Serious mental illnesses, also known as SMI, are among the most pressing health concerns as people with these illnesses die, on average, 25 years earlier than others. Beyond that, these diseases are among the top 5 conditions for direct medical spending in the United States, with annual costs exceeding $30 billion and requiring ongoing management and monitoring that are difficult to maintain. Early warning signs of a change in mental well-being or a relapse are often overlooked and overlooked, and there have been studies showing how behavior monitoring devices can help notice these declines in mental health early as well as self- monitoring. As the ability to automatically track self-reported behaviors expands, the potential exists to dramatically improve disease management in a cost-effective manner. Furthermore, this method is unobtrusive, meaning that there may be discrepancies between detection ability and patient acceptance. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Detection can be used as a personalized treatment which will be explained later in this essay. Despite all this, doctors have not yet accepted technological support to the extent that they could benefit from it. Low acceptance can also lead to poor adherence and increased stigma, which is what people try to avoid. If developed closely and appropriately with patients and their doctors, detection technologies would be more widely accepted and provide a more efficient and cost-effective method of managing SMI. Mental health is known to be linked to behaviors, decreased physical activity or social interactions and emotional reactivity are known symptoms. There are many other behavioral changes that can be monitored, which I will talk about in more detail later. Detection of physical activities such as decrease or increase in physical movements, depending on which SMI we are talking about, can be monitored using smartphone applications such as accelerometer data, as well as the compass. They show us in much more detail when someone is walking and whether they are going up the stairs and their direction. This can be very helpful when talking about eating disorders as patients often exercise more and go up and down stairs repeatedly for a long period of time to burn calories. Detecting social engagement may be the most difficult to track since capturing and analyzing social encounters can be difficult to do without violating user privacy. Nonetheless, there is a growing body of evidence showing that changes in speech and language patterns are signs of declining emotional health. Privacy-sensitive audio can be used to show us the duration, change in tone, and more of user conversations. All this can be achieved using smartphone microphones, but thanks to this method we can also identify daily stress. Usually, physiological symptoms of stress are measured through sensors, such as chemical analysis, but these methods use direct interaction and are therefore less likely to be used by patients. New techniques have been developed to passively detect stress episodes using smartphone microphones and are designed to adapt to each individual and can be used even when the position of the microphone relativeto the speaker and the room is not static. To monitor sleep patterns, patients usually have to wear specialized sleep sensors that would most likely not work with them, since the load imposed on users during sleep must be minimized so that there are no other factors influencing the their health. Smartphone features such as light sensor, whether the phone is in use, etc., can be used to estimate sleep duration with a regression model. While this has a large margin of error compared to other methods such as bedside instrumentation, it is less intrusive and therefore has longer use. Furthermore, depending on the users' wishes, the system can offer a more detailed recording of their sleep duration. The authors have unpublished experimental data from college students showing that interaction models alone can be used to estimate sleep duration with 85% accuracy, as smartphone use is increasing among young people. Beyond that, it is also important to go beyond disease detection and provide feedback to patients. When going to doctors to get help for SMI, patients only receive their feedback at the next appointment which could be weeks away while the use of behavioral tracking provides immediate feedback, although there is no guarantee, feedback should consist of easy-to-understand visualizations, so users can understand their illnesses. The authors of this article created an app called BeWell, which measures physical activities, social engagement and sleep patterns in an ambient screen so that users do not feel different from others. Most apps provide general advice that doesn't suit every individual and may lead them back to bad habits since they don't relate to the user. The best way to solve this problem would be to use context-adaptive algorithms. This would not only make people more involved in getting help, but would also help them understand and learn about their illnesses. The problem is that creating these messages would require the help of medical professionals, which would cost the overburdened healthcare system even more. Personalized interventions could be more meaningful and potentially more convincing lessons for patients, although psychoeducational interventions already exist, they are usually uniform approaches and therefore are not limited to individuals. Pharmacological and psychosocial treatments for an SMI are rooted in their pathology and are centered on an evidence-based understanding of the disease; therefore it would be useful to also consider the neural and behavioral characteristics of each SMI. To be effective, sensing solutions must consider the factors that influence patient adherence. Several factors have been associated with non-adherence, mainly the stigma carried by SMIs. This is why many patients refuse to use devices that will make them feel like they stand out by having higher levels of privacy using smartphones, it may be possible to reduce the potential stigma as we would ensure that the user-facing aspects of the system visible to others would be less visible. An important aspect of the Rich Sensing method is gaining user trust, which can be influenced by technological reliability and its accuracy. To gain this trust, it is critical to inform users about data limitations and system uncertainty. Accuracy can be improved by having users fill out reports for a designated training period and report anomalous cases. Patients with SMI are prone to having memory andcognitive ability deficits that may mean doctors won't know if tracking data is misleading or if the patient is experiencing a period of reduced self-awareness. Another important factor in this system is respect for user privacy. Detection algorithms should only store as much of the detected raw data as the user is comfortable sharing and how it is clinically meaningful, which is why the authors came up with a system that does not record the raw audio but instead processes destructively audio data on the fly to extract and store features that are useful for inferring the presence and style of speech, but not sufficient for reconstructing spoken words. It is important that you have control over how your data is viewed and used, so that you play an active role in your treatment and understand your disease, which will help you be more compliant with your treatments. This would include deleting data and turning off tracking at certain times. This will also help fight stigma as they will show that they are still in control of themselves. Moodrythm is an app whose goal is to find that balance between perception and acceptance used mainly for bipolar disorder. This disease is associated with poor functional and clinical outcomes, high suicide rates and large social costs. The app has a specific goal since it is only for an SMI, which is to provide users with a routine since this is one of the main challenges for people suffering from bipolar disorder and the lack of routine can lead them to have new depressive and manic episodes. The app focuses on helping them maintain this routine and uses the social rhythm metric, a five-item validated self-assessment of the regularity of daily routines and moods. With this daily monitoring it is easy to see how a regular routine can positively influence patients' mood, which can help doctors further stabilize routines, however, self-report and self-assessment by patients cannot always be reliable as often happens. misjudging oneself during manic episodes. The smartphone's sensing capabilities monitor how often the user has social interactions and when awake or asleep, uses SRM (social rhythm metric) to model a series of self-reported activities with therapeutic goals. They use a reward-sensitive neural feature associated with bipolar disorder and virtual badges to reward users for completing assigned tasks. This has been shown to help motivate users and help them adhere to treatments. This app calculates the frequency and duration of user interactions throughout the day based on audio data analysis, on which it bases task completion times and the user's social engagement goals. For sleep pattern, they have a sleep module that uses their smartphone applications like the accelerometer to estimate how long a user sleeps with approximately 85% to 90% accuracy. SRM uses an algorithm where the higher your score, the more stable your routine. Their homepage includes floating bubbles that represent daily events, these bubbles can move, and the more stable the routine, the less the bubbles move, the bubbles change color, from green, if it is stable, to red if it is not. Therapists help patients gain insight into their disorder and how they can improve their daily routine so that their mental health improves. During the development of this app, they worked with three doctors and three patients, closely studying the link between patients' acceptance and their understanding of the data to see if.