Tracking Mental Well Being by Using Sensing Technology

Serious Mental Illnesses (SMIs) are among the most critical healthcare challenges, with individuals suffering from these conditions typically living, on average, 25 years less than their peers. In the United States, SMIs rank among the top five conditions for direct medical expenditures, costing over $30 billion annually. These illnesses necessitate lifelong management and monitoring, which can be difficult to sustain. Often, early warning signs of deteriorating mental health or relapse are overlooked, leading to worsening conditions. Research has indicated that behavior-monitoring devices can help identify declines in mental well-being early on, facilitating self-tracking and management. As technology advances, the ability to automatically track self-reported behaviors is expanding, offering the potential to improve illness management in a cost-effective manner. Furthermore, the unobtrusive nature of these methods can lead to discrepancies between sensing capabilities and patient acceptance.

Personalized Treatment through Sensing Technology

Sensing technology can provide a personalized approach to treatment, which will be elaborated upon later in this essay. Despite the potential benefits, clinicians have not fully embraced technological support in managing SMIs. Low acceptance can contribute to poor adherence to treatment plans and increased stigma, which many patients seek to avoid. If developed collaboratively with patients and clinicians, sensing technologies could gain wider acceptance and offer a more effective and cost-efficient means of managing SMIs. The connection between mental health and behaviors—such as reduced physical activity, social interactions, and emotional responsiveness—is well-documented. Various behavioral changes can be monitored, as discussed below.

Tracking Behavioral Changes

Monitoring physical activity, such as fluctuations in movement, is crucial depending on the specific SMI being addressed. Smartphone applications can track physical activity using accelerometer data and compass features. For instance, this technology can reveal when someone is walking or climbing stairs, which is particularly beneficial for patients with eating disorders who may engage in excessive exercise to burn calories. Tracking social engagement presents more challenges, as capturing and analyzing social interactions must be done sensitively to respect user privacy. However, there is growing evidence that changes in vocal and speech patterns can indicate declines in emotional health.

Privacy-sensitive audio technology can analyze the duration and pitch of conversations, utilizing smartphone microphones to detect daily stress levels. Traditionally, physiological symptoms of stress are measured through direct interaction, which may deter patients from participating. New methods that passively detect stress episodes through smartphone microphones have been developed, adapting to individual users even in variable environments. For sleep pattern monitoring, specialized sensors can be burdensome, so smartphone features like light sensors and usage data can estimate sleep duration using regression models. Although this method has a larger error margin compared to bed instrumentation, it is less intrusive and encourages sustained usage.

Immediate Feedback and User Engagement

Providing patients with immediate feedback on their mental health status is crucial. When seeking help for SMIs, patients often wait for weeks before receiving feedback from clinicians. Behavioral sensing can offer timely insights, with visualizations that are easy to understand, enabling users to comprehend their conditions better. The authors of this essay have developed an app called BeWell, which tracks physical activity, social engagement, and sleep patterns in an ambient display, allowing users to feel more integrated with others. Many existing apps provide generic advice that may not resonate with individual users, potentially leading them back to harmful habits.

To enhance user engagement, context-fit algorithms can be employed, tailoring messages to each individual. However, creating these personalized interventions requires collaboration with medical professionals, which could strain an already overburdened healthcare system. While psycho-educational interventions exist, they often take a uniform approach, failing to address individual needs. Effective pharmacological and psychosocial treatments for SMIs are grounded in evidence-based practices, emphasizing the importance of considering the neural and behavioral characteristics of each condition.

Addressing Stigma and Privacy Concerns

To ensure adherence to treatment, it is vital to address factors contributing to nonadherence, particularly the stigma associated with SMIs. Many patients hesitate to use devices that could make them feel conspicuous. By utilizing smartphones, which are widely accepted, potential stigma may be reduced, as the user-facing aspects of the system can remain discreet. Establishing user trust is essential, influenced by the technology's reliability and accuracy. To build this trust, it is important to communicate the limitations and uncertainties of the data being collected.

Improving accuracy may involve users completing reports during a designated training period and reporting any anomalies. Patients with SMIs may experience memory and cognitive deficits, complicating the interpretation of sensing data. Respecting user privacy is paramount; sensing algorithms should store only the raw data that users are comfortable sharing. The authors propose a system that processes audio data on the fly, extracting useful features while preserving user privacy. Allowing users to control their data, including deletion and muting options, empowers them in their treatment process and helps mitigate stigma.

Case Study: Moodrythm App

Moodrythm is an application designed specifically for managing bipolar disorder, which is associated with high functional impairment and suicide rates. The app aims to help users establish and maintain a routine, addressing one of the primary challenges faced by individuals with bipolar disorder. By employing the Social Rhythm Metric (SRM), a validated self-assessment tool, the app tracks daily routines and moods, facilitating insights into how consistency can enhance mental well-being.

While the app supports users in maintaining their routines, self-assessment can be unreliable, particularly during manic episodes. Smartphone sensing capabilities allow for monitoring of social interactions and sleep patterns, integrating SRM to create therapeutic goals. By incorporating reward-sensitive neural characteristics and virtual badges, the app motivates users to adhere to their tasks and goals.

Conclusion

In conclusion, the authors highlight that the benefits of utilizing rich sensing technology in managing SMIs outweigh the drawbacks. This approach is more cost-effective and time-efficient for both patients and clinicians, improving patient understanding of their conditions and promoting daily routines. While potential ethical concerns regarding privacy exist, advancements in technology can mitigate these risks, ensuring that patient data remains secure. Overall, integrating technology into clinical practice presents a promising opportunity to enhance mental health management and improve outcomes for individuals with SMIs.

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