N-of-1 medicine.

ABSTRACT: The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

CURATE.AI COR-Tx platform as a digital therapy and digital diagnostic for cognitive function in patients with brain tumour postradiotherapy treatment: protocol for a prospective mixed-methods feasibility clinical trial.

INTRODUCTION: Conventional interventional modalities for preserving or improving cognitive function in patients with brain tumour undergoing radiotherapy usually involve pharmacological and/or cognitive rehabilitation therapy administered at fixed doses or intensities, often resulting in suboptimal or no response, due to the dynamically evolving patient state over the course of disease. The personalisation of interventions may result in more effective results for this population. We have developed the CURATE.AI COR-Tx platform, which combines a previously validated, artificial intelligence-derived personalised dosing technology with digital cognitive training. METHODS AND ANALYSIS: This is a prospective, single-centre, single-arm, mixed-methods feasibility clinical trial with the primary objective of testing the feasibility of the CURATE.AI COR-Tx platform intervention as both a digital intervention and digital diagnostic for cognitive function. Fifteen patient participants diagnosed with a brain tumour requiring radiotherapy will be recruited. Participants will undergo a remote, home-based 10-week personalised digital intervention using the CURATE.AI COR-Tx platform three times a week. Cognitive function will be assessed via a combined non-digital cognitive evaluation and a digital diagnostic session at five time points: preradiotherapy, preintervention and postintervention and 16-weeks and 32-weeks postintervention. Feasibility outcomes relating to acceptability, demand, implementation, practicality and limited efficacy testing as well as usability and user experience will be assessed at the end of the intervention through semistructured patient interviews and a study team focus group discussion at study completion. All outcomes will be analysed quantitatively and qualitatively. ETHICS AND DISSEMINATION: This study has been approved by the National Healthcare Group (NHG) DSRB (DSRB2020/00249). We will report our findings at scientific conferences and/or in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04848935.

Economic Evaluation Associated With Clinical-Grade Mobile App-Based Digital Therapeutic Interventions: Systematic Review.

BACKGROUND: Digital therapeutics (DTx), a class of software-based clinical interventions, are promising new technologies that can potentially prevent, manage, or treat a spectrum of medical disorders and diseases as well as deliver unprecedented portability for patients and scalability for health care providers. Their adoption and implementation were accelerated by the need for remote care during the COVID-19 pandemic, and awareness about their utility has rapidly grown among providers, payers, and regulators. Despite this, relatively little is known about the capacity of DTx to provide economic value in care. OBJECTIVE: This study aimed to systematically review and summarize the published evidence regarding the cost-effectiveness of clinical-grade mobile app-based DTx and explore the factors affecting such evaluations. METHODS: A systematic review of economic evaluations of clinical-grade mobile app-based DTx was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Major electronic databases, including PubMed, Cochrane Library, and Web of Science, were searched for eligible studies published from inception to October 28, 2022. Two independent reviewers evaluated the eligibility of all the retrieved articles for inclusion in the review. Methodological quality and risk of bias were assessed for each included study. RESULTS: A total of 18 studies were included in this review. Of the 18 studies, 7 (39%) were nonrandomized study-based economic evaluations, 6 (33%) were model-based evaluations, and 5 (28%) were randomized clinical trial-based evaluations. The DTx intervention subject to assessment was found to be cost-effective in 12 (67%) studies, cost saving in 5 (28%) studies, and cost-effective in 1 (6%) study in only 1 of the 3 countries where it was being deployed in the final study. Qualitative deficiencies in methodology and substantial potential for bias, including risks of performance bias and selection bias in participant recruitment, were identified in several included studies. CONCLUSIONS: This systematic review supports the thesis that DTx interventions offer potential economic benefits. However, DTx economic analyses conducted to date exhibit important methodological shortcomings that must be addressed in future evaluations to reduce the uncertainty surrounding the widespread adoption of DTx interventions. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42022358616; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022358616.

Digital game-based interventions for cognitive training in healthy adults and adults with cognitive impairment: protocol for a two-part systematic review and meta-analysis.

INTRODUCTION: Digital game-based training interventions are scalable solutions that may improve cognitive function for many populations. This protocol for a two-part review aims to synthesise the effectiveness and key features of digital game-based interventions for cognitive training in healthy adults across the life span and adults with cognitive impairment, to update current knowledge and impact the development of future interventions for different adult subpopulations. METHODS AND ANALYSIS: This systematic review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines. A systematic search was performed in PubMed, Embase, CINAHL, Cochrane Library, Web of Science, PsycINFO and IEEE Explore on 31 July 2022 for relevant literature published in English from the previous 5 years. Experimental, observational, exploratory, correlational, qualitative and mixed methods studies will be eligible if they report at least one cognitive function outcome and include a digital game-based intervention intended to improve cognitive function. Reviews will be excluded but retained to search their reference lists for other relevant studies. All screening will be done by at least two independent reviewers. The appropriate Joanna Briggs Institute Critical Appraisal Tool, according to the study design, will be applied to perform the risk of bias assessment. Outcomes related to cognitive function and digital game-based intervention features will be extracted. Results will be categorised by adult life span stages in the healthy adult population for part 1 and by neurological disorder in part 2. Extracted data will be analysed quantitatively and qualitatively, according to study type. If a group of sufficiently comparable studies is identified, we will perform a meta-analysis applying the random effects model with consideration of the I2 statistic. ETHICS AND DISSEMINATION: Ethics approval is not applicable for this study since no original data will be collected. The results will be disseminated through peer-reviewed publications and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42022351265.

Outcome Measurement Instruments and Evidence-based Recommendations for Measurement of the Pelvic Girdle Pain Core Outcome Set (PGP-COS): A Systematic Review and Consensus Process.

This study provides evidence- and consensus-based recommendations for the instruments to measure the five Pelvic Girdle Pain Core Outcome Set (PGP-COS): pain frequency, pain intensity/severity, function/disability/activity limitation, health-related quality of life and fear avoidance. Studies evaluating measurement properties of instruments measuring any PGP-COS outcome in women with PGP were identified through a systematic search of MEDLINE, EMBASE and PEDro databases (inception-July 2021). The methodological quality of studies and quality of measurement properties were evaluated using the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) checklist. Quality criteria and the synthesized evidence were graded using the modified grading of recommendations, assessment, development, and evaluation (GRADE) approach. A consensus meeting with PGP stakeholders was then held to establish recommendations, based on the evidence, for the instruments that should be used to measure the PGP-COS. Ten instruments were identified from 17 studies. No instrument showed high quality evidence for all measurement properties and/or measured all PGP-COS outcomes. Based on current evidence and consensus, the Pelvic Girdle Questionnaire (PGQ), the Short Form-8 (SF-8) and the Fear Avoidance Beliefs Questionnaire (FABQ) are recommended for measuring the PGP-COS. Future research should establish additional measurement properties of instruments and to substantiate these recommendations.

IDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention.

Nanomedicine-based and unmodified drug interventions to address COVID-19 have evolved over the course of the pandemic as more information is gleaned and virus variants continue to emerge. For example, some early therapies (e.g., antibodies) have experienced markedly decreased efficacy. Due to a growing concern of future drug resistant variants, current drug development strategies are seeking to find effective drug combinations. In this study, we used IDentif.AI, an artificial intelligence-derived platform, to investigate the drug-drug and drug-dose interaction space of six promising experimental or currently deployed therapies at various concentrations: EIDD-1931, YH-53, nirmatrelvir, AT-511, favipiravir, and auranofin. The drugs were tested in vitro against a live B.1.1.529 (Omicron) virus first in monotherapy and then in 50 strategic combinations designed to interrogate the interaction space of 729 possible combinations. Key findings and interactions were then further explored and validated in an additional experimental round using an expanded concentration range. Overall, we found that few of the tested drugs showed moderate efficacy as monotherapies in the actionable concentration range, but combinatorial drug testing revealed significant dose-dependent drug-drug interactions, specifically between EIDD-1931 and YH-53, as well as nirmatrelvir and YH-53. Checkerboard validation analysis confirmed these synergistic interactions and also identified an interaction between EIDD-1931 and favipiravir in an expanded range. Based on the platform nature of IDentif.AI, these findings may support further explorations of the dose-dependent drug interactions between different drug classes in further pre-clinical and clinical trials as possible combinatorial therapies consisting of unmodified and nanomedicine-enabled drugs, to combat current and future COVID-19 strains and other emerging pathogens.

The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens.

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

What have we learnt from quantitative case reports of acute lateral ankle sprains injuries and episodes of 'giving-way' of the ankle joint, and what shall we further investigate?

Lateral ankle sprains are a commonly incurred injury in sports. They have a high recurrence rate and can lead to the development of persistent injury associated symptoms. We performed a quantitative synthesis of published case reports documenting the kinematics of acute lateral ankle sprains and episodes of 'giving-way' of the ankle joint to provide a comprehensive description of the mechanisms. A systematic literature search was conducted to screen records within MEDLINE® and EMBASE®. Additional strategies included manual search of specific journals, as well as contacting researchers in relevant communities to retrieve unpublished data. Twenty-four cases were included in the quantitative synthesis, 11 from individual case reports and 13 from four separate case series. Two authors independently reviewed all the articles and extracted ankle joint kinematic data. Excessive ankle inversion was the most pronounced kinematic pattern observed across all included cases, with a mean peak inversion angle of 67.5° (range 2.0 to 142) and a mean peak inversion velocity of 974°/s (range 468 to 1752). This was followed by internal rotation and plantar flexion, respectively. A homogeneous linear function revealed a mean inversion velocity across all cases of 337°/s (range 117 to 1400; R2 = 0.78; p < 0.0001).

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores.

BACKGROUND: Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use. OBJECTIVE: The objective of this review is to characterize the current state of mHealth platforms designed for anxiety or depression that are available for research, commercial use, or both. METHODS: A systematic review was conducted using a two-pronged approach: searching relevant literature with prespecified search terms to identify platforms in published research and simultaneously searching 2 major app stores-Google Play Store and Apple App Store-to identify commercially available platforms. Key characteristics of the mHealth platforms were synthesized, such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, and regulatory information. RESULTS: The literature and app store searches yielded 169 and 179 mHealth platforms, respectively. Most platforms developed for research purposes were designed for depression (116/169, 68.6%), whereas the app store search reported a higher number of platforms developed for anxiety (Android: 58/179, 32.4%; iOS: 27/179, 15.1%). The most common purpose of platforms in both searches was treatment (literature search: 122/169, 72.2%; app store search: 129/179, 72.1%). With regard to the types of intervention, cognitive behavioral therapy and referral to care or counseling emerged as the most popular options offered by the platforms identified in the literature and app store searches, respectively. Most platforms from both searches did not have a specific target age group. In addition, most platforms found in app stores lacked clinical and real-world evidence, and a small number of platforms found in the published research were available commercially. CONCLUSIONS: A considerable number of mHealth platforms designed for anxiety or depression are available for research, commercial use, or both. The characteristics of these mHealth platforms greatly vary. Future efforts should focus on assessing the quality-utility, safety, and effectiveness-of the existing platforms and providing developers, from both commercial and research sectors, a reporting guideline for their platform description and a regulatory framework to facilitate the development, validation, and deployment of effective mHealth platforms.

A core outcome set for research and clinical practice in women with pelvic girdle pain: PGP-COS.

BACKGROUND: Inconsistent reporting of outcomes in clinical trials of women with Pelvic Girdle Pain (PGP) hinders comparison of findings and the reliability of evidence synthesis. A core outcome set (COS) can address this issue as it defines a minimum set of outcomes that should be reported in all clinical trials on the condition. The aim of this study was to develop a consensus-based COS for evaluating the effectiveness of interventions in PGP during pregnancy and postpartum for use in research and clinical practice. METHODS: A systematic review of previous studies on PGP and semi-structured interviews with women were undertaken to identify all outcomes that were reported in prior studies and that are relevant to those experiencing the condition. Key stakeholders (clinicians, researchers, service providers/policy makers and individuals with PGP) then rated the importance of these outcomes for including in a preliminary PGP-COS using a 3-round Delphi study. The final COS was agreed at a face-to-face consensus meeting. RESULTS: Consensus was achieved on five outcomes for inclusion in the final PGP-COS. All outcomes are grouped under the "life impact" domain and include: pain frequency, pain intensity/severity, function/disability/activity limitation, health-related quality of life and fear avoidance. CONCLUSION: This study identified a COS for evaluating the effectiveness of interventions in pregnancy-related and postpartum-related PGP in research and clinical settings. It is advocated that all trials, other non-randomised studies and clinicians in this area use this COS by reporting these outcomes as a minimum. This will ensure the reporting of meaningful outcomes and will enable the findings of future studies to be compared and combined. Future work will determine how to measure the outcomes of the PGP-COS. CORE OUTCOME SET REGISTRATION: This PGP-COS was registered with COMET (Core Outcome Measures for Effectiveness Trials) in January 2017 (http://www.comet-initiative.org/studies/details/958).

IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) with digital drug development.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to multiple drug repurposing clinical trials that have yielded largely uncertain outcomes. To overcome this challenge, we used IDentif.AI, a platform that pairs experimental validation with artificial intelligence (AI) and digital drug development to rapidly pinpoint unpredictable drug interactions and optimize infectious disease combination therapy design with clinically relevant dosages. IDentif.AI was paired with a 12-drug candidate therapy set representing over 530,000 drug combinations against the SARS-CoV-2 live virus collected from a patient sample. IDentif.AI pinpointed the optimal combination as remdesivir, ritonavir, and lopinavir, which was experimentally validated to mediate a 6.5-fold enhanced efficacy over remdesivir alone. Additionally, it showed hydroxychloroquine and azithromycin to be relatively ineffective. The study was completed within 2 weeks, with a three-order of magnitude reduction in the number of tests needed. IDentif.AI independently mirrored clinical trial outcomes to date without any data from these trials. The robustness of this digital drug development approach paired with in vitro experimentation and AI-driven optimization suggests that IDentif.AI may be clinically actionable toward current and future outbreaks.

Methodology in core outcome set (COS) development: the impact of patient interviews and using a 5-point versus a 9-point Delphi rating scale on core outcome selection in a COS development study.

BACKGROUND: As the development of core outcome sets (COS) increases, guidance for developing and reporting high-quality COS continues to evolve; however, a number of methodological uncertainties still remain. The objectives of this study were: (1) to explore the impact of including patient interviews in developing a COS, (2) to examine the impact of using a 5-point versus a 9-point rating scale during Delphi consensus methods on outcome selection and (3) to inform and contribute to COS development methodology by advancing the evidence base on COS development techniques. METHODS: Semi-structured patient interviews and a nested randomised controlled parallel group trial as part of the Pelvic Girdle Pain Core Outcome Set project (PGP-COS). Patient interviews, as an adjunct to a systematic review of outcomes reported in previous studies, were undertaken to identify preliminary outcomes for including in a Delphi consensus survey. In the Delphi survey, participants were randomised (1:1) to a 5-point or 9-point rating scale for rating the importance of the list of preliminary outcomes. RESULTS: Four of the eight patient interview derived outcomes were included in the preliminary COS, however, none of these outcomes were included in the final PGP-COS. The 5-point rating scale resulted in twice as many outcomes reaching consensus after the 3-round Delphi survey compared to the 9-point scale. Consensus on all five outcomes included in the final PGP-COS was achieved by participants allocated the 5-point rating scale, whereas consensus on four of these was achieved by those using the 9-point scale. CONCLUSIONS: Using patient interviews to identify preliminary outcomes as an adjunct to conducting a systematic review of outcomes measured in the literature did not appear to influence outcome selection in developing the COS in this study. The use of different rating scales in a Delphi survey, however, did appear to impact on outcome selection. The 5-point scale demonstrated greater congruency than the 9-point scale with the outcomes included in the final PGP-COS. Future research to substantiate our findings and to explore the impact of other rating scales on outcome selection during COS development, however, is warranted.

Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor.

INTRODUCTION: Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. METHODS: Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. RESULTS: Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). CONCLUSIONS: Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

Risk Factors for Lateral Ankle Sprains and Chronic Ankle Instability.

Lateral ankle sprains (LASs) are a common injury sustained by individuals who participate in recreational physical activities and sports. After an LAS, a large proportion of individuals develop long-term symptoms, which contribute to the development of chronic ankle instability (CAI). Due to the prevalence of LASs and the propensity to develop CAI, collective efforts toward reducing the risk of sustaining these injuries should be a priority of the sports medicine and sports physiotherapy communities. The comprehensive injury-causation model was developed to illustrate the interaction of internal and external risk factors in the occurrence of the inciting injury. The ability to mitigate injury risk is contingent on a comprehensive understanding of risk factors for injury. The objective of this current concepts review is to use the comprehensive injury-causation model as a framework to illustrate the risk factors for LAS and CAI based on the literature.

Clinical assessment of acute lateral ankle sprain injuries (ROAST): 2019 consensus statement and recommendations of the International Ankle Consortium.

Lateral ankle sprain injury is the most common musculoskeletal injury incurred by individuals who participate in sports and recreational physical activities. Following initial injury, a high proportion of individuals develop long-term injury-associated symptoms and chronic ankle instability. The development of chronic ankle instability is consequent on the interaction of mechanical and sensorimotor insufficiencies/impairments that manifest following acute lateral ankle sprain injury. To reduce the propensity for developing chronic ankle instability, clinical assessments should evaluate whether patients in the acute phase following lateral ankle sprain injury exhibit any mechanical and/or sensorimotor impairments. This modified Delphi study was undertaken under the auspices of the executive committee of the International Ankle Consortium. The primary aim was to develop recommendations, based on expert (n=14) consensus, for structured clinical assessment of acute lateral ankle sprain injuries. After two modified Delphi rounds, consensus was achieved on the clinical assessment of acute lateral ankle sprain injuries. Consensus was reached on a minimum standard clinical diagnostic assessment. Key components of this clinical diagnostic assessment include: establishing the mechanism of injury, as well as the assessment of ankle joint bones and ligaments. Through consensus, the expert panel also developed the International Ankle Consortium Rehabilitation-Oriented ASsessmenT (ROAST). The International Ankle Consortium ROAST will help clinicians identify mechanical and/or sensorimotor impairments that are associated with chronic ankle instability. This consensus statement from the International Ankle Consortium aims to be a key resource for clinicians who regularly assess individuals with acute lateral ankle sprain injuries.

A laboratory captured "giving way" episode in an individual with chronic ankle instability.

This brief report details the first ever instrument-based motion description of an accidental "giving way" episode of the ankle joint incurred by a recreational male athlete (age = 22 years; height = 1.78 m; body mass = 97 kg) with chronic ankle instability whilst he was performing a change of direction task. Five inertial measurement units, as well as a high-speed video camera captured his lower limb kinematics during the performance of a maximum effort Agility T-Test, including his accidental "giving way" episode. This episode was analysed and compared to a previous trial during which no incident occurred. Analysis of the inertial measurement unit data revealed that the "giving way" episode was characterised by plantar flexion of the ankle joint, as well as internal rotation and adduction of the ankle-foot complex, with peak rotational velocities reaching 797°/s, 1088°/s and 1734°/s, respectively. This instrument-based motion description provides a unique insight into the characteristic features of a "giving way" episode experienced by a recreational athlete with chronic ankle instability. These findings could inform the development of rehabilitation programmes and the design of protective equipment for individuals with chronic ankle instability.