nach oben

Social Network Analysis and Mining

Erschienen in:

01.12.2022 | Original Article

AI-enabled persuasive personal health assistant

verfasst von: Ivan Donadello, Mauro Dragoni

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper discusses the use of the HORUS.AI solution, an AI-enabled persuasive personal health assistant built upon the integration of semantic web technologies and persuasive techniques, for motivating people to adopt a healthy lifestyle and for supporting them to cope with the self-management of chronic diseases associated with bad lifestyle habits. The solution collects data from users’ devices, explicit users’ inputs, or from the external environment (e.g., facts of the world), and interacts with users by using a goal-based metaphor. Persuasive dialogues are used for proposing persuasion goals to users that, through a mobile application, are able to provide the required information and to receive contextual motivational messages helping them to achieve the proposed goals. In this paper, we focus on how behavioral change strategies have been exploited for providing a personalized support concerning the adoption of healthy lifestyle or the management of their chronic diseases based on the results of personal data processing. Such results are produced by reasoning operations, briefly mentioned in this paper, and coded into motivational strategies and messages by a dialogue-based persuasive layer. This layer manages dialogues and generates persuasive messages based on (i) the information provided by the reasoner, (ii) the user’s behavior and profile, and (iii) the implemented behavioral change strategies. This way, messages are tailored to specific users. HORUS.AI has been validated within the context of the Key To Health project. Results demonstrated how the use of proposed approach supported users about improving their habits from the health perspectives as well as the overall good acceptability of the system by the users involved in the pilot study. Finally, the analysis of system’s efficiency shows how HORUS.AI can be deployed within a real-world scenarios.

Vorheriger Artikel From curious hashtags to polarized effect: profiling coordinated actions in indonesian twitter discourse

Nächster Artikel Longitudinal change in language behaviour during protests: a case study of Euromaidan in Ukraine

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

http://www.who.int/nmh/publications/ncd_report_full_en.pdf.

https://www.cdc.gov/media/releases/2014/p0501-preventable-deaths.html.

The architecture of the HORUS.AI solution is briefly summarized in Sect. 3.

Luxembourg Declaration on workplace health promotion in the European Union. 1997.

https://www.w3.org/TR/turtle/.

The reader may refer to (Dragoni et al. 2018, 2018) for the details about how violations are generated when undesired behaviors related to the rules associated with users are detected.

https://www.drools.org/.

The current version of PerSEO supports the generation of messages in English and Italian.

http://sslmitdev-online.sslmit.unibo.it/linguistics/morph-it.php.

Deployment machine: 2 CPUs Intel Xeon X5690 3.47GHz, 8GB RAM, 160GB HDD.

Agnisarman S, Madathil KC, Stanley LM (2018) A survey of empirical studies on persuasive technologies to promote sustainable living. Sustain Comput Inf Syst 19:112–122

op den Akker H, Cabrita M, op den Akker R, Jones VM, Hermens H, (2015) Tailored motivational message generation: a model and practical framework for real-time physical activity coaching. J Biomed Inf 55:104–115

Amato G, Bolettieri P, Monteiro de Lira V, Muntean CI, Perego R, Renso C(2017) Social media image recognition for food trend analysis. In: proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 1333–1336

Balk-Møller CN, Poulsen K, Larsen M (2017) Effect of a nine-month web- and app-based workplace intervention to promote healthy lifestyle and weight loss for employees in the social welfare and health care sector: a randomized controlled trial. J Med Internet Res 19:e108

Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: COLING (Posters), pp 36–44

Berrouiguet S, Baca-García E, Brandt S, Walter M, Courtet P (2016) Fundamentals for future mobile-health (mhealth): a systematic review of mobile phone and web-based text messaging in mental health. J Med Internet Res 18(6):e135

Berryman C, Ferguson CJ, Negy C (2018) Social media use and mental health among young adults. Psychiatr Q 89(2):307–314

Blundell KL, Forwood S (2021) Using a social media app, instagram, to affect what undergraduate university students choose to eat. Appetite 157:104887

Brambilla M, Sabet AJ, Hosseini M (2021) The role of social media in long-running live events: The case of the big four fashion weeks dataset. Data Brief 35:106840

Byrne E, Kearney J, MacEvilly C (2017) The role of influencer marketing and social influencers in public health. Proceedings of the nutrition Society, 76 (OCE3)

Carolis BD, Mazzotta I (2017) A user-adaptive persuasive system based on‘a-rational’theory. Int. J. Hum. Comput. Stud. 108:70–88

Carrillo E, Varela P, Salvador A, Fiszman S (2011) Main factors underlying consumers food choice: a first step for the understanding of attitudes toward healthy eating. J Sens Stud 26(2):85–95

Cawsey A, Jones RB, Pearson J (2000) The evaluation of a personalised health information system for patients with cancer. User Model User-Adapt Interact 10(1):47–72

Chalaguine LA, Hadoux E, Hamilton F, Hayward A, Hunter A, Polberg S, Potts HWW (2018) Domain modelling in computational persuasion for behaviour change in healthcare. CoRR abs/1802.10054

Chiauzzi E, Rodarte C, DasMahapatra P (2015) Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med 13:77

Coates AE, Hardman CA, Halford JCG, Christiansen P, Boyland EJ (2020) It’s just addictive people that make addictive videos: children’s understanding of and attitudes towards influencer marketing of food and beverages by youtube video bloggers. Int J Env Res Public Health 17(2):449

Colineau N, Paris C (2011) Motivating reflection about health within the family: the use of goal setting and tailored feedback. User Model User-Adapt Interact 21(4–5):341–376https://doi.org/10.1007/s11257-010-9089-x

Comito C, Talia D (2004) GDIS: a service-based architecture for data integration on grids. In: Meersman R, Tari Z, Corsaro A (eds) On the move to meaningful internet systems 2004: OTM 2004 workshops: OTM confederated international workshops and posters, GADA, JTRES, MIOS, WORM, WOSE, PhDS, and INTEROP 2004, Agia Napa, Cyprus, 25–29 Oct 2004. Proceedings, lecture notes in computer science, vol 3292, pp 88–98. Springer. 10.1007/978-3-540-30470-8_27. https://doi.org/10.1007/978-3-540-30470-8_27

Comito C, Talia D (2017) Energy consumption of data mining algorithms on mobile phones: evaluation and prediction. Pervasive Mob. Comput. 42:248–264https://doi.org/10.1016/j.pmcj.2017.10.006

De Jans S, Spielvogel I, Naderer B, Hudders L (2021) Digital food marketing to children: how an influencer’s lifestyle can stimulate healthy food choices among children. Appetite 162:105182

Dijkstra A (2014) The persuasive effects of personalization through: name mentioning in a smoking cessation message. User Model User-Adapt Interact 24(5):393–411https://doi.org/10.1007/s11257-014-9147-x

Donadello I, Dragoni M (2019) Ontology-driven food category classification in images. In: ICIAP (2), lecture notes in computer science, vol 11752, pp 607–617. Springer

Donadello I, Hunter A, Teso S, Dragoni M (2022) Machine learning for utility prediction in argument-based computational persuasion. In: proceedings of the AAAI conference on artificial intelligence, pp 5592–5599. AAAI Press

Dragoni M, Bailoni T, Eccher C, Guerini M, Maimone R (2017) A semantic-enabled platform for supporting healthy lifestyles. In: Seffah A, Penzenstadler B, Alves C, Peng X (eds) Proceedings of the symposium on applied computing, SAC 2017, Marrakech, Morocco, 3-7 Apr 2017, pp 315–322. ACM. https://doi.org/10.1145/3019612.3019835

Dragoni M, Bailoni T, Maimone R, Eccher C (2018) Helis: an ontology for supporting healthy lifestyles. In: Vrandecic D, Bontcheva K, Suárez-Figueroa MC, Presutti V, Celino I, Sabou M, Kaffee L, Simperl E (eds) The semantic web - ISWC 2018 - 17th international semantic web conference, Monterey, CA, USA, 8–12 Oct 2018, Proceedings, part II, lecture notes in computer science, vol 11137, pp 53–69. Springer. https://doi.org/10.1007/978-3-030-00668-6_4

Dragoni M, Bailoni T, Maimone R, Marchesoni M, Eccher C (2018) HORUS.AI - a knowledge-based solution supporting health persuasive self-monitoring. In: van Erp M, Atre M, López V, Srinivas K, Fortuna C (eds) Proceedings of the ISWC 2018posters & demonstrations, industry and blue sky ideas tracks co-located with 17th international semantic webconference (ISWC 2018), Monterey, USA, 8–12th Oct 2018., CEUR workshop proceedings, vol 2180. CEUR-WS.org (2018). http://ceur-ws.org/Vol-2180/paper-14.pdf

Dragoni M, Rospocher M, Bailoni T, Maimone R, Eccher C (2018) Semantic technologies for healthy lifestyle monitoring. In: Vrandecic D, Bontcheva K, Suárez-Figueroa MC, Presutti V, Celino I, Sabou M, Kaffee L, Simperl E (eds) The semantic web - ISWC 2018 - 17th international semantic web conference, Monterey, CA, USA, 8–12 Oct 2018, Proceedings, part II, lecturenotes in computer science, vol 11137, pp 307–324. Springer (2018). https://doi.org/10.1007/978-3-030-00668-6_19

Dragoni M, Tamma VAM (2019) Extending helis: from chronic diseases to behavior change. In: Stefanidis K, Rao P, Parry D, Kondylakis H, Troullinou G (eds) Proceedings of the second international workshop on semantic web meets healthdata management (SWH 2019) co-located with the 18th international semantic web conference (ISWC2019), Auckland, New Zealand, 26 Oct 2019, CEUR workshop proceedings, vol 2515, pp 9–19. CEUR-WS.org. http://ceur-ws.org/Vol-2515/paper1.pdf

Egan KG, Moreno MA (2011) Alcohol references on undergraduate males’facebook profiles. Am J Men’s Health 5(5):413–420

Fogg BJ (2002) Persuasive technology: using computers to change what we think and do. Morgan Kaufmann Publishers

Gatt A, Reiter E (2009) Simplenlg: a realisation engine for practical applications. In: ENLG, pp 90–93. The Association for Computer Linguistics (2009)

Goodman MK, Jaworska S (2020) Mapping digital foodscapes: digital food influencers and the grammars of good food. Geoforum 117:183–193

Guerini M, Stock O, Zancanaro M (2007) A taxonomy of strategies for multimodal persuasive message generation. Appl Artif Intell J 21(2):99–136

Harris A, McGregor JC, Perencevich E, Furuno J, Zhu J, Peterson DE, Finkelstein J (2006) Position paper: the use and interpretation of quasi-experimental studies in medical informatics. J Am Med Inf Assoc: JAMIA 13–1:16–23

Head K, Noar S, Iannarino N, Harrington N (2013) Efficacy of text messaging-based interventions for health promotion: a meta-analysis. Soc Sci Med 97:41–48

Hebert JR, Clemow L, Pbert L, Ockene IS, Ockene JK (1995) Social desirability bias in dietary self-report may compromise the validity of dietary intake measures. Int J Epidemiol 24(2):389–398

Hoermann S, McCabe KL, Milne DN, Calvo RA (2017) Application of synchronous text-based dialogue systems in mental health interventions: systematic review. J Med Internet Res 19:e7023https://doi.org/10.2196/jmir.7023

Hudders L, De Jans S, De Veirman M (2021) The commercialization of social media stars: a literature review and conceptual framework on the strategic use of social media influencers. Soc Med Influ Strategic Commun. 24–67

Hunter A, Chalaguine LA, Czernuszenko T, Hadoux E, Polberg S (2019) Towards computational persuasion via natural language argumentation dialogues. In: KI, Lecture notes in computer science, vol 11793, pp 18–33. Springer

Intille SS (2003) Ubiquitous computing technology for just-in-time motivation of behavior change. In: UbiHealth 2003: 2nd int. workshop on ubiquitous computing for pervasive healthcare applications. http://www.healthcare.pervasive.dk/ubicomp2003/papers/Final_Papers/5.pdf

Ivie EJ, Pettitt A, Moses LJ, Allen NB (2020) A meta-analysis of the association between adolescent social media use and depressive symptoms. J Affect Disord 275:165–174

Jaini PA, Hsu J, Manjunath P, Haliburton JR (2020) 39.5 general psychiatry residents’ attitudes toward discussing social media’s influence on mental health with adolescent and young adult patients. J Am Acad Child Adolesc Psychiatry 59(10):S220

Job RJ, Spark CL, Fjeldsoe SB, Eakin GE, Reeves MM (2017) Women’s perceptions of participation in an extended contact text message-based weight loss intervention: an explorative study. JMIR Mhealth Uhealth 5:e21

Kelders S, Oinas-Kukkonen H, Oörni A, van Gemert EWCJ (2016) Health behavior change support systems as a research discipline; a viewpoint. Int J Med Inf 96

Keles B, McCrae N, Grealish A (2020) A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int J Adolesc Youth 25(1):79–93

Kim JH, Ferziger R, Kowaloff HB, Sands DZ, Safran C, Slack WV (2001) A web-based rapid prototyping and clinical conversational system that complements electronic patient record system. Stud Heath Technol Inf 84:628–32. https://doi.org/10.3233/978-1-60750-928-8-628CrossRef

Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R, Surian D, Gallego B, Magrabi F, Lau AYS, Coiera E (2018) Conversational agents in healthcare: a systematic review. J Am Med Inf Assoc, ocy072. 10.1093/jamia/ocy072. https://doi.org/10.1093/jamia/ocy072

Lassen AD, Lehmann C, Andersen EW, Werther MN, Thorsen AV, Trolle E, Gross G, Tetens I (2016) Gender differences in purchase intentions and reasons for meal selection among fast food customers-opportunities for healthier and more sustainable fast food. Food Qual Prefer 47:123–129

Mardon R, Molesworth M, Grigore G (2018) Youtube beauty gurus and the emotional labour of tribal entrepreneurship. J Bus Res 92:443–454

Marks RJ, De Foe A, Collett J (2020) The pursuit of wellness: social media, body image and eating disorders. Child Youth Serv Rev 119:105659

Melzner J, Heinze J, Fritsch T (2014) Mobile health applications in workplace health promotion: an integrated conceptual adoption framework. Procedia Technol 16:1374–1382

Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, Eccles MP, Cane J, Wood CE (2013) The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 46(1):81–95

Miller TM, Abdel-Maksoud MF, Crane LA, Marcus AC, Byers TE (2008) Effects of social approval bias on self-reported fruit and vegetable consumption: a randomized controlled trial. Nutr J 7(1):1–7

Mohr DC, Schueller SM, Enid M, Burns MN, Rashid P (2014) The behavioral intervention technology model: an integrated conceptual and technological framework for ehealth and mhealth interventions. J Med Internet Res 16(6):e146

Montenegro JLZ, da Costa CA, da Rosa Righi R (2019) Survey of conversational agents in health. Expert Syst Appl 129:56–67

Moreno MA, Standiford M, Cody P (2018) Social media and adolescent health. Curr Pediatr Rep 6(2):132–138

Nederhof AJ (1985) Methods of coping with social desirability bias: a review. Eur J Soc Psychol 15(3):263–280

Oinas-Kukkonen H (2013) A foundation for the study of behavior change support systems. Personal Ubiquitous Comput 17(6):1223–1235

Oinas-Kukkonen H, Harjumaa M (2009) Persuasive systems design: Key issues, process model, and system features. CAIS 24:28

Pan S, Zhou M (2014) Pplum: a framework for large-scale personal persuasion. In: proceedings of the 3rd workshop on data-driven user behavioral modeling and mining from social media, ACM pp 5–6

Pauws S, Gatt A, Krahmer E, Reiter E (2019) Making effective use of healthcare data using data-to-text technology. In: Consoli S, Recupero DR, Petkovic M (eds) Data science for healthcare - methodologies and applications, pp 119–145. Springer. 10.1007/978-3-030-05249-2_4. https://doi.org/10.1007/978-3-030-05249-2_4

Pauws S, Gatt A, Krahmer E, Reiter E (2019) Making effective use of healthcare data using data-to-text technology. In: data science for healthcare, pp 119–145. Springer

Reed C, Long D, Fox M (1996) An architecture for argumentative dialogue planning. In: practical reasoning: proceedings of the first international conference on formal and applied practical reasoning (FAPR96), pp 555–566. Springer Verlag, Berlin

Reiter E (2007) An architecture for data-to-text systems. In: Busemann S (ed) Proceedings of the eleventh european workshop on natural language generation, ENLG 2007, Schloss Dagstuhl, Germany, 17–20 June 2007. https://www.aclweb.org/anthology/W07-2315/

Robroek JS, Lindeboom ED, Burdorf A (2012) Initial and sustained participation in an internet-delivered long-term worksite health promotion program on physical activity and nutrition. J Med Internet Res 14(2):e43

Romo DL, Garnett C, Younger AP, Soren K, Stockwell MS, Catallozzi M, Neu N (2016) Understanding adolescent social media use: Association with sexual risk and parental monitoring factors that can influence protection. J Adolescent Health 58(2):S16–S17

Rospocher M, Serafini L (2012) An ontological framework for decision support. In: JIST

Sama PR, Eapen ZJ, Weinfurt KP, Shah BR, Schulman KA (2014) An evaluation of mobile health application tools. JMIR mHealth uHealth 2(2):e19https://doi.org/10.2196/mhealth.3088

Shafi RM, Romanowicz M, Croarkin PE (2018) # switchedon: a call for assessing social media use of adolescents. Lancet Psychiatry 5(11):e27

Walsh MJ, Baker SA (2020) Clean eating and instagram: purity, defilement, and the idealization of food. Food Cult Soc 23(5):570–588

van der Weegen S, Verwey R, Spreeuwenberg M, Tange H, van der Weijden T, de Witte L (2015) It’s life! mobile and web-based monitoring and feedback tool embedded in primary care increases physical activity: a cluster randomized controlled trial. J Med Internet Res 17(7):e184. https://doi.org/10.2196/jmir.4579CrossRef

Wyke S, Bunn C, Andersen E, Silva M, van Nassau F, Mcskimming P, Kolovos S, Gill J, Gray CM, Hunt K, Anderson A, Bosmans J, Jelsma J, Kean S, Lemyre N, Loudon D, Macaulay L, Maxwell D, McConnachie A, Mutrie N, van der Sanden MNDN, Pereira H, Philpott M, Roberts G, Rooksby J, Røynesdal Ø, Sattar N, Sørensen M, Teixeira P, Treweek S, van Achterberg T, van de Glind IM, van Mechelen W, van der Ploeg HP (2019) The effect of a programme to improve men’s sedentary time and physical activity: the European fans in training (eurofit) randomised controlled trial. PLoS Med, 16

Zhang Z, Wu Y, Zhao H, Li Z, Zhang S, Zhou X, Zhou X (2020) Semantics-aware BERT for language understanding. In: AAAI, pp 9628–9635. AAAI Press

Zukerman I, McConachy R, Korb K (2000) Using argumentation strategies in automated argument generation. In: proceedings of the 1st international natural language generation conference, pp 55–62

Titel: AI-enabled persuasive personal health assistant
verfasst von: Ivan Donadello
Mauro Dragoni
Publikationsdatum: 01.12.2022
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2022
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-022-00935-3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2022

Analyzing social media for measuring public attitudes toward controversies and their driving factors: a case study of migration

From curious hashtags to polarized effect: profiling coordinated actions in indonesian twitter discourse

Community deception: from undirected to directed networks

How disinformation operations against Russian opposition leader Alexei Navalny influence the international audience on Twitter

Misogynoir: challenges in detecting intersectional hate

Investigating political polarization in India through the lens of Twitter

Premium Partner