Marketing therapeutic precision: Potential facilitators and barriers to adoption of n-of-1 trials

A qualitative study on the facilitators and barriers to adopting the N-of-1 trial methodology as part of clinical practice: potential versus implementation challenges

PURPOSE

To investigate opinions among healthcare stakeholders whether implementation of the N-of-1 trial approach in clinical practice is a feasible way to optimize evidence-based treatment results for unique patients.

METHODS

We interviewed clinicians, researchers, and a patient advocate (n = 13) with an interest in or experience with N-of-1 trials on the following topics: experience with N-of-1, measurement, validity and reliability, informally gathered data usability, and influence on physician-patient relationship. Interviews were analysed using qualitative, thematic analysis.

RESULTS

The N-of-1 approach has the potential to shift the current healthcare system towards embracing personalized medicine. However, its application in clinical practice carries significant challenges in terms of logistics, time investment and acceptability. New skills will be required from patients and healthcare providers, which may alter the patient-physician relationship. The rise of consumer technology enabling self-measurement may leverage the uptake of N-of-1 approaches in clinical practice.

CONCLUSIONS

There is a strong belief that the N-of-1 approach has the potential to play a prominent role in transitioning the current healthcare system towards embracing personalized medicine. However, there are many barriers deeply ingrained in our healthcare system that hamper the uptake of the N-of-1 approach, making it momentarily only interesting for research purposes.

The development of a set of key points to aid clinicians and researchers in designing and conducting n-of-1 trials

INTRODUCTION

n-of-1 trials are undertaken to optimise the evaluation of health technologies in individual patients. They involve a single patient receiving treatments, both interventional and control, consecutively over set periods of time, the order of which is decided at random. Although n-of-1 trials are undertaken in medical research it could be argued they have the utility to be undertaken more frequently. We undertook the National Institute for Health Research (NIHR) commissioned DIAMOND (Development of generalisable methodology for n-of-1 trials delivery for very low volume treatments) project to develop key points to assist clinicians and researchers in designing and conducting n-of-1 trials.

METHODS

The key points were developed by undertaking a stakeholder workshop, followed by a discussion within the study team and then a stakeholder dissemination and feedback event. The stakeholder workshop sought to gain the perspectives of a variety of stakeholders (including clinicians, researchers and patient representatives) on the design and use of n-of-1 trials. A discussion between the study team was held to reflect on the workshop and draft the key points. Lastly, the stakeholders from the workshop were invited to a dissemination and feedback session where the proposed key points were presented and their feedback gained.

RESULTS

A set of 22 key points were developed based on the insights from the workshop and subsequent discussions. They provide guidance on when an n-of-1 trial might be a viable or appropriate study design and discuss key decisions involved in the design of n-of-1 trials, including determining an appropriate number of treatment periods and cycles, the choice of comparator, recommended approaches to randomisation and blinding, the use of washout periods and approaches to analysis.

CONCLUSIONS

The key points developed in the project will support clinical researchers to understand key considerations when designing n-of-1 trials. It is hoped they will support the wider implementation of the study design.

Individual Treatment Trials—Do Experts Know and Use This Option to Improve the Treatability of Mucopolysaccharidosis?

Mucopolysaccharidoses (MPS) are a group of rare, heterogeneous, lysosomal storage disorders. Patients show a broad spectrum of clinical features with a substantial unmet medical need. Individual treatment trials (ITTs) might be a valid, time- and cost-efficient way to facilitate personalized medicine in the sense of drug repurposing in MPS. However, this treatment option has so far hardly been used—at least hardly been reported or published. Therefore, we aimed to investigate the awareness and utilization of ITTs among MPS clinicians, as well as the potential challenges and innovative approaches to overcome key hurdles, by using an international expert survey on ITTs, namely, ESITT. Although 74% (20/27) were familiar with the concept of ITTs, only 37% (10/27) ever used it, and subsequently only 15% (2/16) published their results. The indicated hurdles of ITTs in MPS were mainly the lack of time and know-how. An evidence-based tool, which provides resources and expertise needed for high-quality ITTs, was highly appreciated by the vast majority (89%; 23/26). The ESITT highlights a serious deficiency of ITT implementation in MPS—a promising option to improve its treatability. Furthermore, we discuss the challenges and innovative approaches to overcome key barriers to ITTs in MPS.

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.

Status of N-of-1 Trials in Chronic Pain Management: A Narrative Review

N-of-1 trials are randomized controlled clinical trials conducted exclusively on a single patient. The ultimate aim of N-of-1 trials is to optimize a strategy in a particular individual. Chronic pain is a common but refractory clinical problem. Its diverse etiologies and broad variations among patients often lead to the requirement of individualizing medicine. Thus, chronic pain represents a classical condition for N-of-1 clinical trials. Studies have indicated that N-of-1 benefits patients with chronic pain, multiple comorbidities, and uncertain variations during therapies; however, this approach it is not yet adopted as the first choice in pain clinics. To dissect the current status of N-of-1 in chronic pain management, as well as the limitations for its implementation, we herein studied all N-of-1 studies related to chronic pain by searching three major databases (PubMed, ClinicalTrial.gov, Cochrane Library) for publications between 1985 and 2020. Of 35 eligibility papers, 19 were selected for analysis. Results confirmed that N-of-1 trials have solved the refractory cases including osteoarthritis, chronic musculoskeletal pain, and neuropathic pain; however, none of the trials dealt with cancer pain. Longer time and more efforts are needed from investigators when carrying out N-of-1 trials, which inevitably result in implementation difficulties. Of note, all recruited trials were conducted in developed countries. As mobile devices have been introduced and protocols improve, renewed interest in the implementation of N-of-1 trials will occur. Collectively, a previously underestimated conflict between "precision medicine" and "poor implementation" has put N-of-1 in a challenging position for chronic pain management.

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".

Translation and validation of the greek version of a questionnaire measuring patient views on participation in clinical trials

Background

The increasing number of clinical research opportunities requires increasing numbers of participants in clinical trials. However, it may become increasingly problematic, as protocols have become increasingly complex. Better understanding of patients’ attitudes towards their potential participation in clinical trials is essential for developing effective clinical trial recruitment strategies. In Greece, limited research has been conducted on this topic so far. This study aims to contribute to filling this gap.

Methods

A cross-sectional study was conducted. Purposive sampling was used to select participants. The Greek version of a recently developed questionnaire measuring patient views on participation in clinical trials, a 27-item scale distributed into four factors, was tested. In addition, participants were asked to provide information regarding their socio-demographics. A demographic comparison was conducted.

Results

The four-factor solution derived in our study consisted of the same 27 items and it was different from the six-factor solution that Arnetz et al. proposed. The factors risks and benefits, that consisted of 5 and 3 items respectively in the six-factor solution, were merged into one factor that consisted of 10 items in the four-factor solution. The four factors produced were Risks and benefits (ten items, α = 0,867), Patient’s expectations (six items, α = 0.864), Patient’s participation (five items, α = 0.827), and Cost and convenience (five items, α = 0,770). We found that demographic factors did not impact patients’ opinions about clinical trials participation, except for gender. The participants reported as important for participating in clinical trial: receiving clear and adequate information (95,5 %) and being given the opportunity to ask questions (97,8 %), take part in discussions regarding their own treatment (94,6 %), and voice their concerns and opinions (91,1 %). As factors strongly associated with participants’ willingness to participate in a clinical trial were reported: concerns about the risks of being in a clinical trial (87,5 %), the possible side effects of clinical trials (86,3 %), the type of treatment given in a clinical trial (83,7 %), and whether participation would improve their quality of life (QoL) (81,5 %).

Conclusions

The preliminary validation of the Greek version of the questionnaire measuring patient perceptions and expectations of participating in clinical trials demonstrated acceptable validity and reliability and could be further tested in larger samples. The findings that emerged from this study are in line with previous literature.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07111-x.

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.

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.

Informing Healthcare Decisions with Observational Research Assessing Causal Effect. An Official American Thoracic Society Research Statement

Rationale: Decisions in medicine are made on the basis of knowledge and reasoning, often in shared conversations with patients and families in consideration of clinical practice guideline recommendations, individual preferences, and individual goals. Observational studies can provide valuable knowledge to inform guidelines, decisions, and policy.

Objectives: The American Thoracic Society (ATS) created a multidisciplinary ad hoc committee to develop a research statement to clarify the role of observational studies—alongside randomized controlled trials (RCTs)—in informing clinical decisions in pulmonary, critical care, and sleep medicine.

Methods: The committee examined the strengths of observational studies assessing causal effects, how they complement RCTs, factors that impact observational study quality, perceptions of observational research, and, finally, the practicalities of incorporating observational research into ATS clinical practice guidelines.

Measurements and Main Results: There are strengths and weakness of observational studies as well as RCTs. Observational studies can provide evidence in representative and diverse patient populations. Quality observational studies should be sought in the development of ATS clinical practice guidelines, and medical decision-making in general, when 1) no RCTs are identified or RCTs are appraised as being of low- or very low-quality (replacement); 2) RCTs are of moderate quality because of indirectness, imprecision, or inconsistency, and observational studies mitigate the reason that RCT evidence was downgraded (complementary); or 3) RCTs do not provide evidence for outcomes that a guideline committee considers essential for decision-making (e.g., rare or long-term outcomes; “sequential”).

Conclusions: Observational studies should be considered in developing clinical practice guidelines and in making clinical decisions.

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.

Factors affecting people's attitude toward participation in medical research: a systematic review

Objective: Human participants play an important role in medical care advances. Recruiting enough participants is perhaps the most significant procedure that determines the success of medical research, and high participation rate brings about many benefits. Therefore, acquiring enough participants is important for medical researchers. To understand how to improve participation rate, we need to clarify factors affecting the public's attitude toward medical research. Through this review, we aim to examine which factors affect the attitude of human participants toward medical research.Methods: The relevant data were searched by using the keywords "Public," "Participants," "Medical" and "Research" in PubMed (MEDLINE), International Pharmaceutical Abstracts, Web of Science, Science Direct, CINAHL Plus, EMBASE, and China Knowledge Resource Integrated databases. A manual search was done to acquire peer-reviewed articles and reports about participation in medical research.Results: Sixty-three studies were identified for inclusion after full text screening. The included studies were of variable quality. Some factors affecting people's attitude toward participating in medical research have been identified and discussed in our review.Conclusion: This review demonstrated that willingness of participants to take part in medical research was influenced by a variety of factors. These factors may be used to predict the public's willingness to take part in medical research and may potentially be used in developing strategies aimed at improving participation rate.

Factors Associated With Trial Completion and Adherence in App-Based N-of-1 Trials: Protocol for a Randomized Trial Evaluating Study Duration, Notification Level, and Meaningful Engagement in the Brain Boost Study

Background

N-of-1 trials promise to help individuals make more informed decisions about treatment selection through structured experiments that compare treatment effectiveness by alternating treatments and measuring their impacts in a single individual. We created a digital platform that automates the design, administration, and analysis of N-of-1 trials. Our first N-of-1 trial, the app-based Brain Boost Study, invited individuals to compare the impacts of two commonly consumed substances (caffeine and L-theanine) on their cognitive performance.

Objective

The purpose of this study is to evaluate critical factors that may impact the completion of N-of-1 trials to inform the design of future app-based N-of-1 trials. We will measure study completion rates for participants that begin the Brain Boost Study and assess their associations with study duration (5, 15, or 27 days) and notification level (light or moderate).

Methods

Participants will be randomized into three study durations and two notification levels. To sufficiently power the study, a minimum of 640 individuals must begin the study, and 97 individuals must complete the study. We will use a multiple logistic regression model to discern whether the study length and notification level are associated with the rate of study completion. For each group, we will also compare participant adherence and the proportion of trials that yield statistically meaningful results.

Results

We completed the beta testing of the N1 app on a convenience sample of users. The Brain Boost Study on the N1 app opened enrollment to the public in October 2019. More than 30 participants enrolled in the first month.

Conclusions

To our knowledge, this will be the first study to rigorously evaluate critical factors associated with study completion in the context of app-based N-of-1 trials.

Trial Registration

ClinicalTrials.gov NCT04056650; https://clinicaltrials.gov/ct2/show/NCT04056650

International Registered Report Identifier (IRRID)

PRR1-10.2196/16362

Exploring the Potential for Collaborative Use of an App-Based Platform for n-of-1 Trials Among Healthcare Professionals That Treat Patients With Insomnia

BACKGROUND

N-of-1 trials are single patient, multiple crossover, and comparative effectiveness experiments. Despite their rating as "level 1" evidence, they are not routinely used in clinical medicine to evaluate the effectiveness of treatments.

OBJECTIVE

We explored the potential for implementing a mobile app-based n-of-1 trial platform for collaborative use by clinicians and patients to support data-driven decisions around the treatment of insomnia.

METHODS

A survey assessing awareness and utilization of n-of-1 trials was administered to healthcare professionals that frequently treat patients with insomnia at the Icahn School of Medicine at Mount Sinai in New York City.

RESULTS

A total of 45 healthcare professionals completed the survey and were included in the analysis. We found that 64% (29/45) of healthcare professionals surveyed had not heard of n-of-1 trials. After a brief description of these methods, 75% (30/40) of healthcare professionals reported that they are likely or highly likely to use an app-based n-of-1 trial at least once in the next year if the service were free and easy to offer to their patients.

CONCLUSIONS

An app-based n-of-1 trials platform might be a valuable tool for clinicians and patients to identify the best treatments for insomnia. The lack of awareness of n-of-1 trials coupled with receptivity to their use suggests that educational interventions may address a current barrier to wider utilization of n-of-1 trials.

Evaluating the Comparative Effectiveness of Two Diets in Pediatric Inflammatory Bowel Disease: A Study Protocol for a Series of N-of-1 Trials

Inflammatory bowel disease (IBD) affects 3 million children and adults in the US. Treatment involves medications with considerable risk profiles. Dietary modification, such as the specific carbohydrate diet (SCD), may be helpful in treating IBD, but there is insufficient evidence of its effectiveness. N-of-1 trials are ideal for addressing this important research question. The Personalized Research on Diet in Ulcerative Colitis and Crohn’s Disease (PRODUCE) study employs a series of 50 individual N-of-1 trials that compare the SCD to a modified SCD. Treatment periods are assigned in blocks of two, with each patient completing two balanced treatment blocks. Patients are randomized to start with the SCD or modified SCD and alternate between conditions for four eight-week periods. A mobile app guides collecting and viewing data, transitioning diets, and reviewing personal results. Primary outcomes include patient reported outcomes (PROs) of stool frequency, stool consistency, pain interference, and gastrointestinal (GI) symptom severity. We examine changes in inflammation via fecal calprotectin. Participants will receive a personalized answer regarding comparative effectiveness between the SCD and a less restrictive diet option (modified SCD), as well as compared to their baseline diet. We will aggregate the results of completed N-of-1 trials across patients to estimate population level comparative effectiveness of these treatments and the effectiveness of each diet.

Making the switch: From case studies to N-of-1 trials

Case studies can generate hypothesis based on unique clinical patient encounters and provide guidance among populations with limited numbers of patients. However, case studies are not blinded and are susceptible to a variety of factors that can influence study outcomes. One potential solution to minimize this bias is to use an N-of-1 trial. N-of-1 trials are a double-blinded randomized crossover trial within a limited number of patients, often as small as a single patient. These trials borrow many concepts from randomized controlled trials (RCTs), which in turn increases the validity of findings compared with a case report. Situations best suited for an N-of-1 trial include chronic disease states and therapies with quick onset and offset, such as in patients with seizures. There are many opportunities to use N-of-1 trials among patients with epilepsy, and providers are encouraged to explore and employ these methods. The purpose of this article was to describe N-of-1 trials along with considerations for conducting, publishing, and evaluating N-of-1 trials.

•

The use of N-of-1 trials can minimize bias found in traditional case studies.

•

N-of-1 trials are a double-blinded randomized crossover trial within a single patient.

•

There are methods and reporting standards to guide the development and interpretation of N-of-1 trials.

Increasing the Precision of Hypertension Treatment Through Personalized Trials: a Pilot Study

BACKGROUND

There are substantial differences in the effects of blood pressure (BP) medications in individual patients. Yet, the current standard approach to prescribing BP medications is not personalized.

OBJECTIVE

To determine the feasibility of individualizing the selection of BP medications through pragmatic personalized (i.e., N-of-1) trials.

DESIGN

Series of N-of-1 trials.

SETTING

Outpatient.

PATIENTS

Hypertensive adults prescribed none or one BP medication.

INTERVENTION

Participation in a flexible, open-label personalized trial of two to three BP medications (NCT02744456).

MEASUREMENTS

BP was measured twice per day with a validated home BP device. Frequency and severity of side effects were assessed at the end of the day via an electronic questionnaire. Patients' BP medication preference was assessed after reviewing BP lowering and side effect results with a study clinician. Feasibility was assessed by determining the proportion of patients who adhered to self-assessments. Benefit was assessed by asking patients to rate the helpfulness of participation and whether they would recommend personalized trials to other hypertensive patients.

KEY RESULTS

Of ten patients enrolled, two dropped out prior to initiation, one discovered white coat hypertension through ambulatory BP monitoring, and seven (mean age 58 years, 71% of women) completed personalized trials. All seven were compliant with home BP monitoring and side effect tracking. All seven recommended personalized trials of BP medications to others. Thiazides were preferred by three patients, renin-angiotensin system-blocking agents by two patients, a combination of thiazide and beta-blocker by one patient, and any of three classes by one patient.

CONCLUSIONS

Personalized trials of BP medications were feasible and led to improved treatment precision. Heterogeneity of patient preferences and of therapeutic BP response for first-line BP medications can be determined through a personalized trial approach.

Designing Robust N-of-1 Studies for Precision Medicine: Simulation Study and Design Recommendations

Background

Recent advances in molecular biology, sensors, and digital medicine have led to an explosion of products and services for high-resolution monitoring of individual health. The N-of-1 study has emerged as an important methodological tool for harnessing these new data sources, enabling researchers to compare the effectiveness of health interventions at the level of a single individual.

Objective

N-of-1 studies are susceptible to several design flaws. We developed a model that generates realistic data for N-of-1 studies to enable researchers to optimize study designs in advance.

Methods

Our stochastic time-series model simulates an N-of-1 study, incorporating all study-relevant effects, such as carryover and wash-in effects, as well as various sources of noise. The model can be used to produce realistic simulated data for a near-infinite number of N-of-1 study designs, treatment profiles, and patient characteristics.

Results

Using simulation, we demonstrate how the number of treatment blocks, ordering of treatments within blocks, duration of each treatment, and sampling frequency affect our ability to detect true differences in treatment efficacy. We provide a set of recommendations for study designs on the basis of treatment, outcomes, and instrument parameters, and make our simulation software publicly available for use by the precision medicine community.

Conclusions

Simulation can facilitate rapid optimization of N-of-1 study designs and increase the likelihood of study success while minimizing participant burden.

Patient Perceptions of Their Own Data in mHealth Technology-Enabled N-of-1 Trials for Chronic Pain: Qualitative Study

Background

N-of-1 (individual comparison) trials are a promising approach for comparing the effectiveness of 2 or more treatments for individual patients; yet, few studies have qualitatively examined how patients use and make sense of their own patient-generated health data (PGHD) in the context of N-of-1 trials.

Objective

The objective of our study was to explore chronic pain patients’ perceptions about the PGHD they compiled while comparing 2 chronic pain treatments and tracking their symptoms using a smartphone N-of-1 app in collaboration with their clinicians.

Methods

Semistructured interviews were recorded with 33 patients, a consecutive subset of the intervention group in a primary study testing the feasibility and effectiveness of the Trialist N-of-1 app. Interviews were transcribed verbatim, and a descriptive thematic analysis was completed.

Results

Patients were enthusiastic about recording and accessing their own data. They valued sharing data with clinicians but also used their data independently.

Conclusions

N-of-1 trials remain a promising approach to evidence-based decision making. Patients appear to value their roles as trial participants but place as much or more importance on the independent use of trial data as on comparative effectiveness results. Future efforts to design patient-centered N-of-1 trials might consider adaptable designs that maximize patient flexibility and autonomy while preserving a collaborative role with clinicians and researchers.

Personalized (N-of-1) Trials for Depression: A Systematic Review

PURPOSE/BACKGROUND

Personalized (N-of-1) trials are single-patient, crossover-design trials that may be useful for personalizing the selection of depression treatments. We conducted a systematic review of published N-of-1 trials for depression to determine the feasibility and suitability of this methodology for personalizing depression care.

METHODS/PROCEDURES

Electronic databases were searched from database inception through October 2016. Studies were selected if they enrolled depressed patients, included a within-subject crossover design, and systematically assessed depressive symptoms during the N-of-1 trial.

FINDINGS/RESULTS

Five eligible studies reporting on 47 depressed patients (range, 1-18 patients) were identified. Two studies were conducted among adults with treatment-resistant depression, 1 study among depressed inpatients, and 2 studies among patients from special populations (geriatric nursing home, human immunodeficiency virus-associated encephalopathy). All studies evaluated the effects of pharmacologic treatments (methylphenidate, D-amphetamine, ketamine, and sulpiride). Three studies compared an off-label treatment with placebo, 1 study compared 2 off-label treatments, and 1 study compared escalating doses of an off-label treatment with placebo. All 4 studies with more than 1 participant demonstrated heterogeneous treatment effects. All studies produced data that could personalize treatment selection for individual patients. No studies reported on recruitment challenges, compliance with self-tracking, or satisfaction with participation.

IMPLICATIONS/CONCLUSIONS

The feasibility of N-of-1 trials for depression was demonstrated for a limited number of second-line pharmacologic treatments in treatment-resistant patients or in patients with comorbidities that would have excluded them from conventional randomized controlled trials. Additional research is needed to determine whether N-of-1 trials are suitable for improving the selection of depression treatments in clinical practice.

Patient preferences for personalized (N-of-1) trials: a conjoint analysis

OBJECTIVE

Despite their promise for increasing treatment precision, Personalized Trials (i.e., N-of-1 trials) have not been widely adopted. We aimed to ascertain patient preferences for Personalized Trials.

STUDY DESIGN AND SETTING

We recruited 501 adults with ≥2 common chronic conditions from Harris Poll Online. We used Sawtooth Software to generate 45 plausible Personalized Trial designs comprising combinations of eight key attributes (treatment selection, treatment type, clinician involvement, blinding, time commitment, self-monitoring frequency, duration, and cost) at different levels. Conditional logistic regression was used to assess relative importance of different attributes using a random utility maximization model.

RESULTS

Overall, participants preferred Personalized Trials with no costs vs. $100 cost (utility difference 1.52 [standard error 0.07], P < 0.001) and with less vs. more time commitment/day (0.16 [0.07], P < 0.015) but did not hold preferences for the other six attributes. In subgroup analyses, participants ≥65 years, white, and with income ≤$50,000 were more averse to costs than their counterparts (P all <0.05).

CONCLUSION

To optimize dissemination, Personalized Trial designers should seek to minimize out-of-pocket costs and time burden of self-monitoring. They should also consider adaptive designs that can accommodate subgroup differences in design preferences.

N-of-1 trials in the clinical care of patients in developing countries: a systematic review

BACKGROUND

N-of-1 trials have a potential role in promoting patient-centered medicine in developing countries. However, there is limited academic literature regarding the use of N-of-1 trials in the clinical care of patients in resource-poor settings.

OBJECTIVE

To assess the extent of use, purpose and treatment outcome of N-of-1 trials in developing countries.

METHOD

A systematic review of clinical N-of-1 trials was conducted between 1985 and September 2015 using PubMed, Embase, CINAHL, Web of Science and the Cochrane Central Register of Controlled Trials. Grey literature databases and clinical trial registers were also searched. This review included randomized, multi-cycle, crossover within individual patient trials involving drug intervention. Quality assessment and data extraction were conducted by two independent reviewers.

RESULT

Out of 131 N-of-1 trials identified, only 6 (4.5%) were conducted in developing countries. The major reason that N-of-1 trials were used was to provide evidence on feasibility, effectiveness and safety of therapies. A total of 72 participants were involved in these trials. Five of the studies were conducted in China and all evaluated Chinese traditional medicine. The remaining study was conducted in Brazil. The completion rate was 93%. More than half, 46 (69%) of subjects made medication changes consistent with trial results after trial completion. A number of threats to the validity of the included evidence limited the validity of the evidence. In particular, the estimated overall effect in four of the included studies could have been affected by the "carry over" of the previous treatment effect as no adequate pharmacokinetic evidence regarding traditional medicines was presented.

CONCLUSION

The prevalence and scope of N-of-1 trials in developing countries is low. A coordinated effort among government, clinicians, researchers and sponsor organizations is needed to increase their uptake and quality in developing countries.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42015026841 .

Practice to research: integrating evidence-based practices with culture and context

There are ways to integrate culturally competent services (CCS) and evidence-based practices (EBP) which can improve the experiences of patients and their families and communities when faced with health problems, as well as the effectiveness and positive experiences of practitioners. CCS and EBP evidence should be jointly deployed for helping patients and clinicians. Partnership research models are useful for achieving the integration of CCS and EBP, since they involve close observation of and participation by clinicians and practitioners in the research process, and often use integrated qualitative and quantitative mixed methods. We illustrate this with 3 examples of work that can help integrate CCS and EBP: ongoing collection of information from patients, clinicians and staff, or "evidence farming"; close study and continuous improvement of activities and accommodations; and use of evidence of tacit, implicit cultural scripts and norms, such as being "productive," as well as explicit scripts. From a research practice point of view, collaborative partnerships will likely produce research with culture and context bracketed in, and will contribute stronger research models, methods, and units of analysis.

The PREEMPT study - evaluating smartphone-assisted n-of-1 trials in patients with chronic pain: study protocol for a randomized controlled trial

Background

Chronic pain is prevalent, costly, and clinically vexatious. Clinicians typically use a trial-and-error approach to treatment selection. Repeated crossover trials in a single patient (n-of-1 trials) may provide greater therapeutic precision. N-of-1 trials are the most direct way to estimate individual treatment effects and are useful in comparing the effectiveness and toxicity of different analgesic regimens. The goal of the PREEMPT study is to test the ‘Trialist’ mobile health smartphone app, which has been developed to make n-of-1 trials easier to accomplish, and to provide patients and clinicians with tools for individualizing treatments for chronic pain.

Methods/design

A randomized controlled trial is being conducted to test the feasibility and effectiveness of the Trialist app. A total of 244 participants will be randomized to either the Trialist app intervention group (122 patients) or a usual care control group (122 patients). Patients assigned to the Trialist app will work with their clinicians to set up an n-of-1 trial comparing two pain regimens, selected from a menu of flexible options. The Trialist app provides treatment reminders and collects data entered daily by the patient on pain levels and treatment side effects. Upon completion of the n-of-1 trial, patients review results with their clinicians and develop a long-term treatment plan. The primary study outcome (comparing Trialist to usual care patients) is pain-related interference with daily functioning at 26 weeks.

Discussion

Trialist will allow patients and clinicians to conduct personalized n-of-1 trials. In prior studies, n-of-1 trials have been shown to encourage greater patient involvement with care, which has in turn been associated with better health outcomes. mHealth technology implemented using smartphones may offer an efficient means of facilitating n-of-1 trials so that more patients can benefit from this approach.

Trial registration

ClinicalTrials.gov: NCT02116621, first registered 15 April 2014.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-015-0590-8) contains supplementary material, which is available to authorized users.

Factors associated with willingness to participate in clinical trials: a nationwide survey study

Background

This study was conducted to investigate awareness of clinical trials (CTs) including perceptions of favorable feelings about, necessity for, and safety of CTs, the ultimate beneficiary of CTs and the factors associated with willingness to participate in CTs among the general population in South Korea.

Methods

A cross sectional survey study was conducted in a randomly selected national sample of 1,515 Korean.

Results

Perception toward CTs was measured using a scale from 0 (strongly disagree) to 10 (strongly agree). Respondents readily understood the necessity for CTs (M = 7.27, SD = 2.15); had moderately favorable feelings (M = 5.32, SD = 2.31) toward CTs and felt that these CTs were moderately safe (M = 4.71, SD = 1.90). Twenty-five percent of the respondents answered that they would be willing to participate in a CT in the future. Perception of the ultimate benefits of CTs, awareness, favorable feelings, safety, and necessity regarding CTs were identified as significant predictors of willingness to participate in CTs.

Conclusion

An awareness of CTs and the perceptions toward CTs were associated with general public willingness to participate in a CT. Findings from this study can be used in planning outreach and recruitment strategies, and to understand the predictors of CT participation.

Optimizing behavioral health interventions with single-case designs: from development to dissemination

Over the past 70 years, single-case design (SCD) research has evolved to include a broad array of methodological and analytic advances. In this article, we describe some of these advances and discuss how SCDs can be used to optimize behavioral health interventions. Specifically, we discuss how parametric analysis, component analysis, and systematic replications can be used to optimize interventions. We also describe how SCDs can address other features of optimization, which include establishing generality and enabling personalized behavioral medicine. Throughout, we highlight how SCDs can be used during both the development and dissemination stages of behavioral health interventions.

The Ontology of Clinical Research (OCRe): an informatics foundation for the science of clinical research

To date, the scientific process for generating, interpreting, and applying knowledge has received less informatics attention than operational processes for conducting clinical studies. The activities of these scientific processes - the science of clinical research - are centered on the study protocol, which is the abstract representation of the scientific design of a clinical study. The Ontology of Clinical Research (OCRe) is an OWL 2 model of the entities and relationships of study design protocols for the purpose of computationally supporting the design and analysis of human studies. OCRe's modeling is independent of any specific study design or clinical domain. It includes a study design typology and a specialized module called ERGO Annotation for capturing the meaning of eligibility criteria. In this paper, we describe the key informatics use cases of each phase of a study's scientific lifecycle, present OCRe and the principles behind its modeling, and describe applications of OCRe and associated technologies to a range of clinical research use cases. OCRe captures the central semantics that underlies the scientific processes of clinical research and can serve as an informatics foundation for supporting the entire range of knowledge activities that constitute the science of clinical research.

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

OBJECTIVE

To raise awareness among clinicians and epidemiologists that single-patient (n-of-1) trials are potentially useful for informing personalized treatment decisions for patients with chronic conditions.

STUDY DESIGN AND SETTING

We reviewed the clinical and statistical literature on methods and applications of single-patient trials and then critically evaluated the needs for further methodological developments.

RESULTS

Existing literature reports application of 2,154 single-patient trials in 108 studies for diverse clinical conditions; various recent commentaries advocate for wider application of such trials in clinical decision making. Preliminary evidence from several recent pilot acceptability studies suggests that single-patient trials have the potential for widespread acceptance by patients and clinicians as an effective modality for increasing the therapeutic precision. Bayesian and adaptive statistical methods hold promise for increasing the informational yield of single-patient trials while reducing participant burden, but are not widely used. Personalized applications of single-patient trials can be enhanced through further development and application of methodologies on adaptive trial design, stopping rules, network meta-analysis, washout methods, and methods for communicating trial findings to patients and clinicians.

CONCLUSIONS

Single-patient trials may be poised to emerge as an important part of the methodological armamentarium for comparative effectiveness research and patient-centered outcomes research. By permitting direct estimation of individual treatment effects, they can facilitate finely graded individualized care, enhance therapeutic precision, improve patient outcomes, and reduce costs.

Making sense of mobile health data: an open architecture to improve individual- and population-level health

Mobile phones and devices, with their constant presence, data connectivity, and multiple intrinsic sensors, can support around-the-clock chronic disease prevention and management that is integrated with daily life. These mobile health (mHealth) devices can produce tremendous amounts of location-rich, real-time, high-frequency data. Unfortunately, these data are often full of bias, noise, variability, and gaps. Robust tools and techniques have not yet been developed to make mHealth data more meaningful to patients and clinicians. To be most useful, health data should be sharable across multiple mHealth applications and connected to electronic health records. The lack of data sharing and dearth of tools and techniques for making sense of health data are critical bottlenecks limiting the impact of mHealth to improve health outcomes. We describe Open mHealth, a nonprofit organization that is building an open software architecture to address these data sharing and "sense-making" bottlenecks. Our architecture consists of open source software modules with well-defined interfaces using a minimal set of common metadata. An initial set of modules, called InfoVis, has been developed for data analysis and visualization. A second set of modules, our Personal Evidence Architecture, will support scientific inferences from mHealth data. These Personal Evidence Architecture modules will include standardized, validated clinical measures to support novel evaluation methods, such as n-of-1 studies. All of Open mHealth's modules are designed to be reusable across multiple applications, disease conditions, and user populations to maximize impact and flexibility. We are also building an open community of developers and health innovators, modeled after the open approach taken in the initial growth of the Internet, to foster meaningful cross-disciplinary collaboration around new tools and techniques. An open mHealth community and architecture will catalyze increased mHealth efficiency, effectiveness, and innovation.

A Novel N of 1 Trial Design and Proposed Utility in Complementary and Alternative Medicine Research

An N of 1 trial is a multiple crossover study in a single participant. N of 1trials can combine the benefits of individualized patient practice and evidence-based medicine and are amenable to complementary and alternative medicine practice and research. This article will review the basic structure of N of 1trials, discuss how they are commonly used, and review their limitations and statistical considerations. The authors also propose a novel use of the N of 1 trial in the form of mixed-methodology add-on N of 1 trials targeted to a parent trial’s responders. This design can help uncover evidence of subgroup effects in small trials, address issues surrounding the small study effect, and explore the role of interparticipant variability and random chance in the parent trial.

N-of-1 trials in the medical literature: a systematic review

BACKGROUND

N-of-1 trials (multiple crossover studies conducted in single individuals) may be ideal for determining individual treatment effects and as a tool to estimate heterogeneity of treatment effects (HTE) in a population. However, comprehensive data on n-of-1 trial methodology and analysis is lacking. We performed this study to describe n-of-1 trial characteristics, examine treatment changes resulting from n-of-1 trial participation, and to determine if trial reporting is adequate for estimating HTE.

METHODS

We undertook a systematic review of n-of-1 trials published between 1985 and December 2010. Included trials were those having individual treatment episodes as the unit of randomization and reporting individual-specific treatment effects. We abstracted trial characteristics, treatment change information, and analytic methods.

RESULTS

We included 108 trials reporting on 2154 participants. Approximately half (49%) of the trials used a statistical cutoff to determine a superior treatment, whereas the remainder used a graphical comparison (25%) or a clinical significance cutoff (20%). Sixty-seven trials, reporting on 488 people, provided treatment change information: 54% of participants had subsequent treatment decisions consistent with the results of the trial, 8% had decisions inconsistent with trial results, and 38% had ambiguous results. Less than half of the trials (45%) reported adequate information to facilitate the calculation of HTE.

CONCLUSION

N-of-1 trials are a useful tool for enhancing therapeutic precision in a range of conditions and should be conducted more often. To facilitate future meta-analysis, and the estimation of HTE, researchers reporting n-of-1 trial results should clearly describe individual data.

The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?

N-of-1 or single subject clinical trials consider an individual patient as the sole unit of observation in a study investigating the efficacy or side-effect profiles of different interventions. The ultimate goal of an n-of-1 trial is to determine the optimal or best intervention for an individual patient using objective data-driven criteria. Such trials can leverage study design and statistical techniques associated with standard population-based clinical trials, including randomization, washout and crossover periods, as well as placebo controls. Despite their obvious appeal and wide use in educational settings, n-of-1 trials have been used sparingly in medical and general clinical settings. We briefly review the history, motivation and design of n-of-1 trials and emphasize the great utility of modern wireless medical monitoring devices in their execution. We ultimately argue that n-of-1 trials demand serious attention among the health research and clinical care communities given the contemporary focus on individualized medicine.