アーメダバード市の夜勤工場労働者における睡眠の質が歯周健康に及ぼす影響の検討
2026 年 48 巻 2 号 p. 87-96
詳細
2026 年 48 巻 2 号 p. 87-96
Periodontal disease is multifactorial, influenced by both local and systemic factors. Environmental variables, such as sleep quality, may affect periodontal health; however, scant literature has addressed this relationship in factory workers. The present study aimed to investigate the impact of sleep quality on periodontal health in night-shift factory workers in Ahmedabad city. A cross-sectional questionnaire study was conducted on 360 workers from six selected factories in Ahmedabad city, Gujarat. Demographic data including age, sex, marital status, and tobacco use were recorded. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index. A clinical assessment was done to assess the oral hygiene status and periodontal status by using the Oral Hygiene Index–Simplified and the WHO 1997 methodology, respectively. There were a total 198 (55%) day-shift workers and 162 (45%) night-shift workers. The global sleep quality mean scores in the day shift and night shift workers were 3.85 ± 2.14 and 4.80 ± 2.27, with a significant difference. The night shift workers had a significantly (P<0.05) worse score as compared to day shift workers when the oral hygiene status and periodontal status mean score were compared. Age (AOR=5.52; 95% CI=1.98–15.39; P<0.001), sex (AOR=5.89; 95% CI=1.74–19.94; P=0.004), oral hygiene (AOR=6.75; 95% CI=3.67–12.42; P<0.001), night shift (AOR=2.13; 95% CI=1.27–3.59; P=0.004), and poor sleep quality (AOR=2.65; 95% CI=1.47–4.78; P<0.001) were the independent risk factors for periodontal status. In conclusion, the findings indicate that night-shift factory workers exhibit significantly poorer sleep quality, oral hygiene, and periodontal health as compared to their day-shift counterparts.
Sleep is essential for the overall health and wellbeing of the body and the brain [1, 2]. Poor sleep quality has been linked to hypertension, obesity, diabetes, cardiovascular issues, neurological degeneration, and mood disorders [3]. Regardless of age, gender, or ethnicity, everyone needs sufficient sleep for optimal function. However, modern demands, such as extended work shifts and high professional pressures, often force individuals to sacrifice sleep, lowering their overall quality of life [4].
Poor sleep is often associated with unfavorable hormonal profiles and immune dysregulation [5]. It also negatively affects the body’s stress response and inflammatory mediation [6]. Disruption of the circadian rhythm arising from poor sleep may further destabilize the body’s physiological defense mechanisms against infectious pathogens, leaving the body vulnerable to diseases that involve inflammatory changes [7].
One important pathology that is closely associated with an individual’s overall immune response, and tendency for inflammation, is periodontitis, an inflammatory disease caused by specific microorganisms that leads to periodontal pocket formation, gingival recession, ligament destruction, and bone loss [8]. The WHO Global Oral Health Status Report (2022) indicates that severe periodontal disease affects 19% of people aged 15 and older [9]. A systematic review found a periodontitis prevalence of 51% among Indian adults [10]. Besides the microbial agent, host factors (such as age, sex, and genetics) and acquired risks (including poor oral habits, poor oral hygiene practices, and lifestyle choices) also contribute to the disease [8, 11].
Considering the etiopathogenesis of chronic periodontitis, a connection between sleep quality and periodontal health may be explored further, due to the negative effects of insufficient sleep on the immune system, which disrupt anti-inflammatory pathways and protective mediators [12]. Additionally, cognitive decline related to poor sleep can impair oral hygiene practices and leads to habits detrimental to periodontal health [13].
Out of the various factors influencing the development of poor sleep quality, an important variable that has been established is consistent shift-based work [14]. Specifically, industrial and factory workers have been found to be at heightened risk for poor sleep quality, with studies documenting a significant association between night shift work and reduced sleep quality [15–17]. With limited data on the link between sleep quality and periodontal health among Indian factory workers [18], and none specifically from the state of Gujarat, this study aims to assess the impact of sleep quality on periodontal health in factory workers in Ahmedabad City, India.
A cross-sectional, questionnaire-based study was conducted from February 2025 to April 2025 on factory workers in Ahmedabad city, following the STROBE guidelines [19].
Ethical Clearance and permission
Before conducting the study, the research protocol was submitted to the Institutional Ethics Committee of the Government Dental College and Hospital, Ahmedabad, and the ethical clearance (No. IEC GDCH/ PHD.7 /2025) was obtained to conduct the study. The permission for data collection from the factories workers was sought from the competent authorities. The workers were approached through their respective human resource department, and the purpose of the study was explained in the language best known to them (Gujarati/Hindi). Those workers who showed interest were asked to give their informed consent and were enrolled in the study. The study was conducted according to the Declaration of Helsinki.
Sample size and sampling methodology
The prevalence of poor sleep quality observed in previous studies was considered to estimate the sample size by using the prevalence formula: n= Ζα2*p*(1–p)/L2 at 95% confidence interval with allowable error (L) of 5% [15–18, 20]. The calculated sample size ranged from 294 to 380. The median value of 360 was considered as a final sample size for the present study.
To obtain a representative sample, a multi-phase sampling methodology was designed and executed to first select six eligible industrial units [21, 22].
A multiphase sampling methodology was employed. Phase 1: Ahmedabad city was geographically divided into four zones- North, East, West, and South. Phase 2: Two zones, the South zone (having three industrial estates) and West zone (having two industrial estates), were randomly selected using computerized random tables. Phase 3: Out of five industrial estates, two industrial estates (one estate from each selected zone) were randomly selected. Phase 4: A total of 64 factories operating continuous night shifts from two estates were identified. Six factories (representing 10 percent of the identified factories) were randomly selected (Figure 1).
All six selected factories were privately owned enterprises. The factories were primarily involved in manufacturing automotive parts and machinery for industrial applications. The selected factories were classified as large scale industries, with an investment in plant and machinery or equipment exceeding INR 1.25 billion, and with an annual turnover exceeding INR 5 billion [22].
To achieve the study’s primary objective, 60 night-shift workers were then randomly sampled from within each of the six selected industrial units, achieving the total target sample size of 360 participants, according to the following eligibility criteria:
Inclusion criteria: Factory workers aged 18 years and above, working in the same shift for at least three months, regardless of gender or work shift, provided they were present on-site during data collection and could provide informed consent.
Exclusion criteria: Administrative staff of factories, those workers who were unable to communicate in the study language, workers with systemic health conditions, workers who had taken antibiotics or received dental treatment within the preceding three months.
Data Collection
Data were collected on predetermined schedule dates at the selected factories. The data were collected into two parts: Part 1, through a personal interview by using a self-designed, pretested proforma; and Part 2, by a clinical examination. The self-designed, pretested proforma includes three sections. The first section is comprised of demographic details such as age, sex, marital status, tobacco use, and work shift. The second section includes the Pittsburgh Sleep Quality Index (PSQI) questionnaire to assess sleep quality [23]. Validated Gujarati and Hindi translated versions were obtained through correspondence with the Mapi Research Trust (Lyon, France) [24]. The PSQI consists of 19 self-reported items that are combined into seven component scores (ranging 0–3 on a Likert scale) to yield a global score (range 0–21) with scores >5 indicating poor sleep quality. The type III clinical examination was conducted by a principal investigator (TS), assisted by a trained recorder. The principal investigator was trained and calibrated by an expert (PS) in the Department of Public Health Dentistry. There was a strong agreement (kappa value=0.86) between the expert and the principal investigator. Oral hygiene status and periodontal status were assessed by using the Oral Hygiene Index–Simplified (OHI-S) [25, 26] and the Community Periodontal Index-Loss of Attachment index (CPI-LOA), according to the WHO methodology 1997, respectively. All necessary infection control protocol was maintained throughout the data collection.
Statistical Analysis
Data were coded and entered in Microsoft Excel 2021 and analyzed using Statistical Package for Social Science (SPSS version 27, IBM, Chicago Il, USA). Normality was assessed by the Kolmogorov–Smirnov test (P<0.05 indicating non-normal distribution), and non-parametric tests were subsequently applied. The data were presented in percentage, mean, standard deviation, and median. Association between proportions of clinical parameters between day- and night-shift workers were analyzed using the Chi-square and Fisher’s Exact test. Mann–Whitney U test was used for comparisons of mean and median score. Multivariate logistic regression analysis with backward elimination (F-to-remove ≥0.10) was performed to assess the impact of risk factors—age, sex, marital status, tobacco consumption, oral hygiene status, work shifts, and sleep quality (independent variable)—on periodontitis as dependent variables. Multicollinearity diagnostics were performed to ensure the stability of the regression estimates. All independent variables demonstrated low Variance Inflation Factor values (all < 3.0), suggesting that multicollinearity did not significantly affect the model. The periodontal status was dichotomized by categorizing CPI scores ≥3 and Loss of Attachment scores ≥1 as indicative of periodontitis. The level of significance was set at 5%.
The demographic characteristics and distribution of tobacco consumption of the enrolled workers are shown in Table 1. The age of the workers ranged from 20 to 62 years with a mean age of 33.99 ± 8.49 years. A majority of the sample (55.00%) was comprised of young workers between 20 and 33 years of age. The male workers (n=330, 91.67%) outnumbered the females. The majority of the workers were married (n=298, 82.78%). Out of 360 workers, 162 (45.0%) were working in night shift, while 198 (55.0%) worked during day hours. A total of 117 (32.50%) workers were current tobacco users. More than half (n=199, 55.28%) of the workers had no habit of tobacco use. The use of smokeless tobacco was popular (n=135, 37.50%) among the workers.
| Variable |
n (%) (N = 360) |
|---|---|
| Age (In years) | |
| 20–33 | 198 (55.00) |
| 34–47 | 133 (36.94) |
| 48–61 | 26 (7.22) |
| 61–74 | 3 (0.84) |
| Mean±SD | 33.99±8.49 |
| Sex | |
| Male | 330 (91.67) |
| Female | 30 (8.33) |
| Marital Status | |
| Married | 298 (82.78) |
| Unmarried | 62 (17.22) |
| Tobacco Habit | |
| Present | 117 (32.50) |
| Past | 44 (12.22) |
| Never | 199 (55.28) |
| Type of Tobacco Consumed | |
| None | 199 (55.28) |
| Smoking | 14 (3.89) |
| Smokeless | 135 (37.50) |
| Both | 12 (3.33) |
| Shift | |
| Day | 198 (55.00) |
| Night | 162 (45.00) |
The proportions of sleep quality and clinical parameters were compared between day and night shift workers (Table 2). A total of 59 workers in night shift had poor sleep quality as compared to 36 workers in day shift. This was statistically significant (P<0.001). A total of 140 workers out of 360 had good oral hygiene. Among the 198 day shift workers, half (n=98, 49.49%) had good hygiene as compared to 42 (25.93%) night shift workers, showing a significant result (P<0.001). Comparing the proportions of CPI and LOA code between the day shift and night shift workers, periodontal status was significantly (P<0.001) poor in the night shift workers.
| Variables (n) |
Day Shift (n = 198) n (%) |
Night Shift (n = 162) n (%) |
Test Value | P-value |
|---|---|---|---|---|
| Sleep Quality | ||||
| Good (265) | 162 (81.82) | 103 (63.59) | 15.257a | <0.001** |
| Poor (95) | 36 (18.18) | 59 (36.41) | ||
| Oral Hygiene Status | ||||
| Good (140) | 98 (49.49) | 42 (25.93) | 21.872b | <0.001** |
| Fair (219) | 100 (50.51) | 119 (73.46) | ||
| Poor (1) | 0 (0.00) | 1 (0.61) | ||
| Highest CPI Code | ||||
| CPI 0 (7) | 4 (2.02) | 3 (1.85) | 33.325b | <0.001** |
| CPI 1 (30) | 23 (11.62) | 7 (4.32) | ||
| CPI 2 (184) | 118 (59.59) | 66 (40.74) | ||
| CPI 3 (22) | 51 (25.76) | 71 (43.83) | ||
| CPI 4 (14) | 2 (1.01) | 12 (7.41) | ||
| CPI X (3) | 0 (0.00) | 3 (1.85) | ||
| Highest LOA Code | ||||
| CPI 0 (295) | 178 (89.89) | 117 (72.22) | 20.611b | <0.001** |
| CPI 1 (47) | 17 (8.59) | 30 (18.52) | ||
| CPI 2 (13) | 3 (1.52) | 10 (6.17) | ||
| CPI 3 (2) | 0 (0.00) | 2 (1.24) | ||
| CPI X (3) | 0 (0.00) | 3 (1.85) | ||
CPI: Community Periodontal Index, LOA: Loss of Attachment, aComparison by Chi square test, bComparison by Fishers exact test, **P < 0.001 highly significant.
Table 3 presents the results of the multivariate logistic regression model that included cofounder variables such as age, sex, marital status, consumption of tobacco, oral hygiene status, working shift, and sleep quality for periodontal status, according to the highest CPI score. The odds ratios were adjusted by performing backward elimination. Risk factors like age (COR=4.38; 95% CI=1.47–13.04; P=0.01, AOR=5.52; 95% CI=1.98–15.39; P<0.001), sex (COR=4.91; 95% CI=1.42–16.97; P=0.01, AOR=5.89; 95% CI=1.74–19.94; P=0.004), oral hygiene status (COR=6.76; 95% CI=3.65–12.53; P<0.001, AOR=6.75; 95% CI=3.67–12.42; P<0.001), night shift (COR=2.06; 95% CI=1.20–3.53; P=0.01, AOR=2.13; 95% CI=1.27–3.59; P=0.004), and sleep quality (COR=2.59; 95% CI=1.42–4.71; P=0.002, AOR=2.65; 95% CI=1.47–4.78; P=0.001) were retained in the final model for CPI.
| Variables | CPI | ||||||
|---|---|---|---|---|---|---|---|
| COR | CI | P | AOR | CI | P | ||
| Age | 20-33 | 1 | 1 | ||||
| 34–47 | 2.98 | 1.64–5.44 | <0.001** | 3.5 | 2.01–6.09 | <0.001** | |
| 48–61 | 4.38 | 1.47–13.04 | 0.008* | 5.52 | 1.98–15.39 | <0.001** | |
| 61–74 | 0 | 0 | 0.999 | 0 | 0 | 0.999 | |
| Sex | Female | 1 | 1 | ||||
| Male | 4.91 | 1.42–16.97 | 0.012* | 5.89 | 1.74–19.94 | 0.004* | |
| Marital Status | Unmarried | 1 | – | – | – | ||
| Married | 1.8 | 0.8–3.96 | 0.147 | – | – | – | |
| Tobacco Consumption | Never | 1 | – | – | – | ||
| Past | 1.24 | 0.70–2.22 | 0.461 | – | – | – | |
| Present | 1.12 | 0.49–2.56 | 0.792 | – | – | – | |
| Oral Hygiene | Good | 1 | 1 | ||||
| Fair/Poor | 6.76 | 3.65–12.53 | <0.001** | 6.75 | 3.67–12.42 | <0.001** | |
| LOA Code | 0 | 1 | – | – | – | ||
| >=1 | 1.24 | 0.61–2.52 | 0.537 | – | – | – | |
| Shift | Day | 1 | 1 | ||||
| Night | 2.06 | 1.20–3.53 | 0.009* | 2.13 | 1.27–3.59 | 0.004* | |
| Sleep Quality | Good | 1 | 1 | ||||
| Poor | 2.59 | 1.42–4.71 | 0.002* | 2.65 | 1.47–4.78 | 0.001* | |
| Constant | 0.01 | – | <0.001** | 0.01 | – | <0.001** | |
COR = Crude Odds Ratio, AOR = Adjusted Odds Ratio, CI = Confidence Interval, CPI: Community Periodontal Index, LOA: Loss of Attachment, *P < 0.05 significant, **P < 0.01 highly significant.
In Table 4, for periodontal status, according to the highest LOA score, risk factors like age (COR=11.52; 95% CI=4.05–32.76; P<0.001, AOR=13.12; 95% CI=4.80–34.82; P<0.001), oral hygiene status (COR=2.61; 95% CI=1.17–5.79; P=0.02, AOR=2.92; 95% CI=1.35–5.99; P=0.005), and night shift (COR=3.47; 95% CI=1.76–6.84; P<0.001, AOR=3.47; 95% CI=1.79–6.69; P<0.001) were retained in the final model.
| Variables | LOA | ||||||
|---|---|---|---|---|---|---|---|
| COR | CI | P | AOR | CI | P | ||
| Age | 20–33 | 1 | 1 | ||||
| 34–47 | 3.04 | 1.45–6.36 | 0.003* | 3.59 | 1.84–6.99 | <0.001** | |
| 48–61 | 11.52 | 4.05–32.76 | <0.001** | 13.12 | 4.80–34.82 | <0.001** | |
| 61–74 | 36.55 | 2.56–520.76 | 0.008* | 48.35 | 3.47–592.53 | 0.003* | |
| Sex | Female | 1 | – | – | – | ||
| Male | 2.45 | 0.50–11.92 | 0.268 | – | – | – | |
| Marital Status | Unmarried | 1 | – | – | – | ||
| Married | 1.19 | 0.40–3.54 | 0.76 | – | – | – | |
| Tobacco Consumption | Never | 1 | – | – | – | ||
| Past | 0.65 | 0.32–1.32 | 0.23 | – | – | – | |
| Present | 1.34 | 0.54–3.23 | 0.53 | – | – | – | |
| Oral Hygiene | Good | 1 | 1 | ||||
| Fair/Poor | 2.61 | 1.17–5.79 | 0.019* | 2.92 | 1.35–5.99 | 0.005* | |
| CPI Code | 0 | 1 | – | – | – | ||
| >=3 | 1.36 | 0.68–2.72 | 0.393 | – | – | – | |
| Shift | Day | 1 | 1 | ||||
| Night | 3.47 | 1.76–6.84 | <0.001** | 3.47 | 1.79–6.69 | <0.001** | |
| Sleep Quality | Good | 1 | – | – | – | ||
| Poor | 1.04 | 0.51–2.11 | 0.912 | – | – | – | |
| Constant | 0.01 | – | <0.001** | 0.021 | – | <0.001** | |
COR = Crude Odds Ratio, AOR = Adjusted Odds Ratio, CI = Confidence Interval, CPI: Community Periodontal Index, LOA: Loss of Attachment, *P < 0.05 significant, **P < 0.01 highly significant
Factory workers face unique challenges that can affect both their overall health and specific outcomes such as sleep quality and periodontal health. In particular, the disruption of natural circadian rhythms experienced by night shift workers can lead to a range of health issues, making it critical to understand these associations in occupational settings. Hence, the present study was conducted to evaluated the impact of sleep quality on periodontal health in 360 factory workers in Ahmedabad city, India. All workers included in the study were working in their particular shift (Day/Night) for at least 3 months, to ensure that effects of chronic circadian misalignment, rather than acute effects of temporary sleep disruption, were studied.
The Pittsburgh Sleep Quality Index (PSQI) was employed in this study to assess sleep quality. The PSQI is a validated, self-reported instrument that evaluates several aspects of sleep, such as latency, duration, efficiency, and disturbances, making it a comprehensive tool for gauging overall sleep health [23]. Its ease of administration, cost-effectiveness, and proven sensitivity in detecting differences between day and night shift workers render it particularly suitable for this cross-sectional study, allowing for direct comparisons with previous research.
The majority of the study subjects were male (91.67%), which aligns with regional workforce demographics [27]. Interestingly, the majority of the workers did not use tobacco, contrasting with the findings of Setia S et al [18], while the predominance of smokeless tobacco use among the users was similar to the results reported by Gaikwad R et al [28]. The lower prevalence of tobacco use might have been due to ongoing active efforts by factory administrations and organizational bodies to discourage the use of tobacco on industrial estates.
Consistent with previous research, night shift workers experienced significantly poorer sleep quality than day shift workers [15, 16, 29, 30]. This disparity might be due to the disruption of the body’s natural circadian rhythms that regulate essential physiological functions, such as the secretion of melatonin and cortisol, and coordinate metabolic, social, and professional activities. Prolonged night work may lead to a maladapted sleep cycle and subsequently deteriorated sleep quality [31, 32].
Oral hygiene status was notably inferior among the night shift workers. This may be attributed to the detrimental effects of poor sleep quality on fine motor skills and neurocognitive processes, leading to reduced adherence to effective oral hygiene routines and thereby worsening periodontal conditions [33, 34].
Poor sleep quality impairs immune function, leading to an increased production of inflammatory cytokines that can exacerbate periodontal tissue inflammation. Disruption of the circadian rhythm arising from poor sleep may further destabilize the body’s defense mechanisms against periodontal pathogens. Additionally, significantly worse oral hygiene was observed in the night-shift factory workers, which may have been due to compromised cognitive function associated with irregular sleep cycles, resulting in poorer oral hygiene practices, thereby accelerating periodontal deterioration [34]. Subsequently, multiple independent risk factors for periodontitis were identified in this study, including age, sex, oral hygiene status, shift work, and sleep quality.
Aging often accompanies diminished immune competence, heightened inflammatory responses, and increased exposure to local risk factors, all contributing to periodontal deterioration [35]. While periodontitis has a documented higher prevalence in men (~57%) compared to women (~39%), signifying a possible sex/gender bias in disease pathogenesis, the present study did not have a comparable representation of both sexes, negating the ability to infer any definitive causalities [36]. However, it has been documented that males appear more predisposed to periodontal disease, potentially due to less favorable oral hygiene practices, a stronger inflammatory response, and higher tobacco consumption, especially within the Indian subcontinent. Men also tend to have lower oral health literacy, as well as a lower utilization rate of available oral healthcare services [37]. Although tobacco use is widely recognized as a risk factor for periodontitis, no significant association was found in this study, likely because over half of the participants were non-tobacco users.
Our multivariate analysis pinpointed several independent risk factors for periodontitis, with poor oral hygiene status emerging as the strongest predictor (AOR=6.76). This aligns with established evidence that plaque and calculus are primary etiological agents in periodontal inflammation [8]. Crucially, however, both working the night shift (AOR=2.13) and experiencing poor sleep quality (AOR=2.65) remained significant and independent risk factors even after adjusting for oral hygiene and other confounders.
This suggests a multifactorial relationship. While the bivariate analysis showed that night shift work was associated with poorer oral hygiene, the persistence of night shift as an independent risk factor in the final model implies that pathways beyond just hygiene are at play. The strong, independent effect of poor sleep quality (AOR=2.65) likely represents this alternative biological pathway. This finding lends support to the hypothesis that sleep-related immune dysregulation, such as the increased production of proinflammatory cytokines or altered hypothalamic-pituitary-adrenal axis activity, directly contributes to periodontal tissue breakdown, separate from its impact on hygiene behaviors [5, 6, 38–40]. This is further supported by our model for LOA, a marker of more severe disease, where night shift work remained a powerful predictor (AOR=3.47), underscoring the potent role of circadian disruption in periodontal destruction.
Despite these important findings, the study’s cross-sectional design limits the ability to draw causal inferences. Future research should incorporate longitudinal designs with larger, more diverse populations, include objective sleep measurement techniques, and explore additional evaluations, such as inflammatory biomarkers and microbiological profiles, to further elucidate the underlying mechanisms. Furthermore, more objective measures of sleep quality like polysomnography or multiple sleep latency test could be used. Overall, these results underscore the complex interplay between circadian regulation, sleep quality, and periodontal health, suggesting that enhancing sleep quality may be a promising approach to improving oral health outcomes in shift workers.
Considering the findings and limitations of the study, it can be concluded that night shift factory workers had significantly poor oral hygiene, periodontal health and sleep quality as compared to day shift factory workers. Sex, age, oral hygiene status, shift work and sleep quality were found to be independent risk factors for periodontitis in factory workers in Ahmedabad city.
There were no sources of funding for this study.
None.
Data will be provided on correspondence with authors upon reasonable request.
Conceptualization: Shubham Trivedi, Sujal Parkar
Investigation: Shubham Trivedi
Supervision: Sujal Parkar
Visualization: Sujal Parkar
Writing-original draft: Shubham Trivedi, Sujal Parkar
Writing-review & editing: Shubham Trivedi, Sujal Parkar
