XP Health

xphealth

How Society is Failing Young Professionals: Workplace Stress and the Mental Health Crisis

Is Artificial Intelligence in Healthcare a Ticking Time Bomb or Timely Solution?

Synthetic Data And Its Potential In Healthcare

البيانات الاصطناعية وإمكاناتها في مجال الرعاية الصحية

Сінтэтычныя дадзеныя і іх патэнцыял у ахове здароўя

সিন্থেটিক ডেটা এবং স্বাস্থ্যসেবায় এর সম্ভাব্যতা

Dades sintètiques i el seu potencial en l'assistència sanitària

Los datos sintéticos y su potencial en la atención sanitaria

合成データと医療におけるその可能性

Синтетикалык маалыматтар жана анын саламаттыкты сактоодогу потенциалы

Sintētiskie dati un to potenciāls veselības aprūpē

Синтетик өгөгдөл ба түүний эрүүл мэнд дэх боломж

Datha ya Maitirelo Le Bokgoni bja Yona Tlhokomelong ya Maphelo

Маълумоти синтетикӣ ва потенсиали он дар соҳаи тандурустӣ

Idatha yokwenziwa kunye nokubanakho kwayo kukhathalelo lwempilo

Amazon has been salivating at the multitrillion-dollar United States healthcare market since 2018. Although Amazon Health played a vital role in public health during the pandemic, most still don’t view it as a disruptor. A new chapter in the American conglomerate’s move into the industry is upon us — Health Condition Programs. How does this initiative fit into the tech titan’s grand healthcare plan? It’s anybody’s guess. I can only speculate about the countless scenarios surrounding the interplay between Health Condition Programs and Amazon’s diverse solutions. However, the real question we should all be asking is — will it buck the trend of the organization’s cybersecurity, patient safety, and privacy scandals? A Treasure Trove of Patient Data On paper, Health Condition Programs aim to encourage millions of Americans to maximize their unused healthcare features. This mission has merit because over one-third of workers don’t fully understand the employee benefits they signed up for. Many American adults might be unaware that they can receive care to manage chronic conditions — such as diabetes and hypertension — between doctor visits at no extra cost to them. Enrolling in these programs allows insured individuals to maximize their coverage and squeeze more value out of the rising premiums or contributions they shoulder. In practice, Health Condition Programs enable the Andy Jassy-led all-encompassing empire to grow its patient data repository by tens of millions. This offering’s coverage check involves eligibility verification, necessitating personal data collection. Enrollees must share their legal name, date of birth, employer’s name, insurer's name, email address, and other sensitive information. This will determine whether their insurance plan or employee benefits cover health programs Amazon’s partners offer. After confirming an applicant is covered, Amazon Health directs the patient to third-party digital health companies — the actual virtual care providers — to complete registration and program enrollment. While Amazon may say it stops collecting personal data after a coverage check, it may still follow patients beyond that point. This Big Tech firm is a known participant in server-to-server tracking, so I would be more surprised if it doesn’t monitor the rest of an enrollee’s telehealth journey through a backdoor. Connected devices are notable program features that covered individuals can access. Continuous glucose monitor sensors, blood sugar meters, blood pressure monitors, and smart scales generate personalized quantitative data that forms a part of medical records and is personal. This is protected health information tech conglomerates could leverage to enrich their subsidiaries. A Slowly Expanding Network As disturbing as it is to think that Amazon could acquire bits and pieces of millions of medical records under the table through its Health Condition Programs, this multinational’s reach is proportional to its network’s size. As of October 2024, only two digital health companies have participated — Omada Health and Talkspace. Omada specializes in diabetes prevention, weight management, and hypertension care, while Talkspace delivers virtual therapy and psychiatric care. Between the two, insured Americans can choose from only three programs. It’s unclear why Amazon Health could only ink two partnerships in nine months — a departure from the online retail behemoth’s “Get Big Fast” mantra. I bet telehealth providers would move mountains to be in this exclusive club, so Amazon must have been overly selective about its partners for reasons unknown. Only time will tell if Amazon will bring more digital health companies into its fold sooner rather than later. A safe guess is that it would prioritize partner diversity to prevent cannibalization and harvest data with variety. A New Piece to the Amazon Health Care Strategy Puzzle Amazon plays the long game. For all we know, it might envision itself as the savior of health care — the ultimate medical data synthesizer the sector sorely needs. It’s impossible to connect all the dots when many of them haven’t come to light. Still, recognizing this offering’s potential value to this tech colossus isn’t brain surgery. If Health Condition Programs gain currency and the number of participating digital health companies on the network balloons, the company will better understand our health needs. These insights can be the rising tide that lifts the boats in the Amazon Health sub-ecosystem and other related subsidiaries. A quick thought experiment would reveal that more program enrollees could increase Amazon One Medical’s in-person visits. Health Condition Programs almost always require a prior medical diagnosis. Health care professionals on the network — health coaches, licensed therapists and psychiatrists, nutritionists, dieticians, and personal care specialists — could help train AWS HealthScribe with large volumes of recordings. The aim would be to churn out error-free transcripts and clinical notes consistently. The company could expand Alexa’s footprint by integrating its partners’ services into the voice assistant. It’s a tried-and-true move, as evidenced by the partnership between Amazon and Teladoc Health in 2022. Prescriptions from digital health companies can also boost Amazon Pharmacy’s sales. Analyzing mountains of medical data can shed light on the usual indicators of events that can endanger patients’ lives. Tech-forward hospitals have taken this route. They proved it’s feasible to predict signs of upcoming acute events — which increase the chances of patient mortality by three to four times — early enough to address them before they happen. Amazon can do the same thing for people living with chronic diseases. It can mobilize Whole Foods to make healthier food options more widely available to those who need them most. Accessible healthy grocery items empower chronically ill individuals to meet their unique nutritional needs and keep symptoms manageable. Are Health Condition Programs a Scandal Waiting to Happen? Amazon’s venture into health care has been riddled with controversies. In 2023, hackers successfully victimized more than 19,000 PillPack customers, stealing their names, residential addresses, phone numbers, and email addresses. About 3,600 of the impacted accounts included prescription information. That same year, senators grilled the company for forcing patients to waive their right to health information privacy to enroll in Amazon Clinic. In mid-2024, Amazon found itself in hot water for allegedly compromising the safety of One Medical patients. Incidents of awful misjudgments by staff assisting customers by phone have risen since the subsidiary entrusted the job to inadequately trained agents. Amazon has failed to give a good account of itself in the sector on various occasions. At this rate, I feel like someone would expose a dubious practice underpinning Health Condition Programs any moment now. Can We Trust Amazon’s Health Condition Programs? Criticisms toward this initiative are unsubstantiated and premature. However, it would be naive to expect Amazon not to supercharge its health data collection prowess through coverage checks and its partners when the alternative does nothing to improve other parts of its business. We’ll just have to wait and see whether this Amazon Health offering’s practical benefits outweigh its perceived privacy drawbacks — but let’s not hold our breaths. Amazon has been salivating at the multitrillion-dollar United States healthcare market since 2018. Although Amazon Health played a vital role in public health during the pandemic, most still don’t view it as a disruptor. A new chapter in the American conglomerate’s move into the industry is upon us — Health Condition Programs. How does this initiative fit into the tech titan’s grand healthcare plan? It’s anybody’s guess. I can only speculate about the countless scenarios surrounding the interplay between Health Condition Programs and Amazon’s diverse solutions. However, the real question we should all be asking is — will it buck the trend of the organization’s cybersecurity, patient safety, and privacy scandals? A Treasure Trove of Patient Data On paper, Health Condition Programs aim to encourage millions of Americans to maximize their unused healthcare features. This mission has merit because over one-third of workers don’t fully understand the employee benefits they signed up for. one-third of workers don’t fully understand one-third of workers don’t fully understand Many American adults might be unaware that they can receive care to manage chronic conditions — such as diabetes and hypertension — between doctor visits at no extra cost to them. Enrolling in these programs allows insured individuals to maximize their coverage and squeeze more value out of the rising premiums or contributions they shoulder. In practice, Health Condition Programs enable the Andy Jassy-led all-encompassing empire to grow its patient data repository by tens of millions. This offering’s coverage check involves eligibility verification, necessitating personal data collection. Enrollees must share their legal name, date of birth, employer’s name, insurer's name, email address, and other sensitive information. This will determine whether their insurance plan or employee benefits cover health programs Amazon’s partners offer. After confirming an applicant is covered, Amazon Health directs the patient to third-party digital health companies — the actual virtual care providers — to complete registration and program enrollment. While Amazon may say it stops collecting personal data after a coverage check, it may still follow patients beyond that point. This Big Tech firm is a known participant in server-to-server tracking , so I would be more surprised if it doesn’t monitor the rest of an enrollee’s telehealth journey through a backdoor. a known participant in server-to-server tracking a known participant in server-to-server tracking Connected devices are notable program features that covered individuals can access. Continuous glucose monitor sensors, blood sugar meters, blood pressure monitors, and smart scales generate personalized quantitative data that forms a part of medical records and is personal. This is protected health information tech conglomerates could leverage to enrich their subsidiaries. forms a part of medical records forms a part of medical records A Slowly Expanding Network As disturbing as it is to think that Amazon could acquire bits and pieces of millions of medical records under the table through its Health Condition Programs, this multinational’s reach is proportional to its network’s size. As of October 2024, only two digital health companies have participated — Omada Health and Talkspace. Omada specializes in diabetes prevention, weight management, and hypertension care, while Talkspace delivers virtual therapy and psychiatric care. Between the two, insured Americans can choose from only three programs. It’s unclear why Amazon Health could only ink two partnerships in nine months — a departure from the online retail behemoth’s “Get Big Fast” mantra. I bet telehealth providers would move mountains to be in this exclusive club, so Amazon must have been overly selective about its partners for reasons unknown. Only time will tell if Amazon will bring more digital health companies into its fold sooner rather than later. A safe guess is that it would prioritize partner diversity to prevent cannibalization and harvest data with variety. A New Piece to the Amazon Health Care Strategy Puzzle Amazon plays the long game. For all we know, it might envision itself as the savior of health care — the ultimate medical data synthesizer the sector sorely needs. It’s impossible to connect all the dots when many of them haven’t come to light. Still, recognizing this offering’s potential value to this tech colossus isn’t brain surgery. If Health Condition Programs gain currency and the number of participating digital health companies on the network balloons, the company will better understand our health needs. These insights can be the rising tide that lifts the boats in the Amazon Health sub-ecosystem and other related subsidiaries. A quick thought experiment would reveal that more program enrollees could increase Amazon One Medical’s in-person visits. Health Condition Programs almost always require a prior medical diagnosis. Health care professionals on the network — health coaches, licensed therapists and psychiatrists, nutritionists, dieticians, and personal care specialists — could help train AWS HealthScribe with large volumes of recordings. The aim would be to churn out error-free transcripts and clinical notes consistently. The company could expand Alexa’s footprint by integrating its partners’ services into the voice assistant. It’s a tried-and-true move, as evidenced by the partnership between Amazon and Teladoc Health in 2022. Prescriptions from digital health companies can also boost Amazon Pharmacy’s sales. partnership between Amazon and Teladoc Health partnership between Amazon and Teladoc Health Analyzing mountains of medical data can shed light on the usual indicators of events that can endanger patients’ lives. Tech-forward hospitals have taken this route. They proved it’s feasible to predict signs of upcoming acute events — which increase the chances of patient mortality by three to four times — early enough to address them before they happen. by three to four times by three to four times Amazon can do the same thing for people living with chronic diseases. It can mobilize Whole Foods to make healthier food options more widely available to those who need them most. Accessible healthy grocery items empower chronically ill individuals to meet their unique nutritional needs and keep symptoms manageable. Are Health Condition Programs a Scandal Waiting to Happen? Amazon’s venture into health care has been riddled with controversies. In 2023, hackers successfully victimized more than 19,000 PillPack customers, stealing their names, residential addresses, phone numbers, and email addresses. About 3,600 of the impacted accounts included prescription information. 3,600 of the impacted accounts 3,600 of the impacted accounts That same year, senators grilled the company for forcing patients to waive their right to health information privacy to enroll in Amazon Clinic. In mid-2024, Amazon found itself in hot water for allegedly compromising the safety of One Medical patients. Incidents of awful misjudgments by staff assisting customers by phone have risen since the subsidiary entrusted the job to inadequately trained agents. Amazon has failed to give a good account of itself in the sector on various occasions. At this rate, I feel like someone would expose a dubious practice underpinning Health Condition Programs any moment now. Can We Trust Amazon’s Health Condition Programs? Criticisms toward this initiative are unsubstantiated and premature. However, it would be naive to expect Amazon not to supercharge its health data collection prowess through coverage checks and its partners when the alternative does nothing to improve other parts of its business. We’ll just have to wait and see whether this Amazon Health offering’s practical benefits outweigh its perceived privacy drawbacks — but let’s not hold our breaths.

Did You Know About Amazon’s Health Condition Programs? What This Means for Users

Hey Hackers, Zapmii has been nominated in HackerNoon's annual Startups of The Year awards in Sutton, England, UK. Please vote for us here: https://hackernoon.com/startups/europe/europe-sutton-england-uk?stup=66f31c7f6ea4677e23dbed4c Read more about us below to understand why we deserve your vote. Meet Zapmii Zapmii: Innovating Digital Networking and Life-Saving Health Management. Zapmii bridges the gap between digital networking and personal health management with two innovative platforms: Zapmii Social and Zapmii Health. Through cutting-edge NFC technology, we empower professionals to share dynamic digital profiles with ease and offer families peace of mind by ensuring their loved ones’ health information and safety needs are securely managed. Whether connecting at events or safeguarding vulnerable individuals, Zapmii is the solution that puts people and their data first. How Zapmii achieved product-market fit We built Zapmii Social to address a critical need: modern, secure networking in a fast-paced, tech-driven world. Professionals attending events often struggle with outdated paper business cards and lack insights into how connections can lead to real business opportunities. Zapmii Social’s NFC cards and tags solve that, providing real-time engagement analytics so users can track who interacted with their profiles and build stronger, more meaningful connections. Zapmii Health emerged from a personal challenge experienced by one of our co-founders—having a non-verbal family member needing constant care and a way to share medical information quickly. Zapmii Health’s NFC devices allow individuals with critical health needs, such as non-verbal people, children, and the elderly, to share medical data instantly in emergencies. Additionally, caregivers are alerted if vulnerable loved ones leave a predefined safe zone, ensuring safety and peace of mind. Both platforms have already found their footing in two distinct but rapidly growing markets—business networking and personal health management—each worth billions globally. Our early adopters have demonstrated the tangible value these solutions bring, from streamlining professional connections to saving lives in emergencies. The evolution of Zapmii Zapmii’s journey began with Zapmii Social, which was launched three years ago as a response to the growing need for digital-first networking solutions. In the wake of the COVID-19 pandemic, professionals around the world were searching for a contactless, tech-savvy way to share business information. Since then, Zapmii Social has grown its user base by 30% year-over-year, with rave reviews about its ease of use, customization options, and data-driven insights. In 2023, we recognized an even larger, underserved market, families caring for vulnerable individuals, such as those with dementia, autism, or chronic illnesses. This insight led to the development and launch of Zapmii Health. The platform quickly gained traction, with numerous stories of how it has improved care management and even saved lives by alerting caregivers when loved ones strayed from safe zones. Milestones and achievements Won the 2024 Netty Awards for Best Up-and-Coming Consumer Tech Company. Lessons learned We’ve faced several challenges along the way, especially in convincing users to trust a new technology with their sensitive health and personal data. However, we learned that transparent communication about security and offering early product trials were key to gaining user trust. We also realized that our customers value control over their data, so we built features allowing users to determine exactly what information is shared and under which circumstances. Another lesson was that both health and professional markets require different communication strategies. Zapmii Social users are drawn to networking efficiency and personalization, whereas Zapmii Health users are concerned with data privacy and real-time alerts. Recognizing and adapting to these needs has been critical to our success. What Startups of The Year means to us Participating in HackerNoon’s Startups of the Year is more than just an honor for Zapmii, it’s a validation of our vision to make life both simpler and safer for millions of people around the world. This recognition will help us build credibility, especially in the competitive health-tech and networking-tech spaces, where trust is paramount. Additionally, this award will give us the visibility needed to attract key investors and partners who believe in our mission to improve people’s professional lives and safeguard health in emergencies. Conclusion Zapmii is ready to change both business networking and health safety with simple, secure, and life-saving tools. We've only just started, but the impact we've already made is clear, from helping people grow their businesses to saving lives with our instant alerts. As we keep innovating, we're ready to become a leader in both areas, making sure our customers can trust us with their most important things: their connections and their health. Vote for Zapmii today and help us keep changing and protecting lives! Vote for us today! Hey Hackers, Zapmii has been nominated in HackerNoon's annual Startups of The Year awards in Sutton, England, UK. Please vote for us here: Please vote for us here: https://hackernoon.com/startups/europe/europe-sutton-england-uk?stup=66f31c7f6ea4677e23dbed4c https://hackernoon.com/startups/europe/europe-sutton-england-uk?stup=66f31c7f6ea4677e23dbed4c Read more about us below to understand why we deserve your vote. Meet Zapmii Zapmii: Innovating Digital Networking and Life-Saving Health Management. Zapmii: Innovating Digital Networking and Life-Saving Health Management. Zapmii bridges the gap between digital networking and personal health management with two innovative platforms: Zapmii Social and Zapmii Health . Through cutting-edge NFC technology, we empower professionals to share dynamic digital profiles with ease and offer families peace of mind by ensuring their loved ones’ health information and safety needs are securely managed. Whether connecting at events or safeguarding vulnerable individuals, Zapmii is the solution that puts people and their data first. digital networking personal health management Zapmii Social Zapmii Health Zapmii is the solution that puts people and their data first. How Zapmii achieved product-market fit We built Zapmii Social to address a critical need: modern, secure networking in a fast-paced, tech-driven world . Professionals attending events often struggle with outdated paper business cards and lack insights into how connections can lead to real business opportunities. Zapmii Social’s NFC cards and tags solve that , providing real-time engagement analytics so users can track who interacted with their profiles and build stronger, more meaningful connections. Zapmii Social modern, secure networking in a fast-paced, tech-driven world Zapmii Social’s NFC cards and tags solve that real-time engagement analytics Zapmii Health emerged from a personal challenge experienced by one of our co-founders—having a non-verbal family member needing constant care and a way to share medical information quickly. Zapmii Health’s NFC devices allow individuals with critical health needs, such as non-verbal people, children, and the elderly , to share medical data instantly in emergencies. Additionally, caregivers are alerted if vulnerable loved ones leave a predefined safe zone, ensuring safety and peace of mind. Zapmii Health Zapmii Health’s NFC devices non-verbal people, children, and the elderly ensuring safety and peace of mind. Both platforms have already found their footing in two distinct but rapidly growing markets— business networking and personal health management —each worth billions globally. Our early adopters have demonstrated the tangible value these solutions bring, from streamlining professional connections to saving lives in emergencies . business networking personal health management streamlining professional connections saving lives in emergencies The evolution of Zapmii Zapmii’s journey began with Zapmii Social , which was launched three years ago as a response to the growing need for digital-first networking solutions . In the wake of the COVID-19 pandemic, professionals around the world were searching for a contactless, tech-savvy way to share business information. Since then, Zapmii Social has grown its user base by 30% year-over-year, with rave reviews about its ease of use, customization options , and data-driven insights . Zapmii Social digital-first networking solutions contactless, tech-savvy way Zapmii Social has grown its user base by 30% ease of use, customization options data-driven insights In 2023, we recognized an even larger, underserved market , families caring for vulnerable individuals , such as those with dementia, autism, or chronic illnesses . This insight led to the development and launch of Zapmii Health . The platform quickly gained traction, with numerous stories of how it has improved care management and even saved lives by alerting caregivers when loved ones strayed from safe zones. underserved market families caring for vulnerable individuals dementia, autism, or chronic illnesses Zapmii Health improved care management Milestones and achievements Won the 2024 Netty Awards for Best Up-and-Coming Consumer Tech Company. Lessons learned We’ve faced several challenges along the way, especially in convincing users to trust a new technology with their sensitive health and personal data . However, we learned that transparent communication about security and offering early product trials were key to gaining user trust. We also realized that our customers value control over their data , so we built features allowing users to determine exactly what information is shared and under which circumstances. sensitive health and personal data transparent communication about security early product trials our customers value control over their data exactly what information is shared Another lesson was that both health and professional markets require different communication strategies . Zapmii Social users are drawn to networking efficiency and personalization , whereas Zapmii Health users are concerned with data privacy and real-time alerts . Recognizing and adapting to these needs has been critical to our success. both health and professional markets different communication strategies Zapmii Social networking efficiency and personalization Zapmii Health data privacy real-time alerts What Startups of The Year means to us Participating in HackerNoon’s Startups of the Year is more than just an honor for Zapmii, it’s a validation of our vision to make life both simpler and safer for millions of people around the world. This recognition will help us build credibility , especially in the competitive health-tech and networking-tech spaces, where trust is paramount . HackerNoon’s Startups of the Year validation of our vision simpler safer credibility trust is paramount Additionally, this award will give us the visibility needed to attract key investors and partners who believe in our mission to improve people’s professional lives and safeguard health in emergencies . visibility needed key investors and partners people’s professional lives safeguard health in emergencies Conclusion Zapmii is ready to change both business networking and health safety with simple, secure, and life-saving tools. We've only just started, but the impact we've already made is clear, from helping people grow their businesses to saving lives with our instant alerts. As we keep innovating, we're ready to become a leader in both areas, making sure our customers can trust us with their most important things: their connections and their health. business networking health safety Vote for Zapmii today and help us keep changing and protecting lives! Vote for Zapmii today changing and protecting Vote for us today! Vote for us today! V ote for us today! ote for us today!

Innovating Digital Networking & Health Safety: Interview with SOTY 2024 Nominee, Zapmii

Gamified Surveys and Cognitive Load Detection in mHealth: Conclusion, Acknowledgment, and References

Authors:
(1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland;
(2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland;
(7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland;
(8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland;
(9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;
(10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland;
(12) EngD. Table of Links Abstract and Introduction Related Work Methods Results and Discussion Limitations Conclusion, Acknowledgment, and References Limitations The main limitation of this work is the small sample size of participants used for both developing the cognitive load detector and evaluating the cognitive effort required to complete the two survey versions. To mitigate this issue, we pre-trained the detector on a similar task, but further improvements in its performance may be possible with a larger sample size. The generalizability of our method can also be impacted by the homogeneous nature of the study participants. The volunteers were healthy individuals from a similar age group. To ensure the robustness of our method, future studies should include a more diverse and bigger sample size. Finally, we conducted experiments in controlled laboratory settings. In the future, we plan to conduct similar studies in real-world conditions[7]. In this feasibility study, we focused solely on developing automatic detection of high cognitive load, which is the first step towards estimating cognitive effort in the real world. This may not be sufficient to distinguish more subtle differences in cognitive demand caused by the mobile application. To identify the game element or combination of such elements that is the most helpful in reducing cognitive burden we would like to conduct an ablation study in the context of well-being questionnaires in our future work. We would also like to include the self-reported cognitive load after survey completion for a better understanding of survey difficulty. Moreover, it would be interesting to collect data during semi-demanding tasks, such as text reading, in order to train a more finely-graded classifier. Finally, we would like to include other tasks requiring a high cognitive load to ensure the efficiency of our method across various cognitively demanding activities. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland; (2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland; (7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland; (8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland; (9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; (10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland; (12) EngD. Authors: Authors: (1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland; (2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland; (7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland; (8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland; (9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; (10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland; (12) EngD. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Methods Methods Results and Discussion Results and Discussion Limitations Limitations Conclusion, Acknowledgment, and References Conclusion, Acknowledgment, and References Limitations The main limitation of this work is the small sample size of participants used for both developing the cognitive load detector and evaluating the cognitive effort required to complete the two survey versions. To mitigate this issue, we pre-trained the detector on a similar task, but further improvements in its performance may be possible with a larger sample size. The generalizability of our method can also be impacted by the homogeneous nature of the study participants. The volunteers were healthy individuals from a similar age group. To ensure the robustness of our method, future studies should include a more diverse and bigger sample size. Finally, we conducted experiments in controlled laboratory settings. In the future, we plan to conduct similar studies in real-world conditions[7]. In this feasibility study, we focused solely on developing automatic detection of high cognitive load, which is the first step towards estimating cognitive effort in the real world. This may not be sufficient to distinguish more subtle differences in cognitive demand caused by the mobile application. To identify the game element or combination of such elements that is the most helpful in reducing cognitive burden we would like to conduct an ablation study in the context of well-being questionnaires in our future work. We would also like to include the self-reported cognitive load after survey completion for a better understanding of survey difficulty. Moreover, it would be interesting to collect data during semi-demanding tasks, such as text reading, in order to train a more finely-graded classifier. Finally, we would like to include other tasks requiring a high cognitive load to ensure the efficiency of our method across various cognitively demanding activities. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Gamified Surveys and Cognitive Load Detection in mHealth: Limitations

Jinesh Kumar Chinnathambi, a Solution Architect at a leading health insurance company, is using his real-world experience in the fields of Machine Learning (ML) and Artificial Intelligence (AI) to make the healthcare sector more patient-centric and improve patient care. People pursuing a career in healthcare IT are often driven by the aspiration to have a significant impact on people's lives. For Jinesh, the best way to do this in 2024 is by utilizing the problem-solving capabilities of technology to improve and streamline the healthcare industry. As he says, "The ever-changing healthcare landscape, with advancements such as Electronic Health Records (EHR), telemedicine, artificial intelligence, and predictive analytics transforming patient care, presents captivating challenges and opportunities for innovation. It provides the satisfaction of knowing that work in this field directly contributes to enhancing the quality, accessibility, and effectiveness of patient care, ultimately making healthcare more patient-centric." Combining the innovations and technologies in this healthcare IT, such as AI-driven diagnostics, predictive analytics, and personalized treatment plans, with the soft skills required in Healthcare roles, including problem-solving, analytical thinking, communication, and understanding of healthcare operations, has proven incredibly beneficial to both patients and healthcare professionals. Introducing Jinesh Kumar Chinnathambi Jinesh Kumar Chinnathambi's education began with a Bachelor's degree in computer science engineering, which gave him a strong foundation for his work in healthcare IT. During his studies, Jinesh focused on subjects at the intersection of technology and healthcare, including databases, data analysis, and healthcare systems. After graduating, he gained practical IT experience through entry-level roles and tech positions in the healthcare industry. He achieved further specialization in healthcare IT through certifications offered by AHIP (America's Health Insurance Plan) and has kept updated on new healthcare regulations, emerging technologies, and industry best practices to stay ahead of the curve. Networking with professionals, attending industry conferences, and keeping abreast of healthcare IT journals have provided valuable insights and helped him garner success in his career. As he says himself, "The journey into Healthcare IT involves a combination of education, practical experience, ongoing learning, and a dedication to enhancing healthcare delivery through technology." Jinesh has published numerous publications and pieces of academic research, including the well-received "Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence," "Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps," “Harnessing Data Analytics and Artificial Intelligence in Healthcare,” and “Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration.” His wide range of professional certifications and important achievements demonstrate his expertise in a highly competitive field. He is the only one of Elevance Health’s approximately 100,000 employees to hold four AWS certifications, including AWS Certified DevOps Professional, AWS Certified Solutions Architect Associate, AWS Certified Developer Associate, and AWS Certified SysOps Administrator Associate. He also holds a Sun Certified Java Professional (SCJP) certification and multiple America's Health Insurance Plans (AHIP) certifications, including Fundamentals of Healthcare Parts A and B and Basics of Managed Care Part A. In addition to all of this, Jinesh has been recognized with a 2024 Global Recognition Award for his notable healthcare and IT industry achievements. In August 2024, he also won the Cloud Innovator of the Year Award at the Business Innovation Awards 2024 in the Health Care/Information Technology category. This award recognizes outstanding achievements in cloud innovation. In addition to being awarded for his work, Jinesh was also named a judge for the Globee® Awards for Leadership in 2024, signifying a formal recognition of his commitment and significant contributions to his field, and received recognition for his role as judge at the Virginia Tech College Hackathon event. The Role of AI, ML, Data Analytics, Data Warehouses & Cloud Migration in Healthcare IT One of the major benefits of implementing technological advancements like AI and ML into healthcare IT is how they can be utilized to analyze vast amounts of healthcare data, accurately predict patient outcomes and improve treatment plans. A pivotal moment in Jinesh's IT career came with the advent of cloud computing, which reshaped the entire technology landscape. It was here, at the precipice of such a monumental technological change, that Jinesh realized just how beneficial such systems could be to the work he was doing. "Before the rise of cloud computing, businesses were required to establish, manage, and uphold their own expensive IT infrastructures,” recalls Jinesh. “Companies like Amazon, Google, and Microsoft began providing platforms that allowed access to databases, servers, software, and analytics via the internet. This led to a substantial reduction in the cost and complexity of IT operations, hastened innovation, and offered the flexibility to scale resources as needed. It empowered businesses to concentrate more on their core operations while leveraging state-of-the-art technology. Furthermore, cloud computing paved the way for a multitude of other technological advancements seen today, such as big data analytics and AI, which I am currently working on. It stands as a milestone that has redefined the possibilities of IT and continues to shape the future of technology." All of this works in tandem with the dedication of people in the healthcare field to craft a more patient-centric approach, providing personalized and timely care with revolutionary speed and precision. Real-World Studies and Applications By implementing these technological advancements into his established healthcare IT system, Jinesh has been able to successfully develop predictive analytics products to enhance patient care. His work has even been able to identify the signs and likelihood of cancer recurrence far earlier than previously thought possible, all thanks to data analytics, AI, and ML. By configuring AI models to run seamlessly on the cloud, Jinesh has contributed to accelerating predictive diagnoses, enhancing treatment plans, automating administrative tasks, and improving patient care. The strategic designs and technical expertise of solution architects are a vital part of shaping the future of AI-enabled, cloud-based healthcare systems. The impact of these projects has improved patient outcomes, reduced costs, improved efficiency, and helped to deliver more personalized care than ever before. Jinesh has worked tirelessly to guide and develop technology and tool solutions that improve data lineage adoption and governance by collaborating with technology partners to create a cloud-based data operation strategy. He oversaw a comprehensive cloud migration and data governance framework at Elevance Health, ensuring alignment with business objectives and HIPAA regulatory requirements, and also established a data council of senior executives across the enterprise to facilitate efficient collaboration on data initiatives. Furthermore, during his time at Elevance, he worked closely with the enterprise infrastructure and technology team to explore a cloud-based lake-house solution. Jinesh led the implementation of a data migration framework, including issue research, resolution processes, root cause analysis, reporting, data classifications and standards, and data quality validation and controls. He also developed a data governance framework to align with business objectives and HIPAA regulatory requirements, which supported the implementation of monolithic microservices for various use cases. He researched and recommended the appropriate Snowflake warehouse sizes after conducting several proofs of concept, resulting in optimal performance for middleware queries and reducing annual costs by 30%. One study estimated the national cost-savings in the United States from early diagnosis to be $26 billion per year. The Future of Healthcare Technology Jinesh's vision for the future of healthcare technology is one full of promise, continued improvement, and the potential to create a more patient-centric healthcare system that benefits everyone. He plans to work at the convergence of healthcare and technology to improve patient care by introducing new and advanced solutions and developing and managing Electronic Health Records (EHRs), health information exchanges, telemedicine services, and other initiatives to enhance healthcare coordination and accessibility, ultimately benefiting local communities. This will contribute to early disease detection, effective monitoring, decreased medical errors, and better communication between healthcare providers and patients. Jinesh Kumar Chinnathambi , a Solution Architect at a leading health insurance company, is using his real-world experience in the fields of Machine Learning (ML) and Artificial Intelligence (AI) to make the healthcare sector more patient-centric and improve patient care. Jinesh Kumar Chinnathambi Jinesh Kumar Chinnathambi People pursuing a career in healthcare IT are often driven by the aspiration to have a significant impact on people's lives. For Jinesh, the best way to do this in 2024 is by utilizing the problem-solving capabilities of technology to improve and streamline the healthcare industry. As he says, "The ever-changing healthcare landscape, with advancements such as Electronic Health Records (EHR), telemedicine, artificial intelligence, and predictive analytics transforming patient care, presents captivating challenges and opportunities for innovation. It provides the satisfaction of knowing that work in this field directly contributes to enhancing the quality, accessibility, and effectiveness of patient care, ultimately making healthcare more patient-centric." Combining the innovations and technologies in this healthcare IT, such as AI-driven diagnostics, predictive analytics, and personalized treatment plans, with the soft skills required in Healthcare roles, including problem-solving, analytical thinking, communication, and understanding of healthcare operations, has proven incredibly beneficial to both patients and healthcare professionals. Introducing Jinesh Kumar Chinnathambi Jinesh Kumar Chinnathambi's education began with a Bachelor's degree in computer science engineering, which gave him a strong foundation for his work in healthcare IT. During his studies, Jinesh focused on subjects at the intersection of technology and healthcare, including databases, data analysis, and healthcare systems. After graduating, he gained practical IT experience through entry-level roles and tech positions in the healthcare industry. He achieved further specialization in healthcare IT through certifications offered by AHIP (America's Health Insurance Plan) and has kept updated on new healthcare regulations, emerging technologies, and industry best practices to stay ahead of the curve. Networking with professionals, attending industry conferences, and keeping abreast of healthcare IT journals have provided valuable insights and helped him garner success in his career. As he says himself, "The journey into Healthcare IT involves a combination of education, practical experience, ongoing learning, and a dedication to enhancing healthcare delivery through technology." Jinesh has published numerous publications and pieces of academic research, including the well-received " Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence ," " Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps ," “ Harnessing Data Analytics and Artificial Intelligence in Healthcare ,” and “ Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration .” Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps Harnessing Data Analytics and Artificial Intelligence in Healthcare Harnessing Data Analytics and Artificial Intelligence in Healthcare Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration His wide range of professional certifications and important achievements demonstrate his expertise in a highly competitive field. He is the only one of Elevance Health’s approximately 100,000 employees to hold four AWS certifications, including AWS Certified DevOps Professional, AWS Certified Solutions Architect Associate, AWS Certified Developer Associate, and AWS Certified SysOps Administrator Associate. He also holds a Sun Certified Java Professional (SCJP) certification and multiple America's Health Insurance Plans (AHIP) certifications, including Fundamentals of Healthcare Parts A and B and Basics of Managed Care Part A. In addition to all of this, Jinesh has been recognized with a 2024 Global Recognition Award for his notable healthcare and IT industry achievements. Global Recognition Award Global Recognition Award In August 2024, he also won the Cloud Innovator of the Year Award at the Business Innovation Awards 2024 in the Health Care/Information Technology category. This award recognizes outstanding achievements in cloud innovation. In addition to being awarded for his work, Jinesh was also named a judge for the Globee® Awards for Leadership in 2024, signifying a formal recognition of his commitment and significant contributions to his field, and received recognition for his role as judge at the Virginia Tech College Hackathon event . Globee® Awards for Leadership Globee® Awards for Leadership Virginia Tech College Hackathon event Virginia Tech College Hackathon event The Role of AI, ML, Data Analytics, Data Warehouses & Cloud Migration in Healthcare IT One of the major benefits of implementing technological advancements like AI and ML into healthcare IT is how they can be utilized to analyze vast amounts of healthcare data, accurately predict patient outcomes and improve treatment plans. A pivotal moment in Jinesh's IT career came with the advent of cloud computing, which reshaped the entire technology landscape. It was here, at the precipice of such a monumental technological change, that Jinesh realized just how beneficial such systems could be to the work he was doing. "Before the rise of cloud computing, businesses were required to establish, manage, and uphold their own expensive IT infrastructures,” recalls Jinesh. “Companies like Amazon, Google, and Microsoft began providing platforms that allowed access to databases, servers, software, and analytics via the internet. This led to a substantial reduction in the cost and complexity of IT operations, hastened innovation, and offered the flexibility to scale resources as needed. It empowered businesses to concentrate more on their core operations while leveraging state-of-the-art technology. Furthermore, cloud computing paved the way for a multitude of other technological advancements seen today, such as big data analytics and AI, which I am currently working on. It stands as a milestone that has redefined the possibilities of IT and continues to shape the future of technology." All of this works in tandem with the dedication of people in the healthcare field to craft a more patient-centric approach, providing personalized and timely care with revolutionary speed and precision. Real-World Studies and Applications By implementing these technological advancements into his established healthcare IT system, Jinesh has been able to successfully develop predictive analytics products to enhance patient care. His work has even been able to identify the signs and likelihood of cancer recurrence far earlier than previously thought possible, all thanks to data analytics, AI, and ML. By configuring AI models to run seamlessly on the cloud, Jinesh has contributed to accelerating predictive diagnoses, enhancing treatment plans, automating administrative tasks, and improving patient care. The strategic designs and technical expertise of solution architects are a vital part of shaping the future of AI-enabled, cloud-based healthcare systems. The impact of these projects has improved patient outcomes, reduced costs, improved efficiency, and helped to deliver more personalized care than ever before. Jinesh has worked tirelessly to guide and develop technology and tool solutions that improve data lineage adoption and governance by collaborating with technology partners to create a cloud-based data operation strategy. He oversaw a comprehensive cloud migration and data governance framework at Elevance Health, ensuring alignment with business objectives and HIPAA regulatory requirements, and also established a data council of senior executives across the enterprise to facilitate efficient collaboration on data initiatives. Furthermore, during his time at Elevance, he worked closely with the enterprise infrastructure and technology team to explore a cloud-based lake-house solution. Jinesh led the implementation of a data migration framework, including issue research, resolution processes, root cause analysis, reporting, data classifications and standards, and data quality validation and controls. He also developed a data governance framework to align with business objectives and HIPAA regulatory requirements, which supported the implementation of monolithic microservices for various use cases. He researched and recommended the appropriate Snowflake warehouse sizes after conducting several proofs of concept, resulting in optimal performance for middleware queries and reducing annual costs by 30%. One study estimated the national cost-savings in the United States from early diagnosis to be $26 billion per year. One study estimated the national cost-savings One study estimated the national cost-savings The Future of Healthcare Technology Jinesh's vision for the future of healthcare technology is one full of promise, continued improvement, and the potential to create a more patient-centric healthcare system that benefits everyone. He plans to work at the convergence of healthcare and technology to improve patient care by introducing new and advanced solutions and developing and managing Electronic Health Records (EHRs), health information exchanges, telemedicine services, and other initiatives to enhance healthcare coordination and accessibility, ultimately benefiting local communities. This will contribute to early disease detection, effective monitoring, decreased medical errors, and better communication between healthcare providers and patients.

How AI and Machine Learning Are Making the Healthcare Sector More Patient-centric

እንዴት AI እና ማሽን መማር የጤና አጠባበቅ ሴክተሩን የበለጠ ታጋሽ ተኮር እያደረጉት ነው።

Süni intellekt və Maşın Öyrənməsi Səhiyyə Sektorunu necə daha xəstə mərkəzli edir

Com la IA i l'aprenentatge automàtic estan fent que el sector sanitari sigui més centrat en el pacient

Jak umělá inteligence a strojové učení dělají sektor zdravotnictví více zaměřený na pacienta

Cómo la IA y el aprendizaje automático están haciendo que el sector sanitario esté más centrado en el paciente

Comment l'IA et l'apprentissage automatique rendent le secteur de la santé plus centré sur le patient

Como a IA e a aprendizaxe automática están facendo que o sector sanitario sexa máis centrado no paciente

कैसे AI और मशीन लर्निंग स्वास्थ्य सेवा क्षेत्र को अधिक रोगी-केंद्रित बना रहे हैं

AIと機械学習が医療分野をより患者中心に変える

AI жана машинаны үйрөнүү кантип саламаттыкты сактоо секторун сабырдуураак кылат

Ndenge nini AI mpe Machine Learning Ezali Kosala ete Secteur ya Soins de Santé ezala mingi na ba malades

Paano Ginagawa ng AI at Machine Learning ang Sektor ng Pangangalagang Pangkalusugan na Mas Nakasentro sa Pasyente

Authors:
(1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland;
(2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland;
(7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland;
(8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland;
(9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;
(10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland;
(12) EngD. Table of Links Abstract and Introduction Related Work Methods Results and Discussion Limitations Conclusion, Acknowledgment, and References Results and Discussion Cognitive Load Detection. To understand how to obtain a reliable cognitive load detection model we investigate different approaches to training protocols including varying lengths of the signal input and model pretraining as described above. Due to a small number of data, the model performance was varying between runs and therefore we run each training session 40 times and report the mean and standard deviation of all runs. We find that the number of data samples was insufficient to train the model from scratch on the pilot cognitive load dataset only (see Vanilla in Table 1). Model pre-training on WESAD dataset has shown to be helpful for cognitive load detection (see WESAD pre-trained in Table 1). The length of the signal is also important, in general, the 10 seconds window seems to be insufficient to obtain promising cognitive load detection performance even when the model is pretrained. We expected that the better the performance of the stress detector on the pretraining task the better will be the performance on the fine-tuning task. However, the link between source and target task performance is not that apparent. There is a weak correlation between source training data stress detection and fine-tuned cognitive load detector performance (r=0.18 p=0.04), a slightly stronger correlation between source validation data stress detection and target cognitive load detection (r=0.25, p=0.005), and no correlation between the test subject stress detection and target dataset cognitive load score (see Figure 3). Further, to check if our cognitive load detector is not just detecting stress, we run the best-performing stress detector with a 30s window on all participants from the pilot dataset and check % of samples for each participant from each condition (baseline and cognitive load) that are classified as stressed. We find that signal from participants 1, 3, 4, 7 and 8 had 0 samples classified as stressed, participants 0, 6, 9 and 10 had 9-22% samples classified as stressed and only participants 2 and 5 showed higher levels of stress with 59% and 55% of signal classified as stressed respectively. This suggests that improvements obtained via pre-training are not due to the physiological signal similarity in both conditions (cognitive load is not classified as stress) but rather due to learning of general feature extraction from the 1D signal. Response Time. On average participants spent less time responding to questions in gamified condition than in Non-gamified condition, with 5.5s and 6s per question respectively. However, interestingly, in gamified condition participants spent more time at the start and end questions which potentially could be due to the introduction of the gamified components which could capture respondents' attention (see Figure 4). Note that time spent on the very last question in each survey has been not included in this analysis as they mostly capture the time needed to move between questionnaires. Cognitive Burden. To ensure that the cognitive load detector can distinguish between high and low cognitive load for each participant in the survey dataset we first applied all 440 models (from 40 runs and 11 folds) trained with a 30s window on the pilot dataset to every participant in the survey dataset in cognitive load and baseline conditions and for each participant, we selected a model showing the best performance for this participant. In other words, we matched the models to participants using a small calibration dataset obtained from participants prior to filling the survey application. The performance of the selected best model is reported in Table 2 for each session. We have excluded the prediction of cognitive load during survey completion for those participants for whom we could not get a model with performance above 0.7 F1 score (marked with X in Table 2). Encouragingly, for 10 out of 13 participants, we were able to find a model which could reliably distinguish between their high and low cognitive load. The time spent in the high cognitive load and stress does not differ between gamified and non-gamified survey conditions. Potentially to understand if there is a difference between cognitive burden we would need a classifier trained on at least three different tasks with cognitive load levels: high (demanding task), medium (e.g. reading task) and low. Participants Experience. Only half of the participants noticed the difference in survey application appearance between the sessions. Participants who noted the difference reported preferring the gamified version more. Potentially the gamification was too simplistic to modulate the level of engagement or enjoyment. It is nontrivial to introduce game components in the surveys to reduce the burden but not affect the responses. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland; (2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland; (7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland; (8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland; (9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; (10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland; (12) EngD. Authors: Authors: (1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland; (2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland; (7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland; (8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland; (9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; (10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland; (12) EngD. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Methods Methods Results and Discussion Results and Discussion Limitations Limitations Conclusion, Acknowledgment, and References Conclusion, Acknowledgment, and References Results and Discussion Cognitive Load Detection. To understand how to obtain a reliable cognitive load detection model we investigate different approaches to training protocols including varying lengths of the signal input and model pretraining as described above. Due to a small number of data, the model performance was varying between runs and therefore we run each training session 40 times and report the mean and standard deviation of all runs. We find that the number of data samples was insufficient to train the model from scratch on the pilot cognitive load dataset only (see Vanilla in Table 1). Model pre-training on WESAD dataset has shown to be helpful for cognitive load detection (see WESAD pre-trained in Table 1). The length of the signal is also important, in general, the 10 seconds window seems to be insufficient to obtain promising cognitive load detection performance even when the model is pretrained. Cognitive Load Detection. Cognitive Load Detection. Vanilla WESAD pre-trained We expected that the better the performance of the stress detector on the pretraining task the better will be the performance on the fine-tuning task. However, the link between source and target task performance is not that apparent. There is a weak correlation between source training data stress detection and fine-tuned cognitive load detector performance (r=0.18 p=0.04), a slightly stronger correlation between source validation data stress detection and target cognitive load detection (r=0.25, p=0.005), and no correlation between the test subject stress detection and target dataset cognitive load score (see Figure 3). Further, to check if our cognitive load detector is not just detecting stress, we run the best-performing stress detector with a 30s window on all participants from the pilot dataset and check % of samples for each participant from each condition (baseline and cognitive load) that are classified as stressed. We find that signal from participants 1, 3, 4, 7 and 8 had 0 samples classified as stressed, participants 0, 6, 9 and 10 had 9-22% samples classified as stressed and only participants 2 and 5 showed higher levels of stress with 59% and 55% of signal classified as stressed respectively. This suggests that improvements obtained via pre-training are not due to the physiological signal similarity in both conditions (cognitive load is not classified as stress) but rather due to learning of general feature extraction from the 1D signal. Response Time. On average participants spent less time responding to questions in gamified condition than in Non-gamified condition, with 5.5s and 6s per question respectively. However, interestingly, in gamified condition participants spent more time at the start and end questions which potentially could be due to the introduction of the gamified components which could capture respondents' attention (see Figure 4). Note that time spent on the very last question in each survey has been not included in this analysis as they mostly capture the time needed to move between questionnaires. Response Time. Response Time. Cognitive Burden. To ensure that the cognitive load detector can distinguish between high and low cognitive load for each participant in the survey dataset we first applied all 440 models (from 40 runs and 11 folds) trained with a 30s window on the pilot dataset to every participant in the survey dataset in cognitive load and baseline conditions and for each participant, we selected a model showing the best performance for this participant. In other words, we matched the models to participants using a small calibration dataset obtained from participants prior to filling the survey application. The performance of the selected best model is reported in Table 2 for each session. We have excluded the prediction of cognitive load during survey completion for those participants for whom we could not get a model with performance above 0.7 F1 score (marked with X in Table 2). Encouragingly, for 10 out of 13 participants, we were able to find a model which could reliably distinguish between their high and low cognitive load. The time spent in the high cognitive load and stress does not differ between gamified and non-gamified survey conditions. Potentially to understand if there is a difference between cognitive burden we would need a classifier trained on at least three different tasks with cognitive load levels: high (demanding task), medium (e.g. reading task) and low. Cognitive Burden. Cognitive Burden. Participants Experience. Only half of the participants noticed the difference in survey application appearance between the sessions. Participants who noted the difference reported preferring the gamified version more. Potentially the gamification was too simplistic to modulate the level of engagement or enjoyment. It is nontrivial to introduce game components in the surveys to reduce the burden but not affect the responses. Participants Experience. Participants Experience. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Gamified Surveys and Cognitive Load Detection in mHealth: Results and Discussion

Gamified Surveys and Cognitive Load Detection in mHealth: Methods

Stories, News & Mentions about XPHEALTH

Xphealth’s Stories on HackerNoon