatment options best manage their chronic health concern. Researchers utilizing this approach should present trial results to patients in a clear and understandable manner in order for personalized research trials to be useful to participants. The current study provides participant feedback examples for personalized trial reports using lay summaries and multiple presentation styles from a series of 60 randomized personalized trials examining the effects of massage and yoga versus usual care on chronic lower back pain (CLBP). Researchers generated summary participant reports that describe individual participant results using multiple presentation modalities of data (e.g., visual, written, and auditory) to offer the most appealing style for various participants. The article discusses contents of the participant report as well as participant satisfaction with the personalized summary report, captured using a satisfaction survey administered after study completion. The results from the satisfaction survey in the current study show that participants were generally satisfied with their personalized summary report. Researchers will use feedback from the participants in the current study to refine personalized feedback reports for future studies.

A Series of Virtual Interventions for Chronic Lower Back Pain: A Feasibility Pilot Study for a Series of Personalized (N-of-1) Trials

Chronic lower back pain (CLBP) affects 25% of U.S. adults and is associated with high costs due to physician visits and reduced productivity. Research shows that massage and yoga can be effective nonpharmacological treatments for CLBP, but the feasibility, scalability, individual treatment, and adverse-event heterogeneity of these treatments are unknown. The current study evaluated the feasibility and acceptability of a series of personalized (N-of-1) interventions for virtual delivery of massage and yoga or usual-care treatment for CLBP in 57 participants. We hypothesized that this study would provide valuable information about implementing a virtual, personalized platform for randomized controlled trials of personalized (N-of-1) interventions among individuals with CLBP. The study will do so by determining participants' ratings of usability and satisfaction with the virtual, personalized intervention delivery system and, in the long term, identifying ways to integrate these personalized trials into patient care. Of the 57 participants enrolled, two withdrew from the study and were not eligible to receive the primary outcome assessment. Thirty-seven of the remaining 55 participants (67.3%) completed satisfaction surveys comprising the System Usability Scale (SUS) and items assessing satisfaction with the components of the personalized trial. Participants rated the usability of the personalized trial as excellent (average SUS score = 85.8), 95% were satisfied with the personalized trial overall, and 100% stated they would recommend the trial to others. These results suggest that personalized trials of massage and yoga are highly feasible and acceptable to participants with CLBP.

An Argument in Favor of Deep Brain Stimulation for Uncommon Movement Disorders: The Case for N-of-1 Trials in Holmes Tremor

Deep brain stimulation (DBS) is part of state-of-the-art treatment for medically refractory Parkinson’s disease, essential tremor or primary dystonia. However, there are multiple movement disorders that present after a static brain lesion and that are frequently refractory to medical treatment. Using Holmes tremor (HT) as an example, we discuss the effectiveness of currently available treatments and, performing simulations using a Markov Chain approach, propose that DBS with iterative parameter optimization is expected to be more effective than an approach based on sequential trials of pharmacological agents. Since, in DBS studies for HT, the thalamus is a frequently chosen target, using data from previous studies of lesion connectivity mapping in HT, we compared the connectivity of thalamic and non-thalamic targets with a proxy of the HT network, and found a significantly higher connectivity of thalamic DBS targets in HT. The understanding of brain networks provided by analysis of functional connectivity may thus provide an informed framework for proper surgical targeting of individual patients. Based on these findings, we argue that there is an ethical imperative to at least consider surgical options in patients with uncommon movement disorders, while simultaneously providing consistent information regarding the expected effectiveness and risks, even in a scenario of surgical-risk aversion. An approach based on n-of-1 DBS trials may ultimately significantly improve outcomes while informing on optimal therapeutic targets and parameter settings for HT and other disabling and rare movement disorders.

Brief and Telehealth Acceptance and Commitment Therapy (ACT) Interventions for Stress in Inflammatory Bowel Disease (IBD): A Series of Single Case Experimental Design (SCED) Studies

Psychological intervention targeting distress is now considered an integral component of inflammatory bowel disease (IBD) management. However, significant barriers to access exist which necessitate the development of effective, economic, and accessible brief and remote interventions. Acceptance and commitment therapy (ACT) is a therapy with demonstrated acceptability and a growing evidence base for the treatment of distress in IBD populations. The present paper trialled two brief ACT interventions via randomized multiple baseline designs. Study 1 trialled a single-session ACT intervention (delivered face-to-face and lasting approximately two hours) targeting stress and experiential avoidance, respectively. Participants were seven people with an IBD diagnosis who presented with moderate to extremely severe stress (five females, two males; M age = 39.57, SD = 5.74). The findings of study 1 indicate that a single-session ACT intervention represented an insufficient dosage to reduce stress and experiential avoidance. Study 2 investigated a brief telehealth ACT intervention (delivered via a video conferencing platform and lasting approximately four hours) targeting stress and increased psychological flexibility. Participants (N = 12 people with an IBD diagnosis and mild to extremely severe stress) completed baselines lasting from 21 to 66 days before receiving a two-session ACT telehealth intervention supplemented by a workbook and phone consultation. Approximately half of participants experienced reduced stress, increased engagement in valued action, and increased functioning. Despite shortcomings such as missing data and the context of COVID-19, the present findings suggest that brief ACT interventions in this population may be effective and economic, though further research and replications are necessary.

Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects

Recent research has advanced two degree-of-freedom (DoF), simultaneous, independent and proportional control of hand-wrist prostheses using surface electromyogram signals from remnant muscles as the control input. We evaluated two such regression-based controllers, along with conventional, sequential two-site control with co-contraction mode switching (SeqCon), in box-block, refined-clothespin and door-knob tasks, on 10 able-bodied and 4 limb-absent subjects. Subjects operated a commercial hand and wrist using a socket bypass harness. One 2-DoF controller (DirCon) related the intuitive hand actions of open-close and pronation-supination to the associated prosthesis hand-wrist actions, respectively. The other (MapCon) mapped myoelectrically more distinct, but less intuitive, actions of wrist flexion-extension and ulnar-radial deviation. Each 2-DoF controller was calibrated from separate 90 s calibration contractions. SeqCon performed better statistically than MapCon in the predominantly 1-DoF box-block task (>20 blocks/minute vs. 8-18 blocks/minute, on average). In this task, SeqCon likely benefited from an ability to easily focus on 1-DoF and not inadvertently trigger co-contraction for mode switching. The remaining two tasks require 2-DoFs, and both 2-DoF controllers each performed better (factor of 2-4) than SeqCon. We also compared the use of 12 vs. 6 optimally-selected EMG electrodes as inputs, finding no statistical difference. Overall, we provide further evidence of the benefits of regression-based EMG prosthesis control of 2-DoFs in the hand-wrist.

Efficacy of a Digital Acceptance and Commitment Therapy Intervention for the Improvement of Self-management Behaviors and Psychological Flexibility in Adults With Cardiac Disease: Protocol for a Single Case Experimental Design

BACKGROUND

Research indicates that the management of distress levels in those with cardiac disease is not only important for improving quality of life and functioning but also critical for condition management; adherence to treatment; and, ultimately, disease prognosis and progression. Acceptance and commitment therapy (ACT) has consistently demonstrated positive long-term outcomes across a wide array of conditions, including chronic illness. However, most empirical investigations conducted to date have also involved in-person therapy, which can be difficult to access, particularly for those dealing with the demands of chronic disease.

OBJECTIVE

The objective of our research is to evaluate a digital ACT intervention for improving self-management behaviors and distress levels in those with cardiac conditions.

METHODS

The digital ACT intervention will be delivered via a digital health self-management platform over 6 sessions. This will involve a randomized, multiple baseline, single case experimental design with approximately 3 to 15 adults with cardiac disease. The independent variable for each participant will be the pre-post intervention phase. The dependent variables will be a daily self-report measure of psychological flexibility as well as objective measures of condition self-management (eg, blood pressure readings) and engagement with the app (eg, completing guided mindfulness). One-to-one qualitative interviews will also be conducted to further examine participants' experiences with using the intervention and what factors contribute to or impede successful outcomes.

RESULTS

Participant recruitment and data collection began in October 2021, and it is projected that the study findings will be available for dissemination by spring 2022.

CONCLUSIONS

The findings will be discussed in terms of how a digital ACT intervention can best meet the needs of cardiac patients.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/33783.

Genetically-Guided Medical Nutrition Therapy in Type 2 Diabetes Mellitus and Pre-diabetes: A Series of n-of-1 Superiority Trials

Type 2 diabetes mellitus (T2DM) is a heterogeneous metabolic disorder of multifactorial etiology that includes genetic and dietary influences. By addressing the latter, medical nutrition therapy (MNT) contributes to the management of T2DM or pre-diabetes toward achieving glycaemic control and improved insulin sensitivity. However, the clinical outcomes of MNT vary and may further benefit from personalized nutritional plans that take into consideration genetic variations associated with individual responses to macronutrients. The aim of the present series of n-of-1 trials was to assess the effects of genetically-guided vs. conventional MNT on patients with pre-diabetes or T2DM. A quasi-experimental, cross-over design was adopted in three Caucasian adult men with either diagnosis. Complete diet, bioclinical and anthropometric assessment was performed and a conventional MNT, based on the clinical practice guidelines was applied for 8 weeks. After a week of “wash-out,” a precision MNT was prescribed for an additional 8-week period, based on the genetic characteristics of each patient. Outcomes of interest included changes in body weight (BW), fasting plasma glucose (FPG), and blood pressure (BP). Collectively, the trials indicated improvements in BW, FPG, BP, and glycosylated hemoglobin (HbA1c) following the genetically-guided precision MNT intervention. Moreover, both patients with pre-diabetes experienced remission of the condition. We conclude that improved BW loss and glycemic control can be achieved in patients with pre-diabetes/T2DM, by coupling MNT to their genetic makeup, guiding optimal diet, macronutrient composition, exercise and oral nutrient supplementation in a personalized manner.

The Niche of n-of-1 Trials in Precision Medicine for Weight Loss and Obesity Treatment: Back to the Future

PURPOSE OF REVIEW

The n-of-1 clinical trials are considered the epitome of individualized health care. They are employed to address differences in treatment response and adverse events between patients, in a comparative effectiveness manner, extending beyond the delivery of horizontal recommendations for all.

RECENT FINDINGS

The n-of-1 design has been applied to deliver precision exercise interventions, through eHealth and mHealth technologies. Regarding personalized and precision medical nutrition therapy, few trials have implemented dietary manipulations and one series of n-of-1 trials has applied comprehensive genetic data to improve body weight. With regard to anti-obesity medication, pharmacogenetic data could be applied using the n-of-1 trial design, although none have been implemented yet. The n-of-1 clinical trials consist of the only tool for the delivery of evidence-based, personalized obesity treatment (lifestyle and pharmacotherapy), reducing non-responders, while tailoring the best intervention to each patient, through "trial and error". Their application is expected to improve obesity treatment and mitigate the epidemic.

Topical Cyclosporine in Oral Lichen Planus-A Series of 21 Open-Label, Biphasic, Single-Patient Observations

Topical cyclosporine (CSA) has been reported as an alternative treatment in steroid-refractory oral lichen planus (OLP), but evidence is limited and conflicting. An N-of-1 trial setting could be appropriate to evaluate interindividual differences in treatment response. We studied a series of 21 open-label, biphasic single-patient observations. Patients (15 women, 6 men) with OLP recalcitrant to topical steroids received four weeks of CSA mouth rinse (200 mg/twice daily) followed by four weeks of drug withdrawal. Pain (visual analogue scale (VAS) score), disease extent (physicians’ global assessment (PGA) score) and quality of life (Dermatology Life Quality Index (DLQI) score,) were assessed at baseline (T0), after four weeks of treatment (T1) and after another four weeks without treatment (T2). Median age was 58 years (interquartile range/IQR = 52–67) and median disease duration was 18 months (IQR = 12–44). Median baseline VAS score decreased significantly at T1 (p = 0.0003) and increased at T2 (p = 0.032) (T0 = 5 (IQR = 3–6.5); T1 = 2 (IQR = 0.5–3.4); T2 = 3 (IQR = 2–4.8)). Similarly, median baseline PGA score decreased significantly at T1 (p = 0.001) and increased at T2 (p = 0.007) (T0 = 2 (IQR = 1.3–2.5); T1 = 1 (IQR = 1–2); T2 = 2 (IQR = 1–2)). Median baseline DLQI score also decreased significantly at T1 (p =.027) but did not change at T2 (p = 0.5) (T0 = 2.5 (IQR = 1–5.8); T1 = 1 (IQR = 0–3); T2 = 1 (IQR = 1–4)). CSA responders (n = 16) had significantly higher median baseline VAS scores (5.2 (IQR = 5–6.5)) than nonresponders (n =5) (2 (IQR = 2–3.5) (p = 0.02). In our study, pain, disease extent and quality of life of patients with OLP improved significantly during therapy with low-dose CSA mouth rinse and exacerbated after drug withdrawal. Remarkably, patients with high initial VAS scores seemed to profit most.

N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine-A Review

Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the "evidence puzzle".

Toward more rigorous and informative nutritional epidemiology: The rational space between dismissal and defense of the status quo

To date, nutritional epidemiology has relied heavily on relatively weak methods including simple observational designs and substandard measurements. Despite low internal validity and other sources of bias, claims of causality are made commonly in this literature. Nutritional epidemiology investigations can be improved through greater scientific rigor and adherence to scientific reporting commensurate with research methods used. Some commentators advocate jettisoning nutritional epidemiology entirely, perhaps believing improvements are impossible. Still others support only normative refinements. But neither abolition nor minor tweaks are appropriate. Nutritional epidemiology, in its present state, offers utility, yet also needs marked, reformational renovation. Changing the status quo will require ongoing, unflinching scrutiny of research questions, practices, and reporting-and a willingness to admit that "good enough" is no longer good enough. As such, a workshop entitled "Toward more rigorous and informative nutritional epidemiology: the rational space between dismissal and defense of the status quo" was held from July 15 to August 14, 2020. This virtual symposium focused on: (1) Stronger Designs, (2) Stronger Measurement, (3) Stronger Analyses, and (4) Stronger Execution and Reporting. Participants from several leading academic institutions explored existing, evolving, and new better practices, tools, and techniques to collaboratively advance specific recommendations for strengthening nutritional epidemiology.

A Useful and Sustainable Role for N-of-1 Trials in the Healthcare Ecosystem

Clinicians and patients often try a treatment for an initial period to inform longer‐term therapeutic decisions. A more rigorous approach involves N‐of‐1 trials. In these single‐patient crossover trials, typically conducted in patients with chronic conditions, individual patients are given candidate treatments in a double‐blinded, random sequence of alternating periods to determine the most effective treatment for that patient. However, to date, these trials are rarely done outside of research settings and have not been integrated into general care where they could offer substantial benefit. Designating this classical, N‐of‐1 trial design as type 1, there also are new and evolving uses of N‐of‐1 trials that we designate as type 2. In these, rather than focusing on optimizing treatment for chronic diseases when multiple approved choices are available, as is typical of type 1, a type 2 N‐of‐1 trial tests treatments designed specifically for a patient with a rare disease, to facilitate personalized medicine. While the aims differ, both types face the challenge of collecting individual‐patient evidence using standard, trusted, widely accepted methods. To fulfill their potential for producing both clinical and research benefits, and to be available for wide use, N‐of‐1 trials will have to fit into the current healthcare ecosystem. This will require generalizable and accepted processes, platforms, methods, and standards. This also will require sustainable value‐based arrangements among key stakeholders. In this article, we review opportunities, stakeholders, issues, and possible approaches that could support general use of N‐of‐1 trials and deliver benefit to patients and the healthcare enterprise. To assess and expand the benefits of N‐of‐1 trials, we propose multistakeholder meetings, workshops, and the generation of methods, standards, and platforms that would support wider availability and the value of N‐of‐1 trials.

Using population crossover trials to improve the decision process regarding treatment individualization in N-of-1 trials

Healthcare researchers are showing renewed interest in the utilization of N-of-1 clinical trials for the individualization of pharmacological treatments. Here, we propose a frequentist approach to conducting treatment individualization in N-of-1 trials that we call "partial empirical Bayes." We infer the most beneficial treatment for the patient from combining the information provided by a previously conducted population crossover trial with individual patient data. We propose a method for estimating an optimal number of treatment cycles and investigate the statistical conditions under which N-of-1 trials are more beneficial than traditional clinical approaches. We represent the patient population with a random-coefficients linear model and calculate estimators of posttreatment individual disease severities. We show the estimators' consistency under the most common N-of-1 designs and examine their prediction errors and performance with small numbers of patient's responses. We demonstrate by simulating new patients that our approach is equivalent or superior to both the common clinical practice of recommending the on-average best treatment for all patients and the common individualization method that simply compares average responses to the tested treatments. We conclude that some situations exist in which individualization with N-of-1 trials is highly beneficial while other situations exist in which individualization may be unfruitful.

PRAgmatic Clinical Trial Design of Integrative MediCinE (PRACTICE): A Focus Group Series and Systematic Review on Trials of Diabetes and Kidney Disease

Background: Pragmatic trials inform clinical decision with better generalizability and can bridge different streams of medicine. This study collated the expectations regarding pragmatic trial design of integrative medicine (IM) for diabetes and kidney diseases among patients and physicians. Dissonance between users' perspective and existing pragmatic trial design was identified. The association between risk of bias and pragmatism of study design was assessed. Method: A 10-group semi-structured focus group interview series [21 patients, 14 conventional medicine (ConM) and 15 Chinese medicine (CM) physicians] were purposively sampled from private and public clinics in Hong Kong. Perspectives were qualitatively analyzed by constant comparative method. A systematic search of four databases was performed to identify existing IM pragmatic clinical trials in diabetes or kidney disease. Primary outcomes were the pragmatism, risk of bias, and rationale of the study design. Risk of bias and pragmatism were assessed based on Cochrane risk-of-bias tool and PRECIS-2, respectively. The correlation between risk of bias and pragmatism was assessed by regression models with sensitivity analyses. Results: The subtheme on the motivation to seek IM service was analyzed, covering the perceived limitation of ConM effect, perceived benefits of IM service, and assessment of IM effectiveness. Patients expected IM service to retard disease progression, stabilize concomitant drug dosage, and reduce potential side effects associated with ConM. In the systematic review, 25 studies from six countries were included covering CM, Korean medicine, Ayurvedic medicine, and western herbal medicine. Existing study designs did not include a detailed assessment of concomitant drug change and adverse events. Majority of studies either recruited a non-representative proportion of patients as traditional, complementary, and integrative medicine (TCIM) diagnosis was used as inclusion criteria, or not reflecting the real-world practice of TCIM by completely dropping TCIM diagnosis in the trial design. Consultation follow-up frequency is the least pragmatic domain. Increase in pragmatism did not associate with a higher risk of bias. Conclusion: Existing IM pragmatic trial design does not match the patients' expectation in the analysis of incident concomitant drug change and adverse events. A two-layer design incorporating TCIM diagnosis as a stratification factor maximizes the generalizability of evidence and real-world translation of both ConM and TCIM.

Nutrition to Optimise Human Health-How to Obtain Physiological Substantiation?

Demonstrating in an unambiguous manner that a diet, let alone a single product, 'optimizes' health, presents an enormous challenge. The least complicated is when the starting situation is clearly suboptimal, like with nutritional deficiencies, malnutrition, unfavourable lifestyle, or due to disease or ageing. Here, desired improvements and intervention strategies may to some extent be clear. However, even then situations require approaches that take into account interactions between nutrients and other factors, complex dose-effect relationships etc. More challenging is to substantiate that a diet or a specific product optimizes health in the general population, which comes down to achieve perceived, 'non-medical' or future health benefits in predominantly healthy persons. Presumed underlying mechanisms involve effects of non-nutritional components with subtle and slowly occurring physiological effects that may be difficult to translate into measurable outcomes. Most promising strategies combine classical physiological concepts with those of 'multi-omics' and systems biology. Resilience-the ability to maintain or regain homeostasis in response to stressors-is often used as proxy for a particular health domain. Next to this, quantifying health requires personalized strategies, measurements preferably carried out remotely, real-time and in a normal living environment, and experimental designs other than randomized controlled trials (RCTs), for example N-of-1 trials.

Experiment in a Box (XB): An Interactive Technology Framework for Sustainable Health Practices

This paper presents the Experiment in a Box (XB) framework to support interactive technology design for building health skills. The XB provides a suite of experiments—time-limited, loosely structured evaluations of health heuristics for a user-as-experimenter to select from and then test in order to determine that heuristic’s efficacy, and to explore how it might be incorporated into the person’s life and when necessary, to support their health and wellbeing. The approach leverages self-determination theory to support user autonomy and competence to build actionable, personal health knowledge skills and practice (KSP). In the three studies of XB presented, we show that with even the short engagement of an XB experiment, participants develop health practices from the interventions that are still in use long after the intervention is finished. To situate the XB approach relative to other work around health practices in HCI in particular, we contribute two design continua for this design space: insourcing to outsourcing and habits to heuristics. From this analysis, we demonstrate that XB is situated in a largely under-explored area for interactive health interventions: the insourcing and heuristic oriented area of the design space. Overall, the work offers a new scaffolding, the XB Framework, to instantiate time-limited interactive technology interventions to support building KSP that can thrive in that person, significantly both post-interventions, and independent of that technology.

A Series of N-of-1 Trials for Traditional Chinese Medicine Using a Bayesian Method: Study Rationale and Protocol

Background. Our previous studies showed that N-of-1 trials could reflect the individualized characteristics of traditional Chinese medicine (TCM) syndrome differentiation with good feasibility, but the sensitivity was low. Therefore, this study will use hierarchical Bayesian statistical method to improve the sensitivity and applicability of N-of-1 trials of TCM. Methods/Design. This is a randomized, double-blind, placebo-controlled, three-pair crossover trial for a single subject, including 4-8 weeks of run-in period and 24 weeks of formal trial. In this study, we will recruit a total of 30 participants who are in the stable stage of bronchiectasis. The trial will be divided into three pairs (cycles), and one cycle contains two observation periods. The medications will be taken for three weeks and stopped for one week in the last week of each observation period. The order of syndrome differentiation decoction and placebo will be randomly determined. Patient self-reported symptom score (on a 7-point Likert scale) is the primary outcome. Discussion. Some confounding variables (such as TCM syndrome type and potential carryover effect of TCM) will be introduced into hierarchical Bayesian statistical method to improve the sensitivity and applicability of N-of-1 trials of TCM, and the use of prior available information (e.g., "borrowing from strength" of previous trial results) within the analysis may improve the sensitivity of the results of a series of N-of-1 trials, from both the individual and population level to study the efficacy of TCM syndrome differentiation. It is the exploration of improving the objective evaluation method of the clinical efficacy of TCM and may provide reference value for clinical trials of TCM in other chronic diseases. This trial is registered with ClinicalTrials.gov (ID: NCT04601792).

Leveraging applied behavior analysis research and practice in the service of public health

Human behavior plays a central role in all domains of public health. Applied behavior analysis (ABA) research and practice can contribute to public health solutions that directly address human behavior. In this paper, we describe the field of public health, identify points of interaction between public health and ABA, summarize what ABA research has already contributed, and provide several recommendations for how ABA research and practice could continue to promote public health outcomes. A clearer focus on behavior and widespread adoption of research designs and interventions informed by the ABA literature could lead to better public health outcomes. Reciprocally, better integration of public health goals and strategies into ABA research, harnessing of technology, and more collaboration would help diversify and disseminate our applied science and could yield more effective and scalable interventions to prevent and treat public health problems.

Is the N-of-1 method applicable in bodywork research? Lessons learned using a trial as a methodological pilot

N-of-1 trial designs have rarely been used in bodywork research. Using a recent trial as a methodological pilot, critical issues related to the applicability of N-of-1 trials to bodywork are discussed. These include the issues of carry-over effects, bias-controlling approaches and statistical analysis. The discussion highlights the importance of mixed methods and draws some suggestions for a future research program. N-of-1 trials could be used to provide insights about some essential elements of bodywork modalities and their effectiveness.

A Bayesian-bandit adaptive design for N-of-1 clinical trials

N-of-1 trials, which are randomized, double-blinded, controlled, multiperiod, crossover trials on a single subject, have been applied to determine the heterogeneity of the individual's treatment effect in precision medicine settings. An aggregated N-of-1 design, which can estimate the population effect from these individual trials, is a pragmatic alternative when a randomized controlled trial (RCT) is infeasible. We propose a Bayesian adaptive design for both the individual and aggregated N-of-1 trials using a multiarmed bandit framework that is estimated via efficient Markov chain Monte Carlo. A Bayesian hierarchical structure is used to jointly model the individual and population treatment effects. Our proposed adaptive trial design is based on Thompson sampling, which randomly allocates individuals to treatments based on the Bayesian posterior probability of each treatment being optimal. While we use a subject-specific treatment effect and Bayesian posterior probability estimates to determine an individual's treatment allocation, our hierarchical model facilitates these individual estimates to borrow strength from the population estimates via shrinkage to the population mean. We present the design's operating characteristics and performance via a simulation study motivated by a recently completed N-of-1 clinical trial. We demonstrate that from a patient-centered perspective, subjects are likely to benefit from our adaptive design, in particular, for those individuals that deviate from the overall population effect.

Investigating immediacy in multiple-phase-change single-case experimental designs using a Bayesian unknown change-points model

Immediacy is one of the necessary criteria to show strong evidence of treatment effect in single-case experimental designs (SCEDs). However, with the exception of Natesan and Hedges (2017), no inferential statistical tool has been used to demonstrate or quantify it until now. We investigate and quantify immediacy by treating the change points between the baseline and treatment phases as unknown. We extend Natesan and Hedges' work to multiple-phase-change (e.g. ABAB) designs using a variational Bayesian (VB) unknown change-point model. VB was used instead of Markov chain Monte Carlo methods (MCMC), because MCMC cannot be used effectively to determine multiple change points. Combined and individual probabilities of correctly estimating the change points were used as indicators of the algorithm's accuracy. Unlike MCMC in the Natesan and Hedges (2017) study, the VB method was able to recover the change points with high accuracy even for short time series and in only a fraction of the time for all time-series lengths. We illustrate the algorithm with 13 real data sets. Additionally, we discuss the advantages of the unknown change-point approach, and the Bayesian and variational Bayesian estimation for SCEDs.

Participatory co-design of patient-reported outcome indicators and N-of-1 evaluation of a dressing glove for Epidermolysis bullosa

OBJECTIVE

In autosomal recessive dystrophic Epidermolysis bullosa, repeat blistering results in finger webbing and severe contractures of the hands. The aim of this study was to codesign patient-reported outcome indicators for hand therapy with patients, carers and clinicians, and use these to proof-of-concept test a novel dressing glove for recessive dystrophic Epidermolysis bullosa, with cost analysis.

METHOD

Qualitative interviews and focus groups with patients and carers generated content for the indicators. Validity and reliability were established through expert review, piloting and consensus between patients, carers and clinicians. The indicators were self-reported by patients before and while wearing the dressing glove in an N-of-1 study. Time for dressing changes and use of conventional products were also self-reported.

RESULTS

A total of 11 indicators were initially generated from the thematic analysis. Expert review, piloting and consensus involved six patients, five carers and eight clinicians (total n=19). Participants agreed 14 indicators, covering hand skin condition (n=4), webbing between the digits (n=4), experiences of wearing and changing dressings (n=2), hand function (n=2), wrist function (n=1) and hand pain (n=1). In Phase 3, 12 patients scored indicators before wearing the gloves and four patients completed scoring while wearing the gloves. Statistically significant improvements between pre-glove and with-glove periods were found for most participants' experience scores. Skin appearance also improved for most participants.

CONCLUSIONS

The indicators generated useful data, differentiation between scores and participants demonstrating proof-of-concept for patients with recessive dystrophic Epidermolysis bullosa who could wear the dressing gloves. The indicators are being used in routine practice, supporting clinical follow up, commercialisation and regulatory governance of the dressing glove.

The Path Forward for Digital Measures: Suppressing the Desire to Compare Apples and Pineapples

Digital measures are becoming more prevalent in clinical development. Methods for robust evaluation are increasingly well defined, yet the primary barrier for digital measures to transition beyond exploratory usage often relies on a comparison to the existing standards. This article focuses on how researchers should approach the complex issue of comparing across assessment modalities. We discuss comparisons of subjective versus objective assessments, or performance-based versus behavioral measures, and we pay particular attention to the situation where the expected association may be poor or nonlinear. We propose that, rather than seeking to replace the standard, research should focus on a structured understanding of how the new measure augments established assessments, with the ultimate goal of developing a more complete understanding of what is meaningful to patients.

Application of n-of-1 Clinical Trials in Personalized Nutrition Research: A Trial Protocol for Westlake N-of-1 Trials for Macronutrient Intake (WE-MACNUTR)

Personalized dietary recommendations can help with more effective disease prevention. This study aims to investigate the individual postprandial glucose response to diets with diverse macronutrient proportions at both the individual level and population level, and explore the potential of the novel single-patient (n-of-1) trial for personalization of diet. Secondary outcomes include individual phenotypic responses and the effects of dietary ingredients on the composition of gut microbiota. Westlake N-of-1 Trials for Macronutrient Intake is a multiple crossover feeding trial consisting of 3 successive 12-d dietary intervention pairs including a 6-d washout period before each 6-d isocaloric dietary intervention: a 6-d high-fat, low-carbohydrate diet, and a 6-d low-fat, high-carbohydrate diet. The results will help provide personalized dietary recommendations for macronutrients in terms of postprandial blood glucose responses. The proposed n-of-1 trial methods could help in optimizing individual health and advancing health care. This trial was registered with clinicaltrials.gov (NCT04125602).

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

Although group-level evidence supports the use of behavioral interventions to enhance cognitive and emotional well-being, different interventions may be more acceptable or effective for different people. N-of-1 trials are single-patient crossover trials designed to estimate treatment effectiveness in a single patient. We designed a mobile health (mHealth) supported N-of-1 trial platform permitting US adult volunteers to conduct their own 30-day self-experiments testing a behavioral intervention of their choice (deep breathing/meditation, gratitude journaling, physical activity, or helpful acts) on daily measurements of stress, focus, and happiness. We assessed uptake of the study, perceived usability of the N-of-1 trial system, and influence of results (both reported and perceived) on enthusiasm for the chosen intervention (defined as perceived helpfulness of the chosen intervention and intent to continue performing the intervention in the future). Following a social media and public radio campaign, 447 adults enrolled in the study and 259 completed the post-study survey. Most were highly educated. Perceived system usability was high (mean scale score 4.35/5.0, SD 0.57). Enthusiasm for the chosen intervention was greater among those with higher pre-study expectations that the activity would be beneficial for them (p < 0.001), those who obtained more positive N-of-1 results (as directly reported to participants) (p < 0.001), and those who interpreted their N-of-1 study results more positively (p < 0.001). However, reported results did not significantly influence enthusiasm after controlling for participants' interpretations. The interaction between pre-study expectation of benefit and N-of-1 results interpretation was significant (p < 0.001), such that those with the lowest starting pre-study expectations reported greater intervention enthusiasm when provided with results they interpreted as positive. We conclude that N-of-1 behavioral trials can be appealing to a broad albeit highly educated and mostly female audience, that usability was acceptable, and that N-of-1 behavioral trials may have the greatest utility among those most skeptical of the intervention to begin with.

Individualising care in severe bronchopulmonary dysplasia: a series of N-of-1 trials comparing transpyloric and gastric feeding

OBJECTIVE

Compare rates of hypoxaemia during transpyloric and gastric feedings in very preterm infants with severe bronchopulmonary dysplasia.

DESIGN

N-of-1 multiple crossover trials with individual patient and pooled data analyses.

SETTING

Level IV intensive care nursery.

PATIENTS

Infants receiving positive airway pressure between 36 and 55 weeks postmenstrual age were enrolled between December 2014-July 2016.

INTERVENTION

N-of-1 trial consisting of two blocks, each with a 4-day gastric and 4-day transpyloric feeding period assigned in random order.

MAIN OUTCOME MEASURES

The primary outcome was the frequency of daily intermittent hypoxaemic events (SpO₂ ≤80% lasting 10-180 s). Secondary outcomes included the daily proportion of time with an SpO₂ ≤80% and mean daily fraction of inspired oxygen.

RESULTS

Of 15 infants, 13 completed the trial and 2 stopped early for transient worsening in respiratory status during gastric feedings. In the intention-to-treat analyses, transpyloric feedings resulted in increased rates of intermittent hypoxaemia in five infants, greater time per day in hypoxaemia in three infants and more supplemental oxygen use in three infants. One infant received more supplemental oxygen during gastric feedings. The remaining study outcomes were similar between the feeding routes in all other infants. Pooling all data, transpyloric feedings resulted in a higher frequency of intermittent hypoxaemic events (median 7.5/day (IQR 1-23.5) vs 3/day (1-11); adjusted incidence rate ratio 1.8, 95% CI 1.3 to 2.5) and a greater proportion of daily hypoxaemia time (median 0.8% (IQR 0.1-2.3) vs 0.4% (0.07-1.8); adjusted mean difference 1.6, 95% CI 1.1 to 2.5).

CONCLUSIONS

Transpyloric compared with gastric feedings modestly increased rates of hypoxaemia among study participants.

TRIAL REGISTRATION NUMBER

NCT02142621.

Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey

OBJECTIVE

To describe individual patient preferences for Personalised Trials and to identify factors and conditions associated with patient preferences.

DESIGN

Each participant was presented with 18 conjoint questions via an online survey. Each question provided two choices of Personalised Trials that were defined by up to eight attributes, including treatment types, clinician involvement, study logistics and trial burden on a patient.

SETTING

Online survey of adults with at least two common chronic conditions in the USA.

PARTICIPANTS

A nationally representative sample of 501 individuals were recruited from the Chronic Illness Panel by Harris Poll Online. Participants were recruited from several sources, including emails, social media and telephone recruitment of the target population.

MAIN OUTCOME MEASURES

The choice of Personalised Trial design that the participant preferred with each conjoint question.

RESULTS

There was large variability in participants' preferences for the design of Personalised Trials. On average, they preferred certain attributes, such as a short time commitment and no cost. Notably, a population-level analysis correctly predicted 62% of the conjoint responses. An empirical Bayesian analysis of the conjoint data, which supported the estimation of individual-level preferences, improved the accuracy to 86%. Based on estimates of individual-level preferences, patients with chronic pain preferred a long study duration (p≤0.001). Asthma patients were less averse to participation burden in terms of data-collection frequency than patients with other conditions (p=0.002). Patients with hypertension were more cost-sensitive (p<0.001).

CONCLUSION

These analyses provide a framework for elucidating individual-level preferences when implementing novel patient-centred interventions. The data showed that patient preference in Personalised Trials is highly variable, suggesting that individual differences must be accounted for when marketing Personalised Trials. These results have implications for advancing precise interventions in Personalised Trials by indicating when rigorous scientific principles, such as frequent monitoring, is feasible in a substantial subset of patients.

Treatment Burst Data Points and Single Case Design Studies: A Bayesian N-of-1 Analysis for Estimating Treatment Effect Size

Single-case experimental designs (SCED) evaluate treatment effects for each participant, but it is difficult to aggregate and quantify treatment effects across SCED participants receiving the same type of treatment. We applied Bayesian analytic procedures to SCED data aggregated across participants that have previously only been applied to large-N and group design studies of treatment effect sizes. For the current study, we defined transient elevated treatment data points as (1) above the range of the last five baseline sessions during the first three sessions of treatment (i.e., extinction burst); (2) within or above the range of baseline after the first three treatment sessions (i.e., recurrence burst); or (3) thinning phase data points above the last three prethinning treatment data points (i.e., thinning burst). Results indicated that the treatment effect sizes remained large regardless of controlling for transient elevated treatment data points. Finally, we examined the effects of reinforcer schedule thinning on estimates of treatment effect size. Results indicated a moderate negative impact of schedule thinning on treatment effect size with a 16% decrease in effect size. Recommendations for research and practice are provided, and the utility of using Bayesian analysis in single-case experimental designs is discussed.