## Introduction

There have been intensifying efforts to explore mobile health (mhealth) technologies for delivering healthcare at reduced costs and for facilitating more precise and personalized medicine1,2,3 which have led to 73 apps endorsed (examples in Table 1, a complete list in Supplementary Table S1) and additional ones reviewed1 by the US Food and Drug Administration (FDA) for self-diagnosing acute diseases and monitoring chronic conditions1 based on such physiological biomarkers as body temperature and brainwave4,5 and such simple-analyte biomarkers as glucose and urine protein contents4,5.

Although these physiological and simple-analyte biomarkers cover many disease conditions, their coverage is substantially limited for such prevalent diseases as cancers, infectious, respiratory, digestive, endocrine and nervous system diseases, as indicated by the disease-coverage profiles of the 73 FDA endorsed and 94 physiological and simple-analyte biomarker candidates described in the literatures (Fig. 1, Table 1 and 2, Supplementary Table S1 and S2). Apart from the development of more portable biomarker detection technologies, additional biomarkers are needed for fulfilling the tasks of mhealth technologies as efficient and effective means for providing wider coverage of healthcare and personalized treatments at reduced costs1,2,3.

Some genetic, proteomic and metabolomic molecular biomarkers have been clinically used and many more such molecular biomarker candidates (hitherto also tentatively named biomarkers) have been discovered for diagnosing and monitoring diseases, directing treatments and predicting patient responses6,7,8. Of immediate relevance to mhealth are the hundreds of literature-reported non-invasive and minimally-invasive diagnostic, prognostic and theragnotic molecular biomarkers from such non-invasive sources as urine, breath, saliva, tear, feces, sputum and oral mucosa samples (Examples in Table 3 and complete list in Supplementary Table S3) and from such minimally-invasive sources as finger-prick (the list of serum biomarkers potentially detectable from finger-prick is in Supplementary Table S4), which significantly expand the disease coverage as indicated by the disease-coverage profiles of the 664 (27 clinical trial) non-invasive and 592 serum (69 clinical trial or use) molecular biomarkers with respect to those of 73 FDA endorsed apps and 94 physiological and simple-analyte biomarkers (Fig. 1). Many biomarkers are detectable by the new biomarker-detection technologies that become increasingly portable, faster, user-friendly, inexpensive and accurate9,10,11, some of which have been explored for potential mhealth applications9,12,13,14,15.

From the investigations and opinions described in the literatures listed in Supplementary Table S3, there are good reasons to speculate the readiness of some of these technologies for mhealth applications. But before the acceptance and widespread utilization of these technologies, several key questions need to be answered or resolved. Apart from the development of more portable biomarker detection technologies, an important question is whether the new portable biomarker detection technologies are sufficiently sensitive, fast, convenient and inexpensive for biomarker detection in the typical mhealth settings (low sample volume and biomarker concentrations). Another question is whether the discovered and investigative molecular biomarkers extracted from the non-invasive and minimally invasive sources are relevant to mhealth applications in terms of the detection accuracies and the coverage of disease conditions and patient populations. The third is how to resolve the different readings generated from different mhealth devices and variations in individual operations. The fourth is how to manage the heavy workload in processing and analysing the big data arising from widespread use of mhealth devices.

Here, we address some of these questions by analysing (1) biomarker detection capability of the literature-reported new technologies with specific focus on their detection sensitivity, required sample volume, test time and costs with respect to experimentally-determined biomarker levels in patients and the detection limits and (2) the disease coverage, patient populations and the diagnostic, prognostic and theragnostic sensitivity and specificity of the literature-reported non-invasive and minimally-invasive finger-prick molecular biomarkers for mhealth applications with respect to the detection limits of the new detection technologies. We also discuss the feasibility and practical issues of adopting a new strategy for reducing the heavy workload of mhealth data processing by automated electronic pre-screening of the big biomarker screening data.

## Literature Search

The detailed information of 73 mhealth apps endorsed by the US FDA was obtained by manually checking the descriptions of the apps listed in FDA 510(k) medical device database16. The physiological and molecular biomarkers were obtained by the comprehensive literature search of the Pubmed database by using the combination of the keyword “biomarker” together with one of the keywords of “clinical”, “patient”, “disease”, “drug” and specific disease names such as “cancer”, “inflammation” and “hypertension”. We also searched and evaluated biomarker review papers from reputable journals by using the combination of the keywords “biomarker” and “review”, with the cited original articles checked to collect detailed information about the discussed biomarker, such as the name, source, specific disease and function, specificity and sensitivity of the biomarker. The detailed information of these 254 evaluated review and research papers are listed in Supplementary Table S6. Additional sources such as the abstracts of the American society of clinical oncology were also systematically searched, with 658 biomarker conference abstracts in 1995–2013 extracted and evaluated by data mining and manual curation. Non-invasive biomarkers were selected if they were detected in non-invasive tissues such as urine, breath, saliva, tear, feces, sputum and oral mucosa samples. The information of disease conditions was searched from the websites of professional medical associations such as WHO17 and American Cancer Society18 and such additional sources as reputable books and review articles, using combinations of keywords such as the disease name and “prevalence” or “incidence”. These biomarkers were organized based on their international classification ICD-10 codes19 and were displayed with respect to these codes in a tree graph by using the automatic tree generator module in iTOL20.

The performance of the biomarkers in diagnosing, prognosing or theragnosing specific conditions has been statistically measured by sensitivity (the proportion of the condition-positive samples that are correctly identified as positive) and specificity (the proportion of the condition-negative samples that are correctly identified as negative)21. Wherever reported in the literature, these statistical performance measures were recorded. Apart from the collection of the biomarker detection technologies described in our searched biomarker literatures, additional literature search was conducted for searching biomarker detection technologies of potential mhealth applications by using the keyword “biomarker” in combination with one of the keywords “detection”, “detector”, “device”, “technology”, “technique” and “assay”. These detection technologies were analysed for selecting those with potential mhealth applications based on their detection performance, portability, detection time, cost and ease of use.

## Coping with the heavy workload in mhealth: Feasibility of automated electronic pre-screening of big mhealth data

There are concerns about the increased workload in processing and analysing the big data arising from widespread use of mhealth devices1. On the hand, mhealth devices as digital tools may conveniently facilitate electronic pre-screening of the biomarker readings for filtering potential patients likely in need of further attention and evaluation, which helps to significantly reduce the workload. A digitally-coded biomarker, disease and therapeutic information processing system may be developed for automatically receiving, processing, pre-screening and dispatching the biomarker readings transmitted from mhealth devices (Fig. 2).

It is feasible to develop such a system using available tools such as the International Classification of Diseases (ICD) codes for defining, studying and managing diseases and treatments43, the Systematized nomenclature of medicine for clinical documentation and reporting44, the Unified medical language system for biomedical terminology45, the Therapeutic target database biomarker and target information and links to the ICD and drug codes46 and the Drugbank drug information47. Further efforts are needed for additional information refinement and integration, determination and clinical validation of biomarker pre-screening thresholds and development and education of testing protocols. There are also potential issues arising from missed detection or misidentification by an electronic system, lack of data security and insufficient regulation standards.

## Concluding Remarks

Molecular biomarker-based mobile health technologies have the potential to significantly improve the efficiency and quality of healthcare for a variety disease conditions particularly those with large patient populations that cannot be solely covered by physiological and simple-analyte biomarkers. Some of these biomarkers combined with the new detection technologies are readily applicable for mhealth applications. The increased workload in processing and analyzing high volumes of mhealth data may be efficiently managed by an electronic system that facilitate automatic pre-screening and analysis of the biomarker data for filtering potential patients likely in need of further attention and evaluation.