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

Relationship between nonexercise activity and mood in patients with eating disorders

INTRODUCTION

Many patients with eating disorders (EDs) engage in excessive and compulsive physical activity (pathological exercise, PE) to regulate negative mood or to "burn calories." PE can lead to negative health consequences. Non-exercise activity (NEA) bears the potential to serve as intervention target to counteract PE and problematic eating behaviors since it has been associated with positive mood effects. However, to date, there is no investigation on whether the positive link between NEA and mood seen in the healthy translates to patients with ED.

MATERIAL AND METHODS

To study potential associations of NEA and mood in ED, we subjected 29 ED-patients and 35 healthy controls (HCs) to an ambulatory assessment study across 7 days. We measured NEA via accelerometers and repeatedly assessed mood on electronic smartphone diaries via a mixed sampling strategy based on events, activity and time. Within- and between-subject effects of NEA on mood, PE as moderator, and the temporal course of effects were analyzed via multilevel modeling.

RESULTS

NEA increased valence (β = 2.12, p < 0.001) and energetic arousal (β = 4.02, p < 0.001) but showed no significant effect on calmness. The effects of NEA on energetic arousal where significantly stronger for HCs (βHC = 6.26, p < 0.001) than for EDs (βED = 4.02, p < 0.001; βinteraction = 2.24, p = 0.0135). Effects of NEA were robust across most timeframes of NEA and significantly moderated by PE, that is, Lower PE levels exhibited stronger NEA effects on energetic arousal.

CONCLUSION

Patients with ED and HC show an affective benefit from NEA, partly depending on the level of PE. If replicated in experimental daily life studies, this evidence may pave the way towards expedient NEA interventions to cope with negative mood. Interventions could be especially promising if delivered as Just-in-time adaptive interventions (JITAIs) and should be tailored according to the PE level.

Shared and distinct abnormalities in sleep-wake patterns and their relationship with the negative symptoms of Schizophrenia Spectrum Disorder patients

Sleep and rest-activity-rhythm (RAR) abnormalities are commonly reported in schizophrenia spectrum disorder (SSD) patients. However, an in-depth characterization of sleep/RAR alterations in SSD, including patients in different treatment settings, and the relationship between these alterations and SSD clinical features (e.g., negative symptoms) is lacking. SSD (N = 137 altogether, N = 79 residential and N = 58 outpatients) and healthy control (HC) subjects (N = 113) were recruited for the DiAPAson project. Participants wore an ActiGraph for seven consecutive days to monitor habitual sleep-RAR patterns. Sleep/rest duration, activity (i.e., M10, calculated on the 10 most active hours), rhythm fragmentation within days (i.e., intra-daily variability, IV; beta, steepness of rest-active changes), and rhythm regularity across days (i.e., inter-daily stability, IS) were computed in each study participant. Negative symptoms were assessed in SSD patients with the Brief Negative Symptom Scale (BNSS). Both SSD groups showed lower M10 and longer sleep/rest duration vs. HC, while only residential patients had more fragmented and irregular rhythms than HC. Compared to outpatients, residential patients had lower M10 and higher beta, IV and IS. Furthermore, residential patients had worse BNSS scores relative to outpatients, and higher IS contributed to between-group differences in BNSS score severity. Altogether, residentials and outpatients SSD had both shared and unique abnormalities in Sleep/RAR measures vs. HC and relative to one another, which also contributed to the patients' negative symptom severity. Future work will help establish whether improving some of these measures may ameliorate the quality of life and clinical symptoms of SSD patients.

Combining actigraphy and experience sampling to assess physical activity and sleep in patients with psychosis: A feasibility study

BACKGROUND

Sleep disorders and reduced physical activity are common in patients with psychosis and can be related to health-related outcomes such as symptomatology and functioning. Mobile health technologies and wearable sensor methods enable continuous and simultaneous monitoring of physical activity, sleep, and symptoms in one's day-to-day environment. Only a few studies have applied simultaneous assessment of these parameters. Therefore, we aimed to examine the feasibility of the simultaneous monitoring of physical activity, sleep, and symptoms and functioning in psychosis.

METHODS

Thirty three outpatients diagnosed with a schizophrenia or other psychotic disorder used an actigraphy watch and experience sampling method (ESM) smartphone app for 7 consecutive days to monitor physical activity, sleep, symptoms, and functioning. Participants wore the actigraphy watch during day and night and completed multiple short questionnaires (eight daily, one morning, and one evening) on their phone. Hereafter they completed evaluation questionnaires.

RESULTS

Of the 33 patients (25 male), 32 (97.0%) used the ESM and actigraphy during the instructed timeframe. ESM response was good: 64.0% for the daily, 90.6% for morning, and 82.6% for evening questionnaire(s). Participants were positive about the use of actigraphy and ESM.

CONCLUSION

The combination of wrist-worn actigraphy and smartphone-based ESM is feasible and acceptable in outpatients with psychosis. These novel methods can help both clinical practice and future research to gain more valid insight into physical activity and sleep as biobehavioral markers linked to psychopathological symptoms and functioning in psychosis. This can be used to investigate relationships between these outcomes and thereby improve individualized treatment and prediction.

Physical Activity, Positive and Negative Symptoms of Psychosis, and General Psychopathology among People with Psychotic Disorders: A Meta-Analysis

Objective: Existing reviews provided evidence for the associations between higher physical activity (PA) and lower negative symptoms of psychosis among people with schizophrenia. This meta-analysis goes beyond existing syntheses and investigates associations between PA, positive and negative symptoms of psychosis, as well as symptoms of general psychopathology (referring mostly to cognitive functioning) among people with schizophrenia, but also other psychotic disorders. The moderating roles of the type of diagnosis and the type of exercise intervention were explored. Methods: The study was registered with PROSPERO (CRD42018118236). Six electronic databases were searched; n = 27 experimental and observational studies were included, and psychotic symptoms-related data were recorded in one direction (higher values indicate better mental health and lower symptomatology). Results: Higher levels of PA (or participating in PA interventions) were associated with better mental health, that is, lower levels of positive symptoms (all studies: r = 0.170; experimental studies: SMD = 0.677), negative symptoms (all studies: r = 0.214; experimental studies: SMD = 0.838), and general psychopathology (all studies: r = 0.451; experimental studies: SMD = 1.511). The type of diagnosis (schizophrenia vs. other psychotic disorders) did not moderate these associations. Conclusions: We found a consistent pattern of associations between higher levels of PA and lower positive, negative, and general psychopathology symptoms in people with schizophrenia and those with other psychotic disorders.

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

BACKGROUND

Behavioral representations obtained from mobile sensing data can be helpful for the prediction of an oncoming psychotic relapse in patients with schizophrenia and the delivery of timely interventions to mitigate such relapse.

OBJECTIVE

In this study, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data for relapse prediction tasks. The identified clusters can represent different routine behavioral trends related to daily living of patients and atypical behavioral trends associated with impending relapse.

METHODS

We used the mobile sensing data obtained from the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (ambient light, sound, conversation, acceleration, etc) obtained from 63 patients with schizophrenia, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian mixture model (GMM) and partition around medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data, and thus, provide different behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using balanced random forest. The personalization was performed by identifying optimal features for a given patient based on a personalization subset consisting of other patients of similar age.

RESULTS

The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active days but with low communication, etc). Although GMM-based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread, likely indicating heterogeneous behavioral characterizations. On the other hand, PAM model-based clusters had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were observed in the obtained behavioral representation features from the clustering models. The clustering model-based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.23 for the relapse prediction task in a leave-one-patient-out evaluation setting. The obtained F2 score was significantly higher than that of a random classification baseline with an average F2 score of 0.042.

CONCLUSIONS

Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine and atypical behavioral trends. In this study, we used GMM-based and PAM-based cluster models to obtain behavioral trends in patients with schizophrenia. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful in enabling timely interventions.

Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia: protocol for the HOPE-S observational study

INTRODUCTION

The course of schizophrenia illness is characterised by recurrent relapses which are associated with adverse clinical outcomes such as treatment-resistance, functional and cognitive decline. Early identification is essential and relapse prevention remains a primary treatment goal for long-term management of schizophrenia. With the ubiquity of devices such as smartphones, objective digital biomarkers can be harnessed and may offer alternative means for symptom monitoring and relapse prediction. The acceptability of digital sensors (smartphone and wrist-wearable device) and the association between the captured digital data with clinical and health outcomes in individuals with schizophrenia will be examined.

METHODS AND ANALYSIS

In this study, we aim to recruit 100 individuals with schizophrenia spectrum disorders who are recently discharged from the Institute of Mental Health (IMH), Singapore. Participants are followed up for 6 months, where digital, clinical, cognitive and functioning data are collected while health utilisation data are obtained at the 6 month and 1 year timepoint from study enrolment. Associations between digital, clinical and health outcomes data will be examined. A data-driven machine learning approach will be used to develop prediction algorithms to detect clinically significant outcomes. Study findings will inform the design, data collection procedures and protocol of future interventional randomised controlled trial, testing the effectiveness of digital phenotyping in clinical management of individuals with schizophrenia spectrum disorders.

ETHICS AND DISSEMINATION

Ethics approval has been granted by the National Healthcare Group (NHG) Domain Specific Review Board (DSRB Reference no.: 2019/00720). The results will be published in peer-reviewed journals and presented at conferences.

TRIAL REGISTRATION NUMBER

NCT04230590.

Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study

Background

Ecological momentary assessment (EMA) tools appear to be useful interventions for collecting real-time data on patients’ behavior and functioning. However, concerns have been voiced regarding the acceptability of EMA among patients with schizophrenia and the factors influencing EMA acceptability.

Objective

The aim of this study was to investigate the acceptability of a passive smartphone-based EMA app, evidence-based behavior (eB2), among patients with schizophrenia spectrum disorders and the putative variables underlying their acceptance.

Methods

The participants in this study were from an ongoing randomized controlled trial (RCT) of metacognitive training, consisting of outpatients with schizophrenia spectrum disorders (F20-29 of 10th revision of the International Statistical Classification of Diseases and Related Health Problems), aged 18-64 years, none of whom received any financial compensation. Those who consented to installation of the eB2 app (users) were compared with those who did not (nonusers) in sociodemographic, clinical, premorbid adjustment, neurocognitive, psychopathological, insight, and metacognitive variables. A multivariable binary logistic regression tested the influence of the above (independent) variables on “being user versus nonuser” (acceptability), which was the main outcome measure.

Results

Out of the 77 RCT participants, 24 (31%) consented to installing eB2, which remained installed till the end of the study (median follow-up 14.50 weeks) in 14 participants (70%). Users were younger and had a higher education level, better premorbid adjustment, better executive function (according to the Trail Making Test), and higher cognitive insight levels (measured with the Beck Cognitive Insight Scale) than nonusers (univariate analyses) although only age (OR 0.93, 95% CI 0.86-0.99; P=.048) and early adolescence premorbid adjustment (OR 0.75, 95% CI 0.61-0.93; P=.01) survived the multivariable regression model, thus predicting eB2 acceptability.

Conclusions

Acceptability of a passive smartphone-based EMA app among participants with schizophrenia spectrum disorders in this RCT where no participant received financial compensation was, as expected, relatively low, and linked with being young and good premorbid adjustment. Further research should examine how to increase EMA acceptability in patients with schizophrenia spectrum disorders, in particular, older participants and those with poor premorbid adjustment.

Trial Registration

ClinicalTrials.gov NCT04104347; https://clinicaltrials.gov/ct2/show/NCT04104347

Real-Time Monitoring: A Key Element in Personalized Health and Precision Health

Current management of psychiatric disorders relies heavily on retrospective, subjective reports provided by patients and their families. Consequently, psychiatric services are often provisioned inefficiently and with suboptimal outcomes. Recent advances in computing and sensor technologies have enabled the development of real-time monitoring systems for the diagnosis and management of psychiatric disorders. The state of these technologies is rapidly evolving, with passive monitoring and predictive modeling as two areas that have great potential to affect psychiatric care. Although outpatient psychiatry probably stands to benefit the most from the use of real-time monitoring technologies, there are also several ways in which inpatient psychiatry may also benefit. As the capabilities of these technologies increase and their use becomes more common, many ethical and legal issues will need to be considered. The role of governmental regulatory bodies and nongovernmental organizations in providing oversight of the implementation of these technologies is an active area of discussion.

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

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.

Prospective acceptance of distinct mobile mental health features in psychiatric patients and mental health professionals

BACKGROUND

Despite numerous mobile health (mHealth) applications available, current impact on mental healthcare is low. Users face overwhelming variety of applications and sensors. Evidence for distinct features' effectiveness is largely lacking. Along with technical feasibility and data security issues, readiness and preferences of patients predetermine engagement and impact of mHealth in psychiatry.

OBJECTIVE

We aimed to assess the prospective attitudes of psychiatric patients and mental health professionals (MHP) towards mHealth applications in general and with regard to distinct features.

METHODS

We conducted a survey entailing 486 subjects (297 MHP and 189 patients).

RESULTS

Professionals and patients indicate both, considerable acceptance and rejection for most features. Marked concerns across groups relate to data security in general. Actimetry and geotracking were considered particularly skeptical. Importantly, most patients prefer to be prompted timely about health status changes.

CONCLUSION

Altogether, evidence indicates substantial support for mHealth features in mental healthcare despite considerable rejection of distinct features. We conclude that tighter collaboration between researchers, developers and clinicians must address matching mHealth-apps to patients' needs. Improved information on potential risks and possibilities associated with mHealth features is strongly indicated in MHP and psychiatric patients in order to reach an appropriately informed decision on individual involvement.

The use of eHealth to promote physical activity in people with mental health conditions: a systematic review

Background: Achieving adequate amounts of physical activity (PA) confers important physical and mental health benefits. Despite this, people with mental health conditions often do not meet recommended levels of PA. eHealth, the delivery of health information through internet and mobile technologies, is an emerging concept in healthcare which presents opportunities to improve PA. The aim of this systematic review is to describe the use of eHealth to increase or monitor PA levels in people with mental health conditions. Methods: Databases searched included OVID Medline, EMBASE, PsychInfo and Web of Science using a combination of key-words and medical subject headings. Articles were included if they described an eHealth technology designed to improve or monitor PA in people with mental health conditions. Two reviewers screened articles. Articles included in the qualitative synthesis were screened for risk of bias using the Cochrane Risk of Bias Tool for experimental studies and Downs and Black Checklist for non-experimental studies. Results: Seven studies met the eligibility criteria. A variety of eHealth platforms designed to promote or monitor PA were described in these studies; web-based (n=4), web and mobile application (n=3) and e-mail-based (n=1), one study used both a web-based and mobile application. Three studies reported eHealth interventions significantly increased PA levels, however it is unclear if eHealth interventions are superior at promoting PA compared to conventional interventions. Four studies reported that higher levels of PA, measured using eHealth, were associated with better mental health profiles.  Conclusion: eHealth interventions may be an innovative low-cost method to increase PA levels which may have knock-on effects on mental health outcomes. Although some of the included studies in this review demonstrated promising results, methodological restrictions and potential biases from using subjective measures of PA limit the interpretability of these results. Future research should evaluate this promising technology using well-designed trials.

Physical Activity of Patients with Chronic Schizophrenia and Related Clinical Factors

OBJECTIVE

This study aimed to investigate clinical factors contributing to the low physical activity (PA) of patients with chronic schizophrenia.

METHODS

PA 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.

RESULTS

Subjects 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).

CONCLUSION

This study provides preliminary evidence that extrapyramidal symptoms could be a major impediment to the PA of patients with schizophrenia.

The use of eHealth to promote physical activity in people with mental health conditions: a systematic review

Background: Achieving adequate amounts of physical activity (PA) confers important physical and mental health benefits. Despite this, people with mental health conditions often do not meet recommended levels of PA. eHealth, the delivery of health information through internet and mobile technologies, is an emerging concept in healthcare which presents opportunities to improve PA. The aim of this systematic review is to describe the use of eHealth to increase or monitor PA levels in people with mental health conditions. Methods: Databases searched included OVID Medline, EMBASE, PsychInfo and Web of Science using a combination of key-words and medical subject headings. Articles were included if they described an eHealth technology designed to improve or monitor PA in people with mental health conditions. Two reviewers screened articles. Articles included in the qualitative synthesis were screened for risk of bias using the Cochrane Risk of Bias Tool for experimental studies and Downs and Black Checklist for non-experimental studies. Results: Seven studies met the eligibility criteria. A variety of eHealth platforms designed to promote or monitor PA were described in these studies; web-based (n=4), web and mobile application (n=3) and e-mail-based (n=1), one study used both a web-based and mobile application. Three studies reported eHealth interventions significantly increased PA levels, however it is unclear if eHealth interventions are superior at promoting PA compared to conventional interventions. Four studies reported that higher levels of PA, measured using eHealth, were associated with better mental health profiles.  Conclusion: eHealth interventions may be an innovative low-cost method to increase PA levels which may have knock-on effects on mental health outcomes. Although some of the included studies in this review demonstrated promising results, methodological restrictions and potential biases from using subjective measures of PA limit the interpretability of these results. Future research should evaluate this promising technology using well-designed trials.

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

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.

The use of eHealth to promote physical activity in patients with mental health conditions: a systematic review

Background: Achieving adequate amounts of physical activity (PA) confers important physical and mental health benefits. Despite this, individuals with mental health conditions often do not meet recommended levels of PA. eHealth, the delivery of health information through internet and mobile technologies, is an emerging concept in healthcare which presents opportunities to improve PA in people with mental conditions. The aim of this systematic review is to explore if eHealth interventions increase PA levels among individuals with mental health conditions. Methods: Databases searched included OVID Medline, EMBASE, PsychInfo and Web of Science using a combination of key-words and medical subject headings. Articles were included if they described an eHealth intervention designed to improve PA in individuals with mental health conditions. Two reviewers screened articles for inclusion. Results: In total 2,994 articles were identified. Seven studies met the eligibility criteria. A variety of eHealth platforms designed to increase PA were described in these studies; web-based (n=4), web and mobile application (n=3) and e-mail-based (n=1), one study used both a web-based and mobile application. Three studies reported eHealth intervention significantly increased PA levels. Four studies reported that higher levels of PA resulted in improvements in mental health outcomes.  Conclusion: eHealth interventions may be an innovative low cost method to increase PA levels which may have knock-on effects on mental health outcomes. Although some of the included studies in this review demonstrated promising results, methodological restrictions and potential biases from using subjective measures of PA limit the interpretability of these results.  Future research should evaluate this nascent technology using well designed trials.