Avsikten med studien är att ge en fördjupad förståelse av hur terapeuter upplever att relationen till patienten påverkas av att genomföra psykoterapeutisk behandling online. Designen var en kvalitativ studie med strategiskt urval genom semi-strukturerade intervjuer. I studien har 11 terapeuter med erfarenhet av individuell psykoterapeutisk videobehandling medverkat. Alla deltagare har arbetat med psykologi inom olika sjukvårdsregioner och innehar som minst grundläggande utbildning i psykoterapi. Analysmodellen som användes var tematisk textanalys Malterud (2014). Huvudteman som framkom var. 1. Teraputens inställning till mediet och dess förutsättningar kan påverka relationen, 2. Pt-online är inte som sedvanlig terapi- digitala förutsättningar påverkar mötet och 3. Relationellt samspel blir annorlunda (även om vissa aspekter kan vara lika). Resultaten visar deltagarnas olika upplevelser av hur det relationella samspelet förändras under psykoterapeutisk behandling online. Det kan vara värdefullt för terapeuter att veta vad de kan göra för att överbrygga och kompensera för bortfall av sinnesintryck under psykoterapeutiska videosamtal.

Online conversations via video have nowadays partially replaced face to face contact, and there are some challenges that occur in video conversations, e.g., time delay and the placement of the camera which leads to eliminating the possibility for direct eye contact, and time delay. There are experiences that these conditions disturb the social connection with the other, which does not mainly affect problem solving effectiveness, but something relational seems to alter. This study investigated if eye-movement can used to measure social connectedness during video conversation. In order to study this, two custom made units (“NUNAs”) with robotized cameras were built, which e.g. allow for unprocessed eye contact. A feasibility pilot was conducted, which data this report is based on. The experimental conditions were 1, available eye contact, 2, skewed visuality and 3, time delayed signal. The participants (n = 12) took part pairwise, they were familiar with online conversation and did not self report autism spectrum disorder. They were instructed to talk naturally and unprobed through video using the NUNA’s for 30-50 minutes, and the three conditions were changing every 4 minutes. Eye movement behavior within the three conditions was compared, such as the duration of the visit in the eyes area. None of the results showed a significant difference. Factors that might lead to these results, such as limited sample size, are discussed. Eye movement in relational processes may be needed to be studied on a dyadic level, and not on individual. / Onlinekonversationer via video har numera delvis ersatt kommunikation ansikte mot ansikte. Det finns dock vissa utmaningar som uppstår vid videosamtal, exempelvis placeringen av kameran som leder till ett omöjliggörande av ögonkontakt, och tidsfördröjning. Det finns erfarenheter av att dessa förhållanden stör känslan av samhörighet till den andre, vilket inte påverkar problemlösningens effektivitet nämnvärt, istället är det de relationella aspekterna som förändras. Denna studie undersökte om ögonrörelser kan användas för att mäta social anknytning under videokonversation. För att studera detta byggdes två specialtillverkade enheter (”NUNA”) med robotiserade kameror, som t.ex. tillåter obearbetad ögonkontakt. En pilotstudie genomfördes och data ifrån studien har legat till grund för denna rapport. Experimentförhållandena var 1, tillgänglig ögonkontakt, 2, skev visualitet och 3, fördröjd signal. Deltagarna (n = 12) deltog parvis, de var bekanta med onlinekonversationer och rapporterade själva att de inte var inom autism spekrat. De instruerades att prata naturligt genom video med hjälp av NUNA i 30-50 minuter, och de tre förhållandena ändrades var 4:e minut. Ögonrörelsebeteenden inom de tre tillstånden jämfördes, såsom längden på besöket i ögonområdet. Inget av resultaten visade någon signifikant skillnad. Faktorer som kan leda till dessa resultat, såsom begränsad urvalsstorlek samt att ögonrörelser i relationella processer kan behöva studeras på dyadisk nivå, och inte på individnivå, diskuteras.

Human interaction has been a focus of study in psychology for a long time, but until recentlyonly very specific aspects of interaction have been considered. This approach leads to studiesthat gave insights into certain contexts, like parent-child or patient-clinician dynamics [1], [2].There are many theories on narrow subtopics, but no overarching unifying theory describingthe essence of human interaction. To fill this gap, Niclas Kaiser and Emily Butler recentlydeveloped the theory of Social Breathing, considering the participants as parts of a complexsystem [3] which currently lacks a quantitative description. According to their theory, the presence of Social Breathing in an interaction leads to com-plex patterns in multi-variate physiological time series of interacting people. How exactlythis effect could manifest in experimental data remains unclear. However, to distinguishinteraction data from non-interaction data from three independent sources, we tested ex-ploratory analysis methods, including principal component analysis (PCA), cross-wavelettransform and a convolutional neural network. We also developed a Maximum SpectralSimilarity Estimation method based on the cross-wavelet transform. All three data analyzed sets shared the general setup of two participants being in differentvariations of a conversation while one or more (neuro-)physiological variables were tracked. APCA of correlation coefficients we applied to the first data set by Guan et al. from 2015 [4]showed differences in participants’ dynamics, which a support vector machine could capitalizeon with a maximum classification accuracy of 72%. Because physiological dynamics during an interaction are not stable over time, we usedcross-wavelet transforms for time-resolved frequency information. To check for any transientspectral patterns that could be attributed to Social Breathing, we developed Maximum SpectralSimilarity Estimation. It showed that some variables contained spectra that were more similarwithin interaction data compared with less interactional or fake data. This pattern was trueboth for the previously named data source and for the second data source gathered by NiclasKaiser and described in ref. [5]. In this setup, two participants engaged in different stages ofvarying interactional intensity. Contrary to our expectations, interaction distractions resultedin increased similarity. The final experimental setup called NUNA was specifically designed for investigating SocialBreathing in neuro-physiological time series. We used early pilot data from NUNA in this workfor a proof of concept. Training a convolutional neural network on cross-wavelet transformsof functional near-infrared spectroscopy (fNIRS) brain data to recognize reoccurring frequencypatterns of Social Breathing was unsuccessful. Maximum Spectral Similarity Estimation didnot show convincing differences in spectral similarity between different modes of conversationand fake data. We propose adaptations to the experimental setup and the preprocessing of thedata to better identify Social Breathing.