2.1. Study-Design
This research is a cross-sectional, psychometric study in nature that was done between January 2019 and May 2020. This research was approved by the Institutional Ethics Committee (IEC) (Proposal No.: 31/10/181/JMI/IEC/2018). This research was conducted according to the standards of the Helsinki Declaration [2013]. The study was also registered in the Clinical Trials Registry (Registration No.: CTRI/2019/05/01902).
2. 2. Semple-size calculation
Calculation of the sample size was done by Software G. Power 3.1.9.2 using the data from a previous study done by Yapali et al. [14], at an effect size of 0.85, power of 0.90, and alpha of 0.05, which gave a sample size of 88 to be necessary to test the study hypothesis. We overestimated the sample size to 100 to improve the strength of the study.
2.3. Participants
The study included both males and females, aged 20–75 years, who had a history of chronic neck pain for the past 12 weeks and had the proficiency to read and write Hindi and English. The exclusion criteria included a history of tumors, pregnancy, traumatic injuries to the cervical spine, psychiatric and/or other illnesses that led to poor cognition and comprehension, and the inability or unwillingness to complete questionnaires independently.
The physiotherapy clinics of the hospitals and universities in Delhi, India, enrolled a total of 100 participants [70 females and 30 males]. We also placed flyers in and around the university to invite the patients. The researchers screened the willing patients. Prior to their enrollment in the study, the researchers clearly explained the study process to the patients. They also explained the study's outcomes, including the potential value of their responses for clinical setups. Experienced researchers assessed the patients who met the criteria and explained the study's aims, objectives, and significance. We encouraged the participants to ask questions, after which they provided both written and oral consent for the study's recruitment.
2.4. Translation Phase
Every patient underwent a primary medical evaluation prior to the first assessment, which included demographic details such as sex, age, height, weight, body mass index, and a numeric pain rating scale. The university's department of physiotherapy conducted the study. The standard guidelines for the process of cross-cultural adaptation of self-report measures by Beaton et al. [19] guided the procedure. The process was completed in the following phases: -
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)
First, the researchers translated the questionnaire from English to Hindi, and then an expert from the university's Hindi language department translated it from English to Hindi. An expert committee, comprising an author, two translators, and a statistician, evaluated the translation for step 2, reaching a consensus on its forwarding. A typing error occurred during step 2 regarding the order of scoring options. By error, ‘Yes, occasionally, no’ was typed as ‘Nahi, Kabhi Kabhi, Haan’. The error was observed by an expert committee and rectified to its correct order, which was ‘Haan, Kabhi Kabhi, Nahi’. In the third step, two English translators, who were not familiar with the research and lacked medical backgrounds, conducted the backward translation. The expert committee again reviewed the backward translation. In this step, a spelling mistake found in Copenhagen, which was typed as ‘Copelhagen’ was rectified by the committee. A specialist committee, consisting of two physiotherapists, two bilingual translators, and a statistician, reviewed all the reports in the fourth step and reached a consensus. Stage five involved the completion of the questionnaire and the preparation of a pre-final version. Once the pre-final version was ready, we conducted the pilot study on 60 chronic neck pain patients, asking them to complete the Hindi-CNFDS.
The pilot study found some discrepancies in questions 1 and 4. Question 1 was stated as “Kya aap raat me gardan ke dard se pareshan hokar jaagte rehte hai? " and question 4 was cited as “Kya aapko subah kapde pehene me zarurat se zyada samay lagte hai?”. The discrepancies were resolved by the expert committee, and question 1 was modified as “Kya aap raat me gardan dard ke bawajood so pate hai? " and question 4 was modified as “Kya aapko subah kapde pehene me pehle jitna hi samay lagta hai?”. Twenty volunteers who also experienced persistent neck discomfort participated in a qualitative cognitive debriefing [20]. These participants were different from the 60 volunteers in the pilot study. We evaluated three main areas of the tool using a checklist: administration [how long it took to complete, how clear the instructions are], organization [how well the questions flow from one section to the next, whether the questions are logically ordered, and whether all the items are required to assess the construct], and content [directness, measurement of the construct, lack of technical jargon, items that make the participant uncomfortable, cultural acceptability, and cultural appropriateness] [21]. As the final step [step 6], the coordination committee, comprising all the study authors, scrutinized all the documents from the review process, engaging in discussions and assigning ratings at each stage.
Cognitive conversations took between twenty-five and forty-five minutes to complete. Participants did not suggest any changes to any of the items in the questionnaire. Hence, the cognitive conversations revealed that the tool’s contents were well structured, concise, clear, unambiguous, and culturally appropriate. Consequently, we created and prepared the final Hindi version of CNFDS, known as H-CNFDS, for psychometric testing. [Appendix]. Consequently, we developed the final version and incorporated it into the study.
2.5. Statistical Analysis
Microsoft Windows SPSS [version 21] was used for the data analysis. The demographic characteristics [age, gender, height, weight, and BMI] were recorded before filling out the questionnaires and were evaluated descriptively. The Shapiro-Wilk test was used to verify that the data was normal.
2.5.1. Psychometrics
An assessment of test-retest reliability and internal consistency were part of psychometric testing, acceptability, Bland Altman plotting, standard error of measurement, face, content, and construct validity. At least 15% of the participants had to meet the lowest and maximum scores to record floor and ceiling effects. [22].
2.5.2. Reliability Assessment
To establish test-retest reliability and internal consistency, the H-CNFDS questionnaire was administered twice among 50 randomly selected patients. The H-CNFDS questionnaire, which was given on the first day, was termed ‘H-CNFDS 1', and the questionnaire, which was administered for a retest, was termed ‘H-CNFDS 2’. We administered H-CNFDS 2 48 hours after H-CNFDS 1. After all of the questionnaires had been completed, the researchers scored them.
We used Cronbach's to assess the internal consistency of the H-CNFDS between the scores of H-CNFDS 1 and H-CNFDS 2. The Cronbach’s α coefficient value is interpreted as Cronbach’s α ≥ 0.9 = excellent, α ≥ 0.8 = good, α ≥ 0.7 = acceptable, α ≥ 0.6 = questionable, α ≥ 0.5 = poor, and α ≤ 0.5 = unacceptable [23]. We used the intra-class correlation coefficient ICC [2,1] to determine the test-retest reliability. The values above 0.80 indicated excellent test-retest reliability, while the values less than 0.80 and greater than 0.60 indicated very good test-retest reliability [24]. We developed the Bland-Altman plot using the total scores [percentile] of H-CNFDS 1 and H-CNFDS 2 to check the agreement levels between the two tests. We measured the absolute reliability using standard error measurement [SEM].
2.5.3. Validity Assessment
Cronbach’s α was employed to determine the inner consistency of the H-CNFDS between the scores of H-CNFDS 1 and H-CNFDS 2. The Cronbach’s α coefficient value is interpreted as Cronbach’s α ≥ 0.9 = excellent, α ≥ 0.8 = good, α ≥ 0.7 = acceptable, α ≥ 0.6 = questionable, α ≥ 0.5 = poor, and α ≤ 0.5 = unacceptable [23]. We used the intra-class correlation coefficient ICC [2,1] to determine the test-retest reliability. The values above 0.80 indicated excellent test-retest reliability, while the values less than 0.80 and greater than 0.60 indicated very good test-retest reliability [24]. We developed the Bland-Altman plot using the total scores [percentile] of H-CNFDS 1 and H-CNFDS 2 to check the agreement levels between the two tests. We measured the absolute reliability using standard error measurement [SEM].
We classify face validity as the degree to which the measure aligns with its intended purpose [25]. We should assess the questionnaire items for face validity; failing to do so invalidates other aspects of validity [26]. We used a non-face-to-face approach for content validity evaluation. We calculated the content validity index [CVI] using the scores provided by ten expert panelists.