I. INTRODUCTION
The outbreak of the novel coronavirus, officially known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared a Public Health Emergency of International Concern by the World Health Organisation (WHO) in January
2020. (World Health Organization, 2022; Hotez, 2020; Kenny & Mallon 2021) The disease associated with SARS-CoV-2 is called COVID-19.1 In March 2020, WHO declared COVID-19 a global pandemic. (Cucinotta & Vanelli, 2020) As of June 17, 2022, over 535 million confirmed cases of COVID-19, including over 6 million deaths have been reported. (World Health Organization, 2022) Typical symptoms include fever, cough and tiredness. Other symptoms can include, but are not limited to, loss of taste or smell, headaches, and nausea. (Mayo Foundation, 2022)
Besides the direct health effects, COVID-19 has affected human well-being in many other ways. Several virus variants have resulted in waves that have been typically accompanied with lockdowns. (Fisayo & Tsukagoshi, 2021; Zhang et al, 2021) The lockdowns in particular and the pandemic in general have had a severe impact on the world economy and triggered the largest global economic crisis in more than a century. (World Bank Group, 2022) The median global GDP dropped by from 2019 to 2020. (Oum, 2022) Social distancing has been one of the main ways in which communities around the world tried to slow down the spread of the disease. (Qian & Jiang, 2020) Disruption of normal social connections along with economic disruptions plausibly have had detrimental and diverse psychological effects on various segments of the public. (Singh & Singh, 2020; Ruben & Wessely, 2020) Little is known about the psychological effects. (Canet-Juricet al, 2020; Schelhorn et al, 2022) Studies have been conducted to assess these effects on pregnant women, people with preexisting mental health conditions, incarcerated individuals, migrant workers, international students, children and young adults. (Fakari & Simbar, 2020; Li & Zhang, 2020; Cloud et al, 2020; Liem et al, 2020; Zhai & Du 2020; Buheji et al 2020; Shanahan et al 2022) More studies are still needed to fully understand the mental and emotional effects of COVID-19 on various segments of the public across all geographical areas. (Cipollettaet al, 2022; Liu et al, 2020; Yildirim et al, 2021) This study addresses the dearth of research in assessing the emotional state of young adults during a COVID-19 wave in the Detroit Metro area.
II. MATERIAL AND METHODS
Detroit residents between the ages of 18 and 25 self-reported their conditions via a survey instrument hosted on Centiment. Co, an online survey platform that helps to target specific demographics for researchers. (Centiment, 2022) Data were collected between January 19, 2022, and February 7, 2022. The Detroit Metro area was experiencing a COVID wave during the same time. (State of Michigan, 2022) 522 people from the target population responded to the survey. 412 people completed the survey. There are approximately 600,000 people between the ages of 18 and 25 that reside in the Detroit Metro area. (Detroit Regional Chamber, 2022) 384 samples would be needed to achieve confidence level with a margin of error for statistical analysis. (Australian Bureau of Statistics, 2022) The collected responses are greater than the sample size target.
The survey instrument has 4 sections. The first section covered responder demographics. The second section is adapted from the Depression, Anxiety and Stress Scales (DASS-21). The DASS-21 "is a set of three self-report scales designed to measure the emotional states of depression, anxiety and stress. Each of the three DASS-21 scales contains 7 items, divided into subscales with similar content. The depression scale assesses dysphoria, hopelessness, devaluation of life, self-depreciation, lack of interest/involvement, anhedonia and inertia. The anxiety scale assesses autonomic arousal, skeletal muscle effects, situational anxiety, and subjective experience of anxious affect. The stress scale is sensitive to levels of chronic non-specific arousal. It assesses difficulty relaxing, nervous arousal, and being easily upset/agitated, irritable/over-reactive and impatient". Scores for depression, anxiety and stress are calculated by summing the scores for the relevant items. (Motor Accident Insurance Commission, Australia, 2016; Lovibond & Lovibond, 1996)DASS-21 responses are summarized as extremely severe, severe, moderate, mild, and normal.
The third section is based on the Pew Research Center's Teen Survey.(Jiang, 2020) The questions in this section cover the usage of electronic devices by the sampled population. The fourth section was derived from C.S. Mott Children's Hospital National Poll on Children's Health. (Freed, n.d.) This survey measures effects of COVID-19 restrictions on teens, who rely on their peer and social connections for emotional support. In total, the survey instrument had 46 multiple choices questions.
Descriptive analysis of the data collected was performed to better understand the demographics of the participants. Descriptive analysis also included breakdown of responses per question. Analysis of variance (ANOVA) was used to explore whether there are any statistically significant differences between various groups. Further, ANOVA was used to investigate the relationships between depression, anxiety, stress, and self-reported impact of COVID-19 on social interactions. Finally, ANOVA was used to investigate how young adults in the Detroit Metro area tried to deal with problems related to their emotional states.
III. RESULTS
of the respondents identified as female, identified as male. of the respondents self-reported themselves as white or Caucasian, as black of African American, as Latino or Hispanic, as Asian, as Native American or Alaskan Native and as Native Hawaiian or Pacific Islander. A breakdown of respondent by age is shown in table 1.
| Age | Frequency | Percent |
| 18 | 82 | 16% |
| 19 | 50 | 10% |
| 20 | 65 | 12% |
| 21 | 88 | 17% |
| 22 | 58 | 11% |
| 23 | 45 | 9% |
| 24 | 58 | 11% |
| 25 | 68 | 13% |
| Other/Undisclosed | 8 | 2% |
In response to DASS-21 portion of the survey, most respondents reported their levels as normal. Specifically, reported normal depression levels, reported normal anxiety levels and reported normal stress levels. On the other hand, of respondents reported their depression as extremely severe or severe, of respondents reported their anxiety as extremely severe or severe and of respondents reported their stress as extremely severe or severe. Additionally, it can be concluded that largest number of people reported higher than normal levels of depression, anxiety, and stress. A complete breakdown of the relevant responses is included in table 2. A Pearson correlation analysis for the three emotional states was performed. The states demonstrate a high degree of correlation. The correlation analysis is shown in table 3. Furthermore, moderate degree of statistically significant correlation, with coefficients between 0.24and 0.39, were found between the levels of emotional states and various detrimental behaviors reported by the
respondents. Results of the associated Pearson correlation analysis are also shown in table 3.
| Level | Depression | Anxiety | Stress | |||
| Frequency | Percent | Frequency | Percent | Frequency | Percent | |
| Extremely Severe | 57 | 13.8% | 99 | 24.0% | 9 | 2.2% |
| Severe | 54 | 13.1% | 50 | 12.1% | 69 | 16.7% |
| Subtotal | 111 | 26.9% | 149 | 36.2% | 78 | 18.9% |
| Moderate | 102 | 24.8% | 82 | 19.9% | 81 | 19.7% |
| Mild | 39 | 9.5% | 27 | 6.6% | 57 | 13.8% |
| Normal | 160 | 38.8% | 154 | 37.4% | 196 | 47.6% |
| Grand Total | 412 | |||||
| Depression | Anxiety | Stress | Sleep issues | Worry | Sadness | Changes in appetite | Aggressive behavior | Withdrawing from family | |
| Depression | 0.67 | 0.68 | 0.30 | 0.39 | 0.39 | 0.24 | 0.37 | 0.30 | |
| Anxiety | 0.67 | 0.73 | 0.32 | 0.35 | 0.30 | 0.24 | 0.36 | 0.21 | |
| Stress | 0.68 | 0.73 | 0.30 | 0.38 | 0.36 | 0.25 | 0.39 | 0.24 | |
| Sleep issues | 0.30 | 0.32 | 0.30 | 0.40 | 0.41 | 0.39 | 0.31 | 0.23 | |
| Worry | 0.39 | 0.35 | 0.38 | 0.40 | 0.55 | 0.28 | 0.24 | 0.27 | |
| Sadness | 0.39 | 0.30 | 0.36 | 0.41 | 0.55 | 0.38 | 0.29 | 0.37 | |
| Changes in appetite | 0.24 | 0.24 | 0.25 | 0.39 | 0.28 | 0.38 | 0.25 | 0.34 | |
| Aggressive behavior | 0.37 | 0.36 | 0.39 | 0.31 | 0.24 | 0.29 | 0.25 | 0.30 | |
| Withdrawing from family | 0.30 | 0.21 | 0.24 | 0.23 | 0.27 | 0.37 | 0.34 | 0.30 |
Over of the respondents reported that the COVID-19 wave that was prevalent during the data collection phase has very negative or somewhat negative impact on their social interactions. A complete breakdown of the responses is included in table 4. Respondents used various modes of communication to interact with their family members, friends or loved ones. Most common modes of communications reported were phone calls, social media, gaming platforms and in-person interactions. A complete breakdown of the responses is included in table 5. During the COVID-19 wave prevalent during the data collection phase
respondents reported experiencing sleep issues, respondents reported experiencing worry, respondents reported experiencing sadness, respondents reported experiencing changes in appetite, respondents reported experiencing aggressive behavior and respondents reported withdrawing from family. Further, to seek emotional support of respondents looked for information on internet portals, used mobile applications, looked for professional help and talked to people in the family and/or friends.
| Frequency | Percent | |
| Very Negative | 127 | 30.8% |
| Somewhat Negative | 132 | 32.0% |
| Subtotal | 259 | 62.9% |
| No Impact | 114 | 27.7% |
| Somewhat Positive | 27 | 6.6% |
| Very Positive | 12 | 2.9% |
| Grand Total | 412 | |
| Text | Phone Call | Social Media | ||||
| Frequency | Percent | Frequency | Percent | Frequency | Percent | |
| Every day or almost every day | 18 | 4% | 32 | 8% | 40 | 10% |
| A few times a week | 44 | 11% | 85 | 21% | 63 | 15% |
| A few times a month or less | 131 | 32% | 145 | 35% | 116 | 28% |
| Never | 219 | 53% | 150 | 36% | 193 | 47% |
| Total | 412 | |||||
| Gaming Platforms | In-Person (Indoor and/or Outdoor) | |||||
| Frequency | Percent | Frequency | Percent | |||
| Every day or almost every day | 113 | 27% | 16 | 4% | ||
| A few times a week | 91 | 22% | 107 | 26% | ||
| A few times a month or less | 87 | 21% | 127 | 31% | ||
| Never | 121 | 29% | 162 | 39% | ||
| Total | 412 | |||||
ANOVA was performed to assess whether levels of depression, anxiety and stress varied by gender. It was found that the p-values of the F-tests were less than 0.05, hence it can be concluded that there are statistically significant differences between the means from one level of gender to another at the confidence level. The multiple range tests showed that the levels varied significantly between the following groups. People who self-reported their gender as other had statistically significant higher levels of depression and anxiety when compared to people who self-reported their gender as male or female. People who self-reported their gender as female or other had statistically significant higher levels of stress when compared to people who self-reported their gender as male. Results of the ANOVA are shown in tables 6, 7, 8. Multiple Ranges tests are shown in tables 9, 10, 11. The results of ANOVA didn't show any statistically significant differences related to respondents' race.
| Source | Sum of Squares | Df | Mean Square | F-Ratio | P-Value |
| Between groups | 30.21 | 3 | 10.07 | 3.95 | 0.0085 |
| Within groups | 1040.42 | 408 | 2.55 |
| Source | Sum of Squares | Df | Mean Square | F-Ratio | P-Value |
| Between groups | 20.54 | 3 | 6.85 | 3.28 | 0.0210 |
| Within groups | 851.91 | 408 | 2.09 |
| Source | Sum of Squares | Df | Mean Square | F-Ratio | P-Value |
| Between groups | 25.87 | 3 | 8.62 | 5.84 | 0.0006 |
| Within groups | 602.06 | 408 | 1.48 |
| Contrast | Sig. | Difference | +/- Limits |
| Female - Male | -0.28 | 0.32 | |
| Female - Other | * | 1.22 | 0.97 |
| Female - Prefer Not to Say | 0.63 | 1.30 | |
| Male - Other | * | 1.50 | 0.97 |
| Male - Prefer Not to Say | 0.90 | 1.30 | |
| Other - Prefer Not to Say | -0.59 | 1.59 |
| Contrast | Sig. | Difference | +/- Limits |
| Female - Male | -0.26 | 0.29 | |
| Female - Other | * | 1.01 | 0.88 |
| Female - Prefer Not to Say | 0.04 | 1.18 | |
| Male - Other | * | 1.27 | 0.88 |
| Male - Prefer Not to Say | 0.30 | 1.18 | |
| Other - Prefer Not to Say | -0.97 | 1.44 |
| Contrast | Sig. | Difference | +/- Limits |
| Female - Male | * | -0.39 | 0.24 |
| Female - Other | 0.72 | 0.74 | |
| Female - Prefer Not to Say | 0.39 | 0.99 | |
| Male - Other | * | 1.12 | 0.74 |
| Male - Prefer Not to Say | 0.78 | 0.99 | |
| Other - Prefer Not to Say | -0.33 | 1.21 |
ANOVA did not highlight any statistically significant differences between levels of depression, anxiety, stress, and self-reported impact of COVID-19 on social interactions. All p-values were greater than 0.05. Similarly, the analysis did not demonstrate any statistically significant difference in the impact of COVID-19 based on gender or race. Tables 12, 13, 14 show that respondents turned to internet portals and professionals for help with their emotional states at statistically significant levels.
| Source | Sum of Squares | Df | Mean Square | F-Ratio | P-Value |
| MAIN EFFECTS | |||||
| A:Advice from internet | 23.35 | 1 | 23.35 | 11.71 | 0.0007 |
| B:Help from app | 1.01 | 1 | 1.01 | 0.51 | 0.4775 |
| C:Helpirm professional | 12.61 | 1 | 12.61 | 6.32 | 0.0123 |
| D:Helpfirm_fam FRIEND | 4.92 | 1 | 4.92 | 2.47 | 0.1169 |
| Source | Sum of Squares | Df | Mean Square | F-Ratio | P-Value |
| MAIN EFFECTS | |||||
| A:Advice from internet | 48.13 | 1 | 48.13 | 20.62 | 0.0002 |
| B:Help from app | 7.10 | 1 | 7.10 | 3.04 | 0.0819 |
| C:Helpfm professional | 13.97 | 1 | 13.97 | 5.99 | 0.0148 |
| D:Helpfmfam FRIEND | 0.82 | 1 | 0.82 | 0.35 | 0.5536 |
| Source | Sum of Squares | Df | Mean Square | F-Ratio | P-Value |
| MAIN EFFECTS | |||||
| A:Advice from internet | 20.64 | 1 | 20.64 | 14.22 | 0.0002 |
| B:Help from app | 0.02 | 1 | 0.02 | 0.01 | 0.9085 |
| C:Helpfm professional | 4.25 | 1 | 4.25 | 2.93 | 0.0879 |
| D:Helpfmfam FRIEND | 0.004 | 1 | 0.004 | 0.00 | 0.9608 |
IV. DISCUSSION
The analyses show that emotional states of young adults in the Detroit Metro area were concerning. The emotional states were worse for genders other than male. The COVID-19 wave, and the associated lockdown also seems to have coincided with several detrimental behaviors. The young adults used various modes of communication to keep their social interactions active. They turned to various avenues to seek help for their emotional states.
V. CONCLUSION
Public health administrators could use the findings of this study to develop effective remedial programs. At individual level, young adults should keep channels of communications open via various modes with loved ones and professionals to help elevate their emotional states. The study is the first of its kind for the Detroit Metro area. Additional studies should be conducted in other geographical areas to develop a comprehensive understanding of the emotional states of young people in general and during pandemic lockdowns in specific. Further longitudinal studies will also help deepen the depth of knowledge. Regardless, of the COVID-19 related lockdown the emotional states of young people in the Detroit Metro area were found to be distressed.
ACKNOWLEDGMENTS
The authors acknowledge the support of their family in completing this research study.
Disclosure
The authors report no conflicts of interest in this work.