Kamal Hanna1, Paul Sambrook2, Jason M Armfield1, David S Brennan1. 1. Australian Research Centre for Population Oral Health (ARCPOH), Adelaide Dental School, The University of Adelaide, Adelaide, SA, Australia. 2. Oral and Maxillofacial Surgery Unit, Adelaide Dental Hospital, The University of Adelaide, Adelaide, SA, Australia.
Abstract
OBJECTIVES: This study had two objectives: (i) to explore and model domains describing the real-time impact of third molars (TMs) on quality of life (QoL); and (ii) to assess the percentage coverage, in some generic health-related quality of life (HRQoL) and oral health-related quality of life (OHRQoL) instruments, of the TM QoL domains identified in this study. METHODOLOGY: A global cross-sectional sample of tweets containing 'wisdom tooth' over a 1-week period retrieved 3,537 tweets. After random quota sampling, classification and filtering, only 843 tweets were included in the thematic analysis. A TM QoL model was constructed based on the associations of the identified domains. Domains for the selected generic HRQoL and OHRQoL instruments were plotted against the domains identified in the study to calculate the percentage coverage for each. RESULTS: The QoL domains identified were pain (n = 348, 41%), mood (n = 173, 20%), anxiety and fear (n = 54, 7%), enjoying food (n = 41, 4%), coping (n = 37, 4%), daily activities (n = 34, 4%), sleep (n = 24, 2%), social life (n = 19, 2%), physical health (n = 17, 2%), ability to think (n = 9, 1%), self-care (n = 8, 1%) and sporting & recreation (n = 2, <1%). The Assessment Quality of Life instrument (AQoL-8D) covers 87% of the TM QoL domains, while the rest of the HRQoL and OHRQoL instruments cover 33-60%. CONCLUSION: This study shows how Twitter can be used to obtain real-time QoL data, which might be used to model how TMs impact on QoL. The TM QoL domains identified in the study were generally under-represented among the generic OHRQoL instruments assessed while, the HRQoL AQoL-8D covered most of the TM QoL domains. The QoL domains identified in the study might be used to develop a new OHRQoL measure for TMs.
OBJECTIVES: This study had two objectives: (i) to explore and model domains describing the real-time impact of third molars (TMs) on quality of life (QoL); and (ii) to assess the percentage coverage, in some generic health-related quality of life (HRQoL) and oral health-related quality of life (OHRQoL) instruments, of the TM QoL domains identified in this study. METHODOLOGY: A global cross-sectional sample of tweets containing 'wisdom tooth' over a 1-week period retrieved 3,537 tweets. After random quota sampling, classification and filtering, only 843 tweets were included in the thematic analysis. A TM QoL model was constructed based on the associations of the identified domains. Domains for the selected generic HRQoL and OHRQoL instruments were plotted against the domains identified in the study to calculate the percentage coverage for each. RESULTS: The QoL domains identified were pain (n = 348, 41%), mood (n = 173, 20%), anxiety and fear (n = 54, 7%), enjoying food (n = 41, 4%), coping (n = 37, 4%), daily activities (n = 34, 4%), sleep (n = 24, 2%), social life (n = 19, 2%), physical health (n = 17, 2%), ability to think (n = 9, 1%), self-care (n = 8, 1%) and sporting & recreation (n = 2, <1%). The Assessment Quality of Life instrument (AQoL-8D) covers 87% of the TM QoL domains, while the rest of the HRQoL and OHRQoL instruments cover 33-60%. CONCLUSION: This study shows how Twitter can be used to obtain real-time QoL data, which might be used to model how TMs impact on QoL. The TM QoL domains identified in the study were generally under-represented among the generic OHRQoL instruments assessed while, the HRQoL AQoL-8D covered most of the TM QoL domains. The QoL domains identified in the study might be used to develop a new OHRQoL measure for TMs.
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