Does Access to Human Coaches Lead to More Weight Loss Than With AI Coaches Alone?

References

• Abadie A, Imbens GW (2006) Large sample properties of matching estimators for average treatment effects. Econometrica 74(1):235–267.Crossref, Google Scholar
• Altonji JG, Elder TE, Taber CR (2005) Selection on observed and unobserved variables: Assessing the effectiveness of catholic schools. J. Political Econom. 113(1):151–184.Crossref, Google Scholar
• Amorose AJ, Weiss MR (1998) Coaching feedback as a source of information about perceptions of ability: A developmental examination. J. Sport Exercise Psych. 20(4):395–420.Crossref, Google Scholar
• Ashtary-Larky D, Ghanavati M, Lamuchi-Deli N, Payami SA, Alavi-Rad S, Boustaninejad M, Afrisham R, Abbasnezhad A, Alipour M (2017) Rapid weight loss vs. slow weight loss: Which is more effective on body composition and metabolic risk factors? Internat. J. Endocrinol. Metab. 15(3):e13249.Google Scholar
• Bail CA (2024) Can generative ai improve social science? Proc. Natl. Acad. Sci. USA 121(20):e2314021121.Crossref, Google Scholar
• Belkin M, Niyogi P (2002) Using manifold structure for partially labeled classification. Adv. Neural Inform. Processing Systems, 929–936.Google Scholar
• Beyer S (1990) Gender differences in the accuracy of self-evaluations of performance. J. Personality Soc. Psych. 59(5):960–970.Crossref, Google Scholar
• Blüher M (2019) Obesity: Global epidemiology and pathogenesis. Nat. Rev. Endocrinol. 15(5):288–298.Crossref, Google Scholar
• Cadario R, Longoni C, Morewedge C (2021) Understanding, explaining, and utilizing medical artificial intelligence. Nat. Human Behav. 5(12):1636–1642.Crossref, Google Scholar
• Chin SO, Keum C, Woo J, Park J, Choi HJ, Woo J, Rhee SY (2016) Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity. Sci. Rep. 6(1):1–8.Crossref, Google Scholar
• Datta H, Knox G, Bronnenberg BJ (2018) Changing their tune: How consumers’ adoption of online streaming affects music consumption and discovery. Marketing Sci. 37(1):5–21.Link, Google Scholar
• De Maesschalck R, Jouan-Rimbaud D, Massart DL (2000) The Mahalanobis distance. Chemometrics Intelligent Laboratory Systems 50(1):1–18.Crossref, Google Scholar
• DiPrete TA, Gangl M (2004) Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Sociol. Methodol. 34(1):271–310.Crossref, Google Scholar
• Dolatshah M, Hadian A, Minaei-Bidgoli B (2015) Ball*-tree: Efficient spatial indexing for constrained nearest-neighbor search in metric spaces. Preprint, submitted November 2, https://arxiv.org/abs/1511.00628.Google Scholar
• Draganska M, Hartmann WR, Stanglein G (2014) Internet versus television advertising: A brand-building comparison. J. Marketing Res. 51(5):578–590.Crossref, Google Scholar
• Eckles D, Karrer B, Ugander J (2017) Design and analysis of experiments in networks: Reducing bias from interference. J. Causal Inference 5(1):20150021.Crossref, Google Scholar
• Egan DE (1988) Individual differences in human-computer interaction. Helander M, ed. Handbook of Human-Computer Interaction (North-Holland, Amsterdam), 543–568.Crossref, Google Scholar
• Eskreis-Winkler L, Fishbach A (2019) Not learning from failure—The greatest failure of all. Psych. Sci. 30(12):1733–1744.Crossref, Google Scholar
• Ferdinand NK, Kray J (2013) Age-related changes in processing positive and negative feedback: Is there a positivity effect for older adults? Biol. Psych. 94(2):235–241.Crossref, Google Scholar
• Finkelstein SR, Fishbach A (2012) Tell me what I did wrong: Experts seek and respond to negative feedback. J. Consumer Res. 39(1):22–38.Crossref, Google Scholar
• Fishbach A, Eyal T, Finkelstein SR (2010) How positive and negative feedback motivate goal pursuit. Soc. Personality Psych. Compass 4(8):517–530.Crossref, Google Scholar
• Fukunaga K, Narendra PM (1975) A branch and bound algorithm for computing k-nearest neighbors. IEEE Trans. Comput. 100(7):750–753.Crossref, Google Scholar
• Goli A, Chintagunta PK, Sriram S (2022) Effects of payment on user engagement in online courses. J. Marketing Res. 59(1):11–34.Crossref, Google Scholar
• Gordon M, Althoff T, Leskovec J (2019) Goal-setting and achievement in activity tracking apps: A case study of myfitnesspal. World Wide Web Conf., 571–582.Google Scholar
• Goukens C, Dewitte S, Warlop L (2009) Me, myself, and my choices: The influence of private self-awareness on choice. J. Marketing Res. 46(5):682–692.Crossref, Google Scholar
• Heath C, Larrick RP, Wu G (1999) Goals as reference points. Cognitive Psych. 38(1):79–109.Crossref, Google Scholar
• Ingels JS, Misra R, Stewart J, Lucke-Wold B, Shawley-Brzoska S (2017) The effect of adherence to dietary tracking on weight loss: Using HLM to model weight loss over time. J. Diabetes Res. 2017:2017:6951495.Google Scholar
• Jami A (2016) Healthy reflections: The influence of mirror-induced self-awareness on taste perceptions. J. Assoc. Consumer Res. 1(1):57–70.Crossref, Google Scholar
• Jiang X, Wang M (2022) Does personalization increase consumer trust in ads? J. Marketing 86(4):56–72.Google Scholar
• Kast A, Connor K (1988) Sex and age differences in response to informational and controlling feedback. Personality Soc. Psych. Bull. 14(3):514–523.Crossref, Google Scholar
• Knäuper B, Carriere K, Frayn M, Ivanova E, Xu Z, Ames-Bull A, Islam F, et al. (2018) The effects of if-then plans on weight loss: Results of the McGill CHIP healthy weight program randomized controlled trial. Obesity 26(8):1285–1295.Crossref, Google Scholar
• Kozlowski SW, Bell BS (2006) Disentangling achievement orientation and goal setting: Effects on self-regulatory processes. J. Appl. Psych. 91(4):900–916.Crossref, Google Scholar
• Laing BY, Mangione CM, Tseng CH, Leng M, Vaisberg E, Mahida M, Bholat M, Glazier E, Morisky DE, Bell DS (2014) Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: A randomized, controlled trial. Ann. Intern. Med. 161(Suppl 10):S5–S12.Crossref, Google Scholar
• Liu F, Kong X, Cao J, Chen S, Li C, Huang J, Gu D, Kelly TN (2015) Mobile phone intervention and weight loss among overweight and obese adults: A meta-analysis of randomized controlled trials. Amer. J. Epidemiol. 181(5):337–348.Crossref, Google Scholar
• Locke EA, Latham GP (1990) A Theory of Goal Setting & Task Performance (Prentice-Hall, Englewood Cliffs, NJ).Google Scholar
• Longoni C, Bonezzi A, Morewedge CK (2019) Resistance to medical artificial intelligence. J. Consumer Res. 46(4):629–650.Crossref, Google Scholar
• Mehrotra R, McInerney J, Bouchard H, Lalmas M, Diaz F (2018) Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. Proc. 27th ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 2243–2251.Google Scholar
• Meyers-Levy J, Loken B (2015) Revisiting gender differences: What we know and what lies ahead. J. Consumer Psych. 25(1):129–149.Crossref, Google Scholar
• Mitchell EG, Maimone R, Cassells A, Tobin JN, Davidson P, Smaldone AM, Mamykina L (2021) Automated vs. human health coaching: Exploring participant and practitioner experiences. Proc. ACM Human Comput. Interact 5(CSCW1):99.Crossref, Google Scholar
• Moore A (2013) The anchors hierarchy: Using the triangle inequality to survive high dimensional data. Preprint, submitted January 16, https://arxiv.org/abs/1301.3877.Google Scholar
• Motro D, Ellis AP (2017) Boys, don’t cry: Gender and reactions to negative performance feedback. J. Appl. Psych. 102(2):227–235.Crossref, Google Scholar
• Narang U, Shankar V (2019) Mobile app introduction and online and offline purchases and product returns. Marketing Sci. 38(5):756–772.Link, Google Scholar
• Omohundro S (1990) Bumptrees for efficient function, constraint and classification learning. Lippmann RP, Moody JE, Touretzky DS, eds. Adv. Neural Inform. Processing Systems, vol. 3 (Morgan Kaufmann, San Mateo, CA), 693–699.Google Scholar
• Oster E (2019) Unobservable selection and coefficient stability: Theory and evidence. J. Bus. Econom. Statist. 37(2):187–204.Crossref, Google Scholar
• Patel ML, Hopkins CM, Brooks TL, Bennett GG (2019) Comparing self-monitoring strategies for weight loss in a smartphone app: Randomized controlled trial. JMIR Mhealth Uhealth. 7(2):e12209.Crossref, Google Scholar
• Pham MT, Goukens C, Lehmann DR, Stuart JA (2010) Shaping customer satisfaction through self-awareness cues. J. Marketing Res. 47(5):920–932.Crossref, Google Scholar
• Phillips JM, Gully SM (1997) Role of goal orientation, ability, need for achievement, and locus of control in the self-efficacy and goal–Setting process. J. Appl. Psych. 82(5):792–802.Crossref, Google Scholar
• Rosenbaum PR (2002) Observational Studies (Springer, New York).Crossref, Google Scholar
• Rosenbaum PR, Rosenbaum PR (2002) Overt Bias in Observational Studies, Observational Studies, 2nd ed. (Springer, New York), 71–104.Crossref, Google Scholar
• Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55.Crossref, Google Scholar
• Sentyrz SM, Bushman BJ (1998) Mirror, mirror on the wall, who’s the thinnest one of all? Effects of self-awareness on consumption of full-fat, reduced-fat, and no-fat products. J. Appl. Psych. 83(6):944–949.Crossref, Google Scholar
• Sharma A, Lin IW, Miner AS, Atkins DC, Althoff T (2023) Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nature Machine Intelligence 5(1):46–57.Crossref, Google Scholar
• Stypińska J (2021) Ageism in AI: New forms of age discrimination in the era of algorithms and artificial intelligence. CAIP 2021: Proc. 1st Internat. Conf. AI People: Towards Sustainable AI, CAIP 2021, 20–24 November 2021, Bologna, Italy, vol. 39 (European Alliance for Innovation, Slovakia).Google Scholar
• Uetake K, Yang N (2020) Inspiration from the “biggest loser”: Social interactions in a weight loss program. Marketing Sci. 39(3):487–499.Link, Google Scholar
• Volpp KG, John LK, Troxel AB, Norton L, Fassbender J, Loewenstein G (2008) Financial incentive–based approaches for weight loss: A randomized trial. JAMA 300(22):2631–2637.Crossref, Google Scholar
• Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J. Amer. Statist. Assoc. 113(523):1228–1242.Crossref, Google Scholar
• Wharton CM, Johnston CS, Cunningham BK, Sterner D (2014) Dietary self-monitoring, but not dietary quality, improves with use of smartphone app technology in an 8-week weight loss trial. J. Nutrition Educ. Behav. 46(5):440–444.Crossref, Google Scholar
• Yancy WS, Shaw PA, Reale C, Hilbert V, Yan J, Zhu J, Troxel AB, Foster GD, Volpp KG (2019) Effect of escalating financial incentive rewards on maintenance of weight loss: A randomized clinical trial. JAMA Network Open 2(11):e1914393.Crossref, Google Scholar
• Ye T, Shankar V (2025) Close a store to open a pandora’s box? The effects of store closure on sales, omnichannel shopping, and mobile app usage. Marketing Sci. 44(4):820–837.Link, Google Scholar
• Zhao P, Su X, Ge T, Fan J (2016) Propensity score and proximity matching using random forest. Contemporary Clinical Trials 47:85–92.Crossref, Google Scholar