How to use smartphones for less obtrusive ambulatory mood assessment and mood recognition

Bachmann, Anja; Klebsattel, Christoph; Budde, Matthias; Riedel, Till; Beigl, Michael; Reichert, Markus; Santangelo, Philip; Ebner‐Priemer, Ulrich W.

doi:10.1145/2800835.2804394

Cited by 28 publications

(26 citation statements)

References 11 publications

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“…Across studies, 93–100% of respondents found apps easy to learn or use (Dennis et al 2015; Kenny et al 2015; Sacco, 2015). In addition, participants rated apps as useful (Kenny et al 2015), convenient, user-friendly (Bachmann et al 2015), youth-friendly and non-invasive (Reid et al 2009). Despite these positive experiences, technological difficulties (e.g.…”

Section: Resultsmentioning

confidence: 99%

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A systematic review of the psychometric properties, usability and clinical impacts of mobile mood-monitoring applications in young people

et al. 2017

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Background. Mobile mood-monitoring applications are increasingly used by mental health providers, widely advocated within research, and a potentially effective method to engage young people. However, little is known about their efficacy and usability in young populations. Results. We reviewed 25 articles. Studies on the psychometric properties of mobile mood-monitoring applications were sparse, but indicate questionable to excellent internal consistency, moderate concurrent validity and good usability. Participation rates ranged from 30% to 99% across studies, and appeared to be affected by methodological factors (e.g. payments) and individual characteristics (e.g. IQ score). Mobile mood-monitoring applications are positively perceived by youth, may reduce depressive symptoms by increasing emotional awareness, and could aid in the detection of mental health and substance use problems. There was very limited evidence on potential negative impacts.Conclusions. Evidence for the use of mood-monitoring applications in youth is promising but limited due to a lack of high-quality studies. Future work should explicate the effects of mobile mood-monitoring applications on effective selfregulation, clinical outcomes across disorders and young people's engagement with mental health services. Potential negative impacts in this population should also be investigated, as the adult literature suggests that application use could potentially increase negativity and depression symptoms.

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Section: Resultsmentioning

confidence: 99%

“…In addition, participants rated apps as useful (Kenny et al 2015), convenient, userfriendly (Bachmann et al 2015), youth-friendly and non-invasive ). Despite these positive experiences, technological difficulties (e.g.…”

Section: Participants' Perceptionsmentioning

confidence: 99%

A systematic review of the psychometric properties, usability and clinical impacts of mobile mood-monitoring applications in young people

et al. 2017

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“…Several applications have been developed to track personalized mood [18,9,1,4,2]. MoA 2 developed by Bachmann et al [2] is a context-aware smartphone application which provides the ambulatory assessment of mood, tiredness and stress level. The main features of this tool are (1) mood assessment and (2) mood recognition.…”

Section: Related Workmentioning

confidence: 99%

Usability of a mood assessment smartphone prototype based on humor appreciation

Hafiz

Maharjan

Kumar

2018

Proceedings of the 20th International Conference on Human-Computer Interaction With Mobile Devices and Services Adjunct

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Humor appreciation is one of the determinants of individual's mood and can be assessed through jokes. We have developed a functional prototype called Humoris which asks users to select the funniest punchline and register their affective response to the jokes. Based on users' responses, the application predicts and displays their short-term mood using emoticons. Our smartphone prototype is evaluated using the 'think-aloud' method with 9 participants. Usability of Humoris was examined by System Usability Scale questionnaire which gave an average score of 79.44 (SD=8.08). Based on our findings, participants liked the application interface as well as the mood prediction but some of them found some jokes difficult to understand.

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“…As a result of another study the MoA 2 application [16] was developed. This application had the purpose of recognizing and evaluating mood, fatigue and stress levels in a non-intrusive way, based on smartphone sensor readings.…”

Section: Studies Overviewmentioning

confidence: 99%

“…In this case, the number of visits to each web domain are counted and the category that it belongs to is registered [9]. Finally, some studies collect information related with apps usage to evaluate the categories of used apps and how much time the user spent in each category [16]. This kind of data, like web browser history, allows to analyze the user habits.…”

Section: A Monitoring Mechanismsmentioning

confidence: 99%

Overview of context-sensitive technologies for well-being

Freitas¹,

Brito²,

Baras³

et al. 2017

2017 International Conference on Internet of Things for the Global Community (IoTGC)

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Today smart devices such as smartphones, smartwatches and activity trackers are widely available and accepted in most developed societies. These devices present a broad set of sensors capable of extracting detailed information about different situations of daily life, which, if used for good, have the potential to improve the quality of life not only for individuals but also for the society in general. One of the key areas where this type of information can help to improve the quality of life is in healthcare since it allows to monitor and infer the current level of well-being of the smart devices carriers. In this paper, some of the available literature about well-being sensing through context-aware data is reviewed. Also, the main types of mechanisms used in these studies are identified. These mechanisms are related to monitoring, generalization, inference, feedback, energy management and privacy. Furthermore, a description of the mechanisms used in each study is presented.

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How to use smartphones for less obtrusive ambulatory mood assessment and mood recognition

Cited by 28 publications

References 11 publications

A systematic review of the psychometric properties, usability and clinical impacts of mobile mood-monitoring applications in young people

A systematic review of the psychometric properties, usability and clinical impacts of mobile mood-monitoring applications in young people

Usability of a mood assessment smartphone prototype based on humor appreciation

Overview of context-sensitive technologies for well-being

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