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. Author manuscript; available in PMC: 2020 Sep 1.
Published in final edited form as: J Psychiatr Res. 2019 Jun 10;116:112–117. doi: 10.1016/j.jpsychires.2019.06.002

Capturing behavioral indicators of persecutory ideation using mobile technology

Benjamin Buck 1,2,3, Kevin A Hallgren 3, Emily Scherer 4, Rachel Brian 3, Rui Wang 5, Weichen Wang 5, Andrew Campbell 5, Tanzeem Choudhury 6, Marta Hauser 7,8, John M Kane 7,8, Dror Ben-Zeev 3
PMCID: PMC6650264  NIHMSID: NIHMS1532540  PMID: 31226579

Abstract

Most existing measures of persecutory ideation (PI) rely on infrequent in-person visits, and this limits their ability to assess rapid changes or real-world functioning. Mobile health (mHealth) technology may address these limitations. Little is known about passively sensed behavioral indicators associated with PI. In the current study, sixty-two participants with schizophrenia spectrum disorders completed momentary assessments of PI on a smartphone that also passively collected behavioral data for one year. Results suggested that PI was prevalent (n = 50, 82% of sample) but had infrequent incidence (25.2% of EMA responses). PI was also associated with changes in several passively sensed variables, including decreases in distance traveled (Mkilometers = −1.20, SD = 18.88), time spent in a vehicle (Mminutes = −4.15, SD = 49.59), length of outgoing phone calls (Mminutes = −0.79, SD = 13.13), time spent proximal to human speech (Mminutes = −6.26, SD = 153.03), and an increase in time sitting still (Mminutes = 4.04, SD = 94.69). The present study suggests changes associated with PI may be detectable by passive sensors, including reductions in moving or traveling, and time spent around others or in self-initiated phone conversations. These constructs might constitute risk for PI.

Keywords: paranoia, psychosis, technology, mHealth

Introduction

Persecutory delusions are among the most common (Bebbington and Freeman, 2017; Coid et al., 2013; Freeman and Garety, 2014), impairing (Bowie et al., 2006), and treatment-resistant (Freeman et al., 2016a; Garety et al., 2015) of all psychotic symptoms. They are associated with rehospitalization (Castle et al., 1994), social isolation (Kuipers et al., 2006; Smith et al., 2006), and disruption of functioning (Bowie et al., 2006). Treating persecutory delusions is challenging. Medications demonstrate low adherence rates (Leucht et al., 2013), and psychosocial interventions have limited effect on these beliefs’ intractability (Bell et al., 2006; Freeman, 2007). Though impairing and treatment-resistant, persecutory delusions are not static (Freeman, 2016, 2007; Freeman et al., 2002; Zubin and Spring, 1977). Persecutory ideation (PI) comprises a continuum from infrequent, changeable, non-disruptive thoughts about being harmed (Verdoux et al., 1998; Verdoux and Van Os, 2002) to severe delusions (Fusar-Poli et al., 2017; Paolini et al., 2016). PI emerges in response to environmental stressors (Lataster et al., 2013; Lincoln et al., 2015; Myin-Germeys et al., 2003), concurrent with elevations in worry (Freeman and Garety, 1999), negative self-beliefs (Ben-Zeev et al., 2009; Vorontsova et al., 2013), or anomalous sensory experiences (So et al., 2017). It may be possible, with improved assessment of these PI risk factors to intervene early to prevent or mitigate severe episodes (Reininghaus et al., 2016a).

It is challenging to effectively assess PI and its related risk factors in an ongoing manner. Many leading assessment strategies, including in-person self-report measures or clinical interviews (Bell et al., 2017a), have considerable limitations. Changes in PI occur on a brief time scale (Ben-Zeev et al., 2012b, 2011), and often only in one’s lived environment outside the clinic or office (Ben-Zeev et al., 2012a). In-person measures quantify variability between respondents but not changes that occur over time within a single individual. A number of technologies – pagers with accompanying journals (Lataster et al., 2013; Peters et al., 2012; Thewissen et al., 2011, 2008), palm pilots or PDAs (Ben-Zeev et al., 2011), and more recently mobile phones (Depp et al., 2010) – have been used to deploy a mobile data collection technique called ecological momentary assessment, or EMA (Bell et al., 2017b; Granholm et al., 2008). These approaches appear feasible in participants with significant PI and have helped identify a number of risk factors, including reduced self-esteem (Thewissen et al., 2011), increased negative affect (Lataster et al., 2013), and aberrant sensory experiences (Broyd et al., 2017; Reininghaus et al., 2016b). Novel interventions have been developed to target these and related domains (Dennis R. Combs et al., 2007; Freeman et al., 2017, 2016b, 2015; Lahera et al., 2013; Roberts et al., 2014). However, EMA still relies on patient self-report, which is susceptible to minimization, demand characteristics, and poor insight (Sabbag et al., 2012). EMA also places a requirement on patients to complete frequent assessments, which can be perceived as disruptive or burdensome.

Passive sensing of behavior – the automatic collection of data with existing smartphone features (e.g. sound, motion and tracking of calls/texts) – might provide a more sustainable option. Sensing appears to be feasible, acceptable to individuals with SMI, and informative to illness course (Ben-Zeev et al., 2016; Wang et al., 2016). Passive sensors provide frequent observations that do not require active user participation. Existing models (Freeman, 2016) suggest that PI may manifest behaviorally. Individuals experiencing PI report that they are more likely to isolate, stay home, and avoid or escape situations that they perceive to be potentially threatening (Freeman et al., 2007, 2001). Passive sensing technologies can be deployed to indicate whether individuals have left home, traveled extended distances, or spent time around others. Little is known about what passively sensed behavioral indicators might be associated with PI. The ability to detect PI passively using sensors could facilitate more timely and targeted treatment delivery while posing minimal patient burden.

In the current study, we examined data collected via an integrated mobile health system over the course of an entire year to (1) quantify between- and within-person variability in PI, (2) evaluate pre-existing models of indicators of PI (e.g. worry, negative self-beliefs and anomalous perceptions) with EMA gathered over a longer timespan than extant studies, and (3) identify passively sensed indicators of PI.

Methods

Participants

Participants were recruited from outpatient treatment programs at a large psychiatric hospital in New York. Inclusion criteria were (1) being 18 years or older; (2) having a diagnosis of schizophrenia, schizoaffective disorder, or psychosis not otherwise specified; and (3) the occurrence of an inpatient psychiatric hospitalization, daytime psychiatric hospitalization, outpatient crisis management visit, or short-term psychiatric hospital emergency room visit within the last 12 months. Individuals were excluded if they (1) had hearing, vision, or motor impairment making smartphone operation impossible (determined using a demonstration smartphone during screening); (2) had a reading level below the 6th grade (determined using the reading section of the Wide Range Achievement Test (Wilkinson and Robertson, 2004); or (3) were unable to provide informed consent (i.e. pass a competency screener).

Procedure

All participants were given a Samsung Galaxy S5 Android smartphone with an unlimited data, call and text plan for one year. They were asked to carry the phone with them during the course of the study and to make sure to charge the device each night. This device had an integrated mobile health assessment system called CrossCheck pre-installed. CrossCheck comprised EMA self-report scales and multimodal behavioral sensing data from sensors that are come pre-installed on most smartphones (Ben-Zeev et al., 2017; Wang et al., 2017, 2016).

Ecological Momentary Assessment (EMA)

CrossCheck prompted participants to complete a brief, 10-item self-report measure three times per week (i.e. each Monday, Wednesday and Friday during the study period), with the prompt, “Just checking in to see how you’ve been doing over the last few days.” Response options for each items ranges from 0 (not at all) to 3 (extremely). PI was assessed with the following EMA item: “Have you been worried about other people trying to HARM you?”. In addition to symptoms of psychosis, EMA items also assessed mental health (e.g., stress, depression, hopefulness, calmness, clarity of thought), and functioning (e.g., socialization, sleep). These items are listed in Table 1.

Table 1.

Overview of EMA items gathered by CrossCheck examined in the present study.

EMA item
Prompt: Just checking in to see how you’ve been doing over the last few days.
Have you been SOCIAL?
Have you been bothered by VOICES?
Have you been SEEING THINGS other people can’t see?
Have you been feeling STRESSED?
Have you been worried about people trying to HARM you?
Have you been SLEEPING well?
Have you been DEPRESSED?
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0 = Not at all; 1 = A little; 2 = Moderately, 3 = Extremely

Multimodal Behavioral Sensing Measures

Physical activity.

Physical activity is assessed by multiaxial accelerometers through Google Activity Recognition Application Programming. While these data are collected continuously, CrossCheck generates a rating of each activity every ten seconds when the user is moving and every 30 minutes if the device is still. For the present analysis, physical activity variables were represented as the amount of time (in total hours per day) that the individual was engaged in a number of activities, including sitting still, moving on foot, and riding in a vehicle.

Geospatial activity.

Global positioning system (GPS), Wi-Fi, and cellular tower location services allow for time-stamped estimates of participants’ approximate location. Each new location detected via the positioning system allows for a calculation of distances traveled and locations. For the present analysis, this included the number of discrete locations visited as well as distance traveled (in kilometers). In light of several extreme values in the case of atypical travel (e.g. long flights), the distance traveled variable was adjusted (>95th percentile, and <5th percentile replaced with the 95th and 5th percentile value respectively).

Speech frequency and duration.

CrossCheck applies a speech detection algorithm to quantify the amount of time during which speech is present or absent by activating the smartphone microphone. The device microphone is automatically activated every three minutes to assess surrounding sound. This system does not record raw audio on the device, but instead, classifies this data in real time according to whether speech is recorded, logging the start and end times. Speech duration (overall time classified as speech being present, measured in hours) is calculated and assessed in the present analysis.

Device use measures.

CrossCheck automatically logs telecommunication activity, SMS text message logs, as well as number and duration of phone calls. In order to protect participant privacy, no written or audio content of these messages or calls is recorded. In the present study, number of calls and SMS messages placed are assessed.

Data Analytic Plan

We used an exploratory analytic framework that included (1) quantifying PI descriptively and (2) identifying its momentary digital indicators. First, descriptive statistics, including means, standard deviations, and item frequencies were examined to quantify the frequency, severity, and within- and between-person variability of PI. Second, potential indicators were identified from Freeman’s updated cognitive model of persecutory ideation (Freeman, 2016), with the expectation of positive relationships to indicators of negative affect, worry, anomalous perceptual experiences, and sleep disturbances, and negative relationships to social and physical activity. Multilevel models with repeated EMA observations nested within person were used to examine between-participant and within-participant indicators of PI. Person-mean centering was used to create two variables for each indicator: (1) the mean daily value for each participant across days (i.e., between-participants effect) and (2) a person-mean centered variable with each participant’s mean value subtracted from each daily value (i.e., within-participants effect). In calculating these variables and all models, data were selected on days in which participants provided an EMA response related to PI. Models involving passive sensing variables selected days in which 19 or more hours of data (consistent with prior sensing work; Wang et al., 2016) were recorded by the relevant sensor (e.g. for the speech variable, the audio sensor). The between- and within-person variables for each indicator were simultaneously entered as fixed effects in multilevel models, such that within-participant variability was statistically separated from between-participant variability.1 Separate multilevel models were fit for each indicator (with each model including the within- and between-person variable) to prevent model overfitting and to accommodate the exploratory nature of the analyses, as this is – to our knowledge – the first study using passive sensing to identify characteristics that emerge alongside PI. Models were fit with restricted maximum likelihood and included fixed and random participant-level intercepts. To better characterize significant effects in models (the coefficients of which are reported in Table 2), we also compared the means of significant correlates during periods when PI was reported (at any level) versus periods when PI was reported as “not at all.”

Table 2.

Between and within-participants predictions of persecutory ideation from EMA and passively sensed variables.

Between-participant differences Within-participant changes
B SE p B SE p
EMA Self-Report EMA Self-Report
Depressed  0.45 0.09  <.000*** Depressed  0.29 0.01  <.000***
Stressed  0.33 0.08  <.000*** Stressed  0.34 0.01  <.000***
Voices  0.79 0.10  <.000*** Voices  0.41 0.01  <.000***
Seeing Things  0.92 0.10  <.000*** Seeing Things  0.50 0.01  <.000***
Social  −0.22 0.08  .008** Social  0.07 0.01  <.000***
Sleeping  −0.38 0.08  <.000*** Sleeping  0.06 0.01  <.000***
 
Passive Behavioral Sensing Passive Behavioral Sensing
Activity and Location Activity and Location
Still  −1.75 3.24 .59 Still  1.35 0.67  .04*
Vehicle  4.16 9.02 .65 Vehicle  −3.92 1.23  .001**
On foot  1.15 9.40 .90 On foot  −1.28 1.64  .44
Distance  0.40 0.43 .36 Distance traveled  −0.20 0.05  <.001***
Locations visited  4.70 9.56 .63 Locations visited  −0.97 0.93  .30
 
Social  Social
Speech  −0.20 2.82 .94 Speech  −0.65 0.32  .04*
Calls out  −12.34 29.85 .68 Call out  −6.08 2.83  .03*
SMS out  −0.43 0.51 .40 SMS out  −0.02 0.05  .71
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^

p < .10

*

p < .05

**

p < .01

***

p < .001

For passive sensing variables, to aid in interpretability of findings, persecutory ideation variables are multiplied by 100. All time-related predictors entered in hours. Distance is measured in kilometers.

Results

The majority (n = 45) of the 62 randomized participants fully completed the study. One participant did not begin participation after randomization. On average, CrossCheck generated a data entry for 262.80 days (SD = 97.17) per participant, and on average, participants provided 84.52 (SD = 45.79) responses to the EMA persecutory ideation item. Within those days, passive sensors varied with regard to the hours of data they collected per day on average, as audio (Mhours = 17.81, SD = 9.03) and location sensors (Mhours = 17.12, SD = 9.51) logged more data than activity sensors (Mhours = 12.65, SD = 8.55). As mentioned, to reduce the impact of these missing data, models involving passive sensors selected days on which CrossCheck logged 19 or more hours of data. EMA responses were provided on slightly less than one third of the days for which participants’ devices logged data (MEMA% = 31.48% SD = 10.65%). If a participant were to respond to all EMAs when they were prompted (three times per week), this value would equal approximately 42.8%.

Frequency and Distribution of PI

PI varied considerably within and between participants during the study period. Fifty participants (82%) reported at least “a little” PI on one or more occasions, whereas 11 (18%) reported none for the duration of the study. Among participants who endorsed PI at least once, the majority (n = 39, 78%) still reported having none on a majority of days. At the aggregate level, the overall mean PI severity score across all participants and time points was relatively low; about three quarters (75%) of all responses across participants were zero, and fewer were spread across the responses of “a little” (16%), “moderately” (6%), and “extremely” (4%).

Ecological Momentary Assessment Indicators

Between-participant differences.

All EMA indicators demonstrated between-participant relationships to PI (ps < .01, see Table 2). Average PI reported was higher for participants who on average reported elevated rates of depression, stress, hearing voices, seeing things, and reduced rates of reporting feeling social and sleeping well.

Within-participant differences.

When examining within-participant changes, we similarly found significant relationships between all EMA indicators and PI, and most but not all were in the expected direction. Reports of increases in depression, stress, hearing voices, and seeing things all were related to reports of PI, as expected. However, additionally, there were small but significant positive relationships with reports of feeling social and sleeping well, such that these variables were associated with increased PI (rather than decreased PI as we expected).

Passive Sensing

Between-participant differences.

No between-participants passive sensing variables were significantly related to PI (see Table 2).

Within-participant differences.

Several within-participant passive sensing variables were associated with greater PI in a manner generally consistent with study hypotheses (see Table 2). When individuals reported PI, they demonstrated an increase relative to typical behavior in time sitting still (Mminutes = 4.04, SD = 94.69) and a decrease in distance traveled (Mminutes = −1.20 km, SD = 18.89) and time spent in a vehicle (Mminutes = −4.15, SD = 49.59). Participants’ social behavior was affected as well, as those reporting greater PI spent significantly less time around speech (M minutes = −6.26, SD = 153.03) and on outgoing phone calls (Mminutes = −0.79, SD = 13.13).

Discussion

Our study adds to a growing body of evidence supporting the feasibility and utility of mobile technologies to assess psychotic symptoms. As participants responded to EMA prompts over the course of a 12-month data collection period, present results provide support for the scalability of integrated mobile assessment. Results suggest that PI is non-static and occurs in a manner that is consistent with existing conceptual models of PI (Freeman, 2016). In our sample, PI was a common but dynamic phenomenon. While the majority of participants reported at least some occurrences of PI at some point during the data collection period, most of those individuals reported it in less than half of their EMA entries. Three quarters of all EMA entries across all participants reported no PI. This is consistent with stress-vulnerability models of psychosis (Zubin and Spring, 1977) suggesting that psychotic symptoms fluctuate over time.

EMA variables generally demonstrated relationships to behavioral indicators related to domains described in existing models of PI. As hypothesized, individuals who reported more PI tended to report being more depressed and stressed, more severe auditory and visual hallucinations, and being less social or sleeping less well. Further, PI tended to increase within-individuals as they experienced worse depression, stress, voices, and seeing things. Notably, two unexpected findings emerged when examining within-participants changes associated with PI: small but significant increases in self-reported socializing and sleeping well. It’s possible that reports of PI might coincide with more intense affective states in general, differing from non-acute phases of illness characterized primarily by negative symptoms (Strauss et al., 2010).

Alternately, these patterns might reflect that PI coincides with the participants’ perception of increased social contact (e.g. I feel paranoid because I have been social) or greater time spent in bed avoiding others.

Third, several passive sensing indicators similarly adhered to existing models of PI. Experimental (Freeman and Garety, 1999; Vorontsova et al., 2013) and EMA (Bell et al., 2017b; Ben-Zeev et al., 2011; Peters et al., 2012; Thewissen et al., 2011, 2008) studies have suggested that certain behaviors are linked to the emergence and maintenance of PI; the present study provides additional support assessing these behaviors directly and in the moment. When experiencing PI, affected individuals tend to self-report avoiding potentially threatening situations to maintain safety, and the tendency to do this is associated with heightened distress (Freeman et al., 2007). The present study suggests that traces of these safety behaviors may be detectable by passive sensors, including reductions in activity, traveling (particularly in a vehicle), time spent with others and in self-initiated phone conversations. Importantly, the characteristics linked to PI in the present study were those that represented within-participant change, rather than between-participants averages. This provides additional support for stress-vulnerability models of PI. It also suggests that existing assessment strategies that provide aggregate estimates over extended periods miss potentially important within-participants changes. Person-centered assessments provided by mHealth might detect changes with greater granularity and improve the ability of providers to quickly detect risk.

This study is not without limitations. These results provide an exploratory examination of in-the-moment passively sensed indicators associated with PI. Given the correlational nature of our analyses, it is possible that related psychiatric symptoms (e.g. mood symptoms) or a third unknown or unmeasured variable (e.g. medication side effects) partially accounted for or affected identified relationships. This might be further complicated if specific factors correlated with a participants’ decreased likelihood to respond to EMA items. Also, while our data allow examination of several key pieces of cognitive models of PI, they are not exhaustive. Several other phenomena that have been suggested to play a role in the emergence and maintenance of PI – e.g. aberrant salience (Reininghaus et al., 2016b), cognitive biases (Dudley et al., 2016), hostile attribution bias (Buck et al., 2017, 2016; Dennis R Combs et al., 2007) – were not examined. Further, it was also the case that our measure of PI was brief and narrow, operationalized only as harm, when PI often comprises a range of varied concerns. There is a need for more extensive psychometric validation of differing forms of EMA, which is lacking given the relative novelty of such approaches. Last, relationships between passive sensing variables and PI, though significant, were small. It is possible that examining in-the-moment indicators of psychotic symptoms across individuals may obscure idiosyncrasies that more closely track their emergence. For example, other CrossCheck studies have demonstrated that rather than having consistent patterns across individuals, each person might present with their own specific “relapse signature” (Ben-Zeev et al., 2017) that could be identified and used in clinical risk prediction.

Overall, the present study provides replication and support of several components of existing models of PI (Freeman, 2016; Freeman et al., 2002) and introduces a new methodology that adds a dimension to the assessment of PI. These findings contributes to evidence supporting individuals with paranoia engaging with mobile assessments including EMA and passive sensing. Proximally, these results suggest that there might be value in closely monitoring changes in behavior, particularly travel and contact with others. More distally, these results add to the growing support for the development of integrated mobile health systems in routine clinical care. Such systems may be feasible, scalable, and particularly well-suited for the treatment of PI, as PI appears to fluctuate over time and coincide with specific behavioral changes that these systems can detect.

Acknowledgements

The research reported in this article was supported by a grant from the National Institute of Mental Health (R01MH103148). Dr. Buck is supported by a VA Academic Affiliations’ Advanced Fellowship in Health Services Research and Development (TPH 61–000-24).

Footnotes

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Conflicts of Interest

Dr. Ben-Zeev has an intervention content licensing and consulting agreement with Pear Therapeutics. Dr. Campbell is a consultant for Verily Life Sciences. Dr. Choudhury is a co-founder and holds equity stake at HealthRhythms Inc. Dr. Kane has been a consultant for or received honoraria from Alkermes, Eli Lilly, EnVivo Pharmaceuticals (Forum), Forest (Allergan), Genentech, H. Lundbeck. Intracellular Therapies, Janssen Pharmaceutica, Johnson and Johnson, LB Pharmaceuticals, Merck, Minerva, Neurocrine, Otsuka, Pierre Fabre, Reviva, Roche, Sunovion, Takeda, LB Pharmaceuticals and Teva. He has received grant support from Otsuka, Lundbeck and Janssen, and participated in Advisory Boards for Alkermes, Intracellular Therapies, Lundbeck, Neurocrine, Otsuka, Pierre Fabre, Takeda, Teva. Dr. Kane is also a shareholder in Vanguard Research Group and LB Pharmaceuticals, Inc. The other authors have no other potential conflicts to disclose.

1

For passive sensing variables, to aid in interpretability of findings, persecutory ideation variables are multiplied by 100 such that parameter estimates represent a hundredth of a point change in the outcome for one point change in the indicator.

References

  1. Bebbington P, Freeman D, 2017. Transdiagnostic Extension of Delusions: Schizophrenia and Beyond. Schizophr. Bull 43, 273–282. 10.1093/schbul/sbw191 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bell IH, Lim MH, Rossell SL, Thomas N, 2017a. Ecological Momentary Assessment and Intervention in the Treatment of Psychotic Disorders: A Systematic Review. Psychiatr. Serv appi.ps.2016005. 10.1176/appi.ps.201600523 [DOI] [PubMed] [Google Scholar]
  3. Bell IH, Lim MH, Rossell SL, Thomas N, 2017b. Ecological Momentary Assessment and Intervention in the Treatment of Psychotic Disorders: A Systematic Review. Psychiatr. Serv appi.ps.2016005. 10.1176/appi.ps.201600523 [DOI] [PubMed] [Google Scholar]
  4. Bell V, Halligan PW, Ellis HD, 2006. Explaining delusions: A cognitive perspective. Trends Cogn. Sci 10, 220–226. 10.1093/med [DOI] [PubMed] [Google Scholar]
  5. Ben-Zeev D, Brian R, Wang R, Wang W, Campbell AT, Aung MSH, Merrill M, Tseng VWS, Choudhury T, Hauser M, Kane JM, Scherer EA, 2017. CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse. Psychiatr. Rehabil. J. 40, 266–275. 10.1037/prj0000243 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Ben-Zeev D, Ellington K, Swendsen J, Granholm E, 2011. Examining a cognitive model of persecutory ideation in the daily life of people with schizophrenia: A computerized experience sampling study. Schizophr. Bull 37, 1248–1256. 10.1093/schbul/sbq041 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Ben-Zeev D, Granholm E, Cafri G, 2009. Symptoms of depression and anxiety mediate the relationship between self-esteem and paranoia. Schizophr. Res 10.1016/j.schres.2009.06.00s9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Ben-Zeev D, McHugo GJ, Xie H, Dobbins K, Young MA, 2012a. Comparing retrospective reports to real-time/real-place mobile assessments in individuals with schizophrenia and a nonclinical comparison group. Schizophr. Bull 38, 396–404. 10.1093/schbul/sbr171 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Ben-Zeev D, Morris S, Swendsen J, Granholm E, 2012b. Predicting the occurrence, conviction, distress, and disruption of different delusional experiences in the daily life of people with schizophrenia. Schizophr. Bull 38, 826–837. 10.1093/schbul/sbq167 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ben-Zeev D, Wang R, Abdullah S, Brian R, Scherer EA, Mistler LA, Hauser M, Kane JM, Campbell A, Choudhury T, 2016. Mobile Behavioral Sensing for Outpatients and Inpatients With Schizophrenia. Psychiatr. Serv 67, 558–561. 10.1176/appi.ps.201500130 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Bowie CR, Reichenberg A, Patterson TL, Heaton RK, Harvey PD, 2006. Determinants of real-world functional performance in schizophrenia subjects: Correlations with cognition, functional capacity, and symptoms. Am. J. Psychiatry 163, 418–425. 10.1176/appi.ajp.163.3.418 [DOI] [PubMed] [Google Scholar]
  12. Broyd A, Balzan RP, Woodward TS, Allen P, 2017. Dopamine, cognitive biases and assessment of certainty: A neurocognitive model of delusions. Clin. Psychol. Rev 54, 96– 106. 10.1016/j.cpr.2017.04.006 [DOI] [PubMed] [Google Scholar]
  13. Buck B, Iwanski C, Healey KM, Green MF, Horan WP, Kern RS, Lee J, Marder SR, Reise SP, Penn DL, 2017. Improving measurement of attributional style in schizophrenia; A psychometric evaluation of the Ambiguous Intentions Hostility Questionnaire (AIHQ). J. Psychiatr. Res 89, 48–54. 10.1016/j.jpsychires.2017.01.004 [DOI] [PubMed] [Google Scholar]
  14. Buck BE, Pinkham AE, Harvey PD, Penn DL, 2016. Revisiting the validity of measures of social cognitive bias in schizophrenia: Additional results from the Social Cognition Psychometric Evaluation (SCOPE) study. Br. J. Clin. Psychol 55, 441–454. 10.1111/bjc.12113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Castle DJ, Phelan M, Wessely S, Murray RM, 1994. Which patients with non-affective functional psychosis are not admitted at first psychiatric contact? Br. J. Psychiatry 164, 101–106. 10.1192/bjp.165.1.101 [DOI] [PubMed] [Google Scholar]
  16. Coid JW, Ullrich S, Kallis C, Keers R, Barker D, Cowden F, Stamps R, 2013. The Relationship Between Delusions and Violence: Findings From the East London First Episode Psychosis Study. JAMA Psychiatry 70, 1–7. 10.1001/jamapsychiatry.2013.12 [DOI] [PubMed] [Google Scholar]
  17. Combs DR, Adams SD, Penn DL, Roberts D, Tiegreen J, Stem P, 2007. Social Cognition and Interaction Training (SCIT) for inpatients with schizophrenia spectrum disorders: Preliminary findings. Schizophr. Res 91, 112–116. 10.1016/j.schres.2006.12.010 [DOI] [PubMed] [Google Scholar]
  18. Combs DR, Penn DL, Wicher M, Waldheter E, 2007. The ambiguous intentions hostility questionnaire (AIHQ): A new measure for evaluating hostile social-cognitive biases in paranoia. Cogn. Neuropsychiatry 12, 128–143. [DOI] [PubMed] [Google Scholar]
  19. Depp CA, Mausbach B, Granholm E, Cardenas V, Ben-Zeev D, Patterson TL, Lebowitz BD, Jeste DV, 2010. Mobile Interventions for Severe Mental Illness. J. Nerv. Ment. Dis 198, 715–721. 10.1097/NMD.0b013e3181f49ea3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Dudley R, Taylor P, Wickham S, Hutton P, 2016. Psychosis, delusions and the “Jumping to Conclusions” reasoning bias: A systematic review and meta-analysis. Schizophr. Bull 10.1093/schbul/sbv150 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Freeman D, 2016. Persecutory delusions: a cognitive perspective on understanding and treatment. The Lancet Psychiatry 10.1016/S2215-0366(16)00066-3 [DOI] [PubMed] [Google Scholar]
  22. Freeman D, 2007. Suspicious minds: The psychology of persecutory delusions. Clin. Psychol. Rev 27, 425–457. 10.1016/j.cpr.2006.10.004 [DOI] [PubMed] [Google Scholar]
  23. Freeman D, Bradley J, Antley A, Bourke E, DeWeever N, Evans N, Černis E, Sheaves B, Waite F, Dunn G, Slater M, Clark DM, 2016a. Virtual reality in the treatment of persecutory delusions: Randomised controlled experimental study testing how to reduce delusional conviction. Br. J. Psychiatry 209, 62–67. 10.1192/bjp.bp.115.176438 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Freeman D, Bradley J, Waite F, Sheaves B, DeWeever N, Bourke E, McInerney J, Evans N, Černis E, Lister R, Garety P, Dunn G, 2016b. Targeting Recovery in Persistent Persecutory Delusions: A Proof of Principle Study of a New Translational Psychological Treatment (the Feeling Safe Programme). Behav. Cogn. Psychother 10.1017/S1352465816000060 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Freeman D, Dunn G, Startup H, Pugh K, Cordwell J, Mander H, Černis E, Wingham G, Shirvell K, Kingdon D, 2015. Effects of cognitive behaviour therapy for worry on persecutory delusions in patients with psychosis (WIT): A parallel, single-blind, randomised controlled trial with a mediation analysis. The Lancet Psychiatry 10.1016/S2215-0366(15)00039-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Freeman D, Garety P, 2014. Advances in understanding and treating persecutory delusions: A review. Soc. Psychiatry Psychiatr. Epidemiol 49, 1179–1189. 10.1007/s00127-014-0928-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Freeman D, Garety P, 1999. Worry, worry processes & dimensions of delusions: An exploratory investigation of a role for anxiety processes in the maintenance of delusional distress. Behav. Cogn. Psychother 27, 47–52. [Google Scholar]
  28. Freeman D, Garety PA, Kuipers E, 2001. Persecutory delusions: Developing the understanding of belief maintenance and emotional distress. Psychol. Med 10.1017/S003329170100455X [DOI] [PubMed] [Google Scholar]
  29. Freeman D, Garety PA, Kuipers E, Fowler D, Bebbington PE, 2002. A cognitive model of persecutory delusions. Br. J. Clin. Psychol 41, 331–347. [DOI] [PubMed] [Google Scholar]
  30. Freeman D, Garety PA, Kuipers E, Fowler D, Bebbington PE, Dunn G, 2007. Acting on persecutory delusions: The importance of safety seeking. Behav. Res. Ther 45, 89–99. 10.1016/j.brat.2006.01.014 [DOI] [PubMed] [Google Scholar]
  31. Freeman D, Sheaves B, Goodwin GM, Yu LM, Nickless A, Harrison PJ, Emsley R, Luik AI, Foster RG, Wadekar V, Hinds C, Gumley A, Jones R, Lightman S, Jones S, Bentall R, Kinderman P, Rowse G, Brugha T, Blagrove M, Gregory AM, Fleming L, Walklet E, Glazebrook C, Davies EB, Hollis C, Haddock G, John B, Coulson M, Fowler D, Pugh K, Cape J, Moseley P, Brown G, Hughes C, Obonsawin M, Coker S, Watkins E, Schwannauer M, MacMahon K, Siriwardena AN, Espie CA, 2017. The effects of improving sleep on mental health (OASIS): a randomised controlled trial with mediation analysis. The Lancet Psychiatry 10.1016/S2215-0366(17)30328-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Fusar-Poli P, Cappucciati M, De Micheli A, Rutigliano G, Bonoldi I, Tognin S, Ramella-Cravaro V, Castagnini A, McGuire P, 2017. Diagnostic and prognostic significance of brief limited intermittent psychotic symptoms (BLIPS) in individuals at ultra high risk. Schizophr. Bull 43, 48–56. 10.1093/schbul/sbw151 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Garety P, Waller H, Emsley R, Jolley S, Kuipers E, Bebbington P, Dunn G, Fowler D, Hardy A, Freeman D, 2015. Cognitive Mechanisms of Change in Delusions: An Experimental Investigation Targeting Reasoning to Effect Change in Paranoia. Schizophr. Bull 41, 400–410. 10.1093/schbul/sbu103 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Granholm E, Loh C, Swendsen J, 2008. Feasibility and validity of computerized ecological momentary assessment in schizophrenia. Schizophr. Bull 34, 507–514. 10.1093/schbul/sbm113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kuipers E, Garety P, Fowler D, Freeman D, Dunn G, Bebbington P, 2006. Cognitive, emotional, and social processes in psychosis: refining cognitive behavioral therapy for persistent positive symptoms. Schizophr. Bull 32, S24–S31. 10.1093/schbul/sbl014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lahera G, Benito A, Montes JM, Fernández-Liria A, Olbert CM, Penn DL, 2013. Social cognition and interaction training (SCIT) for outpatients with bipolar disorder. J. Affect. Disord 146, 132–136. 10.1016/j.jad.2012.06.032 [DOI] [PubMed] [Google Scholar]
  37. Lataster T, Valmaggia L, Lardinois M, van Os J, Myin-Germeys I, 2013. Increased stress reactivity: a mechanism specifically associated with the positive symptoms of psychotic disorder. Psychol. Med 43, 1389–400. 10.1017/S0033291712002279 [DOI] [PubMed] [Google Scholar]
  38. Leucht S, Cipriani A, Spineli L, Mavridis D, Örey D, Richter F, Samara M, Barbui C, Engel RR, Geddes JR, Kissling W, Stapf MP, Lässig B, Salanti G, Davis JM, 2013. Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: A multiple-treatments meta-analysis. Lancet 382, 951–962. 10.1016/S0140-6736(13)60733-3 [DOI] [PubMed] [Google Scholar]
  39. Lincoln TM, Köther U, Hartmann M, Kempkensteffen J, Moritz S, 2015. Responses to stress in patients with psychotic disorders compared to persons with varying levels of vulnerability to psychosis, persons with depression and healthy controls. J. Behav. Ther. Exp. Psychiatry 47, 92–101. 10.1016/j.jbtep.2014.11.011 [DOI] [PubMed] [Google Scholar]
  40. Myin-Germeys I, Peeters F, Havermans R, Nicolson NA, DeVries MW, Delespaul P, van Os J, 2003. Emotional reactivity to daily life stress in psychosis and affective disorder: An experience sampling study. Acta Psychiatr. Scand 107, 124–131. [DOI] [PubMed] [Google Scholar]
  41. Paolini E, Moretti P, Compton MT, 2016. Delusions in first-episode psychosis: Principal component analysis of twelve types of delusions and demographic and clinical correlates of resulting domains. Psychiatry Res 243, 5–13. 10.1016/j.psychres.2016.06.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Peters E, Lataster T, Greenwood K, Kuipers E, Scott J, Williams S, Garety P, Myin-Germeys I, 2012. Appraisals, psychotic symptoms and affect in daily life. Psychol. Med 42, 1013–1023. 10.1017/S0033291711001802 [DOI] [PubMed] [Google Scholar]
  43. Reininghaus U, Depp CA, Myin-Germeys I, 2016a. Ecological interventionist causal models in psychosis: Targeting psychological mechanisms in daily life. Schizophr. Bull 42, 264–269. 10.1093/schbul/sbv193 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Reininghaus U, Kempton MJ, Valmaggia L, Craig TKJ, Garety P, Onyejiaka A, Gayer-Anderson C, So SH, Hubbard K, Beards S, Dazzan P, Pariante C, Mondelli V, Fisher HL, Mills JG, Viechtbauer W, Mcguire P, Van Os J, Murray RM, Wykes T, Myin-Germeys I, Morgan C, 2016b. Stress sensitivity, aberrant salience, and threat anticipation in early psychosis: An experience sampling study. Schizophr. Bull 42, 712–722. 10.1093/schbul/sbv190 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Roberts DL, Combs DR, Willoughby M, Mintz J, Gibson C, Rupp B, Penn DL, 2014. A randomized, controlled trial of Social Cognition and Interaction Training (SCIT) for outpatients with schizophrenia spectrum disorders. Br. J. Clin. Psychol 53, 281–298. 10.1111/bjc.12044 [DOI] [PubMed] [Google Scholar]
  46. Sabbag S, Twamley EW, Vella L, Heaton RK, Patterson TL, Harvey PD, 2012. Predictors of the accuracy of self assessment of everyday functioning in people with schizophrenia. Schizophr. Res 137, 190–195. 10.1016/j.schres.2012.02.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Smith B, Fowler DG, Freeman D, Bebbington P, Bashforth H, Garety P, Dunn G, Kuipers E, 2006. Emotion and psychosis: Links between depression, self-esteem, negative schematic beliefs and delusions and hallucinations. Schizophr. Res 86, 181–188. 10.1016/j.schres.2006.06.018 [DOI] [PubMed] [Google Scholar]
  48. So SH, Chau AKC, Peters E, Swendsen J, Garety P, Kapur S, 2017. Moment-to-moment associations between emotional disturbances, aberrant salience and persecutory delusions. Eur. Psychiatry 41, S838 10.1016/j.eurpsy.2017.01.1653 [DOI] [Google Scholar]
  49. Strauss GP, Harrow M, Grossman LS, Rosen C, 2010. Periods of recovery in deficit syndrome schizophrenia: A 20-year multi-follow-up longitudinal study. Schizophr. Bull 36, 788–799. 10.1093/schbul/sbn167 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Thewissen V, Bentall RP, Lecomte T, van Os J, Myin-Germeys I, 2008. Fluctuations in self-esteem and paranoia in the context of daily life. J. Abnorm. Psychol 117, 143–153. 10.1037/0021-843X.117.1.143 [DOI] [PubMed] [Google Scholar]
  51. Thewissen V, Bentall RP, Oorschot M, A Campo J, van Lierop T, van Os J, Myin-Germeys I, 2011. Emotions, self-esteem, and paranoid episodes: an experience sampling study. Br. J. Clin. Psychol 50, 178–195. 10.1348/014466510X508677 [DOI] [PubMed] [Google Scholar]
  52. Verdoux H, Maurice-Tison S, Gay B, van Os J, Salamon R, Bourgeois ML, 1998. A survey of delusional ideation in primary-care patients. Psychol. Med 28, 127–134. 10.1017/S0033291797005667 [DOI] [PubMed] [Google Scholar]
  53. Verdoux H, Van Os J, 2002. Psychotic symptoms in non-clinical populations and the continuum of psychosis. Schizophr. Res 54, 59–65. 10.1016/S0920-9964(01)00352-8 [DOI] [PubMed] [Google Scholar]
  54. Vorontsova N, Garety P, Freeman D, 2013. Cognitive factors maintaining persecutory delusions in psychosis: The contribution of depression. J. Abnorm. Psychol 122, 1121– 1131. 10.1037/a0034952 [DOI] [PubMed] [Google Scholar]
  55. Wang R, Aung MSH, Abdullah S, Brian R, Campbell AT, Choudhury T, Hauser M, Kane J, Merrill M, Scherer EA, Tseng VWS, Ben-zeev D, 2016. CrossCheck: Towards passive sensing and detection of mental health changes in people with schizophrenia. Proc. Int. Conf. Ubiquitous Comput 10.1145/2971648.2971740 [DOI] [Google Scholar]
  56. Wang R, Scherer EA, Walsh M, Wang W, Aung MSH, Ben-Zeev D, Brian R, Campbell AT, Choudhury T, Hauser M, Kane J, 2017. Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing. Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol 1, 1–24. 10.1145/3130976 [DOI] [Google Scholar]
  57. Wilkinson G, Robertson G, 2004. Wide Range Achievement Test Fourth Edition (WRAT-4) professional manual Psychological Assessment Resources, Lutz, FL. [Google Scholar]
  58. Zubin J, Spring B, 1977. Vulnerability: A new view of schizophrenia. J. Abnorm. Psychol 86, 103–126. 10.1037/0021-843X.86.2.103 [DOI] [PubMed] [Google Scholar]

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