The ethical and regulatory oversight of any clinical activity related to human subjects is commonly determined based on its categorization as either clinical practice or research. Prominent bioethicists have criticized the traditional distinctions used to delineate these categories, calling them counterproductive and outmoded, and arguing that learning and clinical practice should be deliberately and appropriately integrated. Personalized trials represent a clinical activity with characteristics that overlap both categories, making ethical and regulatory oversight requirements less straightforward. When the primary intent of the personalized trial is to assist in the conduct of individualized patient care with an emphasis on protecting the clinical decision from the biases inherent in usual clinical practice, how should this activity be regulated? In this article, we will explore the ethical underpinnings of personalized trials and propose various approaches to meeting regulatory requirements. Instead of imposing standard research regulations on the conduct of all personalized trials, we recommend that personalized trialists and IRB panels should consider whether participation in a personalized trial results in any foreseeable incremental increase in risk to the participant compared with usual care. This approach may reduce regulatory barriers, which could promote more widespread uptake of personalized trials.

BACKGROUND: Retinitis pigmentosa (RP) is an inherited disease characterized by a progressive loss of rod photoreceptors of the eye, leading to irreversible blindness. To date, to our knowledge, no clinical prospective studies have been undertaken that could document the effect of interventions that could reverse or reduce the progression of this disease. The application of microcurrent stimulation (ES) of the eye in the treatment of chronic eye diseases such as glaucoma and age-related macular degeneration has been used over several decades and has been reported to have beneficial effects to reduce the progression of these blinding diseases and has been supported by animal studies and smaller clinical studies, but to date, no large randomized clinical trials on the use of microcurrent therapy have been published. More recent clinical reports have also shown beneficial effects of ES on slowing the progression of RP but also lacks data from robust prospective clinical outcome studies. To our knowledge, this is the first prospective randomized study to evaluate the safety and clinical effectiveness of transpalpebral electrical stimulation (TpES) on the progression of RP.
METHODS: Randomized prospective study using N-of-1 trial 3 single-blind, crossover comparisons. The intervention period of each comparison is divided into treatment period and control period which are randomized arranged. Twelve participants will be strictly recruited in N-of-1 trial by the researcher in accordance with the inclusion and exclusion criteria. The main outcome of interest examined after each cycle of the 8-week intervention period is the assessment of the visual field (VF). Other variables of interest are best corrected visual acuity (BCVA), retinal function using electroretinogram (ERG), and visual function using NEI VFQ-25 questionnaire. Objective assessments of retinal changes will be undertaken using optical coherence tomography (OCT) and fundus autofluorescence (FAF).
CONCLUSIONS: The trial will evaluate the efficacy and safety of microcurrent stimulation on RP and provide high-quality evidence for clinical application through N-of-1 trial.
BACKGROUND: Chinese Clinical Trial Registry; ChiCTR2300067357; https://www.chictr.org.cn/showproj.html?proj=174635 . Registered on 5 January 2023.

UNASSIGNED: Ideally, real-world data (RWD) collected to generate real-world evidence (RWE) should lead to impact on the care and health of real-world patients. Deriving from care in which clinicians and patients try various treatments to inform therapeutic decisions, N-of-1 trials bring scientific methods to real-world practice.
UNASSIGNED: These single-patient crossover trials generate RWD and RWE by giving individual patients various treatments in a double-blinded way in sequential periods to determine the most effective treatment for a given patient.
UNASSIGNED: This approach is most often used for patients with chronic, relatively stable conditions that provide the opportunity to make comparisons over multiple treatment periods, termed Type 1 N-of-1 trials. These are most helpful when there is heterogeneity of treatment effects among patients and no a priori best option. N-of-1 trials also can be done for patients with rare diseases, potentially testing only one treatment, to generate evidence for personalized treatment decisions, designated as Type 2 N-of-1 trials. With both types, in addition to informing individual\'s treatments, when uniform protocols are used for multiple patients with the same condition, the data collected in the individual N-of-1 trials can be aggregated to provide RWD/RWE to inform more general use of the treatments. Thereby, N-of-1 trials can provide RWE for the care of individuals and for populations.
UNASSIGNED: To fulfill this potential, we believe N-of-1 trials should be built into our current healthcare ecosystem. To this end, we are building the needed infrastructure and engaging the stakeholders who should receive value from this approach.

BACKGROUND: The main aim of this paper is to present the feasibility of rigorously designed multiple N-of-1 design in prosthetics research. While research of adequate power and high quality is often lacking in rehabilitation, N-of-1 trials can offer a feasible alternative to randomized controlled group trials, both increasing design power at group level and allowing a rigorous, statistically confirmed evaluation of effectiveness at a single patient level. The paper presents a multiple N-of-1 trial protocol, which aim is to evaluate the effectiveness of Unity, a prosthetic add-on suspension system for amputees, on patient-reported comfort during daily activities (main outcome measure), prosthesis wearing time, perception of limb-prosthesis fitting and stump volume and functional walking parameters.
METHODS: Multicenter, randomized, prospective, double-blind multiple N-of-1 trial using an introduction/withdrawal design alternating Unity connected/disconnected phases of randomized length on twenty patients with unilateral transtibial amputation. The primary outcome measure is the Prosthetic Socket Comfort Score (SCS), a validated measure of comfort, administered daily by an phone app designed for the study. Secondary outcomes measures will be collected during the 50 days period of the N-of-1 trial: (1) by the same app, daily for patient-reported limb-prosthesis fitting, stump volume variation, and daily wearing time of the prosthesis; (2) by a pedometer for the number of steps per day; (3) by blind assessors in the rehabilitation center during adjustment visits for functional walking parameter (L-Test, 6-minute walk test), and by the patient for the QUEST, and ABC-S. Effectiveness of the Unity system regarding SCS and daily secondary outcome measures will be tested by randomization test. The secondary outcome measures assessed during visits in the rehabilitation center will be analyzed by Non Overlap of All pairs. An estimate of the effect on the amputee population will be generated by aggregating each individual clinical trial (N-of-1 trial) by Hierarchical Bayesian methods.
CONCLUSIONS: This study protocol was designed to answer the question \"which device is best for THIS patient\" and to conclude at a group level on the effectiveness of a new devic, using a Multiple N-of-1 trial, which is promising but underused in prosthetics research so far.
BACKGROUND: N° ID-RCB 2020-A01309-30 Clintrial.gov : NCT04804150 - Retrospectively registered March 20th 2021.

The simulation study investigated the relationship between the local linear trend model\'s data-comparison accuracy, baseline-data variability, and changes in level and slope after introducing the N-of-1 intervention. Contour maps were constructed, which included baseline-data variability, change in level or slope, and percentage of non-overlapping data between the state and forecast values by the local linear trend model. Simulation results showed that baseline-data variability and changes in level and slope after intervention affect the data-comparison accuracy based on the local linear trend model. The field study investigated the intervention effects for actual field data using the local linear trend model, which confirmed 100% effectiveness of previous N-of-1 studies. These results imply that baseline-data variability affects the data-comparison accuracy using a local linear trend model, which could accurately predict the intervention effects. The local linear trend model may help assess the intervention effects of effective personalized interventions in precision rehabilitation.

UNASSIGNED: Blood pressure [BP] should be measured using a bare upper arm with an appropriately sized cuff. In practice, it is more convenient to measure BP on a bare arm below a rolled-up sleeve or on a sleeved arm.
UNASSIGNED: A n-of-1 randomized controlled trial was performed to assess the difference between BP measurements over a sleeve or below a rolled-up sleeve.
UNASSIGNED: The study subject was male, white, 72 years old, BMI 26 kg/m2, arm circumference 29 cm, and under stable antihypertensive treatment. The mean of three BP measurements over a thin sleeve was compared with measurements below a rolled-up sleeve. Additional measurements on a completely bare arm, with thicker sleeves and up to three layers were performed. The order of measurements was determined by chance and two oscillometric devices were used. Descriptive statistics, Bland-Altman test and 2-side T test were used for comparisons.
UNASSIGNED: 504 measurements were performed: 50 % over the sleeve and 50 % below the rolled-up sleeve. The mean systolic blood pressure (SBP) was respectively 116.9 ± 9. 2 [95% CI 115.7-118.0, range 96-135] and 122.8 ± 9.2 [95% CI 121.7-124.0, range 103-139, p = 0.001] mm Hg. The mean diastolic blood pressure [DBP] was respectively 67.6 ± 6.8 [95% CI 66.8-68.4, range 52-84] and 71.8 ± 6.8 [95% CI 71.0-72.7, range 55-85, p = 0.001] mm Hg. There was no significant difference between the measurements over the sleeve and on the completely bare arm [n = 94, p = 0.97]. Sleeve thickness with 2 layers up to 3 mm thick did not affect the results.
UNASSIGNED: Blood pressure measurements over a thin sleeve were significantly lower than measurements below a rolled-up sleeve and match measurements on a completely bare arm.

Blood pressure (BP) is often inadequately controlled in children treated for hypertension, and personalized (n-of-1) trials show promise for tailoring treatment choices. We assessed whether patients whose treatment choices are informed by an n-of-1 trial have improved BP control compared to usual care.
A randomized clinical trial was conducted in a pediatric hypertension clinic in Houston from April 2018 to September 2020. Hypertensive adolescents and young adults 10-22 years old were randomized 1:1 to a strategy of n-of-1 trial using ambulatory BP monitoring to inform treatment choice or usual care, with treatment selected by physician preference. The primary outcome was the proportion of patients with ambulatory BP control at 6 months in a Bayesian analysis.
Among 49 participants (23 randomized to n-of-1 trials and 26 to usual care), mean age was 15.6 years. Using skeptical priors, we found a 69% probability that n-of-1 trials increased BP control at 6 months (Bayesian odds ratio (OR) 1.24 (95% credible interval (CrI) 0.51, 2.97), and 74% probability using neutral informed priors (OR 1.45 (95% CrI 0.48, 4.53)). Systolic BP was reduced in both groups, with a 93% probability of greater reduction in the n-of-1 trial group (mean difference between groups = -3.6 mm Hg (95% CrI -8.3, 1.28). There was no significant difference in side effect experience or caregiver satisfaction.
Among hypertensive adolescents and young adults, n-of-1 trials with ambulatory BP monitoring likely increased the probability of BP control. A large trial is needed to assess their use in clinical practice.
NCT03461003.
ClinicalTrials.gov; NCT03461003.

UNASSIGNED: Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or \"n-of-1\" observational study (N1OS) is useful in exploring such suggested effects by examining each subject\'s exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual.
UNASSIGNED: Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population.
UNASSIGNED: We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life.
UNASSIGNED: We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate.
UNASSIGNED: This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.

Researchers and practitioners often use single-case designs (SCDs), or n-of-1 trials, to develop and validate novel treatments. Standards and guidelines have been published to provide guidance as to how to implement SCDs, but many of their recommendations are not derived from the research literature. For example, one of these recommendations suggests that researchers and practitioners should wait for baseline stability prior to introducing an independent variable. However, this recommendation is not strongly supported by empirical evidence. To address this issue, we used Monte Carlo simulations to generate graphs with fixed, response-guided, and random baseline lengths while manipulating trend and variability. Then, our analyses compared the type I error rate and power produced by two methods of analysis: the conservative dual-criteria method (a structured visual aid) and a support vector classifier (a model derived from machine learning). The conservative dual-criteria method produced fewer errors when using response-guided decision-making (i.e., waiting for stability) and random baseline lengths. In contrast, waiting for stability did not reduce decision-making errors with the support vector classifier. Our findings question the necessity of waiting for baseline stability when using SCDs with machine learning, but the study must be replicated with other designs and graph parameters that change over time to support our results.