Introduction
The global healthcare landscape is undergoing a transformation to provide efficient, accessible and affordable services through technological advancements and increased internet and smartphone usage [1,2]. The term “digital health,” which encompasses eHealth, mobile health, telehealth and telemedicine, leverages technology to enhance medical care [3]. In line with the United Nations’ Sustainable Development Goals, the World Health Organisation advocates for digital health technologies through the Global Strategy on Digital Health 2020-2025, aiming for universal health and welfare [4,5].
Concurrently, digital biomarkers have emerged as a secure means of health data collection [6]. Defined by the National Institute of Environmental Health Sciences, digital biomarkers digitally collect and monitor physiological and behavioural patterns to predict health outcomes [7]. These non invasive devices are wearable, portable and even ingestible, capturing various patterns of normal and abnormal functioning [8-10].
Digital biomarkers offer several advantages, replacing conventional vital sign measurements with rapid, user-friendly methods [11]. They are cost-effective for analysing blood samples, enhancing patient safety and facilitating timely diagnosis [12,13]. Categorised as passive and active, digital biomarkers revolutionise healthcare by aiding in early disease detection and assessing treatment effectiveness through digital phenotyping [14,15]. Notably, digital biomarkers, which facilitate remote patient monitoring, proved valuable during the Coronavirus Disease-2019 (COVID-19) pandemic and are expected to optimise clinical trials in the post-COVID era [16]. Future research aims to develop digital resilience biomarkers for tailored health feedback [17]. With broad applications, digital biomarkers shift healthcare from a reactive to a preventive approach, providing rich data for accurate clinical studies [18,19].
Thus, research on digital biomarkers and their utility in healthcare settings is expected to soar in the future, with higher investments in wearable technologies and technology-based medicine. The market size of digital biomarkers is projected to reach USD 27.17 billion by the year 2030 [20]. Digital biomarkers will reduce the number of clinical trials required and consequently decrease the overall cost of drug development [18]. However, challenges do exist in this nascent field that impede the market growth of digital biomarkers. Concerns include validation testing, privacy, misuse of data, data bias, credibility of the data, interoperability, integration and cost-benefit analysis [20]. Research on digital biomarkers lacks clear protocols for rural use and definitive evidence of their superiority over traditional health practices. Unexplored areas include the risks of self-medication, decreased clinical visits and ethical concerns about safety, privacy and data accuracy. Additionally, issues such as counterfeit products and subpar device quality require further investigation [19]. Thus, the present review was conducted to explore the utility and usability of digital biomarkers in healthcare, while also highlighting potential concerns regarding their full implementation in day-to-day practice.
Impact and Challenges of Digital Biomarkers
Digital biomarkers are rapidly transforming the landscape of medical practice, signifying a significant shift in health data collection, analysis and application [21]. This evolution is largely driven by technological advancements, such as wearables, mobile devices and a variety of sensor technologies. These tools facilitate continuous, real-time monitoring of various physiological and behavioural parameters, including heart rate, physical activity, sleep patterns and even more nuanced data like fecal matter, voice and typing behaviour [22]. This wealth of information offers unprecedented insights into individual health statuses, paving the way for more personalised and effective healthcare [23].
The impact of digital biomarkers in medicine is multifaceted. They enhance personalised healthcare by providing data specific to individuals, moving away from the reliance on broader, population-based norms. This personalisation is crucial in tailoring treatments and interventions to individual needs. Additionally, digital biomarkers play a pivotal role in preventive healthcare [22]. By constantly monitoring health indicators, they enable early detection of potential health issues, allowing for preventive measures and timely interventions. This approach is particularly beneficial in managing chronic diseases like diabetes and heart disease, where continuous monitoring can lead to better disease management and improved outcomes [24].
However, the rise of digital biomarkers is not without challenges. Data privacy and security concerns are paramount, as managing sensitive health data demands stringent protection and ethical use [25]. Furthermore, integrating these biomarkers into the healthcare system involves overcoming regulatory and standardisation hurdles to ensure data accuracy, reliability and interoperability across various platforms. These challenges necessitate not only technological integration but also adaptations in healthcare practices and policies, as well as shifts in patient and provider mindsets. Despite these obstacles, the potential of digital biomarkers to revolutionize healthcare is immense, offering new avenues for personalised medicine, preventive care and enhanced patient outcomes [26].
Types of Digital Biomarkers
Digital biomarkers, which encompass various data types and technologies, are proving to be invaluable tools in modern healthcare [27]. These markers come in different forms, each with its own distinct applications, collectively reshaping the landscape of healthcare delivery [28]. The different types of digital biomarkers and their applications [Table/Fig-1] [29-33].
Digital biomarkers and their application [29-33].
| Type of biomarker | Primary function | Applications in healthcare and research |
|---|
| Physiological biomarkers [29] | Real-time insights into vital signs | Monitoring health conditions and chronic diseases |
| Behavioural / psychological biomarkers [30,31] | Tracking daily activitiesMonitoring sleep patterns, social media usage, speech, etc. | Assessing lifestyle-related risks and mental health conditionsDiagnosing and managing mental health conditions (e.g., depression, insomnia); remote monitoring and telehealth; reducing hospitalisations; improving patient outcomes; pivotal in drug development and clinical trials; offering real-time data on drug efficacy and safety |
| Genomic biomarkers [30] | Derived from Deoxyribonucleic Acid (DNA) sequencing | Personalised medicine; identifying genetic susceptibilities to diseases |
| Biometric biomarkers [32] | Enhancing patient identification and data security | Used in healthcare settings |
| Environmental biomarkers [33] | Assessing exposure to environmental factors | Shedding light on health risks |
Latest Trends in Digital Biomarkers
The articles were studied in depth and the following relevant variables were identified: the disease under study, the system under study and the type of digital biomarker. There are three types of digital biomarkers: i) wearable devices, which are worn on the body, such as smartwatches and fitness bands that monitor parameters like heart rate and physical activity; ii) portable devices, which are easily carried and include mobile apps and handheld monitors that provide flexibility in measuring health data; and iii) implantable devices, which are placed inside the body, such as cardioverter-defibrillators and neurostimulators, that continuously monitor and regulate specific health aspects. Other relevant variables include the sensor, mode of data collection, domain of focus, type of data and data sensed.
In the context of mode of data collection, active data collection refers to situations where users actively provide data, such as responding to surveys, manually entering information, or engaging with applications to gather data. In contrast, passive data collection occurs when data is automatically gathered by devices without user interaction, tracking metrics like sleep activity or respiration. Data from previous studies is represented in [Table/Fig-2] [34-47].
Characteristics of data extracted [34-47].
| Author name | Place/year of study | Disease under study(system involved) | Type of digital biomarker/ Mode | Name of digital biomarker | Sensor | Domain of focus | Type of data | Data sensed |
|---|
| Kourtis LC et al., [34] | USA, 2019 | Alzheimer’s disease/ Central nervous system | Touch screen, barometer/Active and passive | Not mentioned | Camera, microphone, barometer, geo positioned, light sensor, Ultraviolet (UV) sensor | Early detection and monitoring | Physiological | Heartbeat, voice, gait, reflexes, memory, eye movements |
| Dillenseger A et al., [35] | Germany, 2021 | Multiple sclerosis/ Central nervous system | Smart phone applications/ Active | MSPT (Multiple sclerosis Performance Test), FitBit, Garmin | Software applications | Treatment and monitoring | Physiological | Eye movements, gait, coordination, balance |
| Dorsey ER et al., [36] | USA, 2017 | Neurodegenerative disorders/Central nervous system | Smart phones, wearables and implantable devices/Passive | Q motor | Software applications | Detection, treatment and monitoring | Biochemical and physiological | Blood glucose, Blood Pressure (BP), Serum sodium and heart rate |
| Paolo F et al., [37] | UK, 2019 | Schizophrenia and bipolar disorder/Central nervous system | GPS, Bluetooth, GSM cellular network, WIFI/Active | Not mentioned | GPS and GSM mobile network | Early detection and monitoring | Psychological | Distance travelled, location visited, time spent outdoors |
| Youn BY et al., [38] | Korea, 2021 | Neuromuscular disorder/Central nervous system and musculoskeletal system | Mobile application/Active | Open smile and Praat | Home monitoring sensor, magneto inertial sensors, Microsoft Kinect sensor | Early detection and monitoring | Physiological | Motor movements |
| Alfalahi H et al., [39] | United Arab Emirates, 2022 | Neuropsychiatric disorder/ Central nervous system | Smart phones, iPad,Smartwatches/ Active | Not mentioned | Camera, microphone | Early detection and monitoring | Physiological | Motor movements |
| Wesselius FJ et al., [40] | Netherlands, 2021 | Atrial fibrillation/ Cardiovascular system | Electrocardiogram (ECG)/ Active | Not mentioned | Electric leads | Early detection and monitoring | Physiological | Electric activity |
| Chen I M et al., [41] | Taiwan, 2022 | Mental illness/ Central nervous system | Mobile apps/ Active | Know addiction and Mental | Software applications | Early detection | Physiological | Computing circadian rhythm, sleep patterns, record working hours |
| Piau A et al., [42] | USA,2019 | Mild cognitive impairment and mild Alzheimer’s disease/ Central nervous system | Embedded environmental devices and wrist worn device/Passive | Not mentioned | Cameras, software applications | Monitoring | Psychological | Cognitive functions, memory, gait, hand movements |
| Gold M et al., [43] | USA, 2018 | Alzheimer’s disease/ Central nervous system | Wearables and embedded devices/Active and Passive | Not mentioned | Cameras, software applications | Treatment and monitoring | Psychological | Sleep movements, gait pattern, eating pattern, time and location of sleep opening and closing of doors, use of gadgets |
| Frohlich H et al., [44] | Germany, 2022 | Parkinson’s disease/ Central nervous system | Wearables pressure sensors embedded cameras/Active | Not mentioned | Cameras, voice recorder, GPS sensors | Treatment and monitoring | Physiological and psychological | Speech and voice, facial expressions, gait parameters, muscular activity, handwriting |
| Ding Z et al., [45] | China, 2022 | Mild cognitive impairment and dementia/Central nervous system | Computers and testing devices/Active and Passive | Not mentioned | Cameras, speed detectors, smart phone applications, video games. | Early detection and monitoring | Physiological and psychological | Voice pattern, handwriting, attention testing, psychomotor speed, emotion testing, finding a way home, gaming, memory testing |
| Brasier N and Eckstein J, [46] | Switzerland 2019 | Tuberculosis, psoriasis, arthritis, Parkinson’s disease, schizophrenia, diabetes mellitus, cystic fibrosis, kidney damage, lung cancer/ pulmonary system, articular system, central nervous system, endocrine system, renal system, immune system | Sweat patches/Active | Not mentioned | Biosensors | Early detection | Biochemical | Sweat droplets |
| Brasier N et al., [47] | Switzerland 2021 | Tuberculosis/ Respiratory system | On -skin sweat biosensor, tooth mounted biosensor, e-nose for exhaled breath/Passive | Not mentioned | Biosensors | Early detection | Biochemical | Sweat droplets, saliva, exhaled breath, coughing, spirometry |
Passive digital biomarkers ($): refer to the data collected with the help of wearable devices. The sensors installed within these devices detect mild actions done by the user.
Active devices (#): track prompt movements alone and monitor with the help of a transmitter device, like a smartphone, tablet, etc.,
Most research in the field of digital biomarkers has focused on their application in the early detection, treatment and prevention of neurodegenerative disorders such as Alzheimer’s disease and Parkinsonism [34,35]. These biomarkers are collected using wearable, portable and implantable devices, with portable devices being preferred over smartwatches or bands due to their lower maintenance requirements [36-38]. Digital biomarkers are also used to assess motor functions in the management of Alzheimer’s disease. Tasks such as drawing shapes, tapping screens and virtual object placement are employed to detect abnormal changes and provide personalised care, including gender-based care [39].
Digital biomarkers have also been explored for monitoring pulmonary health in diseases such as tuberculosis, cystic fibrosis and lung cancer [46]. Sweat samples are collected using sweat patches and analysed for predictive diagnosis, leveraging night sweats as a diagnostic marker for tuberculosis. Cystic fibrosis diagnosis relies on analysing sweat constituents, particularly chloride concentrations [46]. Additionally, studies have demonstrated the effectiveness of using smartphones and smartwatches to collect lung biomarkers, including breathing patterns, coughing, spirometry and breathlessness [47].
Digital devices have shown promise in managing mental health conditions such as schizophrenia, neuropsychological manifestations, impaired cognitive function, dementia, bipolar disorder and depression [29,37,41,43]. These devices analyse various parameters, including cognitive functions, memory, eye movements, voice changes, sleep patterns, location, activity and social participation to detect and prevent the progression of mental illnesses [38,42]. These digital biomarkers can be incorporated into wearables or implants equipped with built-in cameras or microphones, simplifying data collection and aiding in diagnosis and treatment decisions [41,42,45]. Therefore, digital biomarkers offer a cost-effective and convenient approach to complement traditional diagnostic techniques, leading to accurate diagnosis, personalised treatment strategies and improved healthcare outcomes [38,40].
Directions for Future Research
Digital biomarkers are a rapidly evolving field within healthcare and medical research, combining technology and medicine to revolutionise patient monitoring and disease diagnosis. Future research in this area should focus on the development of more sophisticated algorithms for data analysis, ensuring the accuracy and reliability of digital biomarkers [48]. There is a pressing need to standardise these markers across different platforms and devices for consistency in data interpretation. Ethical considerations, particularly regarding patient privacy and data security, should be a priority in future studies [49]. Additionally, research should explore the integration of digital biomarkers into existing healthcare systems for seamless patient management. It is also crucial to conduct extensive clinical trials to validate the effectiveness and applicability of these biomarkers in various medical conditions. Finally, there should be an emphasis on increasing patient engagement and education about digital biomarkers, as patient participation is critical for the successful implementation of these technologies in routine healthcare [50].
Limitation(s)
The present study is subject to certain limitations. The evidence for the use of digital biomarkers in healthcare was primarily obtained from review articles; therefore, experimental proof established through other research methods is not included in the present study. Additionally, only two databases-PubMed and Google Scholar-were used for the screening and selection of articles. Furthermore, only manuscripts published in English were selected.
Conclusion(s)
The field of digital biomarkers is poised to transform healthcare through its innovative integration of technology and medicine. The research conducted so far has shown promising applications in the early detection, treatment and prevention of a wide range of conditions. These biomarkers, collected via various digital devices, offer a more personalised, accurate and efficient approach to healthcare, allowing for early intervention and better management of diseases. However, the path forward requires careful navigation. Integrating these biomarkers into existing healthcare systems and conducting extensive clinical trials are also crucial steps toward their broader adoption. Ultimately, the success of digital biomarkers hinges not only on technological advancements but also on patient involvement. Educating and engaging patients in the use of these technologies is essential for realising their full potential. As we continue to advance in this field, digital biomarkers stand as a beacon of hope for a more personalised, effective and accessible healthcare system.
Authors contribution: Conceptualisation: JNB, SP and SKM; Methodology: JNB, SP and SKM; Software: SKM; Validation: JNB, SP and SKM; Formal Analysis: JNB, SP and SKM; Investigation: JNB, SP and SKM; Resources: SKM; Data Curation: SKM; Writing- Original draft preparation: JNB, SP and SKM; Writing- Review and Editing: JNB, SP and SKM; Visualisation: JNB, SP and SKM; Supervision: SKM; Project Administration: SKM; and Funding Acquisition: SKM All authors have read and agreed to the published version of the manuscript.
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