Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing

Vaid, Sumer S.; Harari, Gabriella M.

doi:10.1007/978-3-030-31620-4_5

Cited by 16 publications

(8 citation statements)

References 87 publications

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“…Modern smartphones are ubiquitous devices that are equipped with a number of sensors that can sense physical activity, geolocation, communication patterns, and the speech of their owners as they go about their day-to-day lives. This sensing capability offers a potential new paradigm for diagnosis and assessment, where instead of asking patients to report their feelings and behaviors relevant to their mental health, it might be possible to infer this information passively and objectively from smartphone-collected data [ 3 ]. Given enough data over time, these inferences may prove sufficient to act as a novel severity measure for depression and anxiety disorders.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study

et al. 2021

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Background The ability to objectively measure the severity of depression and anxiety disorders in a passive manner could have a profound impact on the way in which these disorders are diagnosed, assessed, and treated. Existing studies have demonstrated links between both depression and anxiety and the linguistic properties of words that people use to communicate. Smartphones offer the ability to passively and continuously detect spoken words to monitor and analyze the linguistic properties of speech produced by the speaker and other sources of ambient speech in their environment. The linguistic properties of automatically detected and recognized speech may be used to build objective severity measures of depression and anxiety. Objective The aim of this study was to determine if the linguistic properties of words passively detected from environmental audio recorded using a participant’s smartphone can be used to find correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment. Methods An Android app was designed to collect periodic audiorecordings of participants’ environments and to detect English words using automatic speech recognition. Participants were recruited into a 2-week observational study. The app was installed on the participants’ personal smartphones to record and analyze audio. The participants also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Words detected from audiorecordings were categorized, and correlations were measured between words counts in each category and the 4 self-report measures to determine if any categories could serve as correlates of social anxiety disorder, generalized anxiety disorder, depression, or general impairment. Results The participants were 112 adults who resided in Canada from a nonclinical population; 86 participants yielded sufficient data for analysis. Correlations between word counts in 67 word categories and each of the 4 self-report measures revealed a strong relationship between the usage rates of death-related words and depressive symptoms (r=0.41, P<.001). There were also interesting correlations between rates of word usage in the categories of reward-related words with depression (r=–0.22, P=.04) and generalized anxiety (r=–0.29, P=.007), and vision-related words with social anxiety (r=0.31, P=.003). Conclusions In this study, words automatically recognized from environmental audio were shown to contain a number of potential associations with severity of depression and anxiety. This work suggests that sparsely sampled audio could provide relevant insight into individuals’ mental health.

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Section: Introductionmentioning

confidence: 99%

Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study

et al. 2021

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“…Different domains, such as health care, medical research, and technological and pharmaceutical companies, have become increasingly dependent on collecting and sharing data digitally to develop health care and new medical and technological products [ 1 - 3 ]. It has also led individuals to take a more active role in seeking out health information, thus managing and promoting their own health by having access to new health websites and mobile apps [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Preferences of the Public for Sharing Health Data: Discrete Choice Experiment

Johansson¹,

Bentzen²,

Shah³

et al. 2021

JMIR Med Inform

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Background Digital technological development in the last 20 years has led to significant growth in digital collection, use, and sharing of health data. To maintain public trust in the digital society and to enable acceptable policy-making in the future, it is important to investigate people’s preferences for sharing digital health data. Objective The aim of this study is to elicit the preferences of the public in different Northern European countries (the United Kingdom, Norway, Iceland, and Sweden) for sharing health information in different contexts. Methods Respondents in this discrete choice experiment completed several choice tasks, in which they were asked if data sharing in the described hypothetical situation was acceptable to them. Latent class logistic regression models were used to determine attribute-level estimates and heterogeneity in preferences. We calculated the relative importance of the attributes and the predicted acceptability for different contexts in which the data were shared from the estimates. Results In the final analysis, we used 37.83% (1967/5199) questionnaires. All attributes influenced the respondents’ willingness to share health information (P<.001). The most important attribute was whether the respondents were informed about their data being shared. The possibility of opting out from sharing data was preferred over the opportunity to consent (opt-in). Four classes were identified in the latent class model, and the average probabilities of belonging were 27% for class 1, 32% for class 2, 23% for class 3, and 18% for class 4. The uptake probability varied between 14% and 85%, depending on the least to most preferred combination of levels. Conclusions Respondents from different countries have different preferences for sharing their health data regarding the value of a review process and the reason for their new use. Offering respondents information about the use of their data and the possibility to opt out is the most preferred governance mechanism.

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“…The feasibility of building passive EMA systems has been greatly improved by the smartphone revolution. Smartphones are ubiquitous and affordable consumer electronics, which are equipped with a wide range of sensors that can enable the type of sensing or monitoring necessary to perform passive EMA [ 7 ]. A brief survey of some of the existing work using passive EMA in the mental health space follows, including a more detailed review of studies that have used ecological audio data to predict mental health state.…”

Section: Introductionmentioning

confidence: 99%

The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study

Matteo¹,

Fotinos²,

Lokuge³

et al. 2020

JMIR Form Res

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Background Objective and continuous severity measures of anxiety and depression are highly valuable and would have many applications in psychiatry and psychology. A collective source of data for objective measures are the sensors in a person’s smartphone, and a particularly rich source is the microphone that can be used to sample the audio environment. This may give broad insight into activity, sleep, and social interaction, which may be associated with quality of life and severity of anxiety and depression. Objective This study aimed to explore the properties of passively recorded environmental audio from a subject’s smartphone to find potential correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment. Methods An Android app was designed, together with a centralized server system, to collect periodic measurements of the volume of sounds in the environment and to detect the presence or absence of English-speaking voices. Subjects were recruited into a 2-week observational study during which the app was run on their personal smartphone to collect audio data. Subjects also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the environmental audio of 84 participants with sufficient data, and correlations were measured between the 4 audio features and the 4 self-report measures. Results The regularity in daily patterns of activity and inactivity inferred from the environmental audio volume was correlated with the severity of depression (r=−0.37; P<.001). A measure of sleep disturbance inferred from the environmental audio volume was also correlated with the severity of depression (r=0.23; P=.03). A proxy measure of social interaction based on the detection of speaking voices in the environmental audio was correlated with depression (r=−0.37; P<.001) and functional impairment (r=−0.29; P=.01). None of the 4 environmental audio-based features tested showed significant correlations with the measures of generalized anxiety or social anxiety. Conclusions In this study group, the environmental audio was shown to contain signals that were associated with the severity of depression and functional impairment. Associations with the severity of social anxiety disorder and generalized anxiety disorder were much weaker in comparison and not statistically significant at the 5% significance level. This work also confirmed previous work showing that the presence of voices is associated with depression. Furthermore, this study suggests that sparsely sampled audio volume could provide potentially relevant insight into subjects’ mental health.

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Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing

Cited by 16 publications

References 87 publications

Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study

Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study

Preferences of the Public for Sharing Health Data: Discrete Choice Experiment

The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study

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