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Prevalence, healthcare utilisation and economic burden of chronic musculoskeletal disorders among adults in Puducherry: A concurrent mixed method study
For correspondence: Dr Sitanshu Sekhar Kar, Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry 605 006, Indiae-mail: drsitanshukar@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Gola A, Kavadichanda C, Kar SS. Prevalence, healthcare utilisation and economic burden of chronic musculoskeletal disorders among adults in Puducherry: A concurrent mixed method study. Indian J Med Res. 2026;163:778-85. doi: 10.25259/IJMR_3109_2025.
Abstract
Background and objectives
Chronic musculoskeletal disorders (MSDs) are a significant contributor to pain and disability worldwide, with burden varying across different regions. The study aimed to estimate the prevalence of chronic MSDs in Puducherry, assess health-seeking behaviour, and evaluate the economic burden associated with MSD-related healthcare.
Methods
A community-based concurrent mixed-method study was conducted in selected areas of Puducherry. Households were selected using systematic random sampling, and individuals were chosen using simple random sampling. The WHO-ILAR COPCORD questionnaire identified individuals with MSDs. Direct and indirect costs from a patient perspective were assessed using a structured questionnaire. Data were collected through Epicollect5 and analysed using STATA v14. Prevalence was reported as proportions with 95% confidence intervals (CI). Focused group discussions (FGDs) were conducted to identify challenges in seeking healthcare.
Results
Among 400 participants, the prevalence of chronic MSDs was 16% (95% CI: 12.5-19.9), with the majority reporting mild disability and more common in women, but there was no urban-rural difference. Around half (48.4%) of participants reported out-of-pocket expenditures (OOPE) in the past three months, with a higher proportion in urban (53.8%) compared to rural areas (45.9%). The median expenditure was ₹204, with urban participants spending ₹308 and rural participants ₹154. FGDs revealed that while many initially preferred private facilities for quicker access to pain medications, they later returned to public facilities due to financial constraints.
Interpretation and Conclusions
The prevalence of chronic musculoskeletal disorders is high in Puducherry, particularly among women and older adults, causing mild disability. Despite a preference for government healthcare, many incurred out of pocket expenditures, highlighting financial challenges in accessing care.
Keywords
Chronic musculoskeletal diseases
Economic burden
Healthcare access
Mixed-method study
Prevalence
Chronic musculoskeletal disorders (MSDs) are considered to be an important cause of pain and disability with a varying spectrum of burden in different parts of the world.1 With improvement in socioeconomic conditions and an increase in life expectancy of the population, the incidence of non-communicable diseases (NCDs), including MSDs is on the rise.2 Global rank of MSDs as a cause of disability adjusted life years (DALY) went up from twelfth place in 1990 to fifth place in 2017. MSDs also rank as the first and primary cause for years lived with disability (YLD) globally. In India, it ranks as the second cause for YLD when compared with all other causes (13.6%) and accounts for 4.5% of the total DALYs. The increase in disability leads to loss of work and enormous health care expenditure.3
In a comprehensive nationwide study conducted in India, joint disorders and pain emerged as the second and fourth most prevalent reasons for outpatient clinic visits and out-of-pocket expenses, respectively, among all NCDs.4 Hence, MSDs have a cascading economic effect not only on individuals suffering from the condition but also on their care takers. Though there are few studies looking at the health care resource utilisation behaviour, this area needs to be further explored as the health care utilisation behaviour changes with factors specific to the geographical area and cultural practices. There is limited understanding of how individuals interpret and ascribe meaning to prognosis, which may adversely affect patient-centred care. This gap can contribute to a mismatch between outcome expectations of individuals with MSDs and those of providers, potentially compromising the completeness and effectiveness of care.
This study was conducted to study the prevalence, healthcare utilisation behaviour, and economic impact of chronic musculoskeletal disease among adults in Puducherry. Findings from this mixed methods study can inform targeted healthcare planning, resource allocation, and policy formulation, enabling tailored interventions to address the burden of MSDs in different settings.
Methods
This mixed method study was undertaken by the department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India. The study protocol and other relevant documents received approval from the Institutional Ethics Committee of the institute.
Study design and setting
Study employed a concurrent mixed-method approach, quantitative part consisting of a cross-sectional survey using a standardised questionnaire and qualitative component consisting of focus group discussions (FGDs). Both methods were used to gather relevant information and were analysed to provide additional insights and explanations that complement findings from each other. A study was conducted in selected rural and urban areas of Puducherry, which are served by outreach health centres of a tertiary teaching hospital. The service areas include four wards and four villages with an approximate population of 10,000 each in urban and rural settings, respectively (Supplementary Figure).
Quantitative component
We interviewed adults in both urban and rural areas from January 2022 to May 2023. Based on a study conducted in southern India, MSDs were found to have a prevalence of 33.9%.5 To achieve a relative precision of 10% and a confidence interval of 95%, calculated sample size was determined to be 188. Accounting for a non-response rate of 5%, the final total sample size was set at 200. Given that the study covered both rural and urban areas, a sample size of 200 was examined at each location.
We used the household line list from the enumeration data to select households from each service area using systematic random sampling. From each selected household, one eligible adult (≥18 yr) was chosen by lottery. Individuals with serious documented illnesses (e.g., cancer, end-stage renal disease), those bedridden, or unable to respond were excluded.
The investigator visited participants at their homes and screened adults for chronic MSDs using the community oriented programme for control of rheumatic diseases (COPCORD) phase I and II questionnaires.6 These tools captured information on demographics, symptoms, duration, functional limitations, healthcare-utilisation behaviour, and medication use. Functional status was assessed using a modified health assessment questionnaire (HAQ) covering 23 daily activities under eight domains (Dressing, arising, eating, walking, bathing, reaching, gripping, and performing errands) among individuals identified with MSDs. A HAQ-DI score of ≤1 indicated mild, >1–2 moderate, and >2–3 severe disability.7 Data collection was carried out by a single investigator who received training in administering the COCORD questionnaire in the Department of Clinical Immunology.
Individuals with chronic MSDs were asked for details regarding their healthcare-utilisation behaviour and the economic burden in seeking care using standardised questionnaires, which included questions related to healthcare utilisation (hospital visits, medication expenses, laboratory tests, and other medical services). It also explored productivity losses due to absenteeism from work, reduced efficiency while at work, and caregiving responsibilities.
Statistical analysis
Continuous variables are reported as mean and standard deviation or median and interquartile range, based on distribution, while categorical variables are summarised as frequency with proportion. Statistical significance level was set at 0.05. The prevalence of chronic MSDs was reported as proportions with 95% confidence interval (CI). Univariate log binomial regression was performed to identify factors associated with chronic MSDs, and results were presented as prevalence ratios with 95% confidence interval (CI). In the multivariable regression model, we included variables with a P value of less than 0.2 in the univariate analysis. Model robustness was assessed based on the Bayesian information criterion (BIC). Data were analysed using STATA ver.17 (StataCorp, Texas, USA).
Qualitative component
For the qualitative component, participants were purposively selected from individuals with chronic MSDs, ensuring representation across both male and female strata, facilitating inclusion of participants with diverse yet relevant experiences, enabling a deeper understanding of perspectives related to chronic MSDs. The FGDs were conducted by a trained investigator at a time and place of participants’ convenience, in the local language (Tamil), in the presence of note taker. Each session was audio recorded and transcribed on the same day and was translated into English.
Manual descriptive content analysis was conducted by two coders independently using both inductive and deductive coding. Deductive codes were developed a priori based on a literature review. To enhance credibility and reduce bias, transcripts were independently reviewed by another researcher. Coding rules and theme development followed standard procedures, with disagreements resolved through discussion.
Results
Quantitative component
A total of 400 participants were surveyed, with an average age of 42.4 years, and women accounted for 53.7% of the sample. Most participants were married n=286 (71.5%), with a higher proportion in rural areas n=149 (74.5%). Nuclear families constituted the majority n=267 (66.7%), with a higher proportion in urban areas n=145 (72.5%), whereas joint families were comparatively more frequent in rural regions n=65 (32.5%). Only difference in education status was statistically significant between rural and urban settings. Distribution of other socio-demographic characteristics is presented in Table I.
| Variables | Total (N=400), n (%) | Urban (n=200), n (%) | Rural (n=200), n (%) |
|---|---|---|---|
| Age (yr) | |||
| 18-45 | 237 (59.3) | 114 (57) | 123 (61.5) |
| ≥45 | 163 (40.7) | 86 (43) | 77 (38.5) |
| Gender | |||
| Men | 185 (46.2) | 87 (43.5) | 98 (49) |
| Women | 215 (53.7) | 113 (56.5) | 102 (51.0) |
| Education status | |||
| No formal education | 47 (11.7) | 21 (10.5) | 26 (13.0) |
| 1st - 8th standard | 96 (24.0) | 36 (18.0) | 60 (30.0) |
| 9th – 12th standard | 164 (41.0) | 98 (49.0) | 66 (33.0) |
| Graduation and above | 93 (23.2) | 45 (22.5) | 48 (24.0) |
| Perceived physical nature of work | |||
| Light | 181 (45.2) | 98 (49.0) | 83 (41.5) |
| Moderate | 138 (34.5) | 66 (33.0) | 72 (36.0) |
| Heavy | 81 (20.2) | 36 (18.0) | 45 (22.5) |
| Socioeconomic status* | |||
| Lower class | 253 (63.3) | 128 (64.0) | 125 (62.5) |
| Upper class | 147 (36.7) | 72 (36.0) | 75 (37.5) |
| Self-reported chronic conditions | |||
| Hypertension | 31 (7.7) | 15 (7.5) | 16 (8.0) |
| Diabetes Mellitus (DM) | 15 (3.7) | 8 (4.0) | 7 (3.5) |
| Hypertension and DM | 30 (7.5) | 13 (6.5) | 17 (8.5) |
| Others† | 24 (6.0) | 10 (5.0) | 14 (7.0) |
| Substance use | |||
| Alcohol†† | 89 (22.2) | 48 (22.0) | 41 (20.5) |
| Tobacco use¶ | 46 (11.5) | 20 (10.0) | 26 (13.0) |
*Based on Modified B.G. Prasad Scale: Lower class includes class I, II and III i.e monthly per capita income <4110; Upper class includes class IV and V ie per capita income ≥4100
†Others include Asthma, Hypothyroidism, Tuberculosis, Hyperlipidemia
††Alcohol consumption in the last one year
Tobacco consumption in the last one month
Prevalence of MSDs of any duration at the time of survey was 42.7% (95% CI: 37.8 – 47.7) in the study population. Prevalence was higher in rural areas as compared to urban areas, but no statistically significant difference was observed. Chronic MSDs, defined as lasting for 90 days or more, were reported by 64 (16%, 95% CI: 12.5 - 19.9) out of the 400 study participants ( Table II). Among the self-marked pain sites among individuals with MSDs (n=171), the knee was the most common n=115 (67.2%), followed by the lower back n=97 (56.3%). Most of the individuals had pain at two or more sites at the time of the interview. The distribution of the HAQ-DI scores among individuals with chronic MSDs, with a mean of 0.7±0.3, exhibited a leftward skew, signifying that most individuals experienced low HAQ disability. Fifty-two participants (81.2%) had mild disability, and 12 participants (18.8%) reported moderate disability according to HAQ-DI among individuals with chronic MSDs (n=62). HAQ-DI scores were found to be higher in rural settings (0.8±0.3) as compared to urban wards (0.5±0.4) ( Table II).
| Total (N=400) | Urban (N=200) | Rural (N=200) | |
|---|---|---|---|
| Prevalence, n (%, 95% CI) | |||
| Any duration | 171 (42.7; 37.8 - 47.7) | 83 (41.5; 34.8 - 48.4) | 88 (44.0; 37.0 - 51.2) |
| ≤7 days | 61 (15.3; 11.9 -19.1) | 35 (17.5; 12.5 - 23.5) | 26 (13.0; 8.7 - 18.5) |
| >7 to 89 days | 46 (11.5; 8.5 - 15.1) | 21 (10.5; 6.6 - 15.6) | 25 (12.5; 8.3 - 17.9) |
| ≥90 days | 64 (16.0; 12.5 - 19.9) | 27 (13.5; 9.1 - 19.1) | 37 (18.5; 13.4 - 24.6) |
| HAQ score (mean±SD) | 0.7 ± 0.3 | 0.5 ± 0.4 | 0.8 ± 0.3 |
SD, standard deviation; CI, confidence interval
Healthcare-utilisation patterns among individuals with chronic MSDs revealed diverse approaches to treatment. Nine out of ten participants sought care for their illness with a median delay of 1.5 months (IQR: 1-5 months) in seeking care, with eight individuals reporting no utilisation of healthcare services for their MSDs. The predominant choice for care was allopathy, with three out of four individuals opting for treatment at public health facilities. Commonly employed treatment modalities included oral medications and oil application, which were reported as effective by 80% (n=45) of individuals. The study also investigated the financial burden on individuals with MSDs. About 48.4% (n=31) of participants among those who sought care incurred out-of-pocket expenses (OOPE) in the last three months, with a median expenditure of ₹ 204.4 for all participants. The comparison of healthcare utilisation between rural and urban areas is displayed in Table III.
| Variable | Total (n=64), n (%) | Urban (n=27), n (%) | Rural (n=37), n (%) |
|---|---|---|---|
| System of medicine followed* | |||
| Modern medicine (includes physiotherapy) | 56 (87.5) | 24 (88.9) | 32 (86.4) |
| Others (homeopathy, herbal, Yoga) | 10 (15.6) | 3 (11.1) | 7 (18.9) |
| Not availing any healthcare | 8 (12.5) | 3 (11.1) | 5 (13.5) |
| Type of health facility | |||
| Government | 49 (76.5) | 20 (74.1) | 29 (78.3) |
| Private | 7 (10.9) | 4 (14.8) | 3 (8.1) |
| Treatment modality*† | |||
| Medications | 49 (76.5) | 24 (100.0) | 25 (78.1) |
| Injections | 10 (15.6) | 6 (25.0) | 4 (12.5) |
| Topical oils/creams/ointments etc. | 42 (65.6) | 16 (66.7) | 26 (81.2) |
| Exercise (Physiotherapy) | 7 (10.9) | 5 (20.8) | 2 (6.2) |
| Perceived effectiveness of treatment† | |||
| Effective | 45 (80.3) | 20 (83.3) | 25 (78.1) |
| Not effective | 11 (19.7) | 4 (16.6) | 7 (21.8) |
| Out-of-pocket expenditure (OOPE) during last 3 months | |||
| OOPE | 31 (48.4) | 14 (53.8) | 17 (45.9) |
| Cost in INR, [Median (IQR)]†† | ₹204.4 (99.7-598.3) | ₹308.3 (225.2-820.2) | ₹153.7 (102.2-673.1) |
| Hindrance at work due to pain during last 2 wk | |||
| No | 4 (6.2) | 3 (27.3) | 1 (9.1) |
| Yes – to a degree | 14 (21.8) | 6 (54.5) | 8 (72.7) |
| Yes – very much | 4 (6.2) | 2 (18.2) | 2 (18.1) |
| Difficulty in performing household tasks due to pain | |||
| Yes | 28 (43.7) | 14 (51.8) | 14 (36.1) |
| No | 36 (56.2) | 13 (48.2) | 23 (63.9) |
*Multiple responses applicable
†Results presented only for individuals availing healthcare (Urban, n=24; Rural, n=32)
††Currency conversion from INR to USD was done using the exchange rate of 1 USD=83.10 INR, as reported by the Reserve Bank of India on August 1, 2023
A regression model was developed to identify predictors of chronic MSDs in the community, incorporating factors deemed significant in univariate analysis. In the univariate model, age ≥46 years, female gender, upper socio-economic class, and alcohol use were found to be significantly associated. Only four variables were included in the multivariable analysis, taking into account the number of outcome events and model diagnostic considerations. In the multivariate model, only age and gender demonstrated significant associations with chronic MSDs. Final multivariable model exhibited a BIC of -2086.5 ( Table IV).
| Total | Chronic MSDs | Unadjusted PR (95% CI) | Adjusted PR† (95% CI) | P value | |
|---|---|---|---|---|---|
| Age (yr) | |||||
| 18-45 | 237 | 10 (4.2) | Ref | Ref | <0.001 |
| ≥46 | 163 | 54 (33.1) | 7.8 (4.1-14.9) | 7.6 (4.1-14.3) | |
| Gender | |||||
| Men | 185 | 17 (9.2) | Ref | Ref | 0.002 |
| Women | 215 | 47 (21.8) | 2.4 (1.4-3.9) | 2.2 (1.3-3.5) | |
| Perceived nature of work | |||||
| Light | 181 | 24 (13.3) | Ref | - | - |
| Moderate | 138 | 24 (17.4) | 1.3 (0.8-2.2) | ||
| Heavy | 81 | 16 (19.7) | 1.5 (0.8-2.6) | ||
| Socio-economic status | |||||
| Lower class | 253 | 49 (19.3) | Ref | Ref | 0.21 |
| Upper class | 147 | 15 (10.2) | 0.5 (0.3-0.9) | 0.7 (0.4-1.2) | |
| Smoking | |||||
| No | 254 | 61 (17.2) | Ref | - | - |
| Yes | 46 | 3 (6.5) | 0.4 (0.1-1.2) | ||
| Alcohol | |||||
| No | 211 | 58 (18.7) | Ref | - | |
| Yes | 89 | 6 (6.7) | 0.4 (0.2-0.8) | ||
| Place of residence | |||||
| Urban | 200 | 27 (13.5) | Ref | Ref | 0.055 |
| Rural | 200 | 37 (18.5) | 1.4 (0.9-2.2) | 1.4 (0.9-2.2) | |
†Age, gender, socio-economic status, and place of residence were included in the multivariable analysis based on P<0.2 in univariate analysis and sample size considerations. PR, prevalence ratio
Qualitative component
Five FGDs were conducted—four with women and one with men—comprising 31 women and 5 men. Participants identified chronic MSDs as a major barrier to quality of life, citing pain and limited mobility as key challenges affecting daily function, social life, and work productivity. MSDs were often attributed to physically demanding work, ageing, and past injuries. Rural participants commonly viewed chronic pain as an inevitable outcome of labour-intensive lifestyles and ageing. Cultural norms strongly influenced health-seeking behaviours. Home remedies and traditional treatments were often preferred initially, especially in rural areas, where formal care was typically sought only when symptoms became severe. Family and peer influence played a significant role in healthcare decisions.
Access to care was hindered by financial constraints, long wait times at public facilities, and transportation issues, particularly in rural settings, as the nearest healthcare centre was several kilometres away, making it difficult to visit regularly. Lack of nearby specialised care further limited treatment options for rural participants. The economic burden of chronic MSDs led to delayed or skipped treatment. Perceptions of treatment efficacy varied widely, while some found relief through combined therapies, others remained dissatisfied due to persistent symptoms. Those adhering to physiotherapy and regular check-ups reported better outcomes, though overall adherence was low due to cost and accessibility barriers. However, individuals developed acceptance over time for the discomfort.
Care-seeking was shaped by perceptions of susceptibility, severity, benefits, and barriers, as reflected in the health belief model provided ( Figure).8
Discussion
This study provides insights into the prevalence, healthcare utilisation, and economic burden of chronic MSDs among adults in rural and urban areas of Puducherry. The prevalence of chronic MSDs was 16%, with women, older adults, and individuals engaged in manual labour being at higher risk. Delays in seeking treatment, reliance on informal healthcare, and high OOPE were prominent findings, underscoring the economic impact of chronic MSDs. Our qualitative analysis sheds light on the quantitative trends: financial barriers and the belief that pain should be tolerated explain, in part, why participants incurred substantial OOPE and postponed medical care. There was concordance between the qualitative and quantitative findings, particularly regarding themes such as financial barriers to care-seeking, pain acceptance, moderate limitations in daily activities, and use of AYUSH therapies and home remedies for symptom management.
Prevalence and associated factors in this study resonate with observations from other settings, reinforcing the consistency of MSD trends across different geographies and cultures. A 2015 study in rural Puducherry found a one-year prevalence of musculoskeletal pain at 33.9%.5 Our study observed a higher prevalence in the same setting, which may be attributable to differences in the questionnaire used and the time period of data collection. A similar study in Calicut, Kerala, closer to our setting, reported a crude point prevalence of 30.13% (95% CI 29.2-31.2).9 The prevalence of MSDs in this study is comparable but a little higher as compared to other low- and middle-income countries like Iran, Bangladesh, Pakistan, and Indonesia 44.7%, 26.3%, 28.9%, and 36.19%, respectively.10-13 The heterogeneity in the prevalence of MSDs in studies conducted in India and other countries among various studies can be attributed to the socio-demographic factors, as well as the time reference followed. Since all of the aforementioned studies utilised the WHO-ILAR COPCORD questionnaire, the comparability of their findings can be relied upon.
Our study observed a higher prevalence of chronic MSDs in rural areas compared to urban settings. Similar findings were reported by Chopra et al,14 with 18.2% of rural and 14.1% of urban respondents in Pune reporting musculoskeletal pain. In Bhigwan’s rural population, 54% experienced unclassified aches or soft tissue rheumatism versus 44% in urban Pune.15 Likewise, Haldiya et al16 reported a higher rural prevalence (11.6%) than urban (9.5%). In contrast, Sharma17 found higher MSD prevalence in urban areas: in Delhi, 7.2% of urban versus 6.9% of rural residents; in Dibrugarh, 13.4% urban versus 9.7% rural.17 These differing trends may be due to variations in occupational demands and demographic shifts, such as increased life expectancy in rural areas, influencing the MSD burden.18
In this study, 87.5% of individuals with chronic MSDs preferred allopathic treatment, and 12.5% of individuals did not seek any care. No significant rural-urban difference in healthcare-seeking was noted. In contrast, a 2017 study by Alok et al19 reported higher treatment-seeking in urban areas (58.7%) compared to rural (49.5%), with 42% of urban and 36% of rural participants preferring modern medicine. Deshmukh et al,20 in rural Gadchiroli, found 76.7% sought care mostly from private providers for back or joint pain within six months, though local trained workers were preferred for village-level care. Similar differences were noted in other studies as well.21,22 These findings highlight differences in treatment-seeking behaviour across settings and the importance of contextualised interventions. In Puducherry, comparable access to public facilities in both urban and rural areas likely contributed to uniform healthcare utilisation across regions.
A mixed-method study by Kirubakaran et al23 in Tamil Nadu echoed similar findings as observed by our qualitative analysis participants found relief through medication, diet, family support, and home remedies. While allopathic treatments were effective, cost and side effects were concerns. The shared cultural context likely influenced these parallel findings, emphasising the need to incorporate cultural considerations in MSD interventions. A recent study in rural West Bengal also highlighted functional limitations and mental health impacts among those with chronic MSDs.24 These findings support a holistic, patient-centred approach to MSD care.
Although limited literature exists on the economic burden of chronic MSDs, a study by Bang et al21 estimated the annual per capita medical expense to be ₹379, with an average loss of 11 workdays per person per year due to pain. The OOPE findings in our study align with this estimate. However, in our sample, only two participants reported missing work due to MSD-related pain in the two wk preceding the survey.
Study strengths include the use of a validated questionnaire, ensuring comparability with other studies, and digital data collection, minimising entry errors. A 100% response rate further reduced selection bias, providing a representative sample. However, the study had some limitations. Its cross-sectional design prevents any causal inferences about factors associated with chronic MSDs. The setting (field area of a tertiary teaching hospital) may have influenced healthcare-utilisation behaviour. Key factors like nutritional status, physical activity, BMI, and body fat composition were not assessed in this study. Sample size was derived using prevalence estimates; therefore, it may not be adequately powered to detect associations with other factors. Even though the qualitative insights aligned well with the quantitative findings, the purposive sampling approach limits the extent to which these results can be generalised beyond the study setting.
India’s rapid demographic and epidemiological transitions have led to a rising burden of NCDs, yet MSDs remain under recognised compared to other NCDs. The findings of this study carry significant public health relevance, revealing a high prevalence of chronic MSDs and notable delays in seeking care. These results emphasise the need for targeted interventions to promote their early diagnosis and treatment.
Author contributions
AG: Concepts, design, literature search, data acquisition, data analysis and interpretation, statistical analysis, manuscript writing; CK: Concepts, design, definition of intellectual content, literature search, interpretation of results, manuscript writing; SSK: Concepts, design, definition of intellectual content, clinical studies, interpretation of results, statistical analysis, manuscript writing. All authors have read and approve the final printed version of the manuscript.
Financial support and sponsorship
None.
Conflicts of Interest
None.
Use of Artificial Intelligence (AI)-Assisted Technology for manuscript preparation
The authors confirm that there was no use of AI-assisted technology for assisting in the writing of the manuscript and no images were manipulated using AI.
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