IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i12p6600-d577896.html
   My bibliography  Save this article

How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program

Author

Listed:
  • Heather Behr

    (Department of Integrative Health, Saybrook University, 55 W Eureka St, Pasadena, CA 91103, USA
    Academic Research, Noom, 229 W 28th St., New York, NY 10461, USA)

  • Annabell Suh Ho

    (Academic Research, Noom, 229 W 28th St., New York, NY 10461, USA)

  • Ellen Siobhan Mitchell

    (Academic Research, Noom, 229 W 28th St., New York, NY 10461, USA)

  • Qiuchen Yang

    (Academic Research, Noom, 229 W 28th St., New York, NY 10461, USA)

  • Laura DeLuca

    (Academic Research, Noom, 229 W 28th St., New York, NY 10461, USA
    Ferkauf Graduate School of Psychology, Yeshiva University, 1165 Morris Park Ave., Bronx, NY 10461, USA)

  • Andreas Michealides

    (Academic Research, Noom, 229 W 28th St., New York, NY 10461, USA)

Abstract

During behavioral weight management, individuals reflect on their progress and barriers through goal pursuit (goal setting and goal striving). Emotions during goal pursuit are largely unknown, and previous investigations of emotions in weight management have primarily relied on self-report. In this retrospective study, we used a well-validated computational text analysis approach to explore how emotion words changed over time during goal setting and striving conversations with a coach in a mobile weight loss program. Linear mixed models examined changes in emotion words each month from baseline to program end and compared emotion words between individuals who set an overall concrete goal for the program (concrete goal setters) and those who set an overall abstract goal (abstract goal setters). Contrary to findings using self-report, positive emotion words were stable and negative emotion words significantly increased over time. There was a marginal trend towards greater negative emotion word use being associated with greater weight loss. Concrete goal setters used more positive words than abstract goal setters, with no differences in negative emotion words and weight loss. Implications include the possibility that individuals may need increasing support over time for negative emotions expressed during goal setting and striving, and concrete goals could boost positive emotion. Future research should investigate these possibilities.

Suggested Citation

  • Heather Behr & Annabell Suh Ho & Ellen Siobhan Mitchell & Qiuchen Yang & Laura DeLuca & Andreas Michealides, 2021. "How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program," IJERPH, MDPI, vol. 18(12), pages 1-15, June.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6600-:d:577896
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/12/6600/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/12/6600/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kurt Benke & Geza Benke, 2018. "Artificial Intelligence and Big Data in Public Health," IJERPH, MDPI, vol. 15(12), pages 1-9, December.
    2. Mike Jones & Frank DeRuyter & John Morris, 2020. "The Digital Health Revolution and People with Disabilities: Perspective from the United States," IJERPH, MDPI, vol. 17(2), pages 1-10, January.
    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. Claus Zippel & Sabine Bohnet-Joschko, 2021. "Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    2. Wen-Yu Ou Yang & Cheng-Chien Lai & Meng-Ting Tsou & Lee-Ching Hwang, 2021. "Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data," IJERPH, MDPI, vol. 18(14), pages 1-12, July.
    3. Han-Nu-Ri Kang & Kang-Sook Lee & JuYeon Koh & YuJin Park & HyunKyung Shin, 2021. "The Factors Associated with Attempted Smoking Cessation and Successful Four-Week Smoking Abstinence According to the Types of Disability in Seoul, Korea," IJERPH, MDPI, vol. 18(7), pages 1-14, March.
    4. Julien Issa & Raphael Olszewski & Marta Dyszkiewicz-Konwińska, 2022. "The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review," IJERPH, MDPI, vol. 19(1), pages 1-10, January.
    5. T. Bradley Willingham & Julie Stowell & George Collier & Deborah Backus, 2024. "Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities," IJERPH, MDPI, vol. 21(1), pages 1-28, January.
    6. Daniele Piovani & Stefanos Bonovas, 2022. "Real World—Big Data Analytics in Healthcare," IJERPH, MDPI, vol. 19(18), pages 1-3, September.
    7. Andrea Spini & Giulia Hyeraci & Claudia Bartolini & Sandra Donnini & Pietro Rosellini & Rosa Gini & Marina Ziche & Francesco Salvo & Giuseppe Roberto, 2021. "Real-World Utilization of Target- and Immunotherapies for Lung Cancer: A Scoping Review of Studies Based on Routinely Collected Electronic Healthcare Data," IJERPH, MDPI, vol. 18(14), pages 1-21, July.

    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:18:y:2021:i:12:p:6600-:d:577896. 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.