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REVIEW ARTICLE |
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Year : 2018 | Volume
: 4
| Issue : 2 | Page : 71-76 |
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The digital medicine ATM: Noninvasive point-of-care diagnostics
Justin M Wright1, Graham B Jones2
1 Technical Research and Development, Novartis Pharmaceuticals, NJ, USA 2 Tufts University Medical Center, Clinical and Translational Science Institute, Boston, MA, USA
Date of Web Publication | 23-Aug-2018 |
Correspondence Address: Graham B Jones Tufts University Medical Center, Clinical and Translational Science Institute, 800 Washington Street, Boston, MA 02111 USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/digm.digm_4_18
Rapid developments in sensing and imaging technologies are transforming our ability to detect, diagnose, and manage disease. Given financial pressures on managed health care, there is increasing emphasis on disease prevention and wellness programs have become a feature of many plans. Recent market developments include the merger of pharmacy and health-care organizations, which promises to open new avenues in health maintenance and diagnosis. Herein, we review recent developments in the field and present a vision for how point-of-care providers can play a pivotal role in prodromal diagnostics and wellness programs. Emphasis is placed on recent advances in digital detection technologies which have the potential to accelerate evolution of such models. An additional potential benefit of large-scale community-based screening centers lies in the identification of patients for recruitment into clinical trials, and mechanisms are proposed.
Keywords: Community health care, diagnostics, digital biomarkers, imaging, remote monitoring
How to cite this article: Wright JM, Jones GB. The digital medicine ATM: Noninvasive point-of-care diagnostics. Digit Med 2018;4:71-6 |
Introduction | |  |
The health-care sector is undergoing a major revolution as a consequence of some factors. Life expectancy is increasing dramatically as is our understanding of disease etiology, allowing highly personalized approaches to disease management.[1] This said, the consequence of debilitating neurodegenerative disorders coupled to increased patient lifespan will likely place enormous financial burden on managed health care, and efforts to diagnose these diseases at prodromal stage have assumed increased emphasis.[2] Given incentive programs designed to promote patient wellness in other life-threatening diseases (including cardiovascular disease and diabetes), there is every reason to believe that progress will be forthcoming in terms of screening and diagnostic strategies applied to neurodegenerative disease. Advances in imaging technology and the identification of digital biomarkers for these central nervous system (CNS) disorders continue at pace,[3] but it seems likely that the very early stages of certain of these diseases (Alzheimer's, Parkinson's) involve very subtle changes which take place over an extended time period. It is therefore incumbent on the health-care industry to develop models and incentives which are user-friendly and promote regular screening, allowing longitudinal analysis over a sufficient period.[4] Recent developments in point of care and pharmacy services offer the potential to respond to this challenge and coupled with state-of-the-art sensing and imaging technology could provide needed breakthroughs in the early detection and thus management of these and other diseases.
The Community Pharmacy | |  |
Mission growth of retail pharmacy corporations has been marked over the past decade, expanding from prescription dispensing to provision of seasonal influenza vaccines and more recently so-called “5 min clinics,” where consumers can book appointments for routine screens and diagnoses, e.g., Streptococcal pharyngitis testing.[5] The appeal of this model is obvious and multifold. Compared with traditional providers (hospitals, health-care clinics, and physician offices), waiting times are typically less, overheads and thus charges are lower (especially so in comparison to co-pays associated with emergency room visits), deductibles are not assessed, and business hours are typically longer, catering to those at work. In addition, transportation costs are often lower as most pharmacies are located in the community, parking charges are not incurred, and for clients required to wait for a process/dispensation, other amenities (shopping, services) are often close by allowing efficient use of time. Coupled to some customers' aversion to visiting hospitals and health-care centers (e.g., fear of contracting communicable diseases such as influenza or methicillin-resistant Staphylococcus aureus), the pharmacy offers myriad consumer benefits. In the case of elderly patients, who often consume multiple prescription drugs, visits to the pharmacy are often very frequent, providing another potentially useful factor – the capture of monitoring biomarkers, which can be assessed longitudinally.[6] This is precisely the information of most value when tracking neurodegenerative disorders including Alzheimer's and Parkinson's. The recent announcement of the merger of Aetna healthcare with CVS pharmacy suggests that such screening centers may have the potential to be established in the near term.[7]
Noninvasive Diagnostics | |  |
The wide geographic presence of retail facilities capable of offering medical screening services opens up new paradigms in health care. Already embracing basic point-of-care diagnostic provisions, such facilities would be ideal locations to house high-technology systems capable of providing clinical-grade precision data for interpretation by care providers. Although such services could also be provided by traditional facilities (hospitals, clinics, and medical centers), this would be, especially relevant for conditions requiring repeat analyses over a given time period, based on their proximity to patients. Examples of this could include follow-up care, wound management, and detection of monitoring biomarkers.[6]
Monitoring biomarkers
The use of monitoring biomarkers for longitudinal tracking of prodromal and diseased states offers a powerful means to inform disease management. On the most basic level, single-nucleotide polymorphism-based genomic analysis has now become a commodity business, and though limited in scope, signaled a step-change in how individuals can approach the hereditary basis for disease.[8] Although such vendors offer mail-back service of the salivary fluid (although real-time applications have also now been developed using mobile phone-based microscopy),[9] it has spurred general interest in the on-demand analysis of biological specimens using noninvasive methods. Handheld devices capable of analyzing saliva for biomarkers have now been reduced to practice,[10] and it would be a logical extension for a stand-alone analyzer housed in a local pharmacy to provide economies of scale in offering the latest and most accurate analytic detection technologies. Similarly, analysis of tear fluids on demand could reveal important biomarker data on systemic disease.[11],[12],[13] Examples include proteomic analysis for glaucoma,[14] diabetic retinopathy,[15] cancer,[16] and Sjogren's syndrome.[17] Proteomic analysis of tears has also revealed changes in levels of potential biomarkers for Alzheimer's disease (lipocalin 1, dermcidin, lysozyme-C, and lacritin).[18] In addition, tear analyses are of obvious relevance to contact lens users seeking to confirm origins of use related conditions, e.g., dry eye and pink eye.[17] Likewise, sweat contains a number of useful biomarkers including mineral content which may be of importance to those suffering from hypertensive conditions.[19] More invasive (though potentially rendered user-friendly) would be the analysis of urine and fecal matter, through deposition to an instrument from a provided customized capture reticule. In the latter case, very rich data on patient microbiome are derivable,[20] and in the former case details which may be significant for a number of liquid biopsies, including ketoacidosis [21] and oncologic markers.[22] The manipulation of blood for such would require additional considerations and precautions relating to the potential for bloodborne pathogens, but smaller scale applications, e.g., finger pricks (commonplace among diabetes mellitus patients) could be rendered safe, and the potential to link to a sophisticated analyzer could yield myriad data points in addition to glucose measurement. Examples could include tracking of monitoring biomarkers, for example, prostate-specific antigen and mRNA for prostate cancer [23] and blood-[24] and plasma-[25] borne biomarkers for Alzheimer's and Parkinson's disease where exosome-derived markers for amyloid-β and α-synuclein are beginning to show promise.[26]
The real benefit of a stand-alone analyzer, however, could be in providing research grade information derived from high-resolution analytical imaging tools. In this regard, one of the most promising technologies in development is the use of scanning retinopathy. This technology allows circulating protein content to be assessed noninvasively using a variety of methods [Figure 1]. Among these, blue laser autofluorescence [27] and optical coherence tomography [28] have been shown to image inclusion bodies which correlate with β-amyloid protein associated with Alzheimer's disease.[29] Other developments could include optical scanning of so-called nanotattoo's, wherein a specific analyte of interest is captured by a custom engineered dermally embedded tattoo.[30] This technology has already been used to detect circulating glucose levels and also cations.[31] In the case of Li +, one could envision application for screening of lithium levels in patients prescribed lithium carbonate for the management of type I bipolar disorder. Maintenance of these levels is notoriously difficult, typically requiring extensive and regular blood work conducted at an outpatient clinic.[32] The availability of such, locally on demand, would be a great boon to these patients [Figure 1].
Follow-up care
Another potential application of a remote diagnostics facility would be in follow-up care for patients who have transitioned from a hospital setting. One area could be wound management, where imaging technology could be used assess multiple factors using colorimetric analysis and additional biomarker analysis recommend as needed, e.g., temperature, heart rate, and white blood cell count if wound infection was suspected. Another patient group who could benefit from this approach would be those who have transitioned from intravenous (IV) drug infusion to subcutaneous (SQ) delivery.[33] Given reported cost savings, the SQ administration route is becoming widespread for delivery of biologic agents, in increasingly large volumes.[34] Switching from a supervised infusion center (IV) to self-administration at home (SQ) requires some degree of user training (c.f. type I diabetes patients) and injection site reactions (edema, erythema, and bleb formation) are expected.[35] Such can be exacerbated based on injection rotation regimens and patient body mass index (BMI). The ability to capture precision-grade images of injection site and upload to a health professional for analysis in real time would help educate this group of patients, allowing them to administer treatments within the locale of their neighborhood.
Cognitive assessment
A third application of deployed technology could be in cognitive assessment methods. Given the need to track patients with high risk of developing neurodegenerative disorders over time, this could be a useful screening environment where patients engage on specific motor function tasks. Several protocols have been developed based on iPad gaming programs,[3],[36],[37] and these could be extended to measure other factors, e.g., gait analysis using smart footpads coupled with retina tracking.[38] It is known that regular motor exercises can delay onset in Parkinson's disease,[39] and the prescribed series of tests and exercises could be used alongside noninvasive imaging methods (vide supra) to classify disease onset and progression. Such could also serve as means to identify patients where positron emission tomography scanning is justified to confirm diagnoses.[40]
The Digital Exhaust Fingerprints | |  |
The central feature of the envisioned neighborhood approach to screening and monitoring would position the local pharmacy as an arm of the physician, in the form of a technical laboratory. Further embellishments could involve the integration of these data streams with those derived from consumer worn devices (iPhone, smartwatch, and fit bit), which would allow composite and holistic monitoring.[41] The result would be real-time tracking of the patients “digital exhaust” offering wide-ranging benefits to the patient, health provider, and in some conditions (e.g., geriatric disorder, CNS diseases) their local care providers who may include family members (dementia).[42] The impact on patients would be obvious, but such a framework could also contribute to new clinical findings and approaches. Aggregate data obtained across large populations at different life stages and with definable characteristics (e.g., gender, ethnicity, BMI, and medical history) would provide compelling evidence on the value of specific risk factors.[43]
A New Model for Health Care | |  |
The proposed model could transform clinical medicine if implemented in a carefully designed fashion. Awareness of the power of digital biomarkers is widespread among generation X and millennial populations, and the ability to import clinically relevant information onto a health dashboard (e.g., smartphone or tablet) viewed as attractive.[44],[45] It could also play an enabling role in the rapidly emerging field of coached/managed subscription programs that track data longitudinally.[46] These programs are also being utilized by large employers who offer financial incentives for enrollees to monitor key indicators [4] and might ultimately be adopted by traditional end-payors. These activities are also likely to stimulate the pharmaceutical and biotechnology sector to consider new models of engagement with patients and potential patients regarding the awareness of their products. Interestingly, in a recent survey, respondents indicated high comfort levels in sharing personal digital medical data with health providers, but substantially less so with pharmaceutical companies.[47] This suggests some ground needs to be covered by the industry if it is to be associated with the wellness sector. This challenge represents a major opportunity, however, and would go some way to establishing confidence in the innovation model for the pharmaceutical industry, which has been called into question.[48] What is becoming abundantly clear is that any and all methods to facilitate early detection of disease will pay dividends in terms of outcome. The nation faces a growing crisis in the form of neurodegenerative diseases and dementia [49] and identification of biomarkers at prodromal stages will help stimulate accelerated development of new therapeutic options.[50]
Barriers and Obstacles | |  |
The evolution of clinical grade monitoring centers in local pharmacies offers myriad potential benefits to both patients and the health-care industry. Deriving maximum value from such facilities will require attention to a number of barriers and obstacles however. A major requirement will be the identification of agreed end-points and standards for the various conventional and digital biomarkers to be screened. Progress is continually being made in this area, and the fact that patient data would be uploaded for analysis by a health-care professional will add a degree of scrutiny on its interpretation. Depending on the biomarker being studied, there will also need to be processes implemented for the calibration of diagnostic instruments to ensure data is usable. There may also be natural concerns related to patient anxiety, both from the (comparatively) impersonal nature of the processes themselves and from a data security standpoint. For widespread adoption, there may need to be incentive mechanisms introduced to maximize patient participation and compliance. Models could include financial incentives offered by the end payor (e.g., lower premiums) or from a pharmaceutical company that the patient agrees to share data with (e.g., vouchers). Such models are now becoming commonplace in sharing of genomic information,[51] and the availability of rich proteomic and metabolomics data streams from patients would seem fertile ground for further engagement. One possibility would be the identification of patients for recruitment into clinical trials, which remains a hurdle for clinical researchers and pharmaceutical companies alike.[52] Ultimately, it can be expected that a combination of patient desire (convenience) and industry drivers (end-payor plan requirements and pharmaceutical industry incentives) will shape and define the field.
Conclusions | |  |
The advent of remote patient monitoring is upon us and looks set to redefine managed health care. The drivers for such include economic pressures and patient preference/convenience. It is interesting to note that overburdening of the health-care sector in China has already led to the development of such systems, e.g., We Doctor.[53] A driving force behind this was patient frustration with wait times despite sustained federal investments in their traditional health-care systems.[54] Implementing such in the US could have myriad other benefits as exemplified by the much heralded Framingham longitudinal cardiovascular study.[55] This study, which tracked individuals and families over generations, is now playing a role in follow-up studies on neurodegenerative disorders.[56] Given increased life expectancies and the looming threat of these debilitating diseases, it is timely and appropriate to implement a managed system which can help address the problem.[57] In addition to providing vital health information to providers, such patient-centered systems may also offer the potential to help identify and recruit candidates for clinical trials. The vaunted phrase “think globally but act locally” seems, particularly apt in terms of medical parlance and neighborhood screening centers represent an appropriate vehicle to help achieve this objective.[58]
Methods used
The author team conducted in-depth literature searches using Medline, PubMed, and SciFinder Scholar over the period 2013–2018. Search terms included remote monitoring, patient engagement, patient-provider programs, wellness, reimbursement programs, digital biomarkers, digital medicine, m-health, and e-health. Additional searches of industry reports, corporate announcements, and abstracts from conferences were also conducted.
Acknowledgments
The authors would like to thank William A. Jones for creating [Figure 1].
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References | |  |
1. | Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015;372:793-5. |
2. | Meister S, Deitersand W, Becker S. Digital health and digital biomarkers – Enabling value chains on health data. Curr Dir Biomed Eng 2016;2:577-81. |
3. | Giggins OM, Clay I, Walsh L. Physical activity monitoring in patients with neurological disorders: A review of novel body-worn devices. Digit Biomark 2017;1:14-42. |
4. | Steinberg D, Horwitz G, Zohar D. Building a business model in digital medicine. Nat Biotechnol 2015;33:910-20. |
5. | Chen CE, Chen CT, Hu J, Mehrotra A. Walk-in clinics versus physician offices and emergency rooms for urgent care and chronic disease management. Cochrane Database Syst Rev 2017;2:CD011774 |
6. | |
7. | |
8. | Do CB, Hinds DA, Francke U, Eriksson N. Comparison of family history and SNPs for predicting risk of complex disease. PLoS Genet 2012;8:e1002973. |
9. | Kühnemund M, Wei Q, Darai E, Wang Y, Hernández-Neuta I, Yang Z, et al. Targeted DNA sequencing and in situ mutation analysis using mobile phone microscopy. Nat Commun 2017;8:13913. |
10. | Lima DP, Diniz DG, Moimaz SA, Sumida DH, Okamoto AC. Saliva: Reflection of the body. Int J Infect Dis 2010;14:e184-8. |
11. | Farandos NM, Yetisen AK, Monteiro MJ, Lowe CR, Yun SH. Contact lens sensors in ocular diagnostics. Adv Healthc Mater 2015;4:792-810. |
12. | von Thun Und Hohenstein-Blaul N, Funke S, Grus FH. Tears as a source of biomarkers for ocular and systemic diseases. Exp Eye Res 2013;117:126-37. |
13. | Hagan S, Martin E, Enríquez-de-Salamanca A. Tear fluid biomarkers in ocular and systemic disease: Potential use for predictive, preventive and personalised medicine. EPMA J 2016;7:15. |
14. | Pieragostino D, Bucci S, Agnifili L, Fasanella V, D'Aguanno S, Mastropasqua A, et al. Differential protein expression in tears of patients with primary open angle and pseudoexfoliative glaucoma. Mol Biosyst 2012;8:1017-28. |
15. | Csősz É, Boross P, Csutak A, Berta A, Tóth F, Póliska S, et al. Quantitative analysis of proteins in the tear fluid of patients with diabetic retinopathy. J Proteomics 2012;75:2196-204. |
16. | Kukumberg P, Karlík M, Beňová-Liszeková D, Beño M, Pechan T, Farkaš R, et al. New perspectives in human tear analysis? Neuro Endocrinol Lett 2015;36:185-6. |
17. | Li B, Sheng M, Li J, Yan G, Lin A, Li M, et al. Tear proteomic analysis of sjögren syndrome patients with dry eye syndrome by two-dimensional-nano-liquid chromatography coupled with tandem mass spectrometry. Sci Rep 2014;4:5772. |
18. | Kalló G, Emri M, Varga Z, Ujhelyi B, Tőzsér J, Csutak A, et al. Changes in the chemical barrier composition of tears in Alzheimer's disease reveal potential tear diagnostic biomarkers. PLoS One 2016;11:e0158000. |
19. | Tang YM, Wang DG, Li J, Li XH, Wang Q, Liu N, et al. Relationships between micronutrient losses in sweat and blood pressure among heat-exposed steelworkers. Ind Health 2016;54:215-23. |
20. | Sinha R, Chen J, Amir A, Vogtmann E, Shi J, Inman KS, et al. Collecting fecal samples for microbiome analyses in epidemiology studies. Cancer Epidemiol Biomarkers Prev 2016;25:407-16. |
21. | Marsden J, Pickering D. Urine testing for diabetic analysis. Community Eye Health 2015;28:77. |
22. | Chiu L, Wong E, DeAngelis C, Chiu N, Lam H, McDonald R, et al. Use of urinary markers in cancer setting: A literature review. J Bone Oncol 2015;4:18-23. |
23. | Zambon CF, Basso D, Prayer-Galetti T, Navaglia F, Fasolo M, Fogar P, et al. Quantitative PSA mRNA determination in blood: A biochemical tool for scoring localized prostate cancer. Clin Biochem 2006;39:333-8. |
24. | O'Bryant SE, Mielke MM, Rissman RA, Lista S, Vanderstichele H, Zetterberg H, et al. Blood-based biomarkers in Alzheimer disease: Current state of the science and a novel collaborative paradigm for advancing from discovery to clinic. Alzheimers Dement 2017;13:45-58. |
25. | Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Doré V, et al. High performance plasma amyloid-β biomarkers for Alzheimer's disease. Nature 2018;554:249-54. |
26. | Shi M, Liu C, Cook TJ, Bullock KM, Zhao Y, Ginghina C, et al. Plasma exosomal α-synuclein is likely CNS-derived and increased in Parkinson's disease. Acta Neuropathol 2014;128:639-50. |
27. | Gabai A, Veritti D, Lanzetta P. Fundus autofluorescence applications in retinal imaging. Indian J Ophthalmol 2015;63:406-15.  [ PUBMED] [Full text] |
28. | Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, et al. Optical coherence tomography. Science 1991;254:1178-81. |
29. | Lim YY, Johnson LN, Fernandez BM, Santos CY, Maruff P, Snyder PJ. High-resolution retinal imaging in the identification of preclinical Alzheimer's disease. Alzheimers Demen 2015;11(7):158-9. |
30. | Bennett MG, Naranja RJ Jr. Getting nano tattoos right – A checklist of legal and ethical hurdles for an emerging nanomedical technology. Nanomedicine 2013;9:729-31. |
31. | Cash KJ, Clark HA. Nanosensors and nanomaterials for monitoring glucose in diabetes. Trends Mol Med 2010;16:584-93. |
32. | Gitlin M. Lithium side effects and toxicity: Prevalence and management strategies. Int J Bipolar Disord 2016;4:27. |
33. | Jones GB, Collins DS, Harrison MW, Thayagarjapuram NR, Wright JM. Personalizing drug delivery: Exploiting the subcutaneous revolution. Sci Transl Med 2017;9:eaaf9166. |
34. | Collins DS, Kourtis LC, Thyagarajapuram NR, Sirkar R, Kapur S, Harrison MW, et al. Optimizing the bioavailability of subcutaneously administered biotherapeutics through mechanochemical drivers. Pharm Res 2017;34:2000-11. |
35. | Wright JM, Jones GB. Developing the subcutaneous drug delivery route. Med Res Arch 2017;5(12). [doi: 10.18103/mra.v5i12]. |
36. | Adams JL, Dinesh K, Xiong M, Tarolli CG, Sharma S, Sheth N, et al. Multiple wearable sensors in Parkinson and huntington disease individuals: A pilot study in clinic and at home. Digit Biomark 2017;1:52-63. |
37. | Heldman DA, Harris DA, Felong T, Andrzejewski KL, Dorsey ER, Giuffrida JP, et al. Telehealth management of Parkinson's disease using wearable sensors: An exploratory study. Digit Biomark 2017;1:43-51. |
38. | Marx S, Respondek G, Stamelou M, Dowiasch S, Stoll J, Bremmer F, et al. Validation of mobile eye-tracking as novel and efficient means for differentiating progressive supranuclear palsy from Parkinson's disease. Front Behav Neurosci 2012;6:88. |
39. | Goodwin VA, Richards SH, Taylor RS, Taylor AH, Campbell JL. The effectiveness of exercise interventions for people with Parkinson's disease: A systematic review and meta-analysis. Mov Disord 2008;23:631-40. |
40. | Landau SM, Thomas BA, Thurfjell L, Schmidt M, Margolin R, Mintun M, et al. Amyloid PET imaging in Alzheimer's disease: A comparison of three radiotracers. Eur J Nucl Med Mol Imaging 2014;41:1398-407. |
41. | Maron JL, Jones GB. How sensors, devices, and biomarkers can transform precision medicine: Perspectives from a clinical and translational science institute. Clin Ther 2018;40:345-8. |
42. | Galvin J. The importance of family and caregiver in the care and management of people with Alzheimer's disease. Alzheimers Demen 2013;9(4):1-2. |
43. | |
44. | Li X, Dunn J, Salins D, Zhou G, Zhou W, Schüssler-Fiorenza Rose SM, et al. Digital health: Tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol 2017;15:e2001402. |
45. | Vashist SK, Schneider EM, Luong JH. Commercial smartphone-based devices and smart applications for personalized healthcare monitoring and management. Diagnostics (Basel) 2014;4:104-28. |
46. | Price ND, Magis AT, Earls JC, Glusman G, Levy R, Lausted C, et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol 2017;35:747-56. |
47. | |
48. | |
49. | Hurd MD, Martorell P, Langa KM. Monetary costs of dementia in the United States. N Engl J Med 2013;369:489-90. |
50. | Stella F, Radanovic M, Balthazar ML, Canineu PR, de Souza LC, Forlenza OV, et al. Neuropsychiatric symptoms in the prodromal stages of dementia. Curr Opin Psychiatry 2014;27:230-5. |
51. | |
52. | Kaur G, Smyth RL, Williamson P. Developing a survey of barriers and facilitators to recruitment in randomized controlled trials. Trials 2012;13:218. |
53. | Blumenthal D, Hsiao W. Lessons from the East – China's rapidly evolving health care system. N Engl J Med 2015;372:1281-5. |
54. | |
55. | Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham heart study and the epidemiology of cardiovascular disease: A historical perspective. Lancet 2014;383:999-1008. |
56. | Satizabal C, Beiser AS, Seshadri S. Incidence of dementia over three decades in the Framingham heart study. N Engl J Med 2016;375:93-4. |
57. | Jones GB, Wright JM. Network medicine: Harnessing the potential of digital biomarkers. J Network Med Target Ther 2017;1. [doi: 10.16966/jnmtt.e101]. |
58. | McLeroy KR, Norton BL, Kegler MC, Burdine JN, Sumaya CV. Community-based interventions. Am J Public Health 2003;93:529-33. |
[Figure 1]
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