Abstract
Predictive pioneers are bridging the mental health gap in older adults by leveraging advanced digital technologies and user-centric insights. This study explores the potential of machine learning and digital tools in predicting mental health conditions in older adults, facilitating early intervention and support. Through an extensive literature review, user interactions, and healthcare staff interviews, the research aims to bridge gaps in existing knowledge and emphasize real-world application. The research culminates in the creation of highly accurate predictive models for depression in older adults, with valuable insights from user perspectives and healthcare staff guiding future directions. This study contributes innovative methods for mental health prediction and emphasizes the importance of user-centred design, paving the way for effective and accessible mental health interventions for older adults.
Top1. Introduction
In recent years, the intersection of mental health, digital technology, and predictive analytics has paved the way for innovative solutions in healthcare (Koutsouleris et al., 2022). The statistics are both staggering and disconcerting, a significant portion of the elderly population grapples with melancholy and anxiety. What compounds the issue is the tendency for individuals to defer seeking therapy until crisis strikes, often driven by a complex interplay of shame and misinformation (Karatas et al., 2022). Against this backdrop, the imperative for early intervention and support becomes paramount. In order to assist older folks with early intervention and support, this chapter explores a ground-breaking method that uses digital technology and machine learning to forecast their anxiety and depression conditions in the future (Biswas, 2022). To understand the landscape comprehensively, this chapter thoroughly explores existing literature. The background is laid via a narrative review. These provide a detailed grasp of the possibilities and difficulties of employing digital technology to forecast mental health issues in older persons (Buenaventura et al., 2020). Central to this study are the predictive models developed, which exhibit notably high accuracy in contrast to prior research outlined in the review (Kadylak & Cotten, 2020). An essential revelation surfaced during this process: the nuances of predicting anxiety status proved to be more complex than anticipated, shedding light on the need for careful consideration of input data. This chapter delves into the human aspect of technology implementation. Important usability and motivational concerns were highlighted during interactive sessions with older persons. Understanding their perspectives is pivotal in designing effective, user-friendly digital mental health monitoring and management tools. Incorporating the viewpoint of healthcare staff is integral to any implementation strategy. The present research uses in-depth interviews to investigate current procedures and past encounters with technology in community healthcare contexts. These insights provide valuable guidance for seamless integration into existing healthcare frameworks. Quantitatively, the efficacy of digital mental health tools in predicting anxiety and depression is underscored by the potential to significantly enhance the determination of depression presence and severity in the elderly. Machine learning algorithms, particularly adept at processing self-reported mood data, emerge as a powerful tool in this endeavor. The notable predictive power exhibited by these models lends credence to the viability of the prediction strategy, laying the foundation for a data-driven paradigm shift in mental health diagnostics. Qualitatively, the chapter advocates for a nuanced approach to policymaking and practitioner integration. Beyond the mere introduction of digital tools, the emphasis lies on tailoring applications to the unique needs of the elderly population. This creative and practical strategy bridges the gap between theoretical advancements and practical implementation, offering a more personalized and effective mental health care experience.
Key Terms in this Chapter
Machine Learning: Artificial intelligence, known as machine learning, focuses on teaching computers new skills by exposing them to data and analyzing it statistically. The main goal is to build systems that can improve with time and do not need to be programmed to do anything.
Ageing: Physical, mental, and social changes are expected outcomes of ageing, defined as the standard and progressive process of becoming older. It includes changes in a person's physical, mental, and social selves accompanying ageing.
Anxiety: A continuous and uncontrolled fear of the unknown is the physical and mental manifestation of anxiety. As a typical emotional response to stress, it may range from mild to severe and significantly impact people's day-to-day functioning.
Digital Technology: refers to the systems, networks, and applications that process and transmit information using binary code. It consists of the internet, smartphones, and computers, all of which collaborate to facilitate data storage and communication, among other things.
Depression: Symptoms of depression include a lack of interest in once enjoyable activities, a pervasive feeling of hopelessness, and an inability to improve one's spirits. It could significantly affect a person's emotional, mental, and physical well-being, making it hard for them to carry out their daily activities.