ABSTRACT :

Unlike most other health conditions, the treatment of mental illness relies on subjective measurement. In addition, the criteria for diagnosing mental illnesses are based on broad categories of symptoms that do not account for individual deviations from these criteria. The increasing availability of personal digital devices, such as smartphones that are equipped with sensors, offers a new opportunity to continuously and passively measure human behaviour in situ. This promises to lead to more precise assessment of human behaviour and ultimately individual mental health. More refined modelling of individual mental health and a consideration of individual context, assessed through continuous monitoring, opens the way for more precise and personalized digital interventions that may help increase the number of positive clinical outcomes in mental healthcare. In this paper, we provide a conceptual review of such techniques for measuring, modelling, and treating mental illness and maintaining mental health.

SCOPE AND OBJECTIVE:

SCOPE:

Significant gender differences in help seeking have also been found, with 11% of female students looking for help in comparison to 6% of males [25]. A global survey found that while males made up 43.8% of the student body, they only comprised of 33.9% of clients who presented to college counselling centres [26], suggesting that males tend not to seek help for mental health problems. While females generally have higher rates of mood and anxiety disorders [26] this only partially accounts for the gender difference found in help seeking.

OBJECTIVE:

Studies corroborate that mental health problems can impact severely on a student’s life [30]. Indeed, mental health problems considerably disrupt learning ability [10], with psychopathology, particularly anxiety and depression, being associated with lower grades [31]. In addition, students who had lifetime suicide plans and attempts when entering university obtained significantly lower grades [3], as did those who engaged in non-suicidal self-injury [32]. Issues with attention and concentration can also impact on grades in addition to mental wellbeing. For example, ADHD is often co-morbid with a range of mental health disorders [33]. Moreover, research has found that of those with DSM IV/CIDI mental health disorders in the previous 12 months, 83.1% of disorders commenced before students started college and that pre-matriculation onset was associated with higher attrition rates and lower university entry rates [4]. It is important therefore to establish baseline prevalence rates of disorders and to understand the socio-demographic predictors of mental health and behavioural problems when students first enter universities.

 This point of entry information may be very beneficial for universities, helping them to provide adequate support for students and addressing problems early, minimising risk and improving grades and retention rates. A report examining the mental health of students in higher education recommends the use of longitudinal studies to gain greater insight into psychopathology in the student body [9]. Research such as that carried out by the WHO World Mental Health Surveys International College Student Project (WMH-ICS) will gather important information about the wellbeing of the student population. Conducted as part of this initiative, the Ulster University Student Wellbeing Study aims to examine and monitor student health and wellbeing during their time at university.

EXISTING SYSTEM:

Mental health and behavioural problems are common among students commencing university. University life can be stressful and problems often exacerbate during their course of study, while others develop disorders for the first time. The WHO World Mental Health Surveys International College Student Project aims to conduct longitudinal research to examine and monitor student mental health and wellbeing. The Ulster University Student Wellbeing study, which commenced in September 2015 in Northern Ireland (NI), was conducted as part of this initiative (wave 1, n = 739), using the WMH-CIDI to examine psychopathology. Baseline prevalence rates of lifetime and 12-month mental health and substance disorders, ADHD and suicidality were high, with more than half of new undergraduate students reporting any lifetime disorder. Co-morbidity was common with 19.1% of students experiencing three or more disorders. Logistic regression models revealed that females, those over 21, non-heterosexual students, and those from a lower SES background were more likely to have a range of mental health and behavioural problems. Overall, 10% of new entry students received treatment for emotional problems in the previous year. However, 22.3% of students with problems said they would not seek help. The study provides important information for universities, policy makers and practice, on mental health and wellbeing in young people generally but particularly for students commencing university. The findings will assist in the development and implementation of protection and prevention strategies in the university setting and beyond.

EXISTING SYTEM ADVANTAGES:

•        It is used for general conversation not for the specific task.

3.3 PROPOSED SYSTEM:

•        1st stage :

•        First, is the measurement stage. It is recommended to collect a broad range of behavioural data through the smartphone over an extended time. Generally, the range of measures collected through the smartphone is relatively consistent regardless of condition. This is in part because much of current research is exploring the relationships between these new signals in order to identify new digital biomarkers. Hence, it makes sense to cast a wide net. Another reason for this is because many of these data streams underpin behaviours that are known to be relevant across mental illnesses (i.e., accelerometer data as a measure of physical activity). Machine learning techniques can be used to find novel connections or structures in the data.

•        2nd stage:

•        Second, the raw data collected at the measurement stage is processed to create new features with a goal to create clinically meaningful inferences. This process can be guided by current clinical thresholds (e.g., for depression), observations from clinical practice, or novel features (such as various mobility measures made possible by GPS). The output of this phase can be the identification of novel digital biomarkers—behavioural patterns derived from mobile sensing—that are significant of or predictive of various mental states of interest. For example, various features collected via the smartphone such as a decrease in locations visited might be strongly related to psychomotor retardation. Furthermore, this phase can generate models that can then be used on their own as a real-time monitor of mental health

•        Final stage:

•        The final phase focuses on how to integrate these inferences into the management of the target condition. This could range from using the inferences alongside a traditional clinical intervention such as psychotherapy or pharmacotherapy to create highly personalized digital interventions such as psychoeducation modules or chrono-therapeutic interventions like light therapy. If at the measurement phase, commonly used measures of mental health such as the PHQ are used, then it may be possible to create models that effectively can translate inferences into outputs that practitioners are already familiar with (i.e., PHQ scores). The identification of biomarkers for depression could help personalize clinical decisions identifying treatments that have a greater chance of success for each individual and then automatically monitoring individual progress when on that prescribed treatment.

PROPOSED SYSTEM ADVANTAGES:

•        It will be more useful for the Mental people.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10