Submitted:
19 June 2025
Posted:
20 June 2025
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Abstract
Keywords:
1. Introduction
2. Materials and Methods
3. Results
Summary of Included Studies
Type of Study and Study Design
Population and Sample Size
Data Collection Methods
Statistical Methodology, Analyses and Recommendations
Limitations Reported In the Reviewed Studies
Comparative and Cross-Cultural Analysis
Overall Implications
4. Discussion
Nutrition Policy, Consumer Practices, and Sustainability in Dietary Choices
Health-Promoting and Sustainable Dietary Patterns
Implications for Dentists' Health and Practice
Psychological Impact of Poor Dietary Habits on Dentists
Shift Work and Its Impact on Dentists' Eating Habits
Interventions to Improve Dietary Habits in Dentists
Strengths and Limitations of the Study
Future Research Directions
Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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| Authors | Type of study- | Population | Exposure | Comparators | Statistical significance | Limitations | confounders | Outcomes |
|---|---|---|---|---|---|---|---|---|
| 1. Olesinska et al 2025 [44] | Systematic review – Multi-study analysis | healthcare staff from 10 studies | Workplace nutrition interventions (Mediterranean diet, telehealth, education, counseling) | No intervention, usual routine, or standard programs in original studies | Significant improvements in weight, LDL cholesterol, food literacy, and dietary adherence in most studies | Small sample sizes in some studies, high heterogeneity, limited long-term data, few studies with blinding | Variability in design, participant demographics, and adherence levels | Improvements in BMI, diet quality, cardiometabolic markers, food literacy, and quality of life |
| 2. Mangoulia et al 2025 [18] | Cross-sectional – Single-center (National and Kapodistrian University of Athens) | 271 undergraduate and postgraduate dentistry and nursing students in Greece | Psychological well-being: stress, anxiety, depression, resilience, hope, spiritual well-being | Comparisons between nursing and dentistry students, as well as subgroups based on gender, education level, income | Yes – significant differences found across departments and demographic factors; hierarchical regression showed hope as predictor of resilience | Limited generalizability (single institution); predominance of females; different academic levels per department; low response rate; cross-sectional design | Gender, income, educational level, department | High prevalence of stress, anxiety, depression; dentistry students showed higher stress/anxiety; hope strongly predicted resilience; need for targeted interventions identified |
| 3. Di Prinzio et al. 2025 [45] |
Pilot intervention study (pre-post design) | Healthcare workers in an Italian hospital | Food education program based on Total Worker Health© approach | Baseline (pre-intervention) vs. post-intervention comparisons | Yes, significant improvements in dietary behaviors and knowledge (p < 0.05) | Small sample size (pilot), short follow-up, single-center, lack of control group | Gender, age, baseline health status, occupation | Improved dietary knowledge, increased fruit/vegetable intake, reduced intake of sugary snacks and processed foods, positive changes in self-reported eating habits |
| 4.Vivarelli & Fenga 2024 [46] |
Systematic review (multi-country) | 37 studies on dentists | Prevalence of burnout in dentists via MBI subscales | Not applicable (meta-analysis of observational studies) | Pooled prevalence estimates with 95% CI and heterogeneity (I²); Stata 13.0 used | High heterogeneity (I²>90%), variable study quality, no study met all Joanna Briggs criteria | Study design, population differences | Burnout affected 13% of dentists overall, with high emotional exhaustion in 25%, high depersonalization in 18%, and low personal accomplishment in 32%, making emotional exhaustion the most common dimension. |
| 5.Hobby et al 2024 [24] |
Cross-sectional – Single-center (Australia, qualitative study via interviews) | 22 health professionals (including dietitians) with patient contact | Personal dietary behaviors and self-efficacy in providing nutrition care | No direct comparators; thematic contrasts drawn across narratives | Not applicable (qualitative study); thematic saturation reached | Small sample size; self-selection bias; only Australian participants; qualitative limits to generalizability | Life experiences, social interactions, role modeling | Personal diet and lived experiences influence confidence in providing nutrition care; social support and environment impact self-efficacy |
| 6. Gilbert et al 2023 [47] |
Prospective observational – Single-center (University Hospital ‘G. Martino’, Messina, Italy) | Healthcare workers (20–65 years), with or without night shift work | Night shift work (NSW), occupational stress, sleep quality, diet, physiological biomarkers | HCWs without night shifts | Yes – significant associations (e.g., stress, sleep, diet, biomarkers; p<0.05) | Self-report bias, confounding variables (lifestyle), resource-intensive protocol, only one center | Age, sex, smoking, comorbidities, work conditions | Improved well-being, dietary habits, stress, and sleep patterns; physiological biomarkers monitored; 12-month follow-up planned |
| 7.Shiri et al 2023 [33] | Narrative Review of 108 RCTs – Multi-country | Health and social service workers (various roles and countries) | Workplace interventions (mindfulness, ergonomics, coaching, exercise, nutrition, scheduling, resilience) | No intervention or alternative workplace programs | Yes – modest but statistically significant effects across multiple RCTs | Review limited to PubMed; no quality appraisal of studies; short follow-up; clustering effects not always accounted for; small samples in some RCTs | Various (e.g., age, gender, occupation, baseline stress levels) | Modest improvements in burnout, job satisfaction, work ability, well-being; limited effect on occupational injuries; barriers to participation include workload, lack of support, off-hours scheduling |
| 8.Chen et al 2023 [35] | Cross-sectional – Multi-center (Mainland China, 31 regions) | In-service physicians working in Chinese hospitals (N=9,196) | Unhealthy eating habits (e.g., eating too fast, irregular meals, frequent out-of-home meals) | Comparisons made across groups with and without specific eating habits | Significant associations between unhealthy eating habits and suboptimal health/disease (p<0.05) | Self-report bias, gender imbalance (83.3% women), online survey format, possible information bias | Age, sex, working time, sleep quality, exercise, sedentary time, smoking, alcohol consumption | Unhealthy eating habits significantly associated with subhealth and metabolic/micronutrient-related diseases |
| 9.Mancin et al. 2023 [38] |
Educational intervention study (pre-post design, single center) | Healthcare professionals (physicians, nurses, dietitians) in Italy | Active learning–based clinical nutrition education program (interactive workshops, case-based learning) | Baseline (pre-intervention) vs. post-intervention comparisons | Yes — significant improvement in nutrition knowledge scores and self-reported confidence (p < 0.001) | Small sample size, single center, no long-term follow-up, no control group | Baseline knowledge, profession (nurse/doctor/dietitian), prior training | Improved nutrition knowledge, higher confidence in delivering nutrition advice, greater engagement in nutrition-related clinical practice |
| 10. Moro et al 2022 [48] |
Single-center, randomized controlled field trial | 104 male petrochemical workers | Priority-oriented nutrition education program based on Theory of Planned Behavior | Control group received no intervention | Significant improvements in knowledge, FBS, Hcy (all p<0.001), and BMI (p<0.05). | Short intervention duration (3 months), only male participants, possible recall bias via FFQ, no family evaluation | Smoking, alcohol use, medication use excluded; groups balanced on demographics | Improved nutritional knowledge, dietary behavior, BMI, FBS, and homocysteine levels in the intervention group compared to control group; no significant change in hs-CRP |
| 11.Yaman 2022 [14] | Single-center (online nationwide sample via snowball technique) | 405 healthcare workers in Turkey (aged 19–67) | Perceived stress, emotional eating, and changes in nutritional habits during COVID-19 | Participants with vs. without changes in eating habits | Statistical significance found (e.g., P < 0.001 for stress and dietary changes) | Self-report bias, cross-sectional design, no causality, online sampling bias | Psychiatric history, COVID experience, socioeconomic concerns | 58% changed eating habits, 51% gained weight, significant associations with stress and emotional eating |
| 12. Panchbhaya et al, 2022 [49] | Systematic Review & Meta-analysis | Healthcare workers (various professions) from 26 studies | Workplace dietary interventions (education, environment, behavior change) | No intervention or usual care | Yes, significant improvements in fruit & vegetable intake (+0.49 servings/day), small but significant reduction in energy intake and body weight | Heterogeneity in intervention types, duration, and outcomes; publication bias possible; limited long-term follow-up in primary studies | Variability in study design, population characteristics, workplace settings | Increased fruit/vegetable intake, reduced energy intake and body weight, improved overall diet quality |
| 13.Rachmah et al 2021 [22] | Systematic review of 11 intervention studies from multiple countries | Workers aged 19–64 in various settings (industrial, manufacturing, garment, etc, healthcare sector.) | Nutrition and health interventions: education, behavioral change, physical activity, meal/supplement interventions, or combinations | Mostly RCTs or controlled trials; 2 studies without control groups | Most studies reported significant improvements in BMI, blood markers, self-efficacy, knowledge, diet, and behaviors (e.g., more fruit/veg, less alcohol/fat). | Heterogeneity in methods and duration, 2 studies lacked control groups, no risk of bias assessment, English-only studies included | Population/worksite heterogeneity; differences in baseline characteristics and implementation | Improved BMI, biochemical indices (e.g., cholesterol, fasting blood sugar), nutrition knowledge, physical activity, reduced risky behavior, increased fruit/vegetable intake |
| 14.Sovold et al 2021 [50] | Systematic Review – Multi-source, not center-based | General population; focus on public health, diagnostic, and nutrition data | Diagnostic, nutritional deficiencies (Vitamin D, C, E, zinc, selenium), psychosocial impact | Not applicable (multiple studies compared) | Significance varies per included study (some RCTs, some observational); mixed results | Heterogeneity in study designs, inconsistent RCT outcomes, lack of long-term data, many observational designs, regional bias (many Polish studies) | Potential variability across populations and designs included in review | Combined assessment of diagnostics, immune-nutritional status, mental health effects, and pandemic preparedness strategies. |
| 15. Mehrotra et al 2021 [51] | Single-center (Riobamba, Ecuador) | 380 students in healthcare | Eating habits (fruits, vegetables, dairy, snacks, etc.) | Frequency of consumption | Significant association between DMFT and fruit (p=0.049), vegetables (p=0.028) | Single age group, geographic limitation, outdated national data | None adjusted statistically (e.g., brushing habits, socio-economic status) | DMFT index; associations with eating habits |
| 16. Kris-Etherton et al 2021 [8] | Narrative Review – Multi-study based | Global population, all ages | Dietary patterns, specific nutrients (e.g., omega-3s, B-vitamins, zinc, magnesium) | No direct comparator; studies compared various diets/nutrients with behavioral health outcomes | Statistically significant in many included studies; RCTs show benefit of Mediterranean diet | Narrative nature, no meta-analysis; possible residual confounding and reverse causation | Nutritional status, comorbidities, lifestyle factors, socioeconomic status | Improved mood, reduced depression and anxiety with healthy diets; recommendation for dietary guidelines inclusion |
| 17. Özarslan & Caliskan 2021 [52] | Single center | 473 HCWs (doctors, nurses, pharmacists, technicians, interns) | Oral health knowledge, attitude, and practices (questionnaire-based survey) | Differences among HCW groups, gender, education level | Yes (p < 0.05 in multiple comparisons) | Cross-sectional design, limited sample size, self-reporting bias | Gender, profession, education level | Knowledge scores, attitudes (e.g., dental visit frequency), hygiene practices |
| 18. Peixoto et al 2021 [53] | Multicenter, Cross-sectional | 706 dentists in Turkey (FP & FN) | Working in filiation service vs not during COVID-19 | Dentists not involved in filiation (FN group) | Yes (p<0.05 for many variables) | Internet-based survey, self-reported data, single timepoint, possible selection bias | Confounders: age, gender, academic degree; Outcomes: MBI scores, stress, burnout | Habits etc |
| 19. Pai et al 2021 [54] | Single-center, cross-sectional | 641 Brazilian dentists (G1: quarantine, G2: outpatient care, G3: frontline) | COVID-19 pandemic-related psychosocial factors, sleep quality, TMD and bruxism symptoms | Groups based on work status during the pandemic (quarantine, outpatient, frontline) | Yes (p<0.016 for Bonferroni adjusted; p<0.001 for regressions) | Small sample of frontline dentists, self-reported data, cross-sectional design | Working status, pandemic concerns, psychological symptoms | Significant correlation between sleep and depression, stress, anxiety; poor sleep is associated with TMD and bruxism |
| 20. Hassani et al 2020 [55] | Single-center, cross-sectional | 180 dentists across India | COVID-19 lockdown's effect on work-life balance | Gender, relationship status, workplace factors | SEM showed significant associations (e.g., R² = 0.627; p-values < 0.05) | Self-report bias, low response rate (37.5%), limited geographical representation | Non-inclusion of dentists from all states; self-reporting; limited stress scale use | Work-life balance affected by mental/physical health, relationships, workplace; more imbalance in females |
| 21. Tenelanda-Lopez et al 2020 [56] |
Observational – Single-center (Washington University) | 1,994 employees (university & medical center), with ≥2 responses across 3 survey waves | Self-reported physical activity, diet, and mental well-being during COVID-19 | Clinical vs. nonclinical roles; working from home vs. onsite | Yes – maintained/increased PA and diet significantly linked to better well-being outcomes (p<0.05); multivariate regressions used | Self-reported data; limited generalizability; no baseline pre-pandemic data; recall and perception bias | Age, race, gender, income, ethnicity, clinical role | Participants maintaining or improving health behaviors had lower odds of moderate/severe depression, anxiety, and stress; onsite clinical workers reported worse well-being |
| 22. Schliemann & Woodside 2019 [21] | Systematic review of systematic reviews (multi-study, international) | Adult employees across healthcare sectors; general and “at risk” populations in workplace settings | Dietary workplace interventions (education, environmental changes, multicomponent programs) | Usual care, no intervention, or non-diet-focused interventions | Small but positive effects: improved FV intake (+0.2–0.7 portions), reduced fat intake, weight loss (−4.4 to −1.0 kg), HDL increase (+0.06 mmol/L), cholesterol reduction | High heterogeneity, self-reporting bias, lack of long-term data, limited economic outcome reporting, generalizability concerns | Inconsistent population/workplace reporting; unclear effect of dietary component in multicomponent interventions | Improvements in FV and fat intake, BMI, cholesterol; mixed/limited results on productivity, absenteeism, and economic outcomes |
| Country / Region | Freq. (n) | Percent (%) | Cumulative (%) | Sector | Freq. (n) | Percent (%) |
|---|---|---|---|---|---|---|
| Italy | 3 | 13.6% | 13.6% | Dental / Dentists | 6 | 27.3% |
| Greece | 1 | 4.5% | 18.1% | Physicians | 2 | 9.1% |
| Turkey | 3 | 13.6% | 31.7% | Healthcare workers (mixed) | 7 | 31.8% |
| China | 1 | 4.5% | 36.2% | Nurses | 1 | 4.5% |
| India | 1 | 4.5% | 40.7% | Public health / community health | 1 | 4.5% |
| Ecuador | 1 | 4.5% | 45.2% | Students (healthcare) | 1 | 4.5% |
| Brazil | 1 | 4.5% | 49.7% | Petrochemical workers (industrial) | 1 | 4.5% |
| USA | 1 | 4.5% | 54.2% | Academic medical staff | 1 | 4.5% |
| Australia | 1 | 4.5% | 58.7% | Dietitians & healthcare professionals | 1 | 4.5% |
| Poland | 1 | 4.5% | 63.2% | Public health | 1 | 4.5% |
| UK | 1 | 4.5% | 67.7% | Mixed HCWs | 1 | 4.5% |
| Multi-country (Europe, Global) | 6 | 27.3% | 95% | Multi-sector (systematic reviews) | 6 | 27.3% |
| Finland | 1 | 4.5% | 100% | Multi-professional (health sector) | 1 | 4.5% |
| Total | 22 | 100% | 100% | Total | 22 | 100% |
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