2020
DOI: 10.1038/s41598-020-71689-1
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Abstract: Schizophrenia is a severe and complex psychiatric disorder with heterogeneous and dynamic multi-dimensional symptoms. Behavioral rhythms, such as sleep rhythm, are usually disrupted in people with schizophrenia. As such, behavioral rhythm sensing with smartphones and machine learning can help better understand and predict their symptoms. Our goal is to predict fine-grained symptom changes with interpretable models. We computed rhythm-based features from 61 participants with 6,132 days of data and used multi-ta… Show more

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Cited by 36 publications
(29 citation statements)
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“…Loss of privacy, misuse of sensitive healthcare information remains a risk, with known cases of mobile technologies selling patient data to third parties (O’neil, 2016; Cohen and Mello, 2018; Zuboff, 2019). Hence, while there is considerable scope for mobile health innovations in improving patient care (Porras-Segovia et al, 2020; Tseng et al, 2020), there is also a pressing need to formulate clear recommendations for these apps among patients and clinicians. Mental health patients remain among the most of vulnerable patient populations and are especially at risk of privacy violations via the exploitation of their data.…”
Section: Discussionmentioning
confidence: 99%
“…Loss of privacy, misuse of sensitive healthcare information remains a risk, with known cases of mobile technologies selling patient data to third parties (O’neil, 2016; Cohen and Mello, 2018; Zuboff, 2019). Hence, while there is considerable scope for mobile health innovations in improving patient care (Porras-Segovia et al, 2020; Tseng et al, 2020), there is also a pressing need to formulate clear recommendations for these apps among patients and clinicians. Mental health patients remain among the most of vulnerable patient populations and are especially at risk of privacy violations via the exploitation of their data.…”
Section: Discussionmentioning
confidence: 99%
“…We obtained a daily template for each of the mobile sensing signals by computing the hourly averages of the signal in a given day of monitoring (thus the template consists of 24 points corresponding to each hour of the day). The templates capture daily rhythmic behaviors which are relevant for monitoring behavioral changes in schizophrenia patients [17]. An example of a daily template obtained for the light level signal is shown in Figure 2.…”
Section: Featuresmentioning
confidence: 99%
“…Relapse prediction is framed as a binary classification problem, associating an upcoming period as relapse or non-relapse based on the features observed in the current period. We extracted daily behavioral rhythm based features from mobile sensing data, which was also effective in predicting self-reported schizophrenia symptoms in our previous work [17], complemented by self-reported symptoms collected through EMA and demographics features, and evaluated different classifiers for relapse prediction. Daily template based rhythm features were found to outperform feature sets proposed in previous works for relapse prediction.…”
Section: Introductionmentioning
confidence: 99%
“…To date, it is estimated that there are more than 10,000 health apps available for download, yet most have never been subject to robust standards of evidence-based medicine (2,17). While there is considerable scope for mobile health innovations in improving patient care (18,19), there is also a pressing need to formulate clear recommendations for these apps among patients and clinicians.…”
Section: Summary Of Major Findingsmentioning
confidence: 99%