From Mobile Mental Health to Mobile Wellbeing: Opportunities and Challenges Andrea GAGGIOLIa,b1 and Giuseppe RIVA a,b Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy b Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy a
Abstract. The combination of smart phones, wearable sensor devices and social media offer new ways of monitoring and promoting mental and physical wellbeing. In this contribution, we describe recent developments in the field of mobile healthcare (or mHealth), by focusing in particular on mobile mental health applications. First, we examine the potential benefits associated with this approach, providing examples from existing projects. Next, we identify and explain possible differences in focus between mobile mental health and mobile wellbeing applications. Finally, we discuss some open challenges associated with the implementation of this vision, ranging from the lack of evidence-based validation to privacy, security and ethical concerns. Keywords. mHealth, wearable sensors, mobile mental health, positive technology
Introduction Mobile technologies are expanding rapidly. In 2011, global mobile penetration was 85 percent, totalizing about 6 billion users; in the same year, the number of subscribers to high-speed mobile connections (3G and 4G systems) reached 1 billion and one in every three mobile phones sold in the world was a smartphone [1]. The increasing diffusion of computationally-powerful, broadband-enabled mobile devices is opening up new opportunities for delivering health information and care in a more efficient and personalized manner. The WHO broadly defines mobile health (or mHealth) as “the use of mobile and wireless technologies to support the achievement of health objectives” [2]. Applications of this approach include diagnostic and treatment support, remote monitoring/data collection, education services and tools to improve communication and training for healthcare workers [3]. The most frequently reported benefits of mHealth are improved patient-physician interaction, increased access to healthcare services, higher treatment compliance and cost savings due to reduced needs for hospitalization and intensive care. Considering these advantages, it is not surprising that the number of mHealth users has increased exponentially over the past few years. In 2011, 17 percent of U.S. adults were using their phones to look up health and medical information [4]; in the same year, 44 million health applications were downloaded [5]. The economic 1
Corresponding Author. Send inquires to: Andrea Gaggioli, Department of Psychology, Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123 Milan, Italy. E-mail:
[email protected]
impact of mHealth is also huge, with annual revenues projected to reach $23 billion worldwide [6]. In particular, it is estimated that about two-thirds of this market will be covered by remote monitoring, which involves patient self-testing using medical sensors or wearable devices and remote transmission of the medical data to healthcare providers for disease management. The rapid adoption of remote monitoring is pushing the development of a whole spectrum of wearable tools capable of measuring vital signals cheaply and not invasively for a large number of medical applications. Examples of these bio-monitoring devices include electrocardiogram (ECG), electroencephalogram (EEG), blood pressure and glucometer; research on implantable in vivo monitoring devices is also emerging. Mobile health tools have the potential to revolutionize the way mental health care services are delivered. In the next, we examine the potential opportunities and challenges of this approach, by discussing examples from existing projects.
1. Mobile Mental Health: The Rise of Mobile Cybertherapy A recent survey carried out by Luxton and coll. [7] found over than 200 unique mobile phone apps specifically associated with behavioral health, addressing a wide array of clinical areas which include developmental disorders, cognitive disorders, substancerelated disorders as well as psychotic and mood disorders. Such “applification” of mental health is a steady-growing trend, which is explained by several advantages that mobile cybertherapy can bring about. 1.1. Advanced Assessment Tools for Individualized Therapy One of the main limitations of conventional assessment procedures is the lack of possibility to gather information from the individual subject during daily life. Advanced monitoring capabilities provided by smart phones allow the researcher to unobtrusively record a variety of behavioral data in real time, which can improve the sensitiveness of measurement of many of the common outcomes measured by studies in clinical psychology [7]. As pointed out by Raento [8], since the phone is an integrated part of both the individual and the social life, it provides access to domains of behavioral data not previously available without either constant observation or reliance on self-reports only. For example, T2 MoodTracker [9] is an application that allows users to selfmonitor, track and reference their emotional experience over a period of days, weeks and months using a visual analogue rating scale. Using this application, patients can self-monitor emotional experiences associated with common deployment-related behavioral health issues like post-traumatic stress, brain injury, life stress, depression and anxiety. Self-monitoring results can be used as self-help tool or they shared with a therapist or health care professional, providing a record of the patient’s emotional experience over a selected time frame. 1.2. Analysis of Real-World Behaviors and Experiences Mental health professionals are becoming increasingly aware of the pivotal role that social and contextual factors play in the development of disease. This has prompted the
definition of methods that allow analyzing behavior within in naturalistic settings, instead of relying only on data obtainable from retrospective self-reports. Since the late 1970s, different ecological momentary assessment methods (EMA), have been introduced to this end [10]. An advantage of EMA over conventional psychological assessment include the ability to assess the temporal relationship between variables, high ecological validity and highly detailed information on subjective experience [10]. EMA techniques have been widely used to study a wide range of psychological disorders, including mood disorders and mood dysregulation, anxiety disorders, substance use disorders, and psychosis [11]. In the past, EMA-based studies have been mainly done via paper and pencil measures. Today, smart phones allow researchers to develop EMA-tools that take advantage of latest advances in computational recognition and sensing technologies, to automatically detect events that can trigger data collection [13]. One such tool is MyExperience, a mobile platform that allows the combination of sensing and self-report to collect both quantitative and qualitative data on user experience and activity [12]. The platform supports 50 built-in smartphone sensors which include GPS, GSM-based motion sensors and device usage information. Sensed events can be used to trigger custom actions such as send SMS messages to the researcher and/or present in situ self-report surveys. Morris and coll. [13] used this platform to develop a mobile application that combines experience sampling of mood with exercises of emotional awareness and self-regulation inspired by cognitive behavioral therapy. Participants were prompted via their mobile phones to report their moods several times a day on a Mood Map and a series of single-dimension mood scales. Using the prototype, participants could also activate different mobile therapy contents as needed. 1.3. Objective Measurement of Emotional States Through Biosensors Measuring objective correlatives of subjectively-reported emotional states is an important concern in research and clinical applications. Physiological and physical activity information provide mental health professionals with integrative measures, which can be used to improve understanding of patients’ self-reported feelings and emotions. The combined use of wearable biosensors and smart phones offer an unprecedented opportunity to collect, elaborate and transmit real-time body signals to the remote therapist. This approach is also useful to allow the patient collecting realtime information related to his/her health conditions and identifying specific trends (i.e. increasing levels of stress). Insights gained by means of this feedback can empower the user to self-engage and manage his/her own health status, minimizing any interaction with other health care actors. Several research groups are experimenting with mobile, non-invasive data collection solutions for the automatic detection/reporting of affective states. For example, Gaggioli et al. developed PsychLog [14], a free, open source mobile psychophysiological data collection platform that allows gathering self-reported psychological information and ECG data. These are sensed and wirelessly transmitted to the mobile phone and gathered by a computing module, which stores and process the signals for the extraction of heart’s beat-to-beat variability (HRV). HRV is considered a useful psycho-physiological measure, because it reflects the natural variability of heart rate in response to affective and cognitive states. HRV indexes have been used to characterize a number of psychological illnesses, including major depression and panic disorders
[11]. Using PsychLog, ECG data can be correlated with user’s self-reported feelings and activities. In this way, it is possible to investigate the relationship between behavioral, psychological and physiological variables, as well as to monitor their dynamic fluctuations over time [15]. Besides monitoring applications, other possible applications of mobile biosensors include the implementation of biofeedback exercises for managing anxiety, stress and health-related problems, ranging from insomnia to pain to hyperthension [7]. 1.4. Enhancement of Patient Engagement with Treatment The effectiveness of a mental intervention is strongly dependent on the quality of interaction between the therapist and the client. This bond does not collapse when patients leave the consulting room, but should continue in daily life. Moreover, the level of patient engagement with their treatment is an important predictor of positive therapeutic outcomes [16]. Therefore, identifying ways for facilitating client-therapist interaction, promoting compliance with treatment and counteracting the possibility of client dissatisfactions are critical challenges in the delivery of mental health interventions. Mobile technologies provide useful means to address these issues [17]. In fact, smartphones are widely-adopted communication tools that can be used to integrate any kind of applications into standard patient treatment regimes. Further, the interactive features of these tools enable patient involvement through the provision of ubiquitous and immediate feedback to support target behaviours. Finally, it is possible to take advantage from the social features of mobile technologies to implement peer support programs. For example, the project Interstress [18] has developed a web-based therapy management platform designed to facilitate patient-therapist communication. The platform allows the therapist to manage several clinical services, including treatment schedule, survey administration, homework assignments, warnings and motivating feedbacks. In addition, the platform provides the patient with a self-tracking service that allows him/her to monitor distress levels on the mobile phone and share them with the therapist, who can in turn use these data to personalize treatment and monitor compliance. 1.5. Transfer of Skills to Everyday Life Patient’s ability to transfer skills from the therapy setting to real-life situations is another process that can be enhanced by the use of mobile tools. In this regard, Riva and coll [19, 20] introduced the concept of Interreality, a novel approach to design mobile cybertherapy interventions enabled by the convergence between mHealth and virtual worlds. At the center of this strategy is the idea that bridging virtual experiences (fully controlled by the therapist, used to learn healthy behaviors and coping skills) with real experiences (the therapist can identify critical situations and assess clinical change) using sensors and smart phones is a feasible and potentially effective way to address the complexity of mental disorders. While conventional behavioral and cognitive-behavioral approaches focus on directly modifying the content of dysfunctional behaviors and thoughts through a rational and deliberate process, Interreality focuses on modifying the patient’s relationship with his/her behavior and thinking through more contextualized experiential processes. On one hand, the patient is continuously assessed in the virtual and real worlds by tracking their behavioral and
emotional status in the context of challenging tasks (customization of the therapy according to the characteristics of the patient). On the other hand, feedback is continuously provided to improve the patient’s skills through a conditioned association between performance and execution of assigned tasks (improvement of self-efficacy). 2. From Mobile Mental Health to Mobile Wellbeing In addition to designing mobile mental health tools, a growing number of researchers and developers are focusing their attention on creating applications that support general wellbeing. Differently from mobile cybertherapy, which is mostly concerned with supporting assessment and treatment of mental diseases, these tools are more oriented to empower users with information for making better decisions, preventing life-style related conditions and preserving/enhancing cognitive performance. For example, Lane and colleagues developed BeWell [21-22], a real-time, continuous sensing application that allows monitoring different user activities (sleep, physical activity, social interaction) and provides feedback that should promote healthier lifestyle decisions. Other mobile wellbeing applications help users to monitor and manage stress levels. Gaggioli and coll. [23] describe a mobile system designed to automatically detect psychological stress events during daily activities from heart-rate and activity data collected with a wearable ECG platform coupled with a smartphone. Detected stress levels are provided to the user in form of graphs displayed on the mobile phone application; apart from the instantaneous values, the user can check the history of stress-level variations during the monitoring period. Riva and coll. [24] define this emerging category of applications “positive technologies” to distinguish them from the more disease-oriented approach that characterizes existing cybertherapy tools. According to these authors, positive technology tools can be used to manipulate the features of an experience in three separate but related ways: •
By structuring it using a goal, rules, and a feedback system. The goal provides subjects with a sense of purpose focusing attention and orienting his/her participation in the experience. The rules, by removing or limiting the obvious ways of getting to the goal, push subjects to see the experience in a different way. The feedback system tells users how close they are to achieving the goal and provides motivation to keep trying.
•
By augmenting it to achieve multimodal and mixed experiences. Technology allows multisensory experiences in which content and its interaction is offered through more than one of the senses. It is even possible to use technology to overlay virtual objects onto real scenes.
•
By replacing it with a synthetic one. Using virtual worlds, even on mobile phones, it is possible to simulate physical presence in a synthetic world that reacts to the action of the subject as if he/she was really there. Moreover, the replacement possibilities offered by technology even extend to the induction of an illusion of ownership over a virtual body.
In addition, these authors positive technologies according to their effects on these three features of personal experience:
•
Hedonic: technologies used to induce positive and pleasant experiences;
•
Eudaimonic: technologies used to support individuals in reaching engaging and self-actualizing experiences;
•
Social/Interpersonal: technologies used to support and improve the connectedness between individuals, groups, and organizations.
3. Open Challenges and Conclusion The emergence of mobile mental health and mobile wellbeing suggests beginning of a new era in cyberpsychology, yet this vision is not without caveats. As recently emphasized by Boyce [25], the main concern associated with the use of apps as a selfmanagement tool is the limited evidence of their effectiveness in improving health and wellbeing. Differently from other health interventions, generally mHealth apps have not been subject to rigorous testing. A potential reason for the lack of randomized evaluations is the fact that most of these apps reach consumers/patients directly, without passing through the traditional medical gatekeepers. However, as Boyce suggests, the availability of trial data would not only benefit patients, but also app developers, who could bring to the market more effective and reliable products. A further concern is related to privacy and security of medical data. Although most smartphone-based medical applications apply state-of-the-art secure protocols, the wireless utilization of these devices opens up new vulnerabilities to patients and medical facilities. These include keystroke loggers and Trojans, email-based attack carrying malicious code seeking specific information, and lost equipment. Finally, as emphasized by Matthews and [17] research into the use of mobile technology in mental health care settings must adhere to strict ethical requirements. According to these authors, to meet these requirements the new system should be (i) informed by accepted scientific models, (ii) designed in collaboration with therapists, (iii) designed to integrate with existing clinical practice and (iv) used by clients under the supervision of a professional therapist. In conclusion, the “applification of mental wellbeing” is at the same time a great opportunity for patients and a great challenge for therapists and researchers. In order to exploit this potential while mitigating risks, it is essential to put in place quality evaluation procedures and integrate mobile solutions into existing activities of patients and healthcare providers to ensure the support needed for new behaviors.
4. Acknowledgments This work was supported by the European funded project ‘‘INTERSTRESS-Interreality in the management and treatment of stress-related disorders’’, FP7-247685.
References [1] Ericsson interim update, Traffic and Market Data Report – On the pulse of the Networked Society (2012), Retrieved from: http://www.ericsson.com/res/docs/2012/tmd_report_feb_web.pdf, accessed October 2012.
[2] World Health Organization, mHealth: New Horizons for Health Through Mobile Technologies, Global Observatory for eHealth Series, 3 (2011), Retrieved from: http://www.who.int/goe/publications/goe_mhealth_web.pdf, accessed October, 2012. [3] Vital Wave Consulting, mHealth for Development: The Opportunity of Mobile Technology for Healthcare in the Developing World. Washington, D.C. and Berkshire, UK: UN Foundation-Vodafone Foundation Partnership, 2009. [4] S. Fox, The Social Life of Health Information, 2011, Pew Research Center’s Internet & American Life Project, Washington, D.C., 2011. Retrieved from: http://pewinternet.org/~/media/Files/Reports/2011/PIP_Social_Life_of_Health_Info.pdf, accessed November, 2012. [5] Juniper Research, Mobile Healthcare Opportunities: Smartphone Apps, Monitoring & mHealth Strategies 2011-2016, 2011. Retrieved from: http://www.juniperresearch.com/reports/mobile_healthcare_opportunities, accessed October 2012. [6] Price Waterhouse Cooper, Emerging mHealth: Paths for growth, 2012. Retrieved from: http://www.pwc.com/en_GX/gx/healthcare/mhealth/assets/pwc-emerging-mhealth-full.pdf [7] D. Luxton, R. McCann, N. Bush, M. Mishkind, G. Reger, mHealth for Mental Health: Integrating Smartphone Technology in Behavioral Healthcare, Professional Psychology: Research and Practice, 42(6) (2011), 505–512. [8] M. Raento, A. Oulasvirta, N. Eagle, Smartphones: An Emerging Tool for Social Scientists. Sociological Methods and Research 37 (2009), 426-454. [9] T2 Mood Tracker [Mobile application software]. Retrieved from: http://t2health.org/apps/t2-moodtracker, accessed October, 2012. [10] L. F. Barrett, D. J. Barrett, An introduction to computerized experience sampling in psychology, Social Science Computer Review, 19(2) (2001) 175–185. [11] D. Kimhy, P. Delespaul, H. Ahn, S. Cai, M. Shikhman, J.A. Lieberman, D. Malaspina, R.P. Sloan, Concurrent measurement of "real-world" stress and arousal in individuals with psychosis: assessing the feasibility and validity of a novel methodology. Schizophr Bull, 36(6) (2010), 1131-1139. [12] MyExperience [Mobile application software]. Retrieved from: http://myexperience.sourceforge.net/ [13] M.E. Morris, Q. Kathawala, T.K. Leen, E.E Gorenstein, F. Guilak, M. Labhard, W. Deleeuw, Mobile therapy: case study evaluations of a cell phone application for emotional self-awareness, Journal of Medical Internet Research 12 (10) (2010). [14] PsychLog [Mobile application software]. Retrieved from: http://sourceforge.net/projects/psychlog/ [15] A Gaggioli, G. Pioggia, G. Tartarisco, G. Baldus, D. Corda, P. Cipresso, G. Riva, A mobile data collection platform for mental health research, Personal and Ubiquitous Computing (in press) DOI: 10.1007/s00779-011-0465-2. [16] T.P. Assay, M.J. Lambert. The empirical case for the common factors in therapy: Quantitative findings. In M. A. Hubble, B. L. Duncan, & S. D. Miller (Eds.), The heart and soul of change: What works in therapy (pp. 33-56). American Psychological Association, Washington, D.C., 1999. [17] M. Matthews, G. Doherty, D. Coyle, J. Sharry, Designing mobile applications to support mental health interventions, in J. Lumsden (Ed.), Handbook of Research on User Interface Design and Evaluation for Mobile Technology, IGI, Information Science Reference, Hershey, PA, 2008, 635 – 656. [18] A. Popleteev, V. Osmani, R. Maimone, S. Gabrielli, O. Mayora, Mobile Stress Treatment: The Interstress Approach, in Proc. 6th International ICST Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health 2012), 2012. [19] G. Riva, B.K. Wiederhold, F. Mantovani, A. Gaggioli, Interreality: The experiential use of technology in the treatment of obesity, Clinical Practice & Epidemiology in Mental Health 7 (2011), 51-61. [20] C. Repetto, G. Riva, From virtual reality to interreality in the treatment of anxiety disorders, Neuropsychiatry 1(1) (2011), 31-43. [21] BeWell [Mobile application software]. Retrieved from: https://www.bewellapp.org/, accessed October, 2012. [22] N. D. Lane, T. Choudhury, A. Campbell, M. Mohammod, M. Lin, X. Yang, A. Doryab, H. Lu, S. Ali, E. Berke, BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing, in Proc. 5th International ICST Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health 2011), 2011. [23] A. Gaggioli, G. Pioggia, G. Tartarisco, G. Baldus, M. Ferro, P. Cipresso, S. Serino, A. Popleteev, S. Gabrielli, R. Maimone, G. Riva, A System for Automatic Detection of Momentary Stress in Naturalistic Settings, Studies in Health Technology and Informatics 181 (2012), 182-186. [24] G. Riva, R. Banos, C. Botella, B.K. Wiederhold, A. Gaggioli, Positive Technology: Using Interactive Technologies to Promote Positive Functioning, Cyberpsychology, Behavior, and Social Networking, 15 (2) (2012), 69-77. [25] N. Boyce, Maps, apps—and evidence? The Lancet, 379(9833) 2012, 2231-2231.
