This study examined mental health issues affecting the group of individuals who are in the social media contact list of the researchers. This was done by administering a closed structured questionnaire, covering information from participants’ demographic characteristics, duration of time spent on ICT usage, to their perceived health effects thereof. Male respondents, largely unemployed are active ICT users, addicted to the Internet, and also suffer from unspecified ailments. Aged 31–35 and above, half of men and all women participants spend more than 5 h per day. High-intensive ICT use is associated, in general, with concurrent symptoms of information overload, depression and Internet addiction. It is insignificantly associated with composite stress. Medium intensive ICT use is correlated with fatigue and depression in equal measure but in opposite direction. There are significant differences in demographic attributes as they explain intensity of ICT Usage. The older the employed survey participants are, the less they suffer from composite stress. However, black South Africans suffer significantly from composite stress, fatigue and depression, though negatively from internet addiction. The results also indicate that South African men have negative experiences of composite stress, depression and internet addiction. Educated South Africans have negative experiences of fatigue, but positive experiences of composite stress, depression and internet addiction. Low intensive use of ICT has no impact on composite stress, fatigue and depressive moods of survey participants. It impacts negatively on internet addiction. Medium intensive use of ICT impacts positively on survey participants’ experiences of fatigue, but negatively on composite stress, depression and internet addiction. High-intensive use of ICT impacts positively on survey participants’ experiences of composite stress and depression.
There are limited studies that focus on information and communication technology (ICT) usage in South Africa, let alone on the mental health aspects of this phenomenon. This stimulates the interests of these researchers on this important aspect in a developing country that is confronted with serious challenges in the area of health.
South Africa is undergoing socio-economic transformation from its racially divisive, social segregation policy of apartheid to an all-inclusive society. The challenges of growing the economy have also emerged in the recent past. Government has laid strong emphasis on tackling unemployment, which currently stands at 29.1% [
Social integration has been difficult; and it is particularly challenging in the workplace. Employees having tight deadlines to meet could be affected by stress. Those who get jobs in the country work very hard. Workers put in about 8.6 h per day, but have very low productivity, about $98 per hours worked [
The overwhelming body of opinion is that when an ICT user obtains relevant and interesting information, the likelihood of facing overload is low; irrelevant information is the one that increases the chances of information overload [
There is still controversy in the literature about the exact association between health and information and communication technology (ICT), with some evidence suggesting links between ICT usage and health problems. Symptoms of mental overload among ICT users has been reported to also be increasing [
Fatigue has been linked in the literature the condition involving a strong and persistent form of mental and/or physical tiredness, pain, weakness, exhaustion, and inability to concentrate [
Stress suffered by individuals is identified as a contributory factor to reductions in organizational performance. This is largely due to the fact that individuals suffering from stress make a lot of errors leading to poor quality of work, absenteeism, emotional disorder and difficulties with balancing work and daily life commitments.
Some of the earlier research on ICT usage, and the associated mental health impacts are open to the criticism of not focusing on the relevant developmental perspective by ignoring thorough analysis of demographic differences of the mental impacts of ICT usage. By ignoring this, they are unable to answer pertinent questions on the interaction between demographic variables that are impacted by ICT usage. There are potential differential impacts of ICT usage by intensity of use, on men, women, young people and adults, the educated and those who are not educated etc. This aspect is what this study tries to uncover when determining the health effects on survey participants as a result of information and communication technology (ICT) usage in South Africa. We seek to test the hypothesis that differential usage in the intensity of ICT use is associated with problems of perceived health. This paper consists of five sections. Section one is introductory; while the second section reviews salient literature on the topic. Section three is the research methodology section. In section four, data collected from the online survey are analyzed; results are also interpreted and presented. The last section concludes the study and makes some recommendations.
ICT represents essential tools used for collecting, processing, storing, transmitting and dissemination of information [
Low-cost Internet access makes it easier to achieve information retrieval [
Cost of data is expensive in South Africa, compared to neighboring countries. Hence, regulators in the country, concerned about expanding access to the Internet and are recently advocating the lowering of data costs. South Africa’s Competition Commission, late in 2019 ordered Vodacom and MTN, the main network service providers, to reduce the cost of connectivity and make available free access to the poorer segment of the society. Government is also prioritizing access to free public Wi-Fi, as a way of providing an alternative to broad-band access especially among underserviced communities [
The South African population is generally well connected as shown in
ICT platform type | ICT platform description | ICT_User_Population (%) |
---|---|---|
Mobile phones | Mobile phone types | 85 |
Types of smartphone | 60 | |
Television | Any kind of TV | 82 |
Streaming TV |
3 | |
Computers | Notebook or desktop | 24 |
Portable Tablet | 12 | |
Other electronic gadgets | Electronic-reader device | 1 |
Wearable technology device | 2 |
Source: [<
Opportunities presented by access to the Internet are substantial! There were 31.18 million active Internet users in South Africa in 2018; of these 28.99 million were active mobile Internet users [
Source: [
In South Africa, there’s an increasing desire to know more about current affairs and to participate in trending discussions. Tweets, texts and even WhatsApp voice notes from the public are now being broadcast on air to express opinions or drive content [
The notion of information overload (hereafter referred to as IO) has a long history [
In the field of psychology, overload is defined as a situation where the input of information is greater than the capacity of the human recipient to process it [
Results reveal that of the 15 platforms studied, only the use of 5 media outlets respectively substantively predicted IO [
Overall, information overload as a result of ICT usage has brought health related dimensions like stress, back pain, upper limb pain, fatigue, continuous headache, sleep loss, addiction, and other related issues. This study will examine these psychological effect on individuals, resulting from their use of ICT devices, in South Africa.
Concerns about what is happening in the public space is growing and there are difficulties with knowing the difference between the truth and fake information [
The South African Stress and Health (SASH) community survey estimates that 4.5% of South Africans suffer from alcohol abuse, 4.9% from major depression, One third of all South Africans have mental illnesses and more than 17 million people in South Africa are dealing with depression, substance abuse, anxiety, bipolar disorder and schizophrenia. The survey estimated the prevalence of major depressive disorder (MDD) at 8.9% and that of anxiety-related disorders at 14.6% [
Therefore, policy makers and opinion leaders are increasing concerned about how distributed information imparts and impacts on their constituents. Considering the widespread usage of information and technology devices, it is essential to examine the mental health effects of ICT usage on South Africans, De Wet et al. [
Research into ICT use in relation to the health and well-being of workers have been conducted by authors such as [
Women in general often use ICT to seek information or communicate while men use it to entertain. For women, the high combined use of computer and mobile phone and frequency of SMS messaging per day, are associated with increased risk of internalized behavioural problems such as stress and symptoms of risk [
It has been observed that individuals depend practically on internet search engines to source for information these days. Diverse search engines such as Google, Yahoo, Altavista, and networking sites have also affected the information seeking behaviours of many individuals and academic researchers [
However, recent research has not paid sufficient attention to the possible influences that ICT devices could have on the individuals or employees who navigate the internet regularly. They tend to be limited in scale and scope [
The South African Stress and Health (SASH) community survey estimates that 4.5% of South Africans suffer from alcohol abuse, 4.9% from major depression, One third of all South Africans have mental illnesses and more than 17 million people in South Africa are dealing with depression, substance abuse, anxiety, bipolar disorder and schizophrenia. The survey estimated the prevalence of major depressive disorder (MDD) at 8.9% and that of anxiety-related disorders at 14.6% [
They however suggest conducting further research on the impact of ICT usage on communication between employees, their colleagues and family members, particularly in South Africa. Authors like [
Some authors attribute the concurrent increase in intensive ICT usage and health problems to the deteriorated sleeping habits. They cite evidence of reduced sleeping times among individuals adolescents in recent years suggesting that sleeping time has decreased by approximately one hour since the end of the 1970s, particularly in the latter half of the 1990s. On the other hand, evidence reviewed by [
Message overload is a situation where engagement in social messaging exceeds users’ communicative and cooperative capacities [
Studies on extreme technology usage suggest that “overload” is the primary reason for concerns about undesirable consequences of ICT usage [
Winkle (1998) cited in [
Total time spent, using all ICT platforms, on social media, do not show a significant negative emotional effect on participants [
Sometimes referred to as “technostress”, information overload (IO) is usually associated with ICT users losing control of the situation such that ICT now controls users, instead of empowering them [
In European countries such as France, the use of the Internet to search for mental health information and other purposes is positively associated with poor mental health but not physical health [
Some studies show that fatigue is moderately to strongly associated with various psychiatric disorders particularly anxiety and depression, and general psychological distress [
In South Africa, the prevalence of alcohol and other drug use disorders is high, with males having higher rates than females, though the gender gap has been narrowing recently [
Experts find a significant association between substance use and unemployment in South Africa, especially by men (
The nationally representative South African Stress and Health (SASH) study shows that approximately 20% of youth in the country suffer from depression and stress-related conditions every year. Studies conducted among Sesotho-speaking depressive patients in the Free State Province, South Africa show high incidence of symptoms such as a depressed mood, lack of energy, and thoughts of death and suicide [
Interest in researching on social media usage has been driven by the rapidly broadening user base for social media technologies, which is related to the continuing spread of Internet use itself. These sites are characterized by their ease of use, their generic nature, focusing on a particular subject or area of interest, and their wide penetration, meaning that significant portions of the population have created an account [
Many researchers suggest that the wide variety of social characteristics on social networks such as age, gender, and location could be used to construct more representative samples than might be achieved by simply selecting users at random [
The Internet, hence, provides new opportunities for non-random or semi-random survey data [
A non-list-based random sampling survey, was carried out online by posting a questionnaire link on different social media platforms (different response modes), to participants who freely participated. This was done by administering a closed structured questionnaire, covering information from participants’ demographic characteristics, duration of time spent on ICT usage, to their perceived health effects thereof.
Demographic details are captured as follows: Age is stated in 6 cohorts of age groups with intervals of 5 years, falling between 20 and 40 years of age and over. There are no uneducated participants! As such educational attainment is measured starting at the undergraduate level. There are 4 racial categories and gender is either male or female, otherwise it is unspecified.
Information about computer use was collected from the online survey participants, including average time spent daily on general computer use, on emailing or chatting in leisure, and on computer gaming, as well as how often the computer was used for more than 2 h without breaks, and how often sleep was lost because of getting stuck late at night by the computer. The study later adopts the approaches of [
Information overload is categorized into two (1) Perceived general information overload and (2) message overload. To assess information overload we asked participants, how often have they felt overwhelmed with the amount of information they have had to deal with in the last month. Also, for message overload we assessed the variable by posing the question: In the last month, how often have respondents felt that they received too many messages (e.g., SMSs, WhatsApp, general emails and/or spam emails, wall postings, event notifications, updates, etc.). Responses to the questions follow a 5-item Likert scale ranging from (0) not at all, (1) just a little, (2) to some extent, (3) quite a lot, and (4) very much.
Perceived information overload, refers to participants own evaluation of experiences of information overload consequence of ICT use, and is scaled from 1–5, 1 being the lowest and 5 being the highest. Since this variable is not dis-aggregated, information overload being measured is thus its composite form.
Health impact of ICT use is, however, first measured in its composite form and later disaggregated into individual components. In order to disaggregate composite stress, participants were asked if they had complained of (1) anxiety, (2) fatigue, (3) depression or depressed mood, reduced performance due to stress, Internet addiction, sleep disturbance/loss and perceived stress which have influenced their performance over the past 14 days. This question allowed participants to offer a range of multiple choice answers.
The possibly destructive quality (or contents) of information and communication as identified by [
However, stress is defined to mean a situation whereby a person feels tense, restless, nervous, or anxious or is unable to sleep at night because his/her mind is troubled all the time. We asked respondents whether they were currently experiencing this kind of stress. Similarly, responses follow a 5-item Likert scale ranging from (0) not at all, (1) just a little, (2) to some extent, (3) quite a lot, and (4) very much. Sleep disturbances were similarly assessed.
Following [
The theoretical underpinning of
We concur with [
The survey was carried out during the second half of 2019, July to August. This is an exploratory study that covered one month during the academic year. This is for reason of the University’s academic time constraint. In all, 48 completed questionnaires were received and 8 were discarded for being incomplete and lacking in crucial information. Typically, online survey response rates are low! A similar study by [
Data used were first filtered by composite stress experiences of participants. This enabled the researchers to correlate relevant demographic variables with information overload. Then, the study estimated a number of models to determine the relation between composite stress and individual stress impacts on survey participants analyzed by demographic variables and ICT usage.
Spearman correlation analysis was used to examine associations between the different information and communication technology (ICT) use variables, and between ICT use and demographic variables.
ICT Intensity use variables | Demographic variables | |||||||
---|---|---|---|---|---|---|---|---|
Daily ICT use | Low ICT use | Medium ICT use | High ICT use | Age | Race | Gender | Education | |
Daily ICT use | 1.000 | |||||||
Low ICT use | –0.95*** | 1.000 | ||||||
Medium ICT use | –0.11 | –0.22 | 1.000 | |||||
High ICT use | 0.95*** | –0.81*** | –0.41*** | 1.000 | ||||
Age | 0.25 | –0.26* | 0.06 | 0.21 | 1.000 | |||
Race | 0.26* | –0.22 | –0.11 | 0.27* | 0.06 | 1.000 | ||
Gender | 0.39*** | –0.33** | –0.17 | 0.41*** | 0.09 | –0.17 | 1.000 | |
Education | 0.28* | –0.35** | –0.21 | 0.19 | 0.01 | –0.02 | 0.67*** | 1.000 |
Note: ***, **, * denote significance at the 1%, 5% and 10%, respectively.
Like [
In
Low_ICT Usage | Medium ICT Usage | High_ICT Usage | |
---|---|---|---|
Men | 12 (30%) | 4 (10%) | 16 (40%) |
Women | 0 (0%) | 0 (0) | 8 (20%) |
Race1 | 0.22 | 0.11 | –0.27* |
Race3 | –0.22 | –0.11 | 0.27* |
Age cohort 2 | 0.51*** | –0.11 | –0.41*** |
Age cohort 4 | –0.33** | –0.17 | 0.41*** |
Age cohort 5 | –0.09 | 0.41*** | –0.17 |
Age cohort 6 | 0.05 | –0.22 | 0.09 |
Education cohort1 | 0.36** | –0.27* | –0.17 |
Education cohort2 | –0.09 | –0.27* | 0.25 |
Education cohort3 | –0.22 | 1.000*** | –0.41*** |
Education cohort6 | –0.22 | –0.11 | 0.27* |
Note: ***, **, * denote significance at the 1%, 5% and 10%, respectively
Thirty percent of study participants are men, making low intensive ICT usage, using ICT for longer than 2 hours continuously with break. Ten percent and forty of the overall sample are men making medium and high intensive use of ICT. Women are not at all represented in the low and medium ICT intensive use categories, but make up twenty percent of overall ICT usage though they only make high intensive ICT use. Other studies suggest that females, young people from Spain, and Nursing students, in general, are more likely to use the Internet for mental health information-seeking than Computer Science students, while studying in Ireland decreased the probability of doing so [
There were also no respondents in age cohorts 1 and 3, just like there were no respondents in the education cohorts 4 and 5. In
There were no respondents possessing educational qualifications in the 4th and fifth categories. Low intensive ICT usage is positively associated with belonging education cohort1. Belonging to educational cohorts 1 and 2 is negatively associated with making medium intensive use of ICT. Participants belonging to educational cohort 3 appear to make significant and positive use of ICT in the medium category. High intensive use of ICT is associated negatively with belonging to educational cohort 3 and positively with belonging to educational cohort 6.
In
Primary consequence of ICT use | Aggregate measure | Gender of users by intensity of ICT use | |||||
---|---|---|---|---|---|---|---|
Information overload | Sleep loss | Daily ICT use | Low ICT use by men | Medium ICT use by men | High ICT use by men | Female high ICT use | |
Information overload | 1.000 | ||||||
Sleep loss | 0.30** | 1.000 | |||||
Daily ICT use | 0.37*** | 0.49*** | 1.000 | ||||
Male low ICT use | –0.29* | –0.39*** | –0.95*** | 1.000 | |||
Male Medium ICT use | –0.23 | –0.29* | –0.111 | –0.22 | 1.000 | ||
Male high ICT use | 0.41*** | 0.53*** | 0.95*** | –0.53*** | –0.27* | 1.000 | |
Female high ICT use | 0.42*** | –0.04 | 0.39*** | --- | --- | ---- | 1.000 |
Note: ***, **, * denote significance at the 1%, 5% and 10%, respectively.
We present results of the associations between ICT use variables and mental health outcomes.
Male respondents | Female | Total | |||
---|---|---|---|---|---|
Mental health/ |
Low ICT use intensity | Medium ICT use intensity | High ICT use intensity | High ICT use intensity | High ICT use intensity |
Composite stress | –0.12 | –0.06 | 0.23 | –0.09 | 0.15 |
Fatigue | –0.22 | 0.33** | –0.0000 | 0.00 | 0.00 |
Depression | –0.22 | –0.33** | 0.41*** | 0.00 | 0.41*** |
Internet addiction | –0.43*** | –0.22 | 0.37** | 0.22 | 0.53*** |
Note: ***, **, * denote significance at the 1%, 5% and 10%, respectively.
In
Mental health/ |
Age cohort 2 | Age cohort 4 | Age cohort 5 | Age cohort 6 |
---|---|---|---|---|
Composite stress | –0.06 | 0.37** | 0.23 | –0.53*** |
Information overload | –0.49*** | 0.81*** | –0.09 | –0.29* |
Fatigue | –0.33** | –0.50*** | 0.82*** | –0.22 |
Depression | –0.33** | 0.0000 | –0.0000 | 0.22 |
Internet addiction | –0.22 | 0.76*** | –0.09 | –0.43*** |
Note: ***, **, * denote significance at the 1%, 5% and 10%, respectively.
Mental health/ |
Educational cohort 1 | Educational cohort 2 | Educational cohort 3 | Educational cohort 6 |
---|---|---|---|---|
Composite stress | –0.15 | –0.15 | –0.06 | 0.56*** |
Information overload | 0.06 | –0.25 | –0.23 | 0.54*** |
Fatigue | –0.000 | –0.000 | 0.33** | –0.33** |
Depression | –0.41*** | 0.41*** | –0.33** | 0.33** |
Internet addiction | 0.36** | –0.53*** | –0.22 | 0.51*** |
Note: ***, **, * denote significance at the 1%, 5% and 10%, respectively.
In
The study adopts a multivariate regression approach being a statistical method that examines the effect of two or more independent variables on two or more dependent variables. Multivariate regression analysis designs are appropriate when multiple dependent variables are included in the analysis. A 99%, 95% and 90% confidence intervals (CIs) have been used for multivariate analyses of prospective associations between ICT use variables (low, medium and high intensities) and mental health outcomes [
Models estimated in the study covered the dimensions of individuals’ perception of composite stress experience resulting from ICT usage. Items loaded on the three dimensions of composite stress; and the second model is based on the composite stress experience (fatigue, depression and Internet addiction) in intensity of ICT use and are based on the following hypotheses:
Hypothesis 1: The psychological experience of composite stress on ICT users is composed of three independent but positively interrelated components, namely: fatigue, depression, and feelings of Internet addiction to ICT use.
Composite stress | Fatigue | Depression | Internet addiction | |
---|---|---|---|---|
Composite stress | 1.000 | |||
Fatigue | –0.19 | 1.000 | ||
Depression | 0.56*** | –0.20 | 1.000 | |
Internet addiction | 0.28* | –0.22 | 0.22 | 1.00 |
Note: ***, **, * denote significance at the 1%, 5% and 10%, respectively.
The prescribed condition of linearity of dependent variables means they should be moderately correlated, i.e., having correlation of reasonably between 0.3 and 0.7 [
A series of hierarchical regression analyses (incorporating key covariates) were conducted to test the hypothesized relationships among independent/predictor, moderator, and predicted/dependent variables. Descriptive analysis, correlations (Pearson r), and Generalized Least Square Method (GLM) were implemented. This was done in order to test whether or not survey participants differed in their use of ICT; it was carried out to distinguish those with low-intensive ICT use with those making medium and high-intensive ICT use. All demographic variables were included in the models. Only the impact of significant stress types is reported in
The multivariate analysis examining the impact of ICT use (low-intensive, medium-intensive and high-intensive users) on the dimensions of techno-strain we specified fatigue, depression, Internet addiction as the dependent variables and included demographic variables with ICT use categories also as independent variables. The specified models show the significance of the multivariate effects of ICT use in impacting on technostress variables.
The older the employed survey participants are, the less they suffer from composite stress. However, black South Africans suffer from significantly from composite stress, fatigue and depression, though negatively from internet addiction. The results shown by the gender variable indicate that South African men have negative experiences of composite stress, depression and internet addiction. Educated South Africans have negative experiences of fatigue, but positive experiences of composite stress, depression and internet addiction.
Low intensive use of ICT has no impact on composite stress, fatigue and depressive moods of survey participants. It impacts negatively on internet addiction. Medium intensive use of ICT impacts positively on survey participants’ experiences of fatigue, but negatively on composite stress, depression and internet addiction. High-intensive use of ICT impacts positively on survey participants’ experiences of composite stress and depression.
Demographic variables | Univariate intensity of ICT intensity use categories as included in models | ||||||||
---|---|---|---|---|---|---|---|---|---|
Age | Race | Gender | Education | Low ICT | Medium ICT | High ICT | R2 | ||
Composite stress | –0.12*** | 0.46*** | –0.76*** | 0.34*** | 0.06 | --- | --- | 0.84 | |
–0.11*** | 0.38*** | –1.07*** | 0.41*** | --- | –0.65*** | --- | 0.94 | ||
–0.14*** | 0.39*** | –0.92*** | 0.34*** | --- | --- | 0.21*** | 0.86 | ||
Fatigue | 0.12* | 0.27** | 0.33 | –0.15** | –0.15 | --- | --- | 0.22 | |
0.10** | 0.41*** | 0.86*** | –0.27*** | --- | 1.09*** | --- | 0.56 | ||
0.15** | 0.38*** | 0.58** | –0.15** | --- | --- | −0.30* | 0.27 | ||
Depression | 0.15*** | 0.22* | –0.47** | 0.21*** | 0.02 | --- | --- | 0.33 | |
0.18*** | 0.10 | –0.99*** | 0.35*** | --- | −1.14*** | --- | 0.73 | ||
0.12*** | 0.09 | –0.78*** | 0.23*** | --- | --- | 0.45*** | 0.48 | ||
Internet addiction | –0.16*** | –0.25** | –0.06 | 0.03 | –0.59*** | --- | --- | 0.36 | |
–0.09 | –0.19* | –0.15 | 0.13* | --- | –0.52** | --- | 0.16 | ||
–0.15*** | –0.37*** | –0.46*** | 0.11*** | --- | --- | 0.79 | 0.61 |
Note: ***, **, * denote significance at the 1%, 5% and 10%, respectively.
This study is about seeking to understand mental health impacts of ICT use by intensity of usage in South Africa. This was done among survey participants who are in the online contact list of the researchers. This group includes followers on Facebook, WhatsApp contact, email contact list and twitter contact. The group of unemployed survey participants report being unable to use the information received. This might suggest that they were receiving irrelevant information especially for their job search.
High intensive ICT use is associated, in general, with concurrent symptoms of information overload, depression and Internet addiction. It is insignificantly associated with composite stress. Low intensive ICT use, on the other hand, is negatively correlated with all mental health variables though the association appears to run from information overload to Internet addiction. Medium intensive ICT use is correlated with fatigue and depression in equal measure but in opposite direction.
The multivariate results showed significant differences in demographic attributes as they explain intensity of ICT Usage of survey participants. The older the employed survey participants are, especially of black South Africans, the less they suffer from composite stress and internet addiction but the more they suffer from fatigue and depression when they intensely make use of ICT. This is indicated by the negative coefficients of these variables in the regression table. Educated participants suffer more from internet addiction and composite stress and less from fatigue and depression as they make intensive use of ICT. Older educated black women suffer from depression while younger educated women suffer from depression and internet addiction.
The question about whether Internet can be a viable scientific research tool is still being investigated. The main query is related with the possibilities of online methodologies to produce valid and reliable data. One limitation of social media research is the inability to guarantee their longevity going forward. Social media sites such as Bebo and Myspace have come and gone implying that relying on social media outlets for data gathering has its shortcomings. However, there is also a sense in which the current leaders appear to have learnt from some of the mistakes made by these previously dominant sites, and certainly appear to be well positioned to last for a significant amount of time.
Experts have raised questions around the existence of sample bias when collecting social media usage data. This is because Internet population constitutes a biased sample of the total population in terms of demographic characteristics. This raises doubts about its usefulness in social research [
Authors have mentioned that social media can be useful for tracking expressed reactions to a particular policy. These reactions cannot be easily generalized to broader public opinion. Methods for analyzing public sentiments via the social media outlets are still a work in progress [
The overall data collection period for this study was also small, especially in the context of collecting data on the health impact ICT usage and getting this to penetrate into public consciousness in South Africa. Further work taking in a much longer time period would be useful in terms of further validating results obtained in this study. There could be much to gain by encouraging future researchers to increase the pool of participants in their contact list. Such an approach ensures that much more interesting policy-relevant research finding is produced in a transparent way, is computationally reproducible manner.
Experts have noted that some individuals, particularly young people experience difficulties in accessing mainstream mental health services and are not inclined to seek professional help [
Authors such as [36] have discussed some of the salient advantages of online surveys. These include: (1) flexibility of formatting to accommodate language differences of respondents (Facebook has more than 110 languages), (2) time-efficiency, (3) cost-efficiency (4) ease of use, (5) convenience of administration, (6) ease of database compilation, (7) accommodates flexibility of questionnaire formats (multiple choice, open-ended types, scale types, dichotomous, etc.), (8) makes follow up easy, (9) questionnaires could be systematically answered, and (10) can be structured for orderly responses.
Some Authors explain that the response rate of online studies is influenced by personalization of contact strategies [