158 Angewandte Psychologie
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- 2021 (3) (entfernen)
This dissertation presents an analysis of the relations to self and technology that emerge from and in the use of self-tracking technologies. The ethnographical study, combined with the Grounded Theory approach and a media analysis, demonstrates the complex intertwining or duality of control and care towards oneself that emerge or become possible in and through the application of ST technologies. ST devices assist in strengthening one's health and well-being in a playful way, building and maintaining a positive self-feeling, self-image and agency, and discovering unknown abilities and potentials within oneself. The ST technologies used provide orientation through complexity-reducing visualizations, highlighting patterns, and trend progression. They challenge through self-overload, dissatisfaction when not achieving goals, self-deception and distraction, narcissism and even loss of control - internally through compulsion to control as well as externally through loss of data otection and exploitation of private data by third parties, as well as handing over responsibility (in the form of decisions) to technology (algorithms) instead of self-responsibility. These two seemingly opposed yet concurrently existing self-relations reflect the dynamic between today's demands for self-responsibility (in health and performance terms) and the need for self-care and guidance for the many relevant, sometimes daily, decisions. They balance possibly existing tensions and ambiguities between the modes of self-relations that at first glance seem to be opposed and yet ultimately are jointly oriented towards the same goal, namely to master one's life (life maintenance) and to be in balance. The self-relations described in this thesis are supported, reinforced, or enabled by ST technology (and practice). Three different roles that ST technology can take in self-care and self-control were elaborated: technology as a means, a counterpart, and a promise. In relation to technology, another dialectic is visible, which shows the apparent contrast between its conception as a tool and means to achieve something and the approach to technology as an intimate counterpart (partner, nanny, coach) and a promise of salvation. The relationship with technology seems to intensify in and through the ST experience and takes on or is assigned a partner-like role by the users. Finally, the results indicate that the concept of (self-)optimization, contrary to its etymological meaning of a logic of increase, can also be understood differently, namely balancing. In this context, optimization does not necessarily mean the fastest, the highest, the strongest, but something that is achievable and satisfactory for the self - within the framework of the given and the desired. At the same time, the optimization understood as harmonizing and balancing in self-tracking becomes a lifelong task that, in principle, can never be completed because with the addition of new vital areas in life and throughout a lifetime also the individually understood and conceived balance often shifts.
Depressionen spielen eine gewichtige Rolle im Forschungsfeld der mentalen Gesundheit. Durch eine zunehmende Digitalisierung erscheint es naheliegend, depressive Störungen auch mithilfe internetbasierter Maßnahmen zu behandeln. Für den effektiven Einsatz internetbasierter interventionen existiert bereits vielfältige Evidenz. Bisher gibt es allerdings nur begrenzte Erkenntnisse darüber, ob internetbasierte Maßnahmen zur Behandlung von majoren Depressionen auch aktiven Kontrollbedingungen überlegen sind. Die Ergebnisse einer randomisiert-kontrollierten Studie (RCT = randomized controlled trial) zum Vergleich einer internetbasierten Intervention mit reiner Online-Psychoedukation (Studie 1) zeigen, dass dies zutrifft. Darüber hinaus ist die Erkenntnislage für Personen mit subklinischen depressiven Symptomen hinsichtlich ihrer langfristigen Wirksamkeit inkonsistent. Eine Meta-Analyse auf Basis der individuellen Teilnehmerdaten (IPD-MA = individual participant data meta-analysis) zur Evaluation der Wirksamkeit internetbasierter Maßnahmen zur Behandlung von subklinischen depressiven Symptomen (Studie 2) führte zu einer kurz-, mittel- und langfristigen Überlegenheit der Behandlungsgruppe im Vergleich zur Kontrollgruppe. Eine zusätzliche Analyse ergab, dass das Risiko für die Entwicklung einer majoren Depression innerhalb von 12 Monaten in der Interventionsgruppe im Vergleich zur Kontrollgruppe 28 % geringer ist. Für die Implementierung internetbasierter Maßnahmen in die Routineversorgung ist es gegebenenfalls erforderlich, geeignete Maßnahmen zu ergreifen, um mit den Studienergebnissen vergleichbar hohe Effekte bei den Betroffenen zu erreichen. Die Identifizierung von Faktoren, die den Behandlungserfolg beeinflussen, ist von großem Interesse, um internetbasierte Maßnahmen geeigneten Populationen kosteneffektiv und mit maximalem Nutzen zur Verfügung stellen zu können. Die IPD-MA für Personen mit subklinischen Symptomen (Studie 2) zeigte, dass eine hohe initiale Symptomschwere und höheres Alter zu einer niedrigeren depressiven Symptomatik zum Post-Messzeitpunkt führten. Eine weitere IPD-MA für Personen mit majorer Depression (Studie 3) identifizierte darüber hinaus ein geringes Bildungsniveau als Risikofaktor für eine Symptomverschlechterung. Die Ergebnisse des RCT (Studie 1) lassen vermuten, dass für Teilnehmer mit vorangehender Psychotherapieerfahrung Online-Psychoedukation bereits hilfreich ist, während diese Maßnahme für Therapie-Neulinge keinen Nutzen zeigt, sie aber erheblich von der internetbasierten Intervention zur Behandlung ihrer Symptome profitieren. Angesichts der zunehmenden Nutzung internetbasierter Maßnahmen zur Behandlung von depressiven Symptomen erscheint es erforderlich, das Augenmerk neben dem Behandlungsnutzen auch auf die unerwünschten Nebenwirkungen zu lenken, für deren Berichterstattung und Handhabung es in diesem Forschungsfeld bisher kaum einen Konsens gibt. Die IPD-MA zur Behandlung von majoren Depressionen (Studie 3) konnte zeigen, dass das Risiko für eine reliable Verschlechterung von der Ausgangssituation bis zum Post-Messzeitpunkt in der Interventionsgruppe im Vergleich zur Kontrollgruppe signifikant geringer war. Eine langfristige Überlegenheit ließ sich nicht konsistent bestätigen. Der RCT (Studie 1) zeigte keinen signifikanten Unterschied in den Verschlechterungsraten zwischen den beiden Versuchsgruppen. In Studie 2 war die Interventionsgruppe der Kontrollgruppe zum Post-Messzeitpunkt und nach 12 Monaten hinsichtlich einer Symptomsteigerung um 50 % überlegen. Wie negative Effekte von internetbasierten Maßnahmen zukünftig idealerweise definiert und berichtet werden sollten, bedarf weiterer Klärung.
Mental health is an important factor in an individuals' life. Online-based interventions have been developed for the treatment of various mental disorders. During these interventions, a large amount of patient-specific data is gathered that can be utilized to increase treatment outcomes by informing decision-making processes of psychotherapists, experts in the field, and patients. The articles included in this dissertation focus on the analysis of such data collected in digital psychological treatments by using machine learning approaches. This dissertation utilizes various machine learning methods such as Bayesian models, regularization techniques, or decision trees to predict different psychological factors, such as mood or self-esteem, dropout of patients, or treatment outcomes and costs. These models are evaluated using a variety of performance metrics, for example, receiver operating characteristics curve, root mean square error, or specialized performance metrics for Bayesian inference. These types of analyses can support decision- making for psychologists and patients, which can, in turn, lead to better recommendations and subsequently to increased outcomes for patients and simultaneously more insight about the interplay between psychological factors. The analysis of user journey data has not yet been fully examined in the field of psychological research. A process for this endeavor is developed and a technical implementation is provided for the research community. The application of machine learning in this context is still in its infancy. Thus, another contribution is the exploration and application of machine learning techniques for the revelation of correlations between psychological factors or characteristics and treatment outcomes as well as their prediction. Additionally, economic factors are predicted to develop a process for treatment type recommendations. This approach can be utilized for finding the optimal treatment type for patients on an individual level considering predicted treatment outcomes and costs. By evaluating the predictive accuracy of multiple machine learning techniques based on various performance metrics, the importance of considering heterogeneity among patients' behavior and affect is highlighted in some articles. Furthermore, the potential of machine learning-based decision support systems in clinical practice has been examined from a psychotherapists' point of view.