A survey of people from 31 different countries around the world found that mental health was the biggest health problem respondents said was facing their country in 2024. Other health problems reported by respondents included cancer, stress, and obesity. The COVID-19 pandemic The COVID-19 pandemic impacted almost every country in the world and was the biggest global health crisis in recent history. It resulted in hundreds of millions of cases and millions of deaths, causing unprecedented disruption in health care systems. Lockdowns imposed in many countries to halt the spread of the virus also resulted in a rise of mental health issues as feelings of stress, isolation, and hopelessness arose. However, vaccines to combat the virus were developed at record speed, and many countries have now vaccinated large shares of their population. Nevertheless, in 2024, ** percent of respondents still stated that COVID-19 was the biggest health problem facing their country. Mental health issues One side effect of the COVID-19 pandemic has been a focus on mental health around the world. The two most common mental health issues worldwide are anxiety disorders and depression. In 2021, it was estimated that around *** percent of the global population had an anxiety disorder, while **** percent suffered from depression. Rates of depression are higher among females than males, with some *** percent of females suffering from depression, compared to *** percent of men. However, rates of suicide in most countries are higher among men than women. One positive outcome of the COVID-19 pandemic and the spotlight it shined on mental health may be a decrease in stigma surrounding mental health issues and seeking help for such issues. This would be a positive development as many people around the world do not or cannot receive the necessary treatment they need for their mental health.

A survey conducted in 2022 found that young adults aged between 18 and 24 were more likely to suffer from moderate to severe stress, depression, and anxiety symptoms. That year, around 66 percent of respondents from this age group reported stress-related symptoms, while 25 percent of those aged 65 and older had similar symptoms. Similar age group trends were also noted for negative effects on wellbeing from feelings of loneliness. Additionally, fewer men than women were likely to report having mental health conditions such as stress, anxiety, and depression. Anxiety, depression, and stress in Europe In Europe, there is a notable variation in the prevalence of mental illnesses. Throughout the epidemic, depression and anxiety increased in all EU nations, affecting young people, particularly young women and those in financial difficulties. A study published by the OECD reported that 55 percent of adults residing in the EU were at risk of depression in 2022. Another study, published in the same year, found that more than half of the respondents surveyed in Ireland, the United Kingdom, and Italy had reported experiencing stress. Anxiety, depression, and loneliness in the U.S. In the United States, anxiety, depression, and loneliness are prevalent issues affecting a significant portion of the population. According to a survey done in 2022, one-third of U.S. adults aged between 18 and 29 years mentioned that they “always” or “often” felt depressed or lonely in the past year. Over half of U.S. adults in the same age group reported that they felt anxious. Furthermore, adults with lower household incomes were more likely than those with higher household incomes to mention that they felt anxious, lonely, or depressed.

BackgroundThe mental health of healthcare workers during the coronavirus-2019 pandemic was seriously affected, and the risk of mental health problems was high. The present study sought to systematically evaluate the mental health problems of healthcare workers worldwide during the pandemic and to determine the latest global frequency of COVID-19 associated mental health problems.MethodsData in the Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Elsevier, MEDLINE, PubMed, PsycINFO and the Web of Science before November 11, 2022, were systematically searched. Cohort, case-control and cross-sectional studies were included. The meta-analysis used a random effects model to synthesize the comprehensive prevalence rate of mental health problems. Subgroup analyses were performed based on time of data collection; whether the country was or was not developed; continent; doctors and nurses; doctors/nurses vs. other healthcare workers; and psychological evaluation scale.ResultsA total of 161 studies were included, including 341,014 healthcare workers worldwide, with women accounting for 82.8%. Occupationally, 16.2% of the healthcare workers were doctors, 63.6% were nurses and 13.3% were other medical staff. During the pandemic, 47% (95% confidence interval [CI], 35–60%) of healthcare workers reported job burnout, 38% (95% CI, 35–41%) experienced anxiety, 34% (95% CI 30–38%) reported depression, 30% (95% CI, 29–31%) had acute stress disorder, and 26% (95% CI, 21–31%) had post-traumatic stress disorder.ConclusionsThe study found that there were common mental health problems among health care workers during the COVID-19 pandemic. The most common was job burnout, followed by anxiety, depression, acute stress and post-traumatic stress disorder. Although the global pandemic has been brought under control, its long-term impact on the mental health of healthcare workers cannot be ignored. Additional research is required to develop measures to prevent, monitor and treat psychological disorders among healthcare workers.

It was estimated that in 2018 roughly 31 million men in the United States suffered from an anxiety disorder. The statistic illustrates the number of lifetime prevalent cases of anxiety disorders among adults in select countries worldwide in 2018, by gender.

BackgroundAnxiety disorders, depression and schizophrenia are the focus of global mental health attention, resulting in a significant number of disability-adjusted life years and a considerable social and economic burden. It’s can affect the socioeconomic landscape as a result of experiencing a global epidemic. And rarely, different Socio-demographic Index (SDI) levels and Age-Period-Cohort (APC) have been used to evaluate the prevalence of mental disorders worldwide.MethodsUsing data from the Global Burden of Disease 2021 (GBD) database, this study assessed trends in the incidence and prevalence of anxiety disorders, depression, and schizophrenia in countries with different SDI levels from 1990 to 2021. Joinpoint and periodic cohort (APC) models were used to sort out the effects of age, period and cohort on incidence. Data were categorized into 5-year age groups and 95% uncertainty intervals (UI) were calculated to account for data variability.ResultsIn countries with different SDI levels, the age-standardized average annual percentage change (AAPC) in the incidence of anxiety were all shown to be increasing, and there were large gender differences between the different SDI levels, with a maximum of 0.97 (0.76–1.18) for females in countries with a high SDI level, Age-standardized more rates per 100,000 people in high SDI countries, from 658.87 in 1990 to 841.56 in 2021, and the largest gender differences in countries with a low to moderate SDI level, with AAPCs for males and females of 0.04 (0.04–0.05), 0.86 (0.63–1.09); for depression, only the countries with medium-high SDI levels were statistically significant compared to the countries with medium-low SDI levels, with AAPCs of 0.05 (0.04–0.07), 0.04 (0.04–0.05); for schizophrenia in addition to the AAPCs of the countries with medium-high SDI levels showed an increase of 0.16 (0.13–0.18); the rest decreased.ConclusionThis study highlights the current status of global incidence and prevalence of mental disorders and examines the complex interactions between the period of onset and cohort of onset of mental disorders using APC modeling, with differences in gender differences in mental disorders in countries with different SDIs, and significant differences in countries with low to medium SDI levels, requiring further exploration of the mechanisms by which socio-economic development influences gender-specific mental health. Countries with different SDI levels have responded to unique trends within their specific socioeconomic, cultural, and historical contexts, suggesting the need for contextualized public health strategies to effectively respond to and manage the incidence and prevalence of mental disorders in these different settings. Prevalence of mental disorders. This points the way to more in-depth future research on treatments and interventions for mental disorders.

As of August 2024, ** percent of adults surveyed worldwide believed that mental health was the biggest health problem in their country, the highest in the provided time interval. This statistic illustrates the share of adults worldwide who believed that mental health was the biggest health concern in their country from 2018 to 2024.

The Anxiety Disorders and Depression Treatment market has become a critical sector within the healthcare industry, driven by the alarming rise in mental health issues worldwide. As per the latest report by STATS N DATA, the market has seen significant growth, reflecting broader trends in societal awareness and the u

BackgroundThe mental health of healthcare workers during the coronavirus-2019 pandemic was seriously affected, and the risk of mental health problems was high. The present study sought to systematically evaluate the mental health problems of healthcare workers worldwide during the pandemic and to determine the latest global frequency of COVID-19 associated mental health problems.MethodsData in the Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Elsevier, MEDLINE, PubMed, PsycINFO and the Web of Science before November 11, 2022, were systematically searched. Cohort, case-control and cross-sectional studies were included. The meta-analysis used a random effects model to synthesize the comprehensive prevalence rate of mental health problems. Subgroup analyses were performed based on time of data collection; whether the country was or was not developed; continent; doctors and nurses; doctors/nurses vs. other healthcare workers; and psychological evaluation scale.ResultsA total of 161 studies were included, including 341,014 healthcare workers worldwide, with women accounting for 82.8%. Occupationally, 16.2% of the healthcare workers were doctors, 63.6% were nurses and 13.3% were other medical staff. During the pandemic, 47% (95% confidence interval [CI], 35–60%) of healthcare workers reported job burnout, 38% (95% CI, 35–41%) experienced anxiety, 34% (95% CI 30–38%) reported depression, 30% (95% CI, 29–31%) had acute stress disorder, and 26% (95% CI, 21–31%) had post-traumatic stress disorder.ConclusionsThe study found that there were common mental health problems among health care workers during the COVID-19 pandemic. The most common was job burnout, followed by anxiety, depression, acute stress and post-traumatic stress disorder. Although the global pandemic has been brought under control, its long-term impact on the mental health of healthcare workers cannot be ignored. Additional research is required to develop measures to prevent, monitor and treat psychological disorders among healthcare workers.

BackgroundDespite the known link between poor living conditions and mental health, there has been little research on the mental health of slum dwellers worldwide. Although the Coronavirus disease 2019 (COVID-19) pandemic has led to an increase in mental health issues, little focus has been given to the impact on slum dwellers. The study aimed to investigate the association between recent COVID-19 diagnosis and the risk of depression and anxiety symptoms among people living in an urban slum in Uganda.MethodsA cross-sectional study was conducted among 284 adults (at least 18 years of age) in a slum settlement in Kampala, Uganda between April and May 2022. We assessed depression symptoms and anxiety using validated Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder assessment tool (GAD-7) questionnaires respectively. We collected data on sociodemographic characteristics, and self-reported recent COVID-19 diagnosis (in the previous 30 days). Using a modified Poisson regression, adjusted for age, sex, gender and household income, we separately provided prevalence ratios and 95% confidence intervals for the associations between recent COVID-19 diagnosis and depressive and anxiety symptoms.ResultsOverall, 33.8% and 13.4% of the participants met the depression and generalized anxiety screening criteria respectively and 11.3% were reportedly diagnosed with COVID-19 in the previous 30 days. People with recent COVID-19 diagnosis were more likely to be depressed (53.1%) than those with no recent diagnosis (31.4%) (p

Surveys fielded in 2023 in 142 countries around the world found that the countries/regions in which people were most likely to state that they experienced stress in the previous day were Northern Cyprus, Israel, and Nigeria. In Israel, around 62 percent of respondents reported feeling stressed in the day prior to being surveyed. Stress is a major health problem around the world In 2023, a survey of adults from 31 countries asked what the biggest health problems facing their country were, and stress was the third most common response, behind cancer and mental health. The countries most likely to report stress as the biggest health problem in their country were South Korea, Turkey, and Switzerland. At that time, around 44 percent of people in South Korea felt stress was their country’s biggest health problem, compared to just 15 percent of people in India and Great Britain. Young people worldwide are more likely to report moderate to severe symptoms of stress, but still, a quarter of those aged 65 years and older reported having moderate to severe symptoms of stress in 2022. Causes of stress and its impact Although stress is a global problem, the causes of stress can vary from country to country. In the United States, some common sources of stress include personal finances, politics and current events, relationships with family and friends, and work. How stress manifests itself also differs, but common symptoms of stress include headache, fatigue, feeling nervous or anxious, feeling sad or depressed, and a loss of interest, motivation, and energy. A recent survey of U.S. adults found that around 36 percent stated that their mental health is negatively impacted when they feel stressed, while 32 percent said stress impacts their physical health. Some healthy ways to cope with stress include avoiding possible stress triggers like reading the news too often, exercising, eating healthy, talking to others, and avoiding drugs and alcohol.

Context and background. Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Additionally, the shortage of specialized personnel is very concerning since depression diagnosis is highly dependent on expert professionals and is time-consuming. Research problems. Recent research has evidenced that machine learning (ML) and natural language processing (NLP) tools and techniques have significantly benefited the diagnosis of depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present. These challenges include assessing alternatives in terms of data cleaning and pre-processing techniques, feature selection, and appropriate ML classification algorithms. Purpose of the study. This paper tackles such an assessment based on a case study that compares different ML classifiers, specifically in terms of data cleaning and pre-processing, feature selection, parameter setting, and model choices. Methodology. The experimental case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD. Major findings. Besides the assessment of alternative techniques, we were able to build models with accuracy levels around 84% with Random Forest and XGBoost models, which is significantly higher than the results from the comparable literature which presented the level of accuracy of 72% from the SVM model. Conclusions. More comprehensive assessments of ML classification algorithms and NLP techniques for depression detection can advance the state of the art in terms of improved experimental settings and performance.

Cranial Electrotherapy Stimulation Market Outlook

The global cranial electrotherapy stimulation (CES) market size is expected to grow from USD 1.2 billion in 2023 to USD 2.3 billion by 2032, at a compound annual growth rate (CAGR) of 7.1%. This growth is primarily driven by the increasing prevalence of mental health disorders and the rising adoption of non-invasive treatment methods. CES devices have gained substantial traction in recent years due to their effectiveness and minimal side effects compared to traditional pharmacological treatments, making them an appealing option for both patients and healthcare providers.

One of the major growth factors for the CES market is the rising prevalence of mental health conditions such as depression and anxiety. According to recent statistics, mental health disorders affect more than 300 million people worldwide, making them a significant public health concern. The increased awareness and diagnosis of these conditions have necessitated the development of effective and accessible treatment options. CES devices have emerged as a promising solution due to their ability to alter brain chemistry and improve symptoms of depression and anxiety without the need for medication.

The growing preference for non-invasive treatment methods is another key driver of the CES market. Traditional treatments for mental health conditions often involve medications that come with a host of side effects and potential for dependency. In contrast, CES devices offer a non-invasive, low-risk alternative that can be used in conjunction with other treatments or as a standalone therapy. This has led to a surge in demand from patients seeking safer and more manageable treatment options, thereby driving market growth.

Technological advancements in the field of neuromodulation have also significantly contributed to the growth of the CES market. Innovations such as portable and user-friendly CES devices have made it easier for patients to incorporate these treatments into their daily lives. Additionally, ongoing research and development efforts are continuously improving the efficacy and safety profiles of these devices, making them more appealing to a broader range of patients and healthcare providers. This has resulted in an increased adoption rate and favorable market dynamics.

Regionally, North America holds the largest share of the CES market, attributed to the high prevalence of mental health disorders, advanced healthcare infrastructure, and a strong focus on research and development. The region's robust regulatory framework and supportive reimbursement policies have further facilitated the adoption of CES devices. However, significant growth is also expected in the Asia Pacific region due to rising healthcare expenditure, increasing awareness of mental health, and the growing acceptance of non-traditional treatment methods. Europe and Latin America are also anticipated to experience steady growth, driven by similar factors.

The introduction of Chronic Cerebellar Stimulator technology represents a significant advancement in the field of neuromodulation. This innovative approach targets the cerebellum, a region of the brain that plays a crucial role in motor control and cognitive functions. By delivering precise electrical stimulation to this area, the Chronic Cerebellar Stimulator aims to enhance neural activity and improve outcomes for patients with various neurological disorders. This technology is particularly promising for conditions that have been resistant to traditional treatments, offering new hope for improved quality of life. As research in this area progresses, the potential applications of Chronic Cerebellar Stimulator devices are expected to expand, further driving interest and investment in this cutting-edge technology.

Device Type Analysis

The cranial electrotherapy stimulation market is segmented into various device types, including transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS). Each of these device types offers unique mechanisms of action and therapeutic benefits, catering to different patient needs and clinical applications.

Transcranial direct current stimulation (tDCS) is one of the most widely used and studied types of CES devices. tDCS involves the application of a low, constant electrical current to specific areas of the brain to modulate neuronal activ

This statistic shows the share of independent music makers who have ever experienced stress, anxiety or depression in relation to their music creation worldwide as of April 2019, sorted by age group. The data reveals that younger music artists were more likely to have experienced some kind of mental health problems, with 80 percent of independent musicians aged between 18 and 25 years old saying they had suffered from stress, anxiety and/or depression in relation to their music creation. Conversely, 49 percent of musicians aged 46 or above said the same.

The Anxiety and Stress Management market has rapidly evolved into a critical sector, addressing the growing prevalence of mental health issues across diverse populations. As mental health awareness rises globally, the market has demonstrated significant growth, with a current value estimated at several billion dolla

Context

I was developing a chatbot called Amdere Bot that identifies people suffering from depression and helps them to cope up with depression. There wasn't much data available online to train the bot to identify depression. Hence I decided to create a conversation dataset from scratch that can be used to train the bot.

Content

I created a .yml file that contains questions and answers. It contains technical questions about depression and anxiety as well as questions that a depressed person is most likely to ask a bot and answers to those question.

Inspiration

Depression is a common mental disorder. Globally, more than 264 million people of all ages suffer from depression. It is a leading cause of disability worldwide. Especially in this current pandemic, the rate of depression and anxiety has increased exponentially. At present, it is even difficult to attend physical therapy. This motivated me to create a bot that can not only answer trivia but is also trained to answer questions related to depression and will help people suffering from depression and anxiety. Not only this it can even aid the suicide helplines by talking to the person until one of their workers is free to attend the call. I would love people to use this data and create a bot that would help people suffering from mental issues.s

According to Cognitive Market Research, The Global Stress Ball market size is USD XX billion in 2023 and will grow at a compound annual growth rate (CAGR) of 5.00% from 2023 to 2030.

Stress balls are becoming increasingly popular due to their benefits, such as their capacity to alleviate excessive levels of tension or anxiety.
Demand for bean bags remains higher in the stress ball market.
The online channel held the highest stress ball market revenue share in 2023.
North America will continue to lead, whereas the European stress ball market will experience the fastest growth until 2030.

Increased Prevalence of Stress to Provide Viable Market Output

A number of different reasons may have contributed to the rise in human stress. In many regions of the world, the prevalence of stress has been rising recently.

In the past year, stress has had an impact on the daily lives of about 3 in 5 persons worldwide (62%) and in the United States (56%). Stress, according to a third (35% of Americans and 34% worldwide), has impacted their everyday life on multiple occasions.

(Source: www.ipsos.com/en-us/news-polls/world-mental-health-day-monitor-2022#:~:text=Experience%20with%20stress%20and%20depression%20in%20the%20past%20year&text=About%203%20in%205%20adults,their%20daily%20lives%20several%20times.)

A variety of factors can cause stress, but growing living expenses, job insecurity, and economic uncertainty rank at the top of the list. To make ends meet, people could feel under pressure to put in more hours or take on several jobs. Children are suffering from stress as well. Nearly one-third of kids said they had suffered a bodily ailment, such as headaches, stomach problems, or trouble falling or staying asleep, within the previous month. Even while the underlying stress levels haven't altered dramatically, more people are recognizing and reporting their stress as a result of increased awareness of mental health issues, thus driving the sales of stress balls.

Growing Government Initiatives for Stress Management to Propel Market Growth

As evidenced by the inclusion of mental health in the Sustainable Development Goals, there has been growing recognition of the crucial role mental health plays in achieving global development goals in recent years. The need for increased investment in mental health awareness, efforts to increase access to high-quality mental health care and effective treatments, and research to find new treatments and enhance existing ones for all mental disorders are all areas that need attention.

In order to provide 100 million additional people with access to high-quality and reasonably priced care for mental health conditions in 12 priority countries, WHO established the WHO Special Initiative for Mental Health (2019-2023): Universal Health Coverage for Mental Health in 2019.

(Source: www.who.int/health-topics/mental-health#tab=tab_1)

Comprehensive mental health policies that address stress as a factor in mental well-being have been developed in some nations. These regulations may include measures for the treatment, de-stigmatization, and prevention of mental health problems. At the same time, some countries have rules and rewards to encourage business owners to establish psychologically healthy workplaces, which can help employees feel less stressed.

Key Dynamics of

Stress Ball Market

Key Drivers of

Stress Ball Market

Increasing Awareness of Mental Health and Demand for Stress Relief Tools: With an increasing emphasis on mental wellness and stress in the workplace, consumers are turning to stress balls as cost-effective, non-invasive solutions for alleviating anxiety, enhancing concentration, and alleviating tension—particularly in office, educational, and home settings. Corporate Gifting and Demand for Promotional Products: Stress balls are commonly utilized in promotional initiatives due to their affordability, ability to be customized, and practical use in daily life. Companies frequently distribute them as promotional items at events or to enhance brand visibility within employee wellness initiatives. Growing Utilization in Occupational and Physical Therapy: Healthcare professionals advocate for the use of stress balls to strengthen hand muscles, develop motor skills, and assist in rehabilitation therapy. Their significance in physiotherapy and recovery support is...

BACKGROUND: Coronavirus disease caused by the novel coronavirus Covid-19 is a current worldwide outbreak. The use of quarantine and isolation proved effective in containing the spread of infection. OBJECTIVES: The purpose of this cross-sectional study was to assess the mental health of Albanian people residing in the country and abroad during the quarantine period for the Covid-19 pandemic. DESIGN: This study was carried out from 25th March – 20th April 2020 through a web survey shared on social networks. The goal was to reach at least the minimum sample size for cross-sectional studies. The Patient Health Questionnaire (PHQ-9) was used to assess mental health. Chi-square (χ2) and Fisher –Exact test were used to assess the statistical significance among variables. P values ≤0.05 were considered statistically significant. RESULTS: 715 participants were included in the final analyses (78.41% females and 21.53% males). Most were residents in Albania (80.41%) and the others resided mainly in Italy (6.89%), Greece (3.51%), Germany (2.43%), Kosovo (1.62%) and the UK (1.69%). Statistical association was found between gender, country of residency and measures taken. Summary score of PHQ-9 items was 6.4662. The total score of depression classification shows that 31.82% and 12.90% of participants have respectively mild and moderate depression. Female participants showed the highest score for some items of PHQ-9, p≤0.05. CONCLUSIONS: Findings suggest that health care professionals should recognize and address mental health problems associated with Covid-19 especially in vulnerable groups. Acting in a timely and proper manner is essential in preventing these problems from becoming chronic.

Context and background. Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Additionally, the shortage of specialized personnel is very concerning since depression diagnosis is highly dependent on expert professionals and is time-consuming. Research problems. Recent research has evidenced that machine learning (ML) and natural language processing (NLP) tools and techniques have significantly benefited the diagnosis of depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present. These challenges include assessing alternatives in terms of data cleaning and pre-processing techniques, feature selection, and appropriate ML classification algorithms. Purpose of the study. This paper tackles such an assessment based on a case study that compares different ML classifiers, specifically in terms of data cleaning and pre-processing, feature selection, parameter setting, and model choices. Methodology. The experimental case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD. Major findings. Besides the assessment of alternative techniques, we were able to build models with accuracy levels around 84% with Random Forest and XGBoost models, which is significantly higher than the results from the comparable literature which presented the level of accuracy of 72% from the SVM model. Conclusions. More comprehensive assessments of ML classification algorithms and NLP techniques for depression detection can advance the state of the art in terms of improved experimental settings and performance.

Subgroup analyses for studies on post-traumatic stress disorder.

Context and background. Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Additionally, the shortage of specialized personnel is very concerning since depression diagnosis is highly dependent on expert professionals and is time-consuming. Research problems. Recent research has evidenced that machine learning (ML) and natural language processing (NLP) tools and techniques have significantly benefited the diagnosis of depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present. These challenges include assessing alternatives in terms of data cleaning and pre-processing techniques, feature selection, and appropriate ML classification algorithms. Purpose of the study. This paper tackles such an assessment based on a case study that compares different ML classifiers, specifically in terms of data cleaning and pre-processing, feature selection, parameter setting, and model choices. Methodology. The experimental case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD. Major findings. Besides the assessment of alternative techniques, we were able to build models with accuracy levels around 84% with Random Forest and XGBoost models, which is significantly higher than the results from the comparable literature which presented the level of accuracy of 72% from the SVM model. Conclusions. More comprehensive assessments of ML classification algorithms and NLP techniques for depression detection can advance the state of the art in terms of improved experimental settings and performance.