Application of Intelligent Data Systems in The Reduction of Psychological Stress in Older Populations

This research paper aims to build an intelligence data system with the help of pilot data. The intelligence data system is collected from banking facilities, health and wellness centers, government schemes, etc. Initially, the data will be collected from the older populations aged from 62 to 85 years age group. The methodology adopted Snowball sampling techniques. This technique adopts a non-probability method to increase the snowball effect sample size. Hence, we selected a small sample size of 18 participants, comprising 13 males and 5 females, with a mean age of 69.2 years (SD = 6.69, range = 22 years). Participants represented diverse professional backgrounds including government service, private employment, business, and household roles through educational qualifications that is ranging from higher secondary to postgraduate and doctoral levels. The sample was purposively associated with the study’s stress measure as all participants reported moderate to high levels of psychological stress where it evaluated through self-reported measures adapted from the Perceived Stress Scale through particular importance on financial and emotional domains. Particularly, 14 participants (77.8%) indicated high stress attributable to financial insecurity also including concerns surrounding healthcare costs, retirement planning inadequacy, children’s educational and marriage expenses, retirement pension insufficiency, debt management, tax obligations, inheritance decisions and susceptibility to financial scams. Moreover, 12 participants (66.7%) are reported significant stress associated with loneliness or extended family absence while it emphasizing the dual burden of financial and emotional adversity dominant among this demographic cohort. Concerning technology engagement twelve participants reported using mobile banking daily, while one participant trusted exclusively on internet banking and another merely on offline banking and also reflecting a moderate yet heterogeneous level of digital adoption within the sample. Furthermore, four participants self-assessed as knowledgeable about artificial intelligence through news media exposure whereas the majority identified themselves as novices also having encountered AI-embedded tools such as machine translators, smart speakers, and AI-assisted automobiles incidentally including without deliberate awareness. Especially, all 18 participants expressed undisputed interest in AI-assisted emotional support although affirming the viability and receptivity of this population toward technology-mediated psychological interventions. The results evaluate the effectiveness of intelligence data to mitigate psychological stress in older adults. It is tailored to find the qualitative stress-reduction strategies for older adults based on real-time intelligence data for enhancing their emotional well-being and quality of life. In this research paper, snowball sampling methodology is used for selection of participants and the AI pilot study aimed to preliminarily examine the role of intelligence data in lightening psychological stress among older populations. The primary objective of the study was to examine older adults’ attitudes, expectations, and potential requirements regarding the use of AI-based data services for managing daily financial concerns and reducing psychological stress, and to clarify preferred contexts for AI interaction and explore the potential of such technologies to provide both emotional companionship and practical support. The conclusion shows that managing psychological stress support in the form of emotional friendship and compassionate engagement holds significant promise for this population. This study recommends that for higher generalizability, a larger potential sample size data may be collected in future investigations.