The application of mHealth to mental health: opportunities and challenges
Open accessAbstract
Advances in smartphones and wearable biosensors enable real-time psychological, behavioural, and physiological data to be gathered in increasingly precise and unobtrusive ways. Thus, moment-to-moment information about an individual's moods, cognitions, and activities can be collected, in addition to automated data about their whereabouts, behaviour, and physiological states. In this report, we discuss the potential of these new mobile digital technologies to transform mental health research and clinical practice. By drawing on results from the INSIGHT research project, we show how traditional boundaries between research and clinical practice are becoming increasingly blurred and how, in turn, this is leading to exciting new developments in the assessment and management of common mental disorders. Furthermore, we discuss the potential risks and key challenges associated with applying mobile technology to mental health.
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Another meta-analysis reported that iCBT was significantly more effective than passive controls but did not differ from active controls.
“Another meta-analysis reported that iCBT was significantly more effective than passive controls but did not differ from active controls [45].”
From Clinical Advances in Treatment Strategies for Obsessive-compulsive Disorder in Adults (2023) · cites this paper as [45]
The sections on real-time monitoring and clinical challenges discuss mHealth and self-monitoring, not a meta-analysis of internet-based CBT. Searches for iCBT and for passive-versus-active control comparisons found no passage reporting the claimed result. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe nature of smartphone interventions appears to position them as a possible low-intensity intervention tool for those with less severe symptoms or as a first step in a stepped-care approach to service delivery.
“Nonetheless, the nature of smartphone interventions does appear to position them as a possible low-intensity intervention tool for those with less severe levels of symptoms or as a first step in a stepped-care approach to service delivery [55].”
From Mobile Apps for Mental Health Issues: Meta-Review of Meta-Analyses (2020) · cites this paper as [55]
These studies have relied on quantitative self-report or simple sensing and monitoring technologies.
“However, these studies have relied on quantitative self-report or simple sensing and monitoring technologies [26].”
From Wearable technology for high-frequency cognitive and mood assessment in major depressive disorder: longitudinal observational study (2019) · cites this paper as [26]
Mobile digital technologies allow for sampling outside of the clinic and in the patient's home or work environment, providing a shared platform for clinicians and patients to monitor symptoms.
“Mobile digital technologies allow for sampling outside of the clinic and in the patient’s home or work environment, providing a shared platform for clinicians and patients to monitor symptoms [26].”
From Wearable technology for high-frequency cognitive and mood assessment in major depressive disorder: longitudinal observational study (2019) · cites this paper as [26]
Sonde Health is a voice-based technology that can be used as a digital biomarker for depression, with a sensitivity and specificity of 0.60 and 0.70, respectively.
“Sonde Health is a voice-based technology that can be used as a digital biomarker for depression, with a sensitivity and specificity of 0.60 and 0.70, respectively.”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [119]
Unobtrusive wearable biosensors can continuously monitor physiological parameters throughout the day with high precision.
“Unobtrusive wearable biosensors can continuously monitor physiological parameters throughout the day with high precision [17], whereas smartphone embedded sensors make it possible to indirectly collect data about patients’ behaviours and habits, such as their social media use, physical activity, or social interactions [18,19].”
From Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review (2019) · cites this paper as [17]
An accurate understanding and assessment of emotion regulation should not be based only on self-report questionnaires but also on associated behavioral and physiological mechanisms provided by sensors and biosensors.
“Ideally, an accurate understanding and assessment of ER should not be based only on self-report questionnaires but also on the associated behavioral and physiological mechanisms that could be provided by sensors (Harari et al., 2016) and biosensors (Figure 1; Marzano et al., 2015).”
From New Technologies for the Understanding, Assessment, and Intervention of Emotion Regulation (2019) · cites this paper as Marzano et al., 2015
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Cites
Ecological momentary assessment
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091415 · Cited by 6,454 · see citing works
Developing and Evaluating Complex Interventions: New Guidance
Craig, P., Dieppe, P. A., MacIntyre, S., Michie, S., Nazareth, I., & Petticrew, M. P. (2006) · UCL Discovery (University College London) · Cited by 1,877 · see citing works
The NIMH Research Domain Criteria (RDoC) Project: Precision Medicine for Psychiatry
Insel, T. R. (2014) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.14020138 · Cited by 1,472 · see citing works
FUNCTIONAL ANALYSIS OF PROBLEM BEHAVIOR: A REVIEW
Hanley, G. P., Iwata, B. A., & McCord, B. E. (2003) · Journal of Applied Behavior Analysis · doi:10.1901/jaba.2003.36-147 · Cited by 1,317 · see citing works
Smartphones for smarter delivery of mental health programs: A systematic review
Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M.-R., & Christensen, H. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2791 · Cited by 1,136 · see citing works
(2001) · Medical Entomology and Zoology · Cited by 915 · see citing works
Mobile Health Technology Evaluation
Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., Riley, W. T., Shar, A., Spring, B., Spruijt-Metz, D., et al. (2013) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2013.03.017 · Cited by 900 · see citing works
mHealth for mental health: Integrating smartphone technology in behavioral healthcare.
Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011) · Professional Psychology: Research and Practice · doi:10.1037/a0024485 · Cited by 809 · see citing works
Trull, T. J., & Ebner-Priemer, U. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185510 · Cited by 761 · see citing works
The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?
Lillie, E. O., Patay, B., Diamant, J., Issell, B., Topol, E. J., & Schork, N. J. (2011) · Personalized Medicine · doi:10.2217/pme.11.7 · Cited by 706 · see citing works
Mohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2013.03.008 · Cited by 693 · cited 1× · see citing works
“…it can shape timely personalised interventions, including behavioural prompts to highlight vulnerability and to encourage alternative behaviours (e.g. via behavioural activation for depressive symptoms and mindfulness-based exercises to enhance emotion regulation and distress tolerance) (20, 21).”
BackgroundHarnessing context sensing to develop a mobile intervention for depression
Burns, M. N., Begale, M., Duffecy, J., Gergle, D., Karr, C. J., Giangrande, E., & Mohr, D. C. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1838 · Cited by 646 · cited 1× · see citing works
“…in scope, mostly relying on quantitative self-report (mainly of mood and activities via patient reported outcome or experience measures) and/or employing a simple collection of sensing and monitoring technologies, in selected diagnostic groups (e.g., unipolar (13) or bipolar depression (14)).”
BackgroundThe Smartphone Psychology Manifesto
Miller, G. F. (2012) · Perspectives on Psychological Science · doi:10.1177/1745691612441215 · Cited by 618 · see citing works
Kvedar, J., Coye, M., & Everett, W. (2014) · Health Affairs · doi:10.1377/hlthaff.2013.0992 · Cited by 606 · see citing works
Nock, M. K., & Mendes, W. B. (2008) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.76.1.28 · Cited by 572 · see citing works
Ehlers, A., Clark, D. M., Hackmann, A., McManus, F., Fennell, M., Herbert, C., & Mayou, R. (2003) · Archives of General Psychiatry · doi:10.1001/archpsyc.60.10.1024 · Cited by 485 · see citing works
Klonsky, E. D. (2009) · Psychiatry Research · doi:10.1016/j.psychres.2008.02.008 · Cited by 404 · see citing works
aan het Rot, M., Hogenelst, K., & Schoevers, R. A. (2012) · Clinical Psychology Review · doi:10.1016/j.cpr.2012.05.007 · Cited by 325 · see citing works
Experience sampling research in individuals with mental illness: reflections and guidance
Palmier-Claus, J. E., Myin-Germeys, I., Barkus, E., Bentley, L., Udachina, A., Delespaul, P. A. E. G., Lewis, S. W., & Dunn, G. (2011) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.2010.01596.x · Cited by 287 · see citing works
Mobile Interventions for Severe Mental Illness: Design and Preliminary Data From Three Approaches
Depp, C. A., Mausbach, B., Granholm, E., Cardenas, V., Ben-Zeev, D., Patterson, T. L., Lebowitz, B. D., & Jeste, D. V. (2010) · Journal of Nervous & Mental Disease · doi:10.1097/nmd.0b013e3181f49ea3 · Cited by 265 · see citing works
Use of ecological momentary assessment in mood disorders research
Wenze, S. J., & Miller, I. W. (2010) · Clinical Psychology Review · doi:10.1016/j.cpr.2010.06.007 · Cited by 205 · see citing works
Walz, L. C., Nauta, M. H., & aan het Rot, M. (2014) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2014.09.022 · Cited by 195 · see citing works
Continuous Evaluation of Evolving Behavioral Intervention Technologies
Mohr, D. C., Cheung, K., Schueller, S. M., Brown, C. H., & Duan, N. (2013) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2013.06.006 · Cited by 166 · see citing works
Santangelo, P., Bohus, M., & Ebner-Priemer, U. W. (2014) · Journal of Personality Disorders · doi:10.1521/pedi_2012_26_067 · Cited by 152 · see citing works
Momentary assessment technology as a tool to help patients with depression help themselves
Wichers, M., Simons, C. J. P., Kramer, I., Hartmann, J., Menne‐Lothmann, C., Myin‐Germeys, I., van Bemmel, A. L., Peeters, F. P. M. L., Delespaul, P., & van Os, J. (2011) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.2011.01749.x · Cited by 150 · see citing works
Davies, S. C. (2014) · Discovery Research Portal (University of Dundee) · Cited by 132 · see citing works
Therapeutic applications of the mobile phone
Preziosa, A., Grassi, A., Gaggioli, A., & Riva, G. (2009) · British Journal of Guidance and Counselling · doi:10.1080/03069880902957031 · Cited by 107 · see citing works
Atienza, A. A., & Patrick, K. (2011) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2011.01.008 · Cited by 76 · see citing works
Rizvi, S. L., & Nock, M. K. (2008) · Suicide and Life-Threatening Behavior · doi:10.1521/suli.2008.38.5.498 · Cited by 46 · see citing works
Contemporary Approaches to Frequent Mood Monitoring in Bipolar Disorder
Malik, A., Goodwin, G. M., & Holmes, E. A. (2011) · Journal of Experimental Psychopathology · doi:10.5127/jep.014311 · Cited by 28 · see citing works
The Lancet Technology: November, 2014
Lee, N. (2014) · The Lancet · doi:10.1016/s0140-6736(14)62267-4 · Cited by 17 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Marzano L, Bardill A, Fields B. Towards a Comprehensive Ecological Model of NSSI: Integrating Psychosocial, Biological and Environmental Real-Time Data Using Digital Diary Methods and Smartphone Technology. 9th Annual Conference of the International Society for the Study of Self-Injury (ISSS). Chicago, Illinois; 2014.
- World Health Organization. mHealth: New horizons for health through mobile technologies. Observatory. 2011;3:112. Available from: http://www.who.int/goe/publications/goe_mhealth_web.pdf
- Lee N. Trouble on the radar. Lancet. 2014;384:1917.
Cited by
Sorted by citations
Mobile Apps for Mental Health Issues: Meta-Review of Meta-Analyses
Lecomte, T., Potvin, S., Corbière, M., Guay, S., Samson, C., Cloutier, B., Francoeur, A., Pennou, A., & Khazaal, Y. (2020) · JMIR mHealth and uHealth · doi:10.2196/17458 · Cited by 336 · see citing works
Lunde, P., Nilsson, B. B., Bergland, A., Kværner, K. J., & Bye, A. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9751 · Cited by 214 · see citing works
Smartphone Apps for the Treatment of Mental Disorders: Systematic Review
Miralles, I., Granell, C., Díaz-Sanahuja, L., Van Woensel, W., Bretón-López, J., Mira, A., Castilla, D., & Casteleyn, S. (2020) · JMIR mHealth and uHealth · doi:10.2196/14897 · Cited by 188 · see citing works
Colombo, D., Fernández-Álvarez, J., Patané, A., Semonella, M., Kwiatkowska, M., García-Palacios, A., Cipresso, P., Riva, G., & Botella, C. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8040465 · Cited by 194 · see citing works
Ben-Zeev, D., Brian, R. M., Jonathan, G., Razzano, L., Pashka, N., Carpenter-Song, E., Drake, R. E., & Scherer, E. A. (2018) · Psychiatric Services · doi:10.1176/appi.ps.201800063 · Cited by 173 · see citing works
Fineberg, N. A., Hollander, E., Pallanti, S., Walitza, S., Grünblatt, E., Dell’Osso, B. M., Albert, U., Geller, D. A., Brakoulias, V., Janardhan Reddy, Y. C., et al. (2020) · International Clinical Psychopharmacology · doi:10.1097/yic.0000000000000314 · Cited by 161 · see citing works
Santangelo, P., Bohus, M., & Ebner-Priemer, U. W. (2014) · Journal of Personality Disorders · doi:10.1521/pedi_2012_26_067 · Cited by 152 · see citing works
Ben-Zeev, D., Scherer, E. A., Gottlieb, J. D., Rotondi, A. J., Brunette, M. F., Achtyes, E. D., Mueser, K. T., Gingerich, S., Brenner, C. J., Begale, M., et al. (2016) · JMIR Mental Health · doi:10.2196/mental.6348 · Cited by 137 · see citing works
Actigraphy for evaluation of mood disorders: A systematic review and meta-analysis
Tazawa, Y., Wada, M., Mitsukura, Y., Takamiya, A., Kitazawa, M., Yoshimura, M., Mimura, M., & Kishimoto, T. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.04.087 · Cited by 115 · see citing works
Czyz, E. K., King, C. A., & Nahum-Shani, I. (2018) · Psychiatry Research · doi:10.1016/j.psychres.2018.06.031 · Cited by 112 · see citing works
Tazawa, Y., Liang, K.-C., Yoshimura, M., Kitazawa, M., Kaise, Y., Takamiya, A., Kishi, A., Horigome, T., Mitsukura, Y., Mimura, M., et al. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03274 · Cited by 101 · see citing works
A mobile app–based intervention for depression: end-user and expert usability testing study
Fuller-Tyszkiewicz, M., Richardson, B., Klein, B., Skouteris, H., Christensen, H., Austin, D., Castle, D., Mihalopoulos, C., O'Donnell, R., Arulkadacham, L., et al. (2018) · JMIR Mental Health · doi:10.2196/mental.9445 · Cited by 98 · see citing works
Porras-Segovia, A., Molina-Madueño, R. M., Berrouiguet, S., López-Castroman, J., Barrigón, M. L., Pérez-Rodríguez, M. S., Marco, J. H., Díaz-Oliván, I., de León, S., Courtet, P., et al. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.05.067 · Cited by 85 · see citing works
Austin, J., Drossaert, C. H. C., Schroevers, M. J., Sanderman, R., Kirby, J. N., & Bohlmeijer, E. T. (2021) · Psychology & Health · doi:10.1080/08870446.2019.1699090 · Cited by 88 · see citing works
Cormack, F., McCue, M., Taptiklis, N., Skirrow, C., Glazer, E., Panagopoulos, E., van Schaik, T. A., Fehnert, B., King, J., & Barnett, J. H. (2019) · JMIR Mental Health · doi:10.2196/12814 · Cited by 79 · see citing works
New Technologies for the Understanding, Assessment, and Intervention of Emotion Regulation
Colombo, D., Fernández-Álvarez, J., García Palacios, A., Cipresso, P., Botella, C., & Riva, G. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01261 · Cited by 75 · see citing works
Ebert, D. D., Harrer, M., Apolinário-Hagen, J., & Baumeister, H. (2019) · Advances in Experimental Medicine and Biology · doi:10.1007/978-981-32-9721-0_29 · Cited by 76 · see citing works
van Genugten, C. R., Schuurmans, J., Lamers, F., Riese, H., Penninx, B. W. J. H., Schoevers, R. A., Riper, H. M., & Smit, J. H. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9020322 · Cited by 71 · see citing works
Meaningless comparisons lead to false optimism in medical machine learning
DeMasi, O., Kording, K., & Recht, B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184604 · Cited by 63 · see citing works
Digital Health Solutions for Indigenous Mental Well-Being
Hensel, J. M., Ellard, K., Koltek, M., Wilson, G., & Sareen, J. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1056-6 · Cited by 61 · see citing works
Hensel, J. M., Shaw, J., Ivers, N. M., Desveaux, L., Vigod, S. N., Cohen, A., Onabajo, N., Agarwal, P., Mukerji, G., Yang, R., et al. (2019) · Journal of Medical Internet Research · doi:10.2196/10838 · Cited by 39 · see citing works
Digital health technologies and major depressive disorder
McIntyre, R. S., Greenleaf, W., Bulaj, G., Taylor, S. T., Mitsi, G., Saliu, D., Czysz, A., Silvesti, G., Garcia, M., & Jain, R. (2023) · CNS Spectrums · doi:10.1017/s1092852923002225 · Cited by 38 · see citing works
Gaffney, H., Mansell, W., & Tai, S. (2020) · Digital Health · doi:10.1177/2055207620911580 · Cited by 26 · see citing works
“Continued and rapid advances in digital technology are now able to facilitate this vision.(10) One way of achieving greater collaboration and flexibility in computerized interventions is through relational agents.”
BackgroundAI-enabled remote and objective quantification of stress at scale
Al-Jebrni, A. H., Chwyl, B., Wang, X. Y., Wong, A., & Saab, B. J. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101929 · Cited by 21 · see citing works
Clinical Advances in Treatment Strategies for Obsessive-compulsive Disorder in Adults
Roh, D., Jang, K. W., & Kim, C.-H. (2023) · Clinical Psychopharmacology and Neuroscience · doi:10.9758/cpn.23.1075 · Cited by 20 · see citing works
“Another meta-analysis reported that iCBT was significantly more effective than passive controls but did not differ from active controls [45].”
BackgroundMobile Mental Health Virtual Communities: Challenges and Opportunities
Eftychiou, L., & El Morr, C. (2017) · The Digitization of Healthcare · doi:10.1057/978-1-349-95173-4_15 · Cited by 3 · see citing works
- Authors
- Marzano, L., Bardill, A., Fields, B., Herd, K., Veale, D., Grey, N., & Moran, P.
- Affiliations
- Middlesex University London (GB)
- King's College London (GB)
- Corresponding author
- Marzano, Lisa
- Venue
- The Lancet Psychiatry
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 2
- Issue
- 10
- Pages
- 942-948
- ISSN
- 2215-0366 (ISSN-L)
- OpenAlex
- W2118154726
- Cited by
- 200
- References
- 31
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 13 · 2023: 22 · 2022: 19 · 2021: 23 · 2020: 32 · 2019: 30 · 2018: 14 · 2017: 14 · 2016: 7 · 2012: 1
- FWCI
- 1.61
- Type
- journal article
- Language
- en
- Open access
- green
- kclpure.kcl.ac.uk
- Licence
- Locations
- The Lancet Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Middlesex University Research Repository (Middlesex University Of London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Funders
- National Institute for Health and Care Research
- King's College London
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- mobile health, mental health, smartphones, wearable biosensors, psychological assessment, common mental disorders, research-practice integration, digital mental health interventions
- MeSH terms
- Emotions, Humans, Mental Health, Monitoring, Physiologic, Research, Risk, Social Behavior, Telemedicine, Cell Phone
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