Automatic detection of social rhythms in bipolar disorder

Abdullah, Saeed; Matthews, Mark; Frank, Ellen; Doherty, Gavin; Choudhury, Tanzeem

doi:10.1093/jamia/ocv200

Cited by 198 publications

(205 citation statements)

References 20 publications

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“…Only two studies have investigated Activity for clinical samples of patients; Beiwinkel et al [21] reported a negative correlation in a between-subject analysis, but the within subject analysis that we reported on had almost zero correlation (r = .02, p = .87), and we reported on Abdullah et al [54] weighting coefficient as the correlation direction (w = 3.79x10 4 ). Distance was analyzed in the same two studies, again with an almost zero correlation by Beiwinkel et al [21] (r = .03, p = .66).…”

Section: Clinical Samples Of Patients Diagnosed With Ud or Bdcontrasting

confidence: 47%

“…In a study where Muaremi et al [60] used microphone features to classify mood, they achieved an F1 accuracy of 82%, and discovered speaking time as the best-performing feature. By expanding to include GPS and accelerometer-related features, Abdullah et al [54] achieved an F1 accuracy of 85.5% with the GPS feature: Distance achieving largest weighting. The same F1 accuracy was achieved by Palmius et al [62] who used 50 location based features from GPS, WIFI, and GSM cell tower ID.…”

Section: Feature Combined Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Rohani¹,

Faurholt‐Jepsen²,

Kessing³

et al. 2017

Preprint

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Background: A number of studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable technologies and mood symptoms in affective disorders (unipolar disorder and bipolar disorder). However, individual studies have reported on different and sometimes contradicting results and no quantitative systematic review of the correlation between objective behavioral features and mood symptoms has been published. Objective: The objectives of this systematic review were to 1) provide an overview of correlations between objective behavioral features and mood symptoms reported in the literature, and 2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help in the identification on which objective features have shown most promising results across studies. Methods: A systematic review of the scientific literature, reported according to the Preferred Reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines was conducted. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, Pubmed, DBLP computer science bibliography, HTA, DARE, Scopus and Science Direct were searched and supplemented by hand examination of reference lists. The search was limited to studies published 2007-2017. Results: A total of 34 studies were eligible for the present review. They investigated 71 unique objective features divided into six different types covering 16 various sensor data inputs. Several objective features were found to have consistent correlation directionality with mood assessment (e.g., the amount of home stay, sleep duration, vigorous activity), while others showed directionality discrepancies across the studies (e.g., amount of SMS sent, time you spend between locations, frequency of smartphone screen activity).

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Section: Clinical Samples Of Patients Diagnosed With Ud or Bdcontrasting

confidence: 47%

Section: Feature Combined Modelsmentioning

confidence: 99%

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Rohani¹,

Faurholt‐Jepsen²,

Kessing³

et al. 2017

Preprint

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“…Abdullah et al [9] reported that combining self-reported data with data from several smartphone sensors and communication patterns resulted in reliable prediction of Social Rhythm Metric, a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder. A different study [10] collected voice features that were automatically generated using smartphones from 28 outpatients on a daily basis during a period of 12 weeks.…”

Section: The Monitoring Of Activity Of Daily Living and Episodic Epismentioning

confidence: 99%

“…The normal use of a smartphone on a daily basis generates a larger amount of data than the amount that is typically collected in questionnaire-based studies or online interventions. Smartphone sensor-based analysis already showed interesting results assessing bipolar disorder [9], depression symptoms [6] and sleep duration [18]. In this work, we proposed a preliminary assessment of a method for patients with mental health conditions.…”

Section: Future Applicationmentioning

confidence: 99%

Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study (Preprint)

Berrouiguet¹,

Ramírez²,

Barrigón³

et al. 2018

Preprint

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Background:The emergence of smart homes and wearable sensor technologies allows the nonintrusive collection of activity data. Thus, health-related events such as Activities of Daily Living (ADLs, e.g., feeding, sleeping) can be captured without the patient's active participation. We designed a system able to perform continuous monitoring based on smartphone's native sensors and advanced machine learning and signal processing techniques.

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“…HCI research has recently started to focus on how technology can support psychological wellbeing [e.g. 1,21,78]. The growth in use and sophistication of mobile health (mHealth) apps for mental health presents particular opportunities and challenges for design [22,83].…”

Section: Introductionmentioning

confidence: 99%

mHealth for Maternal Mental Health

Barry

Doherty

Belisario

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

Self Cite

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Health and wellbeing applications increasingly raise ethical issues for design. User-centred and participatory design approaches, while grounded in everyday wisdom, cannot be expected to address ethical reflection consistently, as multiple value systems come into play. We explore the potential of phronesis, a concept from Aristotelian virtue ethics, for mHealth design. Phronesis describes wisdom and judgment garnered from practical experience of specific situations in context. Applied phronesis contributes everyday wisdom to challenging issues for vulnerable target users. Drawing on research into mHealth technologies for psychological wellbeing, we explore how phronesis can inform ethical design. Using a case study on an app for selfreporting symptoms of depression during pregnancy, we present a framework for incorporating a phronetic approach into design, involving: (a) a wide feedback net to capture phronetic input early in design; (b) observing the order of feedback, which directly affects value priorities in design; (c) ethical pluralism recognising different coexisting value systems; (d) acknowledging subjectivity in the disclosure and recognition of individual researcher and participant values. We offer insights into how a phronetic approach can contribute everyday wisdom to designing mHealth technologies to help designers foster the values that promote human flourishing.

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Automatic detection of social rhythms in bipolar disorder

Abstract: Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.

Cited by 198 publications

References 20 publications

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study (Preprint)

mHealth for Maternal Mental Health

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