Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability

Abstract

Increased stability in one’s daily routine is associated with well-being in the general population and often a goal of behavioral interventions for people with serious mental illnesses like schizophrenia. Assessing behavioral stability has been limited in clinical research by the use of retrospective scales, which are susceptible to reporting biases and memory inaccuracies. Mobile passive sensors, which are less susceptible to these sources of error, have emerged as tools to assess behavioral patterns in a range of populations. The present study developed and examined a metric of behavioral stability from data generated by a passive sensing system carried by 61 individuals with schizophrenia for one year. This metric—the Stability Index—appeared orthogonal from existing measures drawn from passive sensors and matched the predictive performance of state-of-the-art features. Specifically, greater stability in social activity (e.g., calls and messages) were associated with lower symptoms, and greater stability in physical activity (e.g., being still) appeared associated with elevated symptoms. This study provides additional support for the predictive value of individualized over population-level data in psychiatric populations. The Stability Index offers also a promising tool for generating insights about the impact of behavioral stability in schizophrenia-spectrum disorders.

Phenomenology of the COVID-19 Pandemic Experience in Patients Suffering from Chronic Schizophrenia—A Qualitative Analysis

“…Patients who experienced more significant changes in their functioning and everyday routine due to the pandemic, such as changes in regular psychiatric care or limited social contact, experienced a greater decline in their mental condition. This is consistent with previous studies [ 18 ] which showed that stability in social connections was associated with less severe schizophrenia symptoms and better functioning. The results of this study suggest that their earlier experiences may help people diagnosed with schizophrenia to cope with difficulties such as those encountered during the pandemic.…”

“…The everyday life of most people has changed due to the pandemic; however, schizophrenia patients had often experienced social isolation on a daily basis before the pandemic [ 15 , 16 ], so they may be more able to comply with social distancing directives and better tolerate the emotional load they cause [ 11 ]. On the other hand, it also needs to be considered that there is strong evidence in previous research [ 16 , 17 , 18 ] of an association between social isolation and worse functioning of people with mental health history.…”

Associations Between Relapses and Psychosocial Outcomes in Patients With Schizophrenia in Real-World Settings in the United States

“…An inability to perform normal daily tasks is likely to cause further distress to these patients, as a stable daily routine has been linked to feelings of wellbeing across the general population ( 39 ). This may be true to an even greater extent in those with schizophrenia ( 40 ). These findings suggest that reducing relapse rates will be a key factor in enabling patients to continue their daily routine, which may have further benefits for their psychological well-being.…”

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis (Preprint)

“…Stability features such as deviation of daily templates were found to be significant predictors of schizophrenia symptoms such as being depressed. The authors in [22] also proposed a stability metric for behaviors with a fine temporal resolution by calculating the distance between two cumulative sum functions describing behaviors in a certain minute of the day. The computed Stability Index had similar predictive power as the state-of-the-art behavioral features (mean and standard deviation of each behavior) in [23], while being complementary.…”

“…Future studies can focus on establishing personalized cluster models. As suggested in [22], every patient's relapse signatures and the extent to which they adhere to their daily routine are different. The same study found that individual-level models could achieve better performance in predicting symptom severity.…”