Schizophrenia Research 166 (2015) 347–348
Contents lists available at ScienceDirect
Schizophrenia Research journal homepage: www.elsevier.com/locate/schres
Letter to the Editor Possibilities and challenges of online, social media, and mobile technologies for psychosis treatment
Alvarez-Jimenez et al. (2014) contributed a review of innovative user-led internet and mobile technologies to improve clinical and recovery outcomes for people with schizophrenia spectrum disorders. I offer one additional prospect for these technologies, and two challenges.
1. Prospect: mobile technologies can monitor psychotic experiences Upwards of 7% of the general population report lifetime psychotic experiences (PE), which are anomalous sensory experiences and delusional ideations. It is hard to tell whether PE require treatment. PE can be associated with help seeking behaviors, and are sometimes prodromal signs of illness that transition into full psychotic disorder, in which case early intervention at the sub-clinical stage would improve prognoses. But most of the time, PE are fleeting and innocuous, and so receiving costly treatments and bearing the stigma associated with psychosis may end up doing more harm than good (Oh et al., 2014). The proliferation of mobile technologies may unravel this treatment dilemma. Researchers have developed apps that randomly prompt users to fill out questionnaires to track the occurrence of psychotic symptoms throughout the day, illuminating the context of psychotic experiences — that is, the environment and the daily activities that surround and potentially trigger psychosis (Van Os et al., 2014). This kind of monitoring is ideal for subthreshold psychotic state because apps are minimally invasive – that is, they are self-directed, do not require visits to treatment providers, and cause little if any disruption to daily life – but are more vigilant than the ‘watchful waiting’ approach. A person with PE can download the app and calibrate it to match the intensity/specificity of the experiences, similar to the clinical staging model (McGorry et al., 2006). The app may start out with simple monitoring that becomes more elaborate depending on the severity of distress, sense of control, and valence of symptoms. Apps for monitoring PE may one day be able to make sophisticated risk assessments by weighing the user's demographic data, geographic location (e.g. urbanicity, ethnic composition of neighborhood), and other risk factors (e.g. history of trauma, substance use, mood and anxiety problems) to ascertain a risk level for the individual. Taking all of this into consideration, apps can then determine when and how the user should seek help. At low risk levels, apps might advise the user to seek help from social supports, or refer the user to local support groups, web-based psychoeducation, or moderated online forums led by peers and experts. At moderate risk levels, the application may employ text-messaging interventions or web-based therapy, such as CBT. Personalized data collected can be used idiographically to inform coping strategies. At clinical high risk, the app may notify a designated provider (e.g. Španiel et al., 2008), or may use GPS to guide the person to a nearby clinic, agency,
http://dx.doi.org/10.1016/j.schres.2015.05.008 0920-9964/© 2015 Elsevier B.V. All rights reserved.
or emergency room (providing the phone number, map/directions, hours of operation, insurance coverage/eligibility). This could dramatically reduce the duration of untreated psychosis. A recent study found that PE are associated with increased likelihood of making a suicide attempt among individuals with suicidal ideation (DeVylder et al., 2015), and so apps can alert family, friends, and suicide prevention teams to check in with the person if the person misses a set number of prompts, with thresholds configured more conservatively for high-risk individuals. These features exist only in theory at the moment, and seem to be relevant for various at-risk populations. Perhaps it would most effective to develop a comprehensive monitoring system that seamlessly integrates these features as add-ons/plug-ins/packages given the comorbidity between PE and other mental health conditions. 2. Challenge: technologies can exacerbate health disparities Advancing mobile devices does not address (and may even exacerbate) fundamental causes of health disparities. While it is true that the prevalence of smartphones is growing and that patients are becoming interested in using mobile applications for treatment (Torous et al., 2014), not everyone has access to these technologies. Those deprived individuals face significant disadvantages in securing resources, pursuing opportunities, navigating the environment, and staying connected to social supports. Despite the studies that suggest people with schizophrenia frequently use the internet (e.g. Schrank et al., 2010), this notion has limited generalizability. Further, accessing the internet is not the same as using the internet effectively or using smartphones or mobile-based interventions. We need to examine the moderating impact of SES, race/ethnicity, and age on whether and to what extent people adopt technologies to take care of themselves, and then use a community-centered socio-ecological model of training (Vroman et al., 2015) to disseminate both the technologies themselves and the skills to use them. 3. Challenge: technology continues to evolve and can outpace pilot studies Without intuitive design, people may struggle to incorporate apps into their daily habits. Receiving a stream of pop-ups, push notifications, and alerts throughout the day can become annoying – especially for someone who feels paranoid or disorganized – thus generating resistance to the technology. One possibility is to make the app more entertaining, like a videogame (Dennis and O'Toole, 2014), though this will be expensive and may not sustain the user's interest. Instead of self-report prompts, technologies are working toward collecting data naturally through passive behavioral sensing systems, without significant disruption to daily routine. Fitness trackers and the new digital watches capture heart rate, sleep patterns, and other behaviors without the user actively entering any information. Devices can already analyze speech rate and volume to detect depression and
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Letter to the Editor
mania. What else can be done using the phone's camera lens? Might GPS detect social isolating behaviors? Might words typed on the keyboard reveal suicidality? How can these features be effective without violating the user's privacy? Technologies are learning and adapting to the user, while pilot studies lag behind (see Ben-Zeev et al., 2014). Journals should rapidly review and publish reports on technologies, and allow researchers to post results immediately on blogs ahead of publication before findings become obsolete. References Alvarez-Jimenez, M., Alcazar-Corcoles, M.A., Gonzalez-Blanch, C., Bendall, S., McGorry, P.D., Gleeson, J.F., 2014. Online, social media and mobile technologies for psychosis treatment: a systematic review on novel user-led interventions. Schizophr. Res. 156, 96–106. Ben-Zeev, D., Schueller, S.M., Begale, M., Duffecy, J., Kane, J.M., Mohr, D.C., 2014. Strategies for mHealth research: lessons from 3 mobile intervention studies. Adm. Policy Ment. Health Ment. Health Serv. Res. 1–11. Dennis, T.A., O'Toole, L.J., 2014. Mental health on the go effects of a Gamified attentionbias modification mobile application in trait-anxious adults. Clin. Psychol. Sci. 2 (5), 576–590. DeVylder, J.E., Lukens, E.P., Link, B.G., Lieberman, J.A., 2015. Suicidal ideation and suicide attempts among adults with psychotic experiences: data from the Collaborative Psychiatric Epidemiology Surveys. JAMA Psychiatry 72, 219–225. McGorry, P.D., Hickie, I.B., Yung, A.R., Pantelis, C., Jackson, H.J., 2006. Clinical staging of psychiatric disorders: a heuristic framework for choosing earlier, safer and more effective interventions. Aust. N. Z. J. Psychiatry 40, 616–622.
Oh, H., DeVylder, J.E., Chen, F., 2014. To treat or not to treat: responding to psychotic experiences. Br. J. Soc. Work. (bct199). Schrank, B., Sibitz, I., Unger, A., Amering, M., 2010. How patients with schizophrenia use the internet: qualitative study. J. Med. Internet Res. 12. Španiel, F., Vohlídka, P., Hrdlička, J., Koženỳ, J., Novák, T., Motlová, L., Čermák, J., Bednařík, J., Novák, D., Höschl, C., 2008. ITAREPS: information technology aided relapse prevention programme in schizophrenia. Schizophr. Res. 98, 312–317. Torous, J., Friedman, R., Keshavan, M., 2014. Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. JMIR mHealth uHealth 2. Van Os, J., Delespaul, P., Barge, D., Bakker, R.P., 2014. Testing an mHealth momentary assessment routine outcome monitoring application: a focus on restoration of daily life positive mood states. PLoS One 9, e115254. Vroman, K.G., Arthanat, S., Lysack, C., 2015. “Who over 65 is online?” Older adults' dispositions toward information communication technology. Comput. Hum. Behav. 43, 156–166.
Hans Oh Columbia University, School of Social Work, 1255 Amsterdam Avenue, New York, NY 10027, United States Corresponding author. E-mail address: [email protected]. Jordan DeVylder University of Maryland, Baltimore, School of Social Work, United States 30 January 2015
Contents lists available at ScienceDirect
Schizophrenia Research journal homepage: www.elsevier.com/locate/schres
Letter to the Editor Possibilities and challenges of online, social media, and mobile technologies for psychosis treatment
Alvarez-Jimenez et al. (2014) contributed a review of innovative user-led internet and mobile technologies to improve clinical and recovery outcomes for people with schizophrenia spectrum disorders. I offer one additional prospect for these technologies, and two challenges.
1. Prospect: mobile technologies can monitor psychotic experiences Upwards of 7% of the general population report lifetime psychotic experiences (PE), which are anomalous sensory experiences and delusional ideations. It is hard to tell whether PE require treatment. PE can be associated with help seeking behaviors, and are sometimes prodromal signs of illness that transition into full psychotic disorder, in which case early intervention at the sub-clinical stage would improve prognoses. But most of the time, PE are fleeting and innocuous, and so receiving costly treatments and bearing the stigma associated with psychosis may end up doing more harm than good (Oh et al., 2014). The proliferation of mobile technologies may unravel this treatment dilemma. Researchers have developed apps that randomly prompt users to fill out questionnaires to track the occurrence of psychotic symptoms throughout the day, illuminating the context of psychotic experiences — that is, the environment and the daily activities that surround and potentially trigger psychosis (Van Os et al., 2014). This kind of monitoring is ideal for subthreshold psychotic state because apps are minimally invasive – that is, they are self-directed, do not require visits to treatment providers, and cause little if any disruption to daily life – but are more vigilant than the ‘watchful waiting’ approach. A person with PE can download the app and calibrate it to match the intensity/specificity of the experiences, similar to the clinical staging model (McGorry et al., 2006). The app may start out with simple monitoring that becomes more elaborate depending on the severity of distress, sense of control, and valence of symptoms. Apps for monitoring PE may one day be able to make sophisticated risk assessments by weighing the user's demographic data, geographic location (e.g. urbanicity, ethnic composition of neighborhood), and other risk factors (e.g. history of trauma, substance use, mood and anxiety problems) to ascertain a risk level for the individual. Taking all of this into consideration, apps can then determine when and how the user should seek help. At low risk levels, apps might advise the user to seek help from social supports, or refer the user to local support groups, web-based psychoeducation, or moderated online forums led by peers and experts. At moderate risk levels, the application may employ text-messaging interventions or web-based therapy, such as CBT. Personalized data collected can be used idiographically to inform coping strategies. At clinical high risk, the app may notify a designated provider (e.g. Španiel et al., 2008), or may use GPS to guide the person to a nearby clinic, agency,
http://dx.doi.org/10.1016/j.schres.2015.05.008 0920-9964/© 2015 Elsevier B.V. All rights reserved.
or emergency room (providing the phone number, map/directions, hours of operation, insurance coverage/eligibility). This could dramatically reduce the duration of untreated psychosis. A recent study found that PE are associated with increased likelihood of making a suicide attempt among individuals with suicidal ideation (DeVylder et al., 2015), and so apps can alert family, friends, and suicide prevention teams to check in with the person if the person misses a set number of prompts, with thresholds configured more conservatively for high-risk individuals. These features exist only in theory at the moment, and seem to be relevant for various at-risk populations. Perhaps it would most effective to develop a comprehensive monitoring system that seamlessly integrates these features as add-ons/plug-ins/packages given the comorbidity between PE and other mental health conditions. 2. Challenge: technologies can exacerbate health disparities Advancing mobile devices does not address (and may even exacerbate) fundamental causes of health disparities. While it is true that the prevalence of smartphones is growing and that patients are becoming interested in using mobile applications for treatment (Torous et al., 2014), not everyone has access to these technologies. Those deprived individuals face significant disadvantages in securing resources, pursuing opportunities, navigating the environment, and staying connected to social supports. Despite the studies that suggest people with schizophrenia frequently use the internet (e.g. Schrank et al., 2010), this notion has limited generalizability. Further, accessing the internet is not the same as using the internet effectively or using smartphones or mobile-based interventions. We need to examine the moderating impact of SES, race/ethnicity, and age on whether and to what extent people adopt technologies to take care of themselves, and then use a community-centered socio-ecological model of training (Vroman et al., 2015) to disseminate both the technologies themselves and the skills to use them. 3. Challenge: technology continues to evolve and can outpace pilot studies Without intuitive design, people may struggle to incorporate apps into their daily habits. Receiving a stream of pop-ups, push notifications, and alerts throughout the day can become annoying – especially for someone who feels paranoid or disorganized – thus generating resistance to the technology. One possibility is to make the app more entertaining, like a videogame (Dennis and O'Toole, 2014), though this will be expensive and may not sustain the user's interest. Instead of self-report prompts, technologies are working toward collecting data naturally through passive behavioral sensing systems, without significant disruption to daily routine. Fitness trackers and the new digital watches capture heart rate, sleep patterns, and other behaviors without the user actively entering any information. Devices can already analyze speech rate and volume to detect depression and
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Letter to the Editor
mania. What else can be done using the phone's camera lens? Might GPS detect social isolating behaviors? Might words typed on the keyboard reveal suicidality? How can these features be effective without violating the user's privacy? Technologies are learning and adapting to the user, while pilot studies lag behind (see Ben-Zeev et al., 2014). Journals should rapidly review and publish reports on technologies, and allow researchers to post results immediately on blogs ahead of publication before findings become obsolete. References Alvarez-Jimenez, M., Alcazar-Corcoles, M.A., Gonzalez-Blanch, C., Bendall, S., McGorry, P.D., Gleeson, J.F., 2014. Online, social media and mobile technologies for psychosis treatment: a systematic review on novel user-led interventions. Schizophr. Res. 156, 96–106. Ben-Zeev, D., Schueller, S.M., Begale, M., Duffecy, J., Kane, J.M., Mohr, D.C., 2014. Strategies for mHealth research: lessons from 3 mobile intervention studies. Adm. Policy Ment. Health Ment. Health Serv. Res. 1–11. Dennis, T.A., O'Toole, L.J., 2014. Mental health on the go effects of a Gamified attentionbias modification mobile application in trait-anxious adults. Clin. Psychol. Sci. 2 (5), 576–590. DeVylder, J.E., Lukens, E.P., Link, B.G., Lieberman, J.A., 2015. Suicidal ideation and suicide attempts among adults with psychotic experiences: data from the Collaborative Psychiatric Epidemiology Surveys. JAMA Psychiatry 72, 219–225. McGorry, P.D., Hickie, I.B., Yung, A.R., Pantelis, C., Jackson, H.J., 2006. Clinical staging of psychiatric disorders: a heuristic framework for choosing earlier, safer and more effective interventions. Aust. N. Z. J. Psychiatry 40, 616–622.
Oh, H., DeVylder, J.E., Chen, F., 2014. To treat or not to treat: responding to psychotic experiences. Br. J. Soc. Work. (bct199). Schrank, B., Sibitz, I., Unger, A., Amering, M., 2010. How patients with schizophrenia use the internet: qualitative study. J. Med. Internet Res. 12. Španiel, F., Vohlídka, P., Hrdlička, J., Koženỳ, J., Novák, T., Motlová, L., Čermák, J., Bednařík, J., Novák, D., Höschl, C., 2008. ITAREPS: information technology aided relapse prevention programme in schizophrenia. Schizophr. Res. 98, 312–317. Torous, J., Friedman, R., Keshavan, M., 2014. Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. JMIR mHealth uHealth 2. Van Os, J., Delespaul, P., Barge, D., Bakker, R.P., 2014. Testing an mHealth momentary assessment routine outcome monitoring application: a focus on restoration of daily life positive mood states. PLoS One 9, e115254. Vroman, K.G., Arthanat, S., Lysack, C., 2015. “Who over 65 is online?” Older adults' dispositions toward information communication technology. Comput. Hum. Behav. 43, 156–166.
Hans Oh Columbia University, School of Social Work, 1255 Amsterdam Avenue, New York, NY 10027, United States Corresponding author. E-mail address: [email protected]. Jordan DeVylder University of Maryland, Baltimore, School of Social Work, United States 30 January 2015
