Future Research
1. Decentralized Clinical Trials (DCT)
Decentralized Clinical Trials (DCTs) have emerged as a transformative model, addressing the inherent limitations of traditional, multi-centered clinical trials, which often struggle with inclusivity and practicality (Jean-Louis & Seixas, 2024). Traditional trials necessitate participant travel to designated sites for screening, interventions, and follow-ups, introducing biases that favor those with means and knowledge of the healthcare system, or those with few alternatives. Such site-based models exclude many, particularly the elderly, women, and marginalized communities, while logistical obstacles like transportation costs and work absences further limit participation.
DCTs address these issues by utilizing digital technologies and real-world data to extend clinical trial access. Through telemedicine, mobile applications, and remote monitoring, participants can contribute from the comfort of their homes. These innovations increase trial inclusivity and generate comprehensive evidence across diverse demographics, aligning with the growing global trend towards remote healthcare solutions. The COVID-19 pandemic has accelerated this trend, revealing the feasibility and appeal of remote participation, especially in trials of low-to-medium complexity.
Nevertheless, challenges such as secure data management, ethical consent processes, and digital literacy disparities persist. DCTs require well-defined mechanisms for participant support and safety, including maintaining avenues for in-person consultation when necessary. Additionally, limitations arise when trials involve high-complexity interventions or designs unsuitable for decentralization. Issues related to data privacy, platform integration, and the digital divide must be carefully addressed to prevent new forms of inclusion bias. Furthermore, robust strategies are essential to ensure that electronic consent meets ethical standards and that participants have the flexibility to withdraw seamlessly.
Our team is committed to becoming a pioneer in this field in Taiwan. We are actively working to fill critical knowledge and technological gaps necessary for successful implementation. This includes establishing digital phenotyping and conducting pilot studies in digital therapeutics to build foundational expertise. Simultaneously, we are developing robust platforms to ensure a secure and engaging digital patient experience and designing mechanisms to promote equitable access. By systematically addressing these areas, we aim to transform the landscape of mobile health research and eventually provide ecological and representative insights for healthcare interventions.
2. Digital Phenotyping
Previous studies on sleep-wake cycles have relied on the well-established actigraphy method to assess rest-activity rhythms (RARs). However, with the widespread use of smartphones and their deep permeation into our daily lives, smartphone usage patterns may provide a more complete and nuanced understanding of RARs, as smartphone usage reflects social and mental activities. Moreover, the existing research of rest-activity rhythm (RAR) has predominantly focused on healthy participants and has not validated their algorithms in patients with sleep disturbances or disrupted circadian rhythms in clinical settings. This limited clinical evidence restricts the application of circadian knowledge in diagnostic and therapeutic opportunities. By combining actigraphy and human-smartphone interaction patterns in measuring RAR, we can obtain a more comprehensive understanding of the alignment and interaction between physical activity and psycho-social activity, addressing the gaps in current research.
There are essentially two main paradigms when utilizing biomarkers to distinguish phenotypes. The first paradigm manipulates raw data to calculate “new” biomarkers. For example, in initial evaluation of acute kidney injury, we roughly grasp the mechanism of injury by calculating the ratio of blood urea nitrogen (BUN) over serum creatinine, to determine that the injury is pre-renal or not. The other paradigm combines categorization by two or multiple biomarkers to stratify patients. One prominent example is to combine mean corpuscular volume (MCV), mean corpuscular hеmoglobiո concentration (ΜСНС), red cell distribution width (RDW), among others, to determine the etiology of anemia, which is crucial for diagnosis and management approach of such disease.
We aim to establish digital phenotypes based on the digital biomarkers we have developed, the rest-activity rhythms captured through human-smartphone interactions (HSI) and physical activities. Using the two paradigms described above to combine our biomarkers, we will characterize the digital phenotypes of depression and obesity, both as independent conditions and in their comorbid state. Applying the same technique, we also aim to establish digital phenotypes for different exercise frequency patterns, to shed light on the motivations and behaviors related to exercise in population in Taiwan. Once established, these associations will aid in tracking and predicting diseases or behaviors of interest and identify potential therapeutic targets, forming a foundation for implementing decentralized clinical trials (DCT).
3. Enhancing Digital Health Experience
Mobile application developed by our team, “Rhythm”, passively collects users’ digital footprints, identifying sleep/wake cycles and calculating rest-activity rhythm indicators in the smartphone background. Users receive weekly or monthly analyses of their activities on their smartphones, including sleep patterns, sleep statistics, and rest-activity rhythm indicators. All metrics are visualized accordingly, accompanied by explanation of the parameters and basic interpretations in plain language. Users can grasp their rhythmicity of daily activities effortlessly based on the reports, and are allowed to discuss further clinical implications or behavioral modification strategies with attending physicians at appointments based on the analyses. Duty physicians are also provided with panoramic pictures of the users’ sleep/wake cycles, which aid in precise management for the users.
The feedback mechnism of our application and platform is automatic, efficient, reliable, and customized to each user’s circumstances, constructing a unique digital patient experience in contrast to traditional face-to-face encounters. We plan to polish the digital patient experience through refining the UI/UX of the mobile app, including adding more indicators that are relevant to circadian rhythm disruption, e.g. smartphone use before sleep. A well-designed digital patient experience would contribute to the usability of our digital tool, for example, improving users’ self-awareness of their rest-activity rhythm, which potentially would propel changes in health behavior. Additionaly, such experience liberate circadian rhythm evaluation from healthcare and research facilities, which render it an ideal tool for conducting decentralized clinical trials in the future.
Digital therapeutics are potentiated when digital patient experiences sparked changes. One of our mobile app user case demonstrated the potential to develop digital therapeutics based on mobile apps. The users receive summaries of their rest-activity rhythm on a weekly basis, which also contain detailed explanations of the circadian rhythm indicators. One of our users was informed by such reports that he/she has relatively irregular sleep-wake cycles throughout a week, manifest by low interdaily stability (IS) of his/her human-smartphone interactions, as shown by the figure below. Seeing the reports, the user adjusted his/her sleep time spontaneously. This self-adjustment resulted in improved IS of rest-activity rhythm in the following weeks.
Although the clinical implication of the improvement in circadian rhythm indicators still warrants further study, our mobile app and platform has emerged as potential digital therapeutics. Its secure design, ease of use, and spontaneous nature also make it a good fit for conducting decentralized clinical trials.
4. Covid-19
5. Substance Use During Pregnancy
The goal of this four-year project is to investigate the impact of using or combining controlled substances (e.g., sedatives, antidepressants) and illicit drugs during pregnancy on the health of mothers and their newborns in Taiwan. The researches aim to understand differences in the misuse or overdose of these substances between pregnant women who use drugs and those who do not. It will also analyze the effects of maternal drug use on newborn cardiovascular risks and assess the broader social harm, including risks related to accidents such as car crashes and falls.
By leveraging Taiwan’s National Health Insurance Research Database and integrating multiple data sources, such as the Birth Notification System, and Integrated Illegal Drug Database, the project will provide a comprehensive analysis of drug use patterns and their impact, including potential family context comparisons. The findings will deliver critical information to support policy-making, guide clinical decisions, and empower pregnant women and their families to make informed health choices, ultimately enhancing maternal and infant well-being in Taiwan.