Gender differences and mental distress during COVID-19: a cross-sectional study in Japan

Abstract

Background

To identify risk factors for mental distress and investigate whether the factors were different between men and women during the coronavirus disease 2019 (COVID-19), using KOKOROBO data, which is an online platform that aims to facilitate access to mental health services.

Methods

We used baseline data on KOKOROBO users 13 years of age or older in Japan who accessed it from October 11, 2021, to April 6, 2023, excluding those receiving treatment for mental health problems. Global severity, based on the most severe measure on Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Insomnia Severity Index (ISI), was analyzed using multivariable logistic regression with baseline characteristics for each gender, and for under 30 and 30 years of age or older in women. We conducted the same analysis of suicidal ideation for each gender.

Results

In the 686 men and 1274 women, 117 (17.1%) and 100 (7.8%) had minimal global severity respectively, and the rest suffered from mental distress to some extent. For women, ages under 30 years (adjusted OR (aOR): 0.352, 95%CI: 0.231–0.539, P < 0.001), marriage (aOR: 0.453, 95%CI: 0.274–0.746, P = 0.002), and concerns about COVID-19 infection were associated with global severity, while having children (aOR: 0.509, 95% CI: 0.284–0.909, P = 0.023) and decrease of going out during the COVID-19 pandemic had a protective effect on global severity and suicidal ideation for men, respectively. Living with family was a risk factor for mental distress in unmarried women over 30 years of age. Less communication with family or others and responding to the questionnaire late at night (00:00–05:59) were associated with severe global severity in both genders.

Conclusions

Age, living arrangement, marriage, having children, concerns about COVID-19 infection, and lifestyle changes during the COVID-19 pandemic had gender-specific effects on mental distress, while frequent communication and regular life rhythm maintained mental health in both genders. Young women and, unmarried middle-aged women living with their families tended to experience mental distress during the COVID-19 pandemic.

Trial registration

The Ethics Committee of the National Center of Neurology and Psychiatry approved this study (approval number B2020141) on April 15, 2021.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-024-06200-5.

Background

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on the mental health of the public through exposure to high levels of stress due to the loss of normal lifestyles, such as fear of infection and the tendency to stay at home, coupled with bankruptcy and increased unemployment [1]. An increase in the prevalence of anxiety and depression worldwide has been reported [2–5] and the need for support for vulnerable populations has been highlighted [1]. The prevalence of suicidal ideation was also reported to have increased two-fold as compared to pre-pandemic levels in the adult general population [6]. In Japan, the number of suicides increased by 16% during the second wave of the COVID-19 pandemic (July to October 2020), although it declined during the first wave (February to June 2020) [7, 8]. Compared to 2019, the suicide rate by age group in 2022 increased in 20s, 40s, and 50s in that order, and the rate for women increased for three consecutive years in Japan [9]. In addition, the post COVID-19 condition (long COVID) remains a problem [10]; therefore, early identification of mental distress in the general population is essential.

The risk factors for mental health problems associated with the COVID-19 pandemic that have been identified to date are: younger age, being a woman, unemployed, students, health care personnel, pre-existing illness, experiencing economic consequences, perceived infection risk, perceived financial risks, lifestyle changes, perceived discrimination, limited physical exercise, and low perceived social support in Chinese students [11–14]. In Japan, some studies have also reported the risk factors for mental distress: youth, women, low-income level, burden of caregiving, DV, concerns about COVID-19, and short conversation time [15, 16].

Although young women are already known to be at high risk for mental distress during the COVID-19 pandemic, few studies showed whether risk factors were different between men and women [15, 17, 18]. It was found that income level, infection of COVID-19, and concerns about COVID-19 were differently associated with mental distress by gender. However, these studies did not include enough information about their lifestyles, such as relationships or circadian rhythms [16, 19]. Moreover, not enough light has been shed on those other than young women. It would be necessary to develop gender- or age- specific approaches to mitigate mental distress.

We had established an online system to facilitate access to mental health services without the risk of infection, called KOKOROBO, which was launched in Japan in April 2021. Using baseline characteristics of KOKOROBO participants sampled from general population, we investigated gender differences and age differences in women in determinants of mental distress during the COVID-19 pandemic. Our study was closely related to Yoshioka et al. [15]; a nationwide, cross-sectional, web-based study in Japan investigating gender-specific risk factors for serious psychological distress (SPD). They showed that income level, marital status, and having children had different effects on SPD between men and women. We used a new measurement scale “global severity” as a measurement of mental state, which was defined as the most severe measure on depression, anxiety, or insomnia.

Methods

Introduction to KOKOROBO

Conventionally, access to mental health services had been inadequate due to stigma and other barriers; however, since the spread of COVID-19, services in medical and long-term care facilities had been further restricted, and the users of these services were increasingly withholding access due to the risk of infection. Simultaneously, the public was exposed to high levels of stress. Specifically, the need for mental health services increased, but the conditions that inhibited access overlapped, and solving these problems became an urgent issue [1]. Therefore, in order to establish a non-contact consultation system, we aimed to create an online KOKOROBO system to facilitate access to mental health services, which has been in operation since April 2021. Moreover, the burden on staff at mental health and welfare centers and other related facilities has increased significantly owing to the growing number of phone calls for consultation [20]. Recently, an AI-based stress management chatbot using a cognitive-behavioral change approach (AI-chatbot: “COCORO Conditioner”) application has been developed and achieved high user satisfaction [21]. Applying such applications to counseling services is expected to reduce the burden on staff. Therefore, we decided to integrate the AI-chatbot into the system. Specifically, with the owner’s permission, we recommended that it should be used primarily for mildly distressed accessors. Meanwhile, standardized methods for helping people with mental health problems had not been established. Although Psychological First Aid (PFA) is known as humane, supportive, and practical psychological support by non-specialists for people who have experienced a severe crisis event, a more specialized method for mental health professionals, RAPID (Rapport/Reflective listening, Assessment, Prioritization, Intervention, Disposition)-PFA, has been developed [22] and introduced in Japan recently [23]. The system includes online consultations by psychologists who have received RAPID-PFA training and are regularly supervised to ensure quality. When combining AI-chatbot and online counseling using RAPID-PFA, it was necessary to be able to use AI-chatbot and online counseling according to the severity of the mental state. The following stress-related factors were assessed during triage: depression, anxiety, insomnia, and social situations. First, using the criterion values for each measure of depression, anxiety, and insomnia, we decided to recommend observation for individuals with minimal levels in all measures, an AI-chatbot for those with mild levels, and online consultation for those with moderate or higher levels, depending on the most severe measure (Fig. 1). However, considering that some online consultation users may require treatment at a medical facility, we decided to limit the online consultation service to areas where medical facilities for treatment can be guaranteed, and where cooperation with local governments has been established. We have gradually expanded the coverage of this service. One month after receiving the initial assessment, the users will be asked to repeat the above mental assessment, and the goal of the overall plan is to create an algorithm that allows for appropriate triage based on actual interventions and the reassessment results.

Fig. 1.

Flow of the KOKOROBO system. Depression, Anxiety, and Insomnia are assessed using Patient Health Questionnaire (PHQ)-9, Generalized Anxiety Disorder (GAD)-7, and Insomnia Severity Index (ISI), respectively. Severity classification algorithm assesses global severity, which is identified as the most severe measure on PHQ-9, GAD-7, and ISI. We use “COCORO Conditioner” as AI-chatbot. Online consultation is conducted by psychologists who received RAPID-PFA (Rapport/Reflective listening, Assessment, Prioritization, Intervention, Disposition – Psychological First Aid) training

Participants

In the accessors to KOKOROBO from October 11, 2021 to April 6, 2023, participants were those who lived, worked, or went to school in an area with a cooperation system with medical facilities and local governments to ensure smooth access to medical care. All participants whose data were used in this study agreed to provide anonymized data. Baseline data were extracted and used in this study. The study area included 11 Japanese cities: Tokyo, Yokohama, Tokorozawa, Kawaguchi, Chiba, Nagoya, Toyoda, Shinshiro, Shizuoka, Yonago, and Fukuoka. We excluded those who were already receiving treatment for mental health problems. Further, data from those aged 13 years or older at baseline were used in the analyses. This project was approved by the Ethics Committee of the National Center of Neurology and Psychiatry B2020-141 and conforms to the provisions of the Declaration of Helsinki.

Basic information

Each participant completed a self-report questionnaire providing basic information such as age, gender, living situation, marital status, presence of a child, and employment status. Although age was continuous variable, we used a dummy variable for age 30 as a threshold in some of our analyses and we meant “young” people were those under 30 years of age in this paper. The frequencies of communication with family and others were also collected through self-report and classified into four levels. Who the family referred to was up to each participant; for example, their spouses, children, parents or siblings. To examine the behavioral and mental changes associated with COVID-19, participants responded to four levels of change in the frequency of going out after the spread of COVID-19 and concerns about infection with COVID-19. This self-report questionnaire was online and participants were able to respond whenever was convenient for them. Therefore, it could be expected that those who responded late at night would have worse mental state, especially insomnia, than those who responded at other times. Thus, we collected the start time of the questionnaire in four six-hour increments.

Gender

According to the Sex and Gender Equity in Research guidelines [24], sex “refers to a set of biological attributes in humans and animals that are associated with physical and physiological features” while gender “refers to the socially constructed roles, behaviours and identities.” In the present baseline data, we provided only two choices, “men” and “women,” in the questionnaire, but later we added an alternative “not specified” after finalizing the present data. Because gender is a multifaceted concept that includes social and cultural aspects, there are other genders besides men and women. However, only a few participants have chosen “not specified” since the data were finalized, and thus we used men/women in this paper and interpreted the results from a gender perspective.

Depression

Depression was assessed using the Patient Health Questionnaire (PHQ)-9 [25, 26]. The PHQ-9 scores were divided into five levels: 0–4 was minimal, 5–9 was mild, 10–14 was moderate, 15–19 was moderately severe, and 20–27 was severe. The validity of the Japanese version of the PHQ-9 has been confirmed [27].

Anxiety

Initially, we used State-Trait Anxiety Inventory (STAI) forms X-I and X-II [28] to measure state and trait anxiety. However, given the possibility that the large number of items in these scales may have suppressed access, we switched to the Generalized Anxiety Disorder (GAD)-7 [29] scale to assess anxiety during the study. In the present study, we focused on data from participants assessed using the GAD-7. The GAD-7 scores were divided into four levels: 0–4 was minimal, 5–9 was mild, 10–14 was moderate, and 15–21 was severe. The validity of the Japanese version of the GAD-7 has been confirmed [30].

Insomnia

We used the Insomnia Severity Index (ISI) [31, 32] to assess insomnia. The ISI scores were divided into four levels: 0–5 was minimal, 6–10 was mild, 11–17 was moderate, and 18–28 was severe. Insomnia is usually classified into four severity levels using ISI scores: 0–7, no clinically significant insomnia; 8–14 subthreshold insomnia; 15–21 clinical insomnia (moderate severity); 22–28 clinical insomnia (severe) [31]. Nonetheless, different cutoff scores may be helpful depending on the research questions and participant characteristics. This study defined an ISI score of 10 or less as minimal or mild and 11 or more as moderate or severe, as multiple previous studies [31–33] have suggested that an ISI score of 10 is appropriate as the cutoff value for determining the presence of insomnia. For example, Morin et al. [32] reported that a cutoff value of 10 was the highest correct classification rate for community samples, which was the subject of this study. In contrast, a cutoff of 11 yielded the highest correct classification rate for clinical samples. Furthermore, considering that the mean score for people with insomnia and healthy participants was 16.6 and 5.2, respectively, as reported by Munezawa et al. [33], we defined 5 or less as minimal, 6–10 as mild (below clinical threshold), 11–17 as moderate, and 18 or more as severe.

Global severity

We defined a new measurement scale, “global severity” in mental state, which was identified as the most severe measure on depression (PHQ-9), anxiety (GAD-7), or insomnia (ISI). Notably, the PHQ-9 had five categories; thus, we identified moderately severe as severe in PHQ-9 scoring. We recognized if an individual’s global severity is not minimal, then the individual has mental health problems in at least one area of depression, anxiety, or insomnia. According to the global severity level, observation is recommended for individuals with a minimal level, the AI-chatbot for those with a mild level, and online consultation for those with a moderate level or above.

Suicidal ideation

To assess suicidal ideation, we used the 9th item in PHQ-9, “Over the last 2 weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself?” The options were “Not at all,” “Several days,” “More than half the days,” and “Nearly every day.”

Statistical analysis

Descriptive statistics for the study participants were computed for each gender. For each gender, we used multivariable logistic regression to explore the risk factors for global severity, suicidal ideation over the last two weeks, PHQ-9, GAD-7, and ISI. According to global severity, we also conducted a subgroup analysis for women under 30, and 30 years of age or older. The dependent variables were dummy variables that took the value of 1 if the global severity outcome was moderate or severe and 0 otherwise. Concerning suicidal ideation over the last 2 weeks, we used a dummy variable as the dependent variable, which took 0 if a participant had no suicidal ideation at all and 1 if otherwise; that is, one would like to commit suicide for more than a few days. The explanatory variables were age (< 30 or ≥ 30), gender, living situation, marital status, presence of a child, employment status, communication with family and others, change in the frequency of going out after the spread of COVID-19, concerns about infection with COVID-19, and the time when participants responded to the questionnaire. All analyses were conducted using Python version 3.9.7. (Python Software Foundation, https://www.python.org/).

Results

Of all 1,960 participants, 1,274 (65.0%) were women, and women were younger than men (Mean: 41.4, 36.3, SD: 13.6, 13.3 for men and women, respectively). Global severity was more severe in women, and the numbers with minimal, mild, moderate, and severe global severity were 100 (7.8%), 232 (18.2%), 349 (27.4%), and 593 (46.5%) in women, and 117 (17.1%), 164 (23.9%), 177 (25.8%), and 228 (33.2%) in men, respectively. The other baseline characteristics and outcomes are listed in Table 1.

Table 1.

Baseline characteristics

Characteristics	Men (n = 686)	Women (n = 1274)	p value
Age (years), mean (SD)	41.4 (13.6)	36.4 (13.3)	< 0.001
Living arrangement, n (%)			< 0.001
Living alone	181 (26.4)	323 (25.4)
Living with family	488 (71.1)	866 (68.0)
Living with others	17 (2.5)	85 (6.7)
Marital status, n (%)			< 0.001
Never married	280 (40.8)	667 (52.4)
Married	382 (55.7)	522 (41.0)
Divorced / Separated / Widowed	24 (3.5)	85 (6.7)
Having children, n (%)	340 (49.6)	447 (35.1)
Communication with family, n (%)			0.008
Frequently	189 (27.6)	398 (31.2)
Sometimes	290 (42.3)	581 (45.6)
Rarely	172 (25.1)	244 (19.2)
Almost never	35 (5.1)	51 (4.0)
Communication with others, n (%)			0.70
Frequently	46 (6.7)	71 (5.6)
Sometimes	126 (18.4)	239 (18.8)
Rarely	416 (60.6)	793 (62.2)
Almost never	98 (14.3)	171 (13.4)
Employment, n (%)			< 0.001
Permanent staff / Self-employed	484 (70.6)	607 (47.6)
Students	65 (9.5)	170 (13.3)
Others	137 (20.0)	497 (39.0)
Change in frequency of going out after the spread of COVID-19, n (%)			0.32
Not reduced at all	157 (22.9)	253 (19.9)
Little reduced	126 (18.4)	257 (20.2)
Fairly reduced	223 (32.5)	442 (34.7)
Very reduced	180 (26.2)	322 (25.3)
Concern about COVID-19 infection, n (%)			0.022
Not concerned at all	167 (24.3)	237 (18.6)
Little concerned	191 (27.8)	376 (29.5)
Fairly concerned	207 (30.2)	434 (34.1)
Very concerned	121 (17.6)	227 (17.8)
Start time of the questionnaire, n (%)			< 0.001
06:00–11:59	194 (28.3)	255 (20.0)
12:00–17:59	235 (34.3)	376 (29.5)
18:00–23:59	198 (28.9)	424 (33.3)
00:00–05:59	59 (8.6)	219 (17.2)
PHQ-9 score, n (%)			< 0.001
Minimal, 0–4	191 (27.8)	176 (13.8)
Mild, 5–9	163 (23.8)	243 (19.1)
Moderate, 10–14	139 (20.3)	316 (24.8)
Moderately severe, 15–19	105 (15.3)	283 (22.2)
Severe, 20–27	88 (12.8)	256 (20.1)
GAD-7 score, n (%)			< 0.001
Minimal, 0–4	270 (39.4)	311 (24.4)
Mild, 5–9	188 (27.4)	354 (27.8)
Moderate, 10–14	128 (18.7)	358 (28.1)
Severe, 15–21	100 (14.6)	251 (19.7)
ISI score, n (%)			< 0.001
Minimal, 0–5	216 (31.5)	271 (21.3)
Mild, 6–10	203 (29.6)	430 (33.8)
Moderate, 11–17	186 (27.1)	415 (32.6)
Severe, 18–28	81 (11.8)	158 (12.4)
Global severity, n (%)			< 0.001
Minimal	117 (17.1)	100 (7.8)
Mild	164 (23.9)	232 (18.2)
Moderate	177 (25.8)	349 (27.4)
Severe	228 (33.2)	593 (46.5)
Suicidal ideation over the last 2 weeks, n (%)			< 0.001
Not at all	430 (62.7)	611 (48.0)
Several days	132 (19.2)	313 (24.6)
More than half the days	54 (7.9)	148 (11.6)
Nearly every day	70 (10.2)	202 (15.9)

SD, standard deviation; PHQ-9, Patient Health Questionnaire-9; GAD-7, Generalized Anxiety Disorder-7; ISI, Insomnia Severity Index

Global severity is identified as the most severe measure on Patient Health Questionnaire (PHQ)-9, Generalized Anxiety Disorder (GAD)-7, and Insomnia Severity Index (ISI)

p value from chi-squared test or t-test, as appropriate

We found that different factors were associated with global severity between men and women (Table 2). Women over 30 years of age had significantly less severe symptoms than women under 30 years of age (aOR: 0.352, 95% CI: 0.231–0.539, P < 0.001), but there was no significant relationship between age and global severity in men (aOR: 0.646, 95% CI: 0.338–1.236, P = 0.19). Living with family had a significantly negative effect on global severity in women (aOR: 2.023, 95% CI: 1.278–3.202, P = 0.003) but not in men (aOR: 0.978, 95% CI: 0.538–1.781, P = 0.94). Meanwhile, married women were significantly less severe in global severity than unmarried women (aOR: 0.453, 95%CI: 0.274–0.746, P = 0.002). The presence of a child significantly improved global severity in men (aOR: 0.509, 95% CI: 0.284–0.909, P = 0.023) but not in women (aOR: 1.025, 95% CI: 0.696–1.510, P = 0.90). We also found that the following risk factors were associated with global severity for both men and women. The less frequent the communication with family or others, the higher global severity. No employment category was associated with global severity. Change in frequency of going out after the spread of COVID-19 was not associated with global severity, whereas only women who were very concerned about COVID-19 infection were more likely to have higher global severity compared to those who were not concerned at all (aOR: 1.857, 95% CI: 1.084–3.183, P = 0.024). There was a relationship between time of responding to the self-report questionnaire and global severity – both men and women who responded late at night (00:00–05:59) were significantly more severe than those who responded at other times.

Table 2.

Factors associated with global severity, stratified by gender

	Men (n = 686)				Women (n = 1274)
Characteristics	aOR	95%CI	p value		aOR	95%CI	p value
Age
< 30	ref.				ref.
≥30	0.646	[0.338–1.236]	0.19		0.352	[0.231–0.539]	< 0.001
Living arrangement
Living alone	ref.				ref.
Living with family	0.978	[0.538–1.781]	0.94		2.023	[1.278–3.202]	0.003
Living with others	1.113	[0.308–4.025]	0.87		1.021	[0.523–1.993]	0.95
Marital status
Never married	ref.				ref.
Married	0.788	[0.375–1.660]	0.53		0.453	[0.274–0.746]	0.002
Divorced/Separated/Widowed	1.377	[0.448–4.232]	0.58		1.050	[0.533–2.067]	0.89
Having children
No	ref.				ref.
Yes	0.509	[0.284–0.909]	0.023		1.025	[0.696–1.510]	0.90
Communication with family
Frequently	ref.				ref.
Sometimes	1.295	[0.856–1.959]	0.22		1.595	[1.167–2.179]	0.003
Rarely	2.679	[1.473–4.874]	0.001		3.098	[1.889–5.082]	< 0.001
Almost never	3.811	[1.185–12.257]	0.025		4.990	[1.643–15.156]	0.005
Communication with others
Frequently	ref.				ref.
Sometimes	1.133	[0.513–2.504]	0.76		1.216	[0.666–2.220]	0.52
Rarely	1.876	[0.908–3.878]	0.089		2.214	[1.261–3.889]	0.006
Almost never	4.554	[1.841–11.264]	0.001		3.720	[1.817–7.615]	< 0.001
Employment
Permanent staff/ Self-employed	ref.				ref.
Students	1.227	[0.524–2.872]	0.64		1.038	[0.573–1.880]	0.90
Others	1.295	[0.818–2.050]	0.27		1.286	[0.950–1.741]	0.10
Change in frequency of going out after the spread of COVID-19
Not reduced at all	ref.				ref.
Little reduced	0.863	[0.487–1.531]	0.62		1.077	[0.670–1.729]	0.76
Fairly reduced	1.014	[0.609–1.688]	0.96		0.821	[0.532–1.269]	0.37
Very reduced	1.031	[0.603–1.762]	0.91		0.880	[0.553–1.401]	0.59
Concern about COVID-19 infection
Not concerned at all	ref.				ref.
Little concerned	1.128	[0.679–1.872]	0.64		0.665	[0.424–1.043]	0.076
Fairly concerned	0.953	[0.575–1.579]	0.85		0.901	[0.572–1.419]	0.65
Very concerned	1.468	[0.822–2.622]	0.19		1.857	[1.084–3.183]	0.024
Start time of the questionnaire
06:00–11:59	ref.				ref.
12:00–17:59	0.792	[0.514–1.221]	0.29		0.917	[0.631–1.332]	0.65
18:00–23:59	1.243	[0.788–1.962]	0.35		1.230	[0.846–1.789]	0.28
00:00–05:59	3.093	[1.367–6.999]	0.007		3.042	[1.735–5.334]	< 0.001

aOR, adjusted odds ratio; CI, confidence interval

Global severity is identified as the most severe measure on Patient Health Questionnaire (PHQ)-9, Generalized Anxiety Disorder (GAD)-7, and Insomnia Severity Index (ISI)

According to the results of subgroup analysis in women stratified by age (Table 3), women over 30 years of age who lived with their families had significantly higher global severity than those who lived alone (aOR: 3.451, 95% CI: 1.846–6.452, P < 0.001), and marriage had significantly protective effect on women’s global severity regardless of their age. Nonetheless, having children significantly worsened global severity in women under 30 years of age (aOR: 5.97, 95% CI: 1.222–29.164, P = 0.027).

Table 3.

Factors associated with global severity among women, stratified by age

	Women, age < 30 (n = 501)				Women, age ≥ 30 (n = 773)
Characteristics	aOR	95%CI	p value		aOR	95%CI	p value
Living arrangement
Living alone	ref.				ref.
Living with family	0.958	[0.444–2.071]	0.91		3.451	[1.846–6.452]	< 0.001
Living with others	0.567	[0.221–1.454]	0.24		2.141	[0.707–6.487]	0.18
Marital status
Never married	ref.				ref.
Married	0.307	[0.097–0.968]	0.044		0.397	[0.216–0.729]	0.003
Divorced/Separated/Widowed	0.124	[0.007–2.193]	0.15		1.142	[0.553–2.358]	0.72
Having children
No	ref.				ref.
Yes	5.97	[1.222–29.164]	0.027		0.854	[0.566–1.288]	0.45
Communication with family
Frequently	ref.				ref.
Sometimes	2.001	[1.004–3.989]	0.049		1.548	[1.084–2.210]	0.016
Rarely	3.651	[1.386–9.619]	0.009		3.111	[1.711–5.657]	< 0.001
Almost never	7.032	[0.758–65.260]	0.086		5.640	[1.491–21.337]	0.011
Communication with others
Frequently	ref.				ref.
Sometimes	1.042	[0.310–3.499]	0.95		1.219	[0.587–2.531]	0.6
Rarely	1.819	[0.605–5.470]	0.29		2.243	[1.130–4.456]	0.021
Almost never	3.279	[0.778–13.817]	0.11		3.767	[1.591–8.921]	0.003
Employment
Permanent staff/ Self-employed	ref.				ref.
Students	1.235	[0.591–2.582]	0.57		1.461	[0.267–7.999]	0.66
Others	1.949	[0.840–4.526]	0.12		1.195	[0.855–1.669]	0.30
Change in frequency of going out after the spread of COVID-19
Not reduced at all	ref.				ref.
Little reduced	0.744	[0.296–1.870]	0.53		1.321	[0.742–2.352]	0.34
Fairly reduced	0.59	[0.255–1.362]	0.22		0.961	[0.564–1.639]	0.88
Very reduced	0.913	[0.330–2.524]	0.86		1.000	[0.575–1.737]	1
Concern about COVID-19 infection
Not concerned at all	ref.				ref.
Little concerned	0.388	[0.158–0.952]	0.039		0.869	[0.500–1.511]	0.62
Fairly concerned	0.56	[0.226–1.388]	0.21		1.171	[0.672–2.040]	0.58
Very concerned	0.723	[0.217–2.408]	0.6		2.548	[1.359–4.774]	0.004
Start time of the questionnaire
06:00–11:59	ref.				ref.
12:00–17:59	1.143	[0.492–2.653]	0.76		0.864	[0.567–1.317]	0.5
18:00–23:59	2.667	[1.086–6.554]	0.032		1.035	[0.680–1.575]	0.87
00:00–05:59	4.45	[1.556–12.729]	0.005		2.708	[1.363–5.379]	0.004

aOR, adjusted odds ratio; CI, confidence interval

Global severity is identified as the most severe measure on Patient Health Questionnaire (PHQ)-9, Generalized Anxiety Disorder (GAD)-7, and Insomnia Severity Index (ISI)

For suicidal ideation (Table S1), the results were largely similar to those of global severity, but men who did decrease their frequency of going out after the spread of COVID-19 had less suicidal ideation.

The results of PHQ-9, GAD-7, and ISI were shown in the supplementary information (Table S2–S4).

Discussion

We collected baseline data of individuals in KOKOROBO and explored the factors associated with global severity, including depression, anxiety, and insomnia, stratified by gender. As previous studies reported, young women were at high risk for serious global severity [4, 11, 15]. However, younger age was not significantly associated with global severity in men. Although the sample size of men was half that of women, which resulted in lower statistical power, this finding may suggest that the effect of age on global severity was moderated by gender.

We found that living with one’s family had a negative effect on mental health in women. Specifically, women over 30 years of age tended to be negatively affected by living with their family, while women under 30 years of age and men were not. In addition, marriage positively affected mental health in women, and the presence of a child did not worsen it except for young women. These results could be interpreted in two perspectives. First, these results suggest that living with parents or siblings may be an emotional burden for unmarried women over 30 years of age. It is possible that the burden of caregiving and the unusual lifestyle during the COVID-19 pandemic may have increased conflict among family members, leading to mental distress. Previous studies in Japan reported that informal caregiving by family members were associated with mental health distress during the COVID-19 pandemic [15, 34]. Informal caregivers might be concerned about COVID-19 infection because those who needed informal care were likely to be more severely ill if they were infected [35], and our results showed that in particular, serious concerns about COVID-19 infection were associated with global severity in women. A previous study reported that the association between informal caregiving and mental health deterioration was highlighted especially in women [34]. Our results may reflect the social structure in which unmarried middle-aged women are likely to provide informal care to family members with whom they lived with. The second possible reason is that those who experience severe mental distress tend to need others’ help and are therefore likely to live with their family. Because of the cross-sectional nature of the survey, we could not determine the causal relationship between living with family and mental distress.

Regarding young mothers compared to young women without children, the high global severity may indicate that they were more sensitive to the lifestyle changes or the economic downturn [36]. The closure of schools put a burden of childcare and housework on women compared to men in Japan during the COVID-19 pandemic [37]. This made it difficult for women to work outside and would result in the economic downturn. Meanwhile, we also found that the individuals’ global severity was generally better in both men and women who communicated more frequently. Interestingly, although the positive effect of frequent communication with family was evident in both age groups, that with others was significant only in women over 30 years of age. Well-structured communication with family and others is already known to play a key role in coping with mental distress [16, 38], and our findings also showed that sufficient communication with family or others would maintain mental health even during the COVID-19 pandemic. Especially, frequent communication with others strongly supports even women over 30 years of age experiencing severe mental distress.

Regarding COVID-19, although serious concerns about COVID-19 infection worsened global severity in women, changes in the frequency of going out after the spread of COVID-19 were not associated with it in both men and women. It might be expected that decrease in frequency of going out would reduce physical activity, and previous studies reported less physical activity as a risk factor for mental distress [14, 39]. Nonetheless, our study showed that outing frequency was not associated with mental distress. It is possible that staying at home did not necessarily mean less physical activity because population-level interest increased in exercising at home without going out during the COVID-19 pandemic [40]. Meanwhile, those who did not decrease their frequency of going out had more suicidal ideation in men. Although highly speculative, the reason may be that those who did not reduce the frequency of going out were already more withdrawn and were at higher risk for suicidal tendencies before the pandemic [41].

In addition, those who completed the questionnaire late at night were more likely to have severe mental health problems. In studies using data from online platforms, it is relatively easy to collect data from the general population. Simultaneously, participants can respond whenever is convenient for them in the online-based study, and the time of response can be a clue in understanding their lifestyle. That is, our findings showed that responding late at night was associated with mental distress. It is possible that those who responded late at night were less likely to be adhering to a regular life rhythm with a lack of sleep, and sufficient sleep during the night plays a vital role in a healthy lifestyle and has been reported as a factor for better mental state [19]. Thus, not only the questionnaire items, but other available data derived from the platform system can lead to a better understanding of the study population.

This study has certain limitations. First, extrapolation of our findings to other populations is challenging given the high prevalence of mental distress compared to other studies because the subjects were those who accessed the system for mental health concerns. Interestingly, however, the results of the present study did not differ significantly from epidemiological studies targeting the general population [11, 15]. Second, we did not collect data on physical activity, income, or pre-existing physical disorders, which have been reported as risk factors for mental distress [11, 14, 15, 39]. In addition, we did not ask about the use of psychoactive substances, infection of COVID-19, and whether or not anyone lived with a health professional, which are also potential variables associated with mental distress. However, we collected data on lifestyle changes after the pandemic and employment status, which, to some extent might reflect physical activity and income. Finally, we could not investigate the temporal relationship between risk factors and mental distress because our study design is cross-sectional. For example, concern about COVID-19 infection was a risk factor for global severity, but it is possible that mental distress causes concern about infection.

The COVID-19 pandemic has increased mental distress in individuals and limited access to healthcare facilities. These difficulties have raised expectations in using digital devices to detect and manage mental health problems. The KOKOROBO platform in Japan has begun providing online resources, including triage, psychological first aid [22, 23], and recommendations for medical examinations. Worldwide, mental health care has also shifted to remote services [42]. Owing to the high demand for psychiatric services provided remotely, the World Health Organization established the Ensuring Quality of Psychological Support (EQUIP) platform [43].

We found that in addition to young women, there is another high-risk population, unmarried, middle-aged women who live with their families but have less communication with family and others. Social isolation and loneliness are widely known risk factors for mental distress [44], but physical distance from people, such as whether or not they have families living together, could not easily explain this high-risk population. Self-help, identification, and care of individuals needing assistance through digital tools will contribute to their mental well-being.

Finally, mental distress of KOKOROBO users was more intense than expected; thus, we recognized the critical role of AI-chatbots and online counseling. In future work, we plan to study the effects of these programs.

Conclusions

This study has provided gender differences in determinants for mental disorders and risk factors, such as age, living arrangement, marriage, having children, concerns about COVID-19 infections, and lifestyle changes after the pandemic. Sufficient communication and disciplined lifestyle maintained mental health in both men and women. We also found that living with family but having less communication with family and others was a risk factor for mental distress in unmarried women over 30 years of age during the COVID-19 pandemic.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Abbreviations

COVID-19

Coronavirus disease 2019

SPD

Serious psychological distress

PFA

Psychological First Aid

RAPID-PFA

Rapport/Reflective listening, Assessment, Prioritization, Intervention, Disposition-Psychological First Aid

PHQ-9

Patient Health Questionnaire-9

STAI

State-Trait Anxiety Inventory

GAD-7

Generalized Anxiety Disorder-7

ISI

Insomnia Severity Index

EQUIP

Ensuring Quality in Psychological Support

Author contributions

T(akumi)K, KT, TF, RI, FH, YI, TN, KM, KW, T(oshiaki)K, MM, AY, AH, HH, NO, SK, HK, MO, HO, and KN contributed to the conception of this article. T(akumi)K, KT, MO, and KN acquired and analyzed the data, and drafted the manuscript. All authors edited and approved the final manuscript.

Funding

This research was supported by Japan Agency for Medical Research and Development (AMED) under Grant Number JP20dk0307099 and JP23dk0307119, and an Intramural Research Grant (6-1) for Neurological and Psychiatric Disorders of the National Center of Neurologyand Psychiatry from KN and KT.

Data availability

The data analyzed during the current study available from the corresponding author on reasonable request.

Declarations

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Ethic approval and consent to participate

This project was approved by the Ethics Committee of the National Center of Neurology and Psychiatry B2020-141 and conforms to the provisions of the Declaration of Helsinki. All participants whose data were used in this study agreed to provide anonymized data. The informed consent to participate in the study should be obtained from participants (or their parents or legal guardians in the case of children under 16).