Providing nutrition tailored to the specific needs in early life can be complex but with the use of digital technology, it can greatly benefit research by improving accuracy, providing new insights and tailored interventions.
With the global increase in smartphone and Internet penetration online health information as well as investment and development of digital healthcare technologies, the tech-enabled healthcare (TEC) sector is growing at an exponential rate. In Asia, the value of the mobile health (mHealth) market which encompasses all health services that uses mobile network, is estimated to be worth US$6.4 billion this year. There is consensus that digital healthcare transformation can alleviate acute resource and manpower shortages needed to tackle the rising disease burden. One potential application of digital technology is in paediatric healthcare, where the right nutritional interventions in early life can help improve health outcomes in later life stages.
A growing body of research has shown that various diseases such as allergies are linked to imbalance of gut microbiota, or ‘dysbiosis’ that may originate from pregnancy and continue through the first 1,000 days. Experts have concluded that nutritional interventions promoting healthy microbiome environment in the 1,000-day window is critical in disease prevention.
Despite the positive effects of early life nutrition on the modulation of gut microbiota, the final impact on individual health is dependent on the inter-relationships between a large variety of genetic and environmental factors within the gut microbiota system, such as mode of delivery and antibiotic administration in babies. Thus, predicting the impact of nutrition on an individual’s health is a complex and multidisciplinary task.
Identifying and developing tailored nutritional solutions targeted to the specific needs in early life rely on a wealth of data and research, as well as close collaboration between healthcare professionals, researchers and parents. This article looks at how adopting digital technologies can help in research and development of early life nutritional solutions.
Digital technology enables the transmission of real-time data at greater convenience with little cost. Healthcare professionals and researchers can remotely capture, store and process large data samples and cut down significant time for data collection. Conventionally, babies’ data are collected over multiple clinic visits, but, with the usage of smart connected devices, it equips healthcare professionals and researchers with comprehensive data without multiple face-to-face consultations and may help prevent drop-outs in clinical trials and research.
According to a study published in the Journal of Pediatric Psychology, many participants in longitudinal studies involving infants often drop out mid-way due to inconvenience. Therefore, utilising wearable devices to capture data in research can provide convenience for parents due to user familiarity, given that close to 1 in 3 parents own wearable devices, thereby resulting in prolonged participation of clinical trials.
Digital technology can also gather clinical data about babies accurately. In many published studies involving babies, parents are often asked to judge and report the baby’s symptoms, which could be prone to inaccuracies as parental perceptions can vary. Wearable technology, on the other hand, can gather data free from bias. In a recent pilot study done in the United States on the usage of automated crying device, the device captures the natural sound environment of baby through an embedded audio recorder and is able to distinguish between crying and fussing with up to 90 per cent sensitivity and 91 per cent overall accuracy. This was achieved through the application of machine learning algorithms, which allows the device to reliably and objectively identify and quantify both crying and fussing.
Furthermore, the value of digital technology in research has been recognised by industry leaders. In the US, usage of digital technology in clinical trials has even gotten support from the Food and Drug Administration (FDA) as the technology offers, amongst other benefits, real-time monitoring ofclinical trials. In addition, 58 per cent of pharmaceutical industry executives also agree that digital technology adoption in clinical trials can generate better data.
The data captured by digital devices can complement data from new technologies such as DNA sequencing which has allowed researchers to determine the composition and functions of gut microbiome and its impact on overall health. Through computational systems biology, an integrative data approach, interactions between biochemical and metabolic pathways and external environment can be quantified and analysed. This aids nutritional research as the system can account for various biological feedback loops, which can be hard to analyse with human inference. As such, systems biology can be applied to generate novel scientific insights for complex and poorly understood conditions, such as infantile colic and allergy, which has multifactorial etiology.
In addition, wearable technology provides an avenue for researchers to constantly track health indicators during a child’s developmental stage by analysing their risk of developing health problems such as obesity, asthma and allergies later in life.
In clinical practices, the development of new analytical approaches in machine learning and artificial intelligence have increased the actionable insights we can derive from raw data. Machine learning algorithms can be designed to ‘learn’ from data points received and generate accurate predictions and analysis when validated. Such digital technological advances could potentially inform healthcare professionals and parents on babies’ health and nutritional needs by identifying novel correlations between clinical observations and health outcomes. For instance, a smart diaper is being tested to gather babies’ urination data, allowing parents to know when their babies are experiencing urinary tract infections or dehydration. The data will then be sent to a physician who would be able to make proper diagnosis on the baby’s health and nutritional needs.
In research, better understanding of the interplay between nutrition, genetics and environmental exposures, as well as the integration of human multiomic data with microbiome data can help researchers develop strategy for personalised nutrition. As research has shown that nutritional needs and responses to dietary intake may vary from person to person, it is vital for healthcare professionals to provide tailored nutritional recommendations to ensure optimal health. This can be facilitated by digital personalised care tools.
Personalised care is relevant in a parent’s daily life where they can seek quick diagnosis, treatment and reassurance through chatbots and telemedicine services to provide timely advise and reassurance while reducing hospital services usage. The use of the deep-learning and AI approaches to analyse patient interactions are leading to the development of increasingly sophisticated chatbots, which are able to provide parents with tailored advice based on past interactions.
In Singapore, virtual consultations are expected to translate to five times greater in cost savings for patients and their payers in the long-term. A study in Sweden on the usage of telemedicine to facilitate follow-ups for babies discharged from neonatal paediatric unit found that virtual consultation and a specially designed web page eliminated the need for parents to bring their children to the hospital since parents have access to timely advice. Soon enough, digital health platforms can be used to reassure distressed parents and help cut unnecessary clinic or hospital visits.
According to a study by the School of Nursing and Midwifery in Ireland, parents want to be involved in caring for their child but lack guidance from healthcare professionals. Digital technology can strengthen parentprovider relationships as it facilitates regular communication. For instance, in a Danish study assessing the usage of a smartphone app for new parents following postnatal discharge, nurses reported feeling that they could support at-home parents better through the provision of timely, professional information.
Parents are increasingly open to personalised virtual support. According to the Nemours Children’s Health System survey in the United States, 64 per cent of parents are interested in using digital health platforms for their children, while almost 75 per cent of them feel that virtual telemedicine services are better than in-person visits. In Asia, as more parents turn to online resources for parenting advise and support, there may be increasing adoption and usage of these services. One report exploring opportunities and sentiments towards technology adoption in healthcare revealed that 72 per cent of healthcare organisations are already using virtual assistants to improve patient engagement.
Further more, digital technology can aid the product innovation process by gathering real-time data from babies and parents, such as stool patterns. Accurate data of babies’ stool can help further validate current research on the impact of nutritional ingredients, such as synbiotics, in supporting gut health of young children.
Adopting digital technology in healthcare for early life nutrition benefits parents, researchers and healthcare professionals. It can increase engagement between parents and healthcare professionals and improve service satisfaction while also strengthening collaboration between multiple parties to provide babies with better nutrition in early life. Harnessing digital technology in research enables researchers to discover new biological mechanisms, predict the risks of certain diseases and eventually drive long-term nutritional solutions. It also provides an exciting platform for partnership between technology companies, healthcare professionals and researchers to come together and devise practical evidencebased nutritional solutions.
1. Poushter, J. (2016). Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies. Pew Research Center. Retrieved from http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/
2. Atluri, V., Cordina, J., Mango, P., & Velamoor, S. (2016). How tech-enabled consumers are reordering the healthcare landscape. Retrieved from https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/how-tech-enabled-consumers-are-reordering-the-healthcare-landscape
3. mHealth Alliance. Leveraging Mobile Technologies to Promote Maternal & Newborn Health: The Current Landscape & Opportunities for Advancement in Low-Resource Settings. mHealth Alliance. Retrieved from http://www.mhealthknowledge.org/sites/default/files/17_leveraging_mobile_technologies_to_promote_maternal_newborn_health.pdf
4. The Deloitte Centre for Health Solutions. (2015). How digital technology is transforming health and social care. Deloitte. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/life-sciences-health-care/deloitte-uk-connected-health.pdf
5. PricewaterhouseCoopers. (2017). The Digital Healthcare Leap. PricewaterhouseCoopers. Retrieved from https://www.pwc.com/gx/en/issues/high-growth-markets/assets/the-digital-healthcare-leap.pdf
6. Dietert, R. R., & Dietert, J. M. (2015, March). The microbiome and sustainable healthcare. In Healthcare (Vol. 3, No. 1, pp. 100-129). Multidisciplinary Digital Publishing Institute. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4934527/pdf/healthcare-03-00100.pdf
7. Martin, R., Makino, H., Yavuz, A. C., Ben-Amor, K., Roelofs, M., Ishikawa, E., ... & Kushiro, A. (2016). Early-life events, including mode of delivery and type of feeding, siblings and gender, shape the developing gut microbiota. PLoS One, 11(6), e0158498.
8. Munos, B., Baker, P. C., Bot, B. M., Crouthamel, M., Vries, G., Ferguson, I., ... & Ozcan, A. (2016). Mobile health: the power of wearables, sensors, and apps to transform clinical trials. Annals of 5 the New York Academy of Sciences, 1375(1), 3-18. Retrieved from https://nyaspubs.onlinelibrary.wiley.com/doi/full/10.1111/nyas.13117
9. Aylward, G. P. (2002). Methodological Issues in Outcome Studies Of At-Risk Infants. Journal of Pediatric Psychology, 27(1), 37-45. Retrieved from https://academic.oup.com/jpepsy/article/27/1/37/883243
10. PricewaterhouseCoopers (2016). The Wearable Life 2.0 Connected living in a wearable world. Consumer Intelligence Series. PricewaterhouseCoopers. Retrieved from https://www.pwc.com/ee/et/publications/pub/pwc-cis-wearables.pdf
11. Stifter, C. A., Bono, M., & Spinrad, T. (2003). Parent Characteristics and Conceptualizations Associated With The Emergence of Infant Colic. Journal of Reproductive and Infant Psychology, 21(4), 309-322. Retrieved from https://pdfs.semanticscholar.org/b198/2150972be379ade7e552cce27b31ee80814b.pdf
12. Ludwig, T., Richards, J.A., Coulter, K.K, Roy, P., Foussat, A.C., Hannon, S.M. (2018, May). Automated detection of infant crying and fussing for clinical applications. Paper presented at the European Society for Paediatric Gastroenterology Hepatology and Nutrition Conference, Geneva, Switzerland.
13. Admati, C., Dolan, Y. and McManus, M. (2017). AI and Wearables Bring New Data and Analytics to Clinical Trials. Solutions Brief. Intel. Retrieved from https://www.intel.com/content/dam/www/public/us/en/documents/solution-briefs/ai-and-wearables-bring-new-data-and-analytics-to-clinical-trials-solution-brief.pdf
14. Arnold, J. W., Roach, J., & Azcarate-Peril, M. A. (2016). Emerging technologies for gut microbiome research. Trends in microbiology, 24(11), 887-901. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5074899/
15. Mc Auley, M. T., Proctor, C. J., Corfe, B. M., Cuskelly, G. J., & Mooney, K. M. (2013). Nutrition Research and The Impact of Computational Systems Biology. Journal of Computer Science & Systems Biology 6: 271-285., 6(5), 271-285. Retrieved from https://www.omicsonline.org/nutrition-research-and-the-impact-of-computational-systems-biology-jcsb.1000122.php?aid=19492
16. Tintore, M., Colome, G., Santas, J., & Espadaler, J. (2017). Gut Microbiota Dysbiosis and Role of Probiotics in Infant Colic. Archives of Clinical Microbiology, 8(4). Retrieved from http://www.acmicrob.com/microbiology/gut-microbiota-dysbiosis-and-roleof-probiotics-in-infant-colic.php?aid=19957
17. Haghi, M., Thurow, K., & Stoll, R. (2017). Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices. Healthcare Informatics Research, 23(1), 4–15. http://doi.org/10.4258/hir.2017.23.1.4
18. Luo, G. (2015). MLBCD: A Machine Learning Tool for Big Clinical Data. Health Information Science and Systems, 3(1), 3. Retrieved from https://link.springer.com/article/10.1186%2Fs13755-015-0011-0
19. Stern, J. (2013). Smart Diapers Work With a Smartphone. [online] ABC News. Retrieved from https://abcnews.go.com/Technology/smart-diapers-aim-gather-urine-data-provide-child/story?id=19620280
20. Brunkwall, L., & Orho-Melander, M. (2017). The gut microbiome as a target for prevention and treatment of hyperglycaemia in type 2 diabetes: from current human evidence to future possibilities. Diabetologia, 60(6), 943–951. http://doi.org/10.1007/s00125-017-4278-36
21. Ferguson, L. R., De Caterina, R., Görman, U., Allayee, H., Kohlmeier, M., Prasad, C., ... & Kang, J. X. (2016). Guide and position of the international society of nutrigenetics/nutrigenomics on personalised nutrition: part 1-fields of precision nutrition. Journal of Nutrigenetics and Nutrigenomics, 9(1), 12-27.
22. Anderson, D., Fox, J. and Elsner, N. (2018). Digital R&D: Transforming the future of clinical development. Deloitte Insights. Retrieved from https://www2.deloitte.com/insights/us/en/industry/life-sciences/digital-research-and-development-clinical-strategy.html
23. Robinson, C., Gund, A., Sjöqvist, B. A., & Bry, K. (2016). Using Telemedicine in The Care Of Newborn Infants After Discharge From A Neonatal Intensive Care Unit Reduced The Need Of Hospital Visits. Acta Paediatrica, 105(8), 902-909. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5074256/
24. Maruti Techlabs. (2018). Is Conversational AI the future of Healthcare?. Chatbots Magazine. Retrieved from https://chatbotsmagazine.com/is-conversational-ai-the-future-of-healthcare-658a3d8e9dd5
25. PricewaterhouseCoopers & National University of Singapore (2017). Digital health: Challenges and solutions to measuring Return on Investment (ROI). PricewaterhouseCoopers. Retrieved from https://www.pwc.com/sg/en/publications/assets/digital-health-roi-2017.pdf
26. Yager, P. H., Clark, M., Cummings, B. M., & Noviski, N. (2017). Parent Participation in Pediatric Intensive Care Unit Rounds via Telemedicine: Feasibility and Impact. The Journal of Pediatrics, 185, 181-186. Retrieved from http://www.jpeds.com/article/S0022-3476(17)30320-7/pdf
27. Coyne, I. (2013). Families and Health?Care Professionals' Perspectives And Expectations of Family?Centred Care: Hidden Expectations And Unclear Roles. Health Expectations, 18(5), 796-808. Retrieved from https://onlinelibrary.wiley.com/doi/epdf/10.1111/hex.12104
28. Danbjørg, D. B., Wagner, L., Kristensen, B. R., & Clemensen, J. (2015). Nurses’ Experience Of Using An Application To Support New Parents After Early Discharge: An Intervention Study. International Journal of Telemedicine And Applications, 2015, 4. Retrieved from https://www.hindawi.com/journals/ijta/2015/851803/
29. Wicklund, E. (2017). ATA Survey: Parents Like Telehealth for Primary Care Needs. mHealthIntelligence. Retrieved from https://mhealthintelligence.com/news/ata-survey-parents-like-telehealth-for-primary-care-needs
30. The Asian Parent. (n.d.). Asian Digital Mum Survey 2015. Retrieved from https://www.digitalnewsasia.com/sites/default/files/files_upload/Asian%20Digital%20Mum%20Survey%202015%20-%2019%20Mar%202015.pdf
31. Accenture (2017). Digital Health Technology Vision 2017. [online] Accenture. Retrieved from https://www.accenture.com/t20171213T060208Z__w__/us-en/_acnmedia/PDF-49/Accenture-Digital-Health-Technology-Vision-2017.pdf
32. Kosuwon, P., Lao-Araya, M., Uthaisangsook, S., Lay, C., Bindels, J., Knol, J., & Chatchatee, P. (2018). A synbiotic mixture of scGOS/lcFOS and Bifidobacterium breve M-16V increases faecal Bifidobacterium in healthy young children. Beneficial Microbes, 1-12.