Discussion

Our study found that the prevalence of voice disorders among teleoperators in our centre was 48.1 per cent, predominantly affecting female workers (84.6 per cent) with a mean working experience of 11 years. The findings in our study are parallel to the systematic review by Nair et al., which reported a prevalence of between 33 and 68 per cent of voice disorders among call centre operators.^{Reference Nair, Nayak, Maruthy, Krishnan and Devadas2} Similarly, a high prevalence rate of approximately 70 per cent was reported among telemarketers by several other studies.^{Reference Jones, Sigmon, Hock, Nelson, Sullivan and Ogren7,Reference Rechenberg, Goulart and Roithmann8} The work burden of teleoperators working in a hospital may be reckoned to be lower compared with telemarketers. However, our data show that the impact on the voice pre- and post-shift is similar. In contrast, Cutiva et al. reported that the prevalence of voice disorders among occupational voice users was less than 11 per cent when shorter recall period studies were included.^{Reference Cutiva3} The assessment method demonstrated a significant contribution to the prevalence rates: studies based on clinical examinations such as stroboscopy and video laryngoscopy showed higher prevalence rates of voice disorders compared with studies using questionnaires to assess voice disorders.^{Reference Cutiva3}

We found that the subjective voice analysis using maximum phonation time and Voice Handicap Index-10 showed significant differences pre- and post-shift among teleoperators (maximum phonation time (p < 0.018) and Voice Handicap Index (p < 0.011)). Schindler et al., in a study of patients with functional and structural dysphonia, found that Voice Handicap Index is comparable with maximum phonation time; correlation was noted between maximum phonation time and the functional and physical domain of Voice Handicap Index (r = 0.583 and r = 0.683, respectively).^{Reference Schindler, Mozzanica, Vedrody, Maruzzi and Ottaviani9} Grade, Roughness, Breathiness, Asthenia, Strain scoring showed no statistically significant changes in our study; this was also reported by Girardi et al. in a study in which a severe degree of alteration in voice parameters was not demonstrable among patients using Grade, Roughness, Breathiness, Asthenia, Strain and Instability scale.^{Reference Girardi, Marchand, Moreira, Drummond and Cassol10}

Although the pre- and post-shift objective voice analysis using Praat software showed changes pre- and post-shift, these were not statistically significant (frequency (p = 0.259), jitter (p = 0.122) and shimmer (p = 0.17)). This could be attributed to the small sample size of our study. Akin to that, Amorim et al. found no difference in acoustic analysis data (p = 0.738) when comparing pre- and post-shift voice among their patients. Nonetheless, emerging non-standardised acoustic analysis programmes could contribute to addressing the inconclusive outcome and varying data. Moreover, differences in the algorithms hinder the normalisation of objective voice measures (Tables 5 and 6).^{Reference Amorim, Bommarito, Kanashiro and Chiari4}

Table 5. Correlation between pre-shift measurements

There was no correlation between pre-shift measurements. MPT = maximum phonation time; VHI = Voice Handicap Index

Table 6. Correlation between post-shift measurements

There was no correlation between post-shift measurements. MPT = maximum phonation time; VHI = Voice Handicap Index

To date, the ‘gold standard’ voice assessment tool is still unknown. When comparing subjective and objective acoustic analysis, most studies showed varying results. No significant correlation could be demonstrated between Voice Handicap Index and specific acoustic measures in any consistent manner.^{Reference Wheeler, Collins and Sapienza11,Reference Woisard, Bodin, Yardeni and Puech12} Woisard et al. demonstrated that there was no significant correlation between the different scores (total and subscales) and the acoustic parameters (harmonics-to-noise ratio, jitter and shimmer), the aerodynamic parameters (maximum phonation time, mean flow and subglottal pressure) and the Dysphonia Severity Index.^{Reference Woisard, Bodin, Yardeni and Puech12} In our study, we found no correlation between acoustic analysis (frequency, jitter and shimmer) and subjective analysis using maximum phonation time and Voice Handicap Index-10 pre- and post-shift, most likely because of the small sample size. On the other hand, Niebudek-Bogusz et al. reported that acoustic parameters among their patients significantly correlated with the functional and emotional subscales of the Voice Handicap Index.^{Reference Niebudek-Bogusz, Woznicka, Zamyslowska-Szmytke and Sliwinska-Kowalska13}

Professionals who rely on voice should have formal speech-language training and advice for voice intervention as a part of training. Speech therapists need to identify vocal characteristics that are common to this group.^{Reference Oliveira, Raize, Algodoal, Moreira-Ferreira, Marchion, Oliveira, Almeida and Raize14} In our study, 92.6 per cent of our patients had never undergone formal speech training, resulting in no daily vocal warm-up prior to commencement of their shift. This could have contributed to the voice complaints amongst our patients. Voice training should be advocated as a part of teleoperators’ training before job commencement. In addition to that, regular intake of water, proper air humidity and avoidance of risk factors that could result in dryness of the vocal fold mucosa need to be highlighted to all occupational voice users.^{Reference Oliveira, Raize, Algodoal, Moreira-Ferreira, Marchion, Oliveira, Almeida and Raize14–Reference Titze17}