|
 
 |
| ORIGINAL ARTICLE |
|
| Year : 2020 | Volume
: 16
| Issue : 3 | Page : 124-129 |
|
New age technology in promoting healthy aging
Soumya S Inamadar1, Ashish Goel1, SV Madhu2
1 Department of Medicine, UCMS and GTB Hospital, New Delhi, India
2 Department of Endocrinology and Metabolism, UCMS and GTB Hospital, New Delhi, India
| Date of Submission | 01-Jul-2020 |
| Date of Decision | 06-Aug-2020 |
| Date of Acceptance | 07-Sep-2020 |
| Date of Web Publication | 23-Feb-2021 |
Correspondence Address:
Dr. Ashish Goel
Department of General Medicine, University College of Medical Sciences and Guru Teg Bahadur Hospital, New Delhi - 110 095
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/jiag.jiag_9_20
Introduction: Digitalization of the world and the Internet of things have completely transformed social life and health care. Technologies such as artificial intelligence (AI), wearable and implantable devices, mobile applications, and the Internet have revolutionized health-care delivery. With the betterment of health care and control of infectious diseases, longevity has increased, resulting in the bane of chronic noncommunicable illnesses and comorbidities in the aging population. This study aims to review the existing literature on technology applications in health care and the scope for promoting healthy aging. Methods: An online search was done on electronic databases such as PubMed and Google in July 2019 using a combination of keywords mobile, Android, cellphone, artificial intelligence, machine learning, natural language processing, diabetes, blood sugar, hypertension, blood pressure, dyslipidemia, cognitive impairment, and falls. About 80,964 citations were found. On applying the filter title, 997 articles were obtained. In the title and abstract review, 15 unique articles were selected, by eliminating similar, duplicate, and nonrelevant articles. In addition, using cross-reference and Google search, ten articles were found. Discussion: Various applications of the technology in the management of noncommunicable diseases (NCDs) such as diabetes, hypertension, and obesity and geriatric syndromes such as falls and cognitive impairment have been classified into three sections: prevention and prediction; diagnosis and detection; and medication and management. The names of a few applications have been cited as examples. Conclusion: The new age technology has shown promise at various health-care intervention levels in a futuristic hope of taking medicine to the next level - Precision Medicine. The present work will allow physicians involved in older patients with NCDs and geriatric comorbidities to help them have an independent, comfortable, and functional aging.
Keywords: Artificial intelligence, healthy aging, mobile applications, noncommunicable disease, wearable devices
How to cite this article:
Inamadar SS, Goel A, Madhu S V. New age technology in promoting healthy aging. J Indian Acad Geriatr 2020;16:124-9 |
| Introduction | |  |
The advancements in technology over the last two decades have revolutionized human life like never before. With the digitalization of the world and the Internet of things, social life has been completely transformed. Technologies such as artificial intelligence (AI), wearable and implantable devices, mobile applications, and the Internet help us live more independent and efficient lives. AI is the simulation of human intelligence processes by machines, especially computer systems using learning, reasoning, and self-correction. AI involves (a) machine learning, the ability of the system to learn a task without a preexisting code by permutations and combinations of few thousand data points; (b) deep learning, the ability to analyze and interpret millions of data input using the artificial neural network which is an algorithm inspired by human brain; and (c) natural language processing, which includes the analysis of human voice or text, its syntax and semantics, and interpreting the same. The devices, both wearable, implantable, and mobile, have taken health-care facilities to become personalized medicine. Although the application of technology in health-care delivery has been slow, it is being foreseen to bring sweeping and far-reaching changes in the way we practice medicine shortly. In the not too distant future, we are likely to see medical decisions, practices, and products tailored to an individual's risks, behavior, and health status in what is being increasingly recognized as precision medicine.[1]
With the discovery of antibiotics, improvement in public hygiene, and advances in preventive medicine, we have been able to control infectious diseases and increase life expectancy considerably over the last century. Unfortunately, longevity has come with the bane of chronic noncommunicable illnesses and comorbidities in the aging population. In addition, with changes in social life, smaller family size and high aspiration of the younger generation leading to migration for better job opportunities have left back the older persons with loneliness and depression, reflected in the decreased daily living activity. All these together have led to an increased dependency ratio.
Noncommunicable diseases (NCD's) are the leading causes of death globally, killing more people each year than all other causes combined. Of 56.9 million global deaths in 2016, 40.5 million, or 71%, were due to NCDs. The four main NCDs are cardiovascular diseases, cancers, diabetes, and chronic lung diseases.[2] Diabetes is a leading NCD in India. The estimated number of people who have diabetes in India is 40.9, and it is expected to rise to 69. (million by 2025. Diabetes accounts for 1.09 lakh deaths in a year.[3] The risk factors for NCDs can be classified into two main groups. The behavioral risk factors include smoking, alcohol intake, and tobacco chewing. and the metabolic risk factors include high blood pressure (BP), hyperglycemia, obesity, and dyslipidemia. In terms of attributable deaths, the leading metabolic risk factor globally is elevated BP (to which 19% of global deaths are attributed), followed by overweight and obesity and raised blood glucose.[4]
The present work reviews the available literature on the current role and future impact of technology, including AI, wearable or implantable devices, mobile applications, and the Internet of healthy aging. This review has included the following NCDs and comorbidities, i.e., diabetes, hypertension, obesity, dyslipidemia, falls, and cognitive impairment. The present work will allow physicians involved in older patients with NCDs and geriatric comorbidities, to help them have an independent, comfortable, and functional aging.
| Methods | | |
A literature search was conducted in July 2019 on the electronic database PubMed, using combinations of keywords, mobile, android, cellphone, AI, machine learning, natural language processing, diabetes, blood sugar, hypertension, BP, dyslipidemia, cognitive impairment, and falls.
With the initial search, a total of 80,964 citations were found. Applying filters such as keywords appearing in the paper title, we were able to narrow down to 997 articles. The titles of these publications were then screened, and those found duplicate or those relating to identical (or very similar) technology applications were eliminated. The abstracts of the remaining 80 publications were then reviewed. A further 65 articles were eliminated because they were found irrelevant to our objective, were prospective studies with pending results, did not have a transparent methodology, or were repetitive, describing similar applications. Fifteen selected publications were reviewed to classify the approach in health care of the technology used for selected diseases. After reviewing the cross-references from these publications and after a limited Google search, we included an additional ten citations. The current analysis presents a summary of our findings from these articles. A flow diagram for the study has been presented in [Figure 1]
| Discussion | | |
The application of new technology portends a revolution in health-care delivery very shortly. The present review discusses the recent advances in technology in the care of diabetes, hypertension, obesity, falls, and cognitive impairment under three broad areas, (a) prevention and prediction; (b) detection and diagnosis; and (c) medication and management. A summary of the applications of technology in the healthcare of the elderly is provided in [Table 1].
Prevention and prediction
Technology has become a formidable tool in making the unaffected understand the disease. Big data and gamification are two essential concepts applied in health care to improve understanding of the disease and human behavior.[5],[6] With advanced sensors and wearable devices involved in tracking and monitoring disease round the clock, we see a rapid inflow of enormous volumes of data never seen before. Data have become significant in terms of variety, value, and integrity. The arrival of big data has mandated newer approaches to analyze and address. AI has given wings to this analysis approach. Gamification is the science of using game mechanics and principles in a nongame context. Gamification works on three principles, building game mechanics, observing player behavior dynamics, and emotional responses. It can encourage a healthy lifestyle based on positive reinforcement at the same time, allow behavior change through negative feedback.
Several dynamic applications have been seen working on preventing and predicting disease in unaffected individuals in the community. A few exciting advances are discussed here.
Application of gamification principles for incurring behavioral changes
There are mobile applications that create games designed to change behaviors to develop novice habits that will help them improve their health and live longer. These games provide peer-to-peer networking, a useful way to provide the necessary social encouragement to keep patients on the right track. By using a combination of emotional connection-psychology, behavioral economics, and playful design, applications such as Ayogo design games that are interesting to play and at the same time engages patients, so they make better decisions about their health.[7] Ayogo has partnered with several pharmaceutical companies to design different games. One such example is type 2 travelers intended for the newly detected type 2 diabetes patients. Picture it! Is another such app intended for obese patients?[8]
Android applications to create awareness of dementia
There are applications which can put the user in the shoes of someone with dementia. These are designed to help the public think beyond memory loss to gain a fully immersive insight into the varied symptoms people with dementia can experience in everyday life. Walkthrough dementia is one such game which features three scenarios, buying ingredients in the supermarket, going home, and making a cup of tea.[9] Walkthrough dementia, virtual reality is a freely available application in the Google Play store.[10]
Wearable devices to prevent falls
There are devices which have been designed to avoid falls. Walk Joy is one such device which has to be worn over the knees.[11] It replaces the foot's lost sensation, striking the ground by providing a signal to healthy nerves around the knee, and enables the brain to respond as if there is no loss of sensation in the feet. It contains a small computer and sensors that measure the angle and speed during the leg movement, a vibrating or buzzing sensation to the nerves below the knee as the foot strikes the floor. This buzzing sensation replaces the pressure feeling and helps in synchronized walking and prevents falls. The device costs about 14 lakh rupees.
Application of artificial intelligence in the prediction of dementia
A deep learning algorithm has been developed for early prediction of Alzheimer's disease with 82% specificity at 100% sensitivity by Ding et al.[12] This helps in early prediction in the patients with a family history or risk factors favoring the onset of dementia using fluorodeoxyglucose - positron emission tomography and, hence, helps prevent and care.
Detection and diagnosis
Wearable devices for blood pressure monitoring
For ambulatory BP monitoring and early detection of hypertension, many wearable devices have been designed. Withings wifi BP application is one such FDA-approved high accuracy BP application.[13] It is easy to launch, and it provides instant color-coded results. Data from every measurement automatically appear in the Nokia Health Mate app (supported by iOS8+ and Android 5+). These data can be easily shared as a weeklong/month-long report to the health-care providers. The device costs about 15 thousand rupees.
A wearable device for recording vital parameters
There are various devices designed to keep track of critical parameters. Viacom Chekme is an FDA-approved, most versatile, portable health recorder.[14],[15] It functions as a health tracker (just like a wearable sensor). It records electrocardiogram, measures blood oxygen saturation, the number of steps taken in a day, and serves as a thermometer, a BP tracker, and sleep monitor and a reminder. It enables wireless transmission of the data to health-care providers and helps in quick decision making. It costs around 10,000 Rs. MC10 BioStamp point is a wearable health technology made up of discreet sensors that can be applied anywhere on the body for targeted data collection.[16],[17] It can help monitor sleep, posture, activity, and vital signs. The collected information can be shared with mobile phones and personal computers and data analyzed. It is FDA approved and has been primarily used in research trials and carries a potential role in nonresearch health care.
Mobile applications for cognitive impairment
There is an app-based game for neurocognitive assessment and therapy.[18] Akili Evo is one such app-based game, which aids in detecting Alzheimer's disease.[19] The game involves steering the alien through the stream in the presence of distractions like fish or birds. The performance data are compared to the standards.
Application of artificial intelligence in the assessment of cognitive impairment
There is a considerable change in voice tone, form, and intensity in patients with cognitive impairment. Praat Voice toolkit is a toolkit to analyze phonetics by computers.[20] This tool helps in the detection of the same. It can differentiate between subjective CI is cognitive impairment, mild CI, and Alzheimer's disease.
Deep learning in detecting diabetic retinopathy
With the increasing prevalence of diabetes and associated retinal complications throughout the world, manual methods of diagnosis cannot keep pace with the demand for screening services. This gap has now been filled with the application of AI.[21] A convolution neural network processes an input image with a defined weight matrix to extract specific image features without losing spatial arrangement information and classifying into different DR stages.
Wearable devices to detect falls
Wearable devices are designed to see falls and send alerts to the caregivers. Task fall detector is a wearable smart device with a help call button and an automatic fall detector.[22] When a significant impact occurs, the fall detector will start analyzing the fall. Before the alert is transmitted, a vibration prealarm starts. During this time, false alarms can be canceled by moving the arm. The device can also avoid any false alarm by analyzing the fall and detecting if it should send an alarm or not. The automatic trigger gets activated if the person wearing the detector cannot push the button, for example, in case of unconsciousness or immobility.
Medication and management
Mobile applications delivering health information and reminders
There are 500 and above applications for diabetes alone, which are meant to provide diabetes-related information, advise medication adherence, and record the medical data. One such application is Glucose Buddy.[23] This is available freely on Google play stores. This acts as a logbook for the patients where they can enter their investigation reports. The application analyzes the data and depicts it in the form of graphs, which can be printed out before a hospital visit and helps the treating physician make a quick decision on your health status.
Wearable sensors for real-time monitoring
There are wearable biosensors which continuously monitor vitals in real time such as electrocardiogram, heart rate, respiratory rate, body temperature, body posture, fall detection, and activity detection. HealthPatch is a wearable sensor which measures heart rate variability in response to hypoglycemia and sets in an alarm in the system to which it is connected through wifi/bluetooth and prevents complications.[24]
Mobile applications to maintain health record and analysis
There are applications such as MI-BP which are designed for the care of hypertensive patients.[25] MI-BP includes the following components: (1) Home BP monitoring (2) physical activity monitoring (3) sodium intake self-monitoring (4) physical activity and sodium intake goal setting (5) educational messaging through push notification and in-app messaging (6) motivational messaging (7) tailored messaging relevant to individual participants, and (8) medication reminders.
Mobile applications help weight reduction for obese patients
Some applications act as a virtual guide in training for adequate exercise and weight reduction in obese individuals. Fat burner deluxe is one such application that decides the limit of exercise per day by the smartphone's accelerometer. It also provides dietetic advice.[26]
Remote monitoring using artificial intelligence
AI has made possible remote monitoring of patients with dementia. Sensors are linked to objects in the patient's living space such as a pillbox, refrigerators, dining table, and cupboard. The sensor detects each item's use, e.g., pill-taking, and sends the information to the caretaker/receiver sitting at a different place and enables them to monitor the activity of their dear ones. My lively is one such example.[27]
Mobile applications to assist patients with cognitive impairment
Simple and easy to use mobile applications have been designed for the patients of Alzheimer's disease, which help them remember the events/person and let them stay connected and engaged with friends and family. These also slow down memory impairment. Timeless is one such mobile application.[28]
Applications to prevent falls
There is app-based training for the elderly to maintain balance in different postures. Nymbl science.[29] It is one such application which includes a set of exercise or training postures which can be practiced anywhere. It helps in reducing the risk of falling.
A wearable device for tracking patients with dementia
There are hidden wearable trackers like Smart Sole in the individuals' shoes. It helps in monitoring the movement of patients with dementia and easy tracing out when missing.[30]
Continuous glucose monitoring and drug delivery
It is a small device that is worn just under the skin. It measures glucose (sugar) levels continuously throughout the day and night, letting us see trends in sugar levels and alerts to highs and lows. Conventional continuous glucose monitoring (CGM's) are of two types: (1) real-time CGM, where sugar levels can be obtained at any time and (2) intermittently scanned CGM, where sugar levels can be available only after a specific duration of monitoring. CGM has got three components. They are (1) sensors, which can of glucose-oxidase type, fluorescence type, or biosensor type; (2) transmitter and; (3) display device. The physician or patient himself adjusts insulin dose after seeing the display device. CGM has made it possible to ensure strict glycemic control without landing into complications of hypoglycemia.[31]
Artificial pancreas/closed-loop insulin delivery system
Using AI, the loop of CGM and patient/doctor in insulin delivery has been closed by adding a control algorithm. The control algorithm receives information from the CGM and performs a series of mathematical calculations and sends input to the insulin delivery pump to release a specific dose of insulin. This innovation has eliminated human intervention, making it a self-acting, just like the human pancreas.[32]
Strengths and limitations
The present review is a reasonable attempt to look at the implications of technology on health-care systems and focused manner without getting lost in the broad spectrum of disease. We have restricted ourselves to cover only hypertension, diabetes, obesity, cognitive impairment, and falls in this review. Since the field is transforming so rapidly, any attempt to review technologial advances would be come outdated, even before it is completed. While we have made every attempt to include the latest and the most impressive technological advances, we cannot pretend that the present review would be comprehensive or complete. The current study also does not address some other important diseases such as cardiovascular diseases, stroke, and chronic respiratory disorders.
| Conclusion | | |
The new age technology has shown promise at various health-care intervention levels in a futuristic hope of taking medicine to the next level - Precision medicine. With further advances, patients shall receive treatment for life-threatening situations even before the onset of symptoms. In the coming days, as concerns regarding privacy and security, and trust will get resolved, we will likely see more and more translation of technology from our handheld devices to bedside patient care. Increased disease diagnostic efficiency, positive lifestyle and behavior changes, reduced hospital visits, electronic data monitoring, and integration, are about to bring a revolution to health care like never seen before in human history.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Genetics Home Reference, Your Guide to Understanding Genetic Conditions. Genetics Home Reference; 2019. Available from: https://www.ghr.nlm.nih.gov..[Last accessed on 2019 Sep 05].  |
| 2. | |
| 3. | |
| 4. | GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016;388:1659-724. |
| 5. | |
| 6. | |
| 7. | Ayogo Experts in Patient-perceived Self-efficacy. Ayogo Health Inc.; 2019. Available from: https://www.ayogo.com..[Last accessed on 2019 Sep 05]. |
| 8. | Pereira P, Duarte E, Rebelo F, Noriega P. A Review of Gamification for Health-related Contexts. Springer, Cham: International Conference of Design, User Experience, and Usability; 2014. p. 742-53. |
| 9. | Wijma EM, Veerbeek MA, Prins M, Pot AM, Willemse BM. A virtual reality intervention to improve the understanding and empathy for people with dementia in informal caregivers: A pilot study results. Ageing Ment Health 2018;22:1121-9. |
| 10. | |
| 11. | |
| 12. | Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology 2018;290:456-64. |
| 13. | Goldberg EM, Levy PD. New approaches to evaluating and monitoring blood pressure. Curr Hypertens Rep 2016;18:49. |
| 14. | |
| 15. | Mesko B. Digital health technologies to support human missions to mars. New Space 2018;6:109-16. |
| 16. | Wearable Healthcare Technology and Devices MC10; 2019. Available from: https://www.mc10inc.com..[Last accessed on 2019 Sep 05]. |
| 17. | Sen-Gupta E, Wright DE, Caccese JW, Wright JA Jr., Jortberg E, Bhatkar V, et al. A pivotal study to validate the performance of a novel wearable sensor and system for biometric monitoring in clinical and remote environments. Digit Biomark 2019;3:1-3. |
| 18. | Klimova B. Mobile phone apps in the management and assessment of mild cognitive impairment and/or mild-to-moderate dementia: An opinion article on recent findings. Front Hum Neurosci 2017;11:461. |
| 19. | |
| 20. | Satt A, Hooray R, König A, Aalten P, Robert PH. Speech-based Automatic and Robust Detection of Very Early Dementia. In: 5 th Annual Conference of the International Speech Communication Association; 2014. |
| 21. | Dagliati A, Marini S, Sacchi L, Cogni G, Teliti M, Tibollo V, et al. Machine learning methods to predict diabetes complications. J Diabetes Sci Technol 2018;12:295-302. |
| 22. | |
| 23. | |
| 24. | |
| 25. | Buis LR, Dawood K, Kadri R, Dawood R, Richardson CR, Djuric Z, et al. Improving blood pressure among African Americans with hypertension using a mobile health approach (the MI-BP App): Protocol for a randomized controlled trial. JMIR Res Protoc 2019;8:e12601. |
| 26. | |
| 27. | |
| 28. | Sisson S, George K. Programmable Errorless Face-Name Association Device with Real-Time Processing. In: 9 th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference; 2018. p. 82-8. |
| 29. | |
| 30. | Kasliwal MH, Patil HY. Smart Location Tracking System for Dementia Patients. In: 2017 International Conference on Advances in Computing, Communication and Control; 2017. p. 1-6. |
| 31. | Schwartz, FL, Marling CR, Bunescu RC. The promise and perils of wearable physiological sensors for diabetes management. J Diabetes Sci Technol 2018;12:587-91. |
| 32. | Blauw H, Keith-Hynes P, Koops R, DeVries JH. A review of the safety and design requirements of the artificial pancreas. Ann Biomed Eng 2016;44:3158-72. |
[Figure 1]
[Table 1]
|
Search Pubmed for