Introduction
Mental health problems are a significant issue in Bangladesh, where a large number of individuals of different ages suffer from mental illness due to a shortage of mental health services. The causes of mental health problems in BD are due to a combination of various reasons, including inadequate mental health facilities, insufficient mental health care, stigma, panic, and insecurity.
Mental health care facilities in the country are limited. In recent years, mobile health (m-Health) technology has been introduced, significantly improving access to mental health support and solutions. This technology enhances the quality, accessibility, and affordability of mental healthcare. Unfortunately, societal stigma often leads individuals and families t avoid seeking help, but m-Health technology allows them to receive treatment discreetly.
The m-Health technology employs mobile devices to support healthcare services, including smartphones, tablets, wearables, and other devices. Well-known m-Health services in BD include Relaxy, Moner Bondhu, and the like. Relaxy is a locally developed app that connects users with mental health professionals, while Moner Bondhu offers online and in-person counseling, as well as a 24/7 crisis and emergency response helpline. These services aim to make mental health support more accessible and affordable for everyone in Bangladesh. Currently, BD has numerous mobile apps, online counseling platforms, and other services that play a significant role in addressing mental health issues.
m-Health is one of the digital solutions tackling health problems, and in recent years, people of BD have increasingly turned to this technology for medical support. However, the quality, accessibility, and affordability of m-Health technology for mental health solutions remain insufficient.
This research adopts the technology acceptance model (TAM) to evaluate the perceived benefits (PBs) and usefulness of m-Health technology for mental support. It also focuses on the attitudes of the people of the Bangladeshi population toward m-Health technology. In this research, the TAM framework helps to efficiently identify the relationships between various factors such as perceived usefulness (PU) and users’ attitude toward m-Health technology, as well as the correlation between usefulness and the intention to use this technology.
Additionally, the research also discusses the role of m-Health technology in preventing mental health problems in BD and examines various factors associated with these issues.
This research was conducted from January 2023 to June 2024, adhering to ethical considerations to ensure the confidentiality and anonymity of all respondents.
Objective of the research
1. To evaluate the impact of m-Health technology on the physiological mental health development in Bangladesh.
2. To identify the association between PU, ease of use, and respondents’ attitude toward m-Health technology.
3. To investigate the factors that contribute to mental health problems in Bangladesh.
4. To evaluate how m-Health technology provides discreet support for mental health patient due to societal stigma in Bangladesh.
Research question
1. To what extent is gender related to the types of mental health problems in Bangladesh?
2. To what extent does m-Health technology prevent mental health problems in Bangladesh?
3. To what extent is occupation associated with mental health problems in Bangladesh?
Literature review
In Bangladesh, over 6 million people suffer from depressive disorders, while 7 million experience anxiety disorders. It is estimated that more than 10,000 individuals die by suicide each year in the country. Among students aged 13–17 in Bangladesh, around 4% of boys and 6% of girls contemplate suicide.
This guideline addresses common mental health disorders in adults (18 years and older), including depression (including sub-threshold disorders) and various anxiety disorders [such as generalized anxiety disorder, panic disorders, phobias, social anxiety disorder, obsessive–compulsive disorder, and post-traumatic stress disorder (PTSD)] (1).
Mental health issues are a significant concern in Bangladesh. Unfortunately, those with mental health conditions often do not receive adequate treatment, and mental health is not recognized as a serious public health issue. The prevalence of mental disorders is high across different population groups, which is compounded by negative societal perceptions. People with mental illnesses are often stigmatized and labeled as “mad,” leading to a poor reputation within society. m-Health technology provides discreet support to individuals experiencing mental health challenges (2).
The increasing number of mental health patients in BD can be attributed to several factors, including a lack of well-trained mental health professionals, shortage of public mental health facilities, limited financial resources, inadequately stewarded mental health policies, and social stigma.
Various forms of stigma against mental illness exist in BD such as self-stigma, public stigma, professional stigma, and institutional stigma. Self-stigma is a negative attitude of patients against themselves due to having mental issues. Public stigma toward mental patients is very common in Bangladesh; this denotes the general people’s negative and discriminatory behaviors toward mental health patients, hindering their access to medical support. In this regard, m-Health technology is able to conceal the records of patients with mental illness and support them secretly (3).
Professional stigma indicates the negative and prejudiced attitude of patients, general people, or other healthcare professionals toward mental healthcare professionals due to their association with stigmatized patients, affecting their practice. So, through m-Health technology, mental healthcare doctors secretly prescribed to their patients. Lastly, institutional stigma is defined as the intentional or unintentional policies of an organization and negative attitudes toward mental patients that restrict the opportunities for them. m-Health technology can help conceal the identities of mental health patients and provide them with support in a confidential manner (3).
In Bangladesh, community care facilities for psychiatric patients are limited, and the BD government’s budget for mental health care is minimal. Only a small percent of mental patients receive free essential psychotropic medications.
Recently, the Bangladeshi government has prescribed some mental health policies, such as
1. In 2018, BD Parliament approved a new Mental Health Act,
2. In 2019, the Ministry of Health approved a new Mental Health Policy.
There are several online counseling platforms available in Bangladesh such as.
1. Online counseling platforms: Offers virtual counseling sessions with mental health professionals.
2. Tele-psychiatry services: Connects mental health professionals with individuals seeking support or other services.
3. Centre for the Rehabilitation (CRP)-Ganakbari Day Center: Offers psychiatric consultancy, psychotherapy, counseling, occupational therapy, and more.
4. CRP-Mirpur Day Center: Offers professional mental health services on a daily and weekly package.
5. MindSheba: Offers therapy, counseling, and psychological assessments.
6. Esho Nije Kori: Focuses on developing the mental well-being of young professionals and youth.
m-Health technology uses mobile phones and other wireless technology to help with healthcare, such as tracking health, managing chronic diseases, and providing medical advice. The most common application of m-Health is the use of mobile devices to educate consumers about preventive healthcare services.
m-Health technology significantly influences mental health development by delivering accessible, convenient, and often discreet tools for self-management, symptom monitoring, and therapeutic interventions, allowing individuals to engage with mental health support more readily and consistently, particularly in situations where traditional access might be limited (4).
Key ways m-Health technology contributes to mental health development
Increased accessibility
Mobile apps and platforms can be accessed anytime and anywhere on a smartphone, making mental health support readily available to a wider population, including those in rural areas or with busy schedules.
Self-monitoring and tracking
Apps can facilitate self-tracking of mood, anxiety levels, sleep patterns, and other relevant mental health indicators, providing valuable insights for individuals and clinicians to monitor progress and identify potential triggers.
Evidence-based interventions
Many m-Health apps incorporate evidence-based therapies such as cognitive–behavioral therapy (CBT) techniques, mindfulness exercises, relaxation strategies, and journaling prompts, enabling users to practice coping skills at their own pace.
Personalized treatment plans
Apps can be tailored to individual needs, offering customized interventions and support based on specific diagnoses and symptom profiles.
Social support and connection
Some m-Health platforms facilitate peer-to-peer connections, allowing users to share experiences and provide support to one another, reducing feelings of isolation.
Examples of m-Health applications in mental health
Depression management apps
Apps such as “Headspace” and “Calm” offer guided meditations, breathing exercises, and mindfulness practices to manage stress and anxiety.
CBT-based apps
“Moodpath” and “Daylio” use CBT techniques to help users identify negative thought patterns and develop healthier coping mechanisms.
Anxiety management apps
Apps such as “Relax Melodies” provide soothing sounds and relaxation techniques to manage anxiety symptoms.
Mental health check-in apps
Platforms such as “Wysa” use AI-powered chatbots to provide immediate emotional support and check-in on users’ mental well-being.
Challenges and considerations
Data privacy
Ensuring user data security and privacy is crucial when developing and utilizing m-Health apps.
Accessibility and digital divide
Ensuring equitable access to m-Health technologies is important, considering limitations related to internet connectivity and device availability in certain populations.
Clinical validation and regulation
m-Health apps need to be clinically validated and meet regulatory standards to ensure their effectiveness and safety.
Overall, m-Health technology has the potential to revolutionize mental health care by making interventions more accessible, engaging, and personalized, potentially improving treatment outcomes for individuals struggling with mental health conditions (4).
Causes of mental health problems in Bangladesh
Mental health issues in BD are caused by a number of factors, including a lack of services, stigma, and trauma, lack of proper education, family problem, and so forth.
Shortage of mental health professionals
There are not enough qualified mental health professionals to meet the needs of the population.
Poorly managed policies
Mental health policies are not well managed, and there is a lack of a well-established referral system.
Limited access
Many people do not have access to mental health services, especially in rural area.
Stigma
Social stigma
Mental health issues are associated with stigma, taboo, and discrimination in Bangladesh.
Gender-based violence
Gender-based violence, such as dowry-related acid attacks, rape, and forced abortion, can lead to psychological and psychosomatic symptoms in this country.
Trauma
Childhood trauma: Childhood abuse, neglect, or trauma can lead to mental health issues.
Traumatic life events: Stressful life events, such as financial problems, the death of a loved one, or divorce, can lead to mental health issues.
Substance misuse: Substance misuse is a growing concern in Bangladesh, especially among young people.
Other factors
Chronic medical conditions: Chronic medical conditions, such as diabetes, can increase the risk of developing mental health issues.
Brain injury: Brain damage from a serious injury can lead to mental health issues.
Gender is a factor that can impact mental health, and there are many different factors that contribute to mental health issues in both men and women.
Mental health disorders in women
• Women are more likely to experience depression, anxiety, and eating disorders
• Women are more likely to experience PTSD due to sexual assault, rape, and child sexual abuse
• Women may experience depression during or after pregnancy (perinatal depression)
Mental health disorders in men
• Men are more likely to be diagnosed with substance abuse and antisocial personality disorder
• Men are more likely to die by suicide than women
• Men may be more likely to use alcohol to relieve stress
Other factors
• Gender identity and gender dysphoria
• Social expectations, discrimination, and violence
• Hormones, such as estrogen and progesterone, which impact mood, stress, and cognition
Treatment
The earlier treatment begins, the more effective it can be.
Understanding gender and mental health. Understanding the differences between men and women in mental health, and the underlying causes, could improve mental health treatment.
A significant relationship exists between occupation and mental health issues, where certain job characteristics like high stress levels, low job control, and demanding work environments can contribute to the development of mental health problems such as depression, anxiety, and burnout; conversely, having a meaningful and fulfilling job can positively impact mental well-being (5).
Key points about the occupation mental health
Job strain
A major factor linking occupation to mental health issues is “job strain,” which occurs when employees face high job demands with little control over their work processes, leading to increased stress and potential mental health problems.
Occupational stressors
Specific stressors, depending on the occupation, such as long working hours, shift work, heavy workloads, tight deadlines, and exposure to traumatic events, can significantly impact mental health.
Social factors
Aspects such as job insecurity, limited career advancement opportunities, and poor workplace relationships can also contribute to mental health concerns.
Positive impacts of employment
Conversely, having a stable job with a sense of purpose and belonging can promote positive mental health by providing structure, social interaction, and a sense of accomplishment.
Examples of occupations with potential high mental health risks:
• Healthcare workers (nurses, doctors) due to high stress and demanding patient care
• First responders (police officers, firefighters) due to exposure to traumatic events
• Social workers dealing with complex client situations
• Shift workers with irregular schedules
• Highly competitive professions with high pressure to perform
What can be done to mitigate the impact of occupation on mental health:
Workplace interventions
Implementing stress management programs, flexible work arrangements, supportive work environments, and access to employee assistance programs.
Individual coping mechanisms
Maintaining a healthy lifestyle, practicing relaxation techniques, setting boundaries, and seeking professional help when needed.
Impact of mental problem
Most people who die by suicide have a mental health issue. It may be a depressive or substance abuse disorder. They may feel lonely, depressed, or isolated. They may have had a traumatic life experience.
m-Health: definitions, classifications, and contradictions
m-Health refers to the use of mobile devices such as smartphones and tablets to support healthcare services, facilitating health data tracking, medical consultations, and health management. The features of m-Health technologies are healthcare practices; technological modality of the mobile device; intended user group; the stakeholders.
How it works
Data tracking: m-Health apps and wearable devices can monitor vital signs, activity levels, and other health data.
Medical consultations: Tele medicine platforms allow for remote consultations with doctors and other healthcare professionals.
Health management: m-Health tools can help individuals manage chronic conditions, track medications, and receive reminders for appointments.
Benefits m-Health technology
Convenience: m-Health provides accessible and convenient healthcare services, especially in remote areas or for individuals with limited mobility.
Improved health outcomes: By enabling continuous monitoring and real-time data collection, m-Health can help healthcare providers and patients make informed decisions about treatment and care.
Empowerment: m-Health empowers individuals to take an active role in their health management and decision-making.
Examples
1. Apps for tracking fitness, sleep, and nutrition.
2. Telemedicine platforms for remote consultations.
3. Wearable devices for monitoring vital signs.
Classifies m-Health apps into six categories: health record apps, lifestyle apps, remote counseling and monitoring apps, health education apps, apps for contacting healthcare providers, and diagnostic and treatment apps. Mobile fitness applications fall within the realm of lifestyle and health applications.
• Wellness and Lifestyle Management.
• Chronic Disease Management.
• Mental Health and Behavioral Apps.
• Telemedicine and Consultation.
• Health Monitoring and Medical Devices Integration.
• Personal Health Records and Management.
• Women’s Health and Maternity.
Health care information system
The healthcare sector of BD tries to establish online and instant medical facilities for the patients. As a result healthcare sectors are implementing new digital solutions for medical support. Now, m-Health technology is used in administering clinical, administrative, and financial operations of medical organizations.
The health care information system provides healthcare data, information technology, instant medical support, mental support, gives instant solutions to various mental and physical problems, and the like. Technology implementation plays a significant role in boosting m-Health sector (6).
Health Information System (HIS), generally acknowledged as HIS, is one of the most influential technological boons for the medical sector. HIS performs a significant role in developing mental health management with utter efficiency.
Brief description of TAM model & Unified Theory of Acceptance and Use of Technology
The TAM and the unified theory of acceptance and use of technology (UTAUT) are used to better understand why users accept or reject a given technology, as well as how user acceptance can be improved through better and modified technological strategies.
In this research, the author used TAM and UTAUT theory to predict the importance of m-Health technology on the mental health development in Bangladesh. The author successfully used the theory of TAM to predict technology adoption in the healthcare service of Bangladesh. Both TAM and UTAUT models are effectively applied in the mental health care sector of Bangladesh. In this paper, the author showed that TAM and UTAUT models are able to explain stable predictive capabilities for acceptance and use of technologies in the mental healthcare sector of Bangladesh. Mental health issues may be a specific context of health care, where not only the technology, but also socio-organizational and cultural factors influence technology acceptance (4).
By the principle of TAM and UTAUT, we know that the actual use of a specific technology is affected by one’s behavioral intention to use it.
The theory of TAM explains that the intended use is determined by attitude toward using the technology, which in turn is determined by two perceptions of the system: PU and perceived ease of use (PEU).
There are various external factors that influence the perceptions of TAM and UTAUT. The perceptions of UTAUT are the extension of TAM as well as seven other theoretical frameworks. It proposes four constructs that affect usage intention—performance expectancy, effort expectancy, social influence, and facilitating conditions. Age, gender, experience, and voluntariness of use mediate the impact of these expediencies and facilitating conditions on intention (6).
Data analysis
Here the author used questionnaire to evaluate the impact of m-Health and E-health technology as a mental health development tool. The author interviewed personally 435 respondents. According to TAM, PU, UTAUT, and perceived ease (PE) are primary motivational factors for accepting and using new technologies. Based on the variables in TAM, the following hypotheses were formulated:
H01: m-Health technology has no impact on the prevention of mental health problems in BD.
H02: There is no association between gender and type of mental health problems in BD.
H03: There is no association between employment status and type of mental health problems in BD.
H04: Activities of m-Health technology have no association with the protection of mental health problems in BD.
H05: Purpose of m-Health technology is not associated with raising awareness toward mental health.
H06: There is no significant relationship between PEU and attitude of customers toward using m-Health technology.
H07: There is no significant relationship between PU and attitude of customers toward using m-Health technology.
H08: There is no significant relationship between PB and attitude of customers toward using m-Health technology.
H09: There is no significant relationship between social influence and benefits toward using m-Health technology.
H10: There is no significant relationship between usefulness and attitude of customers toward using m-Health technology.
H11: There is no significant relationship between usefulness and intention of customers toward using m-Health technology.
H12: There is no significant relationship between occupation and mental health disorder.
Data analysis
The population for this study consisted of 435 respondents from various locations across Bangladesh. Participants were selected using intentional random sampling and completed a questionnaire based on a 1–5 Likert scale. The research data analysis utilized several statistical tests including Pearson correlation R test, independent t-test, validity test, goodness-of-fit test, and chi-square test. Participants were then directed to the questionnaire, where they shared their experiences with M & E health technology that enhances mental health development in Bangladesh.
All questionnaires were self-administered. The testing process was completed once participants finished answering the questions.
The research method employed was a quantitative descriptive method.
Table 1 shows that young age people are highest number of respondents in this area. SD is a widely used measurement of variability in statistics. It shows how much variation there is from the average (mean). A low SD indicates that the data points tend to be close to the mean, whereas a high SD indicates that the data are spread out over a large range of values. The mean value is 35.03, indicating that most of the respondents agreed to that variable. The SD value is 6.78, indicating that there is a moderate stability of ideas on the specific variable.
Table 1. Distribution of respondents according to their age.
Table 2 shows that Under-graduate and graduate people are the highest number of respondents in this area; higher education level people are very lower percentage.
Table 2. Distribution of respondents according to their education level.
The mean value is 1.76, indicating that most of the respondents did not agree on that variable. The SD value is 12.74, indicating that there is a low stability of ideas on the specific variable.
Table 3 shows that students are the highest number of respondents in this research. The highest mean indicates that most of the respondents agreed on that variable. The lowest SD indicates that there is a stability of ideas on the specific variable.
Table 3. Distribution of respondents according to their occupation.
The mean value is 3.26, indicating that most of the respondents agreed on that variable. The SD value is 9.41, indicating that there is a low stability of ideas on the specific variable.
The range of interpreting the Likert scale mean score is as follows: 1.0–2.4 (Negative attitude), 2.5–3.4 (Neutral attitude), and 3.5–5.0 (Positive attitude).
A low SD means there was a lot of agreement about the answers. High SD means there was a wide range of answers, indicating disagreement. Here, the author gets high SD, indicating high disagreement among the answers.
The range of interpreting the Likert scale mean score is as follows: 1.0–2.4 (Negative attitude), 2.5–3.4 (Neutral attitude), and 3.5–5.0 (Positive attitude). Here, the author gets high SD, indicating high disagreement among the answers.
The mean of all statements is greater than 3.5, respectively, which indicates positive attitude of PEU of ride sharing app. SD of all the statements are greater than 10, which indicates high disagreement among the answers.
The mean of all five statements is greater than 3.5, which indicates positive attitude of PU of ride sharing app. SD of all the statements are greater than 10, which indicates high disagreement among the answers.
The mean of all the statements is greater than 3.5, which indicates positive attitude of PB of ride sharing app. SD of all the five statements are greater than 10, which indicates high disagreement among the answers.
The mean of all the statements is greater than 3.5, which indicates positive attitude toward ride sharing app. SD of all the statements are greater than 10, which indicates high disagreement among the answers.
The mean of all the statements is greater than 3.5, which indicates positive attitude of intention toward ride sharing app. SD of all the statements are greater than 10, which indicates high disagreement among the answers.
Here, the mean of all the statements is greater than 3.5, which indicates positive attitude of social impact of ride sharing app. The author gets high SD, which indicates that there was a wide range of answers, indicating disagreement.
The range of interpreting the Likert scale mean score is as follows: 1.0–2.4 (Negative attitude), 2.5–3.4 (Neutral attitude), and 3.5–5.0 (Positive attitude). Here, the author gets high SD, indicating disagreement among the answers.
The range of interpreting the Likert scale mean score is as follows: 1.0–2.4 (Negative attitude), 2.5–3.4 (Neutral attitude), and 3.5–5.0 (Positive attitude).
A low SD means there was a lot of agreement about the answers. High SD means there was a wide range of answers, indicating disagreement. Here, the author gets high SD, indicating high disagreement among the answers.
Test of hypothesis
H01: m-Health technology has no impact on prevention of mental health problem in BD.
Here the author used independent t-test
Calculated t-value = 0.06436206
Table value t = 0.00000
If the absolute value of the t-value is greater than the critical value, we reject the null hypothesis. So, the null hypothesis m-Health technology has no impact on prevention of mental problem in BD is rejected or not true. There is a significant impact of m-Health technology on prevention of mental health problem of Bangladesh.
H02: There is no association between gender and type of mental health problem in BD.
Here the author used chi-square test.
The calculated chi-square value = 13.31.
Degrees of freedom = (2–1)*(7–1) = 6
At 0.05 level of significance and 6 degrees of freedom the table value of chi-square value = 12.592.
So, chi-square cal > chi-square table.
If our chi-square calculated value is greater than the chi-square critical value, then we reject our null hypothesis. So, in the null hypothesis, there is no significant association observed between gender and type of mental problem in BD, which is rejected or not true. So, there exists a significant association between gender and type of mental health problem in BD.
H03: There is no association between employment status and type of mental health problems in BD.
Here, the author used chi-square test.
The calculated chi-square value = 42.7316.
Degrees of freedom = (2–1)*(7–1) = 6.
At 0.05 level of significance and 6 degrees of freedom, the table value of chi-square value = 12.592.
So, chi-square cal > chi-square table.
If our chi-square, calculated value is greater than the chi-square critical value, then we reject our null hypothesis. So, in the null hypothesis, there is no significant association observed between employment status and type of mental health problem in BD, which is rejected or not true. So there exists a significant association between employment status and type of mental health problem in BD.
H04: Activities of m-Health technology has no association with the protection of mental health problem in BD.
Here, the author used chi-square test.
The calculated chi-square value = 43.12.
Degrees of freedom = (3–1)*(6–1) = 2*5 = 10.
At 0.05 level of significance and 10 degrees of freedom, the table value of chi-square value is = 18.307.
So, chi-square cal > chi-square table.
So, in the null hypothesis, activities of m-Health technology have no association with the protection of mental health problem in BD, which is rejected or not true. So, activities of m-Health technology have a direct association with the protection of mental health problems in BD.
H05: Purpose of m-Health technology is not associated with raising awareness toward mental health development.
Here, the author used chi-square test.
The calculated chi-square value = 38.659.
Degrees of freedom = (3–1)*(3–1) = 2*2 = 4.
At 0.05 level of significance and 4 degrees of freedom, the table value of chi-square value = 9.488.
So, chi-square cal > chi-square table.
So, in the null hypothesis, the purpose of m-Health technology is not associated with raising awareness toward mental health development, which is rejected or not true So, the purpose of m-Health technology is directly associated with raising awareness toward mental health development in Bangladesh.
H05: There exist no remarkable association between PEU and attitude of customers toward using m-Health technology.
The consequence in Tables 22 and 23 shows that there is a remarkable association between PEU and attitude of customers toward m-Health technology, because the P-value of 0.000 in this association is less than the pre-set level of significance in this study, which is 0.05 (P = 0.00 < 0.05). The beta value (r = 0.9998) discloses that there is a strong positive correlation between attitude and PEU. The R square proved that 0.9996 of the total variation of attitude is being explained by PEU. On that basis, therefore, the null hypothesis is accepted, and the alternative rejected.
H06: There exist no remarkable association between PU and attitude of customers toward using m-Health technology.
The consequence of Table 24 expressed a strong evidence against the null hypothesis based on “t” value results.
The outcomes in Table 24 disclosed that there exist a remarkable association between PU and attitude of respondents toward the use of m-Health technology, that is, the P-value of 0.00 in this association is less than the pre-set level of significance in this study, which is 0.05 (P = 0.00 < 0.05). The beta value (r = 0.999) also disclosed that there is a strong association between attitude and PU. The R square value proved that 99.99% of the total variation of attitude is being explained by PEU. Consequently, it can be concluded that there is a positive association exists between attitude of customers and their PU of m-Health technology. On that ground, the null hypothesis is accepted and the alternative rejected.
H07: There exist no remarkable association between PB and attitude of customers toward using m-Health technology.
The consequence of Table 26 expressed a strong evidence against the null hypothesis based on “t” value results.
The outcomes in Table 27 disclose that there exist a remarkable association between PU and attitude of customers toward the use of m-Health technology, that is, the P-value of 0.00 in this relationship is less than the pre-set level of significance in this study, which is 0.05 (P = 0.00<0.05). The beta value (r = 0.999940717) also proves that there exist a strong association between attitude and PBs. The R square disclosed that 99.99% of the total variation of attitude is being explained by PEU. Hence, it can be established that there exist a positive correlation between attitude of respondents and their PB of m-Health technology. On that basis, the null hypothesis is accepted and the alternative rejected.
H08: There is no significant relationship between social influence and benefits toward using m-Health technology.
Here, the author used Pearson r test., r = 0.7067
So, there exists a strong positive relationship between social influence and benefits toward using m-Health technology.
H09: There is no significant relationship between usefulness and attitude of customers toward using m-Health technology.
The consequence of Table 28 expressed a strong evidence against the null hypothesis based on “t” value results.
The outcomes in Table 29 disclose that there exist a remarkable association between PU and attitude of customers toward the use of m-Health technology, that is, the P-value of 0.00 in this relationship is less than the pre-set level of significance in this study, which is 0.05 (P = 0.00 < 0.05). The beta value (r = 0.99994) also disclosed that there is a strong relationship between attitude and PU. The R square proves that 99.99% of the total variation of attitude is being explained by PEU. Hence, it can be established that there exist a positive correlation between attitude of respondents and their PU of m-Health technology. On that ground, the null hypothesis is accepted and the alternative rejected.
Discussion & analysis
From the consequence of the above test, it can be found that there exists a strong relationship between PEU and attitude. Of patients towards m-Health technology. Maximum of the respondents agree that they found m-Health technology service easy to use. The respondents also admit that the medical support they get in terms of mental health issues from m-Health technology in BD is very useful for them.
The research showed that young age people are highest number of mental patients in Bangladesh (Table 1).
Table 2 showed that no education or graduate people are the highest number of mental patients in Bangladesh. Higher education level people have very lower percentage of mental issues in Bangladesh.
The data of Table 3 showed that students are the highest number of respondents in this research.
The research found that male participated more in the interview. This paper also showed that unemployed people are more susceptible to mental health issues than employed people in Bangladesh. The research also showed that people whose education level is in undergraduate level are highly susceptible to mental health problems in Bangladesh. This paper also showed that urban people presented more mental health problems than rural people of Bangladesh. The paper found that married people are more susceptible to mental health crises in Bangladesh (Table 4).
Table 4. Demographic attributes of the respondents.
The research disclosed that maximum of the respondents agreed that the role of m-Health technology is smoothly performed as a mental health development tool (Table 5).
Table 5. Role of m-Health technology as a mental health development tool.
The research found that activities of m-Health technology had a positive impact on the mental development in Bangladesh (Table 6).
Table 6. Activities of m-Health technology on mental health development in BD.
The research showed that maximum of the respondents accorded that it would be easy to use these m-Health tools to find information regarding mental health issues, which indicated that PEU has an optimistic effect on attitude toward m-Health technology in BD (Table 7).
Table 7. PEU of m-Health technology.
The research found that most of the respondents agreed that PU of m-Health technology is very effective (Table 8).
Table 8. PU of m-Health technology.
The research showed that PB of m-Health technology on mental health development is recognized by maximum of respondents (Table 9).
Table 9. Perceived benefit (PB) of m-Health technology.
The research found that maximum of the respondents showed positive attitude toward m-Health technology (Table 10).
Table 10. Respondent’s attitude toward m-Health technology.
The research disclosed that most of the respondents have strong intention toward m-Health technology in Bangladesh (Table 11).
Table 11. Respondent’s intention toward m-Health technology.
The research found that m-Health technology has a positive social influence in Bangladesh (Table 12).
Table 12. Social influence of m-Health technology.
Most of the respondents in this research expressed moderate satisfaction toward m-Health technology (Table 13).
Table 13. Respondent’s satisfaction toward m-Health technology.
In this research, the author disclosed the percentage of suicidal tendency according to age (Table 14). The research found that 16–25 age people have more suicidal tendency in this country.
Table 14. Suicidal tendency at various age in BD.
Here, the author also mentioned the type of mental problem and percentage of mental patient of each type of mental problem in BD (Table 15). This research showed that depression is the most common mental problem in Bangladesh. Then most of the respondents are victim of insomnia, addictive behavior, anxiety disorders, stress, and schizophrenia.
Table 15. Various types of mental problems in BD.
In this research, the author also explained the mental disorder rate according to their profession (Table 16). The research found that students in BD are most susceptible to stress, depression, insomnia, and addictive behavior. Bankers and businessmen are most suffered by stress. Teachers experienced depression the most. Housewives are also victims of depression and stress. Most of the corporate jobholders also were susceptible to depression.
Table 16. Mental disorder according to their profession.
The research found that schizophrenia patients had more tendency to commit suicide than any other mental patient in Bangladesh. Then, depression, PTSD, and stress caused most suicidal tendency in Bangladesh (Table 17).
Table 17. Disease cause and suicidal tendency in BD.
In this research the author found an association between gender and type of mental health problems (Table 18). This research found that males are the most susceptible to depression, stress, insomnia, and additive behavior. And females are the most affected by depression and stress.
Table 18. Gender and type of mental health problem.
Here, the author found a relationship between employment and types of mental problems (Table 19). The research proved that unemployed are the most victim of depression than employed people in Bangladesh. Unemployed people have more additive behavior than employed people. Again, employed people are the victim of stress than unemployed people in Bangladesh.
Table 19. Employment and type of mental health problems.
The research found that activities of m-Health technology has a direct association with the protection of mental health problem in BD (Table 20).
Table 20. Activity and protection of mental health problem.
The research showed that the purpose of m-Health technology is directly associated with raising awareness toward mental health development in BD (Table 21).
Table 21. Purpose and awareness.
This research defends Davies TAM that the PEU affects the attitude and the intention to use m-Health technology in Bangladesh.
The research showed that the degree of PU of m-Health technology positively influences the attitude of respondents toward m-Health technology (Tables 24 and 25). The PU of m-Health care service upgrades the job performance of mental health care industry in Bangladesh, which indicates that m-Health technology with a high level of PU is one for which a user believes that there is a positive user performance relationship. The results of this research found that there is a strong positive relationship between PU and attitude of respondents toward using m-Health technology (Tables 28 and 29).
Table 22. Association between PEU & attitude.
Table 23. Regression between PEU & attitude.
Table 24. Association between PU & attitude.
Table 25. Regression analysis of PU & attitude.
Table 26. Association between PB & attitude.
Table 27. Regression analysis of PB & attitude.
Table 28. Association between usefulness and attitude.
Table 29. Regression analysis of usefulness and attitude.
In this research the author also revealed that there is a significant impact of m-Health technology on prevention of mental health problem of Bangladesh. The author also found that there exists a significant association between gender and type of mental problem in BD. Male and female responded differently to mental health crises (Table 18)
The author also showed that there exists a significant association between employment status and type of mental health problem in BD. Employed people are mostly affected by depression, insomnia, and stress because of workload and career pressure. While unemployed people are mostly affected by depression and additive behavior, because of disappointment, family and social stress, and the like.
The research found that more than 70% respondents have positive attitude toward m-Health technology. The research showed that the most significant barriers of m-Health technology are the unwillingness and low literacy rate of respondents toward m-Health technology.
Limitation
However, this research is not beyond its limitations. This is a perception-based research where a limited number of convenience samples are considered to determine the benefit of m-Health and E-health technology and factors that affect mental health problems in Bangladesh.
There also exist some other limitations in this research, such as all respondents are not so qualified to replied and respond accurately. Time limitation is also a constraint here.
Conclusion
The research showed that m-Health technology plays an important role in preventing mental illness in Bangladesh. m-Health service application is rising gradually in Bangladesh. At present, the number of mental health patients in BD increased rapidly as a result of rate of suicide, crimes, and rape reported. As a consequence, family life, social life, and productivity are hampered seriously. m-Health technology performs a crucial part in protecting mental illness and provide mental support to the patient immediately, promptly, secretly, and remotely.
This research showed that m-Health service performed its activity smoothly because of modern technology. The use of technology eases the use of m-Health service. As a result, more people are interested in m-Health technology.
The research found that Information & Communication Technology (ICT) application of the m-Health technology is in both rural and urban areas of Bangladesh. This research frequently used the perception of TAM and UTAUT implying that acceptance of m-Health applications of all class of people of this country. Now BD mostly exploits ICT to develop m-Health service organizations.
This research explained the role of m-Health technology on the development of mental soundness in Bangladesh. This paper discussed how m-Health tools can be implemented and sustained over time for diverse types of people in this country.
This paper is an implementation science research that emphasized the mental soundness of all class of people through technology-based support system such as m-Health care implementation. This m-Health technology works actively against the societal stigma toward mental patient in Bangladesh.
Besides the crucial part of this paper, the outcomes may identify the correlation among various demographic factors, set up a mental health care development strategy to capture outcomes in a systematic way that prioritizes usefulness of m-Health technology, factors cause mental problem in Bangladesh, inclusion, and robust measurement science.
Conclusively this paper recommended that m-Health technology is a very efficient tool to ensure innovative healthcare service. This technology is meeting the emerging needs of people all over the country. On account of technological innovation m-Health service is very easy to access and implement.
Implication and future study
The application of m-Health service is contiguously accelerating. The m-Health technologies provide a solution to support a lonely person live in a remote place, who may not be able to access in-person medical care services.
The impact of m-Health technology on the development of mental health issues has been increased rapidly.
Future researcher, policymakers, and service providers can study the core research done in this paper in order to conduct more research, issue advance policy, and offer more flexible service.
The result of this research demonstrated a range of studies to capture the exponential growth of m-Health interventions for people. This research showed that m-Health technology had a significant potential to solve various mental health problems of different classes of people and for the youth generation of BD who are facing mental health challenges and seeking support to overcome these challenges.
However, the research found out that the factors that affect mental health problem of people in Bangladesh. The research focused on the reasons for mental health problem in Bangladesh. Again, the paper discussed how m-Health technology lessens the mental health crisis issues of the people of Bangladesh.
However, the review also highlighted notable attention in research that include benefits of m-Health technology, social influence of m-Health technology, satisfaction toward m-Health service, and the like.
Future research may be conducted on adoption of equity, diversity, and inclusion lens of m-Health services. The upcoming research prioritizes understanding how current m-Health technologies can be adopted into existing models of care and develop guidelines, standards, and evaluation frameworks to support future m-Health development and implementation.
The technological advancement expand rapidly in all sectors of modern life including healthcare. Now more global resources are needed to monitor technological advancements to provide quality mental health support through m-Health technology to all people of BD where and when they need them.
Funding
This research was not funded.
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