How Can Geography and Mobile Phones Contribute to Psychotherapy?

Interdisciplinary relationships between Geography and Psychotherapy are an opportunity for innovation. Indeed, scientific works found on bibliographic databases and concerning this theme are scarce. Geographical sub-fields, such as the Geography of Emotions or Psychoanalytical Geography have started to emerge, theorizing about and interpreting feelings, emotions, moods, sufferings, of the chronically ill or diversified social groups and sites. But a less theoretical and more practical approach, in the sense of proposing, predicting and intervening, is lacking; as well as research into the possibilities offered by communication technologies and mobile phones. In the present work, we present the results of a review of the most relevant scientific works published internationally; we reflect on the contributions of Geography and mobile phones to psychosocial therapies and define the orientation and questions that should be posed in future research, from the point of view of geography and regarding psychotherapy. We conclude that the production of georeferenced data via mobile phones concerning the daily lives of people opens great possibilities for cognitive behavioural therapy and mental health. They allow for the development of personalized mood maps that locate the places where a person experiences greater or lesser stress on a daily basis; they allow for a cartography of emotions, a cognitive cartography of the places we access physically or through the Internet, of our feelings and psychosocial experiences. They open the door to the possibility of offering personalized psychotherapy treatments focusing on the ecological-environmental analysis of the places frequented by the person on a daily basis

Social Vices Associated with the use of Information Communication Technologies (ICTs) in a Private Christian Mission University, Southern Nigeria

This study is designed to address social problems associated with Information Communication Technologies (ICTs) and implications they portend on studentship in a Private Christian Mission University, Southern Nigeria. It tries to find out how the engagement of ICT devices results in social vices on campus. Drawing from recorded data between 2006 and 2012 academic year, the study reported six ICT tools associated with eight social- ills. Relying on raw data of 900 students disciplined within this period, the study reported that 187 students were expelled while 46 were advised to withdraw due to their involvement in ICT-related vices. Moreover, the study shows that 78 students served 1 year suspension while 589 students were suspended for one month. Findings of the study also revealed loss of all student rights infinitely for expelled students, nearly all rights for those advised-to-withdraw and all for a specified period for the suspended students. Practical implications of these disciplinary actions are discussed and potential future directions on this subject are proposed

Serious games in mental health treatment: Review of literature

Serious games are considered as powerful tools in learning and instructional purposes. Besides, serious games also have gained-their own popularity among children and adolescents. In these recent years, psychotherapists have realized the advantages of using games as the assistive tool in psychotherapy, so called therapeutic games. The use of serious games is already being applied in various types of mental illnesses, such as anxiety, depression, phobia,panic disorder, and eating disorder.This position paper describes the effectiveness of serious games in treating various mental illnesses among young patients.A systematic review points out the development and research in the circle of serious games for mental illnesses focusing on young patients.In this paper, the literature review consists of relevant serious games for therapeutic development since the year 2005

MIPA-Mobile: Monitoring Psychotherapy with Adolescents using Mobile Applications

Quality of life and well-being have gained increasing importance in psychological assessment and psychological intervention, since research has shown that psychopathology has a negative impact on quality of life and this impact varies in degree and how the impact of the effects themselves is combined (Stevanovic, 2013). In this sense, psychological interventions should not only be aimed at reducing psychopathological symptoms, but also increasing quality of life and well-being (Oliveira, Dias, Gonçalves, & Machado, 2008), assumed to be a mediator between health and spirituality (Oliveira & Junges, 2012). Over the last years, a remarkable development has been shown in the study of psychotherapy outcome monitoring, defined as a systematic and repeated assessment of psychological variables during the process of psychological intervention; the psychological intervention process may change as a result of feedback from the monitoring process as well (McAleavey, Nordberg, Kraus, & Castonguay, 2012). Outcome monitoring contributes to an individualized intervention with the client, adjusted to real needs. This work aims to present the MIPA-Mobile project, directed to develop and to test a new model for monitoring psychological intervention with adolescents, based on a computer application, integrating information from different key-informants.info:eu-repo/semantics/publishedVersio

The words of the body: psychophysiological patterns in dissociative narratives

Trauma has severe consequences on both psychological and somatic levels, even affecting the genetic expression and the cell\u2019s DNA repair ability. A key mechanism in the understanding of clinical disorders deriving from trauma is identified in dissociation, as a primitive defense against the fragmentation of the self originated by overwhelming experiences. The dysregulation of the interpersonal patterns due to the traumatic experience and its detrimental effects on the body are supported by influent neuroscientific models such as Damasio\u2019s somatic markers and Porges\u2019 polyvagal theory. On the basis of these premises, and supported by our previous empirical observations on 40 simulated clinical sessions, we will discuss the longitudinal process of a brief psychodynamic psychotherapy (16 sessions, weekly frequency) with a patient who suffered a relational trauma. The research design consists of the collection of self-report and projective tests, pre-post therapy and after each clinical session, in order to assess personality, empathy, clinical alliance and clinical progress, along with the verbatim analysis of the transcripts trough the Psychotherapy Process Q-Set and the Collaborative Interactions Scale. Furthermore, we collected simultaneous psychophysiological measures of the therapeutic dyad: skin conductance and hearth rate. Lastly, we employed a computerized analysis of non-verbal behaviors to assess synchrony in posture and gestures. These automated measures are able to highlight moments of affective concordance and discordance, allowing for a deep understanding of the mutual regulations between the patient and the therapist. Preliminary results showed that psychophysiological changes in dyadic synchrony, observed in body movements, skin conductance and hearth rate, occurred within sessions during the discussion of traumatic experiences, with levels of attunement that changed in both therapist and the patient depending on the quality of the emotional representation of the experience. These results go in the direction of understanding the relational process in trauma therapy, using an integrative language in which both clinical and neurophysiological knowledge may take advantage of each other

EXPLORING THE CLINICAL UTILITY OF MOBILE APPLICATIONS FOR PROMOTING AFFECT REGULATION AMONG CLIENTS WITH BEHAVIORAL HEALTH PROBLEMS

Nearly 25 percent of adults in the United States are diagnosed with a behavioral health condition, most commonly depression, anxiety disorders and substance use disorders. Each of these diagnoses is associated with significant disruptions in affect regulation which encompasses the capacity to up and down regulate emotions. Best practice treatment for these conditions includes psychotropic medications combined with individual or group-based psychotherapeutic modalities which regardless of the therapist’s theoretical orientation attempt to promote affect regulation through skill transfer and strategies for observing one’s ability to regulate emotions. Similarly, attention to regulatory capacity is central to many emerging self-help technologies involving smart phone applications. These technologies encourage users to observe, track, and offer strategies for regulating feelings through sleep, exercise, nutrition, alcohol use and many others. However, while anecdotally reported, few studies have examined the ways in which smart phone applications are incorporated into psychotherapy. In response, the current exploratory study used focus groups comprised of masters prepared behavioral health clinicians (N=25) to examine the appropriateness, accessibility, practicality and acceptableness of smart phone technologies as an adjuvant tool in the clinical setting. More specifically this study explored the use of technology to promote self-observation, skill transfer and subsequently affect regulation. Results suggested clinicians frequently use smart phone technologies in their practice and find these applications to be appropriate for tracking a range of symptoms (e.g. mood, substance use, sleep disruptions) and for promoting coping skills (e.g. meditation applications). Clinicians also reported these applications were fairly accessible and practical for use. Results indicated clinicians are judicious in their use of smart phone applications based on the client’s developmental needs and their particular symptom presentation. While these technologies were deemed effective, accessible and practical, focus group participants were wary of the impact of technology on society and the developing mind, citing that overuse of technology could promote an exacerbation of social isolation and loneliness. Further, practitioners reported that use of technology in psychotherapy could disrupt the interpersonal relationship in treatment. Respondents also reported they were unclear how to vet applications and desired additional training on their use in treatment. In conclusion, while smart phone applications were used and helpful for promoting affect regulation, future research needs to further examine best practice strategies for integrating smart phone applications into psychotherapeutic treatment, as well as refine technologies to fit more closely with the goals of psychotherapy. Keywords: Technology, Mobile Applications, Affect Regulation, Clinical Utilit

Digital Interventions for Depression : Predictors and Moderators of Treatment Adherence and Outcomes

Background. Depression is the leading cause of disability worldwide. Although evidence-based treatments exist, less than one-in-five people in high-income countries and less than one-in-twenty-seven in low-income countries receive treatment, giving rise to a treatment gap in mental healthcare. Digital interventions have been proposed as a solution to address the treatment gap. As an increasing number of public and private healthcare providers adopt digital interventions to meet the growing demand for treatment, the current thesis set out to examine the latest evidence-base for digital depression interventions and the extent to which new technologies may be used to identify at-risk individuals. Methods. Study 1 assessed the efficacy of digital interventions for the treatment of depressive symptoms based on the largest meta-analysis of digital depression interventions conducted to-date. Databases were searched for RCTs of a computer-, internet-, or smartphone-based interventions for depression versus an active or passive control condition. Participants were individuals with elevated symptoms of depression at baseline. Using a random-effects multilevel metaregression model, we examined effect size of treatment versus control (Hedges’ g) and explored moderators of treatment outcome. Study II was a secondary analysis of data from two RCTs (N=253) of a digital intervention for the prevention and treatment of major depression. Using logistic regression, we first examined participant characteristics as potential predictors of intervention dropout. We then assessed to what extent dropout could be predicted following completion of the first module using a combination of participant characteristics and intervention usage data. Dropout was defined as completing less than six modules. Study III was an observational study of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring. A smartphone app (Delphi) continuously monitored participants’ location and smartphone usage behavior over a 4- week period. The Oura Ring provided measures of activity, sleep and heart rate variability (HRV). Participants were prompted to report their daily mood and self-reported measures of depression, anxiety and stress were collected at baseline, midpoint and the end of the study using the DASS-21. Multilevel regression models were used to predict the association between smartphone and wearable data and mental health scores. Study IV was a secondary analysis of data from Study III in which we compared the accuracy of five supervised machine learning algorithms in the classification of individuals with normal versus above normal symptoms of depression, as defined by the DASS-21. Results. A systematic search of the literature in Study I identified 83 trials (N = 15,530). The overall effect size of digital interventions versus all controls was g = .52. Significantly lower effect sizes were found in studies conducted in real-world settings (effectiveness trials; g = .30) versus laboratory settings (efficacy trials; g = .59). Significantly higher effect sizes were found in interventions that involved human therapeutic guidance (g = .63) compared with unguided, self- help interventions (g = .34). Additionally, we found significant differences in effect size depending on the type of control used (WLC: g = .70; attention: g = .36; TAU: g = .31). No significant difference in outcomes was found between human-guided digital interventions and face-to-face therapy, although the number of studies was low. In Study II we found that lower level of education (OR=3.33) and both lower and higher age (a quadratic effect; age: OR=0.62, age^2: OR=1.55) were significantly associated with higher risk of dropout. In the analysis that aimed to predict dropout following completion of the first module, lower and higher age (age: OR=0.61, age^2: OR=1.58), medium versus high social support (OR=3.40) and a higher number of days to module completion (OR=1.05) predicted higher risk of dropout, whilst a self-reported negative event in the previous week was associated with lower risk of dropout (OR=0.22). In Study III, we found a significant negative association between the variability of locations visited and symptoms of depression (b = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (b = 0.24, p = 0.023) and time in bed and depression (b = 0.26, p = 0.020). Additionally, we found that wake after sleep onset significantly predicted symptoms of anxiety (b = 0.23, p = 0.035). Study IV revealed that a Support Vector Machine using only sensor-based predictors had an accuracy of 75.90% and an Area Under the Curve of 74.89%, whilst an XGBoost model that combined mood and sensor data as predictors classified participants as belonging to the group with normal or above normal levels of depressive symptoms with an accuracy of 81.43% and an Area Under the Curve of 82.31%. Conclusion. The current thesis provided evidence of the efficacy of digital interventions for the treatment of depression in a variety of populations. Importantly, we provided the first meta-analytic evidence that digital interventions are effective in routine healthcare settings, but only when accompanied by human guidance. Notwithstanding, adherence to digital interventions remains a major challenge with little more than 25% of patients completing the full intervention on average in real-world settings. Finally, we demonstrated that data from smartphone and wearable devices may provide valuable sources of data in predicting symptoms of depression, thereby helping to identify at-risk individuals.Tausta. Masennus on maailmanlaajuisesti keskeisimpiä toimintakykyä alentavia tekijöitä. Vaikka masennuksen hoitoon on kehitetty näyttöön perustuvia hoitomuotoja, hoidon tarjonta ei kohtaa kysyntää: korkean tulotason maissa vain viidennes hoitoa tarvitsevista saa hoitoa, ja matalan tulotason maissa hoitoa saavien osuus on vielä selkeästi alhaisempi. Hoidon saatavuusongelman ratkaisuksi on ehdotettu digitaalisia hoitomuotoja, ja digitaalisten masennushoitojen käyttö yleistyykin sekä julkisissa että yksityisissä hoitokonteksteissa. Tässä tutkimuksessa selvitettiin digitaalisten masennushoitojen tehokkuutta ja teknologiasovellusten käyttöä masennuksen riskiryhmien varhaisen tunnistamisen välineenä. Menetelmät. Ensimmäinen osatutkimus tarkasteli masennusoireiden hoidossa käytettävien digitaalisten hoitojen tehokkuutta. Tutkimuksessa toteutettiin tähän mennessä kattavin meta-analyysi satunnaistettuihin koeasetelmiin perustuvista masennusinterventiotutkimuksista, joissa hoitomuotona oli digitaalinen ohjelma ja aktiivinen tai passiivinen kontrollitilanne. Digitaaliset hoidot olivat internetissä tai muulla digitaalisella alustalla toteutettuja hoitoja (esimerkiksi tietokone- tai älypuhelinperustaisia hoitoja). Analyyseissä käytettiin monitasometaregressiomallinnusta, joka estimoi efektikoon koeryhmälle verrattuna kontrolliryhmään (Hedgesin g). Lisäksi tarkasteltiin digitaalisten hoitojen tehokkuuteen mahdollisesti vaikuttavia muokkaavia tekijöitä. Toisessa osatutkimuksessa selvitettiin digitaalisen hoidon keskeyttämistä ennakoivia tekijöitä kahden satunnaistetun vertailututkimuksen aineistossa (N=253) logistisilla regressiomalleilla. Tutkimuksessa tarkasteltiin yksilöllisten ominaisuuksien yhteyttä digitaalisen hoidon keskeyttämistodennäköisyyteen ja lisäksi sitä, miten yksilölliset ominaisuudet ja osallistujan käyttäytyminen digitaalisella alustalla ennustivat keskeyttämistodennäköisyyttä osallistujien suoritettua hoidon ensimmäisen moduulin. Kolmannessa osatutkimuksessa selvitettiin, voidaanko älylaitteilla kerätyillä käyttäytymiseen ja hyvinvointiin liittyvillä tiedoilla ennustaa mielenterveysoireilua. Tutkimuksen aineistona 60 24–68-vuotiasta aikuista, joita seurattiin Applen iPhone-sovelluksen (Delphi) ja Oura-sormuksen avulla neljän viikon ajan. Kerätty aineisto sisälsi osallistujien sijaintia ja puhelimen käyttöä koskevat tiedot sekä aktiivisuuden, unen ja syketaajuuden vaihtelun mittaukset ja päivittäin raportoidun mielialan. Masennus-, ahdistus- ja stressioireet mitattiin osallistujilta tutkimuksen alussa, puolivälissä ja lopussa itseraportointikyselyillä (DASS-21). Kerätyn aineiston ja päivittäin raportoidun mielialan yhteyttä masennus-, ahdistus- ja stressioireiluun tutkittiin monitasoregressiomalleilla. Neljäs osatutkimus toteutettiin samassa aineistossa kuin kolmas osatutkimus. Siinä vertailtiin viiden ohjatun koneoppimisalgoritmin tarkkuutta luokitella osallistujat masentuneiden ja terveiden luokkiin. Tulokset. Systemaattisen kirjallisuushaun perusteella ensimmäisen tutkimuksen meta-analyysiin sisällytettiin 83 tutkimusta (N=15,530). Digitaalisen intervention efektikoko kaikkiin kontrollitilanteisiin verrattuna oli g = 0.52. Kun digitaalinen hoito toteutettiin koeolosuhteiden ulkopuolella (ns. todellisessa elämässä), olivat efektikoot huomattavasti pienempiä (vaikuttavuus g = 0.30) kuin koeolosuhteissa havaitut (tehokkuus g = 0.59). Efektikoot olivat suurempia hoidoissa, joihin liittyi ohjaava ihmiskontakti (esimerkiksi terapeutti) (g = 0.63) verrattuna hoitoihin, joihin ei liittynyt ihmiskontaktia (g = 0.34). Efektikoot erosivat merkitsevästi myös kontrollitilanteesta riippuen (WLC: g = .70; attention: g = .36; TAU: g = .31). Ihmiskontaktin sisältävän digitaalisen hoidon havaittiin olevan yhtä tehokasta kuin kasvokkain tapahtuvan hoidon, joskin tutkimuksia tämän arvioimiseksi oli vain vähän. Toisessa osatutkimuksessa havaittiin, että matalampi koulutustaso (OR=3.33) sekä keskimääräistä(?) matalampi ja korkeampi ikä (kvadraattinen yhteys, ikä: OR=0.62, ikä^2: OR=1.55) ennustivat suurempaa todennäköisyyttä keskeyttää digitaalinen hoito. Niillä osallistujilla, jotka olivat suorittaneet hoidon ensimmäisen moduulin, keskeyttämistä ennustivat ikä (ikä: OR=0.61, ikä^2: OR=1.58), vähäisempi sosiaalinen tuki (OR=3.40) ja meneillään olevassa moduulissa jäljellä olevien päivien määrä (OR=1.05). Itseraportoitu ikävä tapahtuma edellisen viikon aikana oli puolestaan yhteydessä matalampaan keskeyttämistodennäköisyyteen (OR=0.22). Kolmannessa osatutkimuksessa havaittiin, että vähäisempi maantieteellinen liikkuvuus (b = −0.21, p = 0.037) ja suurempi unen (b = 0.24, p = 0.023) ja sängyssä vietetyn ajan määrä (b = 0.26, p = 0.020) olivat yhteydessä korkeampiin masennusoirepisteisiin. Lisäksi havaittiin yhteys nukahtamisen jälkeisen heräämisen ja ahdistuneisuusoireiden välillä (b = 0.23, p = 0.035). Neljäs tutkimus osoitti, että sensoripohjaisia ennustajia käyttävistä algoritmeista Support Vector Machine luokitteli ihmiset masennusoirepistemäärän perusteella oikein masentuneisiin ja terveisiin 75.90% tarkkuudella (käyrän alle jäävä pinta-ala (AUC) = 74.89%). Sensoripohjaisia ja päivittäisiin mielialamittauksiin perustuvia ennustajia yhdistävän XGBoost-algoritmin tarkkuus oli 81.43% (AUC = 82.31%). Johtopäätös. Tämä väitöskirjatutkimus tuotti uutta tietoa digitaalisten masennushoitojen tehokkuudesta. Tutkimuksessa esitettiin ensimmäinen kattava meta-analyysi, joka osoitti, että digitaaliset hoidot voivat olla tehokkaita psykiatrisen hoidon välineitä, mikäli digitaaliseen hoitoon sisältyy ohjaava ihmiskontakti. Digitaalisten hoitojen laajamittaisen käytön suurin haaste liittyy yhä hoitoon sitoutumiseen; keskimäärin vain joka neljäs potilas suorittaa hoidon loppuun. Tutkimustulosten mukaan kannettavien ja puettavien älylaitteiden avulla voidaan kerätä arvokasta tietoa, jonka avulla ennakoida masennusoireilua ja siten varhain tunnistaa erityisessä riskissä olevat