Assessing the Clinical Robustness of Digital Health Startups: Cross-sectional Observational Analysis

Peril and Promise: Teens, Tech, and America's Mental Health Crisis

Advances in Internet technologies have implications for the health and development of children and adolescents with potential for both beneficial and harmful outcomes. Similar technological advances also impact how psychiatrists deliver mental health care in clinical settings. Internet tech adds complexities to psychiatric practice in the form of electronic health records, patient portals, and virtual patient contact, which clinicians must understand and successfully incorporate into practice. Digital therapeutics and virtual mental health endeavors offer new treatment delivery options for patients and providers. Some have proven benefits, such as improved accessibility for patients, but all require provider expertise to utilize.

Clinical Simulation in the Regulation of Software as a Medical Device: An eDelphi Study

BACKGROUND

Accelerated digitalization in the health sector requires the development of appropriate evaluation methods to ensure that digital health technologies (DHTs) are safe and effective. Software as a medical device (SaMD) is a commonly used DHT by clinicians to provide care to patients. Traditional research methods for evaluating health care products, such as randomized clinical trials, may not be suitable for DHTs, such as SaMD. However, evidence to show their safety and efficacy is needed by regulators before they can be used in practice. Clinical simulation can be used by researchers to test SaMD in an agile and low-cost way; yet, there is limited research on criteria to assess the robustness of simulations and, subsequently, their relevance for a regulatory decision.

OBJECTIVE

The objective of this study was to gain consensus on the criteria that should be used to assess clinical simulation from a regulatory perspective when it is used to generate evidence for SaMD.

METHODS

An eDelphi study approach was chosen to develop a set of criteria to assess clinical simulation when used to evaluate SaMD. Participants were recruited through purposive and snowball sampling based on their experience and knowledge in relevant sectors. They were guided through an initial scoping questionnaire with key themes identified from the literature to obtain a comprehensive list of criteria. Participants voted upon these criteria in 2 Delphi rounds, with criteria being excluded if consensus was not met. Participants were invited to add qualitative comments during rounds and qualitative analysis was performed on the comments gathered during the first round. Consensus was predefined by 2 criteria: if <10% of the panelists deemed the criteria as "not important" or "not important at all" and >60% "important" or "very important."

RESULTS

In total, 33 international experts in the digital health field, including academics, regulators, policy makers, and industry representatives, completed both Delphi rounds, and 43 criteria gained consensus from the participants. The research team grouped these criteria into 7 domains-background and context, overall study design, study population, delivery of the simulation, fidelity, software and artificial intelligence, and study analysis. These 7 domains were formulated into the simulation for regulation of SaMD framework. There were key areas of concern identified by participants regarding the framework criteria, such as the importance of how simulation fidelity is achieved and reported and the avoidance of bias throughout all stages.

CONCLUSIONS

This study proposes the simulation for regulation of SaMD framework, developed through an eDelphi consensus process, to evaluate clinical simulation when used to assess SaMD. Future research should prioritize the development of safe and effective SaMD, while implementing and refining the framework criteria to adapt to new challenges.

Key Considerations for Designing Clinical Studies to Evaluate Digital Health Solutions

Evidence of clinical impact is critical to unlock the potential of digital health solutions (DHSs), yet many solutions are failing to deliver positive clinical results. We argue in this viewpoint that this failure is linked to current approaches to DHS evaluation design, which neglect numerous key characteristics (KCs) requiring specific scientific and design considerations. We first delineate the KCs of DHSs: (1) they are implemented at health care system and patient levels; (2) they are "complex" interventions; (3) they can drive multiple clinical outcomes indirectly through a multitude of smaller clinical benefits; (4) their mechanism of action can vary between individuals and change over time based on patient needs; and (5) they develop through short, iterative cycles-optimally within a real-world use context. Following our objective to drive better alignment between clinical evaluation design and the unique traits of DHSs, we then provide methodological suggestions that better address these KCs, including tips on mechanism-of-action mapping, alternative randomization methods, control-arm adaptations, and novel end-point selection, as well as innovative methods utilizing real-world data and platform research.

Digital Mental Health's Unstable Dichotomy-Wellness and Health

This Viewpoint discusses the unacknowledged risks and harms and unrealized clinical benefits of digital mental wellness and health technologies and offers suggestions for ways to catalyze the next phase of these technologies by focusing on safety, evidence, and engagement.

Clinical Validation of Digital Healthcare Solutions: State of the Art, Challenges and Opportunities

Digital health technologies (DHTs) at the intersection of health, medical informatics, and business aim to enhance patient care through personalised digital approaches. Ensuring the efficacy and reliability of these innovations demands rigorous clinical validation. A PubMed literature review (January 2006 to July 2023) identified 1250 papers, highlighting growing academic interest. A focused narrative review (January 2018 to July 2023) delved into challenges, highlighting issues such as diverse regulatory landscapes, adoption issues in complex healthcare systems, and a plethora of evaluation frameworks lacking pragmatic guidance. Existing frameworks often omit crucial criteria, neglect empirical evidence, and clinical effectiveness is rarely included as a criterion for DHT quality. The paper underscores the urgency of addressing challenges in accreditation, adoption, business models, and integration to safeguard the quality, efficacy, and safety of DHTs. A pivotal illustration of collaborative efforts to address these challenges is exemplified by the Digital Health Validation Center, dedicated to generating clinical evidence of innovative healthcare technologies and facilitating seamless technology transfer. In conclusion, it is necessary to harmonise evaluation approaches and frameworks, improve regulatory clarity, and commit to collaboration to integrate rigorous clinical validation and empirical evidence throughout the DHT life cycle.

Patient Engagement with Conversational Agents in Health Applications 2016-2022: A Systematic Review and Meta-Analysis

Clinicians and patients seeking electronic health applications face challenges in selecting effective solutions due to a high market failure rate. Conversational agent applications ("chatbots") show promise in increasing healthcare user engagement by creating bonds between the applications and users. It is unclear if chatbots improve patient adherence or if past trends to include chatbots in electronic health applications were due to technology hype dynamics and competitive pressure to innovate. We conducted a systematic literature review using Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology on health chatbot randomized control trials. The goal of this review was to identify if user engagement indicators are published in eHealth chatbot studies. A meta-analysis examined patient clinical trial retention of chatbot apps. The results showed no chatbot arm patient retention effect. The small number of studies suggests a need for ongoing eHealth chatbot research, especially given the claims regarding their effectiveness made outside the scientific literatures.

Growth of Remote Therapeutic Monitoring Lands New Opportunities for Case Management

PURPOSE/OBJECTIVES

An increase in the use of remote therapeutic monitoring (RTM) has been spurred by nationwide factors including the COVID-19 pandemic, authorized reimbursement of RTM by the Centers for Medicare & Medicaid Services, and more frequent use of big data analytics in health care delivery. This article discusses the use of RTM by care teams at the point of care and explores the role of the case manager in RTM to address patients' unmet needs.

PRIMARY PRACTICE SETTINGS

Although RTM may be utilized across inpatient and outpatient levels of care, this article focuses on outpatient care such as community clinics, provider groups, and home health care.

FINDINGS/CONCLUSIONS

When implemented along with care management interventions, RTM applications have the potential to improve patient adherence, enhance communication between patients and their providers, streamline resource allocation, and address social determinants of health impacting patient care and outcomes.

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

RTM reimbursement models are rapidly evolving, utilizing real-world and patient-reported data to identify and initiate timely, individualized solutions that meet the holistic needs of each patient. Use of an RTM system allows the case manager to build rapport with the patient while quickly identifying care gaps and delivering appropriate interventions that can maximize patient outcomes. RTM can drive savings and bring revenue to the system or practice while providing salient documentation of social determinants of health that can be addressed with validation of proven care coordination interventions.

mHealth Apps for the Self-Management of Low Back Pain: Systematic Search in App Stores and Content Analysis

BACKGROUND

With the rapid development of mobile health (mHealth) technology, many health apps have been introduced to the commercial market for people with back pain conditions. However, little is known about their content, quality, approaches to care for low back pain (LBP), and associated risks of use.

OBJECTIVE

The aims of this research were to (1) identify apps for the self-management of LBP currently on the market and (2) assess their quality, intervention content, theoretical approaches, and risk-related approaches.

METHODS

The UK iTunes and Google Play stores were initially searched for apps related to the self-management of LBP in May 2022. A repeat search in June 2023 was conducted to ensure that any relevant new apps developed in the last year were incorporated into the review. A total of 3 keywords recommended by the Cochrane Back and Neck Group were used to search apps "low back pain," "back pain," and "lumbago." The quality of the apps was assessed by using the 5-point Mobile App Rating Scale (MARS).

RESULTS

A total of 69 apps (25 iOS and 44 Android) met the inclusion criteria. These LBP self-management apps mainly provide recommendations on muscle stretching (n=51, 73.9%), muscle strengthening (n=42, 60.9%), core stability exercises (n=32, 46.4%), yoga (n=19, 27.5%), and information about LBP mechanisms (n=17, 24.6%). Most interventions (n=14, 78%) are consistent with the recommendations in the National Institute for Health and Care Excellence (NICE) guidelines. The mean (SD) MARS overall score of included apps was 2.4 (0.44) out of a possible 5 points. The functionality dimension was associated with the highest score (3.0), whereas the engagement and information dimension resulted in the lowest score (2.1). Regarding theoretical and risk-related approaches, 18 (26.1%) of the 69 apps reported the rate of intervention progression, 11 (15.9%) reported safety checks, only 1 (1.4%) reported personalization of care, and none reported the theoretical care model or the age group targeted.

CONCLUSIONS

mHealth apps are potentially promising alternatives to help people manage their LBP; however, most of the LBP self-management apps were of poor quality and did not report the theoretical approaches to care and their associated risks. Although nearly all apps reviewed included a component of care listed in the NICE guidelines, the model of care delivery or embracement of care principles such as the application of a biopsychosocial model was unclear.

The Buzz Surrounding Precision Medicine: The Imperative of Incorporating It into Evidence-Based Medical Practice

Precision medicine (PM), through the integration of omics and environmental data, aims to provide a more precise prevention, diagnosis, and treatment of disease. Currently, PM is one of the emerging approaches in modern healthcare and public health, with wide implications for health care delivery, public health policy making formulation, and entrepreneurial endeavors. In spite of its growing popularity and the buzz surrounding it, PM is still in its nascent phase, facing considerable challenges that need to be addressed and resolved for it to attain the acclaim for which it strives. In this article, we discuss some of the current methodological pitfalls of PM, including the use of big data, and provide a perspective on how these challenges can be overcome by bringing PM closer to evidence-based medicine (EBM). Furthermore, to maximize the potential of PM, we present real-world illustrations of how EBM principles can be integrated into a PM approach.

Digital Health Programs to Reduce Readmissions in Coronary Artery Disease: A Systematic Review and Meta-Analysis

Background

The use of mobile health (mHealth, wireless communication devices, and/or software technologies) in health care delivery has increased rapidly in recent years. Their integration into disease management programs (DMPs) has tremendous potential to improve outcomes for patients with coronary artery disease (CAD), yet a more robust evaluation of the evidence is required.

Objectives

The purpose of this study was to undertake a systematic review and meta-analysis of mHealth-enabled DMPs to determine their effectiveness in reducing readmissions and mortality in patients with CAD.

Methods

We systematically searched English language studies from January 1, 2007, to August 3, 2021, in multiple databases. Studies comparing mHealth-enabled DMPs with standard DMPs without mHealth were included if they had a minimum 30-day follow-up for at least one of all-cause or cardiovascular-related mortality, readmissions, or major adverse cardiovascular events.

Results

Of the 3,411 references from our search, 155 full-text studies were assessed for eligibility, and data were extracted from 18 publications. Pooled findings for all-cause readmissions (10 studies, n = 1,514) and cardiac-related readmissions (9 studies, n = 1,009) indicated that mHealth-enabled DMPs reduced all-cause (RR: 0.68; 95% CI: 0.50-0.91) and cardiac-related hospitalizations (RR: 0.55; 95% CI: 0.44-0.68) and emergency department visits (RR: 0.37; 95% CI: 0.26-0.54) compared to DMPs without mHealth. There was no significant reduction for mortality outcomes (RR: 1.72; 95% CI: 0.64-4.64) or major adverse cardiovascular events (RR: 0.68; 95% CI: 0.40-1.15).

Conclusions

DMPs integrated with mHealth should be considered an effective intervention for better outcomes in patients with CAD.

A Narrow Definition of Clinical Robustness

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Partners in research: The success with therapies research consortium and the CF community unite to improve self-management

BACKGROUND

The daily treatment regimen for an individual with cystic fibrosis (CF) can take more than 2 h to complete, and chronic treatment adherence rates are low. Developing partnerships between CF clinical researchers and the CF community is essential in developing acceptable, feasible, and effective strategies to improve self-management and adherence.

METHODS

The Success with Therapies Research Consortium (STRC) was formed as a multi-center US collaborative to conduct rigorous research studies of adherence to CF treatments. A multidisciplinary team of researchers from 15 sites, collaborating with members of the CF community, is charged with developing, implementing, and disseminating real-world, patient-centered interventions for people living with CF.

RESULTS

Since 2014, the STRC has conducted 8 studies. The CF community, people with CF (pwCF), and caregivers have come to serve in multiple valuable capacities on the STRC, including as members of the Steering Committee and Co-Principal Investigators. Additionally, while people with CF are irreplaceable participants in STRC studies, their influence, and that of their families and healthcare professionals, extends beyond the traditional research participant role.

CONCLUSIONS

Engaging broadly with the CF community is the optimal model for developing interventions to support those living with CF in sustaining daily care. Input and direct involvement from people with CF, their families, and their caregivers has enabled the STRC to advance its mission through innovative clinical research approaches.

Physicians' Use of Digital Health Interventions in the Management of Nonalcoholic Fatty Liver Disease

Globally, the use of digital health interventions (DHIs) is expanding, along with growing scientific evidence of their effectiveness. Given the high and increasing prevalence of noncommunicable liver disease, we surveyed 295 physicians across Spain about their knowledge, beliefs, attitudes, practices, and access with regard to DHIs for patient care and in particular for liver diseases, including nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Physicians reported high familiarity with DHIs, although most had not recommended them in patient care. Addressing concerns, including limited available time, evidence of effectiveness, education, training, and access may contribute to an increased uptake of these technologies.

Digital health in pain assessment, diagnosis, and management: Overview and perspectives

Managing pain is essential for social, psychological, physical, and economic reasons. It is also a human right with a growing incidence of untreated and under-treated pain globally. Barriers to diagnosing, assessing, treating, and managing pain are complicated, subjective, and driven by patient, healthcare provider, payer, policy, and regulatory challenges. In addition, conventional treatment methods pose their own challenges including the subjectivity of assessment, lack of therapeutic innovation over the last decade, opioid use disorder and financial access to treatment. Digital health innovations hold much promise in providing complementary solutions to traditional medical interventions and may reduce cost and speed up recovery or adaptation. There is a growing evidence base for the use of digital health in pain assessment, diagnosis, and management. The challenge is not only to develop new technologies and solutions, but to do this within a framework that supports health equity, scalability, socio-cultural consideration, and evidence-based science. The extensive limits to physical personal interaction during the Covid-19 pandemic 2020/21 has proven the possible role of digital health in the field of pain medicine. This paper provides an overview of the use of digital health in pain management and argues for the use of a systemic framework in evaluating the efficacy of digital health solutions.

What is coming next in health technology startups? Some insights and practice guidelines

Health technology startups are experiencing a significant surge in growth, particularly since the COVID-19 pandemic, as they address gaps in the sector. However, despite their increasing prevalence, there is still relatively limited knowledge about this sector's evolution. This opinion article explores emerging trends in health startups, including their market size, growth, significant challenges, and guidelines for key stakeholders from a global healthcare service industry perspective. By gaining a better understanding of these trends, new research opportunities and evidence-based practices can be identified.

Efficacy and Conflicts of Interest in Randomized Controlled Trials Evaluating Headspace and Calm Apps: Systematic Review

BACKGROUND

Although there are thousands of mental health apps, 2 apps, Headspace and Calm, claim a large percentage of the marketplace. These two mindfulness and meditation apps have reached tens of millions of active users. To guide consumers, clinicians, and researchers, we performed a systematic review of randomized controlled trials (RCTs) of Headspace and Calm.

OBJECTIVE

Our study aimed to evaluate intervention efficacy, risk of bias, and conflicts of interest (COIs) in the evidence base for Headspace and Calm, the two most popular mental health apps at the time of our search.

METHODS

To identify studies, we searched academic databases (Google Scholar, MEDLINE, and PsycINFO) and the websites of Headspace and Calm in May 2021 for RCTs of Headspace and Calm testing efficacy via original data collection, published in English in peer-reviewed journals. For each study, we coded (1) study characteristics (eg, participants, sample size, and outcome measures), (2) intervention characteristics (eg, free vs paid version of the app and intended frequency of app usage), (3) all study outcomes, (4) Cochrane risk of bias variables, and (5) COI variables (eg, presence or absence of a preregistration and the presence or absence of a COI statement involving the company).

RESULTS

We identified 14 RCTs of Headspace and 1 RCT of Calm. Overall, 93% (13/14) of RCTs of Headspace and 100% (1/1) of RCTs of Calm recruited participants from a nonclinical population. Studies commonly measured mindfulness, well-being, stress, depressive symptoms, and anxiety symptoms. Headspace use improved depression in 75% of studies that evaluated it as an outcome. Findings were mixed for mindfulness, well-being, stress, and anxiety, but at least 40% of studies showed improvement for each of these outcomes. Studies were generally underpowered to detect "small" or "medium" effect sizes. Furthermore, 50% (7/14) of RCTs of Headspace and 0% (0/1) of RCTs of Calm reported a COI that involved Headspace or Calm (the companies). The most common COI was the app company providing premium app access for free for participants, and notably, 14% (2/14) of RCTs of Headspace reported Headspace employee involvement in study design, execution, and data analysis. Only 36% (5/14) of RCTs of Headspace were preregistered, and the 1 RCT of Calm was not preregistered.

CONCLUSIONS

The empirical research on Headspace appears promising, whereas there is an absence of randomized trials on Calm. Limitations of this study include an inability to compare Headspace and Calm owing to the dearth of RCTs studying Calm and the reliance on author reports to evaluate COIs. When determining whether or not mental health apps are of high quality, identification of high-quality apps and evaluation of their effectiveness and investigators' COIs should be ensured.

Automatische Bildanalyse und künstliche Intelligenz in der Nephropathologie

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