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AI & SOCIETY

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17-01-2022 | Open Forum

Recommender systems for mental health apps: advantages and ethical challenges

Authors: Lee Valentine, Simon D’Alfonso, Reeva Lederman

Published in: AI & SOCIETY | Issue 4/2023

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Abstract

Recommender systems assist users in receiving preferred or relevant services and information. Using such technology could be instrumental in addressing the lack of relevance digital mental health apps have to the user, a leading cause of low engagement. However, the use of recommender systems for digital mental health apps, particularly those driven by personal data and artificial intelligence, presents a range of ethical considerations. This paper focuses on considerations particular to the juncture of recommender systems and digital mental health technologies. While separate bodies of work have focused on these two areas, to our knowledge, the intersection presented in this paper has not yet been examined. This paper identifies and discusses a set of advantages and ethical concerns related to incorporating recommender systems into the digital mental health (DMH) ecosystem. Advantages of incorporating recommender systems into DMH apps are identified as (1) a reduction in choice overload, (2) improvement to the digital therapeutic alliance, and (3) increased access to personal data & self-management. Ethical challenges identified are (1) lack of explainability, (2) complexities pertaining to the privacy/personalization trade-off and recommendation quality, and (3) the control of app usage history data. These novel considerations will provide a greater understanding of how DMH apps can effectively and ethically implement recommender systems.

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Abdullah S, Choudhury T (2018) Sensing technologies for monitoring serious mental illnesses. IEEE Multimedia. https://doi.org/10.1109/MMUL.2018.011921236CrossRef

Agnew-Davies R, Stiles WB, Hardy GE, Barkham M, Shapiro DA (1998) Alliance structure assessed by the Agnew Relationship Measure (ARM). British J Clin Psychol. https://doi.org/10.1111/j.2044-8260.1998.tb01291.xCrossRef

Alvarez-Jimenez M, Koval P, Schmaal L, Bendall S, O’Sullivan S, Cagliarini D, Gleeson J (2021) The Horyzons project: a randomised controlled trial of a novel moderated online social therapy to maintain treatment effects from specialist first episode psychosis services. SSRN Electron J. https://doi.org/10.2139/ssrn.3734284CrossRef

Amatriain X, Basilico J (2015) Recommender systems in industry: a netflix case study. In Recomm Syst Handbook. https://doi.org/10.1007/978-1-4899-7637-6_11CrossRef

American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders. Am J Psychiatry. https://doi.org/10.1176/appi.books.9780890425596.744053CrossRef

Amft O (2018) How wearable computing is shaping digital health. IEEE Pervasive Comput. https://doi.org/10.1109/MPRV.2018.011591067CrossRef

Ardito RB, Rabellino D (2011) Therapeutic alliance and outcome of psychotherapy: Historical excursus, measurements, and prospects for research. Front Psychol. https://doi.org/10.3389/fpsyg.2011.00270CrossRef

Aware (2021) Sensors. Retrieved March 9, 2021, from https://awareframework.com/sensors/

Berry K, Salter A, Morris R, James S, Bucci S (2018) Assessing therapeutic alliance in the context of mHealth interventions for mental health problems: Development of the mobile agnew relationship measure (mARM) questionnaire. J Med Internet Res. https://doi.org/10.2196/jmir.8252CrossRef

Boonstra TW, Nicholas J, Wong QJ, Shaw F, Townsend S, Christensen H (2018) Using mobile phone sensor technology for mental health research: integrated analysis to identify hidden challenges and potential solutions. J Med Internet Res. https://doi.org/10.2196/10131CrossRef

Cegala DJ, Broz SL (2002) Physician communication skills training: A review of theoretical backgrounds, objectives and skills. Med Educ. https://doi.org/10.1046/j.1365-2923.2002.01331.xCrossRef

Chancellor S, Birnbaum ML, Caine ED, Silenzio VMB, De Choudhury M (2019) A taxonomy of ethical tensions in inferring mental health states from social media. FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3287560.3287587

Clarke J, Proudfoot J, Whitton A, Birch M-R, Boyd M, Parker G, Fogarty A (2016) Therapeutic alliance with a fully automated mobile phone and web-based intervention: secondary analysis of a randomized controlled trial. JMIR Mental Health. https://doi.org/10.2196/mental.4656CrossRef

Cornet VP, Holden RJ (2018) Systematic review of smartphone-based passive sensing for health and wellbeing. J Biomed Inform. https://doi.org/10.1016/j.jbi.2017.12.008CrossRef

D’Alfonso S, Carpenter N, Alvarez-Jimenez M (2018) Making the most out of smartphone opportunities for mental health*. ACM Int Confer Proceed Series. https://doi.org/10.1145/3292147.3292230CrossRef

D’Alfonso S, Phillips J, Valentine L, Gleeson J, Alvarez-Jimenez M (2019) Moderated online social therapy: viewpoint on the ethics and design principles of a web-based therapy system. JMIR Ment Health. https://doi.org/10.2196/14866CrossRef

D’Alfonso S, Lederman R, Bucci S, Berry K (2020) The digital therapeutic alliance and human-computer interaction. JMIR Mental Health. https://doi.org/10.2196/21895CrossRef

de Beurs D, van Bruinessen I, Noordman J, Friele R, van Dulmen S (2017) Active involvement of end users when developing web-based mental health interventions. Front Psych. https://doi.org/10.3389/fpsyt.2017.00072CrossRef

Deci EL, Ryan RM (2015) Self-determination theory. Int Encyclopedia Soc Behav Sci Second Edition. https://doi.org/10.1016/B978-0-08-097086-8.26036-4CrossRef

Druss BG, Walker ER (2011) Mental disorders and medical comorbidity. The Synthesis Project. Res Synth Rep

Fernandez C (2017) The paradox of choice: why more is less. Vikalpa. https://doi.org/10.1177/0256090917732442CrossRef

Fleming T, Bavin L, Lucassen M, Stasiak K, Hopkins S, Merry S (2018) Beyond the trial: Systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety. J Med Internet Res. https://doi.org/10.2196/jmir.9275CrossRef

Fogg BJ (1998) Persuasive computers: perspectives and research directions. Confer Hum Factors Comput Syst Proceed. https://doi.org/10.1145/274644.274677CrossRef

Gooding P (2019) Mapping the rise of digital mental health technologies: emerging issues for law and society. Int J Law Psychiatry. https://doi.org/10.1016/j.ijlp.2019.101498CrossRef

Gumley A, Bradstreet S, Ainsworth J, Allan S, Alvarez-Jimenez M, Beattie L, Gleeson J (2020) Early signs monitoring to prevent relapse in psychosis and promote well-being, engagement, and recovery: Protocol for a feasibility cluster randomized controlled trial harnessing mobile phone technology blended with peer support. JMIR Res Protoc. https://doi.org/10.2196/15058CrossRef

Gunawardana A, Shani G (2009) A survey of accuracy evaluation metrics of recommendation tasks. J Mach Learn Res 10

Hattingh HL, Knox K, Fejzic J, McConnell D, Fowler JL, Mey A, Wheeler AJ (2015) Privacy and confidentiality: perspectives of mental health consumers and carers in pharmacy settings. Int J Pharm Pract. https://doi.org/10.1111/ijpp.12114CrossRef

Hillier L (2018) Exploring the nature of the therapeutic alliance in technology-based interventions for mental health problems (Lancaster University). Retrieved from https://eprints.lancs.ac.uk/id/eprint/127663/1/2018hilliermphil.pdf

Horvath AO, Luborsky L (1993) The role of the therapeutic alliance in psychotherapy. J Consult Clin Psychol. https://doi.org/10.1037//0022-006x.61.4.561CrossRef

Hu P, Zhang J (2017) A pathway to learner autonomy: a self-determination theory perspective. Asia Pac Educ Rev. https://doi.org/10.1007/s12564-016-9468-zCrossRef

Jannach D, Naveed S, Jugovac M (2017) User control in recommender systems: overview and interaction challenges. Lecture Notes Bus Inform Process. https://doi.org/10.1007/978-3-319-53676-7_2CrossRef

Jorm AF (2018) Australia’s ‘Better Access’ scheme: Has it had an impact on population mental health? Australian New Zealand J Psychiatry. https://doi.org/10.1177/0004867418804066CrossRef

Lake J, Turner MS (2017) Urgent need for improved mental health care and a more collaborative model of care. Permanente J. https://doi.org/10.7812/TPP/17-024CrossRef

Lederman R, Gleeson J, Wadley G, D’Alfonso S, Rice S, Santesteban-Echarri O, Alvarez-Jimenez M (2019) Support for carers of young people with mental illness: Design and trial of a technology-mediated therapy. ACM Trans Computer Hum Inter. https://doi.org/10.1145/3301421CrossRef

Lupton D (2013) The digitally engaged patient: self-monitoring and self-care in the digital health era. Soc Theory Health. https://doi.org/10.1057/sth.2013.10CrossRef

Martin DJ, Garske JP, Katherine Davis M (2000) Relation of the therapeutic alliance with outcome and other variables: a meta-analytic review. J Consult Clin Psychol. https://doi.org/10.1037/0022-006X.68.3.438CrossRef

Milano S, Taddeo M, Floridi L (2020) Recommender systems and their ethical challenges. AI Soc. https://doi.org/10.1007/s00146-020-00950-yCrossRef

Mooney JL, Minor KI, Wells JB, Leukefeld C, Oser CB, Staton Tindall M (2008) The relationship of stress, impulsivity, and beliefs to drug use severity in a sample of women prison inmates. Int J Offender Ther Compar Criminol. https://doi.org/10.1177/0306624X07309754CrossRef

Muñoz A, Ramirez M (2015) Teachers’ conceptions of motivation and motivating practices in second-language learning: a self-determination theory perspective. Theory Res Educ. https://doi.org/10.1177/1477878515593885CrossRef

Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA (2018) Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. https://doi.org/10.1007/s12160-016-9830-8CrossRef

Paraschakis D (2017) Towards an ethical recommendation framework. Proceed Int Confer Res Chall Inform Sci. https://doi.org/10.1109/RCIS.2017.7956539CrossRef

Paterson M (2007) Freedom of information. Australian Administrative Law Fundamentals, Principles and Doctrines. https://doi.org/10.1017/CBO9781139168618.009CrossRef

Pedreschi D, Giannotti F, Guidotti R, Monreale A, Ruggieri S, Turini F (2019) Meaningful explanations of black box ai decision systems. 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI https://doi.org/10.1609/aaai.v33i01.33019780

Peters D, Calvo RA, Ryan RM (2018) Designing for motivation, engagement and wellbeing in digital experience. Front Psychol. https://doi.org/10.3389/fpsyg.2018.00797CrossRef

Ricci F, Shapira B, Rokach L (2015) Recommender systems: introduction and challenges. Recomm Syst Handbook Second Edition. https://doi.org/10.1007/978-1-4899-7637-6_1CrossRef

Scheibehenne B, Greifeneder R, Todd PM (2010) Can there ever be too many options a meta-analytic review of choice overload. J Consum Res. https://doi.org/10.1086/651235CrossRef

Schueller SM, Aguilera A, Mohr DC (2017) Ecological momentary interventions for depression and anxiety. Depress Anxiety. https://doi.org/10.1002/da.22649CrossRef

Sharon T (2017) Self-tracking for health and the quantified self: re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcare. Philosophy Technol. https://doi.org/10.1007/s13347-016-0215-5CrossRef

Spinazze P, Rykov Y, Bottle A, Car J (2019) Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol. BMJ Open. https://doi.org/10.1136/bmjopen-2019-032255CrossRef

Srivastava K, Chaudhury S, Dhamija S, Prakash J, Chatterjee K (2020) Digital technological interventions in mental health care. Indust Psychiatry J. https://doi.org/10.4103/ipj.ipj_32_21CrossRef

Torous J, Roberts LW (2017) Needed innovation in digital health and smartphone applications for mental health transparency and trust. JAMA Psychiat. https://doi.org/10.1001/jamapsychiatry.2017.0262CrossRef

Torous J, Nicholas J, Larsen ME, Firth J, Christensen H (2018) Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements. Evid Based Ment Health. https://doi.org/10.1136/eb-2018-102891CrossRef

Tremain H, McEnery C, Fletcher K, Murray G (2020) The therapeutic alliance in digital mental health interventions for serious mental illnesses: narrative review. JMIR Mental Health. https://doi.org/10.2196/17204CrossRef

Trifan A, Oliveira M, Oliveira JL (2019) Passive sensing of health outcomes through smartphones: systematic review of current solutions and possible limitations. JMIR Mhealth Uhealth. https://doi.org/10.2196/12649CrossRef

Trusty WT, Penix EA, Dimmick AA, Swift JK (2019) Shared decision-making in mental and behavioural health interventions. J Eval Clin Pract. https://doi.org/10.1111/jep.13255CrossRef

Valdez AC, Ziefle M, Verbert K (2016) HCI for recommender systems: the past, the present and the future. RecSys 2016 Proceed 10th ACM Confer Recomm Syst. https://doi.org/10.1145/2959100.2959158CrossRef

van Haasteren A, Gille F, Fadda M, Vayena E (2019) Development of the mHealth app trustworthiness checklist. Digital Health. https://doi.org/10.1177/2055207619886463CrossRef

Wang K, Varma DS, Prosperi M (2018) A systematic review of the effectiveness of mobile apps for monitoring and management of mental health symptoms or disorders. J Psychiatr Res. https://doi.org/10.1016/j.jpsychires.2018.10.006CrossRef

Wang CH, Lee TY, Hui KC, Chung MH (2019) Mental disorders and medical comorbidities: association rule mining approach. Perspect Psychiatr Care. https://doi.org/10.1111/ppc.12362CrossRef

Zhang Y, Chen X (2020) Explainable recommendation: a survey and new perspectives. Found Trends Inf Retr. https://doi.org/10.1561/1500000066CrossRef

Title: Recommender systems for mental health apps: advantages and ethical challenges
Authors: Lee Valentine
Simon D’Alfonso
Reeva Lederman
Publication date: 17-01-2022
Publisher: Springer London
Published in: AI & SOCIETY / Issue 4/2023
Print ISSN: 0951-5666
Electronic ISSN: 1435-5655
DOI: https://doi.org/10.1007/s00146-021-01322-w

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