Global Trends and Disparities in Clinical Trials of Digital Health Interventions-Analyzing Data from the WHO ICTRP

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Prior studies on digital technology trials, mainly from ClinicalTrials.gov, often included non-intervention uses. In contrast, this study leverages the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) for a more comprehensive dataset and focuses exclusively on digital health interventions. The study aims to identify and characterize registered DHIs trials from the WHO ICTRP, analyze their geographical and temporal trends, and elucidate current advancements, gaps, and future directions in DHI clinical research. Methods We conducted a comprehensive analysis of 3,685 registered clinical trials from the WHO ICTRP, spanning the period from January 2005 to December 2022. Using the National institute for Health and Care Excellence (NICE) framework Evidence standards framework for digital health technologies, DHIs were systematically categorized into three levels and eight distinct categories. The analysis focused on these key dimensions: trial objectives, technological trends, geographical distribution, and temporal patterns, providing a robust overview of the evolution and global landscape of DHI clinical research. Results Health promotion (26.6%) and disease treatment (21.3%) are key objectives, with mental health and endocrine disorders as common focuses. Mobile apps have surpassed Short Message Service (SMS) as the dominant technology since 2015, peaking in high-income regions by 2019 and growing steadily in middle-income regions through 2022. Teleconsultation technologies surged post-2019, driven by pandemic demands, while SMS remains vital in low-income countries. Regional disparities persist, with high-income areas conducting over 100 times more trials than low-income ones. Methodological and reporting quality of registered DHI trials needs to be further improved. Conclusions This study highlights global trends in DHI adoption, underscores persistent inequalities in trial distribution, and provides actionable insights for optimizing global digital health strategies. The findings emphasize the need for improved methodological rigor and equitable resource allocation to advance DHI research and implementation worldwide. Technological Trends Digital Health Interventions Clinical Trials Geographical Disparities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Humanity has made significant progress and achievements in the field of healthcare, including the control of infectious diseases[ 1 ], increased infant survival rates[ 2 ], the advancement of organ transplantation surgeries[ 3 ], the development of targeted cancer therapies[ 4 ], and breakthroughs in gene-editing technologies[ 5 ]. However, the accessibility of healthcare services[ 6 ], healthcare inequities[ 7 ]. the rising costs of medical care, the insufficient supply of healthcare workforce, the long-term management pressures of chronic diseases and the quality of patient care among others, remain pressing challenges in the current healthcare landscape. In the field of Digital Health Interventions, the extensive applicability, resource optimization, and integrated advantages, along with the capability for continuous health monitoring and feedback, have demonstrated significant potential in enhancing healthcare accessibility and alleviating disparities in healthcare resource distribution[ 8 ]. Furthermore, DHIs have played a crucial role in the long-term management of chronic diseases[ 9 ], as well as in supporting medical diagnostics and examinations, thereby contributing to the optimization and widespread dissemination of healthcare services. DHIs play a multifaceted role in managing diabetes[ 10 ] and reducing cardiovascular risk [11] , integrating health monitoring[ 12 ], telemedicine consultations[ 13 ], disease self-management, behaviour modification[ 14 , 15 ], health promotion, health education[ 16 ], and patient engagement. For instance, continuous glucose monitoring devices[ 17 ] and wearable fitness trackers[ 18 ] are utilized for real-time health monitoring; telemedicine platforms[ 19 ] enable remote consultations with healthcare providers[ 20 ]; mobile applications and online portals assist in disease self-management by tracking medication adherence and lifestyle adjustments; behaviour modification[ 21 ] is encouraged through digital coaching and personalized feedback systems; health promotion and education are provided via interactive e-learning modules and webinars; and patient engagement is strengthened through social support networks[ 22 ]. This study primarily utilized data from the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) spanning over 18 years, focusing on interventional clinical trials. A total of 3685 clinical trials were included in this analysis. Based on the NICE classification framework, we analyzed and categorized the types of trials in DHI, which were classified into three levels and eight categories. Additionally, we analyzed and annotated various features of DHIs, including its classification distribution, technology usage, disease focus, and regional and national variations. We also examined the temporal development trends of DHIs regarding their classifications, technology uses, and disease focuses. Furthermore, we conducted an in-depth analysis of the correlations between DHIs and disease-related technologies, as well as the relationships between diseases and DHI types. The data analysis reveals that most DHI trials focused on health promotion (27%) and disease treatment (23%), with mental/behavioral and endocrine/metabolic disorders as the primary disease targets. Mobile apps and SMS/emails were the most widely used technologies. Regionally, East Asia and the Pacific led in trial frequency, while the Middle East and North Africa had the fewest. High-income regions reported over 100 times more trials than low-income areas. Mobile apps were the dominant technology across all disease categories, with variations in technology use observed among different disease types. Digital Health Interventions have emerged as transformative tools in addressing some of the most critical challenges in contemporary healthcare, including accessibility gaps, inequities in resource distribution, the long-term management of chronic diseases, and the rising demand for personalized patient care. While DHIs have demonstrated substantial potential in augmenting healthcare delivery, their diverse applications across diseases, technologies, and geographical regions remain insufficiently explored in the context of interventional clinical trials. This study is significant as it provides a comprehensive examination of the global landscape of DHI-related clinical trials over the past 18 years. By leveraging data from the WHO ICTRP, this research categorizes DHIs using the NICE framework and investigates their temporal trends, technological implementations, and disease-specific applications. The findings not only illuminate patterns in technology adoption, such as the dominance of mobile applications and SMS-email platforms, but also reveal critical disparities in trial frequency across income levels and regions, highlighting significant gaps in healthcare and research equity. Furthermore, the study delves into the relationship between DHI classifications and disease targets, identifying areas of concentrated focuses, such as mental and behavioral disorders and endocrine diseases, while also shedding light on underrepresented regions and disease categories. By presenting these findings, this study aims to inform policymakers, healthcare practitioners, and researchers about the current state and future potential of DHIs, thereby fostering evidence-based strategies to optimize their implementation and address global healthcare challenges. Methods Platform introduction The WHO ICTRP, fully known as the International Clinical Trials Registry Platform of the World Health Organization, represents a pivotal global platform with a core objective of enhancing accountability and transparency in clinical research and its dissemination of findings. In response to the fragmentation of global clinical trial data and the lack of uniformity in standards, the WHO initiated the development of the International Clinical Trials Registry Platform in 2005. This endeavor aimed to effectively unify clinical trial registration standards, elevate research transparency, and mitigate unnecessary duplication of trials. The platform integrates multiple clinical trial registries that adhere to international standards, providing a convenient one-stop search portal. The public can readily access information on ongoing and completed clinical trials by utilizing the Universal Trial Number (UTN) or other relevant keywords. Search results encompass essential content such as the trial's basic information, design protocols, and research outcomes. The successful establishment of this platform has significantly facilitated the registration of clinical trials, ensuring that the public can seamlessly access detailed information on these trials. Consequently, it has markedly improved the transparency of publicly conducted clinical trials. Additionally, it has effectively prevented the occurrence of duplicate trials, further strengthening the scientific rigor and impartiality of healthcare decision-making. Ultimately, the platform has made substantial contributions to advancing scientific progress. Data source We retrieved relevant data from the official website of the WHO ICTRP, located at https://www.who.int/clinical-trials-registry-platform . The search period was confined to January 2005 through Dec 2022, during which we identified a total of 10,589 clinical trials. Given the primary focus of this study on intervention trials, we initially excluded 4,789 observational trials, yielding a subset of 5,800 intervention trials. Subsequently, we conducted a comprehensive manual analysis and annotation of these 5,800 trials. During this process, we excluded trials with missing critical information, such as those with blank entries in the disease category field, as well as trials where the DHI classification could not be accurately determined based on the provided description. Examples of excluded trials included those where the intervention was merely labeled as “other app” without specifying the specific intervention method. After rigorous screening, we excluded a total of 2115 trials, resulting in a final sample size of 3685. The specific process is shown in Fig. 1 . DHI classification Digital Health Intervention refers to the use of digital and mobile technologies to support and enhance health care services, health systems, and health-related behaviours. This can include a wide range of applications such as mobile health apps, telemedicine, electronic health records, wearable health devices, and online health information platforms. These interventions aim to improve health outcomes, increase access to care, enhance the quality of care, and make health systems more efficient. Digital health technologies offer significant advantages in enhancing healthcare accessibility, improving diagnostic and treatment efficiency, reducing healthcare costs, and facilitating personalized medicine. In this study, we referred to the NICE Evidence Standards Framework for Digital Health Technologies to define and categorize all trials based on their expected functions and objectives. The DHI was categorized into three main types based on the classification standards used in this study. Tier A DHIs are designed primarily to reduce costs or free up healthcare staff time, without directly influencing patient health or care outcomes. These technologies focus on improving operational efficiency rather than providing direct clinical benefits. Tier B DHIs aim to assist individuals, both citizens and patients, in managing their own health and wellness. These technologies empower users to take control of their health through self-monitoring, education, and lifestyle management tools. Tier C DHIs are intended for the treatment, diagnosis, or guidance of care decisions related to medical conditions. This category includes technologies that have direct health outcomes and may be subject to regulatory oversight as medical devices due to their clinical applications. For specific classification criteria, please refer to "DHI Classification" in Supplementary Material. Digital health technology classification Digital health technologies offer significant advantages in enhancing healthcare accessibility, improving diagnostic and treatment efficiency, reducing healthcare costs, and facilitating personalized medicine. Digital health technologies constitute the specific instruments and pathways for implementing digital health intervention strategies. This study, guided by the Evidence Standards Framework for Digital Health Technologies-2018, systematically categorizes and summarizes the digital technology means widely utilized within the field of digital health intervention experiments. Consequently, fifteen categories of digital health technologies have been identified and delineated. The detailed classifications are presented as Table 1 . Table 1 Digital Health Technology Digtal technology Mobile_apps Camera/images Location_tracking SMS_text_or_emailing Wearable_sensor Activity_tracking Telephone Ambient_sensor Internet_of_things Tele-consultation Social_media Platform Patient_reported_outcome Virtual_reality Virtual_reality Disease coding method In the context of the DHI experiments that are the focus of this study, we have classified the diseases associated with these experiments, with reference to the International Classification of Diseases, 11th Revision (ICD-11) coding system ( https://icd.who.int/browse/2024-01/mms/en ). It is noteworthy that, given the extensive of ICD-11, the classification of diseases in this study has been limited to the first-level category of diseases. The specific classifications are outlined in the following Table 2 . Table 2 ICD-11 Primary Classification Code ICD-11 Condition category 1 Certain infectious or parasitic diseases 2 Neoplasms 3 Diseases of the blood or blood-forming organs 4 Diseases of the immune system 5 Endocrine, nutritional or metabolic diseases 6 Mental, behavioral or neurodevelopmental disorders 7 Sleep-wake disorders 8 Diseases of the nervous system 9 Diseases of the visual system 10 Diseases of the ear or mastoid process 11 Diseases of the circulatory system 12 Diseases of the respiratory system 13 Diseases of the digestive system 14 Diseases of the skin 15 Diseases of the musculoskeletal system or connective tissue 16 Diseases of the genitourinary system 17 Conditions related to sexual health 18 Pregnancy, childbirth or the puerperium 19 Certain conditions originating in the perinatal period 20 Developmental anomalies 21 Symptoms, signs or clinical findings, not elsewhere classified 22 Injury, poisoning or certain other consequences of external causes 23 External causes of morbidity or mortality 24 Factors influencing health status or contact with health services Classifications of development levels We examined the influence of country income on clinical trials in the digital health field. Based on the World Bank's classification standards, countries are grouped into four categories: high-income, upper-middle-income, lower-middle-income, and low-income. This classification is primarily based on gross national income (GNI) per capita, reflecting the economic development level of each country or region. Specifically, the World Bank employs the Atlas method to calculate GNI per capita and categorizes global economies into four income groups according to the latest standards, updated in July 2023. The GNI per capita thresholds are as follows: low-income countries have a maximum GNI of $ 1,135; lower-middle-income countries range from $ 1,136 to $ 4,465; upper-middle-income countries range from $ 4,466 to $ 13,845; and high-income countries have a GNI exceeding $ 13,845. Data annotation method This study employed a manual annotation approach to conduct an in-depth analysis of the trials included in the research. To this end, we recruited a team of four annotators, including a doctoral candidate, a master’s student, and two undergraduate students, all with backgrounds in medical and health-related fields. The annotation process was divided into three phases: The first phase involved pre-annotation and rule revision. The annotation team was trained on the classification rules for DHI and each member was assigned to pre-annotate 100 trials independently. Subsequently, the pre-annotation results were collected and analysed, and the feedback was used to revise and standardize the annotation rules. The second phase was the formal annotation stage, during which the annotation team conducted formal annotation and analysis of 5,800 trials, strictly adhering to the revised rules established in the first phase. The third phase involved cross-checking the annotation results. To ensure accuracy and consistency, all annotation results were cross-checked to identify and correct potential errors or inconsistencies. During the annotation process, we focused on the following fields: condition (i.e., disease classification), annotated according to the first-level classification of ICD-11 codes; the DHI classification of the intervention, referring to the specific type of digital health intervention; and the digital technology used, representing the specific technical means employed to implement the intervention. Results We identified a total of 3685 registered DHI trials, including 1004 (27.3%) trials that were actively recruiting participants (Table 1 ). Of the included trials, there were 3471 (94.2%) interventional studies and 208 (5.6%) observational studies. The majority of included DHI trials were registered in ANZCTR (29.8%), ClinicalTrials.gov (25.9%), and ISRCT (13.6%). The intervention model was parallel design in 58.3%, and single arm in 9.9% of the registered DHI trials. Randomised controlled trials accounted for 44.8% of the registered DHI trials. Double masking was used in only 2.5% and single masking in 6.4% of the registered DHI trials. The sample sizes were small ( 2000, were less common, comprising 10.45% and 4.80%, respectively of the registered DHI trials. See Table 3 for the detail. Table 3 The main characteristics of registered DHI trials Frequency Percentage Study Type Observational 208 5.64% Interventional 3471 94.19% No report 6 0.16% Recruitment Status Recruiting 1004 27.25% Not recruiting 2655 72.05% No report 26 0.71% Source Register ANZCTR 1098 29.80% ClinicalTrials.gov 956 25.94% ISRCTN 500 13.57% IRCT 223 6.05% German Clinical Trials Register 186 5.05% CTRI 176 4.78% NTR 134 3.64% JPRN 122 3.31% ChiCTR 75 2.04% CRIS 73 1.98% TCTR 55 1.49% REBEC 49 1.33% PACTR 28 0.76% SLCTR 8 0.22% ITMCTR 2 0.05% Sample Size Range 2000 177 4.80% 0.00% Allocation Allocation: randomized 1650 44.78% Allocation: non-random 378 10.26% Allocation: NA 443 12.02% No report 1214 32.94% 0.00% Masking Masking: double 91 2.47% Masking: single 237 6.43% Masking: none (open) 1540 41.79% Masking: NA 622 16.88% No report 1195 32.43% 0.00% Intervention Model Single Group 364 9.88% Parallel Group 2149 58.32% Crossover 82 2.23% Factorial Assignment 49 1.33% No report 1041 28.25% Frequency of registered DHI trials There was a year-on-year increase in numbers of newly registered DHI trials, with a compound annual growth rate (CAGR) of 28.5% from 2005 to 2022 (Fig. 2 ). A significant growth in registered DHI trials began in 2011. Meanwhile in 2019, there was a downward trend, but the next year, namely 2020, saw a strong rebound. The increases in registered DHI trials and changes over time were dominated by high income countries (Fig. 2 ). Of the 3685 included DHI trials, 2589 (70.3%) were conducted in high-income countries, 314 (8.5%) in upper-middle-income countries, 515 (14.5%) in lower-middle-income countries, and only 25 (0.7%) in low-income countries. (Note: 242 in mixed or unknown countries). Figure 3 shows the number of registered DHI trials in top 15 countries (see Supplementary table 3 − 2 for data in all countries). Globally, Australia registered the largest number of DHI trials (n = 901, 24.5%), followed by USA (n = 480, 13.0%), UK (n = 241, 6.5%), India (n = 188, 5.1%), Germany (182, 4.9%), Iran (172, 4.7%), and China (129, 3.5%). Categories of DHIs Based on the NICE framework, there are 8 functional categories of DHIs at 3 tiers (Table 4 ). The DHI categories, ranked by frequency from highest to lowest, were as follows: promoting health (27%), treating conditions (23%), and informing clinical management (19%). Less than 10% of the registered DHI trials concerned health diaries, system services, communication, driving clinical management, or disease diagnoses. However, additional analysis revealed a sharp increase in the categories of system service and communication during 2019–2020 (see Supplementary Fig. 4 − 1). Table 4 Frequency of registered DHI trials by service category DHI Category Frequency Percentage A-System service 250 6.8% B-Communication 250 6.8% B-Health diaries 335 9.1% B-Promoting health 1010 27.3% C-Diagnose a condition 78 2.1% C-Driving clinical management 218 5.9% C-Inform clinical management 716 19.4% C-Treat a condition 843 22.8% Diseases targeted in registered DHI trials Regarding disease frequency, Fig. 4 shows that the following conditions received noticeable attentions: mental, behavioural, or neurodevelopmental disorders (18.8%), endocrine, nutritional, or metabolic diseases (14.1%), factors influencing health status or contact with health services (10.1%), diseases of the circulatory system (9.9%), neoplasms (6.8%), and diseases of the nervous system (5.9%). Conversely, diseases of the ear or mastoid process (0.5%), diseases of the skin (0.5%), and developmental anomalies were among less frequently addressed conditions (0.4%). Digital technologies used We focused on analysing different technological approaches, including mobile applications, SMS text messaging, tele-consultations, telephone, wearable sensors, and platform (Fig. 5). Mobile applications (Apps) have unequivocally emerged as the most frequently utilized technological tool in registered DHI trials (38.7%), and the usage of SMS or emailing (20.2%) ranks second only to mobile applications. In addition, tele-consultation (8.3%), telephone (7.5%), wearable sensors (7.2%), and platform (4.4%) were commonly used digital technologies in registered DHI trials. Figure 6 illustrates the temporal trends of the top six frequently utilized digital technologies, including mobile applications, SMS text messaging, tele-consultation, telephones, wearable devices, and platforms. The usage of mobile Apps has demonstrated a general upward trend over time, with notable fluctuations observed in 2019. SMS and emailing have similarly exhibited a steady, albeit more gradual, increase in usage over time. Tele-consultation has shown a significant surge in usage, particularly after 2019. The usage of telephones has exhibited a fluctuating pattern, characterized by significant peaks during specific periods. The usage of wearable devices has experienced a marked increase, reflecting the rising interest in self-monitoring and personalized health tracking. The usage of platforms has shown a notable increase since 2020. Digital technologies by country income Registered DHI trials tended to use different digital technologies in countries with different income levels (Fig. 7 ). Mobile applications were involved in more than 40% of registered DHI trials in high-income and middle-income countries, considerably higher than that in low-income countries (30.8%). The use of SMS or emailing was negatively associated with county income. The proportion of DHI trials using SMS or emailing was 21.5%, 22.2%, 33.8% and 53.8%, respectively, in high-income, upper-middle-income, lower-middle income, and low-income countries. The proportion of DHI trials using telephones was also higher in low-income countries (15.4%), compared with high-income (9.1%) or middle-income countries (6.8–9.8%). Tele-consultation technologies were used in 8.0–13.5% of the registered DHI trials in high-income or middle-income countries, while none in low-income countries. There was a positive association between the use of wearable sensors or platform and country income levels. Specifically, the proportion of registered trials using wearable sensors was 9.8%, 6.5%, 2.9%, and 0%, respectively, in high-income, upper-middle-income, lower-middle-income, and low-income countries. Digital technologies by DHI category Table 5 illustrates the distribution of digital technologies[ 23 ] by healthcare functionalities. Mobile applications are the most frequently utilized, with high usage in informing clinical management (51%), driving clinical management (42%), and diagnosing conditions (43%). SMS-text messaging or emailing ranks second, with notable usage in promoting health (38%) and communication (28%). Telephone as a digital technology contributed to disease diagnosis (17%), treatment (9%), and communications (11%). Wearable sensors are primarily used for health diaries (26%), reflecting their role in personalized health tracking. Tele-consultation is widely adopted for treating (17%), driving clinical management (10%), and diagnosing conditions (9%). Activity tracking shows moderate usage for health diaries (12%), while less commonly used technologies, such as cameras/images, ambient sensors, and location tracking, have minimal representation, with cameras/images used most for diagnosing conditions (4%). Virtual reality and gamification, though less adopted, contribute to driving clinical management (4%) and treating conditions (4%). Table 5 Digital Technology Usage and DHI categories System Service Communication Health diaries Promoting health Inform clinical management Driving clinical management Diagnose condition Treat condition Mobile apps 43% 28% 39% 32% 51% 42% 43% 36% SMS or emailing 13% 28% 6% 38% 14% 13% 11% 11% Telephone 7% 11% 6% 7% 7% 6% 17% 9% Camera/images 1% 3% 2% 2% 2% 2% 4% 3% Wearable sensor 5% 2% 26% 3% 6% 6% 8% 8% Ambient sensor 0% 0% 0% 0% 1% 0% 0% 0% Location tracking 1% 1% 0% 0% 1% 2% 0% 0% Activity tracking 5% 4% 12% 2% 4% 4% 3% 3% Internet of things 3% 1% 1% 2% 3% 0% 0% 2% Tele-consultation 6% 6% 2% 5% 6% 10% 9% 17% Digital PRO 2% 1% 1% 0% 1% 2% 0% 0% Social media 1% 1% 0% 1% 0% 0% 0% 0% Virtual reality 2% 1% 0% 1% 1% 2% 1% 4% Gamification 0% 4% 1% 2% 0% 4% 0% 2% Platform 12% 10% 2% 3% 3% 5% 3% 4% Note: PRO – patient reported outcomes Digital technologies by diseases Figure 8 shows the proportional distribution of six commonly used digital technologies by ICD-11 disease category (see supplementary table 3–4 for all digital technologies). Mobile applications showed high usage proportions across all disease categories, as high as 62.5% for certain conditions originating in the perinatal period, 50.0% for external causes of morbidity or mortality, 47.8% for diseases of the ear or mastoid process, and 45.1% for mental, behavioural or neurodevelopmental disorders. The use of mobile apps was below 30% for only two conditions, namely, sleep-wake disorders (25.4%) and injury, poisoning or certain other consequences of external causes (24.7%). SMS-based tools were also prominently utilized, especially certain infectious or parasitic diseases (39.1%), diseases of the visual system (29.2%), pregnancy, childbirth or the puerperium (29.2%), and diseases of the digestive system (28.5%). Diseases targeted at by SMS DHIs are relatively more prevalent in low-income countries. Tele-consultation technologies emerged as key tools in categories such as developmental anomalies (25.0%), Injury, poisoning or certain other consequences of external causes (18.8%), sleep-wake disorders (19.4%), and diseases of the immune system (17.2%). Wearable sensors had significant proportions in categories such as diseases of the blood or blood-forming organs (15.4%), sleep-wake disorders (17.9%), Injury, poisoning or certain other consequences of external causes (12.9%), diseases of the nervous system (12.8%), and diseases of the respiratory system (12.4%). The use of telephone as a digital tool was no more than 20%, and less than 10% for 18 of the 22 disease categories. The use of platform as a DHI tool was also low across disease categories (Fig. 8 ). Other technologies, such as virtual reality, ambient sensors, and internet of things (IoT), were less frequently utilized overall (supplementary table 3–4). However, they exhibited isolated prominence in specific categories like diseases of the skin for camera/image (19.2%) and developmental anomalies for virtual reality (12.5%). These findings indicate that the adoption of specific digital health technologies aligns with the distinct clinical and operational needs of individual disease categories. Discussion This study provides an in-depth analysis of the distribution and trends of digital health interventions (DHIs) in registered clinical trials. The registered DHI trials were mainly registered on ANZCTR, ClinicalTrials.gov, and ISRCTN. Of the registered DHI trials, interventional studies (94.2%) dominated the field, and a large proportion (72.1%) had already completed recruitment. Sample sizes were small or moderate in most of registered DHI trials. The parallel group design is the most prevalent, comprising 58.3 of studies. Randomized allocation was employed in 44.8% of studies, and masking was used in a small proportion of studies (8.9%). A significant number of studies fail to provide detailed information about allocation (32.9%), masking (32.4%), or intervention model (28.3%). These findings offer valuable insights in digital health research, highlighting the need for improved design quality in terms of sample size, appropriate masking, and transparency in reporting trial methodologies. Overall, the number of registered DHI trials each year has been increasing, with a compound annual growth rate (CAGR) of 28.5% from 2005 to 2022. The CAGR in this study was somewhat lower than the 34% reported in a study of the use of connected digital products in registered clinical trials and the 39% reported in a study of registered neurology trials. The difference in estimated growth rates may be due to different trial registries, types of DHI technologies, and medical fields involved[ 24 , 25 ]. The results indicate that mobile applications and text messaging technologies dominated various DHI categories, particularly in health management and mental health interventions. Furthermore, wearable devices had the highest application rate in health log recording, demonstrating their significance in health monitoring and data collection. As digital health technologies continue to develop, the scope of DHI applications in disease treatment and management is expanding, with notable progress in mental health and chronic disease management[ 26 ]. However, regional and income disparities still have a significant impact on the implementation and utilization of DHIs, particularly in low-income countries. In the future, with advancements in technology and the improvement of policy frameworks, digital health interventions are expected to see broader global adoption, particularly in enhancing treatment outcomes, increasing patient engagement, and enabling personalized health management. Digital Health Interventions trials have experienced significant growth globally, with "health promotion" accounting for the largest proportion of applications. Temporal trends show a clear upward trajectory in health promotion, which reflects the advantages of DHIs in this domain. Their core functionalities, such as accessibility and personalized health management, have contributed to this trend[ 27 ]. However, the application of DHIs in disease diagnosis has lagged due to the high complexity of medical diagnosis processes and the potential risks associated with misdiagnosis[ 28 ]. This gap highlights the need for further validation and refinement of these technologies in specialized fields. The COVID-19 pandemic from 2019 to 2022 acted as a catalyst for the adoption of DHIs[ 29 ]. Many healthcare systems quickly embraced telemedicine and digital solutions to address challenges in healthcare delivery during the crisis[ 30 ]. Consequently, the number of clinical trials in categories like system services, online consultations, and remote monitoring surged. These technologies reduced face-to-face interactions and ensured continuity of care. However, the pandemic also led to a temporary decline in clinical trials for routine interventions such as health promotion and management, as healthcare systems prioritized urgent treatment needs[ 31 ]. Mobile applications have emerged as the primary medium for delivering DHIs due to their portability, interactivity, and ability to offer personalized services. They are particularly effective for health monitoring, disease prevention, and management[ 32 ]. As smartphones became more widespread after 2012, mobile applications experienced rapid adoption, while SMS saw slower growth. Despite this, SMS remains relevant, especially in regions with limited smartphone penetration or network access. Its cost-effectiveness and broad reach make it ideal for large-scale implementation in resource-constrained settings[ 33 ]. Teleconsultation technologies gained significant traction after 2019, driven by the pandemic-induced shift toward remote healthcare. These technologies provided an essential alternative to face-to-face medical services, ensuring patient safety and access to care[ 34 ]. Wearable devices, another critical component of DHIs, enable real-time tracking of physiological data such as heart rate, sleep quality, and body temperature. Their integration into health systems has increased due to their ease of use and ability to provide continuous health monitoring[ 35 ]. Emerging technologies like virtual reality and gamification, while less commonly used, hold promise for specific applications such as rehabilitation and patient education. These tools can enhance user engagement and provide innovative approaches to health management. The adoption of DHIs varies significantly across income levels and regions[ 36 ]. High-income regions, characterized by advanced infrastructure and widespread smartphone usage, have led the adoption of mobile applications since 2012. Teleconsultation technologies also saw rapid growth after 2019, reflecting the digital transformation of healthcare systems during the pandemic. In upper-middle-income regions, the adoption of mobile applications accelerated after 2017, albeit with a delayed start compared to high-income regions. The slower decline in SMS usage suggests a prolonged transition period due to infrastructure and affordability constraints[ 37 ]. In lower-middle-income regions, economic barriers and limited technological access delayed the growth of mobile applications until 2020[ 38 ]. SMS remains the dominant technology in these areas, offering a low-cost solution for health communication. Sub-Saharan Africa exemplifies the reliance on SMS for health information dissemination, driven by limited technological infrastructure and internet access[ 39 ]. Significant increases in SMS usage during major health events, such as Ebola and COVID-19, highlight its role in emergency response. However, the gradual rise in mobile app usage since 2019 indicates progress in digital health infrastructure. In South Africa, the pandemic spurred rapid growth in teleconsultation and SMS technologies, reflecting increased demand for remote healthcare solutions. The use of DHIs varies based on the unique management needs of different health conditions. Mobile applications dominate areas requiring continuous patient engagement and self-reporting, such as infectious diseases and digestive disorders[ 40 ]. Wearable devices are particularly effective for mental health and sleep disorders, providing real-time physiological data to support treatment plans[ 41 ]. SMS remains valuable for infectious or parasitic diseases, pregnancy, childbirth or the puerperium, diseases of the digestive system, and health status monitoring, that are more prevalent in low-income countries, where accessibility and simplicity are critical[ 42 ]. Teleconsultation’s prominence in developmental anomalies and injuries underscores its adaptability in providing remote care during emergencies or in resource-limited settings[ 43 ]. In summary, digital health technologies are strategically deployed across ICD-11 disease categories. Mobile applications are versatile and widely used, while SMS serves as a critical tool in regions with limited digital infrastructure. Wearable sensors excel in conditions requiring continuous monitoring, such as neurodevelopmental disorders. Teleconsultation’s significant use in urgent care scenarios highlights its importance in enhancing access to healthcare. While DHIs have demonstrated significant potential, several challenges remain. Regulatory frameworks need to be strengthened to ensure the safety and efficacy of these technologies. Issues such as data privacy, interoperability, and patient trust must be addressed to promote broader adoption[ 44 ]. Additionally, the uneven growth of DHIs across regions underscores the need for targeted strategies to bridge adoption gaps. Emerging technologies like virtual reality and gamification warrant further exploration, particularly for applications in rehabilitation and patient education[ 45 ]. The integration of artificial intelligence into DHIs could enhance their diagnostic and predictive capabilities, but this requires rigorous validation to ensure accuracy and reliability. Conclusion This study demonstrates the rapid growth and expanding applications of digital health interventions (DHIs) in clinical research, with mobile applications, SMS, and wearable devices emerging as foundational technologies across various healthcare domains. While the COVID-19 pandemic accelerated adoption, particularly in telemedicine and remote monitoring, significant challenges remain in ensuring equitable access, improving methodological rigor, and addressing persistent regional disparities. Moving forward, targeted strategies to optimize trial design, validate emerging technologies, and implement context-specific solutions will be crucial for realizing DHIs' full potential in enhancing treatment outcomes, patient engagement, and personalized healthcare delivery globally. Abbreviations ANZCTR Australia New Zealand Clinical Trials Registry CAGR Compound Annual Growth Rate ChiCTR Chinese Clinical Trial Registry CRIS Clinical Research Information Service CTRI Clinical Trials Registry India DHIs Digital health interventions GNI Gross national income ICD-11 International Classification of Diseases, 11th Revision IoT Internet of Things IRCT Iranian Registry of Clinical Trials ISRCTN International Standard Randomised Controlled Trial Number ITMCTR International Traditional Medicine Clinical Trial Registry JPRN Japan Primary Registries Network NICE National institute for Health and Care Excellence NTR Netherlands Trial Register PACTR Pan African Clinical Trial Registry PRO Patient Reported Outcomes REBEC Brazilian Clinical Trials Registry SLCTR Sri Lanka Clinical Trials Registry SMS Short Message Service TCTR Thai Clinical Trials Register UTN Universal Trial Number WHO ICTRP World Health Organization International Clinical Trials Registry Platform. Declarations Acknowledgements Not applicable. Authors’ contributions Y.L. conceptualized the research, designed the methodology, performed primary data analysis, and wrote the original draft. X.G. contributed to study conceptualization, supervised the project, acquired funding, and critically reviewed the manuscript. F.S. initiated the study idea, developed the study protocol, assisted in data collection, advised on statistical analysis, and participated in writing and revision. All authors reviewed and approved the final manuscript. Funding No funding was received for this work. Data availability The datasets generated and analyzed during the current study are available in the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) repository, accessible at https://trialsearch.who.int/ Ethics approval and consent to participate The data used in this study were obtained from a public database, so ethical approval was not required. Consent for publication Not applicable. Competing interests Authors Yang Liu, Xitong Guo and Fujian Song declare no financial competing interests. References Baker RE, Mahmud AS, Miller IF, Rajeev M, Rasambainarivo F, Rice BL et al. Infectious disease in an era of global change. Nature Reviews Microbiology [Internet]. 2021;20(4):193–205. Available from: http://dx.doi.org/10.1038/s41579-021-00639-z Locke A, Kanekar S. Imaging of Premature Infants. Clinics in Perinatology [Internet]. 2022;49(3):641–55. Available from: http://dx.doi.org/10.1016/j.clp.2022.06.001 Watson CJE, Dark JH. Organ transplantation: historical perspective and current practice. British Journal of Anaesthesia [Internet]. 2012;108:i29–42. Available from: http://dx.doi.org/10.1093/bja/aer384 Aggarwal S. Targeted cancer therapies. Nature Reviews Drug Discovery [Internet]. 2010;9(6):427–8. Available from: http://dx.doi.org/10.1038/nrd3186 Kohn DB, Chen YY, Spencer MJ. Successes and challenges in clinical gene therapy. 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Supplementary Files YangLiuXitongGuoFujianSongSupplementaryMaterialsGlobalTrendsandDisparitiesinClinicalTrialsofDigitalHealthInterventionsAnalyzingDatafromtheWHOICTRP0401.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Jul, 2025 Reviews received at journal 29 May, 2025 Reviewers agreed at journal 24 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviews received at journal 12 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers invited by journal 06 May, 2025 Editor invited by journal 09 Apr, 2025 Editor assigned by journal 08 Apr, 2025 Submission checks completed at journal 08 Apr, 2025 First submitted to journal 01 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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2","display":"","copyAsset":false,"role":"figure","size":7208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegistered DHI Trials each year from 2005 to 2022\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6351306/v1/c15c3482263f3061dc9613f9.png"},{"id":82388445,"identity":"fa35dee4-7c9d-4f5e-9713-f92f25a36be4","added_by":"auto","created_at":"2025-05-09 17:23:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of registered DHI trials by country\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinedrawingimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6351306/v1/fe931dd57fd8031a7751a978.png"},{"id":82388832,"identity":"9e7521b3-3f91-4989-ad7c-624c223d17c8","added_by":"auto","created_at":"2025-05-09 17:31:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of registered DHI trials by disease\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinedrawingimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6351306/v1/85db1b971f055fb1c6d39fc4.png"},{"id":82388448,"identity":"fd95d7f4-50a2-4b92-be93-b2b38d964190","added_by":"auto","created_at":"2025-05-09 17:23:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8046,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of registered trials by Digital Technology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: PRO refers to patient reported outcome\u003c/p\u003e","description":"","filename":"Onlinedrawingimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6351306/v1/7de49c7a2284332b11010cff.png"},{"id":82388449,"identity":"e2008dc9-4f24-49f3-be2f-5284bba355ee","added_by":"auto","created_at":"2025-05-09 17:23:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime trends of registered trials by digital technology\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6351306/v1/28138ecb28f5b0a4ff17531f.png"},{"id":82388456,"identity":"3e9652c9-ae4c-4fe0-b878-58bd1c5afde4","added_by":"auto","created_at":"2025-05-09 17:23:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":8466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportion of digital technologies used by country income\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinedrawingimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6351306/v1/db7c3373a8744945448a7b74.png"},{"id":82388834,"identity":"8748d1dd-228c-42a2-a13c-86c26f334016","added_by":"auto","created_at":"2025-05-09 17:31:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":167943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportion of digital technologies used by country income\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6351306/v1/7a7007287663eeccb8d94b02.png"},{"id":82389233,"identity":"63e2919d-4786-4931-97fc-ebe8782120d8","added_by":"auto","created_at":"2025-05-09 17:39:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1668441,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6351306/v1/261343ea-bedd-4e10-bb21-172a7ed86ae5.pdf"},{"id":82388454,"identity":"911ab0ec-8518-4cbd-bbde-72aa5b739ae5","added_by":"auto","created_at":"2025-05-09 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class=\"CitationRef\"\u003e2\u003c/span\u003e], the advancement of organ transplantation surgeries[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the development of targeted cancer therapies[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and breakthroughs in gene-editing technologies[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the accessibility of healthcare services[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], healthcare inequities[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. the rising costs of medical care, the insufficient supply of healthcare workforce, the long-term management pressures of chronic diseases and the quality of patient care among others, remain pressing challenges in the current healthcare landscape.\u003c/p\u003e \u003cp\u003eIn the field of Digital Health Interventions, the extensive applicability, resource optimization, and integrated advantages, along with the capability for continuous health monitoring and feedback, have demonstrated significant potential in enhancing healthcare accessibility and alleviating disparities in healthcare resource distribution[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, DHIs have played a crucial role in the long-term management of chronic diseases[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], as well as in supporting medical diagnostics and examinations, thereby contributing to the optimization and widespread dissemination of healthcare services.\u003c/p\u003e \u003cp\u003eDHIs play a multifaceted role in managing diabetes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and reducing cardiovascular risk\u003csup\u003e[11]\u003c/sup\u003e, integrating health monitoring[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], telemedicine consultations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], disease self-management, behaviour modification[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], health promotion, health education[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and patient engagement. For instance, continuous glucose monitoring devices[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and wearable fitness trackers[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] are utilized for real-time health monitoring; telemedicine platforms[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] enable remote consultations with healthcare providers[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; mobile applications and online portals assist in disease self-management by tracking medication adherence and lifestyle adjustments; behaviour modification[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] is encouraged through digital coaching and personalized feedback systems; health promotion and education are provided via interactive e-learning modules and webinars; and patient engagement is strengthened through social support networks[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study primarily utilized data from the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) spanning over 18 years, focusing on interventional clinical trials. A total of 3685 clinical trials were included in this analysis. Based on the NICE classification framework, we analyzed and categorized the types of trials in DHI, which were classified into three levels and eight categories. Additionally, we analyzed and annotated various features of DHIs, including its classification distribution, technology usage, disease focus, and regional and national variations. We also examined the temporal development trends of DHIs regarding their classifications, technology uses, and disease focuses. Furthermore, we conducted an in-depth analysis of the correlations between DHIs and disease-related technologies, as well as the relationships between diseases and DHI types.\u003c/p\u003e \u003cp\u003eThe data analysis reveals that most DHI trials focused on health promotion (27%) and disease treatment (23%), with mental/behavioral and endocrine/metabolic disorders as the primary disease targets. Mobile apps and SMS/emails were the most widely used technologies. Regionally, East Asia and the Pacific led in trial frequency, while the Middle East and North Africa had the fewest. High-income regions reported over 100 times more trials than low-income areas. Mobile apps were the dominant technology across all disease categories, with variations in technology use observed among different disease types.\u003c/p\u003e \u003cp\u003eDigital Health Interventions have emerged as transformative tools in addressing some of the most critical challenges in contemporary healthcare, including accessibility gaps, inequities in resource distribution, the long-term management of chronic diseases, and the rising demand for personalized patient care. While DHIs have demonstrated substantial potential in augmenting healthcare delivery, their diverse applications across diseases, technologies, and geographical regions remain insufficiently explored in the context of interventional clinical trials.\u003c/p\u003e \u003cp\u003eThis study is significant as it provides a comprehensive examination of the global landscape of DHI-related clinical trials over the past 18 years. By leveraging data from the WHO ICTRP, this research categorizes DHIs using the NICE framework and investigates their temporal trends, technological implementations, and disease-specific applications. The findings not only illuminate patterns in technology adoption, such as the dominance of mobile applications and SMS-email platforms, but also reveal critical disparities in trial frequency across income levels and regions, highlighting significant gaps in healthcare and research equity.\u003c/p\u003e \u003cp\u003eFurthermore, the study delves into the relationship between DHI classifications and disease targets, identifying areas of concentrated focuses, such as mental and behavioral disorders and endocrine diseases, while also shedding light on underrepresented regions and disease categories. By presenting these findings, this study aims to inform policymakers, healthcare practitioners, and researchers about the current state and future potential of DHIs, thereby fostering evidence-based strategies to optimize their implementation and address global healthcare challenges.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlatform introduction\u003c/h2\u003e \u003cp\u003eThe WHO ICTRP, fully known as the International Clinical Trials Registry Platform of the World Health Organization, represents a pivotal global platform with a core objective of enhancing accountability and transparency in clinical research and its dissemination of findings.\u003c/p\u003e \u003cp\u003eIn response to the fragmentation of global clinical trial data and the lack of uniformity in standards, the WHO initiated the development of the International Clinical Trials Registry Platform in 2005. This endeavor aimed to effectively unify clinical trial registration standards, elevate research transparency, and mitigate unnecessary duplication of trials.\u003c/p\u003e \u003cp\u003eThe platform integrates multiple clinical trial registries that adhere to international standards, providing a convenient one-stop search portal. The public can readily access information on ongoing and completed clinical trials by utilizing the Universal Trial Number (UTN) or other relevant keywords. Search results encompass essential content such as the trial's basic information, design protocols, and research outcomes.\u003c/p\u003e \u003cp\u003eThe successful establishment of this platform has significantly facilitated the registration of clinical trials, ensuring that the public can seamlessly access detailed information on these trials. Consequently, it has markedly improved the transparency of publicly conducted clinical trials. Additionally, it has effectively prevented the occurrence of duplicate trials, further strengthening the scientific rigor and impartiality of healthcare decision-making. Ultimately, the platform has made substantial contributions to advancing scientific progress.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData source\u003c/h3\u003e\n\u003cp\u003eWe retrieved relevant data from the official website of the WHO ICTRP, located at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/clinical-trials-registry-platform\u003c/span\u003e\u003cspan address=\"https://www.who.int/clinical-trials-registry-platform\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The search period was confined to January 2005 through Dec 2022, during which we identified a total of 10,589 clinical trials. Given the primary focus of this study on intervention trials, we initially excluded 4,789 observational trials, yielding a subset of 5,800 intervention trials.\u003c/p\u003e \u003cp\u003eSubsequently, we conducted a comprehensive manual analysis and annotation of these 5,800 trials. During this process, we excluded trials with missing critical information, such as those with blank entries in the disease category field, as well as trials where the DHI classification could not be accurately determined based on the provided description. Examples of excluded trials included those where the intervention was merely labeled as \u0026ldquo;other app\u0026rdquo; without specifying the specific intervention method. After rigorous screening, we excluded a total of 2115 trials, resulting in a final sample size of 3685. The specific process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDHI classification\u003c/h3\u003e\n\u003cp\u003eDigital Health Intervention refers to the use of digital and mobile technologies to support and enhance health care services, health systems, and health-related behaviours. This can include a wide range of applications such as mobile health apps, telemedicine, electronic health records, wearable health devices, and online health information platforms. These interventions aim to improve health outcomes, increase access to care, enhance the quality of care, and make health systems more efficient. Digital health technologies offer significant advantages in enhancing healthcare accessibility, improving diagnostic and treatment efficiency, reducing healthcare costs, and facilitating personalized medicine.\u003c/p\u003e \u003cp\u003eIn this study, we referred to the NICE Evidence Standards Framework for Digital Health Technologies to define and categorize all trials based on their expected functions and objectives. The DHI was categorized into three main types based on the classification standards used in this study.\u003c/p\u003e \u003cp\u003eTier A DHIs are designed primarily to reduce costs or free up healthcare staff time, without directly influencing patient health or care outcomes. These technologies focus on improving operational efficiency rather than providing direct clinical benefits.\u003c/p\u003e \u003cp\u003eTier B DHIs aim to assist individuals, both citizens and patients, in managing their own health and wellness. These technologies empower users to take control of their health through self-monitoring, education, and lifestyle management tools.\u003c/p\u003e \u003cp\u003eTier C DHIs are intended for the treatment, diagnosis, or guidance of care decisions related to medical conditions. This category includes technologies that have direct health outcomes and may be subject to regulatory oversight as medical devices due to their clinical applications. For specific classification criteria, please refer to \"DHI Classification\" in Supplementary Material.\u003c/p\u003e\n\u003ch3\u003eDigital health technology classification\u003c/h3\u003e\n\u003cp\u003eDigital health technologies offer significant advantages in enhancing healthcare accessibility, improving diagnostic and treatment efficiency, reducing healthcare costs, and facilitating personalized medicine.\u003c/p\u003e \u003cp\u003eDigital health technologies constitute the specific instruments and pathways for implementing digital health intervention strategies. This study, guided by the Evidence Standards Framework for Digital Health Technologies-2018, systematically categorizes and summarizes the digital technology means widely utilized within the field of digital health intervention experiments. Consequently, fifteen categories of digital health technologies have been identified and delineated. The detailed classifications are presented as Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital Health Technology\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigtal technology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMobile_apps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamera/images\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocation_tracking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMS_text_or_emailing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWearable_sensor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity_tracking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelephone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmbient_sensor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternet_of_things\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTele-consultation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial_media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient_reported_outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVirtual_reality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVirtual_reality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDisease coding method\u003c/h3\u003e\n\u003cp\u003eIn the context of the DHI experiments that are the focus of this study, we have classified the diseases associated with these experiments, with reference to the International Classification of Diseases, 11th Revision (ICD-11) coding system (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icd.who.int/browse/2024-01/mms/en\u003c/span\u003e\u003cspan address=\"https://icd.who.int/browse/2024-01/mms/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). It is noteworthy that, given the extensive of ICD-11, the classification of diseases in this study has been limited to the first-level category of diseases. The specific classifications are outlined in the following Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eICD-11 Primary Classification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICD-11 Condition category\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCertain infectious or parasitic diseases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeoplasms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the blood or blood-forming organs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the immune system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndocrine, nutritional or metabolic diseases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMental, behavioral or neurodevelopmental disorders\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep-wake disorders\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the nervous system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the visual system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the ear or mastoid process\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the circulatory system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the respiratory system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the digestive system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the skin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the musculoskeletal system or connective tissue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases of the genitourinary system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditions related to sexual health\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePregnancy, childbirth or the puerperium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCertain conditions originating in the perinatal period\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopmental anomalies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymptoms, signs or clinical findings, not elsewhere classified\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInjury, poisoning or certain other consequences of external causes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExternal causes of morbidity or mortality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors influencing health status or contact with health services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClassifications of development levels\u003c/h2\u003e \u003cp\u003eWe examined the influence of country income on clinical trials in the digital health field. Based on the World Bank's classification standards, countries are grouped into four categories: high-income, upper-middle-income, lower-middle-income, and low-income. This classification is primarily based on gross national income (GNI) per capita, reflecting the economic development level of each country or region. Specifically, the World Bank employs the Atlas method to calculate GNI per capita and categorizes global economies into four income groups according to the latest standards, updated in July 2023. The GNI per capita thresholds are as follows: low-income countries have a maximum GNI of \u003cspan\u003e$\u003c/span\u003e1,135; lower-middle-income countries range from \u003cspan\u003e$\u003c/span\u003e1,136 to \u003cspan\u003e$\u003c/span\u003e4,465; upper-middle-income countries range from \u003cspan\u003e$\u003c/span\u003e4,466 to \u003cspan\u003e$\u003c/span\u003e13,845; and high-income countries have a GNI exceeding \u003cspan\u003e$\u003c/span\u003e13,845.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData annotation method\u003c/h3\u003e\n\u003cp\u003eThis study employed a manual annotation approach to conduct an in-depth analysis of the trials included in the research. To this end, we recruited a team of four annotators, including a doctoral candidate, a master\u0026rsquo;s student, and two undergraduate students, all with backgrounds in medical and health-related fields. The annotation process was divided into three phases:\u003c/p\u003e \u003cp\u003eThe first phase involved pre-annotation and rule revision. The annotation team was trained on the classification rules for DHI and each member was assigned to pre-annotate 100 trials independently. Subsequently, the pre-annotation results were collected and analysed, and the feedback was used to revise and standardize the annotation rules.\u003c/p\u003e \u003cp\u003eThe second phase was the formal annotation stage, during which the annotation team conducted formal annotation and analysis of 5,800 trials, strictly adhering to the revised rules established in the first phase.\u003c/p\u003e \u003cp\u003eThe third phase involved cross-checking the annotation results. To ensure accuracy and consistency, all annotation results were cross-checked to identify and correct potential errors or inconsistencies.\u003c/p\u003e \u003cp\u003eDuring the annotation process, we focused on the following fields: condition (i.e., disease classification), annotated according to the first-level classification of ICD-11 codes; the DHI classification of the intervention, referring to the specific type of digital health intervention; and the digital technology used, representing the specific technical means employed to implement the intervention.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe identified a total of 3685 registered DHI trials, including 1004 (27.3%) trials that were actively recruiting participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the included trials, there were 3471 (94.2%) interventional studies and 208 (5.6%) observational studies. The majority of included DHI trials were registered in ANZCTR (29.8%), ClinicalTrials.gov (25.9%), and ISRCT (13.6%). The intervention model was parallel design in 58.3%, and single arm in 9.9% of the registered DHI trials. Randomised controlled trials accounted for 44.8% of the registered DHI trials. Double masking was used in only 2.5% and single masking in 6.4% of the registered DHI trials. The sample sizes were small (\u0026lt;\u0026thinsp;100) in 44.6%, or moderate (100\u0026ndash;500) in 40.2%, while larger sample sizes, such as 500\u0026ndash;2000 and \u0026gt;\u0026thinsp;2000, were less common, comprising 10.45% and 4.80%, respectively of the registered DHI trials. See Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for the detail.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe main characteristics of registered DHI trials\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStudy Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecruitment Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecruiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot recruiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource Register\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANZCTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinicalTrials.gov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISRCTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGerman Clinical Trials Register\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJPRN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChiCTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREBEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePACTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLCTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITMCTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample Size Range\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAllocation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllocation: randomized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllocation: non-random\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllocation: NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.02%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMasking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasking: double\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasking: single\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.43%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasking: none (open)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasking: NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.43%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntervention Model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParallel Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactorial Assignment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFrequency of registered DHI trials\u003c/h2\u003e \u003cp\u003eThere was a year-on-year increase in numbers of newly registered DHI trials, with a compound annual growth rate (CAGR) of 28.5% from 2005 to 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A significant growth in registered DHI trials began in 2011. Meanwhile in 2019, there was a downward trend, but the next year, namely 2020, saw a strong rebound.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe increases in registered DHI trials and changes over time were dominated by high income countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of the 3685 included DHI trials, 2589 (70.3%) were conducted in high-income countries, 314 (8.5%) in upper-middle-income countries, 515 (14.5%) in lower-middle-income countries, and only 25 (0.7%) in low-income countries. (Note: 242 in mixed or unknown countries).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the number of registered DHI trials in top 15 countries (see Supplementary table 3\u0026thinsp;\u0026minus;\u0026thinsp;2 for data in all countries). Globally, Australia registered the largest number of DHI trials (n\u0026thinsp;=\u0026thinsp;901, 24.5%), followed by USA (n\u0026thinsp;=\u0026thinsp;480, 13.0%), UK (n\u0026thinsp;=\u0026thinsp;241, 6.5%), India (n\u0026thinsp;=\u0026thinsp;188, 5.1%), Germany (182, 4.9%), Iran (172, 4.7%), and China (129, 3.5%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCategories of DHIs\u003c/h2\u003e \u003cp\u003eBased on the NICE framework, there are 8 functional categories of DHIs at 3 tiers (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The DHI categories, ranked by frequency from highest to lowest, were as follows: promoting health (27%), treating conditions (23%), and informing clinical management (19%). Less than 10% of the registered DHI trials concerned health diaries, system services, communication, driving clinical management, or disease diagnoses. However, additional analysis revealed a sharp increase in the categories of system service and communication during 2019\u0026ndash;2020 (see Supplementary Fig.\u0026nbsp;4\u0026thinsp;\u0026minus;\u0026thinsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of registered DHI trials by service category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDHI Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-System service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-Health diaries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-Promoting health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Diagnose a condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Driving clinical management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Inform clinical management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Treat a condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDiseases targeted in registered DHI trials\u003c/h2\u003e \u003cp\u003eRegarding disease frequency, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the following conditions received noticeable attentions: mental, behavioural, or neurodevelopmental disorders (18.8%), endocrine, nutritional, or metabolic diseases (14.1%), factors influencing health status or contact with health services (10.1%), diseases of the circulatory system (9.9%), neoplasms (6.8%), and diseases of the nervous system (5.9%). Conversely, diseases of the ear or mastoid process (0.5%), diseases of the skin (0.5%), and developmental anomalies were among less frequently addressed conditions (0.4%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDigital technologies used\u003c/h2\u003e \u003cp\u003eWe focused on analysing different technological approaches, including mobile applications, SMS text messaging, tele-consultations, telephone, wearable sensors, and platform (Fig.\u0026nbsp;5). Mobile applications (Apps) have unequivocally emerged as the most frequently utilized technological tool in registered DHI trials (38.7%), and the usage of SMS or emailing (20.2%) ranks second only to mobile applications. In addition, tele-consultation (8.3%), telephone (7.5%), wearable sensors (7.2%), and platform (4.4%) were commonly used digital technologies in registered DHI trials.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the temporal trends of the top six frequently utilized digital technologies, including mobile applications, SMS text messaging, tele-consultation, telephones, wearable devices, and platforms. The usage of mobile Apps has demonstrated a general upward trend over time, with notable fluctuations observed in 2019. SMS and emailing have similarly exhibited a steady, albeit more gradual, increase in usage over time. Tele-consultation has shown a significant surge in usage, particularly after 2019. The usage of telephones has exhibited a fluctuating pattern, characterized by significant peaks during specific periods. The usage of wearable devices has experienced a marked increase, reflecting the rising interest in self-monitoring and personalized health tracking. The usage of platforms has shown a notable increase since 2020.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDigital technologies by country income\u003c/h2\u003e \u003cp\u003eRegistered DHI trials tended to use different digital technologies in countries with different income levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Mobile applications were involved in more than 40% of registered DHI trials in high-income and middle-income countries, considerably higher than that in low-income countries (30.8%). The use of SMS or emailing was negatively associated with county income. The proportion of DHI trials using SMS or emailing was 21.5%, 22.2%, 33.8% and 53.8%, respectively, in high-income, upper-middle-income, lower-middle income, and low-income countries. The proportion of DHI trials using telephones was also higher in low-income countries (15.4%), compared with high-income (9.1%) or middle-income countries (6.8\u0026ndash;9.8%). Tele-consultation technologies were used in 8.0\u0026ndash;13.5% of the registered DHI trials in high-income or middle-income countries, while none in low-income countries. There was a positive association between the use of wearable sensors or platform and country income levels. Specifically, the proportion of registered trials using wearable sensors was 9.8%, 6.5%, 2.9%, and 0%, respectively, in high-income, upper-middle-income, lower-middle-income, and low-income countries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDigital technologies by DHI category\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the distribution of digital technologies[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] by healthcare functionalities. Mobile applications are the most frequently utilized, with high usage in informing clinical management (51%), driving clinical management (42%), and diagnosing conditions (43%). SMS-text messaging or emailing ranks second, with notable usage in promoting health (38%) and communication (28%). Telephone as a digital technology contributed to disease diagnosis (17%), treatment (9%), and communications (11%). Wearable sensors are primarily used for health diaries (26%), reflecting their role in personalized health tracking. Tele-consultation is widely adopted for treating (17%), driving clinical management (10%), and diagnosing conditions (9%). Activity tracking shows moderate usage for health diaries (12%), while less commonly used technologies, such as cameras/images, ambient sensors, and location tracking, have minimal representation, with cameras/images used most for diagnosing conditions (4%). Virtual reality and gamification, though less adopted, contribute to driving clinical management (4%) and treating conditions (4%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital Technology Usage and DHI categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystem Service\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommunication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealth diaries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePromoting health\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInform clinical management\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDriving clinical management\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDiagnose condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTreat condition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMobile apps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMS or emailing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelephone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamera/images\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWearable sensor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmbient sensor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation tracking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity tracking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet of things\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTele-consultation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital PRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVirtual reality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: PRO \u0026ndash; patient reported outcomes\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDigital technologies by diseases\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the proportional distribution of six commonly used digital technologies by ICD-11 disease category (see supplementary table 3\u0026ndash;4 for all digital technologies). Mobile applications showed high usage proportions across all disease categories, as high as 62.5% for certain conditions originating in the perinatal period, 50.0% for external causes of morbidity or mortality, 47.8% for diseases of the ear or mastoid process, and 45.1% for mental, behavioural or neurodevelopmental disorders. The use of mobile apps was below 30% for only two conditions, namely, sleep-wake disorders (25.4%) and injury, poisoning or certain other consequences of external causes (24.7%). SMS-based tools were also prominently utilized, especially certain infectious or parasitic diseases (39.1%), diseases of the visual system (29.2%), pregnancy, childbirth or the puerperium (29.2%), and diseases of the digestive system (28.5%). Diseases targeted at by SMS DHIs are relatively more prevalent in low-income countries.\u003c/p\u003e \u003cp\u003eTele-consultation technologies emerged as key tools in categories such as developmental anomalies (25.0%), Injury, poisoning or certain other consequences of external causes (18.8%), sleep-wake disorders (19.4%), and diseases of the immune system (17.2%). Wearable sensors had significant proportions in categories such as diseases of the blood or blood-forming organs (15.4%), sleep-wake disorders (17.9%), Injury, poisoning or certain other consequences of external causes (12.9%), diseases of the nervous system (12.8%), and diseases of the respiratory system (12.4%). The use of telephone as a digital tool was no more than 20%, and less than 10% for 18 of the 22 disease categories. The use of platform as a DHI tool was also low across disease categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther technologies, such as virtual reality, ambient sensors, and internet of things (IoT), were less frequently utilized overall (supplementary table 3\u0026ndash;4). However, they exhibited isolated prominence in specific categories like diseases of the skin for camera/image (19.2%) and developmental anomalies for virtual reality (12.5%). These findings indicate that the adoption of specific digital health technologies aligns with the distinct clinical and operational needs of individual disease categories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides an in-depth analysis of the distribution and trends of digital health interventions (DHIs) in registered clinical trials. The registered DHI trials were mainly registered on ANZCTR, ClinicalTrials.gov, and ISRCTN. Of the registered DHI trials, interventional studies (94.2%) dominated the field, and a large proportion (72.1%) had already completed recruitment. Sample sizes were small or moderate in most of registered DHI trials. The parallel group design is the most prevalent, comprising 58.3 of studies. Randomized allocation was employed in 44.8% of studies, and masking was used in a small proportion of studies (8.9%). A significant number of studies fail to provide detailed information about allocation (32.9%), masking (32.4%), or intervention model (28.3%). These findings offer valuable insights in digital health research, highlighting the need for improved design quality in terms of sample size, appropriate masking, and transparency in reporting trial methodologies.\u003c/p\u003e \u003cp\u003eOverall, the number of registered DHI trials each year has been increasing, with a compound annual growth rate (CAGR) of 28.5% from 2005 to 2022. The CAGR in this study was somewhat lower than the 34% reported in a study of the use of connected digital products in registered clinical trials and the 39% reported in a study of registered neurology trials. The difference in estimated growth rates may be due to different trial registries, types of DHI technologies, and medical fields involved[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results indicate that mobile applications and text messaging technologies dominated various DHI categories, particularly in health management and mental health interventions. Furthermore, wearable devices had the highest application rate in health log recording, demonstrating their significance in health monitoring and data collection. As digital health technologies continue to develop, the scope of DHI applications in disease treatment and management is expanding, with notable progress in mental health and chronic disease management[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, regional and income disparities still have a significant impact on the implementation and utilization of DHIs, particularly in low-income countries. In the future, with advancements in technology and the improvement of policy frameworks, digital health interventions are expected to see broader global adoption, particularly in enhancing treatment outcomes, increasing patient engagement, and enabling personalized health management.\u003c/p\u003e \u003cp\u003eDigital Health Interventions trials have experienced significant growth globally, with \"health promotion\" accounting for the largest proportion of applications. Temporal trends show a clear upward trajectory in health promotion, which reflects the advantages of DHIs in this domain. Their core functionalities, such as accessibility and personalized health management, have contributed to this trend[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the application of DHIs in disease diagnosis has lagged due to the high complexity of medical diagnosis processes and the potential risks associated with misdiagnosis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This gap highlights the need for further validation and refinement of these technologies in specialized fields.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic from 2019 to 2022 acted as a catalyst for the adoption of DHIs[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Many healthcare systems quickly embraced telemedicine and digital solutions to address challenges in healthcare delivery during the crisis[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Consequently, the number of clinical trials in categories like system services, online consultations, and remote monitoring surged. These technologies reduced face-to-face interactions and ensured continuity of care. However, the pandemic also led to a temporary decline in clinical trials for routine interventions such as health promotion and management, as healthcare systems prioritized urgent treatment needs[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMobile applications have emerged as the primary medium for delivering DHIs due to their portability, interactivity, and ability to offer personalized services. They are particularly effective for health monitoring, disease prevention, and management[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As smartphones became more widespread after 2012, mobile applications experienced rapid adoption, while SMS saw slower growth. Despite this, SMS remains relevant, especially in regions with limited smartphone penetration or network access. Its cost-effectiveness and broad reach make it ideal for large-scale implementation in resource-constrained settings[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTeleconsultation technologies gained significant traction after 2019, driven by the pandemic-induced shift toward remote healthcare. These technologies provided an essential alternative to face-to-face medical services, ensuring patient safety and access to care[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Wearable devices, another critical component of DHIs, enable real-time tracking of physiological data such as heart rate, sleep quality, and body temperature. Their integration into health systems has increased due to their ease of use and ability to provide continuous health monitoring[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging technologies like virtual reality and gamification, while less commonly used, hold promise for specific applications such as rehabilitation and patient education. These tools can enhance user engagement and provide innovative approaches to health management.\u003c/p\u003e \u003cp\u003eThe adoption of DHIs varies significantly across income levels and regions[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. High-income regions, characterized by advanced infrastructure and widespread smartphone usage, have led the adoption of mobile applications since 2012. Teleconsultation technologies also saw rapid growth after 2019, reflecting the digital transformation of healthcare systems during the pandemic.\u003c/p\u003e \u003cp\u003eIn upper-middle-income regions, the adoption of mobile applications accelerated after 2017, albeit with a delayed start compared to high-income regions. The slower decline in SMS usage suggests a prolonged transition period due to infrastructure and affordability constraints[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In lower-middle-income regions, economic barriers and limited technological access delayed the growth of mobile applications until 2020[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. SMS remains the dominant technology in these areas, offering a low-cost solution for health communication.\u003c/p\u003e \u003cp\u003eSub-Saharan Africa exemplifies the reliance on SMS for health information dissemination, driven by limited technological infrastructure and internet access[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Significant increases in SMS usage during major health events, such as Ebola and COVID-19, highlight its role in emergency response. However, the gradual rise in mobile app usage since 2019 indicates progress in digital health infrastructure. In South Africa, the pandemic spurred rapid growth in teleconsultation and SMS technologies, reflecting increased demand for remote healthcare solutions.\u003c/p\u003e \u003cp\u003eThe use of DHIs varies based on the unique management needs of different health conditions. Mobile applications dominate areas requiring continuous patient engagement and self-reporting, such as infectious diseases and digestive disorders[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Wearable devices are particularly effective for mental health and sleep disorders, providing real-time physiological data to support treatment plans[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. SMS remains valuable for infectious or parasitic diseases, pregnancy, childbirth or the puerperium, diseases of the digestive system, and health status monitoring, that are more prevalent in low-income countries, where accessibility and simplicity are critical[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Teleconsultation\u0026rsquo;s prominence in developmental anomalies and injuries underscores its adaptability in providing remote care during emergencies or in resource-limited settings[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, digital health technologies are strategically deployed across ICD-11 disease categories. Mobile applications are versatile and widely used, while SMS serves as a critical tool in regions with limited digital infrastructure. Wearable sensors excel in conditions requiring continuous monitoring, such as neurodevelopmental disorders. Teleconsultation\u0026rsquo;s significant use in urgent care scenarios highlights its importance in enhancing access to healthcare.\u003c/p\u003e \u003cp\u003eWhile DHIs have demonstrated significant potential, several challenges remain. Regulatory frameworks need to be strengthened to ensure the safety and efficacy of these technologies. Issues such as data privacy, interoperability, and patient trust must be addressed to promote broader adoption[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Additionally, the uneven growth of DHIs across regions underscores the need for targeted strategies to bridge adoption gaps.\u003c/p\u003e \u003cp\u003eEmerging technologies like virtual reality and gamification warrant further exploration, particularly for applications in rehabilitation and patient education[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The integration of artificial intelligence into DHIs could enhance their diagnostic and predictive capabilities, but this requires rigorous validation to ensure accuracy and reliability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates the rapid growth and expanding applications of digital health interventions (DHIs) in clinical research, with mobile applications, SMS, and wearable devices emerging as foundational technologies across various healthcare domains. While the COVID-19 pandemic accelerated adoption, particularly in telemedicine and remote monitoring, significant challenges remain in ensuring equitable access, improving methodological rigor, and addressing persistent regional disparities. Moving forward, targeted strategies to optimize trial design, validate emerging technologies, and implement context-specific solutions will be crucial for realizing DHIs' full potential in enhancing treatment outcomes, patient engagement, and personalized healthcare delivery globally.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANZCTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAustralia New Zealand Clinical Trials Registry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAGR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCompound Annual Growth Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eChiCTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChinese Clinical Trial Registry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Research Information Service\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Trials Registry India\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDHIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDigital health interventions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGNI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGross national income\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-11\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases, 11th Revision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIoT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternet of Things\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIranian Registry of Clinical Trials\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISRCTN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Standard Randomised Controlled Trial Number\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITMCTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Traditional Medicine Clinical Trial Registry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eJPRN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eJapan Primary Registries Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNICE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational institute for Health and Care Excellence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNetherlands Trial Register\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePACTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePan African Clinical Trial Registry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Reported Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eREBEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrazilian Clinical Trials Registry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSLCTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSri Lanka Clinical Trials Registry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort Message Service\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThai Clinical Trials Register\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversal Trial Number\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO ICTRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization International Clinical Trials Registry Platform.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eY.L. conceptualized the research, designed the methodology, performed primary data analysis, and wrote the original draft. X.G. contributed to study conceptualization, supervised the project, acquired funding, and critically reviewed the manuscript. F.S. initiated the study idea, developed the study protocol, assisted in data collection, advised on statistical analysis, and participated in writing and revision. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo funding was received for this work.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) repository, accessible at https://trialsearch.who.int/\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe data used in this study were obtained from a public database, so ethical approval was not required.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eAuthors Yang Liu, Xitong Guo and Fujian Song declare no financial competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaker RE, Mahmud AS, Miller IF, Rajeev M, Rasambainarivo F, Rice BL et al. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.3346/jkms.2018.33.e152\u003c/span\u003e\u003cspan address=\"10.3346/jkms.2018.33.e152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Technological Trends, Digital Health Interventions, Clinical Trials, Geographical Disparities","lastPublishedDoi":"10.21203/rs.3.rs-6351306/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6351306/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eDigital health interventions (DHIs) have emerged as transformative tools in healthcare, enhancing effectiveness, accessibility, personalization, and safety. Prior studies on digital technology trials, mainly from ClinicalTrials.gov, often included non-intervention uses. In contrast, this study leverages the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) for a more comprehensive dataset and focuses exclusively on digital health interventions. The study aims to identify and characterize registered DHIs trials from the WHO ICTRP, analyze their geographical and temporal trends, and elucidate current advancements, gaps, and future directions in DHI clinical research.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive analysis of 3,685 registered clinical trials from the WHO ICTRP, spanning the period from January 2005 to December 2022. Using the National institute for Health and Care Excellence (NICE) framework Evidence standards framework for digital health technologies, DHIs were systematically categorized into three levels and eight distinct categories. The analysis focused on these key dimensions: trial objectives, technological trends, geographical distribution, and temporal patterns, providing a robust overview of the evolution and global landscape of DHI clinical research.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHealth promotion (26.6%) and disease treatment (21.3%) are key objectives, with mental health and endocrine disorders as common focuses. Mobile apps have surpassed Short Message Service (SMS) as the dominant technology since 2015, peaking in high-income regions by 2019 and growing steadily in middle-income regions through 2022. Teleconsultation technologies surged post-2019, driven by pandemic demands, while SMS remains vital in low-income countries. Regional disparities persist, with high-income areas conducting over 100 times more trials than low-income ones. Methodological and reporting quality of registered DHI trials needs to be further improved.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study highlights global trends in DHI adoption, underscores persistent inequalities in trial distribution, and provides actionable insights for optimizing global digital health strategies. The findings emphasize the need for improved methodological rigor and equitable resource allocation to advance DHI research and implementation worldwide.\u003c/p\u003e","manuscriptTitle":"Global Trends and Disparities in Clinical Trials of Digital Health Interventions-Analyzing Data from the WHO ICTRP","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 17:23:08","doi":"10.21203/rs.3.rs-6351306/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-01T05:00:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-29T14:37:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208784805732795314376697321962639850692","date":"2025-05-24T17:18:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157291639546640309409813986734899765682","date":"2025-05-21T06:26:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-12T10:45:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70450757980471621295131418223215530850","date":"2025-05-12T10:26:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277048996909907226225277428796486287902","date":"2025-05-06T09:55:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-06T09:38:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-09T11:23:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-08T23:40:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-08T23:40:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-04-01T08:42:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d7c4eaa-ea85-4f8f-98cb-28a15b6a328f","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-19T21:08:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 17:23:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6351306","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6351306","identity":"rs-6351306","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}