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

ABSTRACT

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.

Highlights

Key findings

• The potential merits of adopting N-of-1 trials into clinical practice (in terms of efficacy and participation) was acknowledged by all participants.

• The trade-off between methodological rigidity and practical application for the patient was mentioned by clinicians as an important barrier for the use of N-of-1 trials in clinical practice.

• There appears to be substantial dissensus on the usefulness of “informal/pragmatic” N-of-1 trials in clinical practice; clinicians appear the strongest advocates for strict methodological rigour.

What this adds to what is known

• Previous research suggests that lack of knowledge by researchers, clinicians, and patients on the topic of N-of-1, operational complexity, and costs are primary barriers for adoption of N-of-1 trials in clinical practice.

• Our work confirms the abovementioned barriers and adds to this list: the current design of the healthcare system and the lack of consensus on methodological requirements.

• The Quantified Self movement as well as the advances in the wearable technology were mentioned by (patient)researchers as facilitators for the adoption of N-of-1 methodologies in clinical practice.

What is the implication, what should change now

• Education on N-of-1 trials need to be included in the medical (and thus not only the biomedical sciences) curriculum.

• The N-of-1 approach might help promote shared decision making in which patient can lead using their own data.

• Best practices of N-of-1 adoption in clinical practice need to be identified and used as examples to further inform communication between medical stakeholders and policymakers.

KEYWORDS:

• N-of-1 trials
• individual point of care studies
• single case designs
• single subject research
• personalized healthcare
• personal science

Introduction

In 2005, Dawes et al (Dawes et al., 2005). released the Sicily statement on evidence-based practice (EBP). The authors concluded that EBP requires decisions about healthcare to be based on the best available, current, valid, and relevant evidence. Furthermore, these decisions should be made by those receiving care, informed by the tacit and explicit knowledge of those providing care, within the context of available resources (Dawes et al., 2005). On the one hand, this definition is quite ambitious. The most valid and relevant evidence for a single person receiving care is unlikely to be a directly applicable group-based effect-estimate from a randomized controlled trial (RCT) or a recommendation from a guideline, since such recommendations forgo an individual’s personal context and circumstances (Greenhalgh et al., 2014; Huang & Hood, 2019; RVS, 2017). On the other hand, this definition is not very ambitious as it requires only the decision about healthcare to be evidence-based, and thus not the trajectory after the decision. In other words, a person can make the best possible decision based on the a priori best available evidence, their own values, and the input from the healthcare professional, but there is still a chance that the intervention may not work well enough, requiring to be further tailored to suit the individual. However, this may be a lengthy trajectory of trial and error, which presents a burden for both healthcare professional and patient. One might argue that this fallacy is covered within the fifth step of the EBP circle “Evaluation of performance” (Cook et al., 1992), however performance is typically assessed from the perspective of the healthcare provider rather than from the perspective of the patient (Ivers et al., 2012).

While treatments based on RCTs work well enough for the general population, evidence shows that heterogeneous responses to evidence-based treatments among individuals are commonplace (Kent et al., 2019; Schork, 2015). Group-based recommendations provide persons receiving care or their care providers little to no help; leaving them at their own discretion to tackle the heterogeneity in both treatment effect and side effects. This may be particularly true for those who suffer from rare diseases or specific combinations of comorbidities. However, when interpreting an individual’s treatment effects, it is difficult to distinguish placebo effects, the desire of patients and healthcare providers to please each other, regression to the mean, or one’s expectations towards the treatment with a real effect. All the aforementioned can potentially result in inefficient, trial-and-error care over the course of several visits; a process known as the “trial of therapy” (Guyatt et al., 1988). Even if the therapy is deemed effective by the patient and healthcare provider, no further trials are carried out on other potential treatments (including watchful waiting) to determine whether they are equally or more effective than the first (Demeyin et al., 2017). One might argue that the trial of therapy is unethical from this regard; the trajectory of care needs to be more precise, person-centred and data-driven (Demeyin et al., 2017; Huang & Hood, 2019; Schork, 2015).

Application of the methodology of N-of-1 trials, a clinical trial in which a single patient serves as their own control during the entire trial duration, is frequently mentioned as a potential solution to optimize this trial of therapy in clinical practice (Demeyin et al., 2017; Guyatt et al., 1988; Huang & Hood, 2019; Sacristán & Knottnerus, 2021; Schork, 2015). To date, N-of-1 trials are primarily used to determine how a single patient responds to different (dosages of) interventions. The most common form of N-of-1 trials uses a multiple crossover design; multiple exposures to reversible treatments are given in a random order, and the patient’s response to each treatment can be compared with each of his or her other responses. For example, the A-B-A-B design, in which a patient receives an intervention during period A and a control intervention during period B (Vohra et al., 2015). The critical point is that time periods of treatment exposure are randomized, rather than patients (Kravitz & Duan, 2014). If possible, N-of-1 trials should be blinded and include a random allocation of interventions. As the conduct of N-of-1 trials includes precise assessment of treatment outcomes and adverse effects, it enables patients and healthcare providers to determine the relative benefits and harms of possible treatments that matter to them (Guyatt et al., 1988). Due to the high applicability and validity for individual patients, N-of-1 trials are deemed the most valid method for evaluating individual clinical decision making (Guyatt et al., 1988; OCEBM Levels of Evidence Working Group, 2011). But the potential benefits of using a methodology in clinical practice based on the scientific N-of-1 trial are not limited to understanding the true treatment effect. Using an N-of-1 approach could also increase patient empowerment and participation in treatment, improve shared decision making, increase adherence to treatment, or tailor an intervention to the needs of a patient (G. H. Guyatt et al., 1986, 1990; C. J. Nikles et al., 2007; J. Nikles et al., 2011). Regardless of these evident benefits, the uptake of the N-of-1 approach in clinical practice has been limited (Kravitz et al., 2008).

A possible explanation for this slow uptake in clinical practice is that the merits of N-of-1 trials have primarily been studied as scientific alternatives to RCTs. Studies investigating the superiority of an N-of-1 approach over usual trial of therapy care are fairly scarce. Several studies have investigated the viability of the N-of-1 methodology in clinical practice, with heterogeneous results (Braun & Clarke, 2012, 2006; Kravitz et al., 2009; Saldaña, 2013). In addition, adaptations of the N-of-1 method for integrating EBP and patient-centred medicine are being developed (Sacristán & Knottnerus, 2021). However, the application of N-of-1 trials has been limited to specific diseases or intervention modes, which might not be entirely comparable. Moreover, to the best of our knowledge, only one study has specifically investigated potential barriers and facilitators to the adoption of the N-of-1 approach in clinical practice (Kravitz et al., 2009). Kravitz et al (Kravitz et al., 2009). found that the primary perceived barriers to adoption include education, operational complexity (especially increased time demands), costs, and the threatening paradigm shift in the doctor-patient relationship. Unfortunately, this study was limited to only physicians and patients.

We propose that additional studies on perceived barriers and facilitators are warranted to aid our understanding of the adoption of the N-of-1 approach in clinical practice but should include a broader group of healthcare providers who could benefit from the N-of-1 approach. Therefore, the objective of our study is to identify perceived barriers and facilitators for the adoption of the N-of-1 approach in clinical practice from the perspectives of physicians, allied health professionals, researchers, and patient advocates. Please note that in this study, we use the wording “N-of-1 trial” to refer to the scientific research method, whereas we use the wording “N-of-1 methodology/approach” to refer to the clinical care process based on the N-of-1 trial methodology.

Methods

We conducted a qualitative study of the potential facilitators and barriers to the adoption of the N-of-1 approach in healthcare settings using semi-structured interviews with clinicians, researchers, and a patient advocate. Interviews were qualitatively analysed using thematic analysis, involving an iterative process of coding and induction of common themes (Braun & Clarke, 2012, 2006; Saldaña, 2013). Ethical approval for the study was obtained from the HAN Ethical Commission under reference number ECO 490.09/23.

Sampling

Purposive convenience sampling was used to recruit interview participants directly from the researchers’ interdisciplinary networks. We selected a diverse sample of clinicians, researchers, health practitioners and a patient advocate, specifically chosen in order to maximize the diversity of perspectives on N-of-1 trials. Participants were affiliated with different universities, healthcare centres and a university hospital in the Netherlands. Selection was based on two criteria. First, participants had to have hands-on experience with N-of-1 trials, either in research or clinical settings, or they had to have significant knowledge about N-of-1 trials. Second, they had to be actively involved in either healthcare practice or research related to healthcare. Eleven candidates were contacted through the researchers’ networks by email with a request for participation. Three candidates were directly approached at a conference on N-of-1 methodology, where their expertise was verbally confirmed after agreeing to participate. One candidate declined due to a lack of time. The total sample of 13 experts comprised five medical doctors, four researchers, two physical therapists, one psychologist and one patient advocate from eight different organizations. The participants were between 25–60 years of age, three of whom were women. All participants signed informed consent forms before participating in the interviews.

Data collection

The primary author (IW) conducted 13 semi-structured, one-hour interviews at different universities and clinics in the Netherlands between October 2018 and February 2019. Each interview took place in the interviewee’s office, with only the researcher and the interviewee present. Twelve interviews were conducted in Dutch, one in English. Each interview was audio-recorded, for which separate written consent was obtained from all participants. The main objective of the interview was to gain an overview of what would be needed to implement and use the N-of-1 approach in healthcare practice. An interview guide was prepared based on the following aspects deemed relevant as deduced from literature:

• The interviewee’s current experience with N-of-1 trials,

• Validity and reliability of N-of-1 measurements in clinical practice,

• Usability of data resulting from an N-of-1 approach for both patient and practitioner,

• The influence of the availability of N-of-1 data on the communication and relationship between patient and practitioner,

• The non-clinical context for the facilitation of N-of-1 approaches, such as political policies, the role of health insurance companies, and the use of consumer technology for measurement,

• The relation between N-of-1 methodology and consumer trends such as Quantified Self.

TH reviewed the interview guide. Primary questions based on these aspects were posed, after which the interviewee could talk freely. Probes were given if the interviewee deviated from the topic or fell silent. Interviewees were encouraged to talk about both positive and negative aspects. Each interview was fully transcribed.

Data analysis

The interview transcripts were analysed in Atlas.ti using thematic analysis (Braun & Clarke, 2012, 2006; Saldaña, 2013) following the worked example by Byrne (Byrne, 2022). IW and ML conducted the analysis. Both researchers familiarized themselves with the data and method of coding by reading the transcripts and agreeing on an initial coding focus. Iterations of independent open coding of the transcripts followed by comparison and code merging into a common codebook were performed until all transcripts had been coded. All open codes received the label “F” (Facilitating factor for adoption of N-of-1 approach), “B” (Barrier to adoption of N-of-1 approach) or “O” (Other), the latter serving to code fragments which might prove to be interesting but were of a different category. Upon completion of coding, the analysis of emerging themes was commenced. The final set of themes was reviewed together with TH and checked against the interview transcripts, to ensure that no semantic shifting had occurred.

All our themes resulted directly from our interpretation of the data. No theoretical frameworks were used to identify themes in advance. However, after the first iteration of theme identification, we found the Consolidated Framework for Implementation Research (CFIR) (Damschroder et al., 2009) to be a useful model in which to embed our themes. In particular, the first four domains (Intervention characteristics, Outer Setting, Inner Setting, and Characteristics of individuals) seamlessly encompassed our findings and were therefore used to classify and describe our findings.

Trustworthiness

To ensure thorough reporting, we used the Consolidated Criteria for Reporting Qualitative Research (COREQ) (Tong et al., 2007) checklist as a guide to both design and describe our study. To assess the trustworthiness of the analysis process and the results, we follow Lincoln and Guba’s evaluative criteria (Lincoln & Guba, 1985; Nowell et al., 2017).

First, we used researcher and participant triangulation, as well as peer debriefing, to establish credibility. The researchers (IW and ML) initially independently coded identical portions of the data, after which the resulting coding schemes were compared and calibrated. After this phase, each researcher coded half of the remaining data. The methodological perspective of IW and the clinical expertise of ML proved to be complementary and valuable for data interpretation. Upon completion, all three authors reviewed and calibrated the analysis. In addition, we asked an external expert to critically evaluate the results described in our manuscript.

Second, triangulation of participant expertise helped us establish transferability. This was successful as many of the same issues were touched upon by interviewees from different backgrounds.

Finally, to ensure dependability, we documented our decisions to merge codes, as well as the criteria for when to use a code (where necessary). This was done in Atlas.ti, as comments attached to the codes in question.

Results

A total of 13 experts from eight different organizations were interviewed: five medical doctors, four researchers, two physical therapists, one psychologist and one patient advocate. Our thematic analysis yielded a total of 14 themes. Table I presents an overview of the themes as they have been embedded in the four domains from the Consolidated Framework for Implementation Research (CFIR) (Damschroder et al., 2009) model. All results described are based entirely on the opinions expressed by the interviewees.

Table I. Overview of the major categories and themes based on the CFIR model.

Domain	Theme
1. Intervention characteristics	Reliability and validity
	Trial design
	Financial aspects
2. Outer setting	Paradigm shift towards personalized medicine
	Patient’s needs, resources, and perspective towards care
	External policy and incentives
3. Inner setting	Internal preconditions for implementation
	Continuous methodological education
	(Time) investment
	Ethical issues
4. Characteristics of the individuals	Patient skills
	Physician skills
	Patient acceptance
	Other psychological aspects

Domain 1: intervention characteristics

The application of the N-of-1 approach as an intervention in clinical practice appears to be fraught with barriers, although those interested in the practical application of N-of-1 observe some facilitators. Participants unanimously agreed that a patient’s condition must be suited to N-of-1 testing, which are typically complex, chronic conditions, and rare diseases for which only very little evidence-based results are available. Another application is testing for side effects. Nevertheless, the N-of-1 approach requires further development, both methodologically and in terms of its clinical application. This development relates to the themes of reliability and validity, trial design, and financial aspects. Table II provides an overview of the barriers and facilitators per theme for the domain Intervention characteristics.

Table II. Overview themes, barriers, and facilitators for the domain intervention characteristics.

Theme	Barriers	Facilitators
Reliability and validity	Safeguarding validity in a real-world setting without blinding and randomization.	Triangulating subjective and objective data gives a more comprehensive overview.
	Uncertainty about which measurements patients can accurately do at home.
	Unexpected confounders and difficulty in determining causality.
	Patients may be able to distinguish the intervention from the placebo.
Trial design	Dealing with variation in the progression of a medical condition is challenging.	Informal N-of-1 trials provide great value for hypothesis generation.
	Complex interventions are difficult to standardize.
	Dealing with predefined treatment protocols can hinder tailoring the intervention to the needs of the individual.
Financial aspects	Expensive in the short term.	Reduce long-term costs by eliminating under- and overtreatment.
	Major time investments from healthcare workers.	Innovation grants from insurance providers.
	Insurance providers do not reimburse personalized treatments or medicines tried in a different context than originally intended.

Reliability and validity

Every interviewee touched on the topic of reliability and validity; the main point of discussion being how strictly the N-of-1 methodology should be applied. There is a major trade-off between conducting N-of-1 studies for scientific validation purposes and conducting them to gain insight into the condition of a patient and the response to treatment.

Barriers

The main concern about N-of-1 trials outside of the scientific context was how to safeguard validity. In clinical practice, blinding, randomization, and sufficient challenge—re-challenge phases may be difficult to guarantee, especially if the intervention contains physical exercises, which can be randomized but not blinded. Without these safeguards, the MDs in particular appeared less willing to accept the results.

Directly related is the uncertainty of whether measurements that patients conduct at home are accurate. Furthermore, they may gain insight into their results, influencing their progress and making it impossible to reliably repeat the measurements. Especially if such measurements are negative, it could cause patients to discontinue the intervention. Poor therapy compliance is a major threat to validity and patients may not always want to tell their physician out of guilt.

Well yes therapy compliance, or adherence for medication, differs from patient to patient, and is sometimes very difficult to register, because people say do you take your pills, they say yes, because they dare not say no. (MD1)

Other barriers mentioned were unexpected confounders, difficulty in determining causality, patients reacting to placebo or nocebo effects, and concerns that patients may be able to distinguish the intervention from placebo. Still, developing novel ways of dealing with placebo effects should not be shunned.

Facilitators

Greater validity of N-of-1 trials can be facilitated if the protocol collects subjective and objective data, which can be triangulated. Objective measures are ever more crucial if proper blinding cannot be done, but the question remains what data should be primary if objective and subjective data do not correlate.

In the case in which you cannot do proper blinding, then I think that your objective outcome measures will become much more important. […] Then you actually get something more like what they call “single patient outcome trials” in the literature … SPOT, so you just look at what happens based on the data you have, and then it thus becomes very important to minimize bias and you will need highly objective measures. […] but imagine that your subjective data on group level, on individual level would show an effect for lots of people and you objective data does not, then you have a problem. Then I would not know what you should believe. (MD7)

Trial design

Interviewees mentioned the following aspects as minimal design requirements for N-of-1 trials in clinical practice: an adequate baseline assessment, multiple reversals, blinding and multiple interventions. A placebo was not always deemed essential, as long as multiple intervention blocks showed similar results.

Barriers

Design barriers that interviewees came up with for tailoring the N-of-1 approach to a specific case were: 1) dealing with variation in the progression of a medical condition (i.e., taking exacerbations of symptoms into account, and dealing with non-linear and unstable data), 2) systematically measuring complex interventions such as those involving physical therapy or lifestyle (i.e., needing to take several working mechanisms into account, and finding the optimal unit of measurement), and 3) dealing with predefined treatment protocols (i.e., logistical challenges when working with highly structured, complex interventions).

Does this imply that N-of-1 methodology should be immediately dismissed from less structured contexts? One participant mentioned the need to think outside of the box about further development of N-of-1 methodology to take on design challenges to circumvent existing validity issues.

I am a researcher who sees these things as a challenge. And then you say: okay you cannot blind but are there other smart methods, and why is blinding the only thing that would work? Maybe you should make use of the placebo effect. So it is a matter of smart design and asking the right questions. […] It would be something for such a unit in our clinical research institute, that there is a kind of N-of-1 room where you can discuss with people who have a lot of experience. (MD13)

Facilitators

The only facilitator concerning trial design is that less strictly conducted N-of-1 trials provide great value for hypothesis formulation, and the interviewees stressed the importance of weighing validity and usefulness (i.e., what is to be gained from N-of-1 results).

It can be that the patient is tired, that something is going on, all of that influences the measurement, but by measuring really frequently, of by letting a patient self-measure, measurements will remain snapshots, but you do have more measurements, which probably reflect reality somewhat more. […] certainly, yes absolutely, I think that that would be really valuable [for scientific research]. (R5)

Another participant stressed the need to look at trial design not only from the perspective of scientific research, but also to consider what the patient needs and wants to get out of the trial, and to employ mixed methods research to achieve this.

Financial aspects

Costs are considered an important factor, and the short-term and long-term benefits of the N-of-1 approach must be carefully weighed in this light.

Barriers

N-of-1 trials are expensive in the short term. They require a huge time investment from healthcare workers, especially if placebo pills need to be developed. Additionally, healthcare insurance providers are mentioned as a barrier. Healthcare practitioners receive very little reimbursement for personalized treatments, which is a widely recognized problem. Moreover, insurance providers do not pay for medicines if their effectiveness has not been demonstrated in multiple RCTs.

It is economically advantageous for an insurance provider to say there is not enough evidence, this is a strict rule. Still they would benefit if it can be proven that a drug works well for a patient, even if it is expensive, because they should want to utilize healthcare costs as efficiently as possible. (MD7)

It is of course rather easy and economically useful to hide behind the fact that there are no group trials … . Level 1 evidence available, because that simply means that they do not have to reimburse, that is just a hard rule that exists now, so they will probably be interested in registered drugs, that you look better if it works for a patient, because that will be economically beneficial for them, to use healthcare costs as efficiently as possible, but I think if you want to push that if you have very strong evidence on an individual level that an expensive drug works for 1 patient, without showing it in a group, I cannot imagine that there is much enthusiasm for that. (MD7)

Facilitators

Financial facilitators require a shift of collective mindset. In the long term, N-of-1 trials may help to cut medical costs if, for example, over-treatment with expensive and ineffective drugs/therapies are identified and prevented.

We give people medications for rheumatoid arthritis […] you think, people will get better. […] Only, after a few years we conduct a study, it was done in the Netherlands […] in which we randomized all those people, 800 people, one half quits and the other half does not quit, or actually two-thirds. It turned out that more than 50% of the people still had no problems after 1 year. So more than 50% of the people were using medicines they did not need. (MD11)

Insurance providers can play an important facilitating role too, for example through innovation grants and promoting programs that investigate how to decrease the use of healthcare facilities.

Domain 2: outer setting

The domain Outer setting reflects the array of social, fiscal, and policy factors that are important for implementation success. Within this context, interviewees mentioned the paradigm shift towards personalized medicine, the needs of the increasingly proactive patient community and the need for policy makers and insurance companies to support the transition. Table III provides an overview of the barriers and facilitators per theme for the domain Outer setting.

Table III. Overview themes, barriers, and facilitators for the domain outer setting.

Theme	Barriers	Facilitators
Paradigm shift towards personalized medicine	System built on treating acute diseases instead of chronic conditions.	Personalized medicine is gaining momentum.
		Shared decision making is becoming increasingly commonplace.
		Patient-led communities are using informal forms of N-of-1 to improve their quality of life within chronic conditions.
Patient’s needs, resources, and perspective towards care	Patient attitude and health skills determine success of N-of-1.	Healthcare providers can harness proactive patient desires to try alternatives in a collaborative and relatively controlled setting.
		Patient feels taken seriously.
External policy and incentives	Communication and collaboration with insurance providers is difficult.
	Interprofessional collaboration does not happen enough.
	Data sharing is fragmented and hampered by privacy laws.

Paradigm shift towards personalized medicine

N-of-1 and self-research will likely benefit from the shift towards personalized medicine. However, this shift demands changes in the appreciation of the value of N-of-1, the standard of thinking within the scientific community, and the acknowledgement of the increasingly proactive patient community.

Barriers

All interviewees point out that personalized medicine is gaining momentum in the healthcare system. Developments in fields like genomics facilitate much more precisely tailored medicine than ever before. However, the main barrier is that our healthcare system is based on a model developed for treating acute diseases, which makes it ill-suited to today’s epidemic of chronic diseases. Chronic conditions may progress differently for every individual. Therefore, a systemic transition is needed to facilitate tailored healthcare for individuals with chronic conditions.

We call it the “regime” in transition theory. and it looks at changes and innovations in systems, in a way. […] Today’s diseases are chronic diseases. And the way we’ve designed the organization [for infectious diseases] […] is not working very well for chronic diseases. So in chronic diseases you need constant interaction and constant communication, but the way we do it is for earlier phases of demographic transition and epidemiological transition. (R3)

Facilitators

The first facilitator is the appreciation of the value of the N-of-1 approach in the clinical community. For example, many patients want to find out whether additional therapies or supplements will work for them and want to be taken seriously by their physician. An N-of-1 trial could provide a solution, especially as this type of personalization is embraced by some of the interviewed MDs.

In rheumatology there are quite some alternatives, supplements and such, that often cost a lot of money. So I often say: that has not been proven with research, with studies that show that those treatments are effective. […] So I advise those people, I would try it and try it for example 3 months and then you stop and compare those periods to each other and if you don’t notice a difference I would not use it. And if you do notice a difference, start it again and if you notice that you feel better, then in any case you have an idea that it works for you. Also a type of N-of-1 in daily practice, without attaching, or being able to attach, scientific value to it. But that is something that you regularly do and advise. (MD16)

The mindset of the physician is critical in this transition. The interviewees mentioned that there is an increasing trend towards shared decision-making on treatment plans, and that they believed that the open-mindedness and enthusiasm of the healthcare provider can be of great value when helping patients incorporate their informal self-research initiatives into their clinical treatment plan.

Secondly, societal changes have a facilitating influence. Movements like Citizen Science and Quantified Self are gaining momentum within patient communities, which position N-of-1 as a means to optimize health in single subjects. Patients strive for recognition and acknowledgement of their N-of-1 efforts and emphasize that the outcomes of their initiatives must be shared within the communities and the clinical domain. The danger that the quality of such data may not always be optimal is acknowledged and positioned as a challenge.

There are different possible ways of research in which you apply more or less structure. That determines the quality of the data and what you can do with it. And with some … yes, with Quantified Self, you quantify absolutely everything. But then the question becomes, are they measuring the right thing? Are the research questions appropriate? And many tinkerers at home have good research questions but measure the wrong things. So within that, you have to find something. (PA8)

Part of the solution may be the rapid technological development of consumer home monitoring devices, and the willingness of proactive patients to use this technology to improve or monitor their treatment results. Quality wearables can facilitate more accurate data measurement. Increasing accessibility to such devices can be a strong facilitator, even though data availability and commercial interests are challenges inevitably associated with technology.

Concerning technology […] it is really enormously cheap, small, fast, has become accessible, so what is available to consumers today, a lot of especially the more expensive ones, are of high quality. So I am enthusiastic about that. I am a lot less enthusiastic about that data is often not available or that you are a business model for a company by generating data. (R10)

The systemic changes discussed by the interviewees will require laws for privacy and data governance on an entirely different scale. However, existing patient communities do not consider policy making the starting point; patients should simply get started with their own self-research. As the movement gains momentum, health policy makers will no longer be able to work around it.

Patient’s needs, resources, and perspectives towards care

All interviewees explain that every patient has his/her own story when visiting a health professional. Patients are also shaped by their beliefs and environment, which drives their behaviour.

Barriers

Interviewees recognize that some patients are frustrated because treatment effects do not meet expectations or subjective experiences are not acknowledged. However, the main anticipated barrier is that not all patients might be capable of completing a healthcare trajectory in line with the N-of-1 approach, or want such a proactive role, for example due to insufficient skills, resources, or intrinsic motivation.

I think attitude in particular, uninterested patients I have never managed to get to adhere to treatment, even if they say they do, there, the N-of-1 trial is pointless. (MD1)

Facilitators

On the other hand, frustrations sometimes drive proactive patients to start experimenting on their own with different strategies. Additionally, pressure from their environment to try out alternatives like nutritional supplements or complementary medicine is a driver for self-experimentation. If this type of motivation can be harnessed in collaboration with healthcare providers to achieve a goal that is meaningful to the patient, this may facilitate the trust leading the patient to believe that the treatment is best for him/herself.

The patients I have seen find it okay to do it. The beauty of it is that you go together with the patient, you take each other seriously and the patient receives an answer to ‘is this the best medicine for me’ and not to ‘what is the best medicine against the disease’. (MD13)

External policy and incentives

Interviewees were consistent in their views on external policies. No facilitators were mentioned in this context.

Barriers

Communication and collaboration with insurance companies can be difficult. Interviewees believe that health insurance companies do not recognize the value of N-of-1 trial evidence. Contracts with insurers are believed to hamper the type of therapies that can be administered; apart from reimbursement issues, possibilities for health professionals and patients to experiment together with N-of-1 trials are limited because health professionals feel that they are under tight control. Furthermore, the lack of interprofessional collaboration hampers the data flow necessary for meaningful application of the N-of-1 approach. Data are oftentimes confined in different IT systems, and data sharing is cumbersome because of privacy and confidentiality laws.

Domain 3: inner setting

The Inner setting explores how the internal structures and characteristics of an organization influence the potential of adopting an innovation. In this case, it refers to hospitals, primary care practices and patients’ own living situations. Reliable data infrastructure, methodological education, time investment, and ethics are believed to be important themes. Table IV provides an overview of the barriers and facilitators per theme for the domain Inner setting.

Table IV. Overview themes, barriers, and facilitators for the domain inner setting.

Theme	Barriers	Facilitators
Internal preconditions for implementation	Logistics are challenging if patients live far from the clinic.	N-of-1 trials may be more easily accepted in an academic hospital with an additional research focus.
	Setting up a baseline is challenging, making it difficult to interpret findings.	Informal N-of-1 trials may be good enough for tailoring treatment plans.
	Reliable data infrastructure is lacking.
Continuous methodological education	Clinicians can allocate limited time to staying up to date about new approaches and methodologies.	N-of-1 trials must be incorporated into medical curricula.
(Time) investment	Requires significant time investment from healthcare providers.
	Organizational incentives and rewards for personalized care are often low.
Ethical issues	The patient will be deliberately misinformed during the trial.	Patients are willing to give ethical consent if it will result in health gains for them or their community.
	The patient will not have access to their data until after the trial.	Providing care according to non-optimized processes (i.e., not N-of-1) might be considered unethical.
	The patient will have to engage in blocks of placebo or alternative treatment even if the experimental treatment is effective.

Internal preconditions for implementation

Within any institute, the implementation climate, culture, and priorities must be receptive to experimenting with new alternatives before change can take place, which interviewees think is currently not yet the case.

Barriers

Interviewees foresee many challenges regarding the feasibility of practical implementation. Logistics are an issue since many patients live far away from the clinic. This hampers things like taking measurements that cannot be done at home, and distribution of placebos, which are already difficult to obtain without requiring the apothecary to deliver customized pills identical to the substance being tested. Additionally, setting up a baseline is challenging because time is often short, or because patients take multiple medications and the effects become difficult to disentangle.

All participants agree that organizations need reliable data infrastructures from which data is easy to extract and share, which are currently lacking. It is difficult to safeguard the privacy and ownership of these data, especially if a patient is treated by an interprofessional team.

Facilitators

Academic hospitals may be the best breeding ground for learning how to implement N-of-1, because academic hospitals have both a clinical and a research focus. This facilitates easier access to resources such as an experienced statistician to perform new ways of data analysis, and other expertise without which innovation in methods of analysis will never happen.

The innovation is particularly apparent in Bayesian statistics, so really a more hierarchical analysis, and more in terms of probabilities instead of p-values with a certain significance … and building that model and also the analyses and the … way of … presenting data, I think most innovation is contained there. (MD7)

The MDs suggested that a dedicated N-of-1 trial centre in a hospital would be a great first step towards decreasing the knowledge gap. This centre could support physicians with how to conduct the trial and how to analyse the results, providing protocols for practical N-of-1 implementation, advice on different levels of reliability, and guidelines for which level of evidence may be attributed to the results.

Some interviewees consider the N-of-1 approach not to be too far from usual care, and less rigorous N-of-1 trials may be considered for patients and clinicians who are trying to find the most suitable treatment. They do state that patients need to be much more closely and intensively supervised during such a care process, for which the organization must allocate time.

Continuous methodological education

Lack of exposure is a frequently mentioned issue; the concept of N-of-1 methodology is simply unknown to many practicing physicians, nor does it feature prominently in university curricula. Interviewees expect that increased awareness may contribute to greater acceptance.

Barriers

There is little time allocated in daily schedules for medical staff and researchers to stay up to date about the latest methodological and scientific insights. If groups or departments within a hospital want to prioritize the use of an N-of-1 approach, this will have to change. The interviewees confirm the added value of having a discussion board to deliberate about trial setups and ethical issues, as well as a sustainable infrastructure to share insights, knowledge, experiences, and perhaps even data, both on local and (inter)national levels. Ideally, such boards and infrastructures would involve patients to a certain extent.

Facilitators

One prominently emphasized solution is that N-of-1 trials must be included in the core curriculum of medical studies, as part of a broader perspective on clinical data collection. Development of personalized strategies including N-of-1 can only be facilitated by exposure, preferably as early as possible. A precondition for this is that educational directors and curriculum owners must also endorse this approach.

It’s called a minor these days. Of course you could do that. These minors are about 10 weeks nowadays, you can’t talk about N-of-1 for 10 weeks but you could do a minor about alternatives … “Beyond RCT” … that would be a good title for such a minor, that you offer alternatives and hope that students will use this knowledge when they start working to find new ways of gathering evidence. (MD1)

(Time) investment

Participants point out that successful implementation will depend on the availability of human capital, as well as the organization’s incentives and rewards system. No specific facilitators were mentioned in this context.

Barriers

All interviewees emphasize that N-of-1 trials are by nature a substantial time investment, both in terms of trial duration and a clinician’s time investment. Firstly, clinicians need to explain the process and purpose of N-of-1, and the importance of adherence. Secondly, they must supervise and support the patient in the process of taking personalized measurements. Finally, the data must be analysed. Normally, there is no room in a clinician’s schedule for such additional activities.

Look, I am a clinician so that means that I am expected to spend 80% of my time on patient work. Clinical work, seeing patients, reporting about patients, discussing about patients, look and in the other 20% is all the non-directly patient-related work. Yes, I wonder if I would be capable of doing such a study. (P15)

Apart from the clinician, the apothecary must invest time to manufacture placebos and encapsulate pills to ensure blinding. And the patient must persevere, performing the continuous measurements and adhering to the alternating intervention and control conditions, in some cases even if the results appear poor. Hence, the trade-off between the time invested and the potential impact of the results must be critically evaluated.

Ethical issues

Interviewees indicate that there is a fine line between conducting care according to the N-of-1 approach and trying to help a patient based on the knowledge from group-based research (like RCTs). A clinician should therefore carefully determine the scope of operation. This has several consequences, which have all been labelled as barriers. No facilitators were mentioned.

Barriers

A clear definition for when N-of-1 transcends structured clinical treatment to scientific research is crucial to determine when to engage in an N-of-1 trial. Examples of good reasons for N-of-1 are patients experiencing side effects, long-term patients wanting to experiment with different treatment plans, suspicion of over- or undertreatment, or cost reduction with biosimilars. However, initiating a placebo-controlled experiment inevitably means that patients will deliberately and knowingly be disinformed while participating, and the results will be kept from the patient until after the trial. Additionally, a patient will need to re-engage in a placebo block after potentially having experienced great symptom relief.

Ethically this was tough, that you say to patients, now you have experienced that there is a very good medicine, but now get off of it to again also engage in 4 weeks of placebo again. (MD7)

Therefore, it is important to communicate beforehand that several treatments will be given without the patient knowing which one is being given at any point in time, that side effects may take place without knowing whether they originate from the treatment or a nocebo effect, and that the right to the patient’s own data will be violated for the duration of the trial. If the patient gives explicit consent for this, these ethical issues may be circumvented. The MDs mention that they do expect most patients to be willing to give this consent, given the potential benefits for them.

The ethical side, you can come to an agreement with people: okay you have the feeling that you get these problems from these medications. That could be because of a nocebo, are you okay with us giving you injection A and injection B several times without saying which one it is, and then you have to say from which one you get symptoms and then we will see if it matters. Many people will agree with that. If you do it in that way, you can ask consent, so you can work around the ethical side. (MD11)

One MD offers a different perspective: how ethical is it that patients are currently prescribed pills which might not be the best fit for them? If the result is for the patient only, validation could easily be done during a regular consultation without ethical violations.

Domain 4: characteristics of the individual

Conducting an N-of-1 trial requires specific health skills and strong motivation from both the patient and the physician. Additionally, it is psychologically demanding for the patient. Table V provides an overview of the barriers and facilitators per theme for the domain Characteristics of the individual.

Table V. Overview themes, barriers, and facilitators for the domain characteristics of the individual.

Theme	Barriers	Facilitators
Patient skills	Not all patients can handle the relatively tedious task of continuous data gathering.	Patients who understand why they are given a certain treatment are more likely to be motivated and therapy compliant.
	Not all patients have sufficient insight in the fluctuations in their physical condition.
Physician skills	Not listening well enough to the patient’s needs and conception of their disease.	Shared decision making is becoming more accepted in medical practice.
		The importance of basic coaching skills in shared decision-making is being recognized.
Patient acceptance	Strong patient beliefs may hinder acceptance of treatment.	A physician’s enthusiasm for a certain type of treatment and N-of-1 for the benefit of the patient can help to change a patient’s attitude.
	Cultural differences in beliefs are not always accounted for.
Other psychological aspects	Full trust between patient and healthcare provider is not always present.	Recognition for patients’ efforts and close involvement of healthcare providers can make patients feel understood.
	A patient’s past negative experiences can lead to resistance to new treatments.	Patients altruistically contribute to science, and their results can help their fellow patients.
	Willingness to transition to new treatment block in N-of-1 trial is low if patients do well on a certain treatment.
	Continuous measurement at home may make patients feel more ill than they are.

Patient skills

Patients’ health literacy and ability to accurately measure and collect data were frequently mentioned as preconditions for providing care according to an N-of-1 approach.

Barriers

To gather high-quality data, patients must understand why accurate data are vital for the success of an N-of-1 approach, and what the consequences of missing values are, given that some patients feel there is no point filling out the questionnaires if there has been no change in their condition.

It all depends on the accuracy of data they submit. So we also had patients there, or actually 1 or 2 who did not fill out anything for a whole week, or even 2, or did not use the medicine for a while without consulting with us, yes those are things … yes if you don’t have the data, you have nothing, you may have an intuition, but you also have that during an ordinary consult. So you’re back where you started. (MD7)

Moreover, patients need to have sufficient insight in their condition and its fluctuations. One interviewee stated that some patients do not appreciate the subtlety of using a 0–10 score and consistently score good days as 8 and bad days as 4. This is not necessarily a problem, as long as the patient is consistent, and the clinician or researcher understands the patient’s way of thinking.

When treating patients with lower health literacy, the way of taking measurements must be tailored to what a patient can handle, or the help of family members enlisted. Interviewees acknowledged that it would be a shame if patients with lower health literacy could not participate in care according to the N-of-1 methodology, and that many pain scores (like the Visual Analogue Scale) are not that complicated to fill out at home, either on paper or digitally.

Facilitators

Patients who know why they are being treated and believe in their treatment are often the most compliant ones. Therefore, what is needed most are an open mind and willingness to complete the trial. When questioned about specific failure factors, the MDs in particular remain quite pragmatic: good data collection is hard.

Nothing ever always goes well in medicine. I said it just now, therapy compliance, this is also rather like therapy compliance, properly filling out these types of questionnaires, that is really nasty, that is a reality. (MD1)

Physician skills

An important precondition the interviewees recognized is that the relation between patient and physician must be based on an attitude of equality and that expectations must be clearly communicated beforehand. Elaborating physicians’ skill sets to help them better understand their patients were all labelled as facilitators.

Facilitators

Crucial to success is that both patient and physician intrinsically support the effort, which can only be achieved through shared decision making; high-quality communication between patient and physician is considered a precondition. Essentially, every physician needs coaching skills to help the patient move forward during the treatment period.

I do not heal anyone, but we can propose something which allows patients to gain knowledge about their own actions and to make good choices again. (PT4)

Examples of critical skills proposed are the ability to empathize with the patient psychologically, to visualize outcomes with the patient, to accept that what the patient wants does not always match what the physician thinks is the best solution, to be able to reflect on one’s own code of conduct, to be able to spot when a patient experiences a fallback, and to develop a broad perspective on the possibilities of interdisciplinary healthcare.

Patient acceptance

Even if an N-of-1 trial yields the most reliable results in the world, a patient’s beliefs, experiences, and willingness to change are ultimately going to determine whether they will accept the intervention, and whether the N-of-1 effort will eventually impact a patient’s life.

Barriers

Patient beliefs can be a strong barrier to acceptance of an N-of-1 approach or its results. Even if the N-of-1 shows that symptoms during placebo periods are no different than those during treatment periods, this is not always enough to convince a patient.

You sometimes have a discussion, the patient says: I don’t believe this does anything, I don’t want to take this rubbish. While in my experience he’s doing very well. Then you cannot convince that patient properly that it actually does work and you say, okay we will stop it. I had a patient once, who probably also had a memory disorder, who did that 3 or 4 times already. So we stop treatment, and his rheumatoid arthritis flares up, so he says, this and that. I say: yes but you first had, we give the drug back. And he’s doing very well again. But after a year he starts grumbling again, this rubbish and so on. (MD16)

Interviewees explain that managing patients’ wishes, beliefs and ideas regarding alternative forms of treatment can be really challenging if these do not match with those of their own. For example, a patient may develop resistance to a treatment plan if physicians show resistance to accommodating certain alternatives that patients might want to try. Finally, cultural differences are not to be overlooked when personalizing treatments.

Facilitators

If a physician is enthusiastic about one type of treatment versus another, this could positively shape a patient’s attitude towards an N-of-1 approach. Another prominent facilitator one MD pointed out is that the benefits of the treatment must be sufficiently noticeable for the patient, which is frequently the case in studies in which people with rare disease conditions participate.

Other psychological aspects

All interviewees agree that there must be complete trust between patient and physician and strong mutual motivation if a healthcare trajectory based on the N-of-1 approach is to be successful.

Barriers

If a patient has had either positive or negative experiences in the past, it may influence trial adherence. Similarly, our interviewees expect that the clinician’s expectations and knowledge can influence the trial as well. Additionally, hindsight bias could be introduced if patients report subjective symptoms.

Furthermore, if the time it takes for health improvements to become noticeable is long, enthusiasm for participating in the trial might diminish. On the other hand, if a patient does experience a relatively fast relief in symptoms during one block, they might be unwilling to continue the trial, because switching to the next intervention block means that at least one more placebo block is coming up.

In that study we noticed, because the medicine we tested actually worked very well, so there was, well in any case reluctance on the part of the patients to then move into a new block […] I can imagine that a patient would think, yes I have had two medicines in four weeks, I have a strong preference for that medicine, can we not analyze the data now because I want to continue with this. So you have to make very clear agreements beforehand. (MD7)

Facilitators

Many interviewees reflected on the option of sharing the results with patients and clinicians during the trial. While this may affect the behaviour of both the patient and the clinician, it may also improve motivation, provide opportunities for reflection, and help them better attune their behaviour. This may be helpful if, as some interviewees fear, conducting measurements at home emphasizes the fact that people are dealing with a health condition, which could be demotivating. If their efforts are rewarded with attention and recognition from their clinician, patients might feel more emotionally involved, especially if the N-of-1 approach truly matches their own search for answers. Potentially, two MDs also suggest that once something works for a patient, altruism might be another motive to engage in N-of-1.

I also think that if I think up something entirely new for a patient or think up something entirely new together with the patient, then the patient is always inclined to say: okay well I will participate to show, not that something works for this condition, but that something works for me in any case, because then other patients can also benefit. So it is also something I often say to a patient: this is wonderful that we have discovered this together, but would it not be even more wonderful if we formally researched this in an N-of-1 design so that other patients can also benefit from this. (MD13)

Discussion

In this study, we explored perceived barriers and facilitators for adopting an N-of-1 approach as a method to enhance personalized treatment in regular medical practice as identified among different healthcare stakeholders. Overall, the interviewees were positive about the potential of using N-of-1 methodology in the clinical domain. However, a substantial number of barriers were perceived. The primary barriers lie in the domain of degrees of freedom to tailor the N-of-1 methodology (in terms of validity and ethics), the lack of knowledge about and experience in performing valid N-of-1 trials in clinical practice, the incompatibility of the N-of-1 approach within the current healthcare system, the still somewhat paternalistic doctor-patient relationship, and the pragmatic challenges related to implementation (i.e., multidisciplinary collaborations, costs, time investment, and lack of infrastructure). The most important perceived facilitators were the changing role of the patient, who is increasingly becoming the owner of his/her health problems, and the technological advances to support monitoring of outcomes over time, which is a crucial aspect of N-of-1 trials. Visual overviews of the key barriers and facilitators are given in Figures 1–4.

Figure 1. Barriers and facilitators comprising key barrier 1.

Figure 2. Barriers and facilitators comprising key barrier 2.

Figure 3. Barriers and facilitators comprising key barrier 3.

Figure 4. Barriers and facilitators comprising the key facilitators.

Key barrier 1: methodological rigor

At this moment, N-of-1 implementation is led by pioneering individuals, either driven by professional curiosity or a proactive patient’s wish to get more out of their treatment. The most prominently discussed topic by our interviewees was methodological rigour, calling into question the validity of the trials and how to deal with confounding factors. This concerns issues like how to facilitate an adequate baseline assessment, multiple reversals, blinding and multiple interventions. These are particularly relevant for interventions that cannot be easily blinded, such as physical therapy. Nevertheless, even in the physical therapy domain, a case has been made for the adoption of more N-of-1 single case research to improve problem solving in practice and increase collective knowledge as early as 1996 (Hasson, 1996). However, the greatest strength of N-of-1 trials is their ability to provide reliable data for personalized physiological and lifestyle advice (Greef et al., 2007). N-of-1 trials can provide rich overviews of individual variability (Schork, 2015) and the value of individual outcomes can be translated to improvements in medical treatments (Blackston et al., 2019; Punja et al., 2016). Even though the mechanisms are difficult to deduce from an N-of-1, it does show the effectiveness of an intervention for an individual (Bradbury et al., 2020) which is also reflected by the fact that the N-of-1 trial has been included in the Oxford Centre for Evidence-Based Medicine’s Levels of Evidence on Level 1, next to systematic reviews of randomized trials (OCEBM Levels of Evidence Working Group, 2011).

We suggest that the methodological requirements should be determined based on the purpose of the N-of-1 trial. In the case of answering clinical and scientific questions, a strict methodology should be adhered to (Clough et al., 2018; Porcino et al., 2020). However, in the case of a patient simply wanting a better solution to his or her problem, it would help them if their physician would assist them in conducting their own, relatively informal N-of-1 trials. Whitney et al (Whitney et al., 2018). put it accurately by stating that “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”. On top of this, one interviewee questioned the importance of true scientific validity in clinical practice, suggesting we should make “smart use of the placebo effect”. If an intervention cannot be blinded and insight in data is available, but the patient is nevertheless improving, how important is it whether the effect is brought about by the intervention or simply the personalized attention the patient is receiving? Admittedly, if we are to obtain data from clinical practice, this could be a problem for drawing scientific conclusions. Potentially, aggregation of larger numbers of such N-of-1 approaches could be part of the solution. And it should not be forgotten that such data can serve to generate hypotheses at best.

A final consideration is that in practice, medical assessment currently often comes down to the simple, error-prone question by the care provider: “How are you doing” (Mizra et al., 2017). This question also does not allow one to infer a causal relation between the initiated intervention and one’s experienced response, and we argue that any form of N-of-1 usage will provide more validity than reliance on patients’ memories, as also confirmed by one of our interviewees.

Key barrier 2: time and financial investments

On the topic of costs, interviewees argued that while N-of-1 trials are initially costly for healthcare providers, they may result in future cost reductions because of better tailored medical prescriptions. However, the biggest issue perceived by the interviewees was that insurance companies are not willing to pay for N-of-1 treatment, nor will healthcare insurers refund medications if the outcomes deviate from standardized protocols. This comes across as paradoxical. After all, insurance companies demand evidence-based results, but are not willing/able to facilitate experiments that would establish whether a treatment/medication really works on an individual level. A potential solution suggested by one of the interviewees was that insurance companies could provide special grants for implementing N-of-1 trials in the care processes of (potentially highly expensive) patients or patient populations. Funds should be provided with the goal of facilitating a decrease in use of healthcare facilities and focusing costs to those cases in which effectiveness has been demonstrated.

To cope with the substantial time investment, clinicians will need additional support and dedicated time. One interviewee suggested an N-of-1 department within hospitals, comprising specialized professionals leading the practical implementation and logistics of the trials, providing support for patients, and safeguarding proper data analysis and methodological validity, especially if the outcomes are to be used for research purposes as well. Benefits of involving researchers include that a stricter evaluation of the different treatments could be performed, and it could relieve the burden from the clinicians and patients. The interviewees do, however, emphasize that there should be a clear separation between research and clinical tasks.

Key barrier 3: required education

The lack of knowledge about and exposure to N-of-1 is considered an issue by our panel. The interviewees call for a continuous methodological education on N-of-1 trials and how to interpret their results on all levels; from students to seasoned practitioners and clinicians. This becomes even more apparent when we consider that different statistical approaches will be needed to handle data variation during analysis. Some education is also required at the level of the patient. Patients need to be open-minded and aware of the stakes involved in an N-of-1 trial and to be informed that they have to make trade-offs between the effort involved, the expected impact on their life, and the potential contribution to science. On top of this, some initial training in how to take and interpret measurements will be essential. The abovementioned dedicated N-of-1 centre could play a crucial facilitating role here. Additionally, many of our interviewees were enthusiastic about helping patients with their N-of-1 journey, so allotting some extra time to physicians and researchers can also contribute to solving the problem.

Key facilitators: patient ownership and technological support

The patient advocate we interviewed was clear on the matter: patients are taking matters into their own hands when they cannot find relief in conventional healthcare systems. They are taking ownership of their health, perform their own research on their condition and frequently come to their doctors with questions about their own findings, or requests for certain medical tests that are not part of standardized protocols. The most proactive patients are embracing the paradigm of citizen science and are aided by technological support devices such as smartwatches, smartphones, and infrastructures for data sharing such as the Open Humans initiative. These patients prioritize recognition from their physicians and want their findings to be taken seriously, which requires a change in attitude and physician skills. With the increasing accuracy of home measurement devices, aggregation of individual datasets could yield valuable insights such as the progression of a condition, lifestyle influences, and patient awareness, which would not otherwise easily become apparent if relying solely on physician visits of relatively short duration. In the research domain, initiatives are already being taken to analyse such unstructured datasets. For this proactive patient group, informally conducted N-of-1 trials are of crucial importance. The potential of this group could be leveraged if clinicians are willing to embrace such initiatives, help their patients in the right directions, and keep an open mind to learn from potentially unexpected or new insights. This is also likely to reduce resistance to treatment, as well as optimize treatment based on shared decision making for the unique patient. An overview of how certain barriers and facilitators may interact and influence each other is given in Figure 5.

Figure 5. Potential interaction between barriers and facilitators.

Strengths and limitations

There are several strengths and limitations to this work. Strengths include the fact that we used an experienced interviewer and performed the data analysis with two independent persons with diverse backgrounds. We chose to include a wide variety of expertise in the sample, to get a broad perspective which looks further than solely a clinical setting. With the developments in precision medicine, the popularity of citizen science in the health domain among patients, the use of consumer technology and the positive anecdotal results that emerge from these settings, we considered it important to include the perspectives of researchers, healthcare providers like physical therapists and patients in addition to the more traditional physician perspectives. The added value became apparent when we found that this group is more willing to embrace less strictly conducted N-of-1 trials for both research and individual health optimization purposes than the physician group.

Limitations of this work include the lack of data saturation, which might have resulted in missing other barriers and facilitators, and forgoing member-checking our findings with the participants. Nonetheless, substantial overlap between participants was observed, as well as subtle differences in focus which were remarkably consistent within the professional groups, in particular the clinicians and the researchers.

To our knowledge, we are the first to study the perceived barriers and facilitators of adopting N-of-1 trials in clinical practice among earlier adopters. In 2009, Kravitz et al (Kravitz et al., 2009). studied barriers and facilitators among patients and physicians who had not yet actively worked with N-of-1, but showed interest in the topic. These authors identified the following primary barriers: (lack of) education, operational complexity, costs, and the threatening paradigm shift in the doctor-patient relationship, in which physicians foresee discomfort in taking on the role of scientist. Our findings reaffirm the barriers identified by Kravitz et al (Kravitz et al., 2009) but our data add another layer of complexity (potentially because we interviewed a broader sample) related to incompatibility of N-of-1 trials in the current healthcare systems. Our interviewees suggested that the current healthcare system has not been designed to facilitate monitoring and experimentation within an individual as the system is organized to deal with groups/populations. If this is indeed the case, recommendations for the adoption of N-of-1 trials should be more fundamental than developing checklists for physicians (Porcino et al., 2020) renewing the methodology (Whitney et al., 2018) or using the latest technology (Coppini & Colantonio, 2017). As Davidson et al (Davidson et al., 2018). suggest, we need interdisciplinary and multi-stakeholder networks to determine best practices for delivery of N-of-1 care, we need to set up trial platforms and need to construct an open, transparent, deep phenotype data bank, where N-of-1 trial data can be deposited (Davidson et al., 2018). These suggestions are in line with those provided in our study.

Based on our findings, we believe the adoption of an N-of-1 approach in clinical practice is challenging and are sceptical that efforts from healthcare professionals alone will ever lead to sustainable implementation of N-of-1 methodology. Therefore, we stress the importance of interprofessional and multi-stakeholder initiatives. A noteworthy development on the topic of N-of-1 methodology is patient empowerment through the citizen science movement which is expanding into the health domain (Den Broeder et al., 2018; King et al., 2019; Rosas et al., 2022; Rowbotham et al., 2019; Wiggins & Wilbanks, 2019). Patients are collaborating to gather and share their own data to find solutions for their conditions. Examples range from elaborate data gathering using consumer health sensors and smartphone apps to applying techniques like text mining on patient forum discussions to discover relations between experiences, and how frequently such experiences are being reported (Bietz et al., 2019; van Oortmerssen et al., 2017).

In conclusion, there is a strong belief that an N-of-1 approach has the potential to play a prominent role in transitioning the current health system towards embracing personalized medicine, not only using new technology but by redesigning the system itself. However, to date, there are many barriers which are believed to be deeply ingrained in our healthcare system that hamper the uptake of N-of-1 methodology in daily clinical care, making the N-of-1 trial in the strict sense currently interesting for research purposes only. Nevertheless, patient-initiated movements using self-research are rapidly gaining momentum, and it remains to be seen to what extent they may influence clinical practice.

Dataset considerations

In line with ethical considerations and privacy concerns, the qualitative data presented in this study, due to its sensitive nature and inability to be fully anonymized, cannot be made publicly available. However, we are committed to promoting transparency and facilitating scholarly discussion. Researchers interested in discussing strategies to share or access our data are encouraged to contact us at [email protected]. We are willing to engage in conversations regarding potential methodologies, anonymization techniques, and collaborative approaches to ensure data integrity while respecting participant confidentiality.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Regieorgaan SIA/NWO under Grant [number KIEM.LSH.03.014].

Notes on contributors

Ilona Wilmont

Dr. Ilona Wilmont, PhD is a researcher at HAN University of Applied Sciences and Radboud University Nijmegen, with a background in Information Sciences. Her specialty is qualitative research, with a focus on how personalized healthcare and citizen science methodologies can help patients support their quality of life. She collaborates with Stichting Je Leefstijl Als Medicijn, one of the largest Dutch patient communities, as well as with an outpatient epilepsy clinic, on empowering epilepsy patients to use lifestyle interventions and self-measurement to improve quality of life. She also works with Leiden University Medical Centre and Erasmus University Medical Centre on lifestyle interventions for migraine patients. Furthermore, Dr. Wilmont teaches Research Methods and Academic Writing at Radboud University Nijmegen.

Mark Loeffen

Mark Loeffen, PT is an experienced physical therapist and manual therapist specializing in treating long-term back, neck, and shoulder pain. Apart from his work with clients he participates in scientific research projects to improve interdisciplinary collaboration within physical therapy, as well as to improve outcomes for patients by using personalized approaches.

Thomas Hoogeboom

Dr. Thomas Hoogeboom, PT, MSc, PhD is trained physical therapist and human movement scientist. He currently holds an assistant professor position at IQ healthcare department of the Radboud university medical center. Dr. Hoogeboom specializes in implementing and evaluating personalized interventions, and in the methodologies to facilitate this. He has participated in the development of nationwide medical guidelines, and collaborates with several other universities, including the University of Montana and the University of Utah, on the topic of shared decision making between patients and healthcare providers and how clinicians can integrate physical activity in the lives of medically fragile patient groups. Dr. Hoogeboom has a particular interest in meta-research and is currently setting up the Meta-Phit group together with researchers from the University of Technology Sydney.