Activity monitoring using a mHealth device and correlations with psychopathology in patients with chronic schizophrenia

Shin, Seunghwan; Yeom, Chan-Woo; Shin, Cheolmin; Shin, Jaehyun; Jeong, Jae Hoon; Shin, Jung U; Lee, Young Ryeol

doi:10.1016/j.psychres.2016.10.059

Cited by 23 publications

(12 citation statements)

References 37 publications

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“…Smartphones recorded call and SMS activity. Sleep, stress, and mental health were assessed via PSQI, PSS, and MCS questionnaires respectively Offline retrospective Shin et al 2016 Correlate symptom severity (assessed via the PANSS questionnaire) with activity levels 61 subjects with schizophrenia Wrist-worn devices recorded accelerometry Offline retrospective Stamatakis et al 2013 Classify UPDRS score categories from wearable data 36 subjects with PD and 10 controls Finger-worn sensors recorded accelerometry during a tapping test Offline retrospective Tung et al 2014 Compare area, perimeter, and mean distance from home in subjects with AD versus controls using smartphone data 19 subjects with AD and 33 controls Smartphones recorded GPS Offline retrospective Walther et al 2009b Assess if motor symptoms (assessed via PANSS questionnaires) correlate with wearables data 55 subjects with schizophrenia Wrist-worn devices recorded actigraphy Offline retrospective Walther et al 2009a Assess if activity differs by schizophrenia subtype 60 subjects with schizophrenia Wrist-worn devices recorded actigraphy Offline retrospective Wang et al 2014 Correlate smartphone data with PHQ-9, PSS, flourishing scale, and UCLA loneliness scale scores 48 healthy subjects Smartphones recorded accelerometry, conversations, sleep, and location Offline retrospective Wang et al 2016 Determine associations between EMA survey scores and smartphone data via generalized estimating equations 21 subjects with schizophrenia Smartphones recorded accelerometry, voice audio, light sensor readings, GPS data, and application usage…”

Section: Table A1mentioning

confidence: 99%

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

2018

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Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.

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Section: Table A1mentioning

confidence: 99%

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

2018

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“…Only five studies addressing schizophrenia were included in this review [53,57,66,80,81]. None of them included patients with treatment-resistant schizophrenia.…”

Section: Resultsmentioning

confidence: 99%

“…Staples et al [53] investigated sleep estimation of 17 patients by comparing the Pittsburgh Sleep Quality Index (PSQI), EMAs, and accelerometer data, but did not address the severity of symptoms. Psychiatric symptoms evaluated by the Positive and Negative Syndrome Scale (PANSS) among those with schizophrenia were related to lower activity level [66,80,81], while interbeat intervals correlated negatively with positive symptoms [81].…”

Section: Resultsmentioning

confidence: 99%

Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review

et al. 2019

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Background Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration. Objective To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy. Methods A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied. Results Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression. Conclusions Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.

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“…Some clinical factors might influence the PA of patients with schizophrenia. For example, social withdrawal due to positive or negative symptoms may lead to a decrease in PA of patients with schizophrenia [ 34 , 35 ]. Unemployment or social isolation due to low social functioning in patients with schizophrenia may also reduce their PA and increase the amount of time they spend being sedentary [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Physical Activity of Patients with Chronic Schizophrenia and Related Clinical Factors

Lee¹,

Kim²,

Kim³

et al. 2018

Psychiatry Investig

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ObjectiveThis study aimed to investigate clinical factors contributing to the low physical activity (PA) of patients with chronic schizophrenia. MethodsPA was measured in 50 outpatients with chronic schizophrenia using the International Physical Activity Questionnaire Short Form (IPAQ-SF). Psychopathology, psychosocial functioning, and extrapyramidal symptoms were assessed using the 18 item-Brief Psychiatric Rating Scale (BPRS-18), Global Assessment of Functioning (GAF), and Drug-Induced Extrapyramidal Symptom Scale (DIEPSS), respectively. We examined differences in these clinical variables between “inactive,” “minimally active,” and “health enhancing physical activity” groups. Linear regression analysis was used to examine the clinical factors explaining low PA levels in patients with schizophrenia. ResultsSubjects spent an average of 130.18±238.89 min/wk on moderate/vigorous-intensity PA and only 26% of them met the recommended PA guideline of 150 minutes of at least moderate PA per week. The inactive group showed significantly higher BPRS-18 and DIEPSS scores, and a lower GAF score than the other groups. Linear regression analysis showed that DIEPSS scores independently explained the amount of total PA (p=0.001) and time spent being sedentary (p=0.028). ConclusionThis study provides preliminary evidence that extrapyramidal symptoms could be a major impediment to the PA of patients with schizophrenia.

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Activity monitoring using a mHealth device and correlations with psychopathology in patients with chronic schizophrenia

Cited by 23 publications

References 37 publications

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review

Physical Activity of Patients with Chronic Schizophrenia and Related Clinical Factors

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