Diet therapy for inflammatory bowel disease (IBD) is an international research priority but guidance for IBD-specific diet trial design is lacking. This review critically evaluates key elements of prospective IBD food-based intervention trials and identifies gaps. Electronic databases were searched for interventional IBD diet studies. Prospective primary studies/trials were included if used food-based dietary strategies. Forty studies/trials evaluating 29 food-based strategies as therapy for IBD were identified. Considerable heterogeneity in diets, trial design, and methodology exists. Thirty-one trials (78%) intended the diet to modulate inflammation but 14/31 (46%) did not have a primary endpoint measuring an objective change in inflammatory activity and 20/31 (65%) controlled for medication stability prior to application of diet at baseline. Higher-quality IBD diet trials used symptom-based assessment tools coupled with an objective evaluation of inflammatory activity. Dietary advice trials are the most common. One-third of trials developed and administered diet education without a dietitian. Evaluation and reporting on adherence to diet therapy occurred in <60% of trials. Failure to include or report on key elements of trial design reduced the interpretability and validity of the results. This is a considerable limitation to advancing scientific knowledge in this area. Diet therapy trials should adhere to similar rigorous quality standards used to develop other IBD therapies. Therefore, a set of practical recommendations was generated to provide the authors' perspective to help inform the future design of high-quality IBD diet trials.

The interplay of daily life factors, including mood, physical activity, or light exposure, influences sleep architecture and quality. Laboratory-based studies often isolate these determinants to establish causality, thereby sacrificing ecological validity. Furthermore, little is known about time-of-year changes in sleep and circadian-related variables at high resolution, including the magnitude of individual change across time of year under real-world conditions. The Ecology of Human Sleep (EcoSleep) cohort study will investigate the combined impact of sleep determinants on individuals' daily sleep episodes to elucidate which waking events modify sleep patterns. A second goal is to describe high-resolution individual sleep and circadian-related changes across the year to understand intra- and inter-individual variability. This study is a prospective cohort study with a measurement-burst design. Healthy adults aged 18-35 years (N = 12) will be enrolled for 12 months. Participants will continuously wear actimeters and pendant-attached light loggers. A subgroup will also measure interstitial fluid glucose levels (six paticipants). Every 4 weeks, all participants will undergo three consecutive measurement days of four ecological momentary assessments each day ('bursts') to sample sleep determinants during wake. Participants will also continuously wear temperature loggers (iButtons) during the bursts. Body weight will be captured before and after the bursts in the laboratory. The bursts will be separated by two at-home electroencephalogram recordings each night. Circadian phase and amplitude will be estimated during the bursts from hair follicles, and habitual melatonin onset will be derived through saliva sampling. Environmental parameters (bedroom temperature, humidity, and air pressure) will be recorded continuously.

The experience sampling method (ESM), also known as ecological momentary assessment, is gaining popularity in physical activity research. This method involves assessing participants' behaviors and experiences repeatedly over time. One key advantage of ESM is its ability to temporally separate the dependent and independent variable of interest, reducing the risk of reverse causality. However, temporal separation alone is insufficient for establishing causality. This methodology paper illustrates the importance of the identification phase in drawing causal conclusions from ESM data. In the identification phase the causal effect of interest (or estimand) is specified and the assumptions under which a statistical association can be considered as causal are visualized using causal directed acyclic graphs (DAGs).

We demonstrate how to define a causal estimand and construct a DAG for a specific ESM research question. The example focuses on the causal effect of physical activity performed in real-life on subsequent executive functioning among older adults. The DAG development process combines literature review and expert consultations to identify time-varying and time-invariant confounders.

The developed DAG shows multiple open backdoor paths causing confounding bias, even with temporal separation of the exposure (physical activity) and outcome (executive functioning). Two approaches to address this confounding bias are illustrated: (1) physical control using the within-person encouragement design, where participants receive randomized prompts to perform physical activity in their natural environment, and (2) analytic control, involving assessing all confounding variables and adjusting for these variables in the analysis phase.

Implementing the identification phase enables ESM researchers to make more informed decisions, thereby enhancing the validity of causal inferences in studies aimed at answering causal questions.

Bacteriophage (phage) therapy has emerged as a promising solution to combat the growing crisis of multidrug-resistant (MDR) infections. There are several international centers actively engaged in implementation of phage therapy, and recent case series have reported encouraging success rates in patients receiving personalized, compassionate phage therapy for difficult-to-treat infections. Nonetheless, substantial hurdles remain in the way of more widespread adoption and more consistent success. This Review offers a comprehensive overview of current phage therapy technologies and therapeutic approaches. We first delineate the common steps in phage therapy development, from phage bank establishment to clinical administration, and examine the spectrum of therapeutic approaches, from personalized to fixed phage cocktails. Using the framework of a conventional drug development pipeline, we then identify critical knowledge gaps in areas such as cocktail design, formulation, pharmacology, and clinical trial design. We conclude that, while phage therapy holds promise, a structured drug development pipeline and sustained government support are crucial for widespread adoption of phage therapy for MDR infections.

Dapagliflozin reduces the urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) decline at a population level, but individuals show a large variation in responses. The n-of-1 trial design allows for direct assessment of treatment effects within an individual, and digital technologies and remote study assessments can reduce clinic visits, ease participant burden, and improve trial efficiency.

To assess individual UACR responses to dapagliflozin treatment in a decentralized clinical trial and the feasibility of remote data collection.

This decentralized, randomized, double-blind, placebo-controlled crossover trial using an n-of-1 approach was conducted using data from the Dutch primary and secondary health care systems between May 2021 and September 2022. Participants included adults with type 2 diabetes, a UACR greater than 20 mg/g, and an eGFR greater than 30 mL/min/1.73 m2. Statistical analyses were performed between June and August 2023.

Participants were assigned to two 1-week treatment periods with dapagliflozin, 10 mg/d, and two 1-week treatment periods with placebo in random order, with 1-week washout periods in between.

The primary outcome was the difference in the change in UACR from start to end of treatment between dapagliflozin and placebo in the per-protocol population. A post hoc exploratory analysis assessed the feasibility of remote data collection, including the proportion of urine and capillary blood samples successfully delivered to the central laboratory.

In total, 20 participants (mean [SD] age, 64.9 [8.7] years; 17 [85.0%] male) with a mean (SD) eGFR of 70.2 (20.3) mL/min/1.73 m2 and a median UACR of 94.7 (IQR, 29.8-242.6) mg/g were included in the study. They experienced a relative change in UACR with dapagliflozin compared with placebo of -15.1% (95% CI, -28.2% to -3.3%; P = .01). UACR changes showed considerable variation during both dapagliflozin and placebo treatment (first treatment period: median, -12.8% [range, -56.3% to 36.2%] and 2.9% [range, -86.7% to 35.1%], respectively). UACR changes correlated significantly between the first and second dapagliflozin exposure (r = 0.50; P = .03), with no correlation observed between the placebo exposure periods (r = 0.09; P = .69). With regard to remote data collection, 811 of 816 urine samples (99.4%) and 433 of 440 capillary blood samples (98.4%) were successfully delivered to the central laboratory.

In this crossover trial, individual UACR responses to dapagliflozin reflected a pharmacological response. Remote data collection proved to be reliable, supporting its use in future studies and clinical practice for monitoring individual dapagliflozin responses.

EudraCT identifier: 2020-004929-23.

Sonified biofeedback is a subtype of auditory biofeedback that conveys biological data through specific non-verbal sounds. It can be designed to provide augmented biomechanical feedback in near-real-time when provided as "concurrent" biofeedback. As a practice that developed spanning across engineering and the arts, sonified biofeedback can extend beyond simple tones and beeps, towards more fully incorporating music in movement training. Sonified biofeedback may leverage the motivational aspects of music in movement training, the neuroplasticity benefits demonstrated from participation in music-based interventions, and neurological auditory-motor coupling, all while providing task-relevant cues to facilitate motor (re)learning. Sonified biofeedback may also provide similar benefits as rhythmic cueing (e.g., rhythmic auditory stimulation), or added benefits because sonified biofeedback does not impose a strict isochronous rhythm when it follows rhythms that are driven by outputs of the motor control system. In this review paper, the unique opportunity presented by concurrent sonified biofeedback as a movement training tool for balance and gait is introduced and discussed.

This review paper brings together prior research from clinical, engineering, and artistic design sources using sonified biofeedback in balance and gait training across diverse end-users to highlight trends, reveal gaps in knowledge, and provide perspective for future work in the area. The goal was to review progress and critically assess research using sonified biofeedback during movement training for postural control or gait. 49 papers were selected based on their experimental investigation and statistical analyses of the effects of using sonified biofeedback to assist in movement training for feet-in-place balance tasks (20 papers) or gait tasks such as walking and running (29 papers). The sound design choices, experimental design features, and movement training results are summarized and reviewed. All but two studies reported at least one statistically significant positive effect of training with sonified biofeedback in biomechanical, clinical, or psychosocial measures. Conversely, only seven studies shared any negative effect on one biomechanical, clinical, or psychosocial measure (with five of these studies also reporting at least one other positive effect). After describing these encouraging findings, this review closes by sharing perspectives about future directions for designing and using sonified biofeedback in balance and gait training, and opportunities for more cohesive growth in this practice. One such suggestion is to pursue sonified designs and experimental designs that can translate to the neurorehabilitation field. This includes strategically selecting control groups and evaluation tasks to understand if improvements from training with one task transfer to additional relevant movement tasks. Additionally, it is important that future publications share details about the design processes and sound designs so researchers can more readily learn from one another.

Overall, this review shares the positive impact of using sonified biofeedback in balance and gait training. This review highlights the evidence of existing successes and potential for even more impactful future positive effects from using sonified biofeedback to help diverse populations with a spectrum of balance and mobility challenges and goals.

Randomised trials have long been recognised as the gold standard research tool for evidence-based medicine. The past decade has seen the emergence of several innovative trial designs that are revolutionising how trials are conducted. These innovative designs enable more efficient, pragmatic trials that can address complex research questions which were previously not possible. In this paper, we provide an overview of the key innovative designs that are likely to be useful in the emergency medicine context, namely cluster crossover and stepped wedge designs, sequential multiple assignment randomised trial (SMART) designs, and platform trials. We describe the main features of each design, outline their pros and cons, and describe when they may or may not be useful. We also provide examples of these innovative designs in contexts that are relevant to emergency medicine.

Bespoke therapies represent a promising tool to address a diverse range of genetic and acquired conditions, offering new hope where conventional treatments have fallen short. With the rapid rise of bespoke therapies, profound ethical and regulatory challenges emerge, making it crucial to establish a comprehensive framework that ensures these treatments reach clinical settings and meet patients' needs as quickly as possible while protecting all parties involved. Although current guidelines are continually evolving to address the range of ethical tensions raised by these therapies, several gaps remain. A significant unresolved question is determining where personalized interventions fall on the research-care continuum and understanding the institutional, regulatory, and ethical implications when custom therapies are classified as research, care, or a mix of both. To address these questions, we introduce a conceptual model alongside practical guidance for the development, administration, and evaluation of individualized therapies, using CRISPR/Cas9-based interventions for Duchenne Muscular Dystrophy as a case study. We argue that the goals of an intervention should be as individualized as the bespoke product itself, tailored to the specifics of each case. Rather than attempting to pinpoint the exact location of an intervention on the continuum, which may be hard to operationalize and have limited utility, our approach focuses on the practical details of how such interventions are administered and the individual component parts of an intervention. It advocates for transparent discussions among all partners to anticipate and adjust various components/parameters along the process of administering individualized interventions. Our paper highlights the most critical of these parameters in (1) the planning and development of individualized therapies in laboratory settings, (2) their regulatory oversight, and (3) evaluation. By discussing these stages and parameters in detail, we aim to provide guidance on how to navigate the ethical complexities inherent to individualized interventions and offer a preliminary framework for balancing the interplay between research objectives and patient care needs. Acknowledging that the scientific rigor and adequacy of any new model must be evaluated, we also identify the types of evidence that are required to validate that our model effectively meets individual and societal needs.

Digital twins in precision medicine provide tailored health recommendations by simulating patient-specific trajectories and interventions. We examine the critical role of Verification, Validation, and Uncertainty Quantification (VVUQ) for digital twins in ensuring safety and efficacy, with examples in cardiology and oncology. We highlight challenges and opportunities for developing personalized trial methodologies, validation metrics, and standardizing VVUQ processes. VVUQ frameworks are essential for integrating digital twins into clinical practice.

N-of-1 trials compare two or more treatment options for a single participant. These trials have been used to study options for chronic conditions such as arthritis and attention deficit hyperactivity disorder. In addition, they have been suggested as a means to study interventions in rare populations that may not be tractable to include in standard clinical trials, such as treatment options for HIV-positive patients in need of organ transplant. Sequential monitoring of accruing data has been well-studied in traditional clinical trials, but these methods have not yet been implemented in N-of-1 trials. However, the option to validly stop an N-of-1 trial early could deliver faster decisions that could directly improve the patient's health.

In this work, we propose and evaluate a framework to (1) facilitate sequential monitoring in individual N-of-1 trials with a continuous outcome and (2) combine results across a series of already-completed sequentially monitored N-of-1 trials. By employing the block structure common to N-of-1 trials, we suggest that existing approaches to sequential monitoring may be employed when data from one N-of-1 trial are analyzed with a linear mixed-effects model. To combine results across a series of already-completed sequentially monitored N-of-1 trials, we propose combining the naive estimates from constituent trials in a random-effects model with inverse-variance weighting. We evaluate these proposals via simulation.

We find that type 1 error can be substantially inflated for N-of-1 trials with a small number of planned blocks but can reach the nominal rate for trials with more planned blocks or those with larger numbers of periods per block or by using a t -value correction. For those settings with acceptable type 1 error, sequential monitoring results in similar power and on average earlier stopping compared with trials with no sequential monitoring. And, as expected, we find that including a larger number of constituent trials in a series reduces the mean-squared error of the combined point estimator.

Under suitable design considerations, our proposed framework for sequential monitoring can support clinicians in providing important decisions earlier, on average, for patients engaged in N-of-1 trials.

Mucopolysaccharidosis (MPS) encompasses a group of genetic lysosomal storage disorders, linked to reduced life expectancy and a significant lack of effective treatment options. Immunomodulatory drugs could have the potential to be a relevant medical approach, as the accumulation of undegraded substances initiates an innate immune response, which leads to inflammation and clinical deterioration. However, immunomodulators are not licensed for this indication. Consequently, we aim to provide evidence advocating fast access to innovative individual treatment trials (ITTs) with immunomodulatory drugs and high-quality evaluation of drug effects by implementing a risk-benefit model tailored for MPS. The iterative methodology of our novel decision analysis framework (DAF) involves three key steps: (i) literature review on promising treatment targets and immunomodulators in MPS; (ii) quantitative risk-benefit assessment (RBA) of selected molecules; (iii) assigning phenotypic profiles and quantitative evaluations. The results facilitate a personalized application of the model and are based on published evidence as well as interdisciplinary experts' consensus and patient perspectives. Four promising immunomodulators have been identified: adalimumab, abatacept, anakinra, and cladribine. An improvement in mobility is most likely with adalimumab, while anakinra is anticipated as a treatment of choice for neuronopathic MPS patients. Nevertheless, a comprehensive RBA should always be completed on an individual basis. Our evidence-based DAF tool for ITTs directly addresses the substantial unmet medical need in MPS and characterizes an initial stride toward precision medicine with immunomodulators.

Neuromuscular diseases include conditions that affect the spinal motor neurons, peripheral nerves, neuromuscular junctions, and muscles, and they can result from acquired and inherited causes. The number of genetic therapies targeting the inherited causes of neuromuscular diseases has surged in the last decade. This review aims to highlight the current state of genetic therapies within the framework of precision medicine, focusing on the achievements and the gaps that remain. A major emphasis is on spinal muscular atrophy, Duchenne muscular dystrophy, and amyotrophic lateral sclerosis, as these neuromuscular diseases have seen tremendous recent advancements. We will also discuss the future considerations necessary to accelerate the development of next-generation genetic therapies and enhance therapeutic outcomes for patients with neuromuscular diseases.

Inborn errors of metabolism (IEMs) are rare genetic conditions with significant morbidity and mortality. Technological advances have increased therapeutic options, making it challenging to remain up to date. A centralized therapy knowledgebase is needed for early diagnosis and targeted treatment. This study aimed to identify all treatable IEMs through a scoping literature review, followed by data extraction and analysis according to the Treatabolome principles. Knowledge of treatable IEMs, therapeutic categories, efficacy, and evidence was integrated into the Inborn Errors of Metabolism Knowledgebase (IEMbase), an online database encompassing all IEMs. The study identified 275 treatable IEMs, 18% of all currently known 1564 IEMs, according to the International Classification of Inherited Metabolic Disorders. Disorders of fatty acid and ketone body metabolism had the highest treatability (67%), followed by disorders of vitamin and cofactor metabolism (60%), and disorders of lipoprotein metabolism (42%). The most common treatment strategies were pharmacological therapy (34%), nutritional therapy (34%), and vitamin and trace element supplementation (12%). Treatment effects were most commonly observed in nervous system abnormalities (34%), metabolism/homeostasis abnormalities (33%), and growth (7%). Predominant evidence sources included case reports with evidence levels 4 (48%) and 5 (12%), and individual cohort studies with evidence level 2b (12%). Our study generated the Metabolic Treatabolome 2024. IEMs are the largest group of monogenic disorders amenable to disease-modifying therapy. With drug repurposing efforts and advancements in gene therapies, this number will expand. IEMbase now provides up-to-date, comprehensive information on clinical and biochemical symptoms and therapeutic options, empowering patients, families, healthcare professionals, and researchers in improving patient outcomes.

Heterogeneity of treatment effects in inflammatory bowel disease (IBD) means that many individuals or patient subgroups depart from the average for whom the outcomes from traditional randomized trials may not be applicable. The N-of-1 trial is a design in which a single patient is followed over time with the treatments being randomized from period to period with the intention of finding the most effective treatment for that patient. The aim was to investigate the utility of N-of-1 trials in IBD.

To identify relevant articles for this scoping review, a MEDLINE literature search was conducted through the PubMed platform for articles published in the English language using the search terms "inflammatory bowel disease," "Crohn's disease," "ulcerative colitis," "N-of-1 trials," "single case designs," and "personalized trials."

N-of-1 trials have seen a resurgence across several medical disciplines, driven by a need for more personalized medicine and patient-centered health care; their use in IBD is scarce with only 3 trials identified. Studies involving multiple N-of-1 trials can generate robust evidence for each participant and average effect estimates. The N-of-1 trial may hold potential for studying patients with IBD that are excluded from or underrepresented by randomized trials, such as those with extraintestinal manifestations, pouchitis, and proctitis. Although methodologically sound and akin to the rigor of a randomized controlled trial, the crossover periods inherent to the study design can be perceived as burdensome by patients and researchers.

The N-of-1 trial design provides a patient-centered means of objectively determining individual response to therapy.

Neurodegenerative disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) are characterized by the progressive degeneration of neuronal structure and function, leading to severe cognitive and motor impairments. These conditions present significant challenges to healthcare systems, and traditional treatments often fail to account for genetic variability among patients, resulting in inconsistent therapeutic outcomes. Pharmacogenomics aims to tailor medical treatments based on an individual's genetic profile, thereby improving therapeutic efficacy and reducing adverse effects. This focused review explores the genetic factors influencing drug responses in neurodegenerative diseases and the potential of pharmacogenomics to revolutionize their treatment. Key genetic markers, such as the APOE ε4 allele in AD and the CYP2D6 polymorphisms in PD, are highlighted for their roles in modulating drug efficacy. Additionally, advancements in pharmacogenomic tools, including genome-wide association studies (GWAS), next-generation sequencing (NGS), and CRISPR-Cas9, are discussed for their contributions to personalized medicine. The application of pharmacogenomics in clinical practice and its prospects, including ethical and data integration challenges, are also examined.

Psychedelics have shown early evidence for a range of benefits and low harm profiles in extant research. However, adverse effects (AEs) research in psychedelics has been limited, leading to underspecified AE profiles, inconsistent measurement, and potential undercounting of AEs. The development of safe, effective psychedelic therapies and treatments for AEs when they occur requires a thorough assessment of psychedelic-related AEs, their phenomenology, risk factors, and longitudinal duration. This article proposes that research on meditation-related AEs, which overlap in important ways with the phenomenological and contextual characteristics of psychedelic-related AEs, has engaged many methodological challenges present in the study of psychedelic-related AEs. Thus, meditation-related AE research offers thematic and methodological insights that are valuable to psychedelic AE research. An integrative review of extant AE research in both psychedelics and meditation is provided, and an agenda for leveraging meditation research to advance the investigation of psychedelic AEs is recommended. This includes the utility of meditation-related AEs as a comparator condition for psychedelic-related AEs, as well as recommendations for the adoption of (1) detailed and comprehensive, (2) user-informed, (3) impact-based, (4) standardized, (5) unbiased, and (6) representative measures of AEs and (7) examining factors that influence their impacts and trajectories.

Personalised medicine (PM) research programmes represent the modern paradigm of complex cross-disciplinary research, integrating innovative methodologies and technologies. Methodological research is required to ensure that these programmes generate robust and reproducible evidence. The PERMIT project developed methodological recommendations for each stage of the PM research pipeline. A common methodology was applied to develop the recommendations in collaboration with relevant stakeholders. Each stage was addressed by a dedicated working group, specializing in the subject matter. A series of scoping reviews that mapped the methods used in PM research and a gap analysis were followed by working sessions and workshops where field experts analyzed the gaps and developed recommendations. Through collaborative writing and consensus building exercises, the final recommendations were defined. They provide guidance for the design, implementation and evaluation of PM research, from patient and omics data collection and sample size calculation to the selection of the most appropriate stratification approach, including machine learning modeling, the development and application of reliable preclinical models, and the selection and implementation of the most appropriate clinical trial design. The dissemination and implementation of these recommendations by all stakeholders can improve the quality of PM research, enhance the robustness of evidence, and improve patient care.

Precision oncology tailors treatment strategies to a patient's molecular and health data. Despite the essential clinical value of current diagnostic methods, hematoxylin and eosin morphology, immunohistochemistry, and gene panel sequencing offer an incomplete characterization. In contrast, highly multiplexed tissue imaging allows spatial analysis of dozens of markers at single-cell resolution enabling analysis of complex tumor ecosystems; thereby it has the potential to advance our understanding of cancer biology and supports drug development, biomarker discovery, and patient stratification. We describe available highly multiplexed imaging modalities, discuss their advantages and disadvantages for clinical use, and potential paths to implement these into clinical practice. Significance: This review provides guidance on how high-resolution, multiplexed tissue imaging of patient samples can be integrated into clinical workflows. It systematically compares existing and emerging technologies and outlines potential applications in the field of precision oncology, thereby bridging the ever-evolving landscape of cancer research with practical implementation possibilities of highly multiplexed tissue imaging into routine clinical practice.

The translational gap from the discovery of evidence-based solutions to their implementation in healthcare delivery organizations derives from an incorrect assumption that the need for change among executive, administrative, or clinical personnel is the same as the demand for change. For sickle cell disease (SCD), implementation of evidence-based guidelines is often delayed or obstructed due to lack of demand. This challenge allows for the persistence of resource limitations and care delivery models that do not meet the community's unique needs. Agile Storytelling is a process built on the scientific foundations of behavioral economics, complexity science, and network science to create local demand for the implementation of evidence-based solutions.

Agile Storytelling includes a design phase and a testing phase. The design phase converts the evidence-based solution into a minimally viable story of a hero, a villain, struggle, drama, and a resolution. The testing phase evaluates the effectiveness of the story via a series of storytelling sprints in the target local healthcare delivery organization. The efficacy of Agile Storytelling was tested in an iterative n-of-1 case study design.

Agile Storytelling was used in a large, urban, healthcare system within the United States to facilitate implementation of national SCD best-practice guidelines. After repeated failures attempting to use national and local data regarding the high societal need to hire a SCD-specific social worker, an Agile change conductor using Agile Storytelling was able to create demand for the new position within a week. This decision has ultimately improved patient outcomes and led to the adoption of a specialized collaborative care team for SCD within the health network.

Agile Storytelling can lead to structured, effective, and informed storytelling to create local demand within healthcare delivery organizations.

Wrist function is a top priority for transradial amputees. However, the combined functional, biomechanical, and cognitive impact of using a powered prosthetic wrist is unclear. Here, we quantify task performance, compensatory movements, and cognitive load while three transradial amputees performed a modified Clothespin Relocation Task using two myoelectric prostheses with and without the wrists. The two myoelectric prostheses include a commercial prosthesis with a built-in powered wrist, and a newly developed inexpensive prosthetic wrist for research purposes, called the "Utah wrist", that can be adapted to work with various sockets and prostheses. For these three participants, task failure rate decreased significantly from 66% ± 12% without the wrist to 39% ± 9% with the Utah wrist. Compensatory forward leaning movements also decreased significantly, from 24.2° ± 2.5 without the wrist to 12.6° ± 1.0 with the Utah wrist, and from 23.6° ± 7.6 to 15.3° ± 7.2 with the commercial prosthesis with an integrated wrist. Compensatory leftward bending movements also significantly decreased, from 20.8° ± 8.6 to 12.3° ± 5.3, for the commercial with an integrated wrist. Importantly, simultaneous myoelectric control of either prosthetic wrist had no significant impact on cognitive load, as assessed by the NASA Task Load Index survey and a secondary detection response task. This work suggests that functional prosthetic wrists can improve dexterity and reduce compensation without significantly increasing cognitive effort. These results, and the introduction of a new inexpensive prosthetic wrist for research purposes, can aid future research and development and guide the prescription of upper-limb prostheses.

 N-of-1 trials have emerged as a personalized approach to patient-centered care, where patients can compare evidence-based treatments using their own data. However, little is known about optimal methods to present individual-level data from medication-related N-of-1 trials to patients to promote decision-making.

 We conducted qualitative interviews with patients with heart failure with preserved ejection fraction undergoing N-of-1 trials to iterate, refine, and optimize a patient-facing data visualization tool for displaying the results of N-of-1 medication trials. The goal of optimizing this tool was to promote patients' understanding of their individual health information and to ultimately facilitate shared decision-making about continuing or discontinuing their medication.

 We conducted 32 semistructured qualitative interviews with 9 participants over the course of their participation in N-of-1 trials. The N-of-1 trials were conducted to facilitate a comparison of continuing versus discontinuing a β-blocker. Interviews were conducted in person or over the phone after each treatment period to evaluate participant perspectives on a data visualization tool prototype. Data were coded using directed content analysis by two independent reviewers and included a third reviewer to reach a consensus when needed. Major themes were extracted and iteratively incorporated into the patient-facing data visualization tool.

 Nine participants provided feedback on how their data were displayed in the visualization tool. After qualitative analysis, three major themes emerged that informed our final interface. Participants preferred: (1) clearly stated individual symptom scores, (2) a reference image with labels to guide their interpretation of symptom information, and (3) qualitative language over numbers alone conveying the meaning of changes in their scores (e.g., better, worse).

 Feedback informed the design of a patient-facing data visualization tool for medication-related N-of-1 trials. Future work should include usability and comprehension testing of this interface on a larger scale.

Digital behavior change interventions (DBCIs) are feasibly effective tools for addressing physical activity. However, in-depth understanding of participants' long-term engagement with DBCIs remains sparse. Since the effectiveness of DBCIs to impact behavior change depends, in part, upon participant engagement, there is a need to better understand engagement as a dynamic process in response to an individual's ever-changing biological, psychological, social, and environmental context.

The year-long micro-randomized trial (MRT) HeartSteps II provides an unprecedented opportunity to investigate DBCI engagement among ethnically diverse participants. We combined data streams from wearable sensors (Fitbit Versa, i.e., walking behavior), the HeartSteps II app (i.e. page views), and ecological momentary assessments (EMAs, i.e. perceived intrinsic and extrinsic motivation) to build the idiographic models. A system identification approach and a fluid analogy model were used to conduct autoregressive with exogenous input (ARX) analyses that tested hypothesized relationships between these variables inspired by Self-Determination Theory (SDT) with DBCI engagement through time.

Data from 11 HeartSteps II participants was used to test aspects of the hypothesized SDT dynamic model. The average age was 46.33 (SD=7.4) years, and the average steps per day at baseline was 5,507 steps (SD=6,239). The hypothesized 5-input SDT-inspired ARX model for app engagement resulted in a 31.75 % weighted RMSEA (31.50 % on validation and 31.91 % on estimation), indicating that the model predicted app page views almost 32 % better relative to the mean of the data. Among Hispanic/Latino participants, the average overall model fit across inventories of the SDT fluid analogy was 34.22 % (SD=10.53) compared to 22.39 % (SD=6.36) among non-Hispanic/Latino Whites, a difference of 11.83 %. Across individuals, the number of daily notification prompts received by the participant was positively associated with increased app page views. The weekend/weekday indicator and perceived daily busyness were also found to be key predictors of the number of daily application page views.

This novel approach has significant implications for both personalized and adaptive DBCIs by identifying factors that foster or undermine engagement in an individual's respective context. Once identified, these factors can be tailored to promote engagement and support sustained behavior change over time.

Precision Medicine (PM) uses advanced Machine Learning (ML) techniques and big data to develop personalized treatments, but healthcare still relies on traditional statistical procedures not targeted on individuals. This study investigates the impact of ML on epidemiology.

A quantitative analysis of the articles in PubMed for the years 2000-2019 was conducted to investigate the use of statistical methods and ML in epidemiology. Using structural topic modelling, two groups of topics were identified and analysed over time: topics closer to the clinical side of epidemiology and topics closer to the population side.

The curve of the prevalence of topics associated with population epidemiology basically corresponds to the curve of the relative statistical methods, while the more dynamic curve of clinical epidemiology broadly reproduces the trend of algorithmic methods.

The findings suggest that a renewed separation between clinical epidemiology and population epidemiology is emerging, with clinical epidemiology taking more advantage of recent developments in algorithmic techniques and moving closer to bioinformatics, whereas population epidemiology seems to be slower in this innovation.

Adequate sleep plays a crucial role in maintaining physical, mental, and emotional health. On average, adults require 7-9 h of sleep per night. However, less than two-thirds of women meet this recommendation. During the coronavirus disease 2019 (COVID-19) pandemic, poor sleep quality and moderate-to-severe stress were highly prevalent among healthcare workers (HCWs), especially women. While some interventions have been proposed to address stress/burnout in HCWs, few have focused specifically on women in healthcare. Therefore, this is a protocol for a study that aims to determine the efficacy of a mind-body intervention (MBI) to improve sleep duration among women HCWs aged 40-60 years using the personalized (N-of-1) trial design.

A personalized (N-of-1) trials model will be employed to evaluate the efficacy of an MBI to improve sleep duration (primary endpoint) and explore its effects on sleep quality, physiological factors, and their relationships with participants' perceived stress, anxiety, and depression. The series of personalized trials (n = 60) will be conducted over 16 weeks. The MBI will include mindfulness, yoga, and guided walking, delivered in two 2-week block sequences for 12 weeks, with two 2-week periods for baseline and follow-up. Participants will watch 30-min videos three times weekly and wear an activity tracker to monitor sleep and activity. They will receive daily text messages with questions about sleep quality and bi-weekly questionnaires about their stress, anxiety and depression scores, fatigue, concentration, confidence, mood, and pain levels.

Results from this study will inform the development of N-of-1 methodology for addressing the health and wellness needs of middle-aged women.

Pediatric pulmonary hypertension is a rare condition. Survival remains poor in the current management era. There is a lack of data regarding the medical management of pediatric pulmonary hypertension and most pulmonary vasodilators are used off-label in children.

Pediatric pulmonary hypertension clinical trials' design and realization face many hurdles, including poor recruitment, limited available pharmacologic and physiologic data in children of various ages, ethical issues, and the lack of validated trial endpoint. Innovative clinical trial designs have emerged and may allow us to overcome some of these issues. Extrapolation of adult data to children, with additional pharmacokinetic and safety data, remains extremely important and valid in etiologies where the pediatric and the adult pathophysiologies are believed to be similar.

Close collaboration between sponsors, regulators, patients, caregivers, physicians and researchers is necessary to develop efficacious and safe drugs for pediatric pulmonary hypertension. The increasing involvement of patients' and caregivers' participation in the development of clinical trials should help shape future research that is feasible and meaningful to the patients.

Older adults with multimorbidity experience impaired health-related quality of life and treatment burden. Yoga has the potential to improve several aspects of health and well-being. The British Wheel of Yoga's Gentle Years Yoga© programme was developed specifically for older adults, including those with chronic conditions. A pilot trial demonstrated feasibility of using Gentle Years Yoga in this population, but there was limited evidence of its effectiveness and cost-effectiveness.

To determine the effectiveness and cost-effectiveness of the Gentle Years Yoga programme in addition to usual care versus usual care alone in older adults with multimorbidity.

Pragmatic, multisite, individually randomised controlled trial with embedded economic and process evaluations.

Participants were recruited from 15 general practices in England and Wales from July 2019 with final follow-up in October 2022.

Community-dwelling adults aged 65 years and over with multimorbidity, defined as two or more chronic health conditions from a predefined list.

All participants continued with any usual care provided by primary, secondary, community and social services. The intervention group was offered a 12-week programme of Gentle Years Yoga.

The primary outcome and end point were health-related quality of life measured using the EuroQol-5 Dimensions, five-level version utility index score over 12 months. Secondary outcomes were health-related quality of life, depression, anxiety, loneliness, incidence of falls, adverse events and healthcare resource use.

The mean age of the 454 randomised participants was 73.5 years; 60.6% were female, and participants had a median of three chronic conditions. The primary analysis included 422 participants (intervention, n = 227 of 240, 94.6%; usual care, n = 195 of 214, 91.1%). There was no statistically or clinically significant difference in the EuroQol-5 Dimensions, five-level version utility index score over 12 months: the predicted mean score for the intervention group was 0.729 (95% confidence interval 0.712 to 0.747) and for usual care it was 0.710 [95% confidence interval (CI) 0.691 to 0.729], with an adjusted mean difference of 0.020 favouring intervention (95% CI -0.006 to 0.045, p = 0.14). No statistically significant differences were observed in secondary outcomes, except for the pain items of the Patient-Reported Outcomes Measurement Information System-29. No serious, related adverse events were reported. The intervention cost £80.85 more per participant (95% CI £76.73 to £84.97) than usual care, generated an additional 0.0178 quality-adjusted life-years per participant (95% CI 0.0175 to 0.0180) and had a 79% probability of being cost-effective at the National Institute for Health and Care Excellence threshold of £20,000 per quality-adjusted life-year gained. The intervention was acceptable to participants, with seven courses delivered face to face and 12 online.

Self-reported outcome data raise the potential for bias in an unblinded trial. The COVID-19 pandemic affected recruitment, follow-up and the mode of intervention delivery.

Although the Gentle Years Yoga programme was not associated with any statistically significant benefits in terms of health-related quality of life, mental health, loneliness or falls, the intervention was safe, acceptable to most participants and highly valued by some. The economic evaluation suggests that the intervention could be cost-effective.

Longer-term cost-effectiveness modelling and identifying subgroups of people who are most likely to benefit from this type of intervention.

This trial is registered as ISRCTN13567538.

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/94/36) and is published in full in Health Technology Assessment; Vol. 28, No. 53. See the NIHR Funding and Awards website for further award information.

The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow movement towards improved understanding of pathophysiology and treatments compared with more common illnesses. The push towards evidence-based medicine (EBM), which prioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the methodologies endorsed by EBM, due to a number of constraints. While other trial designs have been suggested to overcome these limitations (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data. This paper critically examines the pitfalls of EBM (and its trial design offshoots) as it pertains to rare diseases, exploring the current landscape of AI as a potential solution to these challenges. This discussion is also taken a step further, providing philosophical commentary on the weaknesses and dangers of AI algorithms applied to rare disease research. While not proposing a singular solution, this article does provide a thoughtful reminder that no 'one-size-fits-all' approach exists in the complex world of rare diseases. We must balance cautious optimism with critical evaluation of new research paradigms and technology, while at the same time not neglecting the ever-important aspect of patient values and preferences, which may be challenging to incorporate into computer-driven models.

Physician underprescribing and patient nonadherence are major barriers to the benefits of guideline-directed medical therapy. An important contributor to both underprescribing and patient nonadherence is concern about medication-related side effects. Yet, there are few to no data on approaches used by physicians to: (1) elicit medication-related side effects, (2) attribute these side effects to specific medications, and (3) take appropriate action.

The authors conducted semistructured interviews with physicians to identify facilitators and barriers to each critical step of heart failure medication management: elicitation of side effects, attribution of side effects to a medication, and action in response to attributed side effects. Interviews were transcribed and coded using directed content analysis. For elicitation of potential side effects, limited patient communication and family discordance in reporting were key barriers, whereas guiding questions, measurement, and open channels of communication were key facilitators. For attribution of side effects, confounding from other medications, limited time for clinical encounters, and nonspecific symptoms were key barriers, whereas time-limited medication discontinuation trials and medication rechallenges were key facilitators. For taking action, challenges with weighing risks and benefits and physician fear about causing harm or interfering with other clinicians were barriers, whereas patient-physician communication and the results of a medication discontinuation trials and medication rechallenge were facilitators.

This study generated key facilitators and barriers to 3 key aspects of heart failure medication management related to side effects that should drive future work to improve heart failure medication management.

Treatment of rare/ultra-rare tumors is an unmet need due to a lack of standardized therapies and clinical trials. We developed the Molecular Tumor Board (MTB), a multidisciplinary team that integrates molecular profiling to generate personalized, N-of-One treatments for advanced cancers. This study evaluates 112 patients with rare/ultra-rare tumors who presented to the MTB and were evaluable for clinical therapeutic outcome. Overall, 46/112 patients (41%) received a treatment regimen with a high degree of matching between tumor molecular alterations and drugs given (reflected by a high Matching Score (≥50%)). Patients with a high versus low Matching Score experienced significantly longer progression-free survival (p = 0.005) and overall survival (p = 0.047), and higher rates of clinical benefit (stable disease ≥6 months, partial response, or complete response) (54% vs. 32% p = 0.027). The MTB facilitated personalized N-of-One matching of drugs to tumor molecular alterations, which was associated with improved clinical outcomes in patients with rare/ultra-rare cancers.

This article will provide clinicians with guidance on helping older adult patients make lifestyle changes to enhance brain health and well-being.

Evidence suggests that physical activity might be helpful in improving cognitive functioning. The data on the benefits of cognitive activity is inconsistent and not as robust. The MediDiet, DASH, and MIND diets have been associated with better cognitive health. Sleep hygiene and cognitive behavioral therapies are considered first line evidence-based treatments for insomnia and the maintenance of healthy sleep patterns. Mindfulness based interventions have been shown to reduce anxiety, depression, and stress, and can help some older adults manage pain more constructively. Evidence-based information regarding the four topics of exercise, nutrition, sleep, and mindfulness is reviewed, so that clinicians may be better able to optimize care for their older adult patients.

In the ever-evolving healthcare landscape, ancient healing traditions cast a profound shadow, offering insights and inspirations that resonate with modern medical practice. This paper explores the enduring influence of shamanic and Hippocratic healing traditions on contemporary healthcare, examining their contributions to holistic health approaches, diagnostic techniques, and ethical standards. The Hippocratic emphasis on observation, clinical experience, and ethical principles laid the foundation for modern Western medicine, while shamanic practices highlight the importance of spiritual and psychological dimensions in healing. The comparative analysis reveals both commonalities and distinctions among various ancient practices, such as Ayurveda, Traditional Chinese Medicine, and indigenous healing systems, emphasizing their holistic understanding of health and the use of natural remedies. Cross-cultural exchanges, from the Silk Road to the Islamic Golden Age and beyond, facilitated the integration and dissemination of medical knowledge, enriching global medical traditions. The paper also discusses the impact of these ancient practices on contemporary healthcare systems, highlighting the resurgence of holistic and integrative medicine, the validation and incorporation of herbal remedies, and the challenges of cultural appropriation and scientific validation. By embracing the wisdom of ancient healing traditions and fostering collaboration between traditional and modern medicine, contemporary healthcare can enhance therapeutic options, promote patient-centered care, and address global health challenges with a more inclusive and compassionate approach. This integration holds promise for the future of healthcare, benefiting individuals and communities worldwide.

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

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

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

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

Repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are increasingly used for major depressive disorder (MDD). Most tDCS and rTMS studies target the left dorsolateral prefrontal cortex, either with or without neuronavigation. We examined the effect of rTMS and tDCS, and the added value of neuronavigation in the treatment of MDD.

A search on PubMed, Embase, and Cochrane databases for rTMS or tDCS randomized controlled trials of MDD up to 1 February 2023, yielded 89 studies. We then performed meta-analyses comparing tDCS efficacy to non-neuronavigated rTMS, tDCS to neuronavigated rTMS, and neuronavigated rTMS to non-neuronavigated rTMS. We assessed the significance of the effect in subgroups and in the whole meta-analysis with a z-test and subgroup differences with a chi-square test.

We found small-to-medium effects of both tDCS and rTMS on MDD, with a slightly greater effect from rTMS. No significant difference was found between neuronavigation and non-neuronavigation.

Although both tDCS and rTMS are effective in treating MDD, many patients do not respond. Additionally, current neuronavigation methods are not significantly improving MDD treatment. It is therefore imperative to seek personalized methods for these interventions.

Patients with relapsed or refractory metastatic cancer unresponsive to standard therapies have motivated nuclear physicians to develop innovative radioligands, precisely targeted to tumor molecular receptors, for effective treatment of specific advanced malignancies. Individual practitioners in departments of nuclear medicine across the world have performed first-in-human studies on compassionate patient usage N-of-One protocols. These physician-sponsored studies then evolved into early-phase clinical trials and obtained real-world data to demonstrate real-world evidence of effectiveness in prolonging survival and enhancing quality of life of many so-called "End-Stage" cancer patients. Virtually all the therapeutic radiopharmaceuticals in current clinical oncology have been discovered and developed into effective specific treatments of targetable cancers by individual doctors in the course of their hospital practice. Pharma industry was not involved until many years later when performance of mandated Phase 3 randomized controlled trials became necessary to achieve regulatory agency approval. This article traces the history of several novel theranostic agents developed from compassionate N-of-One studies by hospital physicians over the past 36 years. It acknowledges the collegiality and collaboration of individual nuclear medicine specialists, worldwide, in pioneering effective humane therapy of particular advanced cancers unresponsive to conventional treatments.

Background/Objectives: Chronic inflammatory demyelinating polyneuropathy (CIDP) is a rare autoimmune disease. Neuropathic pain (NP), related to peripheral inflammation, is among its earliest manifestations. This preliminary open-label investigation aimed to evaluate the efficacy of ultramicronized Palmitoylethanolamide (umPEA) in the management of NP. Methods: A total of 14 patients with CIDP, already undergoing immunoglobulin (Ig) therapy, were divided into two groups: Group A received umPEA 600 mg twice daily in addition to Ig for 60 days, followed by Ig alone until the end of the observation (180 days); Group B received Ig alone for 120 days and subsequently umPEA + Ig in the last 60 days of the study. Painful symptom intensity and quality of life were assessed by the Numeric Rating Scale, Neuropathic Pain Symptoms Inventory, and Five Dimensions Health Questionnaire. The safety umPEA profile was evaluated. Results: UmPEA in addition to immunoglobulins allowed for a significant improvement over time in all NP symptoms intensity (p = 0.0007) and in patients' quality of life (p = 0.0036). Conclusions: This study suggests umPEA as a safe and effective treatment in addition to immunoglobulins to improve NP, ameliorating the patient's health status. These results highlight the importance of neuroinflammation modulation in the management of CIDP's painful symptoms, drawing attention to umPEA's potential use also in neuropathies of different etiologies.

n-of-1 trials are a type of crossover trial designed to optimise the evaluation of health technologies in individual patients. This trial design may be considered for the evaluation of health technologies in rare conditions where fewer patients are available to take part in research. This review describes the characteristics of randomised n-of-1 trials conducted over the span of 12 years, including how the n-of-1 design has been employed to study both rare and non-rare conditions.

Databases and clinical trials registries were searched for articles including "n-of-1" in the title between 2011 and 2023. The reference lists of reviews identified by the searches were searched for any additional eligible articles. Randomised n-of-1 trials were selected for inclusion and data were extracted on a range of design, population, and analysis characteristics. Descriptive statistics were produced for all variables.

We identified 74 studies meeting our eligibility criteria, 13 of which (17.6%) were conducted in rare conditions. They were conducted in a range of clinical areas with the most common being neurological conditions (n = 16, 21.6%). The median (Q1, Q3) number of participants randomised was 9 (4, 20) and 12 trials (16.2%) involved a single patient only. Forty-six (62.2%) trials evaluated pharmaceutical interventions and 49 (66.2%) trials were placebo controlled. Trials had a median (Q1, Q3) of six (4, 8) periods and 61 (82.4%) compared two health technologies. Fifty-seven (77.0%) trials incorporated blinding and 32 (43.2%) had a washout period. Forty-nine trials (66.2%) used patient-reported outcome measures (PROMs) to assess the primary outcome. Trials used a range of approaches to analysis and 48 (64.9%) combined data from multiple patients. The characteristics of the n-of-1 trials conducted in rare conditions were generally consistent with those in non-rare conditions.

n-of-1 trials are still underused and the application of the n-of-1 design for the evaluation of health technologies for rare diseases has been particularly limited. We have summarised the characteristics of randomised n-of-1 trials in rare and non-rare conditions. We hope that it can inform researchers in the design of future n-of-1 studies. Further work is required to provide guidance on specific design considerations, implementation, and statistical analysis of these studies.

Not applicable.

Studying individual causal effects of health interventions is important whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. As an alternative method, we propose to re-analyze existing population-level studies as N-of-1 trials, and use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. As a reference to the N-of-1 trials approach, we first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian repeated-measures models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses revealed differences between participants. Baseline values of the gait parameters varied largely among all participants, and the effects of fatigue and cognitive task were also heterogeneous, with some individuals showing effects in opposite directions. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.

In recent years, the development of sensor and wearable technologies have led to their increased adoption in clinical and health monitoring settings. One area that is in early, but promising, stages of development is the use of biosensors for therapeutic drug monitoring (TDM). Traditionally, TDM could only be performed in certified laboratories and was used in specific scenarios to optimize drug dosage based on measurement of plasma/blood drug concentrations. Although TDM has been typically pursued in settings involving medications that are challenging to manage, the basic approach is useful for characterizing drug activity. TDM is based on the idea that there is likely a clear relationship between plasma/blood drug concentration (or concentration in other matrices) and clinical efficacy. However, these relationships may vary across individuals and may be affected by genetic factors, comorbidities, lifestyle, and diet. TDM technologies will be valuable for enabling precision medicine strategies to determine the clinical efficacy of drugs in individuals, as well as optimizing personalized dosing, especially since therapeutic windows may vary inter-individually. In this mini-review, we discuss emerging TDM technologies and their applications, and factors that influence TDM including drug interactions, polypharmacy, and supplement use. We also discuss how using TDM within single subject (N-of-1) and aggregated N-of-1 clinical trial designs provides opportunities to better capture drug response and activity at the individual level. Individualized TDM solutions have the potential to help optimize treatment selection and dosing regimens so that the right drug and right dose may be matched to the right person and in the right context.

Massage has been used as a treatment for musculoskeletal pain throughout history and across cultures, and yet most meta-analyses have only shown weak support for the efficacy of massage. There is a recognised need for more research in foundational questions including: how massage treatments are constructed; what therapists actually do within a treatment, including their clinical reasoning; and what role therapists play in determining the effectiveness of a massage treatment.

The aim of this study was to explore what experienced orthopaedic massage therapists consider to be the aspects of their work that contribute to effectiveness.

Semi-structured interviews were conducted via Zoom with six experienced orthopaedic massage therapists in Australia.

The interviews were analysed using inductive thematic analysis, seeking insights that might be practically applied, rather than theory-driven interpretations.

The participants focused on the underlying differences between clients, between therapists, and between treatments, and clearly indicated that this concept of "difference" was foundational to their view of their work and was the underlying context for the comments they made. Within that frame of "difference", three key themes were interpreted from the data: (1) "Everyone is different so every treatment is different": how they individualised treatment based on these differences; (2) "How therapists cope with difference": how they managed the challenges of working in this context; and (3) "What makes a difference": the problem-solving processes they used to target each treatment to meeting the client's needs.

Participants did not identify specific techniques or modalities as "effective" or not. Rather, a therapist's ability to provide effective treatment was based on an iterative process of treatment and assessment that allowed them to focus on the individual needs of the client. In this case "effectiveness" could be considered a process rather than a specific massage technique.

In this paper we synthesize an expansive body of literature examining the multifaceted influence of chiropractic care on processes within and modulators of the neuroendocrine-immune (NEI) system, for the purpose of generating an inductive hypothesis regarding the potential impacts of chiropractic care on integrated physiology. Taking a broad, interdisciplinary, and integrative view of two decades of research-documented outcomes of chiropractic care, inclusive of reports ranging from systematic and meta-analysis and randomized and observational trials to case and cohort studies, this review encapsulates a rigorous analysis of research and suggests the appropriateness of a more integrative perspective on the impact of chiropractic care on systemic physiology. A novel perspective on the salutogenic, health-promoting effects of chiropractic adjustment is presented, focused on the improvement of physical indicators of well-being and adaptability such as blood pressure, heart rate variability, and sleep, potential benefits that may be facilitated through multiple neurologically mediated pathways. Our findings support the biological plausibility of complex benefits from chiropractic intervention that is not limited to simple neuromusculoskeletal outcomes and open new avenues for future research, specifically the exploration and mapping of the precise neural pathways and networks influenced by chiropractic adjustment.