The effect of artificial intelligence (AI) in healthcare, in particular with respect to determinants of health or illness, is primarily one of greater depth and certainty in the analysis of population data.  The ethical issues inherent in aggregation and use of massive health data warehouses consist primarily of the risks involved in the potentially expedient means to achieve the value promised through application of AI, along with the usual barely legal methods employed in commercial exploitation of such.  Almost any behavioral or social determinant of health can be studied using artificial intelligence provided sufficient data with relevant content is available.  This includes the value of health data streams directly from sensors worn or implanted in the patient themselves, the advantage of AI being its ability to learn from the vast quantities of detailed data which are thereby made available.  For the purposes of this paper, the focus will be on the utility of health data warehouses for studying determinants of health in populations. 

Population studies of the behavioral determinants of health, such as studies of nutritional and exercise habits and practices along with recreational drug use and the ethically distinct category of individual liberty in drug use, that promoting health, can all be potentially studied through application of deep learning AI to health data warehouses.  Social determinants of health are intertwined with individual behavioral ones primarily to the extent that they influence individual behavior, although the built environment’s contributions to health outcomes cannot be ignored.  Obvious examples include the presence of lead in water plumbing and the prevalence of car exhaust caused smog prior to the legal requirement of catalytic converters on all gasoline combustion engines.  One less obvious example currently under study is that of the presence of microplastics in the environment causing health effects through their tendency to enter through ingestion.  Determinants of health which can be studied with AI applied to data warehouses are the focus of this paper, and ultimately this includes almost all of them.  The primary drivers of ill health therefore should be selected, such including exercise, nutrition, drug use, and cultural membership or beliefs affecting selection or rejection of health protecting or promoting behaviors generally. 

The Health Belief Model provides a framework for study of both cultural influences and beliefs respecting adoption of health promoting behaviors and cessation of unhealthy ones.  It’s basic premise is that the primary motivators of behavior change are beliefs respecting the risks inherent in particular behaviors and those respecting the benefits achievable with adoption of others (Nash et al., 2021).  The determinant in this case, influencing all others, is belief. 

One type of analysis done by social media platforms that addresses beliefs is called sentiment analysis.  According to Wikipedia (Sentiment Analysis), this “is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.”  With the deepening power of AI applied to advertising on such platforms, particular product advertisements could be shown to particular users with a high likelihood of believing that that product would be good to acquire.  Applied to health behavior modification, particular messages could just as easily be targeted to those most susceptible to receiving and acting on them based on the beliefs that can be inferred form the information available on them.  AI applied to health data warehouses in combination with content of social networking sites would be particularly effective in custom tailoring messages that motivate healthy behaviors or inhibit unhealthy ones. Social media platforms can provide escape from physical cultural membership constraints and an avenue to new social experiences, and there is no reason to waste the beneficial influence these new social interactions can have to reinforce beliefs that support healthy behaviors. 

Positive messaging respecting exercise can come from authoritative sources such as the American College of Sports Medicine, which helped craft the exercise promoting health messaging of the United Stated government which started in the early 1990’s (ACSM, 2025).  The dramatic decline in the incidence of chronic diseases caused by sedentary lifestyle induced thrombosis, or abnormal blood coagulation, occurring during the period since then can be no coincidence, as such authority supported the growth of the inexpensive popular gym industry exemplified by Planet Fitness. 

Health equity is frequently frustrated, however, by nutritional behaviors, those of the lower income demographics showing an increasing trend toward lack of attention and discipline in this behavioral determinant of health (Ong et al., 2024).  The prevalence of high energy, low nutritional value foods and the busy, active work life of the low income family create a reliance on convenience food at supermarkets and fast food restaurants.  The recent enactment of requirements for nutritional labeling of fast foot items is a form of messaging which simply makes selections more competent, providing information to the consumer and incentive to the producer to improve the nutritional value of their products.  Determining the effectiveness of such information availability can be a subject of study using AI applied to health data warehouses provided sales information, including historic, can be obtained from fast food restaurant chains.  Dining behaviors can also be inferred from social media content, so with sufficient breadth of sources AI can help here also.  This one example demonstrates the power of providing information needed for competent decision making, which is also the primary value to populations of the availability of inexpensive digital technology, something no software developer could miss.

Another impediment to health equity is the quality and types of recreational drugs available to lower income individuals and communities combined with their general lack of education respecting their short and long term health effects.  Of the domains related to social determinants of health, the two most relevant to this determinant are education access and quality and social and community context.  Both of these can be studied using AI applied to the current health data warehouses, which generally include social networking derived information also, in particular if one accounts for the popular authority of entertainers who are perceived as free from the usual social constraints which limit opportunities to lower socioeconomic strata generally.  The social context provides readily available escapes and other solutions to social stratification with education being the hard road that few are willing to travel for the duration required.  Messages reinforcing positive educational behaviors can be designed and tailored using application of AI to the vast quantities of data already available, and with the emerging ability to re-identify social networking sources, can be targeted for effect.  The potential for re-identification can be seen as entirely ethically negative except for the rare exception which this application would be an example of, which of course must be carefully regulated or government controlled to have a constructive effect.  One need only study the aggressive frauds of the cigarette industry as their campaign to preserve their marketing efforts was steadily shut down through regulation to understand the potential for evil inherent in such targeting.

The determinants of health which AI can be effective with respect to are numerous and diverse since it can be used to study any and produce competent analysis usable for effective policy designs as well as commercial IT product design.  The ethics of application of AI to achieve the benefits of knowing what will motivate people to actually change their behavior, for instance, is a complex question fought with risks of various kinds.  To what extent can we use AI to re-identify a subject for messaging or other interventions and what level of risk to the individual or others would justify it?  Can we ignore the power of AI in identification of individuals at risk or a danger to others and therefore fail to implement the AI needed for re-identification?  The ethics of studying exercise and nutrition attitudes and practices of particular demographics, among other valuable application, cannot be denied, and such study is ethically sound, although it does require a risky levels of health and social networking data integration into vast, high value warehouses which under currently law can be commercially exploited, sending potentially sensitive data to entities with unknown ethical standards.  The risk needs mitigation with governance standards and competent regulation, making possible reasonably safe realization of the potential of AI to analyze and reveal the details of various population determinants of health.

References:

McCarron, T.L., Noseworthy, T., Moffat, K., Wilkinson, G., Zelinsky, S., W, D., Hassay, D.,
Lorenzetti, D. L., & Marlett, N.J. (2019), Understanding the motivations of patients:
A co‐designed project to understand the factors behind patient engagement, 
Health Expectations,  August, 2019.

Nash, D.B., Skoufalos, A., Fabius, R.J., Oglesby, W.H. (2021), Population Health, Creating
a Culture of Wellness, Jones and Bartlett Learning, LLC.

Ong, J. C. L., Seng, B. J. J., Law, J. Z. F., Low, L. L., Kwa, A. L. H., Giacomini, K. M., &
Ting, D. S. W. (2024). Artificial intelligence, ChatGPT, and other large language
models for social determinants of health: Current state and future directions.
Cell Reports. Medicine, 5(1), Article 101356. https://doi.org/10.1016/j.xcrm.2023.101356.

Thompson, W. (senior editor) (2010, 2025). ACSM’w Guidelines for Exercise Testing and
Prescription, 8th and 12th editions. Walters Kluwer, 2010 and 2025 respectively.

Privacy online has many supporters:

18 Privacy Focused Organizations You Should Know About:
https://identityreview.com/18-privacy-or...ould-know/

"Nowadays, with the Internet, any piece of knowledge can be accessed with minimal effort. What many fail to understand is the true cost of such capacity. Oftentimes, big tech firms such as Google and Facebook store user data and share them to other companies, in which they use to personalize ads. Many users are not aware of this considerable breach in user privacy."

2025-09-30:
Some of these shouldn't be trusted, including WhatsApp and Facebook Messenger, since they're owned and run by Meta/Facebook, whose business model requires them to collect as much information about you as possible. Read the article carefully to find the express guarantees of others. They exist.

There is a valid case that we the people are negligent about our health and that many of our short and especially long term illnesses could be prevented if we were. Since we are not required by law to be doing what we can to maintain and to improve our health to the extent reasonably possible to avoid both chronic and shorter term illnesses, however, the governments have failed to enact reasonable requirements to protect us from the pain and expense resulting. Simple exercise and nutrition requirements which are verifiable should be used to help determine whether or not the illness subject to insurance is actually caused by something outside of the reasonable control of the patient. Only in this case can the coverage actually fall under the general understanding of “Insurance,” which is meant only to compensate the injured for injury which is not their own fault. Punishing people arbitrarily for this collective negligence by cutting off necessary medical care in a way which creates profits, however, is not just, and those merely gaming the current legal system and medical establishment to maximize their gain through aggressive and abusive business practices are only exploiting the lack of enforcement of reasonable standards on the insured.
Those who accept their responsibility for their own health are already using digital technology to advance it and to prove both their efforts and their results.  The government's negligence creates opportunities for digital technology developers to support them, and such is a very socially responsible enterprise.  The main question, then, is how much value can we deliver to achieve the goals of developing training competence in the user and generally supporting their health building journey.

For the free version, explain to the user what the usual rules for handling their data are and how we can make money selling, leasing, or or otherwise engaging in business using it.

In the first level of paid, we give them dashboard level visibility of what we collect and time stamped event logs showing all transmission of any part of it.

In the next level of paid licensing, all transmission is off by default and they get the dashboard to view and analyze everything collected and stored on their device, along with notices of request for use of any part. At this level, they own and control all of the collected data pertaining to them.

The user pays for protection.

SQLite is a highly vulnerable db engine which produces files which can be easily read using Android's application interoperability features.  Therefore, all database files must be encrypted, including the block chained data change event record.  How does one execute SQL on an encrypted database file? The only way to access, navigate, filter, and apply analytics is to first read the entire file into RAM and decrypt it into custom maps and other feature convenient collections.  The security problem then is only how to securely store encryption and decryption keys.

User data collection is best done using a trusted forum to which only app users have access.  Like Stack Exchange, only private, it will provide a means of discussing use of the app, experiences applying its features, goals and strategies for achieving them, and can be a source of downloadable workout templates.  Rather than spying on users, collecting cell phone and other sensor data, acting ostensibly as their agent developing wonderful new technology, provide a means for users to share the information they want to share without fear of the usual "use this and you consent" webpage and app privacy policies which permit almost unlimited use of data collected in commerce.  Further, the user is engaged and knowingly contributing to the software's development.  Those that are so engaged form a core of supporters will benefit more, gaining more value from use of the software.

This is relevant to the design of a social media component into the exercise designer and tracker app so that users with a common constructive purpose can interact, share material, find support, etc.

The following is a comment I posted responding to this blog article:
How Social Media Shortens Your Life, 
URL: https://www.gurwinder.blog/p/how-social-...-your-life


Your discussion of the problems with the current status quo of social media platforms is excellent and detailed, and as a software engineer I've both experienced the feeling of futility or lack of purpose that their "engagement" tactics produce, and spent a lot of time thinking about designing better. There are technology redesign solutions to the profitable and powerful social media platform designs prevalent on the Internet today, just a there are cooking and dining solutions to the prevalence of fast food and other convenience addictions. Governance of the user is the main problem since their rewarded goal generally is to attract attention with the most relevant or compelling content posts, and this frequently involves bending or breaking the ordinary rules of constructive restraint in social interaction.
By facilitating communities with constructive purposes, rather than collecting as many users as possible, with any motives or intentions possible, and mixing them all together into the same inflammable pot, you get an inherent governance from the users themselves. They are also not structurally motivated to attract the greatest number of followers or greatest amount of attention, but can if they like, and can simply be ignored, unlike algorithm driven attention grabbing scrolling machines. Search-ability by the user is key to finding content and other members with similar interests, and should be possible with user selected or provided criteria. This capability is crippled in profit driven or social manipulation driven algorithm control of content and ad delivery. Restore agency to the user and you restore civilization.

My proof of simultaneity (which Einstein argued was unprovable):
Pick one point in time. Prove two things happened.

Things might be much simpler than the experts on top would like us to believe.

Social interaction is impossible without common purpose, and prevalent social media designs create an algorithm driven statistical interaction control machine taking purpose out of our control.  This is justified as the normal way to enhance the user experience with relevant content, membership connections, and advertising.  Conveniently for the social media company's power, there is no search capability implemented allowing the user to do this for themselves, nor are they allowed to review what the sentiment analysis and other algorithms are basing their decisions on.