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AI for Mental Health and Greater Wellness: A Myth or the Upcoming Reality?

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As of today, what do you think is the most prospective and growing market looking for the next profound technological innovations?

Note that deep-tech innovations relate to disruptive solutions created around unique, well-protected, and hard-to-reproduce technological and scientific advances.

You might be surprised to find that it is the wellness industry, driven by a rise in consumer interest and purchasing power.

The industry presents significant opportunities and interests for investors with its recent Global Wellness Institute assessment. The wellness sector accounts for $4.4 trillion in the global economy, with an expected 5%-10% annual growth. In addition, as we exit the pandemic, the industry’s rise will increase and is expected to reach around $7.0 trillion in value by 2025.

The wellness economic sector not only relates to fitness and nutrition, but extends to the areas of mental health, overall physical appearance, and even spirituality.

As a result, the wellness concept has become a priority for many people.

Consumer’s view on wellness
(Source: McKinsey & Company)

Our team has compiled compound research explaining how innovative technologies help implement R&D projects to satisfy the growing market needs while resolving users’ existing pain points.

We will elaborate on:

  • Existing challenges and issues for R&D projects within the wellness domain
  • Current examples of digital health and wellness products
  • Ways to use artificial intelligence in behavioral and mental health cares
  • How it is possible to innovate with regard to diet and nutrition
  • Future of AI (artificial intelligence) for staying healthy and fit
  • Critical existing examples of the use of AI for wellness

Let’s dive right into the challenges everyone faces with regard to keeping their mind and body fit.

Pain Points Faced by the Wellness Market & Its Consumers

So what problems exist within the wellness domain? Are there any valuable possibilities for deep tech that could revolutionize the industry?

The pandemic highlighted the importance of mental health as a significant part of the personal health equation. Wellness is not a luxury – it’s an absolute necessity!

Just imagine, before the pandemic, 19.86% of adults (± 50 million Americans) experienced mental illnesses. In addition, suicidal ideation continues to rise.

It is not only the ongoing mental health crisis that needs new AI wellness solutions.

The COVID-19 pandemic forced people to live life online, resulting in minimized movement, closed fitness centers, and a loss of means to travel. As a result, everyone struggled to maintain their usual activity level, maintain normal weight, and experience normal sleep.

These days, many of the following wellness issues are faced by humankind:

  • Levels of anxiety and depression increased substantially
  • The negative influence of COVID-19 and other former pandemics
  • The certainty of keeping track of overall physical health condition
  • The desire to sustain a younger and better appearance
  • The need to stay fit and keep up with exercising
  • Obligation to follow a good diet and nutrition
  • The need to deal with worsened sleep conditions
  • Minimized mindfulness and concentration levels

Still, the question arises whether “the next big thing” with enabled AI/ML technologies will revolutionize the wellness arena.

Wellness tech market overview
(Source: CBInsights, 2018)

Even though over 350,000 health apps are available worldwide, most of these digital health apps cannot solve or catch up with consumer demands.

Another example is the fact that the American Psychological Association estimates that 20,000 mobile mental health products exist. However, only 2.08% of these published services are evidence-based and effective. As a result, most of the available solutions are not only invalid, but users can even end up putting themselves in harm’s way.

You may think relying on the star rating and number of downloads can lead you to good apps. A study of Beth Israel Deaconess Medical Center and Harvard Medical School did not find any correlation between these indicators and an application’s service quality.

Moreover, popular apps dealing with anxiety, depression, suicide, domestic violence, and psychological well-being freely share personal user data. Mozilla researchers state that 25 apps out of 32 failed to have minimum security standards and privacy protection standards like strong password generation and control over app updates and vulnerabilities. In addition, 28 mental health products in the study were even labeled as “privacy not included.”

Another finding by the Economist states that the mental health industry faces a significant challenge with a growing number of ineffective apps that people try to use for their mental problems. In addition, almost anyone can hack “emotional data” since these products do not undergo any reliable checkups.

In addition, mainstream apps like Headspace and Calm act as an “entertainer” and have little to do with solid solutions to deal with depression or anxiety. Instead, these apps provide meditations, sleep stories, and videos that do not address deeper mental health concerns. Also, favorite apps follow similar patterns and deliver standardized approaches to everyone within their “mental health” service.

Does this mean that we find happiness the same way? Is there only one way to deal with depression, and the app knows all possible user reactions?

Not really – yeah right!

Any mental health treatment should be individualized in order to succeed.

Moreover, mental health apps concentrate on “problem fixing” rather than understanding the insight from individual experiences, however negative, followed by coping with the problem.

Lastly, using an app can become habitual, for instance, like listening to relaxing stories while falling asleep. As a result, mainstream apps may create a dependency similar to YouTube that directs users to watch more and more content.

These days, scientists disagree over the nature of emotions that form our state of mind in the long run. In addition, emotions are expressed and treated differently across different cultures and populations. And, of course, this factor is disregarded when it comes to products created for wellness.

Analysis of emotions
(Image source)

To innovate in the wellness area, it is necessary to recognize each person’s individuality. Creating individualized treatment could become available with AI/ML and the rise of Evidence-Based Practices (EBPs) within digital mental health.

Therefore, if left unregulated, solutions based on AI technology risk bringing new disparities to the table. Providing mental help to those who cannot afford one-on-one therapy is most likely aligned with the bot-powered therapist whose help is of uncertain quality.

So the question remains whether or not AI or other deep-tech technologies can add value to the wellness industry.

AI Technologies for Revolutionizing Wellness Industry

In this section, we want to explain how AI technology can improve distinct segments of the wellness industry.

First and foremost, this includes mental health, followed by fitness and dieting.

1. Artificial Intelligence for Mental Health

The market of mental and workplace wellness is estimated at $180 billion in value.

The practice of studying and computing emotions has existed for over two decades. Affective computing means the computing of everything that relates to, arises from, or influences emotions; it is an interdisciplinary field uniting computer science, cognitive science, and psychology.

Emotional AI implies the process of creating technology that can identify, interpret, process, adapt to, and most importantly, express and simulate human emotions.

Mobile EEG headset for medical research

The technology relies on voice, voice tone, sensors, facial expressions, speech, sentiment analysis, computer vision, and other ML techniques for capturing and analyzing physical cues and signals to detect changes in emotions.

The technology has become a valuable source for clinical therapists, and existing applications of emotional AI fall into three categories, namely:

1. Create systems that analyze emotions to adjust consumers’ replies.

These systems use emotions in the machine’s decision-making process. For example, chatbots and conversational IVRs define routes for the customers to create a proper service flow that is more accurate and faster according to the different emotional factors.

Here, the system output is completely emotion-free and points users in the right direction. This can be used to define anger, stress, and lack of attention in distinct areas like security and auto navigation.

For example, Affectiva’s Automotive AI is working to detect human emotions in order to take over car navigation or stop the vehicle to prevent accidents.

2. Implement solutions that analyze emotions for learning purposes.

With the utilization of affective AI, it is possible to create software to monitor stress levels and analyze heightened emotional states, thus aiding in mindfulness, self-awareness, and ultimately self-improvement.

These mental health products will perform for both the heart and mind. For example, a system of Google Glass-type devices helps people with autism understand social cues and emotions.

Another example of AI application in the workplace is the idea of a bracelet that rationalizes a bank trader’s decisions. With pulse tracking, the system notes deviations from the normal emotional state and helps avoid irrational trading decisions.

In addition, for keeping the good condition of corporate workers, Cogitocorp is used to track any signs of anxiety.

3. Develop systems to resemble and replace human-to-human interactions.

Since 2014, smart speakers like Alexa, Google Assistant, Amazon Echo, and Siri by Apple – have entered the household and have been building relationships through interaction.

More and more new products have begun to use conversational user interfaces with the paradigm of “computers are social actors,” meaning that people tend to apply the same social heuristics to computers as humans.

The idea is to facilitate help with mental health issues and disorders related to anxiety. Sentiment analysis used in chatbots combines sophisticated natural language processing (NLP) and machine learning techniques to determine the emotion expressed by the user.

This software attempts to increase mindfulness and apply techniques from behavioral therapy. Ellie is a virtual avatar therapist that can detect depression and deal with post-traumatic stress disorder. The technology is unique, as it detects both words and nonverbal cues like facial expressions, posture, gestures, etc.

Another example, Feel Therapeutics, tracks sweat, skin temperature, and blood pressure via a sensory wristband. The connected app then asks the user’s state in a series of labels from “distressed” to “content,” and helps consumers manage their stress and anxiety.

Still, limited reliability, lack of specificity, and restricted generalizability in emotional AI are limitations. Mental health AI systems require simplifications of psychological models and grand theories in neurobiology. In addition, gestures and voice vary across cultures – thus, emotional AI cannot necessarily capture the diversity of human emotional experience.

Qualitative relationships must be quantified in emotional AI, and the future belongs to those who are capable of producing these results.

2. Artificial Intelligence for Dieting & Nutrition

It is not a secret that we are what we eat and that the food we consume daily affects our well-being. Another example of AI in healthcare lies in the nutrition and food industry for weight loss, nutrition planning, and healthy eating and dieting processes.

The AI-based diet planning model is based on the experience of nutritionists in combination with some recent field studies – most precisely, the proven data available. AI can then be utilized in diet planning based on physical attributes and targets.

A significant development was conducted by Israel’s Weizmann Institute of Science that provided insight into the increase in blood glucose levels in response to eating. Just note that more than one hundred factors are involved in studying glycemic response and personalized diet planning is key, rather than the simply being a food factor. 

Artificial intelligence for dieting & nutrition

AI-based diet planning is in the early evolutionary stages as Cambridge research states, but the technology is up-and-coming.

Personalized diet plans account for machine learning and data analytics for specifics in the user’s digestive system. For instance, FitGenie uses AI to automate nutrition planning that makes dieting simpler and less time-consuming.

The big difference in the project lies in the complex analysis not solely concentrating on calories, but also metrics like body composition, adherence, weight change rate, hunger, fatigue, and much more. These data are analyzed and the interconnections are built to make justified adjustments.

As well, Science 22 bases its predictions on properties like weight-loss meals derived from big-data analytics, social demographic data, and desired weight loss. And this created model predicts a 72% chance that users will stick to the proposed plan and lose the expected weight.

In perspective, AI can analyze metabolism, the digestive system, allergies, and other taste priorities to create an individualized, ideal meal plan following the set need. As a result, many can resolve their health issues with diabetes, obesity, heart problems, issues with malnutrition, etc. Moreover, these AI wellness solutions can act more quickly and effectively with millions of data pieces than nutritionists.

3. Artificial Intelligence for Staying Active

As a top strategic tech trend, AI has begun taking input from fitness professionals and adapting the insights. As presented by the Freeletics app, users can work out anywhere and have physical fitness training using AI.

Freeletics app
(Source: Freeletics)

The AI-based physical system doesn’t claim to be better than a human trainer, but the app works by first taking input from fitness coaches and adapting this knowledge. Then, the system analyzes the constantly-changing outcomes powered by the data from 10 million users, comparing the information to the goal and exercise abilities stated by each user.

The team uses the so-called “human-augmented AI approach,” which can help push the limits in individual training based on an existing capabilities analysis and goal specifications. Engineers combine inference modeling with multiple data mining techniques to generate a custom training experience.

For example, Tonal automatically sets the optimal direction in real-time for every movement, so users get the most of every exercise and get faster results. In addition, Tempo Studio utilized 3D motion capture and AI technology to provide users with biomechanical analysis and insight.

All in all, AI-enabled fitness solutions can help predict exercise, create individual workout plans, make necessary adjustments based on available equipment, and even deliver a human posture estimation. For gym management, AI helps automate repetitive tasks and avoid human errors.

Nonetheless, the field of AI wellness faces some challenges and limitations:

  • Shortage of qualified specialists and engineers.
  • High cost to set up the necessary IT infrastructure for AI projects.
  • Limited availability of the EHR (electronic health records) consisting of physiological measures, medical history, laboratory reports, information on allergies, medical prescriptions, and other related information to make proper correlation analysis.
  • Data deficiency and its unstructured format may result in false conclusions.
  • Authenticity and integration of the data in the field that should use standardized format with ongoing renovation.
  • Complexity of data collection and standardization via new computational and mathematical tools.

Does the Future Belong to AI Wellness & AI Therapy Apps?

As explained, the existing mental health and wellness digital products cannot meet the current user demand, appear to be ineffective in their treatment, lack credibility and clinical trials, and do not even meet the minimum security and privacy standards. In addition, most well-known products for mental well-being provide straightforward, standardized content for entertainment, which in reality cannot help individuals cope with severe mental issues.

The future of the wellness industry is driven by individualization and personalization, possible only with the next generation technologies, including AI. The application lies in improving mental state, assistance with meal consumption and improved nutrition, as well as personalized training assistance.

Beyoncé 22 Days Nutrition is a great instructive example of machine learning and providing individual weekly menus according to particular targets. The backbone of the system’s results is derived from a person’s physical characteristics, health condition, lifestyle, etc.

For mental wellness, AI could become a solution to the leading causes of morbidity and mortality due to mental health disorders. However, many doubt that AI will ever provide empathic care. On the other hand, researchers state that people increasingly connect emotionally to technology and treat robots as unique, living beings.

Lastly, Neurons Lab helped iPlena company develop an AI physical therapy module for better posture, a more symmetrical body with less pain, less muscle stiffness, and a better hormonal situation.

iPlena wellness app screens

The AI-based software provides a more accurate diagnosis and treatment due to the combination of medical expertise and evidence-based technology. The iPlena app makes an accurate visual anamnesis based on medical expertise and algorithms that evaluate the client’s medical history.

The founder of this software is an osteopath and physiotherapist who constantly analyzes MRI pictures and makes a diagnosis based on specific rules. Collecting these rules and correctly clustering this data made for a project that was perfectly tailored for AI adoption.

As a result, the AI-based system helps accept more patients in the clinic. In addition, the software aims to enhance the client’s engagement in their own health and allows them to spend less time, experience less stress, and pay less for medical services.

How iPlena differs from other generic AI wellness apps

Lastly, any AI wellness project requires reliance on several stakeholders and funding sources. The biggest challenge of new deep-tech startups, aside from funding, is cross-functional technical and business expertise.

Are you looking for a tech partner to innovate with in the wellness industry?

The team at Neurons Lab could become your trusted tech partner. We can support you with the latest engineering information and capabilities, business value, and the option to reach out to venture capitalists for funding.

Drop us a line for a detailed conversation