Ontology-based categorization of clinical studies by their conditions.

Objective: The free-text Condition data field in the ClinicalTrials.gov is not amenable to computational processes for retrieving, aggregating and visualizing clinical studies by condition categories. This paper contributes a method for automated ontology-based categorization of clinical studies by their conditions.

Long COVID and Medicine's Two Cultures.

Medicine has separated the two cultures of biological science and social science in research, even though they are intimately connected in the lives of our patients. To understand the cause, progression, and treatment of long COVID , biology and biography, the patient's lived experience, must be studied together.

COVID-19 interventional trials: Analysis of data sharing intentions during a time of pandemic.

Background: This survey of COVID-19 interventional studies encompasses, and expands upon, a previous publication [1] examining individual participant level data (IPD) sharing intentions for COVID-related trials and publications prior to June 30, 2020.

Methods: Replicating our inclusion criteria from the original survey, we evaluated a larger dataset of 2759 trials and 281 publications in this follow-up survey for willingness to share IPD and studied if sharing sentiment has evolved since the beginning of the pandemic.

Results: We found that 18 months into the pandemic, data sharing intentions remained static at 15% for trials registered through ClinicalTrials.

Standardized Integration of Person-Generated Data Into Routine Clinical Care.

Person-generated data (PGD) are a valuable source of information on a person's health state in daily life and in between clinic visits. To fully extract value from PGD, health care organizations must be able to smoothly integrate data from PGD devices into routine clinical workflows. Ideally, to enhance efficiency and flexibility, such integrations should follow reusable processes that can easily be replicated for multiple devices and data types.

Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care.

The term 'data science' usually refers to the process of extracting value from obtained from a large group of individuals. An alternative rendition, which we call (Per-DS), aims to collect, analyze, and interpret to inform decisions. This article describes the main features of Per-DS, and reviews its current state and future outlook.

Perceptions of Older Men Using a Mobile Health App to Monitor Lower Urinary Tract Symptoms and Tamsulosin Side Effects: Mixed Methods Study.

Background: Mobile health (mHealth) apps may provide an efficient way for patients with lower urinary tract symptoms (LUTS) to log and communicate symptoms and medication side effects with their clinicians.

Objective: The aim of this study was to explore the perceptions of older men with LUTS after using an mHealth app to track their symptoms and tamsulosin side effects.

Methods: Structured phone interviews were conducted after a 2-week study piloting the daily use of a mobile app to track the severity of patient-selected LUTS and tamsulosin side effects.

Tracking Lower Urinary Tract Symptoms and Tamsulosin Side Effects Among Older Men Using a Mobile App (PERSONAL): Feasibility and Usability Study.

Background: Continuous α1a-blockade is the first-line treatment for lower urinary tract symptoms (LUTS) among older men with suspected benign prostatic hyperplasia. Variable efficacy and safety for individual men necessitate a more personalized, data-driven approach to prescribing and deprescribing tamsulosin for LUTS in older men.

Objective: We aim to evaluate the feasibility and usability of the PERSONAL (Placebo-Controlled, Randomized, Patient-Selected Outcomes, N-of-1 Trials) mobile app for tracking daily LUTS severity and medication side effects among older men receiving chronic tamsulosin therapy.

Rethinking Table 1.

Clinical and translational medicine studies of disease risk or treatment response typically include a table 1 comparing groups on age, sex, and race and/or ethnicity. Although customarily treated as biological variables, each denote biography, elements of a person's lived experience. Capturing these biographical features is essential to achieving the ambition of personalized medicine.

Individualized Studies of Triggers of Paroxysmal Atrial Fibrillation: The I-STOP-AFib Randomized Clinical Trial.

Importance: Atrial fibrillation (AF) is the most common arrhythmia. Although patients have reported that various exposures determine when and if an AF event will occur, a prospective evaluation of patient-selected triggers has not been conducted, and the utility of characterizing presumed AF-related triggers for individual patients remains unknown.

Objective: To test the hypothesis that n-of-1 trials of self-selected AF triggers would enhance AF-related quality of life.

Biosocial medicine: Biology, biography, and the tailored care of the patient.

Biosocial Medicine, with its emphasis on the full integration of the person's biology and biography, proposes a strategy for clinical research and the practice of medicine that is transformative for the care of individual patients. In this paper, we argue that Biology is one component of what makes a person unique, but it does not do so alone. Biography, the lived experience of the person, integrates with biology to create a unique signature for each individual and is the foundational concept on which Biosocial Medicine is based.

Chronic pain treatment preferences change following participation in N-of-1 trials, but not always in the expected direction.

Objective: To examine pain treatment preferences before and after participation in an N-of-1 trial.

Study Design And Setting: In this observational study nested within a randomized trial, we examined chronic pain patients' preferences before and after treatment in relation to N-of-1 trial results; assessed the influence of different schemes for defining comparative "superiority" on potential conclusions; and generated classification trees illustrating the relationship between pre-treatment preferences, N-of-1 trial results, and post-treatment preferences.

Results: Treatment preferences differed pre- and post-trial for 40% of participants.

Correction: Data Sharing Goals for Nonprofit Funders of Clinical Trials.

[This corrects the article DOI: 10.2196/23011.].

Mobile Health: making the leap to research and clinics.

Health applications for mobile and wearable devices continue to experience tremendous growth both in the commercial and research sectors, but their impact on healthcare has yet to be fully realized. This commentary introduces three articles in a special issue that provides guidance on how to successfully address translational barriers to bringing mobile health technologies into clinical research and care. We also discuss how the cross-organizational sharing of data, software, and other digital resources can lower such barriers and accelerate progress across mobile health.

The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data.

Introduction: Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code.

Data Sharing Goals for Nonprofit Funders of Clinical Trials.

Sharing clinical trial data can provide value to research participants and communities by accelerating the development of new knowledge and therapies as investigators merge data sets to conduct new analyses, reproduce published findings to raise standards for original research, and learn from the work of others to generate new research questions. Nonprofit funders, including disease advocacy and patient-focused organizations, play a pivotal role in the promotion and implementation of data sharing policies. Funders are uniquely positioned to promote and support a culture of data sharing by serving as trusted liaisons between potential research participants and investigators who wish to access these participants' networks for clinical trial recruitment.

COVID-19 trials: declarations of data sharing intentions at trial registration and at publication.

Background: The sharing of individual participant-level data from COVID-19 trials would allow re-use and secondary analysis that can help accelerate the identification of effective treatments. The sharing of trial data is not the norm, but the unprecedented pandemic caused by SARS-CoV-2 may serve as an impetus for greater data sharing. We sought to assess the data sharing intentions of interventional COVID-19 trials as declared in trial registrations and publications.

Digital Medicine Community Perspectives and Challenges: Survey Study.

Background: The field of digital medicine has seen rapid growth over the past decade. With this unfettered growth, challenges surrounding interoperability have emerged as a critical barrier to translating digital medicine into practice. In order to understand how to mitigate challenges in digital medicine research and practice, this community must understand the landscape of digital medicine professionals, which digital medicine tools are being used and how, and user perspectives on current challenges in the field of digital medicine.

Timely access to trial data in the context of a pandemic: the time is now.

Objective: Clinical trial data sharing has the potential to accelerate scientific progress, answer new lines of scientific inquiry, support reproducibility and prevent redundancy. Vivli, a non-profit organisation, operates a global platform for sharing of individual participant-level trial data and associated documents. Sharing of these data collected from each trial participant enables combining of these data to drive new scientific insights or assess reproducibility-not possible with the aggregate or summary data tables historically made available.

Ethical Framework for Assessing Manual and Digital Contact Tracing for COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has challenged the traditional public health balance between benefiting the good of the community through contact tracing and restricting individual liberty. This article first analyzes important technical and ethical issues regarding new smartphone apps that facilitate contact tracing and exposure notification. It then presents a framework for assessing contact tracing, whether manual or digital: the effectiveness at mitigating the pandemic; acceptability of risks, particularly privacy; and equitable distribution of benefits and risks.