Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications

Abstract

Mobile health technologies to detect physiological and simple-analyte biomarkers have been explored for the improvement and cost-reduction of healthcare services, some of which have been endorsed by the US FDA. Advancements in the investigations of non-invasive and minimally-invasive molecular biomarkers and biomarker candidates and the development of portable biomarker detection technologies have fuelled great interests in these new technologies for mhealth applications. But apart from the development of more portable biomarker detection technologies, key questions need to be answered and resolved regarding to the relevance, coverage and performance of these technologies and the big data management issues arising from their wide spread applications. In this work, we analyzed the newly emerging portable biomarker detection technologies, the 664 non-invasive molecular biomarkers and the 592 potential minimally-invasive blood molecular biomarkers, focusing on their detection capability, affordability, relevance and coverage. Our analysis suggests that a substantial percentage of these biomarkers together with the new technologies can be potentially used for a variety of disease conditions in mhealth applications. We further propose a new strategy for reducing the workload in the processing and analysis of the big data arising from widespread use of mhealth products and discuss potential issues of implementing this strategy.

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.

Table 1 Examples of FDA endorsed mobile apps. (For a complete list, please refer to Supplementary Table S1).

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.

Table 2 Examples of physiological biomarkers. (For a complete list of physiological biomarkers, please refer to Supplementary Table S2).
Figure 1
figure1

Disease-coverage profiles of the biomarkers.

664 (27 in clinical trial or use) non-invasive molecular biomarkers are colored in light (deep) red. 592 (69 in clinical trial or use) non-invasive molecular biomarkers are colored in light (deep) green. The 94 (13 in clinical trial or use and 73 FDA endorsed apps) physiological and conventional biomarkers are colored in light (deep) blue. Each leaf in the tree represents a specific ICD code as follows: A00-B99: infectious and parasitic diseases, C00-D49: Neoplasms, D50-D89: Diseases of the blood and related organ and immune disorders, E00-E89: Endocrine, nutritional and metabolic diseases, F01-F99: Mental, Behavioral and Neurodevelopmental disorders, G00-G99: nervous system disorders, H00-H59: eye and adnexa diseases, H60-H95: Diseases of the ear and mastoid process, I00-I99: circulatory system disorders, J00-J99: respiratory system disorders, K00-K95: digestive system disorders, L00-L99: skin and subcutaneous tissue disorders, M00-M99: musculoskeletal system and connective tissue disorders, N00-N99: genitourinary system disorders, O00-O9A: Pregnancy, childbirth and the puerperium, P00-P96: conditions originating in the perinatal period, Q00-Q99: Congenital malformations, deformations and chromosomal abnormalities, R00-R99: conditions not elsewhere classified, S00-T88: Injury, poisoning and certain other consequences of external causes, V00-Y99: External causes of morbidity, Z00-Z99: Factors influencing health status and contact with health services

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.

Table 3 Examples of non-invasive molecular biomarkers. For a complete list of non-invasive molecular biomarkers, please refer to Supplementary Table S3.

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.

New technologies for detecting non-invasive molecular biomarkers and their relevance to mhealth

The new biomarker-detection technologies combined with mobile phone or the equivalent imaging devices have been explored for detecting at least 23 molecular biomarkers including 11 non-invasive ones (Table 4). These new technologies can be categorized into four groups: (1) paper-based and mobile phone enabled, (2) paper-based, (3) mobile-phone enabled and (4) the other point of care technologies. The first group of technologies combines innovative paper-based microfluidic analytical technologies with mobile phone enabled automated image processing tools, which are most relevant to mhealth applications because of the very low cost (~$2.60+ cost plus mobile phone), increasingly enhanced detection sensitivity (0.3–60 ng/mL, 0.13–21.3 μg/mL and 0.81–2000 ng/mL for small molecule, peptide and protein biomarkers respectively), low sample volumes (0.5–25 μL), short detection time (15–60 mins) and the convenient biomarker processing (mobile phone-based) capabilities. The recently developed paper-based microfluidic analytical technologies include paper-based enzyme-linked immunosorbent assays (P-ELISA)9,22, paper lateral flow immunoassays (P- LFIAs)12,23 and paper-based Au-nanoprobes22. These are integrated with or coupled to mobile phones equipped with the colorimetric algorithms22 and the applications for immediate data processing of the detection results without referring to peripheral equipment for read-out and analysis9.

Table 4 New biomarker detection technologies.

The second group of technologies primarily employ innovative P-ELISA in combination with a scanner, printer or digital camera based image-processing facility to achieve a detection sensitivity as high as 33.7 fg/mL24 and 18 pM/mL25 for detecting peptide and protein biomarker respectively. The imaging processing component of these technologies may be potentially replaced by mobile phone-based ones for potential mhealth applications. The third group of technologies integrates mobile phone imaging processing tools with newly developed disposable microfluidic chip26, opto-acoustic immunoassay27, microfluidic capillary array equipped with optical signal amplifier28, microtiterplate based ELISA29 and other technologies. These technologies achieve detection sensitivity up to the level of 60–300 pg/mL for protein biomarkers29,30. Although their costs are more suitable for point of care (POC) rather than mhealth applications, the innovative design may be potentially implemented into paper-based platforms for more extensive mhealth applications. A new POC technology in the fourth group, the negative-pressure-driven microfluidic chip magnetic bead based ELISA, is capable of detecting a small molecule biomarker at sensitivity level of 0.3 ng/mL31,32. If implemented into paper-based and mobile phone-enabled platforms, this technology may potentially find wider applications for detecting small molecule biomarkers in mhealth.

Overall, 12 or 52.2% of the 23 tested molecular biomarkers are detectable by these new technologies at low concentrations (0.3–810 pg/mL and 4–50 ng/mL for 8 and 4 biomarkers respectively). Although the detectable concentrations of these 23 biomarkers are roughly 10-fold higher than those of the conventional technologies24, seven of them are nonetheless within the lower detection limit of the new technologies for non-invasive detection24,27. Of the eight biomarkers with available patient data, only two biomarkers in the corresponding non-invasive source are outside the detection limit of the new technologies. Moreover, 64.3% of these biomarkers are detectable at significantly lower sample volumes (0.5–12 μL) and shorter time (10–60 min) than the volumes (100–300 μL)13,25 and durations (up to 4h)24 of the conventional technologies. The costs of these detection devices are ~$300–$600 US dollars33. The per-test costs are in the range of 0.01–190. Therefore, the new technologies are fairly sensitive, efficient and inexpensive for detecting a substantial percentage of the tested non-invasive biomarkers and there is high likelihood that they can be applied for detecting other non-invasive biomarkers in mhealth applications.

The non-invasive molecular biomarkers and their relevance to mhealth

Analysis of the 664 literature-reported non-invasive molecular biomarkers (examples in Table 5 and a complete list in Supplementary Table S5) showed that 546 and 183 biomarkers are for the diagnosis and prognosis of 85 and 45 disease conditions respectively, with 31 and 14 (or 36.5% and 31.1%) of the disease conditions covered by higher number (4–22) of biomarkers and 10 and 6 (or 11.8% and 13.3%) of the disease conditions by clinically-validated/evaluated biomarkers. Among these, 21 acute diseases and 11 chronic conditions affect large populations of 239,000–235 million and 10–235 million people respectively. Therefore, exploration of these biomarkers may significantly improve the efficiency of the management of these disease conditions.

Table 5 Examples of common diseases covered by non-invasive molecular biomarkers. For a complete list, please refer to Supplementary Table S5.

The diagnostic performance of 88 (or 29.7%) of the 296 diagnostic biomarkers for 43 diseases and the prognostic performance of 24 (25.5%) of the 94 prognostic biomarkers for 14 conditions have been reported in the literature (examples in Tables 3 and 5 and a complete list in Supplementary Table S3, S5) Their performances have been typically measured by sensitivities (the rates for positive identification of disease conditions) and specificities (the rates for correct classification of the negatives). The sensitivities and specificities of the majority of these biomarkers are ≥85% and ≥80% for diagnosis and ≥80% and ≥80% for prognosis respectively, which are roughly at the ≥90% sensitivity and ≥90% specificity levels of the good biomarkers21. Therefore, a substantial percentage of these non-invasive biomarkers are expected to be potentially useful for pre-screening patients in need of further evaluations in mhealth applications.

The utility of these biomarkers for mhealth applications also depends on whether they are detectable by the new detection technologies, i.e., whether the levels of these biomarkers in the non-invasive sources from the patients are within the detection range of the new detection technologies. We searched from the literatures the corresponding biomarker levels for 35 diseases (Supplementary Table S5, examples in Table 5) and compared them to the detection limits of the new technologies. Our analysis showed that 26 (or 74.3%) of the 35 disease conditions with searchable information, including 8 disease conditions with large patient populations, have one or more biomarker detectable by the new technologies (Table 5), suggesting that a substantial percentage of the disease conditions including those with large patient populations may be partly covered by the new technologies.

The potential of the minimally invasive finger-prick biomarker technologies for mhealth applications

The minimally invasive finger-prick biomarker technologies have been developed for POC applications11. Because of their improved detection performance34, portability35 and ease of use36 and because of their decreased detection time34, some of these technologies when combined with smartphone-based processing technologies may find potential mhealth applications. Serum biomarkers are known to be detectable at finger-prick albeit at altered concentrations and thus at re-adjusted detection cut-off values37,38. Therefore, one can hypothesize that most of the serum biomarkers of sufficient level of concentrations may be potentially detectable by finger-prick biomarker technologies. The application of these technologies in mhealth significantly expands the coverage of disease conditions because some biomarkers not found in urine are in the serum (e.g. it has been reported that the blood contains the common markers of liver function that are not found in urine35). Our own literature search results showed that the literature-reported serum biomarkers and biomarker candidates cover additional 62 disease conditions beyond those covered by the existing physiological, simple-analyte and the non-invasive molecular biomarkers and biomarker candidates (Fig. 1 and Supplementary Table S4).

Moreover, the finger-prick biomarker technologies can potentially have more enhanced capabilities in detecting the biomarkers of low concentrations. The levels of biomarkers in blood are typically more concentrated than those biomarkers collected from the non-invasive urine, breath, saliva, tear, feces, sputum or oral mucosa sources{Song, 2014 #89} {Abdalla, 2012 #115}. For those biomarkers with concentrations in the non-invasive and finger-prick sources below and above the detection limit of the mhealth biomarker technologies respectively, some of them are potentially detectable by using finger-prick biomarker technologies even if they are undetectable by the non-invasive biomarker technologies.

Several new technologies have been developed with potential applications for detecting serum biomarkers from a drop of blood (Table 4). To enable the purification and detection of serum biomarkers, specially designed fluid handling and silver reduction devices have been combined with the ELISA microfluidic chip for simplified biomarker detection, which enables the detection of an HIV biomarker from 1 μl of unprocessed whole blood in <15 min39. In another design, a microfluidic purification chip was developed for simultaneously capturing multiple biomarkers from blood samples and releasing them into purified buffer for sensing by a silicon nanoribbon detector, which was able to detect two model cancer antigens from a 10 ml sample of whole blood in <20 min40. A micropatterned paper device that combines a filter membrane and a patterned paper chip for achieving blood plasma erythrocyte separation and biomarker detection from the blood from a fingerstick, which is capable of detecting protein biomarkers at ~50 g/L concentrations35. Progress has been made in developing plasmonic ELISA for the ultrasensitive detection of disease biomarkers with the naked eye with the ability to detect biomarkers in whole serum at the ultralow concentration of 10−18 g mL−1 41.

We have found the reports about the detection of 12 serum biomarkers by means of these new technologies (Table 4). Overall, 5 or 42% of the 12 biomarkers are detectable at concentrations of <1.5 ng/mL. Considering that many serum biomarker concentrations are higher than those collected from the urine or other non-invasive sources, the relevant technologies may be extended for the detection of a more variety of low concentration biomarkers than those coverable by the non-invasive biomarker technologies. These technologies enable serum biomarker detection mostly at low sample volumes of 1–10 uL and short time of 12–30 min comparable to those of the non-invasive biomarker technologies. The cost of a microtiterplate based ELISA device coupled with a smartphone is <$66029. The per test costs of these technologies are in the range of $0.1–34. Three studies reported the sensitivity and specificity of five serum biomarkers, which are in the range of 82–100% (vast majority >90%) and 78%-100% respectively38,39,42. Therefore, these new technologies are fairly sensitive, efficient and inexpensive for detecting a substantial percentage of the tested serum biomarkers with potential mhealth applications.

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).

Figure 2
figure2

Flow chart of mhealth biomarker detection and automated data processing procedures.

(Figure drawn by C.Q.).

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.

Additional Information

How to cite this article: Qin, C. et al. The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications. Sci. Rep. 5, 17854; doi: 10.1038/srep17854 (2015).

References

  1. Steinhubl, S. R., Muse, E. D. & Topol, E. J. Can mobile health technologies transform health care? JAMA 310, 2395–2396, 10.1001/jama.2013.281078 (2013).

    CAS  Article  PubMed  Google Scholar 

  2. Sieverdes, J. C., Treiber, F. & Jenkins, C. Improving diabetes management with mobile health technology. Am. J. Med. Sci. 345, 289–295, 10.1097/MAJ.0b013e3182896cee (2013).

    Article  PubMed  Google Scholar 

  3. Kouris, I., Tsirmpas, C., Mougiakakou, S. G., Iliopoulou, D. & Koutsouris, D. E-Health towards ecumenical framework for personalized medicine via Decision Support System. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2010, 2881–2885, 10.1109/IEMBS.2010.5626308 (2010).

    Article  Google Scholar 

  4. Lee, Y. G., Jeong, W. S. & Yoon, G. Smartphone-based mobile health monitoring. Telemed. J. E. Health 18, 585–590, 10.1089/tmj.2011.0245 (2012).

    Article  PubMed  Google Scholar 

  5. Stuckey, M. I., Shapiro, S., Gill, D. P. & Petrella, R. J. A lifestyle intervention supported by mobile health technologies to improve the cardiometabolic risk profile of individuals at risk for cardiovascular disease and type 2 diabetes: study rationale and protocol. BMC public health 13, 1051, 10.1186/1471-2458-13-1051 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Majewski, I. J. & Bernards, R. Taming the dragon: genomic biomarkers to individualize the treatment of cancer. Nat. Med. 17, 304–312, 10.1038/nm.2311 (2011).

    CAS  Article  PubMed  Google Scholar 

  7. Maisel, A. S. & Choudhary, R. Biomarkers in acute heart failure--state of the art. Nat. Rev. Cardiol 9, 478–490, 10.1038/nrcardio.2012.60 (2012).

    CAS  Article  PubMed  Google Scholar 

  8. Blennow, K. Biomarkers in Alzheimer’s disease drug development. Nat. Med. 16, 1218–1222, 10.1038/nm.2221 (2010).

    CAS  Article  PubMed  Google Scholar 

  9. Wang, S. et al. Integration of cell phone imaging with microchip ELISA to detect ovarian cancer HE4 biomarker in urine at the point-of-care. Lab Chip 11, 3411–3418, 10.1039/c1lc20479c (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Mendes, B., Silva, P., Aveiro, F., Pereira, J. & Camara, J. S. A micro-extraction technique using a new digitally controlled syringe combined with UHPLC for assessment of urinary biomarkers of oxidatively damaged DNA. PloS one 8, e58366, 10.1371/journal.pone.0058366 (2013).

    CAS  ADS  Article  PubMed  PubMed Central  Google Scholar 

  11. Song, Y. et al. Point-of-care technologies for molecular diagnostics using a drop of blood. Trends in biotechnology 32, 132–139, 10.1016/j.tibtech.2014.01.003 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. Costa, M. N. et al. A low cost, safe, disposable, rapid and self-sustainable paper-based platform for diagnostic testing: lab-on-paper. Nanotechnology 25, 094006, 10.1088/0957-4484/25/9/094006 (2014).

    CAS  ADS  Article  PubMed  Google Scholar 

  13. Yamada, K., Takaki, S., Komuro, N., Suzuki, K. & Citterio, D. An antibody-free microfluidic paper-based analytical device for the determination of tear fluid lactoferrin by fluorescence sensitization of Tb3+. Analyst 139, 1637–1643, 10.1039/c3an01926h (2014).

    CAS  ADS  Article  PubMed  Google Scholar 

  14. Vashist, S. K., Mudanyali, O., Schneider, E. M., Zengerle, R. & Ozcan, A. Cellphone-based devices for bioanalytical sciences. Anal. Bioanal. Chem. 406, 3263–3277, 10.1007/s00216-013-7473-1 (2014).

    CAS  Article  Google Scholar 

  15. Warren, A. D., Kwong, G. A., Wood, D. K., Lin, K. Y. & Bhatia, S. N. Point-of-care diagnostics for noncommunicable diseases using synthetic urinary biomarkers and paper microfluidics. Proc. Natl. Acad. Sci. USA 111, 3671–3676, 10.1073/pnas.1314651111 (2014).

    CAS  ADS  Article  PubMed  Google Scholar 

  16. 510(k) Premarket Notification Database. Retrieved from http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm. (Date of access:07/12/2014).

  17. World Health Organization: WHO. Retrieved from http://www.who.int/en/. (Date of access: 07/12/2014).

  18. American Cancer Society. Retrieved from http://www.cancer.org/. (Date of access: 07/12/2014).

  19. Bramer, G. R. International statistical classification of diseases and related health problems. Tenth revision. World Health Stat Q 41, 32–36 (1988).

    CAS  ADS  PubMed  Google Scholar 

  20. Letunic, I. & Bork, P. Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy. Nucleic Acids Res. 39, W475–478, 10.1093/nar/gkr201 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Brower, V. Biomarkers : Portents of malignancy. Nature 471, S19–21, 10.1038/471S19a (2011).

    CAS  ADS  Article  PubMed  Google Scholar 

  22. Murdock, R. C. et al. Optimization of a paper-based ELISA for a human performance biomarker. Anal. Chem. 85, 11634–11642, 10.1021/ac403040a (2013).

    CAS  Article  PubMed  Google Scholar 

  23. Gerbers, R., Foellscher, W., Chen, H., Anagnostopoulos, C. & Faghri, M. A new paper-based platform technology for point-of-care diagnostics. Lab Chip 14, 4042–4049, 10.1039/c4lc00786g (2014).

    CAS  Article  Google Scholar 

  24. Hsu, M. Y. et al. Monitoring the VEGF level in aqueous humor of patients with ophthalmologically relevant diseases via ultrahigh sensitive paper-based ELISA. Biomaterials 35, 3729–3735, 10.1016/j.biomaterials.2014.01.030 (2014).

    CAS  Article  PubMed  Google Scholar 

  25. Cheng, C. M. et al. Paper-based ELISA. Angewandte Chemie 49, 4771–4774, 10.1002/anie.201001005 (2010).

    CAS  Article  PubMed  Google Scholar 

  26. Lillehoj, P. B., Huang, M. C., Truong, N. & Ho, C. M. Rapid electrochemical detection on a mobile phone. Lab Chip 13, 2950–2955, 10.1039/c3lc50306b (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Bourquin, Y., Reboud, J., Wilson, R., Zhang, Y. & Cooper, J. M. Integrated immunoassay using tuneable surface acoustic waves and lensfree detection. Lab Chip 11, 2725–2730, 10.1039/c1lc20320g (2011).

    CAS  Article  PubMed  Google Scholar 

  28. Balsam, J., Rasooly, R., Bruck, H. A. & Rasooly, A. Thousand-fold fluorescent signal amplification for mHealth diagnostics. Biosens. Bioelectron. 51, 1–7, 10.1016/j.bios.2013.06.053 (2014).

    CAS  Article  PubMed  Google Scholar 

  29. Vashist, S. K. et al. A smartphone-based colorimetric reader for bioanalytical applications using the screen-based bottom illumination provided by gadgets. Biosens. Bioelectron. 67, 248–255, 10.1016/j.bios.2014.08.027 (2015).

    CAS  Article  PubMed  Google Scholar 

  30. Preechaburana, P., Macken, S., Suska, A. & Filippini, D. HDR imaging evaluation of a NT-proBNP test with a mobile phone. Biosens. Bioelectron. 26, 2107–2113, 10.1016/j.bios.2010.09.015 (2011).

    CAS  Article  PubMed  Google Scholar 

  31. Lin, Y. H. et al. A negative-pressure-driven microfluidic chip for the rapid detection of a bladder cancer biomarker in urine using bead-based enzyme-linked immunosorbent assay. Biomicrofluidics 7, 24103, 10.1063/1.4794974 (2013).

    CAS  Article  PubMed  Google Scholar 

  32. Li, C. et al. Discovery of Apo-A1 as a potential bladder cancer biomarker by urine proteomics and analysis. Biochem. Biophys. Res. Commun. 446, 1047–1052, 10.1016/j.bbrc.2014.03.053 (2014).

    CAS  Article  PubMed  Google Scholar 

  33. Stedtfeld, R. D. et al. Gene-Z: a device for point of care genetic testing using a smartphone. Lab Chip 12, 1454–1462, 10.1039/c2lc21226a (2012).

    CAS  Article  PubMed  Google Scholar 

  34. Chang, H. K. et al. Rapid, label-free, electrical whole blood bioassay based on nanobiosensor systems. ACS nano 5, 9883–9891, 10.1021/nn2035796 (2011).

    CAS  Article  PubMed  Google Scholar 

  35. Vella, S. J. et al. Measuring markers of liver function using a micropatterned paper device designed for blood from a fingerstick. Anal. Chem. 84, 2883–2891, 10.1021/ac203434x (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. Hawwa, A. F. et al. A novel dried blood spot-LCMS method for the quantification of methotrexate polyglutamates as a potential marker for methotrexate use in children. PloS one 9, e89908, 10.1371/journal.pone.0089908 (2014).

    CAS  ADS  Article  PubMed  PubMed Central  Google Scholar 

  37. Gootjes, J., Tel, R. M., Bergkamp, F. J. & Gorgels, J. P. Laboratory evaluation of a novel capillary blood sampling device for measuring eight clinical chemistry parameters and HbA1c. Clin Chim Acta 401, 152–157, 10.1016/j.cca.2008.12.016 (2009).

    CAS  Article  PubMed  Google Scholar 

  38. Neogi, S. B. et al. Diagnostic accuracy of haemoglobin colour strip (HCS-HLL), a digital haemoglobinometer (TrueHb) and a non-invasive device (TouchHb) for screening patients with anaemia. J. Clin. Pathol. 10.1136/jclinpath-2015-203135 (2015).

  39. Chin, C. D. et al. Microfluidics-based diagnostics of infectious diseases in the developing world. Nature medicine 17, 1015–1019, 10.1038/nm.2408 (2011).

    CAS  Article  PubMed  Google Scholar 

  40. Stern, E. et al. Label-free biomarker detection from whole blood. Nature nanotechnology 5, 138–142, 10.1038/nnano.2009.353 (2010).

    CAS  ADS  Article  PubMed  Google Scholar 

  41. de la Rica, R. & Stevens, M. M. Plasmonic ELISA for the ultrasensitive detection of disease biomarkers with the naked eye. Nature nanotechnology 7, 821–824, 10.1038/nnano.2012.186 (2012).

    CAS  ADS  Article  PubMed  Google Scholar 

  42. Pollock, N. R. et al. A paper-based multiplexed transaminase test for low-cost, point-of-care liver function testing. Sci. Transl. Med. 4, 152ra129, 10.1126/scitranslmed.3003981 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. Wood, P. H. Applications of the International Classification of Diseases. World Health Stat Q 43, 263–268 (1990).

    CAS  PubMed  Google Scholar 

  44. Cote, R. A. & Robboy, S. Progress in medical information management. Systematized nomenclature of medicine (SNOMED). JAMA 243, 756–762 (1980).

    CAS  Article  Google Scholar 

  45. Bodenreider, O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, D267–270, 10.1093/nar/gkh061 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. Qin, C. et al. Therapeutic target database update 2014: a resource for targeted therapeutics. Nucleic Acids Res. 42, D1118–1123, 10.1093/nar/gkt1129 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. Law, V. et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42, D1091–1097, 10.1093/nar/gkt1068 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. Lee, D., Chou, W. P., Yeh, S. H., Chen, P. J. & Chen, P. H. DNA detection using commercial mobile phones. Biosens. Bioelectron. 26, 4349–4354, 10.1016/j.bios.2011.04.036 (2011).

    CAS  Article  PubMed  Google Scholar 

  49. Apilux, A., Ukita, Y., Chikae, M., Chailapakul, O. & Takamura, Y. Development of automated paper-based devices for sequential multistep sandwich enzyme-linked immunosorbent assays using inkjet printing. Lab Chip 13, 126–135, 10.1039/c2lc40690j (2013).

    CAS  Article  Google Scholar 

  50. Hsu, C. K. et al. Paper-based ELISA for the detection of autoimmune antibodies in body fluid-the case of bullous pemphigoid. Anal. Chem. 86, 4605–4610, 10.1021/ac500835k (2014).

    CAS  Article  PubMed  Google Scholar 

  51. Ohashi, Y. et al. Abnormal protein profiles in tears with dry eye syndrome. Am. J. Ophthalmol. 136, 291–299 (2003).

    CAS  Article  Google Scholar 

  52. Fung, A. O. et al. Quantitative detection of PfHRP2 in saliva of malaria patients in the Philippines. Malar. J. 11, 175, 10.1186/1475-2875-11-175 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Hussain, S. et al. Level of interferon gamma in the blood of tuberculosis patients. Iran J. Immunol. 7, 240–246, IJIv7i4A6 (2010).

    CAS  PubMed  Google Scholar 

  54. Zangheri, M. et al. A simple and compact smartphone accessory for quantitative chemiluminescence-based lateral flow immunoassay for salivary cortisol detection. Biosens. Bioelectron. 64, 63–68, 10.1016/j.bios.2014.08.048 (2015).

    CAS  Article  PubMed  Google Scholar 

  55. Bozovic, D., Racic, M. & Ivkovic, N. Salivary cortisol levels as a biological marker of stress reaction. Medical archives 67, 374–377 (2013).

    Article  Google Scholar 

  56. Maisel, A. B-type natriuretic peptide levels: diagnostic and prognostic in congestive heart failure: what’s next? Circulation 105, 2328–2331 (2002).

    Article  Google Scholar 

  57. Long, K. D., Yu, H. & Cunningham, B. T. Smartphone instrument for portable enzyme-linked immunosorbent assays. Biomed. Opt. Express 5, 3792–3806, 10.1364/BOE.5.003792 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. Allin, K. H. & Nordestgaard, B. G. Elevated C-reactive protein in the diagnosis, prognosis and cause of cancer. Crit. Rev. Clin. Lab. Sci. 48, 155–170, 10.3109/10408363.2011.599831 (2011).

    CAS  Article  PubMed  Google Scholar 

  59. Laksanasopin, T. et al. A smartphone dongle for diagnosis of infectious diseases at the point of care. Sci. Transl. Med. 7, 273re271, 10.1126/scitranslmed.aaa0056 (2015).

    Article  Google Scholar 

  60. Parekh, B. S., Pau, C. P., Kennedy, M. S., Dobbs, T. L. & McDougal, J. S. Assessment of antibody assays for identifying and distinguishing recent from long-term HIV type 1 infection. AIDS Res. Hum. Retroviruses 17, 137–146, 10.1089/08892220150217229 (2001).

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Shenzhen SZSITIC grants JSGG20141016150327538, JCYJ20140509151735023, JCYJ20140827150509058 and 20150113A0410006 and Singapore Academic Research Fund R-148-000-208-112.

Author information

Affiliations

Authors

Contributions

Y.Z.C. and Y.Y.J. designed the study. C.Q.,Y.H.P. and Y.Z.C. undertook data collection. C.Q., L.T.,Y.H.P., C.Z., S.Y.C., P.Z., Y.T. and Y.Z.C. analyzed the data and developed drafts of the manuscript. C.Q., L.T., C.Z., S.Y.C., P.Z., Y.T., Y.Y.J. and Y.Z.C. contributed to interpretation of the results, drafting of the paper and revisions of the manuscript. All authors contributed to and approved the final draft for publication.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Electronic supplementary material

Rights and permissions

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Qin, C., Tao, L., Phang, Y. et al. The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications. Sci Rep 5, 17854 (2015). https://doi.org/10.1038/srep17854

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing