Digital health technologies for metabolic disorders in older adults: a scoping review protocol.

Introduction: Metabolic disorders (type 2 diabetes, insulin resistance, hyperglycaemia, obesity, hyperlipidaemia, hypertension, non-alcoholic fatty liver disease and metabolic syndrome) are leading causes of mortality and disability worldwide. These disorders disproportionately affect older adults relative to those younger. Digital health technologies (DHTs), such as patient monitoring, digital diagnostics and digital therapeutics, emerge as promising tools for health promotion in day-to-day life.

Noninvasive, Multimodal Inflammatory Biomarker Discovery for Systemic Inflammation (NOVA Study): Protocol for a Cross-Sectional Study.

Background: Prolonged systemic inflammation is recognized as a major contributor to the development of various chronic inflammatory diseases. Daily measurements of inflammatory biomarkers can significantly improve disease monitoring of systemic inflammation, thus contributing to reducing the burden on patients and the health care system. There exists, however, no scalable, cost-efficient, and noninvasive biomarker for remote assessment of systemic inflammation.

Top-funded companies offering digital health interventions for the prevention and treatment of depression: a systematic market analysis.

Background: Digital innovations can reduce the global burden of depression by facilitating timely and scalable interventions. In recent years, the number of commercial Digital Health Interventions for Depression (DHIDs) has been on the rise. However, there is limited knowledge on their content and underpinning scientific evidence.

Comparative Efficacy of Commercial Wearables for Circadian Rhythm Home Monitoring from Activity, Heart Rate, and Core Body Temperature.

Circadian rhythms govern biological patterns that follow a 24-hour cycle. Dysfunctions in circadian rhythms can contribute to various health problems, such as sleep disorders. Current circadian rhythm assessment methods, often invasive or subjective, limit circadian rhythm monitoring to laboratories.

Using Wearables to Study Biopsychosocial Dynamics in Couples Who Cope With a Chronic Health Condition: Ambulatory Assessment Study.

Background: Technology has become an integral part of our everyday life, and its use to manage and study health is no exception. Romantic partners play a critical role in managing chronic health conditions as they tend to be a primary source of support.

Objective: This study tests the feasibility of using commercial wearables to monitor couples' unique way of communicating and supporting each other and documents the physiological correlates of interpersonal dynamics (ie, heart rate linkage).

Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan.

Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce CosinorAge, a digital biomarker of aging developed from wearable-derived circadian rhythmicity from 80,000 midlife and older adults in the UK and US. A one-year increase in CosinorAge corresponded to 8-12% higher all-cause and cause-specific mortality risks and 3-14% increased prospective incidences of age-related diseases. CosinorAge also captured a non-linear decline in resilience and physical functioning, evidenced by an 8-33% reduction in self-rated health and a 3-23% decline in health-related quality of life score, adjusting for covariates and multiple testing.

Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving.

Background: Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers.

The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers.

Objectives: We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers.

Materials And Methods: The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification.

Results: By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention.

Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial.

Background: Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel.

Can digital health researchers make a difference during the pandemic? Results of the single-arm, chatbot-led Elena+: Care for COVID-19 interventional study.

Background: The current paper details findings from Elena+: Care for COVID-19, an app developed to tackle the collateral damage of lockdowns and social distancing, by offering pandemic lifestyle coaching across seven health areas: anxiety, loneliness, mental resources, sleep, diet and nutrition, physical activity, and COVID-19 information.

Methods: The Elena+ app functions as a single-arm interventional study, with participants recruited predominantly via social media. We used paired samples -tests and within subjects ANOVA to examine changes in health outcome assessments and user experience evaluations over time.

Applying User-Centered Design and Implementation Science to the Early-Stage Development of a Telehealth-Enhanced Hybrid Cardiac Rehabilitation Program: Quality Improvement Study.

Background: Cardiac rehabilitation (CR) is an evidence-based intervention that improves event-free survival in patients with cardiac conditions, yet <27% of all eligible patients use CR in the United States. CR is traditionally delivered in clinic-based settings where implementation barriers abound. Innovative nontraditional program designs and strategies are needed to support widespread CR uptake.

Development of a digital biomarker and intervention for subclinical depression: study protocol for a longitudinal waitlist control study.

Background: Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.

Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014.

Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.

Development of "LvL UP 1.0": a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders.

Background: Non-communicable diseases (NCDs) and common mental disorders (CMDs) are the leading causes of death and disability worldwide. Lifestyle interventions mobile apps and conversational agents present themselves as low-cost, scalable solutions to prevent these conditions. This paper describes the rationale for, and development of, "LvL UP 1.

Smartphone-based cough monitoring as a near real-time digital pneumonia biomarker.

Background: Cough represents a cardinal symptom of acute respiratory tract infections. Generally associated with disease activity, cough holds biomarker potential and might be harnessed for prognosis and personalised treatment decisions. Here, we tested the suitability of cough as a digital biomarker for disease activity in coronavirus disease 2019 (COVID-19) and other lower respiratory tract infections.

Trends in voice characteristics in patients with heart failure (VENTURE) in Switzerland: Protocol for a longitudinal observational pilot study.

Introduction: Heart Failure (HF) is a major health and economic issue worldwide. HF-related expenses are largely driven by hospital admissions and re-admissions, many of which are potentially preventable. Current self-management programs, however, have failed to reduce hospital admissions.

Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data.

Objective: To develop a noninvasive hypoglycemia detection approach using smartwatch data.

Research Design And Methods: We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.

Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes.

Aim: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data.

Materials And Methods: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes.

User-centered design of a telehealth-enhanced hybrid cardiac rehabilitation program as hospital quality improvement.

Background Innovative program designs and strategies are needed to support the widespread uptake of cardiac rehabilitation (CR) programs in the post-COVID19 era. We combined user-centered design (UCD) and implementation science (ImS) principles to design a novel telehealth-enhanced hybrid (home and clinic-based) CR (THCR) program. Methods As part of a New York Presbyterian Hospital (NYPH) quality improvement initiative (March 2020-February 2022), we designed a THCR program using an iterative 3 step UCD process informed by the Theoretical Domains Framework and Consolidated Framework for Implementation Research to: 1) identify user and contextual barriers to CR uptake (stakeholder interviews), 2) design an intervention prototype (design workshops and journey mapping), and 3) refine the prototype (usability testing).

Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study.

Background: Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration.

Breathing as an Input Modality in a Gameful Breathing Training App (Breeze 2): Development and Evaluation Study.

Background: Slow-paced breathing training can have positive effects on physiological and psychological well-being. Unfortunately, use statistics indicate that adherence to breathing training apps is low. Recent work suggests that gameful breathing training may help overcome this challenge.

A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development.

Background: To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards.

Objective: In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches).

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review.

Background: Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions.

TripletCough: Cougher Identification and Verification From Contact-Free Smartphone-Based Audio Recordings Using Metric Learning.

Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors.