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CHAPTER Cyberpsychology and Afective 41 Computing

and

Abstract Key Words:

Introduction clinical change (cybertherapy). On the other side, Clinical psychology has been traditionally based cyberpsychology focuses on the possible use of tech- on face-to-face interactions that involve verbal and nology for improving personal development and nonverbal language, without any technological well-being (positive technology/computing and mediation. However, emerging technologies—the smart health). Internet, mobile devices, virtual reality (VR), and Both aspects of cyberpsychology are related to the like—are modifying these traditional settings and involve a variety of afective processes. Te (Castelnuovo, Gaggioli, Mantovani, & Riva, 2003; discipline’s overlap with afective computing and Preziosa, Grassi, Gaggioli, & Riva, 2009; Riva & human–computer interaction (HCI) in general Mantovani, 2012). As the availability of these tech- are signifcant, yet its psychological origins mean nologies expands the ways in which treatment can that the research communities have somewhat dif- be provided, psychologists are expected to incor- ferent focuses. Afective computing started as an porate these innovations into their practice and engineering discipline, driven by a motivation to research (Barak, 2008). Cyberpsychology is a recent engineer new technologies that could better under- branch of psychology that is trying to support this stand humans and be more efective for humans. process. In particular, it aims at the understanding, Cyberpsychology originated in psychology and has forecasting, and induction of the diferent processes been driven by the quest to help humans deal with of change related to the use of new technologies. their digital environments and use these environ- Within this broad focus, cyberpsychology has ments to promote well-being. Te object of study two faces. On one side, cyberpsychology tries to in cyberpsychology, as it is for many HCI research- understand how technologies can be used to induce ers, is the change introduced by the technology and

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not the technology itself. In this chapter, we review t
Virtual clinics and general practice, in which these histories and discuss how afective computing a professional ofers early interventions and can (or could in the future) assist cyberpsychology treatment through the Internet in terms of both treating existing mental diseases t
Blended approaches, in which a range of (e.g., anxiety disorders, depression, mood disorders, services, such as Internet and face-to-face, are personality disorders) and in terms of preventive integrated to ofer prevention and care approaches to nurture health and well-being (e.g., Tese e-therapy approaches allow the patient promoting healthy lifestyles, behavior change inter- to engage in treatment without having to accom- ventions). Many mental diseases are directly related modate to ofce appointments, often reducing the to a variety of afective processes: emotional expe- social anxiety of face-to-face treatment (Mair & rience (e.g., sadness, stress), mood disorders (e.g., Whitten, 2000). Internet-based therapies have bipolar), depression (hopelessness, helplessness), shown to be economically sound by being efec- and personality (e.g., borderline). Similarly, preven- tive at a low cost (Kadda, 2010). Tey also have tively nurturing health and well-being often involves the potential to reach people in isolated places, making life changes (e.g., toward health-promoting where mental health is often a problem (Hordern, lifestyles), which in themselves are often associated Georgiou, Whetton, & Prgomet, 2011). with a variety of fuctuating afective states (e.g., Furthermore, Internet-based applications allow hopefulness of being healthy, frustration of not for the use of interactive monitoring systems that managing to stay away from fatty foods, discourage- give the therapist instant access to clinical data dur- ment of postponing to join the gym indefnitely, joy ing therapy and gives the individual patient the of having lost 2 pounds in a week, pride in having possibility of monitoring his or her progress. Tis implemented a major lifestyle change). is in line with the “know thyself” motto of recent Afective computing—whose main focus is to HCI research (Li, Forlizzi, & Dey, 2010), which develop technologies to sense, recognize, under- posits that refecting on personal data, such as our stand, and simulate afective processes—can exercise patterns, can help us lead more healthy therefore make important contributions to the lifestyles. enhancement of existing cybertherapies and posi- A great number of studies have shown sig- tive technologies, as well as to the design and devel- nifcant results in Internet-aided psychotherapy opment of novel ones. applied to both individual therapy (Andersson, Cybertherapy and Afective Computing 2009; Bergstrom et al., 2010) and self-help sup- What Is Cybertherapy? port (Andersson et al., 2005; Carlbring, Ekselius, & Andersson, 2003). Journals such as CyberPsychology, Cybertherapy is the branch of psychology that Behavior and Social Networking, IEEE Transactions uses new technology to induce clinical change. on Biomedical Engineering, Journal of Cybertherapy Historically e-therapy—the use of the Internet and Rehabilitation, Journal of Medical Internet and related media for clinical care—has been the Research, Telemedicine and e-health are dedicated to frst area of cyberpsychology to have an impact on reporting progress in this feld. However, cyberther- psychological treatments (Manhal-Baugus, 2001). apy also involves two emerging technologies: VR It is generally agreed that innovative e-therapy and mobile devices. approaches are an opportunity for earlier and better Te characteristics of VR therapy, the use of VR care for the most common mental health problems for clinical care, include a high level of control of (Christensen & Hickie, 2010). Te successful mod- the interaction with the tool and the enriched expe- els of e-therapy services include diferent levels of rience provided to the patient (Riva, 2005; 2009). interactivity and support: Typically in VR, the patient learns to cope with t
Content-centric systems that ofer prevention, problematic situations related to his or her problem. self-help, and self-care to users. Multiple charities For this reason, the most common application of and government-funded projects ofer support that VR in this area is the treatment of anxiety disorders follows this approach. and phobias, such as fear of heights, fear of fying, t
Consumer-assisted support, in which the level and fear of public speaking (Emmelkamp, 2005; of peer interaction is ofered online through Wiederhold & Rizzo, 2005). Emerging applications volunteers with lived experience of a mental of VR in psychotherapy include eating disorders and disorder obesity (Ferrer-Garcia & Gutierrez-Maldonado,

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2012; Riva et al., 2006; Riva, Manzoni, Villani, for 15–20 minutes about traumatic or emotional Gaggioli, & Molinari, 2008), posttraumatic stress experiences. disorder (Reger & Gahm, 2008), addictions A recent meta-analysis of 146 research trials (Bordnick et al., 2008), sexual disorders (Optale, (Frattaroli, 2006) using various unstructured emo- 2003), and pain management (Hofman, 2004). tion writing methods concluded that the impact of M-health—the use of mobile devices such as this type of writing approach may have some ben- smartphones and tablets for clinical care—is also efts for some individuals, but the overall efect size an emerging area of cybertherapy (Istepanian, was very small (r-efect size = 0.075). Jovanov, & Zhang, 2004). Te wide availability One alternative method to unstructured writ- and acceptance of mobile devices—signifcantly ing is to structure how participants write during the higher than PCs—make them the perfect tools to writing task. Writing instructions could be manipu- bridge the gap between inpatient and outpatient lated to increase the likelihood that participants treatment (Preziosa et al., 2009). On one side, write in a way that is suggested to be therapeutic mobile devices ofer a nonintrusive way to moni- (e.g., write about something that you are thinking tor patients in their real-life contexts (Gaggioli, or worrying about too much, about something that Cipresso et al., 2012), thereby afording the thera- you feel is afecting your life in an unhealthy way, pist the possibility of optimizing the patient’s treat- etc.) and therefore increase the likelihood that they ment (Gaggioli, Pioggia et al., 2012; Kauer et al., obtain benefts from the task. 2012). On the other side, advanced multimedia A number of emotion writing studies have capabilities of these devices give developers the manipulated the writing condition in such a ability to create interactive applications that allow manner (King, 2001; King & Miner, 2000) but the patients to autonomously experience clinical could not demonstrate causal links between support (Cipresso et al., 2012). hypothesized theoretical processes and outcomes. Difculties have been due to the absence of clear Afective Computing in Cybertherapy operational defnitions of the processes within Afective computing ofers new interaction the writing sessions and therefore poorly targeted opportunities for the diferent modalities of e-therapy assessment of expected changes according to these just described (Luneski, Konstantinidis, & Bamidis, processes of change. For example, King and Miner 2010), specifcally in the feld of anxiety and stress (2000) found writing about positive benefts from management (Parsons & Rizzo, 2008; Riva, Grassi, past upsetting experiences was benefcial to health Villani, Gaggioli, & Preziosa, 2007; Villani et al., and suggested this may have been due to enhanced 2012; Villani, Lucchetta, Preziosa, & Riva, 2009). self-regulation skills and a sense of self-efcacy. Moreover, afect detection, from verbal or nonver- Te study did not, however, measure changes in bal expressions, can be used to adapt the interac- self-regulation or self-efcacy and could not con- tion with an avatar or other Internet-based systems frm the proposed mechanisms of action. One (Yang & Bhanu, 2012). Many of these techniques fnal area to be studied is how afective computing have or could be used in e-therapy conditions. techniques can be used to detect emotions in text Afect generation techniques such as those discussed (Calvo & Kim 2012). by [see section on Afect Generation in this volume] are being used to make more expressive avatars. In AFFECTIVE COMPUTING AND VIRTUAL REALITY the next paragraphs, we list some examples of the In general, the most common application of use of afective computing in cybertherapy. VR in cybertherapy is in the treatment of anxi- ety disorders and phobias (Emmelkamp, 2005; AFFECTIVE COMPUTING AND EMOTIONAL Wiederhold & Rizzo, 2005). Indeed, VR expo- WRITING sure therapy (VRE) has been proposed as a new Many e-therapy approaches use writing activi- medium for exposure therapy (Riva, 2005) that ties as an essential element for refection. Tis is is safer, less embarrassing, and less costly than based on research that suggests that writing about reproducing real-world situations. Te rationale thoughts and feelings of past upsetting experiences is simple: in VR, the patient is intentionally is benefcial to some individuals. One of the leading confronted with the feared stimuli while allow- researchers in this feld has been J. W. Pennebaker ing the anxiety to attenuate. Avoiding a dreaded (1997) who developed a short-term (3–5 sessions) situation reinforces a phobia, and each succes- writing therapy involving participants writing sive exposure to it reduces the anxiety through

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the processes of habituation and extinction. In decreasing anxiety levels and increasing relaxation fact, VR can be described as an advanced imagi- levels. A similar approach was also used for reduc- nal system: an experiential form of imagery that ing anxiety before outpatient surgery (Mosso et al., is as efective as reality in inducing emotional 2009) and for improving stress management in responses (Vincelli, Molinari, & Riva, 2001). As a sample of nurses working with cancer patients underlined by Baños, Botella, and Perpiña, the (Villani et al., 2012; Villani et al., 2013). All these VR experience can help the course of therapy mobile protocols share a similar approach: frst, (Baños, Botella, & Perpiña, 1999) through “its they use the multimedia capabilities of mobile capability of reducing the distinction between the phones to train the user to the use of easy relax- computer’s reality and the conventional reality.” ation techniques (e.g., breathing control); second, In fact, “VR can be used for experiencing dif- they use the stress inoculation training paradigm ferent identities and . . . even other forms of self, (e.g., exposure to stressful situations) to help the as well” (p. 289). Te possibility of structuring user gain confdence in his or her ability to cope a large amount of realistic or imaginary stimuli with anxiety and fear stemming from the situation. and, simultaneously, of monitoring the possible Several research groups are also experiment- responses generated by the user of the technology ing with mobile, noninvasive data collection solu- ofers a considerable increase in the likelihood of tions for the automatic detection of afective states. therapeutic efectiveness as compared to tradi- For example, Gaggioli et al. developed PsychLog tional procedures (Riva & Davide, 2001). (http://sourceforge.net/projects/psychlog/) a free, A more detailed discussion related to the use of open source mobile psycho-physiological data col- afective computing methodologies in VR has been lection platform that allows gathering self-reported discussed in Bickmore’s chapter in this volume. psychological information and electrocardiogram (ECG) data (Gaggioli, Cipresso et al., 2012). Tese AFFECTIVE COMPUTING AND MOBILE DEVICES signals are sensed and wirelessly transmitted to the Mobile phone usage has already been harnessed mobile phone and gathered by a computing module in health care generally, but in the past few years that stores and processes the signals for the extrac- applications of this technology are also being tion of heart rate variability (HRV). Heart rate vari- explored in the mental health feld. In general, ability is considered a useful psycho-physiological the most common mobile feature used in men- measure because it refects the natural variability tal health is text messaging, both to help patients of heart rate in response to afective and cognitive to express themselves and to support them in states. Heart rate variability indexes have been used real-life settings (Preziosa et al., 2009). However, to characterize a number of psychological illnesses, an emerging group of researchers have tried to test including major depression and panic disorders the efectiveness of multimedia mobile phones (Kimhy et al., 2010). Using PsychLog, ECG data applied to emotion induction. Preziosa and col- can be correlated with user’s self-reported feelings leagues (Preziosa, Villani, Grassi, & Riva, 2006; and activities. In this way, it is possible to investigate Riva, Grassi, et al., 2007; Riva, Preziosa, Grassi, & the relationship between behavioral, psychological, Villani, 2006) tested the efcacy of a mobile proto- and physiological variables, as well as to monitor col for helping students manage exam stress in con- their dynamic fuctuations over time. trolled studies by comparing it with other media (DVD, mobile without video, mobile with video, Afective Computing for Positive CD). Te trial showed a better efcacy of video Technology mobile narratives experienced on mobile phones in Psychologists began to recognize that the reducing the level of exam stress and in helping the discipline’s focus on helping people with men- student to relax. Tis result was recently replicated tal health problems, the diagnostic-treatment with a larger sample (Grassi, Gaggioli, & Riva, model, left many outside their scope. Early in the 2011). In a diferent study, Grassi and colleagues past decade, psychologists such as Seligman and (Grassi, Gaggioli, & Riva, 2009) tested the abil- Csikszentmihalyi proposed increasing the atten- ity of mobile narratives (narrated video) supported tion giving to developing well-being (Seligman & by multimedia mobile phones to enhance positive Csikszentmihalyi, 2000). Positive psychology, as they emotions and reduce work anxiety in a sample of called it, was to study what makes people happier, commuters. Here again, the use of a mobile nar- in the broadest sense. Since then, the positive psy- rative was signifcantly better than other media in chology feld has fourished. In his book Authentic

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Happiness Seligman talked about the “three pillars” Te main objective of this new paradigm is to use of a good life (Seligman, 2002): technology to manipulate and enhance features of our personal experience for increasing wellness and t
the pleasant life: achieved through the presence generating strength and resilience in individuals, of positive emotions; organizations, and society (Wiederhold & Riva, t
the engaged life: achieved through engagement 2012). In the proposed framework (see Figure 41.1), in satisfying activities and utilization of one’s positive technologies are classifed according to their strengths and talents; efects on the pertinent features of personal experi- t
the meaningful life: achieved through serving a ence (Botella et al., 2012): purpose larger than oneself. t
Hedonic technologies are used to induce Notwithstanding its fast growth, some have positive and pleasant experiences. underlined that positive psychology has relevant t
Eudaemonic technologies are used to methodological limitations related to the focus support individuals in reaching engaging and and length of most studies (McNulty & Fincham, self-actualizing experiences. 2012). To address this issue, Riva recently suggested t
Social/Interpersonal technologies are used to that positive psychology may be the science of per- support and improve social integration and/or sonal experience (Riva, 2012a) in that its aim should connectedness between individuals, groups, and be understanding how it is possible to manipulate organizations. the quality of personal experience with the goals of increasing wellness and generating strengths and Afective computing can contribute to systems resilience in individuals, organizations, and society. at all these levels. Te frst dimension of positive In this view, positive functioning is a combina- technology is concerned with how to use technol- tion of three types of well-being (Keyes & Lopez, ogy to foster positive emotional states. At this level, 2002)—high emotional well-being, high psycholog- afective computing can exploit the link between ical well-being, and high social well-being—that are user experience and emotions (Norman, 2004). achieved through the manipulation of three charac- According to the model of emotions developed by teristics of our personal experience—afective qual- James Russell (Russell, 2003; 2005), it is possible to ity, engagement/actualization, and connectedness. modify the afective quality of an experience through Riva and colleagues (Riva, 2012b; Riva, Banos, the manipulation of “core afect.” Simply put, a posi- Botella, Wiederhold, & Gaggioli, 2012) also sug- tive emotion is achieved by increasing the valence gested that it is possible to combine the objectives (positive) and arousal (high) of core afect (afect of positive psychology with enhancements in infor- regulation) and by getting the user to attribute this mation and communication technologies (ICTs) in change to the contents (afective quality) of the pro- a move toward a new paradigm: positive technology. posed technological experience (object).

Features of Positive Involved Link with personal technologies technologies well-being experience targeted level by technology Broaden-and-build Hedonic level Afective computing, Emotional quality model of positive Technologies used to emotional design, (arousal, valence, emotions induce positive and engineering aesthetic, object) (Fredrickson, 2001, pleasant experiences hedonic computing 2004)

Eudaimonic level Persuasive computing Flow Connectedness Technologies used to Presence (Csikszentmihalyi, 2001) (Collective intentions, Serious gaming, support individuals in social presence, Simulations, Transformation of fow reaching engaging and empathy) e-health, (Delle fave, 1996; Riva self-actualizing experiences Virtual reality therapy et al, 2006)

Social & interpersonal Social capital Engagement/ level Persuasive computing Serious gaming, (Coleman, 1998; Hellwell actualization Technologies used to Simulations, & Putnam, 2004) (Challenge/skills, support and improve Social networks, Networked fow goals, presence) social integration and Social presence connectedness (Gaggioli et al, 2013)

Fig. 41.1 Riva Cyberpsychology Applications. Positive technology levels.

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Te second dimension requires exploring sys- t
Relativism, an understanding of the multiple tems that can engage us more with what we do, for perspectives that can be taken with regards to example during learning interactions. Tis has been common life events. a growing research focus of the artifcial intelligence t
Mindfulness, the conscious attention to the in education community (Calvo & D’Mello, 2012). present moment (or task), is of increasing interest For example, diferent authors (Calvo & D’Mello, in HCI (Sengers, 2011). 2011) have investigated the occurrence of engage- t
Refective insight, mainly ways to develop ment, together with confusion, delight, and other refection, emotion-regulation, and dialectical emotions in the context of learning environments. thinking. Engagement, confusion, frustration, boredom, t
Social consciousness, to promote the selfess curiosity, and happiness are the most commonly motivation to help others and take action toward occurring afective states observed across a range improving the human condition. of technologies (D’Mello, in press). Dimensional Te theory of fow, developed by positive psy- representations can also be used to model afect chology pioneer Mihaly Csikszentmihalyi (1990), during learning (Hussain, AlZoubi, Calvo, & provides a theoretical framework for addressing D’Mello, 2011). In one study, a fully automated, this challenge. Flow, or optimal experience, is a afect-sensitive, intelligent tutoring system for positive and complex state of consciousness that computer literacy—Afective AutoTutor—(Graesser is present when individuals act with total involve- et al., 2008) was developed (see D’Mello & Graesser, ment in a task. Te basic feature of this experience is this volume). Te Afective AutoTutor can promote the perceived balance between high environmental engagement by automatically detecting students’ opportunities for action (challenges) and adequate boredom, confusion, and frustration through gross personal resources in facing them (skills). Additional body movements, facial features, and contextual characteristics are intrinsic motivation, deep con- cues. Te afective states detected by the system centration, clear rules in and unambiguous feedback are used to adapt the computer tutor’s responses. from the task at hand, loss of self-consciousness, A pedagogical agent (i.e., avatar) synthesizes afect control of one’s actions and environment, and posi- via its verbal responses and nonverbal facial expres- tive afect. sions and speech intonation. Ghani and Deshpande (1994) identifed three It is less clear how afective computing or HCI factors that infuence the occurrence of fow in can be used to support the eudaemonic level. A pos- HCI: perceived control, ftness of task (i.e., the dif- sible strategy comes from Rogers (2006), who called ference between challenges and skills), and cogni- for a shift from “proactive computing” to “proac- tive spontaneity (“playfulness”). tive people,” in which “technologies are designed Te fnal level of positive technology—the social not to do things for people but to engage them and interpersonal one—is concerned with the use more actively in what they currently do” (p. 406). of technologies to support and improve the con- Following this path, Calvo and Peters (2012) have nectedness among individuals, groups, and orga- speculated on features that would support such sys- nizations. Here, afective computing may be used tems, particularly informed by the psychological to understand how to use technology to create a literature. Tese include: mutual sense of awareness. Following this vision, t
Intrapersonal skills, particularly introspection, Morris (2005) recently described how social net- refection, and self-criticism. working and pervasive computing technologies can t
Interpersonal skills, including social intelligence, be efectively used to help reduce feelings of social empathy, and compassion (see Bickmore’s chapter isolation and depression in elderly individuals. In [this volume] for a discussion of how these are their approach, sensor data measuring phone calls particularly important attributes in healthcare). and visits were used to derive public displays of t
Change and uncertainty features that remind social interactions with relatives and friends, which us how things change, are impermanent, and they introduced into selected elders’ homes. Tese uncertain. ambient displays, which refect data on remote and t
Balance of intrapersonal, interpersonal, and face-to-face interaction gathered by wireless sensor extrapersonal interests over the short and long networks, were intended to raise awareness of social term has been identifed as a key developmental connectedness as a dynamic and controllable aspect achievement (Sternberg, 2001). of well-being. According to fndings, this strategy

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was efective in reducing the feeling of social isola- regaining their own health (Pavel, 2012). As men- tion in elderly users. tioned earlier, preventively nurturing health and well-being often involves making life change (e.g., Afective Technologies for Smart Health toward health-promoting lifestyles), which itself is Recent epidemics of behavioral-related health often associated with a variety of fuctuating afec- issues, such as excessive alcohol, tobacco, or drug tive states. Afective computing with its main focus use, overeating, and lack of exercise, place people on developing technologies to sense, recognize, at risk of serious health problems. In 2013, the understand, and simulate afective processes such World Health Organization reported that world- as emotions, attitudes, personality, and motiva- wide obesity has more than doubled since 1980. tion, can therefore make important contributions It found that 1.5 billion adults were overweight, to novel smart health and well-being approaches of which 500 million were obese, and 43 million because emotion is a major motivating factor in children under the age of 5 were overweight. In decision making. the United States alone, obesity aficts 33.8% of For example, computer vision research- adults, 17% (or 12.5 million) of US children and ers are developing techniques to automatically teens, and obesity has tripled in one generation detect depression from video of the patients’ face (WHO, 2013). As well, excessive alcohol use is (Ellgring, 2008). In one such study, McIntyre et al. the third leading preventable cause of death in (2009) used active appearance models to track local the United States (79,000 deaths annually) and is shape and texture features in the face and then a responsible for a wide range of health and social Multiboost classifer to build the automated detec- problems (e.g., risky sexual behavior, domes- tion model. tic violence). Alcoholism is estimated to afect 10–20% of US males and 5–10% of US females Smart Health Behavior Change at some time in their lifetimes. Similar risks exist Interventions with other forms of substance abuse. Although multiple approaches to smart health Medicine and healthcare have therefore started and well-being involve sensing and monitoring to move toward fnding ways of preventively pro- the patient’s physiological signals related to their moting wellness rather than solely treating already health (e.g., ECG, BVP, GSR) in real-life settings established illness. Health promotion interventions using mobile technologies, communicating them aimed at helping people to change their behavior (in real time if needed) to their physicians, and toward healthier lifestyles are being deployed, but storing them for individual’s self-monitoring, other the epidemic nature of these problems calls for dras- approaches involve computer-based interventions tic measures to rapidly increase access to efective (CBIs) for behavior change and are delivered via the behavior change interventions for diverse popula- internet in the privacy and comfort of one’s home. tions. It is economically impossible for medical Tere are multiple advantages to CBIs for behavior and healthcare professionals to provide appropri- change (see Bewick et al., 2008; Hester, Squires, & ate medical care and health education for millions Delaney, 2005; Krebs, Prochaska, & Rossi, 2010; of people in need (and the numbers are grow- Lustria, Cortese, Noar, & Glueckauf, 2009 for ing). Interventions must involve the use of auto- useful reviews). In particular, research has already mation to provide help to people in need. Smart shown that computer-based assessment and feed- health and well-being technologies that leverage back systems can: the latest technological advances (e.g., sensors and sensors networks, actuators, robots, and vir- t
Increase accessibility and cost-efectiveness and tual assistants) to build intelligent care (e.g., smart decrease barriers to access: Research shows that as few homes for independent living, wearable prosthetics, as one or two motivational interviewing sessions life-style modifcation coaching) are therefore being often yield greater change than no counseling at researched and developed at increasing speed (Pavel, all (Miller & Rollnick, 2002), and yet these short 2012). One important aspect of smart health and interventions are often unavailable. Furthermore, well-being that afective computing is particularly even though follow-up sessions have been shown to relevant to deals with patient-centric approaches, increase positive outcome, they are unfortunately whether home- or mobile-based, to empower peo- not always ofered in medical and public health ple before they get sick (as well patients) to become settings due to a lack of human resources. On the active informed participants about preserving or other hand, there is some evidence that people will

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accept computer-based assessment and feedback or extrinsic motivation. Successful counselors programs (Lustria et al., 2009), which can be as are those who can inhibit their righting refex efective as interventions delivered by a person (Miller & Rollnick, 2002). Although we strive (Hester et al., 2005). Computer-based interventions to enable computers to be more human-like, can easily be reproduced and delivered over the computer-based systems inherently do not have Internet, on mobile devices, or in community-based such drives to overdo helping and therefore can be waiting rooms. at an advantage with respect to the righting refex. t
Increase confdentiality and sensitive information t
Demonstrate infnite patience: Another trap divulgence: Patients who engage in behaviors that for counselors is to try to move a patient toward can put them at risk (e.g., excessive drinking, change more quickly than he or she is ready unsafe sex, overeating) tend to report more for. Respecting the various stages of change information to a computer interviewer than to a (Prochaska & Velicer, 1997) and the patient’s pace human (Servan-Schreiber, 1986). Te knowledge toward change can be challenging for therapists. that a computer does not have an intrinsic value However, computers have infnite patience. system to judge the patient favors the divulgence of sensitive information. Provided with sensitive Other Smart Health Behavior information that a human would not have access Change Interventions to, CBIs can address issues that would otherwise be Personal informatics and quantifed self are yet ignored. another form of intervention, commonly associated t
Tailor information: Tailored communication, with a new motto “Know thyself” (through behav- intended to reach one specifc person’s needs ioral data that we store on the cloud). Te basic tenet versus generic communication (e.g., a brochure) of this research (Li et al., 2010) is that by refecting leads to better patient outcomes and is derived on our past we can improve the way we lead our lives. from individual assessment (see for reviews Krebs For example, if we are shown evidence that we are not et al., 2010; Noar, Benac, & Harris, 2007). getting enough exercise or are eating too much, we Computer-based interventions can assess and are more likely to change our behaviors accordingly. create a user model to deliver tailored information Tis has prompted the defnition of ecologi- and dynamically update the user profle over cal momentary assessment methods (EMA) that multiple adaptive sessions (Yasavur, Amini, & use technology to analyze and record behavior in Lisetti, 2012). Te user model can be produced naturalistic settings. An advantage of EMA over using afective computing models that can be used conventional psychological assessment includes the to target an intervention when certain emotions ability to assess the temporal relationship between are detected. variables, high ecological validity, and recording of t
Diminish variability: Tere is wide variability highly detailed information on subjective experience (from 25% to 100%) in diferent counselors’ rates (Barrett & Barrett, 2001). In the past, EMA-based of improvement among their patients (Miller & studies have been mainly done via paper-and-pencil Rollnick, 2002). In medical or public health measures. Today, smart phones allow researchers to settings, for example, personnel well trained in develop EMA tools that take advantage of the latest delivering motivational interventions are not advances in computational recognition and sensing always available. When trained personnel are not technologies to automatically detect critical (e.g., available, a good CBI can alleviate variability, stressful) events that can trigger data collection thus providing more people with motivating (Gaggioli & Riva, 2013). experiences. One such tool is MyExperience (http://myex t
Avoid righting refex: One of the traps many perience.sourceforge.net/), a mobile platform that counselors experience when they try to help people allows the combination of sensing and self-report is the righting refex or the tendency to set things to collect both quantitative and qualitative data on right, employing direct advocacy for the advantages user experience and activity. Te platform supports of change and thereby acting out patients’ 50 built-in smartphone sensors, which include ambivalence toward changing (increasing resistance GPS, GSM-based motion sensors, and device usage rather than simple awareness of discrepancy). information. Sensed events can be used to trigger Tis behavior is common within the traditional custom actions such as sending SMS messages to biomedical model of counseling, in which the the researcher and/or presenting in situ self-report counselor acts as an expert by providing advice surveys.

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Morris and colleagues (Morris et al., 2010) used Conclusion this platform to develop a mobile application that In this chapter, we reviewed how cyberpsychol- combines experience sampling of mood with exer- ogy and cybertherapy are combining with afective cises of emotional awareness and self-regulation computing to ofer new ways of delivering therapies inspired by cognitive behavioral therapy. Participants for mental health, positive psychology, and behavior were prompted via their mobile phones to report change toward health well-being. Sensors, multi- their moods several times a day on a Mood Map and modal user interfaces, mobile technologies, intelli- a series of single-dimension mood scales. Using the gent virtual characters, user modeling, and natural prototype, participants could also activate diferent language processing are all smart technologies that mobile therapy contents as needed. need to be coupled with progress in psychology, Consolvo and colleagues have developed guide- healthcare, and medicine to promote health and lines (Consolvo, Everitt, Smith, & Landay, 2006) well-being anytime, anywhere, and for everyone. to encourage physical activity: give proper credit; provide history, current status, and performance References Andersson, G. (2009). Using the Internet to provide cognitive measures; support social infuence (i.e., use social behaviour therapy. Behaviour Research and Terapy, 47(3), pressures and support); and consider practical con- 175–180. straints. Tese guidelines are becoming a common Andersson, G., Bergstrom, J., Hollandare, F., Carlbring, P., principle in the design of commercial “motiva- Kaldo, V., & Ekselius, L. (2005). Internet-based self-help tional” products in sports. for depression: Randomised controlled trial. [Randomized Controlled Trial Research Support, Non-U.S. Gov’t]. Te One of the underlying theories for these designs British Journal of Psychiatry, 187, 456–461. doi: 10.1192/ is that of cognitive dissonance (Festinger, 1957) bjp.187.5.456 which describes the psychological discomfort (dis- Aylett, R., Vala, M., Sequeira, P., & Paiva, A. (2007). FearNot! sonance) felt by a person when his or her behavior An emergent narrative approach to virtual dramas for is at odds with his or her attitudes or values. Tese anti-bullying education. Lecture Notes in Computer Science, 4871, 202–205. researchers argue that when the person is motivated Baños, R. M., Botella, C., & Perpiña, C. (1999). Virtual reality and (and has the option) to eliminate this internal con- psychopathology. CyberPsychology & Behavior, 2(4), 283–292. fict, behavior change can be achieved. Barak, A. (Ed.). (2008). Psychological aspects of cyberspace: Teory, For example, Lane and colleagues developed research, applications. Cambridge, UK: Cambridge University BeWell (https://www.bewellapp.org/) a real-time, Press. Barrett, L. F., & Barrett, D. J. (2001). An introduction to com- continuous sensing application that allows monitor- puterized experience sampling in psychology. Social Science ing of diferent user activities (sleep, physical activ- Computer Review, 19(2), 175–185. ity, social interaction) and provides feedback that Bergstrom, J., Andersson, G., Ljotsson, B., Ruck, C., Andreewitch, should promote healthier lifestyle decisions (Lane S., Karlsson, A., . . . Lindefors, N. (2010). Internet-versus et al., 2011). A similar application, YourWellness, group-administered cognitive behaviour therapy for panic disorder in a psychiatric setting: A randomised trial. BMC supports older adults in monitoring their emotional Psychiatry, 10, 54. doi: 10.1186/1471–244X-10–54 well-being, as well as other parameters of well-being Bewick, B. M., Trusler, K., Barkham, M., Hill, A. J., Cahill, J., & they consider important to their overall health Mulhern, B. (2008). Te efectiveness of web-based interven- (Doyle, O’Mullane, McGee, & Knapp, 2012). It tions designed to decrease alcohol consumption–a systematic can also check if some action or behavior change review. Preventive Medicine, 47(1), 17–26. Bordnick, P. S., Traylor, A., Copp, H. L., Graap, K. M., Carter, is required on the part of the older person. Other B., Ferrer, M., & Walton, A. P. (2008). Assessing reactivity to mobile well-being applications help users to moni- virtual reality alcohol based cues. Addictive Behavior, 33(6), tor and manage stress levels. Gaggioli and colleagues 743–756. (Gaggioli, Pioggia et al., 2012) describe a mobile Botella, C., Riva, G., Gaggioli, A., Wiederhold, B. K., Alcaniz, system designed to automatically detect psychologi- M., & Banos, R. M. (2012). Te present and future of positive technologies. [Research Support, Non-U.S. Gov’t]. cal stress events during daily activities from heart Cyberpsychology, Behavior and Social Networking, 15(2), 78– rate and activity data collected with a wearable ECG 84. doi: 10.1089/cyber.2011.0140 platform coupled to a smartphone. Detected stress Calvo, R. A., & D’Mello, S. (2012). Frontiers of afect-aware levels are provided to the user in the form of graphs learning technologies. IEEE Intelligent Systems. Submitted for displayed on the mobile phone application; apart publication. Calvo, R. A., & D’Mello, S. K. (Eds.). (2011). New perspectives from these instantaneous values, the user can check on afect and learning technologies. New York: Springer. the history of stress-level variations during the mon- Calvo, R. A., & Kim, S. (2012). Emotions in text: Dimensional itoring period. and categorical models. Computational Intelligence,.

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Calvo, R. A., & Peters, D. (2012). Positive comput- Graesser, A. C., D’Mello, S. K., Craig, S. D., Witherspoon, A., ing: Technology for a wiser world. ACM Interactions, 19(2), Sullins, J., McDaniel, B., & Gholson, B. (2008). Te rela- 28–31. tionship between afective states and dialog patterns during Carlbring, P., Ekselius, L., & Andersson, G. (2003). Treatment interactions with AutoTutor. Journal of Interactive Learning of panic disorder via the Internet: A randomized trial of Research, 19(2), 293–312. CBT vs. applied relaxation. Journal of Behavior Terapy and Grassi, A., Gaggioli, A., & Riva, G. (2009). Te Green Experimental Psychiatry, 34(2), 129–140. Valley: Te use of mobile narratives for reducing stress in Castelnuovo, G., Gaggioli, A., Mantovani, F., & Riva, G. commuters. CyberPsychology & Behavior, 12(2), 1–7. (2003). New and old tools in psychotherapy: Te use of Grassi, A., Gaggioli, A., & Riva, G. (2011). 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