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Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer’s felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of “orchestration”. By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience; initiating and closing the interaction; habituating; helping improve on the users’ body awareness and engagement with emotional experiences; soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies.

In what way might public statues wrong people? In recent years, philosophers have drawn on speech act theory to answer this question by arguing that statues constitute harmful or disrespectful forms of speech. My aim in this paper will be add a different theoretical perspective to this discussion. I will argue that while the speech act approach provides a useful starting point for thinking about what is wrong with public statues, we can get a fuller understanding of these wrongs by drawing on resources from recent work in situated affectivity. I will argue that public statues can be understood as affective artifacts and that this can both help us understand both the deep affective wrongs caused by public statues and offer a possible explanation as to why some people are so strongly opposed to their removal.

In recent years, the field of deep learning has flourished, not only breaking through many difficult problems that are difficult to be solved by traditional algorithms but also bursting with greater vitality when combined with other fields. For example, product emotional design based on deep learning can integrate users' emotional needs into the actual product design. In this paper, we aim to use deep learning and affective technology in the creation of AR interactive picture books to transform the reading process from static to dynamic, enrich visual stimulation, and increase the fun and interactivity of reading. In this paper, based on the three-level theoretical model of emotion, the emotion labeling results are input to a deep neural network for learning, to establish an emotion-based recognition model for picture book images. The results show that the model can well analyze the emotion of images in AR picture books, and the accuracy of prediction is a big improvement compared with traditional machine recognition algorithms. The application of AR virtual implantation technology in interactive picture books on the market is often just a marketing gimmick while combining deep learning and emotional technology can better create diverse interactive picture books to meet children's emotional reading needs, enhance reading engagement, and stimulate children's creativity.

There is no agreement on how to formally incorporate affective data into statistical analysis and research conclusions. The information systems (IS) literature has recently published several position papers that have established a framework and perspective for using affective technology in IS research though. The frameworks have not been extensively tested, and are likely to evolve over time as empirical studies are conducted, and the validity of the methodologies is confirmed or disproved. A major goal of the current paper is to take the initial steps in translating the frameworks to usable methodologies, with application to improving our understanding of how to make effective empirical tests. This paper also investigates the adoption cycle of one of these technologies—electrodermal response (EDR) technologies—whose incarnation in the polygraph in forensic applications went through a complete adoption cycle in the twentieth century. The use of EDR response data in marketing research and surveys is nascent, but prior experience can help us to forecast and encourage its adoption in new research contexts. This research investigates three key questions: (1) What technology adoption model is appropriate for electrodermal response technology in forensic science? (2) What is the accuracy of affective electrodermal response readings? (3) What information is useful after superimposing affective EDR readings on contemporaneous survey data collection? Affective data acquisition technologies appear to add the most information when survey subjects are inclined to lie and have strong emotional feelings. Such data streams are informative, non-invasive and cost-effective. Informativeness is context-dependent though, and it relies on a complex set of still poorly understood human factors. Survey protocols and statistical analysis methods need to be developed to address these challenges .

Blockchain technology has been highlighted as one of the most promising technologies to emerge in the 21st century. However, the expansion of blockchain applications is progressing much more slowly than initially expected, despite its promising properties. These considerations motivate this study, which evaluates the drivers that facilitate the adoption of this technology through blockchain-based loyalty programs (BBLPs). The analytical framework used is the conceptual groundwork known as the cognitive–affective–normative model. Thus, we propose to explain the behavioural intention to use BBLPs (BEHAV) with two cognitive variables, namely perceived usefulness (USEFUL) and perceived ease of use (EASE); two affective variables, namely positive emotions (PEMO) and negative emotions (NEMO); and a normative factor, namely, the subjective norm (SNORM). A partial least squares-structural equation modelling analysis suggests that, to explain the expected response of BEHAV, only the positive relationships of the cognitive constructs with the response variable are significant. The results of the quantile regression suggest that the cognitive constructs, especially USEFUL, have a consistently significant positive influence across the entire response range of the response variable. The affective variables are significant in explaining the lower quantiles of BEHAV but not across the full response range. NEMO consistently has a significant negative influence on BEHAV in the percentiles at or below the median response. PEMO has a significantly positive influence on some of the BEHAV percentiles below the median, although this impact is not consistent across the lower quantiles of the median. The normative variable appears to have a residual influence on BEHAV, which, when significant (at the 90th quantile), is, contrary to expectations, negative. The results highlight that, while cognitive variables are essential in the acceptance of BBLPs, emotions—particularly negative ones—play an especially significant role among potential users whose level of acceptance falls below the central trend.

This paper proposes the use of social psychological and philosophical foundations for designing affective technology that promotes the experience of love. The adopted theoretical basis is the concept of productive love, which is heavily based on Enrich Fromm but also includes theories and scientific findings of numerous psychoanalysts, social psychologists, and philosophers. We conducted a review of the theory about the nature of love and found that social psychological and philosophical approaches differ regarding peoples' understandings. The findings were used to elaborate eight principles of productive love. Based on these principles, we derived criteria for designing affective technology when the objective is to promote productive love. We reviewed the existent studies on affective technologies and implemented the criteria into a system design, the Pictures' Call. A prototype of the system was pretested to illustrate how productive love technology could be based on established criteria.

The following test of the circadian phase-shift hypothesis for patients with winter depression (seasonal affective disorder, or SAD) uses low-dose melatonin administration in the morning or afternoon/evening to induce phase delays or phase advances, respectively, without causing sleepiness. Correlations between depression ratings and circadian phase revealed a therapeutic window for optimal alignment of circadian rhythms that also appears to be useful for phase-typing SAD patients for the purpose of administering treatment at the correct time. These analyses also provide estimates of the circadian component of SAD that may apply to the antidepressant mechanism of action of appropriately timed bright light exposure, the treatment of choice. SAD may be the first psychiatric disorder in which a physiological marker correlates with symptom severity before, and in the course of, treatment in the same patients. The findings support the phaseshift hypothesis for SAD, as well as suggest a way to assess the circadian component of other psychiatric, sleep, and chronobiologic disorders.

Affect is a critical factor in human decisions and behaviors within many social contexts. In the information and communication technology (ICT) context, a growing number of studies consider the affective dimension of human interaction with ICTs. However, few of these studies take systematic approaches, resulting in inconsistent conclusions and contradictory advice for researchers and practitioners. Many of these issues stem from ambiguous conceptualizations of various affective concepts and their relationships. Before researchers can address questions such as \"what causes affective responses in an ICT context\" and \"what impacts do affective responses have on human interaction with ICTs,\" a theoretical foundation for affective concepts and their relationships has to be established. This theory and review paper addresses three research questions: (I) What are pertinent affective concepts in the ICT context? (2) In what ways are these affective concepts similar to, or different from each other? (3) How do these affective concepts relate to or influence one another? Based on theoretical reasoning and empirical evidence, the affective response model (ARM) is developed. ARM is a theoretically bound conceptual framework that provides a systematic and holistic reference map for any ICT study that considers affect. It includes a taxonomy that classifies affective concepts along five dimensions: the residing, the temporal, the particular/general stimulus, the object/behavior stimulus, and the process/outcome dimensions. ARM also provides a nomological network to indicate the causal or co-occurring relationships among the various types of affective concepts in an ICT interaction episode. ARM has the power for explaining and predicting, as well as prescribing, potential future research directions.

Abstract Background and Objective Chronic low back pain (CLBP) is highly prevalent, with a substantial psychosocial burden. Pain has both sensory and affective components. The latter component is a significant driver of disability and psychiatric comorbidity but is often inadequately treated. Previously we reported that noninvasive transcranial direct current stimulation (tDCS) may modulate pain-associated affective distress. Here we tested whether 10 daily tDCS sessions aimed to inhibit the left dorsal anterior cingulate cortex (dACC), a region strongly implicated in the affective component of pain, would produce selective reduction in pain-related symptoms. Methods In this multisite, double-blinded, randomized placebo-controlled trial (RCT), 21 CLBP patients received 10 weekday sessions of 2-mA active tDCS or sham (20 minutes/session). A cathodal electrode was placed over FC1 (10–20 electroencephalography coordinates), and an identical anodal return electrode was placed over the contralateral mastoid. Participants rated pain intensity, acceptance, interference, disability, and anxiety, plus general anxiety and depression. Results Regression analysis noted significantly less pain interference (P =0.002), pain disability (P =0.001), and depression symptoms (P =0.003) at six-week follow-up for active tDCS vs sham. Omnibus tests suggested that these improvements were not merely due to baseline (day 1) group differences. Conclusions To our knowledge, this is the first double-blinded RCT of multiple tDCS sessions targeting the left dACC to modulate CLBP’s affective symptoms. Results are encouraging, including several possible tDCS-associated improvements. Better-powered RCTs are needed to confirm these effects. Future studies should also consider different stimulation schedules, additional cortical targets, high-density multi-electrode tDCS arrays, and multimodal approaches.

There is limited evidence of the safety and impact of task-shared care for people with severe mental illnesses (SMI; psychotic disorders and bipolar disorder) in low-income countries. The aim of this study was to evaluate the safety and impact of a district-level plan for task-shared mental health care on 6 and 12-month clinical and social outcomes of people with SMI in rural southern Ethiopia. In the Programme for Improving Mental health carE, we conducted an intervention cohort study. Trained primary healthcare (PHC) workers assessed community referrals, diagnosed SMI and initiated treatment, with independent research diagnostic assessments by psychiatric nurses. Primary outcomes were symptom severity and disability. Secondary outcomes included discrimination and restraint. Almost all (94.5%) PHC worker diagnoses of SMI were verified by psychiatric nurses. All prescribing was within recommended dose limits. A total of 245 (81.7%) people with SMI were re-assessed at 12 months. Minimally adequate treatment was received by 29.8%. All clinical and social outcomes improved significantly. The impact on disability (standardised mean difference 0.50; 95% confidence interval (CI) 0.35-0.65) was greater than impact on symptom severity (standardised mean difference 0.28; 95% CI 0.13-0.44). Being restrained in the previous 12 months reduced from 25.3 to 10.6%, and discrimination scores reduced significantly. An integrated district level mental health care plan employing task-sharing safely addressed the large treatment gap for people with SMI in a rural, low-income country setting. Randomised controlled trials of differing models of task-shared care for people with SMI are warranted.