IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i5p3105-d765303.html
   My bibliography  Save this article

Predicting Interest in Orthodontic Aligners: A Google Trends Data Analysis

Author

Listed:
  • Magdalena Sycińska-Dziarnowska

    (Department of Orthodontics, Pomeranian Medical University in Szczecin, Al. Powst. Wlkp. 72, 70111 Szczecin, Poland)

  • Liliana Szyszka-Sommerfeld

    (Department of Orthodontics, Pomeranian Medical University in Szczecin, Al. Powst. Wlkp. 72, 70111 Szczecin, Poland)

  • Krzysztof Woźniak

    (Department of Orthodontics, Pomeranian Medical University in Szczecin, Al. Powst. Wlkp. 72, 70111 Szczecin, Poland)

  • Steven J. Lindauer

    (Department of Orthodontics, School of Dentistry, Virginia Commonwealth University, Richmond, VA 23298, USA)

  • Gianrico Spagnuolo

    (Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, 80131 Naples, Italy)

Abstract

Aligners are an example of how advances in dentistry can develop from innovative combinations of 3D technologies in imaging, planning and printing to provide new treatment modalities. With increasing demand for esthetic orthodontic treatment, aligners have grown in popularity because they are esthetically more pleasing and less obstructive to oral hygiene and other oral functions compared to fixed orthodontic appliances. To observe and estimate aligner treatment interest among Google Search users, Google Trends data were obtained and analyzed for the search term, “Invisalign”. A prediction of interest for the year 2022 for three European Union countries with the highest GDP was developed. “Invisalign” was chosen to represent all orthodontic aligners as the most searched term in Google Trends for aligners. This is the first study to predict interest in the query “Invisalign” in a Google search engine. The Prophet algorithm, which depends on advanced statistical analysis methods, positions itself as an automatic prediction procedure and was used to predict Google Trends data. Seasonality modeling was based on the standard Fourier series to provide a flexible model of periodic effects. The results predict an increase in “Invisalign” in Google Trends queries in the coming year, increasing by around 6%, 9% and 13% by the end of 2022 compared to 2021 for France, Italy and Germany, respectively. Forecasting allows practitioners to plan for growing demand for particular treatments, consider taking continuing education, specifically, aligner certification courses, or introduce modern scanning technology into offices. The oral health community can use similar prediction tools and methods to remain alert to future changes in patient demand to improve the responses of professional organizations as a whole, work more effectively with governments if needed, and provide better coordination of care for patients.

Suggested Citation

  • Magdalena Sycińska-Dziarnowska & Liliana Szyszka-Sommerfeld & Krzysztof Woźniak & Steven J. Lindauer & Gianrico Spagnuolo, 2022. "Predicting Interest in Orthodontic Aligners: A Google Trends Data Analysis," IJERPH, MDPI, vol. 19(5), pages 1-10, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:5:p:3105-:d:765303
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/5/3105/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/5/3105/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Magdalena Sycinska-Dziarnowska & Hanna Bielawska-Victorini & Agata Budzyńska & Krzysztof Woźniak, 2021. "The Implications of the COVID-19 Pandemic on the Interest in Orthodontic Treatment and Perspectives for the Future. Real-Time Surveillance Using Google Trends," IJERPH, MDPI, vol. 18(11), pages 1-9, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    2. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    3. Torsten Heinrich & Jangho Yang & Shuanping Dai, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," Papers 2012.14503, arXiv.org.
    4. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    5. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    6. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.
    7. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    8. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.
    9. Alina Ferecatu & Arnaud Bruyn & Prithwiraj Mukherjee, 2024. "Silently killing your panelists one email at a time: The true cost of email solicitations," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 1216-1239, July.
    10. Burbano, Vanessa & Padilla, Nicolas & Meier, Stephan, 2020. "Gender Differences in Preferences for Meaning at Work," IZA Discussion Papers 13053, Institute of Labor Economics (IZA).
    11. Robert Kubinec & Haillie Na‐Kyung Lee & Andrey Tomashevskiy, 2021. "Politically connected companies are less likely to shutdown due to COVID‐19 restrictions," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2155-2169, September.
    12. Barrington-Leigh, C.P., 2024. "The econometrics of happiness: Are we underestimating the returns to education and income?," Journal of Public Economics, Elsevier, vol. 230(C).
    13. Salvatore Nunnari & Massimiliano Pozzi, 2022. "Meta-Analysis of Inequality Aversion Estimates," CESifo Working Paper Series 9851, CESifo.
    14. Andreas Kryger Jensen & Claus Thorn Ekstrøm, 2021. "Quantifying the trendiness of trends," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 98-121, January.
    15. Lauderdale, Benjamin E. & Bailey, Delia & Blumenau, Jack & Rivers, Douglas, 2020. "Model-based pre-election polling for national and sub-national outcomes in the US and UK," International Journal of Forecasting, Elsevier, vol. 36(2), pages 399-413.
    16. Tamara Broderick & Ryan Giordano & Rachael Meager, 2020. "An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?," Papers 2011.14999, arXiv.org, revised Jul 2023.
    17. Kenneth F. Kellner & Arielle W. Parsons & Roland Kays & Joshua J. Millspaugh & Christopher T. Rota, 2022. "A Two-Species Occupancy Model with a Continuous-Time Detection Process Reveals Spatial and Temporal Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 321-338, June.
    18. Owen G. Ward & Jing Wu & Tian Zheng & Anna L. Smith & James P. Curley, 2022. "Network Hawkes process models for exploring latent hierarchy in social animal interactions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1402-1426, November.
    19. repec:osf:osfxxx:fah3z_v1 is not listed on IDEAS
    20. Gómez-Ugarte, Ana C. & Chen, Irena & Acosta, Enrique & Basellini, Ugofilippo & Alburez-Gutierrez, Diego, 2025. "Accounting for uncertainty in conflict mortality estimation: An application to the Gaza War in 2023-2024," SocArXiv z4e7s_v1, Center for Open Science.
    21. Radka Jersakova & James Lomax & James Hetherington & Brieuc Lehmann & George Nicholson & Mark Briers & Chris Holmes, 2022. "Bayesian imputation of COVID‐19 positive test counts for nowcasting under reporting lag," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 834-860, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:5:p:3105-:d:765303. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.