Background: The maritime work environment necessitates a baseline level of health and fitness to perform job-related roles and safety-critical duties. The Singapore Navy implemented a smoking cessation programme to promote health.
Purpose: To assess the impact of tobacco use on obesity and fitness levels.
Materials and methods: Researchers analysed a pooled dataset of 36 700 survey responses from the smoking cessation programme between 2021 and 2024. These included smoking status; awareness of cessation programmes; reasons for smoking initiation/cessation; and demographic data, including age, body mass index (BMI) and fitness testing results.
Results: The pooled prevalence of smokers was 20.7%, with 83.2% of smokers aware of smoking cessation programmes. The top three factors for smoking initiation included ‘Habit’ (62.4%), ‘Stress reduction’ (58.7%) and ‘Social bonding’ (45.6%). The top three factors for smoking cessation included ‘Health reasons’ (38.1%), ‘Family and friends’ (36.3%) and ‘Cost’ (26.7%).
The ‘Overweight’ category for BMI and the ‘FAILED’ category for fitness testing had the highest prevalence of smokers at 24.1% and 31.2%, respectively. Multinomial logistic regression adjusted for age found that smokers had 59% higher odds of being overweight (p<0.001) and 21% higher odds of being obese (p<0.01) than non-smokers. Smokers were 43% less likely to obtain a SILVER grade (p<0.001) and 72% less likely to obtain a GOLD grade (p<0.001) than non-smokers in fitness testing.
Conclusion: Tobacco use is associated with increased obesity and reduced physical fitness. This may impair work ability in maritime environments. These insights would guide future interventions to reduce smoking prevalence in the Singapore Navy.
Keywords: military personnel, tobacco use, physical fitness, obesity, maritime environment
Introduction
The armed forces provide a unique social environment for tobacco use. In the Turkish Armed Forces, a study in 2002 noted that 31.4% of smokers started smoking during their period of military service, and that environmental factors played a role in increasing the frequency of tobacco use.1 In Singapore, a qualitative study found that superiors and peers had an influence on tobacco use, and certain control measures such as designated smoking points might result in the unintended effect of these areas being regarded as an area for socialisation.2
In the Singapore Navy, occupational demands and the extended distance from medical support during sea deployments require a baseline level of health and fitness to perform job-related roles, safety-critical duties and facilitate rescue in an emergency. Routine roles include scaling vertical ladders on the exterior of the ship superstructure and accessing confined spaces within engineering or technical equipment spaces. Other safety-critical duties include firefighting and damage control.
Singapore adopts the IMO/ILO Guidelines on the medical examination of seafarers, which consider obesity and physical fitness when assessing a worker’s ability to perform their roles in the maritime work environment.3 In addition, the Singapore Navy has internal medical guidelines that govern medical fitness for sea deployment. Obesity and poor physical fitness are deemed risk factors for heat-related illnesses, physical injuries and chronic diseases, and could potentially disqualify sailors from active service.
Furthermore, the benefits of smoking cessation, including reduced healthcare costs4 and all-cause mortality5 are well demonstrated in current literature. Environmental and social factors played a significant role in smoking cessation, as shown by improved smoking cessation rates in grouped-based programme trials.6 In particular, behaviour modification counselling and individualised pharmacotherapy reduced the relative risk for hospitalised readmissions by 44% for hospitalised smokers with acute cardiovascular risk factors.7
Given the unique social/work environment for tobacco use and the long-term benefits of smoking cessation, the Singapore Navy launched a smoking cessation programme, which has since been folded under our Sailor Health strategy, to enhance operational readiness and reduce medical attrition in the Singapore Navy.
The Singapore Navy rolled out its Smoking Cessation Strategy in 2020, with the first Smoking Cessation Survey conducted in Jul 2021. The strategy guided Navy-wide interventions under four main themes: Education, Engagement, Encouragement and Enforcement. Awareness and education were achieved through regular internal broadcasts of smoking cessation messages and the annual ‘World No Tobacco Day Seminar’ where external speakers were invited to share the current regulatory landscape and available smoking cessation programmes.
Sailors who wished to begin their quit journey were also engaged regularly through medical therapy such as the SAF-Singhealth Smoking Cessation Clinic, and by peers who successfully quit. Our encouragement efforts included recognising sailors who encouraged their peers or helped create a conducive environment for smoking cessation. This was coupled with enforcement methods such as designated smoking points and a twice-yearly smoking declaration and survey since 2021.
This retrospective cross-sectional study thus aimed to assess the health impact of tobacco use on BMI and fitness levels. It also aimed to determine the pooled smoking prevalence, level of awareness of smoking cessation programmes, factors influencing smoking initiation and cessation, as well as methods and challenges in smoking cessation to guide future interventions.
Materials and methods
This study retrospectively analysed data from a series of survey responses obtained from a biannual Smoking Cessation Survey conducted for the Singapore Navy between 2021 and 2024. The survey questions were self-developed and modelled after the National Population Health Survey (NPHS). The anonymised dataset comprised pooled data from 6 surveys (1 in 2021, 2 in 2022, 2 in 2023 and 1 in 2024), with a total of 36 700 responses. Participants included full-time national servicemen (NSFs)[1], operationally ready national servicemen (NSmen), regular service personnel and defence executive officers[2].
The anonymised dataset available comprised survey results and demographic data. The survey results included the smoking status, duration/frequency of smoking, awareness of smoking cessation programmes, reasons for smoking initiation/cessation and methods/challenges of smoking cessation. The demographic data included age, BMI and fitness testing award types. All Singapore Navy personnel were invited to participate in the survey. No data on gender, survey year and response rate were available. All responses from the anonymised dataset were included in the study.
The historical crude prevalence and age-standardised rates for the Smoking Cessation surveys were obtained from previous records since 2021. The data was compared with national annual rates to assess trends and differences. The national annual crude prevalence and age-standardised rates (ASR) for Singapore was obtained from the NPHS. The reference population used for the ASR is the Singapore Census 2020 resident population.8
Further analysis of the 7579 self-reported smokers in the survey was performed to determine awareness of smoking cessation programmes. Factors influencing smoking initiation/cessation as well as methods/challenges in smoking cessation in the Singapore Navy were analysed. Because these survey questions were introduced only in 2023, only 3562 entries from self-declared smokers were available. For questions where participants could provide multiple responses, the responses were reported as a percentage of the total number of self-reported smokers.
The anonymised pooled survey data were analysed using the statistical software ‘R Studio’ 2024.09.1. Prevalence, confidence intervals and proportions were obtained. A chi-square test was used to determine the association between smoking and BMI, as well as smoking and fitness levels. Multinomial logistic regression analysis (adjusted for age) quantified the effect size (health impact) of the associations. The reference categories used were ‘Normal weight’ and ‘FAILED IPPT’. DSO National Laboratories – Singapore Armed Forces Institutional Review Board (IRB 0010/2024) approved the study.
Results
A total of 36700 responses from 6 surveys spanning from 2021 to 2024 were analysed. The pooled prevalence of smoking for the Singapore Navy was calculated as 20.7%. Table 1 details the demographic features of the survey respondents.
Table 1: Demographic features of participants
| Category | Total (n) | Non-smokers (%) | Smokers (%) | ||
|---|---|---|---|---|---|
| Pooled prevalence | |||||
| 36700 | 29103 (79.3%) | 7579 (20.7%) | |||
| Vocation | |||||
| Shipboard | 16950 | 12540 (74.0%) | 4410 (26.0%) | ||
| Shore offices | 7558 | 6652 (88.0%) | 906 (12.0%) | ||
| Workshop | 4433 | 3300 (74.4%) | 1133 (25.6%) | ||
| Training | 4076 | 3349 (82.2%) | 727 (17.8%) | ||
| Non-shipboard operational units | 3683 | 3262 (88.6%) | 421 (11.4%) | ||
| Age group | |||||
| 18–20 | 3108 | 3075 (98.9%) | 33 (1.1%) | ||
| 21–30 | 17067 | 13718 (80.4%) | 3349 (19.6%) | ||
| 31–40 | 7623 | 5588 (73.3%) | 2035 (26.7%) | ||
| 41–50 | 6667 | 4929 (73.9%) | 1738 (26.1%) | ||
| 51–60 | 2014 | 1587 (78.8%) | 427 (21.2%) | ||
| 61–74 | 221 | 206 (93.2%) | 15 (6.8%) | ||
| BMI category | |||||
| Underweight (<18.5) | 2446 | 2064 (84.4%) | 382 (15.6%) | ||
| Normal (18.5–24.9) | 18885 | 15461 (81.9%) | 3424 (18.1%) | ||
| Overweight (25–29.9) | 11620 | 8433 (72.6%) | 3187 (27.4%) | ||
| Obese (>=30) | 1161 | 908 (78.2%) | 253 (21.8%) | ||
| Not measured | 2588 | 2237 (86.4%) | 351 (13.6%) | ||
| Fitness level | |||||
| GOLD | 8028 | 7084 (88.2%) | 944 (11.8%) | ||
| SILVER | 9189 | 7300 (79.4%) | 1889 (20.6%) | ||
| PASSED | 11432 | 7930 (69.4%) | 3502 (30.6%) | ||
| FAILED | 2243 | 1511 (67.4%) | 732 (32.6%) | ||
| Not Attempted | 5808 | 5278 (90.9%) | 530 (9.1%) | ||
| Age of first cigarette* | |||||
| Below 18 | 2917 (38.4%) | ||||
| 18–20 | 3401 (44.7%) | ||||
| 21–30 | 1235 (16.2%) | ||||
| 31–40 | 36 (0.5%) | ||||
| 41–50 | 6 (0.1%) | ||||
| Above 50 | 2 (0.1%) | ||||
| Smoking duration (years)* | |||||
| 0–10 | 2756 (36.3%) | ||||
| 11–20 | 2233 (29.4%) | ||||
| 21–30 | 1635 (21.5%) | ||||
| 31–40 | 815 (10.7%) | ||||
| 41–50 | 142 (1.9%) | ||||
| More than 50 | 16 (0.2%) | ||||
| Smoking intensity (cigarettes per day)* | |||||
| Light (1–10) | 6970 (91.8%) | ||||
| Moderate (11–19) | 466 (6.1%) | ||||
| Heavy (>=20) | 161 (2.1%) | ||||
* Percentages out of self-reported smokers.
Respondents were grouped into 5 vocations: Shipboard (46.2%), Shore offices (20.6%), Workshop (12.1%), Training (11.1%) and Non-shipboard Operational Units (10.0%), with Shipboard accounting for the majority of respondents. Most smokers were in the Shipboard vocation (58.1%), followed by Workshop (14.9%) and Shore offices (11.9%). Compared with other vocations, Shipboard had the highest prevalence of smokers (26.0%), followed by Workshop (25.6%) and Training (17.8%).
Most respondents were aged 21–30 years old. In terms of age group distribution, most smokers were 21–30 years old, followed by 31–40 and 41–50. The age-specific prevalence of smoking was highest in 31–40 (26.7%), followed by 41–50 (26.1%) and 51 – 60 (21.2%).
BMI was categorised as Underweight (<18.5), Normal (18.5–24.9), Overweight (25.0–29.9) and Obese (>30), with most respondents in the Normal category. Most smokers were in the Normal BMI category, followed by Overweight and Underweight. Among the BMI categories, the prevalence of smokers was highest in Overweight (27.4%), followed by Obese (21.8%) and Normal (18.1%).
The fitness testing award categories were categorised as GOLD, SILVER, PASSED and FAILED, with most respondents receiving a PASSED award. Most smokers received a PASSED award, followed by SILVER and GOLD. Among the fitness testing award categories, smokers were most likely to receive FAILED (32.6%), followed by PASSED (30.6%) and SILVER (20.6%).
Among the 7579 self-declared smokers, the age of first cigarette was categorised into Below 18 (before National Service), 18–20 (during National Service) and in 10-year intervals thereafter. Most smokers started smoking between 18–20 (44.7%), followed by those below 18 (38.4%) and between 21–30 (16.2%).
Smoking duration (years) was categorised in 10-year intervals. Most smokers had smoked for a duration of 0–10 years (36.3%), followed by 11–20 years (29.4%) and 21–30 years (21.5%).
The smoking intensity was classified as Light (1–10 per day), Moderate (11–19 per day) and Heavy (20 or more per day). Most smokers were light smokers (91.8%), followed by moderate (6.1%) and heavy (2.1%).
The crude smoking prevalence and ASR from previous iterations of the Smoking Cessation Surveys were compared with those reported in the NPHS. Age-adjusted smoking prevalence showed a general downward trend in both the Navy (20.8%–19.0%) and Singapore (10.4%–8.9%) since 2021. The age-adjusted smoking prevalence for 2023 in the Navy (20.3%) was observed to be higher than the national prevalence (8.9%). Similarly, the crude smoking prevalence in the Navy was higher than the national crude prevalence. Table 2 shows a detailed comparison of smoking prevalence and ASR from 2021.
Table 2: Comparison of smoking prevalence and age-standardised rates
| Survey period | 2H 2021 | 1H 2022 | 2H 2022 | 1H 2023 | 2H 2023 | 1H 2024 |
|---|---|---|---|---|---|---|
| Crude prevalence (%) with 95% CI | ||||||
| Smoking cessation survey | 21.4(20.5, 22.4) | 21.8(20.8, 22.9) | 21.8(20.7, 22.8) | 20.6(19.6, 21.6) | 19.6(18.6, 20.6) | 18.9(17.9, 19.9) |
| National population Health survey (males and females) # | 10.4(9.6, 11.2) | N.A. | 9.2(8.5, 10.0) | N.A. | 8.8(8.0, 9.6) | N.A. |
| National population Health survey(males) # | 17.8(16.3, 19.3) | N.A. | 16.0(14.7, 17.3) | N.A. | 15.7(14.2, 17.2) | N.A. |
| Age-standardised rate (%)* | ||||||
| Smoking cessation survey | 20.8 | 21.4 | 21.1 | 21.5 | 19.0 | 19.9 |
| National population Health survey | 10.4 | N.A. | 9.3 | N.A. | 8.9 | N.A. |
* Reference population used was Singapore Census 2020 resident population.
# National Population Health Survey (NPHS) 2024 reports total crude prevalence as 8.4% (7.8, 9.1) and male crude prevalence as 14.9% (13.7, 16.1)
Of the 7597 self-reported smokers, 83.2% were aware of the smoking cessation programmes within the Navy, while 90.0% were aware of the smoking cessation programmes at the national level.
A total of 3562 self-declared smokers responded regarding their intention to quit smoking. The responses were grouped using the Transtheoretical Model to determine the stages of change, with those who were not planning to quit classified as being in the pre-contemplation phase, those who were planning to quit with no dates as being in the contemplation phase and those who planned to quit with dates as being in the action phase. Of the 3562 self-declared smokers, 1711 (48.0%) were in the pre-contemplation phase, 1700 (47.7%) were in the contemplation phase, and 151 (4.2%) in the action phase. Each intention-to-quit group showed high levels of awareness. Refer to Table 3 for a detailed breakdown of awareness levels for each intention-to-quit group.
Table 3: Awareness of smoking cessation programmes among smokers
| Survey question | Total | Yes (%) | No (%) |
|---|---|---|---|
| Overall awareness | |||
| Smoking cessation programmes within the Navy | 7597 | 6317 (83.2%) | 1280 (16.8%) |
| National level smoking cessation programmes | 7597 | 6834 (90.0%) | 763 (10.0%) |
| Intention to Quit* | |||
| Awareness of smoking cessation programmes within the Navy | |||
| Total (3 surveys) | 3562 | 3237 (90.9%) | 325 (9.1%) |
| [Precontemplation phase] Not planning to quit | 1711 | 1602 (93.6%) | 109 (6.4%) |
| [Contemplation phase] Planning to quit with no dates | 1700 | 1505 (88.5%) | 195 (11.5%) |
| [Action phase] Planning to quit with dates | 151 | 130 (86.1%) | 21 (13.9%) |
| Awareness of national level smoking cessation programmes | |||
| Total (3 surveys) | 3562 | 3441 (96.6%) | 121 (3.4%) |
| [Precontemplation phase] Not planning to quit | 1711 | 1663 (97.2%) | 48 (2.8%) |
| [Contemplation phase] Planning to quit with no dates | 1700 | 1633 (96.1%) | 67 (3.9%) |
| [Action phase] Planning to quit with dates | 151 | 145 (96.0%) | 6 (4.0%) |
| * Intention to quit was introduced from 2023 onwards. Percentages within each intention group | |||
Among the 3562 self-reported smokers, the top 3 factors influencing smoking initiation included ‘Out of habit’ (62.4%), ‘Stress reduction’ (58.7%) and ‘Social bonding’ (45.6%). The top 3 factors influencing smoking cessation were ‘Health reasons’ (38.1%), ‘Family & friends’ (36.3%) and ‘Cost’ (26.7%).
The top three methods used in smoking cessation were ‘Gradual reduction’ (40.9%), ‘Cold turkey’ (13.0%) and ‘Nicotine replacement therapy’ (5.5%). The top three challenges in smoking cessation were ‘Out of habit’ (34.8%), ‘Unable to relax without smoking’ (18.1%) and ‘Experience withdrawal symptoms’ (11.5%). Other factors influencing smoking initiation/cessation, and the methods/challenges in smoking cessation were detailed in Table 4 and Table 5, respectively.
Table 4: Factors influencing smoking initiation/cessation
| Factors influencing | Total smokers | Precontemplation phase | Contemplation phase | Action phase |
|---|---|---|---|---|
| Smoking initiation—What are your reasons for smoking?* | ||||
| Out of habit | 2223 (62.4%) | 1084 (48.8%) | 1066 (48.0%) | 73 (3.3%) |
| Stress reduction | 2090 (58.7%) | 1005 (48.1%) | 993 (47.5%) | 92 (4.4%) |
| Social bonding | 1623 (45.6%) | 789 (48.6%) | 770 (47.4%) | 64 (3.9%) |
| Work environment | 1155 (32.4%) | 535 (46.3%) | 570 (49.4%) | 50 (4.3%) |
| Avoid withdrawal symptoms | 494 (13.9%) | 243 (49.2%) | 233 (47.2%) | 18 (3.6%) |
| Others | 92 (2.6%) | 51 (55.4%) | 36 (39.1%) | 5 (5.4%) |
| Smoking cessation—What was your main motivation for quitting smoking?* | ||||
| Health reasons | 1356 (38.1%) | NA | 1242 (34.9%) | 114 (3.2%) |
| Family & friends | 1292 (36.3%) | NA | 1179 (33.1%) | 113 (3.2%) |
| Cost of cigarettes | 950 (26.7%) | NA | 879 (24.7%) | 71 (2.0%) |
| Work environment | 81 (2.3%) | NA | 77 (2.2%) | 4 (0.1%) |
| Others | 46 (1.3%) | NA | 40 (1.1%) | 6 (0.2%) |
| NA | 1711 (48.0%) | 1711 (48.0%) | NA | NA |
* Questions were introduced from 2023 onwards. Percentages out of 3562 self-reported smokers.
Table 5: Methods/challenges in smoking cessation
| Factors influencing | Total smokers | Precontemplation phase | Contemplation phase | Action phase |
|---|---|---|---|---|
| Methods for smoking cessation—How are you trying to quit smoking?* | ||||
| Gradual reduction | 1458 (40.9%) | NA | 1369 (38.4%) | 89 (2.5%) |
| Cold turkey | 463 (13.0%) | NA | 403 (11.3%) | 60 (1.7%) |
| Nicotine replacement therapy | 196 (5.5%) | NA | 182 (5.1%) | 14 (0.4%) |
| Others | 77 (2.2%) | NA | 72 (2.0%) | 5 (0.1%) |
| Health Promotion Board (HPB) Programmes | 45 (1.3%) | NA | 37 (1.0%) | 8 (0.2%) |
| Guidance from a SCORE ambassador | 19 (0.5%) | NA | 17 (0.5%) | 2 (0.1%) |
| RSN Nicotine Cessation Programmes | 9 (0.3%) | NA | 9 (0.3%) | – |
| NA | 1711 (48.0%) | 1711 (48.0%) | NA | NA |
| Challenges in smoking cessation—What are the difficulties you face in quitting smoking?* | ||||
| Out of habit | 1238 (34.8%) | NA | 1153 (32.4%) | 85 (2.4%) |
| Unable to relax without smoking | 643 (18.1%) | NA | 595 (16.7%) | 48 (1.3%) |
| Experience withdrawal symptoms | 411 (11.5%) | NA | 378 (10.6%) | 33 (0.9%) |
| Work environment (from colleagues) | 376 (10.6%) | NA | 344 (9.7%) | 32 (0.9%) |
| Social pressures (from friends) | 267 (7.5%) | NA | 250 (7.0%) | 17 (0.5%) |
| Others | 108 (3.0%) | NA | 97 (2.7%) | 11 (0.3%) |
| NA | 1711 (48.0%) | 1711 (48.0%) | NA | NA |
* Questions were introduced from 2023 onwards. Percentages out of 3562 self-reported smokers.
A multinomial logistic regression analysis of the association between smoking status and obesity (adjusted for age) suggested that smokers had lower odds of being underweight (adjusted OR 0.89, 95% CI 0.79 –1.00, p = 0.06); however, it did not reach statistical significance. Smokers also had 59% higher odds of being overweight (adjusted OR 1.59, 95% CI 1.50–1.68, p<0.001), and 21% higher odds of being obese (adjusted OR 1.21, 95% CI 1.05–1.40, p = 0.01).
Analysing the association between smoking status and physical fitness (adjusted for age), smokers had 9% lower odds of obtaining a PASS award for fitness testing (adjusted OR 0.91, 95% CI 0.83–1.00, p=0.07); however, it did not reach statistical significance. Smokers are 43% less likely to obtain a SILVER grade (adjusted OR 0.57, 95% CI 0.52–0.63, p<0.001) and 72% less likely to obtain a GOLD grade (adjusted OR 0.28, 95% CI 0.25–0.32, p<0.001). See Table 6 for the detailed results of the association between smoking status, obesity and fitness.
Table 6: Multinomial logistic regression analysis (age adjusted) of the association between smoking status vs obesity and smoking status vs fitness level
| Sample | OR | SE | 95% CI | P-value |
|---|---|---|---|---|
| BMI | ||||
| Underweight (<18.5) | 0.89 | 0.06 | 0.79–1.00 | 0.06 |
| Overweight (23–29.9) | 1.59 | 0.03 | 1.50–1.68 | <0.001 |
| Obese (>=30) | 1.21 | 0.07 | 1.05–1.40 | 0.01 |
| Fitness level | ||||
| PASSED | 0.91 | 0.05 | 0.83–1.00 | 0.07 |
| SILVER | 0.57 | 0.05 | 0.52–0.63 | <0.001 |
| GOLD | 0.28 | 0.06 | 0.25–0.32 | <0.001 |
Discussion
The study demonstrated a strong association of tobacco use with increased obesity and reduced physical fitness in the Singapore Navy. These factors can potentially affect work ability in a maritime environment, as obesity and physical fitness are essential components of medical fitness assessments for sea deployment. Obesity and poor physical fitness are known personal risk factors for heat-related illness,9 which could result from participation in firefighting activities or prolonged exposure to hot environments on deck or in engineering spaces.
Overall, smokers were 59% more likely to be overweight, and 21% more likely to be obese. Obesity in the presence of other cardiovascular or musculoskeletal risk factors could impair the ability to perform job requirements. It could complicate medical support/evacuation during extended periods of sea deployment away from advanced medical care. Anecdotally, many sailors engaged in unhealthy eating habits and fewer physical activities due to shift work. These smokers would be at higher risk of cardiovascular diseases and mortality, as smoking and obesity were known major risk factors in atherosclerotic heart diseases.10 Lifestyle modification interventions that address both smoking and obesity could synergistically improve cardiovascular and metabolic health in general.
Similarly, a strong association was observed between smoking status and fitness level, with smokers 43% less likely to obtain ‘SILVER’ and 72% less likely to obtain ‘GOLD’ award. Among the different fitness categories, smokers were most prevalent in the ‘FAILED’ category. This provided strong evidence against the anecdotal comments typically given by smokers that smoking before IPPT (or anything else) gave them better performance. The results correlated with many studies showing decreased fitness levels in smokers.11,12 Poor physical fitness could also affect the ability to move around the vessel routinely, perform routine tasks and carry out emergency duties on board, including escape, firefighting and evacuation. Any impairment in the ability to perform such essential duties is usually accompanied by work restrictions or limitations that affect sea deployability.3
One of the study’s key strengths was the analysis of a large, pooled dataset from a series of biannual surveys from 2021–2024. Although this was an anonymous, self-reported survey, there could be reporting bias due to the minimum legal age for smoking in Singapore (21 years). We note the possibility of recall bias for cigarettes smoked per week. Finally, there might be a possibility of social desirability bias leading to a high level of awareness of smoking cessation programmes.
While the age-adjusted prevalence rate for smoking in the Navy remained higher than national levels, there was a decreasing trend in smoking prevalence for both the Navy and Singapore at the national level. This might be due to the increased education, outreach and enforcement efforts organised both within the Navy and at the national level, for example, World No Tobacco Day events as well as the increase in the minimum legal age to 21 years old for purchase, use, possession and supply of tobacco products in 2021.13
An alternative possibility is increased vaping uptake in the local community. Despite being banned in Singapore since 2018, the ease of obtaining vapes through messaging, e-commerce and social media platforms made enforcement challenging.14 While no official statistics exist on the demographics of vape users, the number of vaping-related offences handled by the Health Sciences Authority had tripled to almost 15 000 cases since 2021.15
Regarding tobacco use habits, most self-reported smokers in the Navy smoked less than 10 sticks per day and had a smoking duration of less than 10 years. This was below the national average of 12 cigarettes per day for male smokers.8 Studies have shown that light smokers were more likely to attempt and succeed in smoking cessation as compared to heavy smokers.16,17 Studies also showed a significant association between successful smoking cessation and lower years of smoking duration.18,19 These data could potentially be used to encourage light smokers with low smoking duration in the Navy to initiate smoking cessation.
Most smokers in the Navy started their first cigarette between 18–20 years old. In Singapore, all male citizens and permanent residents must perform National Service for 2 years upon turning 18 years old.20 While smoking initiation could be in response to a stressful transition from civilian to military life, previous studies have shown that the majority of smokers picked up smoking before National Service (18–20 years old).2 Youths were also shifting towards vaping due to ease of access and low detectability.21 These trends suggested that a concerted whole-of-government effort at the national level might be required to address tobacco use before 18 years of age. The smoking cessation programme within the Singapore Navy thus complements the public health effort at a national level by providing a conducive environment that minimises smoking initiation and smoking cessation during their formative years.
The top three factors for smoking cessation, ‘Habit’, ‘Stress reduction’ and ‘Social bonding’ suggested a strong social nature of tobacco use in the Singapore Navy. Smokers typically take a habitual smoke break at designated smoking points on shore, or on board after meals or activities. During sea deployments, smokers would stock up on cigarettes at each port of call and share their supply on prolonged deployments with fellow smokers. The sale of tobacco products is strictly prohibited on naval bases and ships. Engaging smokers who were direct superiors in discussions about the unintended social outcomes of their smoking habits could create awareness to avoid perpetuating a work culture that is conducive to tobacco use. Healthier alternatives for stress reduction and bonding, such as sports, games and interest groups, could also be provided to reduce the temptation to smoke.
The top three factors for smoking cessation included ‘Health reasons’, ‘Family and friends’ and ‘Cost’. Interventions could incorporate nudges around the factors identified in this study. Some examples include improving understanding of how smoking affects their current health status during medical encounters, enlisting the support of family and friends to help in the quit journey, and highlighting the opportunity cost of spending on smoking.
On the methods and challenges to quit smoking, the top three methods for quitting included ‘Gradual reduction’, ‘Cold turkey’ and ‘Nicotine replacement’. Evidence showed that smokers who gradually reduced the number of cigarettes smoked and those who reduced more than 50% of their daily cigarette use were more likely to achieve smoking cessation.22,23 Successful ex-smokers could be engaged to share these proven quitting methods. Smokers could also be supported in challenges identified in quitting to facilitate their quit journey. For example, reducing the accessibility of smoking points would be helpful, as smoking points on shore are typically located by the back exit outside the building. Shifting and reducing available smoking points such that they require a longer transit time from work spaces and food establishments could reduce the habitual urge to smoke during breaks or after meals.
Tobacco use is associated with increased obesity and reduced physical fitness. This may impair work performance in maritime environments. The insights from the factors for smoking initiation, smoking cessation, motivations for quitting and methods of quitting would guide future interventions to effectively and efficiently reduce smoking prevalence in the Navy. More studies could be conducted in future to determine the incidence and quantify the health impact of smoking on different gender, age and vocation groups.