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Article

The Relationship Between Ventilation and Building-Related Symptoms in Modern High-Performance Japanese Houses: A Cross-Sectional Study Using Building-Specification Data

by
Ryotaro Iwayama
1,2,
Keiichi Shimatani
3,
Yoshitake Nakayama
4,
Kohki Takaguchi
5,
Hiroko Nakaoka
6,
Norimichi Suzuki
5,7,* and
Kenichi Sakurai
5,8
1
Graduate School of Medical and Pharmaceutical Sciences, Chiba University, Chiba 260-8670, Japan
2
Comprehensive Housing R&D Institute, Sekisui House, Ltd., Kizugawa 619-0224, Japan
3
Endowed Course on Housing Environment and Health (Sekisui House, Ltd.), Center for Preventive Medical Sciences, Chiba University, Chiba 263-8522, Japan
4
Department of Design, Faculty of Creative Engineering, Chiba Institute of Technology, Chiba 275-0016, Japan
5
Department of Healthy Cities and Built Environment, Center for Preventive Medical Sciences, Chiba University, Chiba 263-8522, Japan
6
Japan Plant Factory Association, Kashiwanoha Campus, Chiba University, Kashiwa 277-0882, Japan
7
Design Research Institute, Chiba University, Chiba 263-8522, Japan
8
Department of Nutrition and Metabolic Medicine, Center for Preventive Medical Sciences, Chiba University, Chiba 260-8670, Japan
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(17), 3013; https://doi.org/10.3390/buildings15173013
Submission received: 15 July 2025 / Revised: 12 August 2025 / Accepted: 19 August 2025 / Published: 25 August 2025
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
Browse Figure
Versions Notes

Abstract

Ventilation plays a key role in maintaining indoor air quality and reducing building-related symptoms (BRSs). Although prior studies suggest that ventilation volume and system type may influence BRSs, few have examined their combined effects in residential settings. This cross-sectional study investigated associations between ventilation volume, system type, and BRSs among 3970 residents of newly built detached houses in Japan. Data were collected in two waves in 2023, and the ventilation volume per floor area and per person was calculated from building specifications. BRSs were assessed using the MM040EA Sick House Syndrome questionnaire and analyzed using binary logistic regression stratified by system type. In air supply and exhaust systems, higher ventilation volume per person was linked to a lower prevalence of general symptoms (OR = 0.46). In exhaust-only systems, greater ventilation volume was positively associated with mucosal irritation symptoms (OR = 1.60). These findings highlight the complex relationship between ventilation and health and emphasize the importance of system type. Although air quality parameters were not measured directly, the results provide evidence based on building specifications, thereby offering insight to refine building codes, guide post-occupancy assessments, and inform preventive public health policy.

1. Introduction

Building-related symptoms (BRSs) are nonspecific complaints—headaches, fatigue, and irritation of the eyes, nose, and throat, as well as skin dryness and itchiness—that occur across various indoor settings and degrade the health and quality of life of occupants [1,2,3]. Indoor air quality (IAQ), an integrative indicator that is pivotal to health and comfort, encompasses factors such as temperature, humidity, airflow, and indoor and outdoor pollutants [4]. Pollutants like volatile organic compounds (VOCs) and particulate matter (PM) originate from both indoor and outdoor sources [5,6,7]; their concentration is influenced by a building’s ventilation volume [8,9,10]; thus, adequate ventilation is essential for maintaining optimal IAQ and reducing BRSs [11,12]. Recently, various mechanical ventilation systems with differing performance characteristics have been introduced in residential buildings, including air supply and exhaust (ASE) systems and those that are exhaust-only (EO).
Previous research on ventilation and BRSs has produced mixed results. A study of multifamily dwellings in Sweden found no significant differences in BRSs between units using seasonal adaptive ventilation, which reduces ventilation by 20% during the heating season, and units with constant ventilation [13]. In contrast, a study in Tianjin, China, found that lower nocturnal ventilation volumes in bedrooms were significantly associated with mucosal irritation symptoms, a type of BRS [14]. Evidence from Japan is similarly inconsistent: two studies have found no significant association between the use of mechanical ventilation and the prevalence of BRSs [15,16]. However, a 2018 study reported that both adults and children living in indoor environments with mechanical ventilation systems experienced BRSs at a rate approximately 11% lower than those living in homes without such systems [17]. In Seoul, South Korea, occupants of energy-efficient homes equipped with mechanical ventilation systems reported fewer cough symptoms than those in conventional homes [18]. System type also matters: a study of detached houses in Sweden found that ASE systems were significantly associated with a reduced prevalence of BRSs, whereas EO and natural ventilation systems were not [19]. Similarly, a Norwegian study on multifamily dwellings revealed that symptom types differed depending on the ventilation system used (EO vs. ASE), suggesting the complexity of system performance in influencing BRSs [20].
Importantly, most previous studies have focused on either ventilation volume or ventilation systems in isolation. However, in an actual living environment, these two factors are closely related, and evaluating one factor alone may lead to an underestimation or overestimation of the other’s effect. Such a limitation may, at least in part, account for the inconsistent findings across studies to date. To the best of our knowledge, very few studies have examined both factors simultaneously, and none have assessed ventilation volume and system type concurrently using building-specification data in Japanese residential settings. Furthermore, studies that incorporate air quality measurements are often constrained to limited geographic areas and small sample sizes because of cost considerations.
In this study, we conducted a cross-sectional analysis among residents of homes across Japan that were equipped with mechanical ventilation systems, specifically EO and ASE systems, using objective building-specification data. This study aimed to clarify how ventilation volume and system type are associated with the presence of BRSs. This dual-exposure approach is novel for Japan and is not subject to the logistical and financial constraints of on-site IAQ monitoring. By jointly analyzing both factors in a nationwide sample and leveraging routinely available specification data, this study offers scalable, practice-oriented evidence that could inform building codes, post-occupancy evaluations, and preventive public health policies intended to mitigate BRS risk. We hypothesized that higher ventilation volume and ASE systems would each be associated with lower BRS prevalence and would offer the strongest protection against BRSs when they occur together.

2. Materials and Methods

2.1. Study Design, Setting, Participants, and Period

We conducted a cross-sectional analysis using data from the Japan Housing and Health Cohort (J-Hohec). J-Hohec was established to elucidate the relationship between living environments and residents’ health conditions and to address health issues caused by housing conditions. It comprises objective floor plans and building-specification data from houses built by a major housing company (Sekisui House, Ltd., Kizugawa, Japan), along with survey data on residents’ socioeconomic status and health conditions.
For the present study, we restricted the sample to relatively new detached houses completed in 2013 or later and built by the same national developer. This ensured a uniform baseline of construction quality and minimized heterogeneity in the design practices. Web-based survey invitations were sent to approximately 270,000 members who had agreed to receive email newsletters. Participants were provided with an explanation of the survey’s purpose; via a web-based consent screen, they gave their informed consent to share their building information and their personal data before they completed the web-based questionnaire. The consent process was conducted electronically, and no minors were included in the study. Each home purchaser was assigned a unique ID, which allowed the corresponding digitized building specification to be retrieved from the database. Three reminder emails were sent during the two-and-a-half-month survey period. The response rate for Wave 1 and Wave 2, which are the focus of this study, was approximately 5%.
The dataset construction and participant selection process proceeded as follows: The initial sample included 9710 individuals from Wave 1 (24 January to 31 March 2023, n = 5460) and Wave 2 (24 July to 30 September 2023, n = 4250). Within approximately one month after the end of each wave, the survey responses were matched with building information—including personally identifiable data such as residential addresses—using participant ID numbers. The authors had temporary access to identifying information during this data integration process. During the integration, address information was reduced to the postal code. By the time of the analysis, all the data had been anonymized and contained no personally identifiable information.
From this initial sample, we excluded participants who lived in nondetached houses (n = 573), lived in homes that were 10 years old or older (n = 4329), had missing data on ventilation systems (n = 145), had ventilation system capacity outside ±3 standard deviations from the mean (n = 49), had a total floor area outside ±3 standard deviations from the mean (n = 48), did not provide gender information (n = 77), had a history of Sick House Syndrome (SHS) or chemical sensitivity (n = 10), had a history of depression or mental health issues (n = 155), did not report their household income (n = 250), and had a body mass index (BMI) that was outside ±3 standard deviations from the mean (n = 104). The final analysis included 1500 respondents with ASE systems and 2470 respondents with EO systems, for a total of 3970 respondents (Figure 1).
This dataset includes the key variables that were needed to analyze the relationship between ventilation volume and BRSs. It also contains data on modern airtight and highly insulated homes, and thus it is well-suited for this analysis. Sekisui House, Ltd. (the industry collaborator for this study) provided only the building-specification data; all the analytic code was developed and executed solely by the academic investigators, and the company had no involvement in data analysis, data interpretation, or manuscript preparation for publication. This study was approved by the Ethics Committee of the Chiba University Graduate School of Medicine (approval number M10381).

2.2. Outcome Variables

The Japanese-translated version of the SHS questionnaire MM040EA was used to assess BRSs [21,22]. It is a self-administered questionnaire that has been widely used in both international and domestic studies for the epidemiological assessment of BRSs. Accordingly, the MM040EA is regarded as the standard instrument for Sick Building Syndrome/SHS research in Japan and was adopted without modification in the present study. Participants were asked to report the frequency of experiencing, in their home, any of the following 12 symptoms during the six months prior to the study: fatigue, heaviness in the head, headache, nausea/dizziness, difficulty concentrating, itchy eyes, runny/stuffy nose, cough, dry throat, dryness of the face, dryness of the scalp/ears, and dry hands. Symptom assessment was conducted in two stages. First, participants were prompted to select one of three responses for each of the 12 symptoms: “Yes, almost every week,” “Yes, sometimes,” and “No, not at all.” Second, participants were asked if any symptoms they marked as “Yes” were related to their home environment. If they responded “Yes,” the symptom was categorized as a BRS. Participants reporting at least one BRS were considered BRS cases.
All outcome variables in this study were analyzed as binary variables based on the presence or absence of symptoms. In previous studies, symptoms were classified using the MM040EA into three categories: general symptoms, mucous membrane irritation symptoms, and skin dryness symptoms [17,19,23]. In this study, BRSs were classified into three categories: general symptoms (fatigue, heaviness in the head, headache, nausea/dizziness, and difficulty concentrating), mucous irritation symptoms (itchy eyes, runny/stuffy nose, and cough), and skin dryness symptoms (dry throat, dryness of the face, dryness of the scalp/ears, and dry hands).

2.3. Ventilation

Ventilation systems were classified into two categories based on building specifications: ASE systems and EO systems. ASE systems featured mechanically controlled supply and exhaust, high-efficiency air purification filters, and total heat exchangers. These filters remove fine particles, such as PM, to improve IAQ [24,25,26,27,28,29], and total heat exchangers conserve energy by minimizing thermal loss during air exchange between indoor and outdoor environments [30,31]. Conversely, EO systems supply air via natural ventilation and use mechanical exhaust only; they do not include filters or heat exchangers. Older ventilation systems can degrade IAQ if filters are not replaced and ducts are not cleaned [32,33,34]. Thus, to reduce such variability, we included only houses less than 10 years old. Additionally, professional maintenance by the construction company was conducted at intervals of 3 months, 1 year, 2 years, and 5 years after construction. Therefore, we considered the risk of reduced ventilation performance due to poor maintenance to be minimal.
In this study, we evaluated the ventilation volume per total floor area and per person, categorized by ventilation system type. A distinctive feature of the study was the availability of objective building information, as all participants lived in houses constructed by the same housing company. We reviewed each house’s as-built drawings and building specifications to determine their ventilation systems and floor area details. Ventilation volume was calculated based on the model number of the ventilation system as specified in the product specifications. Compliance with the Building Standards Act was verified through a ventilation calculation report, which was prepared based on the rated ventilation capacity described in the documents. The ventilation volume was identified accurately using these specification details, which included the ventilation type, the volume, the presence of a total heat exchanger, and high-performance filters.
There are two methods for determining ventilation volume: one based on the size of the building and the other based on the number of occupants. In this study, ceiling height was set as a fixed standard of 2.5 m by the builder during the construction design. However, owing to the limitation in the available floor plan data, features such as atriums and sloped ceilings could not be accounted for, making it difficult to calculate air volume precisely. Therefore, in this study, the ventilation volume relative to the size of the building was evaluated using the ventilation volume per floor area. It was determined that this would not pose any issues when buildings of different sizes were compared. Furthermore, the living room was treated as the main gathering space in each household, and we focused on ventilation volume per person to evaluate its association with BRSs. In Japan, living rooms and bedrooms are typically prioritized in IAQ measurements due to the amount of time spent in them [35]. In this study, living rooms were identified using blueprint labels. Because the living room is typically the space in which family members gather and is the room in which people spend the most time while awake [36,37], it is a suitable space for assessing the link between the residential environment and the subjective symptom index MM040EA. Thus, we selected the living room for our analyses. Bedrooms, however, were not selected for our analysis, as they were not labeled consistently, and multiple rooms may serve as bedrooms.
To calculate ventilation volumes, we extracted (i) the rated airflow capacity of each ventilation unit directly from the equipment specification sheets, (ii) the living area and total floor area from the as-built architectural plans, and (iii) the household headcount from the questionnaire; these three parameters—the ventilation capacity of the ventilation system (Q), the total floor area and the living area (Atot and Aliv), and the number of occupants (N)—formed the basis of all the ventilation metrics used in our analysis.
The ventilation volume by floor area was calculated using the following formula:
Qf = Q/Atot
where
  • Qf: the ventilation volume by floor area (m3/(h·m2));
  • Q: the ventilation capacity of ventilation systems (m3/h);
  • Atot: total floor area (m2).
The ventilation volume per person was calculated using the following formula:
γ = Aliv/Atot × 100
where
  • γ: the ratio of the living area to the total floor area (%);
  • Aliv: the living area (m2).
Qliv = Q × γ
where
  • Qliv: the ventilation volume of the living room.
Qp = Qliv/N
where
  • Qp: the ventilation volume per person (m3/(h·person));
  • N: the number of occupants (persons).
These formulas were developed in this study based on insights and data reported in previous research, which observed differences in BRSs related to these factors [19,20].
Because ventilation volumes estimated from building specifications may contain nondifferential measurement errors stemming from discrepancies between design and actual airflow, we converted the continuous ventilation indicators (m3/(m2·h)) into tertiles (“low,” “medium,” and “high”). This categorization mitigates bias stemming from nondifferential exposure misclassification and, by coding the tertiles with an ordinal variable (0–2), enables the calculation of a p-value for trends to test monotonic dose–response relationships while still capturing possible non-linear or threshold effects at the category extremes. It also yields threshold-based information that is readily interpreted by practitioners. ASE and EO systems were analyzed separately due to differences in their periods of widespread adoption and regional distribution; therefore, the cutoff values for these tertiles differed between system types.
For floor-area-based volume, the value ranges for ASE were <0.41, 0.41–<0.52, and ≥0.52 m3/(m2·h), and those for EO systems were <0.41, 0.41–<0.46, and ≥0.46 m3/(m2·h).
For person-based volume, the ASE value ranges were <15.68, 15.68–<22.08, and ≥22.08 m3/h, and EO values were <11.71, 11.71–<16.90, and ≥16.90 m3/h.
In Japan, the 2003 amendment to the Building Standards Act established regulations requiring formaldehyde emissions to be below 5 µg/(m2·h) and mandated the installation of 24 h ventilation systems capable of at least 0.5 replacements of the total indoor air volume each hour in residential buildings [38,39]. All houses in this study complied with these standards. Furthermore, primary construction materials were certified to meet formaldehyde emission standards, and the chemicals used in adhesives and paints were managed through safety data sheets [40]. Therefore, we assumed that chemical emissions from building materials would not differ significantly across the houses. However, there is no legal standard for indoor CO2 concentration in Japanese homes [41,42]. Conversely, Germany includes residential buildings in its IAQ guidelines, which define CO2 concentrations below 1000 ppm as harmless [43]. The American Society of Heating, Refrigerating and Air-Conditioning Engineers standard determines the minimum ventilation volume based on a sum per area and per person [44]. At present, approaches to residential ventilation rates differ from country to country.
The design airtightness (C-value) specified for all surveyed dwellings was ≤2.0 cm2/m2, which is consistent with the target set for cold regions in the 1999 Next-Generation Energy-Saving Guideline (abolished in 2009). This uniformly moderate specification reduces the likelihood that uncontrolled air leakage might have materially confounded the observed associations.

2.4. Independent Variables

Participant data for personal characteristics, household characteristics, and subjective perceptions of the environment were obtained through a survey questionnaire. Individual characteristics included gender, age, BMI, medical history, neurotic personality traits, and smoking status (never smoked or current smoker). Medical history covered bronchial asthma, atopic dermatitis, contact dermatitis, hay fever, allergic rhinitis, allergic conjunctivitis, and food allergies; having any of these conditions comprised a positive medical history. Neurotic personality traits were assessed using the Japanese version of Gosling’s Big Five Personality Trait Assessment Scale [45], which measures neuroticism, extraversion, openness, agreeableness, and conscientiousness. Based on previous research, neurotic personality trait scores were classified as low (≤7), moderate (8–9), and high (≥10) [23].
BRSs are related not only to physical environmental factors but also to psychosocial factors such as satisfaction with the living environment and personality traits [46]. Neurotic personality traits were included in the analysis due to their known association with BRSs [23,47]. Although the survey included several items related to both environmental perceptions (such as satisfaction with air quality and airflow, thermal comfort, and visibility of dust) and lifestyle behaviors (including home cleaning habits and the use of air purifiers and various types of heating appliances), no validated questionnaire items were available. As these perceptions are highly subjective and prone to individual bias, it was confirmed that they would not significantly affect the analysis results and were therefore excluded as variables.
Household characteristics included annual household income (<JPY 4 million, JPY 4–8 million, >JPY 8 million, and unknown) and the number of household members.
Information on participants’ homes was obtained from the construction company’s database. Building characteristics included the age of the building, geographic location, insulation grade, and structural type (steel or wooden construction). Construction sites were classified into eight regions, from cold to warm, based on heating load requirements as defined by Japan’s climate classification [48]. No participants were from region 1 (northern Hokkaido) or region 8 (Okinawa). Insulation grade is a measure of thermal performance, with higher grades indicating better insulation. The properties in this study ranged from Grade 4 to Grade 6, which are higher than Grade 2, the most common level in existing homes in Japan [49].

2.5. Statistical Analysis

Descriptive statistics were used to summarize the building information, participant characteristics, and prevalence of BRSs. Seasonal differences in symptom occurrence within each ventilation system were assessed using chi-square (χ2) tests at a significance level of p < 0.05. Binomial logistic regression was used to analyze BRSs. The three symptom categories were treated as dependent variables, whereas the characteristics of the participant, household, and home were treated as independent variables. In the regression model, the low-ventilation group was designated as the reference category.
Multivariate analyses were adjusted for the following potential confounders: survey season, building age, regional classification, insulation grade, structure, gender, age, BMI, medical history, household income, smoking status, and neurotic personality traits. No pairwise Spearman correlations exceeded 0.40, and all variance inflation factors were <2.0, indicating minimal multicollinearity. To account for multiple comparisons in the regression analyses, Bonferroni correction was applied (adjusted p = 0.05/6 ≈ 0.0083).
All analyses were performed using SPSS version 27.0 for Windows (SPSS Inc., Chicago, IL, USA).

3. Results

3.1. Participant Characteristics

In both the ASE and EO groups, approximately 70% of the participants were men; however, the age distribution differed between groups. In the ASE group, the largest proportion was in their 30s (45.6%), and those in their 30s or younger accounted for approximately half of the group. In the EO group, the largest proportion was in their 40s (32.6%), followed closely by those in their 30s (31.5%). In both groups, approximately 70% of participants had a normal BMI (18.5–<25). In terms of household income, 39.1% of the ASE group had an annual income of at least JPY 8 million, compared with 33.9% in the EO group. The proportion of smokers was similar between groups: 13.1% in the ASE group and 12.1% in the EO group. Approximately 40% of participants in both groups had low scores for neurotic personality traits (Table 1).

3.2. Dwelling Characteristics of ASE and EO Systems

The average total floor area of dwellings was similar between the two systems, with ASE homes averaging 129.4 m2 and EO homes averaging 130.1 m2. The average living room area was slightly larger in ASE homes (35.1 m2) than in EO homes (31.6 m2). The average equipment ventilation volume was also higher in ASE homes (153.3 m3/h) than in EO homes (139.3 m3/h). The average number of household members was slightly lower in ASE homes (3.2 persons) than in EO homes (3.4 persons) (Table 1). These variables are essential for calculating the ventilation volume per floor area and per person.
ASE homes were generally newer, with 80.8% built within the past 3 years. In contrast, only 22.4% of EO homes were built within the past 3 years, and 50.8% were more than 5 years old. Regarding regional distribution, ASE homes were more frequently located in colder areas (16.2% in regions classified as four or below), compared with just 1.7% of EO homes. In terms of insulation grade, 95.8% of ASE homes had Grade 5 insulation, compared with 78.5% for EO homes. Among EO homes, an additional 20.8% had Grade 4 insulation, and very few homes in either group had Grade 6 insulation. Steel-framed construction was common in both groups and was used in approximately 60% of homes in each category (Table 1).

3.3. BRSs for ASE and EO Systems

In the ASE group, general symptoms occurred at a rate of 6.9% throughout the year, with the symptoms being slightly more common in summer (7.7%) than in winter (6.4%). Mucosal irritation symptoms had an annual prevalence of 11.3%, with the highest rate of occurrence in winter (12.2%) and the lowest rate in summer (9.7%). Skin dryness symptoms were the most common overall (22.2%), with a marked seasonal increase in winter (26.7%) relative to summer (14.7%).
In homes with the EO system, general symptoms occurred at an annual rate of 6.2%, and the rate of occurrence was slightly higher in winter (6.6%) than in summer (5.5%). Symptoms of mucosal irritation had an overall prevalence of 10.5%, with higher rates in winter (11.3%) than in summer (9.2%). Skin dryness symptoms were also common (17.3% annually), with winter (21.4%) showing a higher prevalence than summer (10.7%) (Table 2).
Skin dryness symptoms exhibited pronounced seasonality in both the ASE systems (χ2 = 28.57, p < 0.001) and the EO systems (χ2 = 45.94, p < 0.001). In contrast, no significant seasonal differences were detected for either general symptoms or mucosal irritation symptoms (Table 3).

3.4. The Relationship Between Ventilation Volume per Floor Area and BRSs

In the ASE-system group, the high-ventilation group showed decreased odds of general symptoms (OR = 0.55, 95% CI = 0.33–0.92, and p = 0.024; p for trend = 0.019) and increased odds of skin dryness symptoms (OR = 1.36, 95% CI = 1.00–1.87, and p = 0.054; p for trend = 0.032) relative to the low-ventilation group; however, neither association met the Bonferroni-adjusted threshold of p < 0.0083. In the EO-system group, the high-ventilation group showed increased odds of mucosal irritation symptoms (OR = 1.50, 95% CI = 0.99–2.26, and p = 0.055; p for trend = 0.030), which also did not reach the adjusted significance level (Table 4).

3.5. Relationship Between Ventilation Volume per Person and BRSs

In the ASE-system group, the high-ventilation group was associated with a significantly lower prevalence of general symptoms (OR = 0.46, 95% CI = 0.27–0.77, and p = 0.003) than the low-ventilation group, and they showed a significant dose–response relationship (p = 0.007). Conversely, in the EO-system group, the medium-ventilation group was associated with a significant increase in mucosal irritation symptoms (OR = 1.60, 95% CI = 1.15–2.21, and p = 0.005) (Table 5).

4. Discussion

4.1. Ventilation Systems

This study focuses on how ventilation volume and the type of mechanical ventilation system—ASE or EO—are associated with the occurrence of BRSs. To the best of our knowledge, this is the first study in a residential setting to comprehensively evaluate both the magnitude of ventilation volume and the system type using objectively verified building-specification data. Our data were derived from construction documents validated by professional staff, allowing for the precise classification of ventilation systems and demonstrating that routinely collected building-specification data can serve as a complementary, scalable proxy measure when direct IAQ monitoring is infeasible.
The main findings in this study revealed that, in the ASE system, a higher ventilation volume per person was associated with a lower prevalence of general symptoms. Conversely, in the EO system, a higher ventilation volume per person was associated with a higher prevalence of mucosal irritation symptoms. These results suggest that evaluating ventilation effectiveness requires considering not only the quantity of airflow but also the characteristics of the ventilation system.
The differences between the ASE and EO systems were distinct. Although their floor area, room size, and number of occupants were similar, the ASE system delivered, on average, 14 m3/h more ventilation. The ASE homes were also newer—over 80% had been built within the past 3 years—and more commonly located in colder regions where higher insulation and energy-efficient systems are prioritized. The residents in ASE homes were generally younger, with more than half under the age of 40, compared with one-third in this age range in the EO homes. This generational difference may be linked to increased demand for mechanical ventilation following public awareness raised during the COVID-19 pandemic [50].
The prevalence of BRSs differed by system: general symptoms (ASE: 6.9%; EO: 6.2%), mucosal irritation symptoms (ASE: 11.3%; EO: 10.5%), and skin dryness (ASE: 22.2%; EO: 17.3%). These values fall within the range of previous reports from Japan using the MM040EA instrument [23], although slight differences may reflect variations in season, demographics, or housing conditions. Although the raw prevalence of BRSs appeared higher in the ASE group, this may reflect age-related vulnerability, as younger adults are more prone to BRSs [23].
The inverse association between ventilation volume per person and general symptoms in the ASE group aligns with previous studies in office settings, where increasing the ventilation to approximately 20 L/(s·person) reduced symptom frequency [51,52,53]. This effect could be explained by improved IAQ and specifically by reduced CO2 concentrations. Elevated CO2 levels have been linked to fatigue and impaired cognitive performance in office workers as well as in children [54,55]. CO2 concentration is considered a key indicator of the relationship between indoor ventilation and symptoms such as fatigue or tiredness. According to the French Agency for Food, Environmental and Occupational Health & Safety (ANSES), an indoor–outdoor CO2 concentration difference of 450 ppm or more, or an indoor CO2 concentration above 850 ppm, is associated with an increased risk of BRSs [56]. Japan’s Building Standards Act mandates a ventilation rate of 20 m3/(h·person) in office spaces to maintain CO2 levels below 1000 ppm [39]. In this study, the middle (15.68–<22.08 m3/(h·person)) and high (≥22.08 m3/(h·person)) groups of the ASE system included ranges that met or exceeded this standard. Conversely, although it was not statistically significant, a similar inverse trend was observed in the EO system, with odds ratios decreasing as ventilation volume per person increased. Likewise, both systems showed a nonsignificant trend of decreasing odds ratios with higher ventilation volume per floor area. The absence of any statistically significant association in these cases may be due to insufficient ventilation volume relative to the number of occupants.
However, a significant association between mucosal irritation symptoms and ventilation volume was observed for homes with EO systems only in the middle tertile of ventilation volume per person. The nasal mucosa plays an important role in the immune system by eliminating foreign substances, aided by the mucin secreted by nasal mucous cells. When exposed to foreign particles—including viruses, dust, pollen, and other particulates—the mucosa responds with increased secretion, vasodilation, and inflammation, which contribute to nasal congestion and irritation [57,58]. PM originates from indoor and outdoor sources, including smoking, cooking, heating, and traffic [5]. A study in London found that 26% to 37% of indoor PM came from indoor sources, and air exchange with outdoor air reduced PM concentrations by 65% to 86% [6]. The effects of ventilation thus depend on the relative balance between indoor emissions and outdoor air quality.
In this study, the proportion of smokers was relatively low (12–13%). In addition, Japanese homes are typically equipped with dedicated exhaust fans in kitchens, and air conditioners are the predominant heating source. Therefore, indoor sources of PM were presumed to be limited. Pollen concentrations rise in Japan from February to March [59], while westerly winds bring yellow dust and PM2.5 from the Asian continent from winter to spring [60,61]. Therefore, the influence of outdoor-derived pollutants may have been greater than that of indoor PM generation.
High-efficiency air purification filters in home ventilation systems can reduce indoor concentrations of PM [27]. ASE systems are typically equipped with filters to clean incoming air, thereby limiting the infiltration of outdoor pollutants. In contrast, EO systems rely on natural air supply without filtration, thereby increasing exposure to outdoor contaminants. A study in Sapporo, Japan, reported that opening windows for ventilation five or more times per week was associated with a 16–20% higher prevalence of mucosal irritation symptoms [17], which is consistent with our findings for the EO system. The lack of a reduction in mucosal irritation symptoms with increasing ventilation volume in ASE systems may be attributable to the limited generation of indoor pollutants.
In this study, chi-square analyses showed that the prevalence of skin dryness symptoms was higher during Wave 1 (winter) than during Wave 2 (summer), increasing by 12.0% for the ASE systems and 10.7% for the EO systems.
Previous studies conducted in student dormitories in Tianjin, China, and in Japanese office environments have identified dry air as a key environmental factor contributing to skin dryness symptoms [62,63]. Indoor factors such as temperature and humidity are particularly influential. An experimental study by Takada et al. demonstrated that low humidity, even at a constant temperature, increases the rate of skin moisture evaporation, thereby intensifying the sensation of dryness [64]. Humidification methods, such as the use of fog-type devices, have been shown to increase moisture in the stratum corneum and alleviate dryness, emphasizing the importance of humidity control for maintaining skin health [65]. In Japan, dry outdoor air introduced via ventilation can lower indoor relative humidity during winter and may exacerbate skin dryness [66].
This study identified several significant associations and trends between the type and volume of mechanical ventilation systems and BRSs. Increasing ventilation volume per person may help reduce general symptoms. Although ventilation is generally considered beneficial for health, the expected benefits were not consistently observed in this study. In some cases, higher ventilation volume was associated with adverse effects, such as mucosal irritation.
When ventilation volume is increased, systems such as ASE systems may be more effective in reducing nasal mucosal irritation caused by outdoor air pollutants than natural ventilation or EO systems. For example, a report on Swedish single-family homes found that, relative to natural ventilation, EO systems showed no significant association with BRSs. Conversely, ASE systems were more effective in reducing general symptoms, with an odds ratio of 0.54 [19]. Similarly, a study on Norwegian apartment buildings reported that EO systems were associated with reductions in eye and nasal symptoms and in coughing, but with increases in throat irritation, facial skin irritation, and fatigue. ASE systems, however, were linked with reductions in eye, nasal, and throat irritation as well as facial skin irritation and fatigue, although coughing and headaches increased [20]. These findings suggest that the relationship between residential ventilation systems and BRSs depends not only on system design and performance but also on contextual factors. These include the size of the indoor space, the number of occupants, and the seasonal or regional variations in outdoor air pollution and humidity. By introducing the concept of ventilation volume alongside system type, this study offers new insights into the complex relationship between ventilation and BRSs. Further research is needed to clarify these mechanisms from a multifaceted perspective, incorporating indoor and outdoor environmental conditions and the lifestyles of residents.

4.2. Study Limitations

This study has several limitations. First, we did not measure IAQ indicators such as CO2 and VOC concentrations or outdoor pollutants, including PM and yellow sand. In addition, official air pollution data were only available up to 2022 and could not be incorporated into this analysis. Thus, comparisons between indoor and outdoor pollutant concentrations could not be made, which limited our ability to assess how ventilation volume and system types influenced indoor pollutant levels. Therefore, the interpretation of any “beneficial” or “adverse” effects should be viewed as associative and hypothesis-generating. Furthermore, although ventilation volume was categorized into tertiles to mitigate measurement error, this approach did not eliminate the potential for bias to arise from unmeasured variations in IAQ.
Second, differences in construction quality or a lack of maintenance could lead to different outcomes. Therefore, the findings of this study should be interpreted in the context of high-performance Japanese homes that were built by the same housing company to meet certain performance standards and that received regular maintenance. The interpretation of the results also assumes a relatively uniform distribution of resident behaviors—such as window opening or cleaning frequency—across the population. Although such behaviors may be influenced by psychosocial factors, we controlled for neurotic personality traits in the analysis and considered subjective perceptions of ventilation and air quality satisfaction.
Third, the cross-sectional design of the current study precludes causal inference. It is possible that individuals with higher sensitivity to BRSs were more likely to choose homes equipped with high-performance mechanical ventilation systems. In addition, participation was restricted to owners who had agreed to receive email or social media communications, and the overall response rate was low (approximately 5%). These factors may limit the generalizability of the findings to all homeowners.
Future longitudinal studies are warranted to explore causal relationships. These studies should incorporate environmental monitoring of both indoor and outdoor air quality in order to verify whether the symptom gradients observed in building-specification data truly reflect actual indoor pollutant exposures while also considering seasonal variations, lifestyle behaviors, and differences in housing performance.

5. Conclusions

This nationwide cross-sectional study of 3970 residents in modern Japanese detached houses is the first in Japan to use building-specification data to simultaneously evaluate ventilation volume and system type. In homes with ASE systems that incorporated high-efficiency filters and heat-recovery units, the highest tertile of ventilation per person was associated with fewer general BRSs (adjusted OR = 0.46; 95% CI 0.28–0.77), whereas in EO systems, the medium tertile of ventilation per person was positively associated with mucosal irritation (OR = 1.60; 95% CI = 1.14–2.25). These results suggest that both adequate ventilation volume and add-on features such as filtration are important for symptom reduction. Because the specification-based approach relies on routinely collected design documents, it is readily scalable to nationwide populations, supporting the inclusion of high-efficiency filters in retrofit programs and providing an evidence base for updating Japanese residential ventilation guidelines.
However, this study is limited by the absence of direct IAQ measurements and its cross-sectional design, which preclude causal inference, as well as by its focus on recent Japanese detached houses, which may restrict its generalizability to other countries or building types. Future research should validate specification-derived ventilation metrics against on-site IAQ measurements and examine these relationships across a wider spectrum of buildings to determine the optimal combination of airflow and filtration for occupant health.

Author Contributions

Conceptualization, R.I., K.S. (Keiichi Shimatani) and N.S.; data curation, R.I.; formal analysis, R.I.; funding acquisition, N.S.; investigation, R.I., K.S. (Keiichi Shimatani), Y.N., and N.S.; methodology, R.I., K.S. (Keiichi Shimatani), Y.N., K.T. and N.S.; project administration, N.S.; supervision, H.N., N.S. and K.S. (Kenichi Sakurai); validation, K.S. (Keiichi Shimatani) and K.T.; visualization, R.I.; writing—original draft, R.I.; writing—review and editing, R.I., K.S. (Keiichi Shimatani), Y.N., K.T., H.N., N.S. and K.S. (Kenichi Sakurai). All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by a grant from Sekisui House Ltd., the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (B) [grant number 21H01487], and the JST OPERA Program, Japan [grant number JPMJOP1831]. The sponsor has no control over the interpretation, writing, or publication of the manuscript.

Institutional Review Board Statement

The Chiba University Graduate School of Medicine Ethics Committee (M10381) granted permission to conduct this study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. Participants were informed about the survey’s purpose and asked for consent to retrieve building information from the construction company at the beginning of the survey.

Data Availability Statement

The individual-level data generated and analyzed during the current study are not publicly available due to privacy and ethical restrictions. Furthermore, as this is part of an ongoing epidemiological investigation, data sharing is not yet permissible.

Acknowledgments

The authors would like to thank all the J-hohec participants and all staff for their dedication. I would like to express my sincere gratitude to my advisor, Chisato Mori.

Conflicts of Interest

Ryotaro Iwayama was enrolled as a graduate student at Chiba University until 31 March 2025, and is an employee of Sekisui House, Ltd. Keiichi Shimatani belongs to the endowed chairs of Sekisui House Ltd., which was established at the Center for Preventive Medical Sciences at Chiba University. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study.

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Buildings 15 03013 g001
Figure 1. Flow diagram of participant data analysis.
Figure 1. Flow diagram of participant data analysis.
Buildings 15 03013 g001
Table 1. Characteristics of participants and dwellings by ventilation system type (ASE and EO).
Table 1. Characteristics of participants and dwellings by ventilation system type (ASE and EO).
ASE System
(n = 1500)		EO System
(n = 2470)
Number (%)
Gender
       Male1068(71.2)1714(69.4)
       Female432(28.8)756(30.6)
Age group
       20–29128(8.5)65(2.6)
       30–39684(45.6)778(31.5)
       40–49391(26.1)805(32.6)
       50–59164(10.9)415(16.8)
       ≥60133(8.9)407(16.5)
BMI a
       <18.593(6.2)171(6.9)
       18.5−<251064(70.9)1790(72.5)
       ≥25343(22.9)509(20.6)
Medical history
       Yes745(49.7)1180(47.8)
Household income (million yen)
       <40060(4.0)182(7.4)
       400–<800854(56.9)1450(58.7)
       ≥800586(39.1)838(33.9)
Smoker
       Yes197(13.1)298(12.1)
Neuroticism tendency
       ≤7656(43.7)1075(43.5)
       8–9515(34.3)919(37.2)
       ≥10329(21.9)476(19.3)
Number of household members b
       Single15(1.0)35(1.4)
       2388(25.9)531(21.5)
       3490(32.7)625(25.3)
       4422(28.1)839(34.0)
       5158(6.4)397(16.1)
       627(1.1)43(1.7)
Building age
       <31212(80.8)553(22.4)
       3–<5124(8.3)663(26.8)
       5–<10164(10.9)1254(50.8)
Regional classification
       ≤379(5.3)4(0.2)
       4163(10.9)36(1.5)
       5278(18.5)296(12.0)
       6861(57.4)1858(75.2)
       7119(7.9)276(11.2)
Insulation grade
       453(3.5)513(20.8)
       51437(95.8)1938(78.5)
       610(0.7)19(0.8)
Structure
       Steel frame946(63.1)1542(62.4)
       Wooden554(36.9)928(37.6)
Season
       Winter941(62.7)1533(62.1)
       Summer559(37.3)937(37.9)
mean (SD)
Total floor area (m2)129.4 (31.0)130.1 (30.6)
Living room area (m2)35.1 (7.0)31.6 (7.1)
Ventilation volume (m3/h)153.3 (34.3)139.3 (34.1)
a BMI, body mass index. b Used as a variable only in the analysis of ventilation volume per area.
Table 2. Descriptive statistics for BRSs by ventilation system type and season (summer, winter, and year-round).
Table 2. Descriptive statistics for BRSs by ventilation system type and season (summer, winter, and year-round).
ASE SystemEO System
Year-RoundWinterSummerYear-RoundWinterSummer
General symptoms a,b103(6.9)60(6.4)43(7.7)153(6.2)101(6.6)52(5.5)
     Fatigue61(4.1)31(3.3)30(5.4)95(3.8)61(4.0)34(3.6)
     Heaviness in the head38(2.5)25(2.7)13(2.3)61(2.5)41(2.7)20(2.1)
     Headache37(2.5)23(2.4)14(2.5)69(2.8)48(3.1)21(2.2)
     Nausea/dizziness8(0.5)5(0.5)3(0.5)28(1.1)16(1.0)12(1.3)
     Difficulty concentrating46(3.1)29(3.1)17(3.0)74(3.0)48(3.1)26(2.8)
Mucosal irritation symptoms a,b169(11.3)115(12.2)54(9.7)259(10.5)173(11.3)86(9.2)
     Itchy eyes55(3.7)36(3.8)19(3.4)96(3.9)69(4.5)27(2.9)
     Runny/stuffy nose107(7.1)75(8.0)32(5.7)176(7.1)109(7.1)67(7.2)
     Cough99(6.6)69(7.3)30(5.4)132(5.3)94(6.1)38(4.1)
Skin dryness symptoms a,b333(22.2)251(26.7)82(14.7)428(17.3)328(21.4)100(10.7)
     Dry throat235(15.7)170(18.1)65(11.6)281(11.4)209(13.6)72(7.7)
     Dryness of the face103(6.9)89(9.5)14(2.5)122(4.9)96(6.3)26(2.8)
     Dryness of the scalp/ears79(5.3)65(6.9)14(2.5)115(4.7)85(5.5)30(3.2)
     Dry hands176(11.7)153(16.3)23(4.1)226(9.1)182(11.9)44(4.7)
a If there was at least one relevant symptom, the subject was classified as having symptoms. b Bold values indicate the symptoms included in the statistical analysis.
Table 3. Chi-square test results for seasonal differences in symptom occurrence a.
Table 3. Chi-square test results for seasonal differences in symptom occurrence a.
ASE
System		EO System
χ2pχ2p
General symptoms0.760.385 0.910.341
Mucosal irritation symptoms2.050.152 2.530.112
Skin dryness symptoms28.57<0.00145.94<0.001
a Boldface indicates statistical significance (p < 0.05).
Table 4. Ventilation systems, volume, and BRS prevalence in Japan: a binomial logistic regression analysis a.
Table 4. Ventilation systems, volume, and BRS prevalence in Japan: a binomial logistic regression analysis a.
General SymptomsMucosal Irritation SymptomsSkin Dryness Symptoms
OR95% CIpOR95% CIpOR95% CIp
ASE system (m3/(h·m2))
Low<0.431.00 1.00 1.00
Middle0.43–<0.520.73(0.44–1.19)0.2011.13(0.82–1.57)0.4550.95(0.69–1.31)0.753
High≥0.520.55(0.33–0.92)0.0241.04(0.75–1.46)0.8021.36(1.00–1.87)0.054
p for trend0.0190.9190.032
EO system (m3/(h·m2))
Low<0.411.00 1.00 1.00
Middle0.41–<0.460.72(0.48–1.09)0.1171.03(0.67–1.59)0.8860.85(0.65–1.11)0.219
High≥0.460.79(0.52–1.18)0.2511.50(0.99–2.26)0.0550.92(0.71–1.21)0.562
p for trend0.3760.0300.568
a The adjusted variables were season, building age, regional classification, insulation grade, structure, gender, age group, BMI, medical history, household income, smoking, neuroticism tendency, and the number of household members.
Table 5. Association between BRSs and ventilation volume per persons using binary logistic regression analysis a,b.
Table 5. Association between BRSs and ventilation volume per persons using binary logistic regression analysis a,b.
General SymptomsMucosal Irritation SymptomsSkin Dryness Symptoms
OR95% CIpOR95% CIpOR95% CIp
ASE system (m3/(h·person))
Low<15.681.00 1.00 1.00
Middle15.68–<22.080.52(0.32–0.86)0.0110.84(0.55–1.28)0.4161.02(0.74–1.41)0.911
High≥22.080.46(0.27–0.77)0.0030.93(0.61–1.41)0.7281.31(0.95–1.81)0.104
p for trend0.0070.3840.147
EO system (m3/(h·person))
Low<11.711.00 1.00 1.00
Middle11.71–<16.900.97(0.66–1.43)0.8811.60(1.15–2.21)0.0051.13(0.87–1.47)0.364
High≥16.900.71(0.46–1.10)0.1211.41(1.00–2.00)0.0511.13(0.86–1.49)0.386
p for trend0.1350.0500.379
a Boldface indicates statistical significance (p < 0.0083). b The adjusted variables were season, building age, regional classification, insulation grade, structure, gender, age group, BMI, medical history, household income, smoking, and neuroticism tendency.
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Iwayama, R.; Shimatani, K.; Nakayama, Y.; Takaguchi, K.; Nakaoka, H.; Suzuki, N.; Sakurai, K. The Relationship Between Ventilation and Building-Related Symptoms in Modern High-Performance Japanese Houses: A Cross-Sectional Study Using Building-Specification Data. Buildings 2025, 15, 3013. https://doi.org/10.3390/buildings15173013

AMA Style

Iwayama R, Shimatani K, Nakayama Y, Takaguchi K, Nakaoka H, Suzuki N, Sakurai K. The Relationship Between Ventilation and Building-Related Symptoms in Modern High-Performance Japanese Houses: A Cross-Sectional Study Using Building-Specification Data. Buildings. 2025; 15(17):3013. https://doi.org/10.3390/buildings15173013

Chicago/Turabian Style

Iwayama, Ryotaro, Keiichi Shimatani, Yoshitake Nakayama, Kohki Takaguchi, Hiroko Nakaoka, Norimichi Suzuki, and Kenichi Sakurai. 2025. "The Relationship Between Ventilation and Building-Related Symptoms in Modern High-Performance Japanese Houses: A Cross-Sectional Study Using Building-Specification Data" Buildings 15, no. 17: 3013. https://doi.org/10.3390/buildings15173013

APA Style

Iwayama, R., Shimatani, K., Nakayama, Y., Takaguchi, K., Nakaoka, H., Suzuki, N., & Sakurai, K. (2025). The Relationship Between Ventilation and Building-Related Symptoms in Modern High-Performance Japanese Houses: A Cross-Sectional Study Using Building-Specification Data. Buildings, 15(17), 3013. https://doi.org/10.3390/buildings15173013

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