The potential of digital health in understanding human behavior

Agree with Dr. Joshi's statement "Advances in digital technologies have created opportunities to assess and modify health behavior. " but at the same time, my question is how to maintain a balance between the speed of research and the speed of innovation? Advanced digital technologies are one of the most important and critical achievements in human history. Such technologies are intriguing, important, and complex. 

as per the group of researchers, types of human behavior, such as health behavior, organization behavior, entrepreneurial behavior, violent behavior, economic behavior, sexual behavior, consumer behavior, social behavior, motor behavior, aggressive behavior, addictive behavior, machine behavior, and cyber behavior to name a few. Technologies need few health care professionals to make mHealth apps its an amalgum of technicians and clinicians and health care professionals. 

There are mental health apps available in the store to balance mental peace. 

Indeed current days digital culture do hold a very wide aspect of human life in terms of health. To identify which this study here investigates the feasibility of using a smartphone app to measure and assess the relationship between social network metrics and mental health. The app collected Bluetooth and mental health data in 63 participants. Social networks of proximity were estimated from Bluetooth data and 95% of the edges were scanned at least every 30 minutes. The majority of participants found this method of data collection acceptable and reported that they would be likely to participate in future studies using this app. These findings demonstrate the feasibility of using a smartphone app that participants can install on their own phone to investigate the relationship between social connectivity and mental health. 

Read more: Trifan A, Oliveira M, Oliveira J. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR Mhealth Uhealth 2019;7(8):e12649. URL: //mhealth.jmir.org/2019/8/e12649. DOI: 10.2196/12649

In last two decades Lot of research has been done in understanding the role of digital technology in influencing human behavior in a positive way. In this article, Digital Health Behavior Change Technology: Bibliometric and Scoping Review of Two Decades of Research. Bibliometric analysis is done and scoping review was carried out to evaluate trends and possible gaps. Results showed that after the introduction of smartphone in 2009, there was an exponential growth in behavior change technology and computer science, public and occupational health, and psychology are the most researched areas. Targeted areas for behavioral modifications were physical activity and healthy eating. Although behavior change techniques were not reported in most of the article but goal settings and self-management were reported as two most used behavior change techniques. It was concluded that for larger societal impact interaction between behavioral sciences and technology could be to be explored and a clear framework is required for the advancement of evaluation and  its research design.

Read here:

//www.ncbi.nlm.nih.gov/pmc/articles/PMC6935048/

The continued advancement of technology and the breakthroughs in the field of health have opened up numerous arenas to improve the health outcomes worldwide. The integration of digital technology with evidence-based behavior modification theories can be advantageously used in expediating digital health interventions. This paper reviews the digital health data-driven approaches to understanding human behavior with a focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. It also discusses that digitally derived empirical data for human behavior can be used for tailored interventions for psychiatric disorders.

//www.nature.com/articles/s41386-020-0761-5#Abs1

The paper talks about using Smartphones to Monitor Bipolar Disorder Symptoms. The paper investigated whether smartphone measurements predicted clinical symptoms levels and clinical symptom change. Pilot testing of the study is based on a smartphone-based monitoring system, Social Information Monitoring for Patients with Bipolar Affective Disorder (SIMBA), that tracked daily mood, physical activity, and social communication. Three smartphone sensors were used to measure physical activity. Self-reported mood states were assessed once a day on the smartphone at random times with a 2-item questionnaire. Appointments with the treating clinician, who was blinded to smartphone data, were scheduled at approximately 8-week intervals and included assessment of manic and depressive symptoms using clinical rating scales. 

Results: An increase in clinical depressive symptoms were predicted by a decline in social communication (ie, outgoing text messages) and a decline in physical activity as measured by the smartphone. Higher overall levels of clinical manic symptoms were predicted by lower physical activity on the smartphone (ie, distance traveled, and higher social communication. An increase in clinical manic symptoms was predicted by a decrease in physical activity on the smartphone. Thus, smartphones have the potential to monitor bipolar disorder symptoms in patient's daily life. 

Reference: Beiwinkel T, Kindermann S, Maier A, Kerl C, Moock J, Barbian G, Rössler W Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study JMIR Ment Health 2016;3(1):e2doi: 10.2196/mental.4560

Technology and health have experienced a convergence in the last few years, leading to a new state of digital health that is transforming how we live and work. But there is a greater science behind it that analyzes tremendous amounts of data to shape and personalize our health experiences. AI now uses these data patterns and interactions to craft specific content and experiences to support patient engagement.

While the same behavioral science principles apply to digital health, it’s important to note that additional security measures are implemented in digital health platforms due to the sensitivity of personal health information and increased risk. Unlike a simple online purchase at Amazon or similar, digital health platforms implement multiple layers of security and data privacy protections to ensure that patients’ connections with providers are private and one-on-one through a secure channel.

By combining advanced behavioral science and machine learning, digital companions can now incorporate patient, brand, and medication variables to create intervention activities and create engagement through a series of push notifications. These components are driven by patient interaction and customized by the thousands of decisions each patient makes every day. Using patient profiles and their behaviors sourced from aggregated patient populations, healthcare systems now have a wealth of information on how patients are likely to interact. Aligning those data outputs to machine learning saw the birth of AI in healthcare.

This Article, Digital Health Tools Offer New Opportunities for Personalized Care talks about “behavioral phenotype” predict how to tailor information so it provides a better experience. Netflix have developed approaches to provide personalized experiences to your consumer’s based on their interests. Another example is Amazon, which can predict from the past purchases if you are more likely to buy a children’s book or a science fiction novel.  These companies track individual attributes such as, behaviors, preferences, and experiences to create a “behavioral phenotype. With behavioral phenotype, digital health solutions become more personalized and effective. Experiment was conducted and they concluded that incorporating behavioral phenotyping to personalize the design of digital health solutions could help in in changing behavior.

Read here:

//hbr.org/2020/11/digital-health-tools-offer-new-opportunities-for-personalized-care

Advancements in digital technologies along with decreased costs and size of the range of sensors in smartphones have expanded the utilization of smartphones by turning them into robust and omnipresent monitoring systems. Smartphones have the potential to incessantly gather data of the users, monitor their behaviors and activities and provide feedback and recommendations.

A systematic review conducted in 2018 examined the passive use of smartphones to generate health and well being related outcomes as well as assessed the users’ engagement and probable challenges faced by them in using self-monitoring devices. The study showed that smartphones have already been utilized in different health and wellbeing related situations such as physical activity and mental health. These two have been reported to be the most studied applications. Additionally, global positioning systems and accelerometers are the most utilized sensors in smartphones for gathering and analysing information about the health and wellbeing status of users.

Although smartphones may have proven to be beneficial for passive monitoring of the health and wellbeing status of users, there seems to be a lack of correlation between outcomes generated through smartphones and clinical knowledge. Further, user engagement and motivation frequently influence adherence and full substantiation of these systems.

Digital health interventions have enabled social interaction (participants interacting with their existing and new connections) at a large scale and therefore larger data collections (both online and offline) over time. Data collected passively from digital health tools facilitate a more accurate estimation of their effects on human behavior.

For instance, the online weight loss community affects weight loss, lifestyle management, diet moderation, and increased physical activity by posting pictures on social media like Facebook, Instagram, and other such applications. Also, drinking and smoking behavior can be influenced a lot by friends/family's online posting behavior. Similarly, search related to  prevention of HIV and reduction of risky sexual behavior can extend to digital media. In addition to this, GPS, sensors, and artificial intelligence are the most utilized tools in everyday gadgets such as smartwatches, smartphones, tabs, etc for assimilating and analyzing information on the user's health-related data. Digital health interventions using a social network approach have demonstrated improved health outcomes for patients with mental health issues.

However, the question remains how to maximize the potential of social interaction, and the number of users using particular digital health tools ?

Reference: Moller, A.C., Merchant, G., Conroy, D.E. et al. Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential. J Behav Med 40, 85–98 (2017). //doi.org/10.1007/s10865-016-9818-7