SleepSight

Kerz, Maximilian; Folarin, Amos; Meyer, Nicholas; Begale, Mark; MacCabe, James H.; Dobson, Richard

doi:10.1145/2968219.2971419

Cited by 14 publications

(3 citation statements)

References 6 publications

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“…Further, the dropout rate in our study was acceptable and similar to that reported in previous studies such as ClinTouch [ 8 ], PeerFIT [ 29 ], and CrossCheck [ 30 ]. However, some studies had almost no dropouts, for example, FOCUS [ 6 ], SleepSight [ 31 ], or App4Independence [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

m-RESIST, a Mobile Therapeutic Intervention for Treatment-Resistant Schizophrenia: Feasibility, Acceptability, and Usability Study

Grasa¹,

Seppälä²,

Alonso-Solís³

et al. 2023

JMIR Form Res

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Background In the European Union, around 5 million people are affected by psychotic disorders, and approximately 30%-50% of people with schizophrenia have treatment-resistant schizophrenia (TRS). Mobile health (mHealth) interventions may be effective in preventing relapses, increasing treatment adherence, and managing some of the symptoms of schizophrenia. People with schizophrenia seem willing and able to use smartphones to monitor their symptoms and engage in therapeutic interventions. mHealth studies have been performed with other clinical populations but not in populations with TRS. Objective The purpose of this study was to present the 3-month prospective results of the m-RESIST intervention. This study aims to assess the feasibility, acceptability, and usability of the m-RESIST intervention and the satisfaction among patients with TRS after using this intervention. Methods A prospective multicenter feasibility study without a control group was undertaken with patients with TRS. This study was performed at 3 sites: Sant Pau Hospital (Barcelona, Spain), Semmelweis University (Budapest, Hungary), and Sheba Medical Center and Gertner Institute of Epidemiology and Health Policy Research (Ramat-Gan, Israel). The m-RESIST intervention consisted of a smartwatch, a mobile app, a web-based platform, and a tailored therapeutic program. The m-RESIST intervention was delivered to patients with TRS and assisted by mental health care providers (psychiatrists and psychologists). Feasibility, usability, acceptability, and user satisfaction were measured. Results This study was performed with 39 patients with TRS. The dropout rate was 18% (7/39), the main reasons being as follows: loss to follow-up, clinical worsening, physical discomfort of the smartwatch, and social stigma. Patients’ acceptance of m-RESIST ranged from moderate to high. The m-RESIST intervention could provide better control of the illness and appropriate care, together with offering user-friendly and easy-to-use technology. In terms of user experience, patients indicated that m-RESIST enabled easier and quicker communication with clinicians and made them feel more protected and safer. Patients’ satisfaction was generally good: 78% (25/32) considered the quality of service as good or excellent, 84% (27/32) reported that they would use it again, and 94% (30/32) reported that they were mostly satisfied. Conclusions The m-RESIST project has provided the basis for a new modular program based on novel technology: the m-RESIST intervention. This program was well-accepted by patients in terms of acceptability, usability, and satisfaction. Our results offer an encouraging starting point regarding mHealth technologies for patients with TRS. Trial Registration ClinicalTrials.gov NCT03064776; https://clinicaltrials.gov/ct2/show/record/NCT03064776 International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2017-021346

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Section: Discussionmentioning

confidence: 99%

m-RESIST, a Mobile Therapeutic Intervention for Treatment-Resistant Schizophrenia: Feasibility, Acceptability, and Usability Study

Grasa¹,

Seppälä²,

Alonso-Solís³

et al. 2023

JMIR Form Res

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“…Relapse is a multidimensional event, and prognostic risk factors for relapse in schizophrenia are a longstanding, complex topic. Despite a large body of research that evaluated the factors associated with the relapse of psychosis in schizophrenia, these studies mainly focused on only a few factors, including maintenance medication, substance abuse, family support, and social adjustment (4)(5)(6)(7)(8)(9)(10)(11). Additionally, the models that have been used to predict relapse, such as multiple regression or Cox proportional hazard regression, are relatively simplistic.…”

Section: Introductionmentioning

confidence: 99%

Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China

Chen

Fan

et al. 2020

Front. Psychiatry

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Background Preventing relapse of schizophrenic patients is really a challenge. The present study sought to provide more explicit evidence and factors of different grades and weights by a series of step-by-step analysis through χ 2 test, logistic regression analysis and decision-tree model. The results of this study may contribute to controlling relapse of schizophrenic patients. Methods A total of 1,487 schizophrenia patients were included who were 18–65 years of age and discharged from 10 hospitals in China from January 2009 to August 2009 and from September 2011 to February 2012 with improvements or recovery of treatment effect. We used a questionnaire to collect information about relapse and correlative factors during one year after discharge by medical record collection and telephone interview. The χ 2 test and logistic regression analysis were used to identify risk factors and high-risk factors firstly, and then a decision-tree model was used to find predictive factors. Results The χ 2 test found nine risk factors which were associated with relapse. Logistic regression analysis also showed four high-risk factors further (medication adherence, occupational status, ability of daily living, payment method of medical costs). At last, a decision-tree model revealed four predictors of relapse; it showed that medication adherence was the first grade and the most powerful predictor of relapse (relapse rate for adherence vs. nonadherence: 22.9 vs. 55.7%, χ 2 = 116.36, p < 0.001). The second grade factor was occupational status (employment vs. unemployment: 19.7 vs. 42.7%, χ 2 = 17.72, p < 0.001); the third grade factors were ability of daily living (normal vs. difficult: 28.4 vs. 54.3%, χ 2 = 8.61, p = 0.010) and household income (household income ≥ 3000 RMB vs. <3000 RMB: 28.6 vs. 42.4%, χ 2 = 6.30, p = 0.036). The overall positive predictive value (PPV) of the logistic regression was 0.740, and the decision-tree model was 0.726. Both models were reliable. Conclusions For schizophrenic patients discharged from hospital, who had good medication adherence, more higher household income, be employed and normal ability of daily living, would be less likely to relapse. Decision tree provides a new path for doctors to find the schizophrenic inpatient’s relapse risk and give them reasonable treatment suggestions after discharge.

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“…Developing methodologies for anomaly detection typically requires specific consideration of the application at hand to frame the problem appropriately [5]. In the field of mental health, new and existing anomaly detection methods have begun to appear as an appealing option for a variety of applications, including relapse prediction [8][9][10], detection of illness [11,12], worsening cognitive impairment [13], motor skills [14], and anomalous traveling patterns [15,16]. For instance, by leveraging passively collected smartphone sensor data and digitally delivered patient surveys, Barnett et al [9] identified increases in the rate of anomalous behavioral patterns in 3 schizophrenia patients up to 7 days before a relapse.…”

Section: Introductionmentioning

confidence: 99%

Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System (Preprint)

Knights¹,

Heidary²,

Cochran³

2020

Preprint

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BACKGROUND Adherence to medication is often represented in the form of a success percentage over a period of time. Although noticeable changes to aggregate adherence levels may be indicative of unstable medication behavior, a lack of noticeable changes in aggregate levels over time does not necessarily indicate stability. The ability to detect developing changes in medication-taking behavior under such conditions in real time would allow patients and care teams to make more timely and informed decisions. OBJECTIVE This study aims to develop a method capable of identifying shifts in behavioral (medication) patterns at the individual level and subsequently assess the presence of such shifts in retrospective clinical trial data from patients with serious mental illness. METHODS We defined the term adherence volatility as “the degree to which medication ingestion behavior fits expected behavior based on historically observed data” and defined a contextual anomaly system around this concept, leveraging the empirical entropy rate of a stochastic process as the basis for formulating anomaly detection. For the presented methodology, each patient’s evolving behavior is used to dynamically construct the expectation bounds for each future interval, eliminating the need to rely on model training or a static reference sequence. RESULTS Simulations demonstrated that the presented methodology identifies anomalous behavior patterns even when aggregate adherence levels remain constant and highlight the temporal dependence inherent in these anomalies. Although a given sequence of events may present as anomalous during one period, that sequence should subsequently contribute to future expectations and may not be considered anomalous at a later period—this feature was demonstrated in retrospective clinical trial data. In the same clinical trial data, anomalous behavioral shifts were identified at both high- and low-adherence levels and were spread across the whole treatment regimen, with 77.1% (81/105) of the population demonstrating at least one behavioral anomaly at some point in their treatment. CONCLUSIONS Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of adherence volatility and develops a new type of contextual anomaly detection, which does not require an a priori definition of normal and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area.

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SleepSight

Cited by 14 publications

References 6 publications

m-RESIST, a Mobile Therapeutic Intervention for Treatment-Resistant Schizophrenia: Feasibility, Acceptability, and Usability Study

m-RESIST, a Mobile Therapeutic Intervention for Treatment-Resistant Schizophrenia: Feasibility, Acceptability, and Usability Study

Identifying Modifiable Risk Factors for Relapse in Patients With Schizophrenia in China

Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System (Preprint)

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