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Posit Science's mobile mood tracker app. Left: the app's intro screen on the iPad. The user clicks on any tile to start the assessment. Right: single example items from PHQ-9, GAD-7, Rumination, and IMS are shown. PHQ-9: patient health questionnaire, 9-item. GAD-7: generalized anxiety disorder, 7-item. IMS: Immediate Mood Scaler. 

Posit Science's mobile mood tracker app. Left: the app's intro screen on the iPad. The user clicks on any tile to start the assessment. Right: single example items from PHQ-9, GAD-7, Rumination, and IMS are shown. PHQ-9: patient health questionnaire, 9-item. GAD-7: generalized anxiety disorder, 7-item. IMS: Immediate Mood Scaler. 

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Background Mood disorders are dynamic disorders characterized by multimodal symptoms. Clinical assessment of symptoms is currently limited to relatively sparse, routine clinic visits, requiring retrospective recollection of symptoms present in the weeks preceding the visit. Novel advances in mobile tools now support ecological momentary assessment...

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... informed consent, participants were given an iPad mini (Model # A1454, iPad mini WiFi 16GB; Apple, Inc) and were asked to log in to PSC's Mobile Mood Tracker app with a unique password-protected login to complete the tasks ( Figure 1). UCSF EMU participants completed the procedure during their hospitalization (in clinic) and UCB or PSC participants completed it in the lab at UC Berkeley or at the PSC offices in San Francisco. ...

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... Based on these two dimensions, Russell created a Circumplex model of emotions [17] most commonly used to test stimuli of emotion words, emotional facial expressions, and affective states. Every language has its mood-related words, and since our study took place in Italy, final terms chosen from the Immediate Mood scale [11] and ITAMS, Italian Mood Scale [15] are: (i) Redquarter -nervosa, tesa, preoccupata, arrabbiata; (ii) Yellowquarter -attenta, energica, felice, euforica; (iii) Green-quarter -soddisfatta, serena, rilassata, calma; (iv) Blu-quarter -apatica, triste, depressa, stanca. ...
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Starting from the assumption that mood has a central role in domain-specific persuasion systems for well-being, the main goal of this study was to investigate the feasibility and acceptability of single-input methods to assess momentary mood as a medium for further interventions in health-related mobile apps destined for mature women. To this aim, we designed a very simple android App providing four user interfaces, each one showing one interactive widget to self-assess mood. Two widgets report a hint about the momentary mood they represent; the last two do not have the hints but were previously refined through questionnaires administered to 63 women (age 45-65) in order to reduce their expressive ambiguity. Next, fifteen women (age 45-65 years) were recruited to use the app for 15 days. Participants were polled about their mood four times a day and data were saved in a remote database. Moreover, users were asked to fill out a preliminary questionnaire, at the first access to the app, and a feedback questionnaire at the end of the testing period. Results appear to prove the feasibility and acceptability of this approach to self-assess momentary mood in the target population and provides some potential input methods to be used in this context.
... Ecological momentary assessment (EMA) has been employed to effectively measure variations in affective and cognitive states in ecological, real-life situations [22,23]. Using EMA, data can be collected in the natural environment and repeatedly across multiple time points, allowing for more accurate representation of momentary changes [23][24][25]. Indeed, daily positive affect was found to be associated with lower depressive and anxiety symptoms, through the enhancement of psychological resilience [23,26]. ...
... Indeed, daily positive affect was found to be associated with lower depressive and anxiety symptoms, through the enhancement of psychological resilience [23,26]. Moreover, EMA studies found that large variability in daily mood reporting is a signi cant predictor of mental health status [23,24,27,28]. Finally, higher mood variability over time was suggested re ect high emotional reactivity, combined with a lack of regulatory control that prevents the emotions from returning to their baseline level [29]. ...
... The total score for the scale is derived from the sum of scores of all ve items, and ranges between 0 and 20, with higher scores re ecting better self-perceived psychological resilience. EMA data of mood and IC was collected during a 2-week period following t0 using the Moodify app (Nahum et al., 2017). The app was installed on participant's mobile phones, and they were asked to provide, twice/day during weekdays (i.e., Sunday-Thursday), a momentary mood reporting and then complete a short IC task. ...
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Psychological resilience - the ability to adapt to adversity - is associated with intact inhibitory control (IC) mechanisms, which support goal-directed behavior. To date, no study has examined the daily fluctuations of IC performance in relation to resilience. The purpose of this study is to examine the relationship between IC and mood in young adults in a stressful situation in relation to psychological resilience. A baseline resilience test was conducted on 156 female and male soldiers during their basic combat training. Afterward, participants completed a 2-week ecological momentary assessment protocol, which included reporting their momentary moods and completing an IC assessment twice/day. A hierarchical linear modeling (HLM) analysis revealed that psychological resilience moderated the relationship between momentary IC and momentary mood, with better IC only being associated with better mood for those with higher, but not lower, baseline psychological resilience. This association was present only for female, but not for male participants. The study demonstrates that psychological resilience manifests itself in the everyday association between IC and mood. Additionally, these results contribute to our understanding of resilient behavior in the real world by supporting cognitive models of resilience. Trial Registration: MOH_2018-0-13_002451
... Novel remote measurement technologies (RMTs), for example, smartphone applications, sensors and wearable technologies, are a subsection of mHealth, and can enable frequent, longitudinal and personalised health monitoring 2 . Currently, the assessment, and subsequent treatment, of many chronic health conditions is limited to retrospective recall during routine clinic visits, which can be biased by cognitive and memory heuristics 3 , and social desirability bias 4 . RMT data offers the potential for high-frequency symptom monitoring, which is more reflective of an individual's daily experience 5 . ...
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The use of remote measurement technologies (RMTs) across mobile health (mHealth) studies is becoming popular, given their potential for providing rich data on symptom change and indicators of future state in recurrent conditions such as major depressive disorder (MDD). Understanding recruitment into RMT research is fundamental for improving historically small sample sizes, reducing loss of statistical power, and ultimately producing results worthy of clinical implementation. There is a need for the standardisation of best practices for successful recruitment into RMT research. The current paper reviews lessons learned from recruitment into the Remote Assessment of Disease and Relapse- Major Depressive Disorder (RADAR-MDD) study, a large-scale, multi-site prospective cohort study using RMT to explore the clinical course of people with depression across the UK, the Netherlands, and Spain. More specifically, the paper reflects on key experiences from the UK site and consolidates these into four key recruitment strategies, alongside a review of barriers to recruitment. Finally, the strategies and barriers outlined are combined into a model of lessons learned. This work provides a foundation for future RMT study design, recruitment and evaluation.
... the Columbia Suicide Severity Rating Scale (C-SSRS), respectively. 19,20 Ketamine side effects were assessed pre and post infusion using the Side Effect Rating Scale for Dissociative Anesthetics (SERSDA). 21 Participants completed the IMS, C-SSRS, and SERSDA at baseline (pre infusion), 2 and 6 hours post infusion, and daily while in the ED until inpatient disposition. ...
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Objective: Mood disorders complicated by suicidal ideation (SI) frequently present to the emergency department (ED) for care. Currently, patients with SI in the ED do not typically receive targeted interventions. Ketamine may have a role in treating SI within the ED because subanesthetic doses have rapid-acting antidepressant and antisuicidal properties. Methods: This single-arm, open-label feasibility study enrolled 14 participants from the ED with acute SI who were awaiting voluntary admission to inpatient psychiatry to receive ketamine at 0.5 mg/kg, administered intravenously. Participants were assessed post administration to evaluate feasibility of administration in the ED and short-term effectiveness. Feasibility was determined by acceptability by patients and physicians as well as tolerability and ability to recruit participants into the study. Efficacy was assessed based on changes in (1) self-reported mood and (2) suicidal ideation pre- and postinfusion of ketamine. Results: All patients reported severe depression and active SI at baseline. No serious adverse events were reported, and acceptability was rated highly by both participants and physicians (>70%). Two hours after receiving ketamine 0.5 mg/kg, the mean SI and somatic symptom burden were decreased compared to baseline (P < 0.001 and P = 0.005, respectively), and the mean self-reported mood was increased (P = 0.006). Improvements in mood and decreases in suicidality persisted at 6 hours. Conclusions: Overall, ketamine was well tolerated, considered feasible by both participants and physicians, and demonstrated short-term efficacy. There is a growing body of evidence demonstrating the feasibility of ketamine administration in the ED, and larger randomized trials should be conducted to establish treatment recommendations for patients with SI in the ED.
... The demographic questionnaire included age, marital status, medical conditions, number of family members near the facility, and the number of times residents had left the facility in the previous week. The mood scale was adopted from Nahum et al. (2017) Immediate Mood Scaler to capture data regarding the current emotions of the participants. The scale ranged from very negative emotions and very positive emotions. ...
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Throughout the COVID-19 pandemic, long-term care residents have been disproportionately affected both physically and mentally. Increased restrictions have worsened long-term care residents’ mental health and have increased feelings of isolation and loneliness. This pilot study explores the feasibility of virtual reality (VR) technology used by long-term care residents for mental health in a rural area of southern Illinois. We captured long-term care residents’ thoughts, feelings, and knowledge of VR using a pre-test and post-test design following an educational session introducing VR. Participants were then offered the opportunity to use the technology, with 9 out of the 11 participants watching a 360⁰ video using the VR headset. All participants who tried the VR headset noted that they were more willing to try VR in the future. While no statistically significant changes in mood from before and after the session were found, the results suggest that the use of VR for mental health in long-term care populations is more feasible when paired with an educational session before intervention.
... However, the measuring interval of STAI ranges from 1 h to 104 days (16), lacking the capability of measuring transitory states in a finer temporal resolution. Immediate Mood Scaler (IMS), a newly proposed self-report tool, could capture current mood states with 22 items but is designed for a daily report with a maximum usage frequency being twice a day (17). Though state anxiety is associated with transient sympathetic activation and vagal deactivation (18), most anxiety-related indicators were reported in discrete time points with long intervals (19,20). ...
Article
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Anxiety induction is widely used in the investigations of the mechanism and treatment of state anxiety. State anxiety is accompanied by immediate psychological and physiological responses. However, the existing state anxiety measurement, such as the commonly used state anxiety subscale of the State-Trait Anxiety Inventory, mainly relies on questionnaires with low temporal resolution. This study aims to develop a tracking model of state anxiety with high temporal resolution. To capture the dynamic changes of state anxiety levels, we induced the participants' state anxiety through exposure to aversive pictures or the risk of electric shocks and simultaneously recorded multi-modal data, including dimensional emotion ratings, electrocardiogram, and galvanic skin response. Using the paired self-reported state anxiety levels and multi-modal measures, we trained and validated machine learning models to predict state anxiety based on psychological and physiological features extracted from the multi-modal data. The prediction model achieved a high correlation between the predicted and self-reported state anxiety levels. This quantitative model provides fine-grained and sensitive measures of state anxiety levels for future affective brain-computer interaction and anxiety modulation studies.
... Digital interventions that collect patient-reported outcomes have been recognized as feasible and acceptable by clinicians and patients alike [17]. However, self-reporting often involves reporting bias, which may result in erroneous judgments such as the reconstruction of memories and excessive reliance on cognitive heuristics [18]. Retrospective self-reports of negative mood states experienced in the past (eg, the most recent 2 weeks) tend to be exaggerated in a negative direction [19]. ...
... Screening for psychological distress in this population is recognized as essential, but several barriers for its successful implementation persist [13,14]. For instance, the most used method of self-reporting of symptoms can result in significant bias in reporting experiences, which is even more pronounced in people with depression [18][19][20][21][22][23]. Therefore, a better way to detect psychological disorders in cancer survivors is needed. ...
Article
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Background: Cancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed. New possibilities have been opening up with advances in artificial intelligence and increasing knowledge on the connection of observable cues and psychological states. Objective: The aim of this scoping meta-review was to identify observable features of depression that can be intercepted using artificial intelligence in order to provide a stepping stone toward better recognition of depression among cancer survivors. Methods: We followed a methodological framework for scoping reviews. We searched SCOPUS and Web of Science for relevant papers on the topic, and data were extracted from the papers that met inclusion criteria. We used thematic analysis within 3 predefined categories of depression cues (ie, language, speech, and facial expression cues) to analyze the papers. Results: The search yielded 1023 papers, of which 9 met the inclusion criteria. Analysis of their findings resulted in several well-supported cues of depression in language, speech, and facial expression domains, which provides a comprehensive list of observable features that are potentially suited to be intercepted by artificial intelligence for early detection of depression. Conclusions: This review provides a synthesis of behavioral features of depression while translating this knowledge into the context of artificial intelligence–supported screening for depression in cancer survivors.
... Leveraging our expertise in the development of digital platforms that provide individualized monitoring and treatment strategies (Biagianti et al., 2016a;Nahum et al., 2017), the effects of two experimental mobile approaches were tested in people with SSD in hopes to reduce negative symptoms and enhance social functioning. ...
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Background Patients with Schizophrenia Spectrum Disorders (SSD) demonstrate poor social functioning. While group-based approaches show long-term improvements, access to treatments is limited. Digital platforms hold promise to overcome barriers to treatment delivery and improve outcomes. Objective In a parallel arm, double-blind RCT, we tested CLIMB, a clinician-assisted, adjunct to treatment that includes computerized social cognition training (SCT), ecological momentary assessments (EMAs), group tele-therapy, and moderated messaging. CLIMB was compared to an active control that includes computerized general cognitive training (GCT), unstructured support groups, and unmoderated messaging. Methods The primary outcome was social functioning. Secondary outcomes were negative symptoms and quality of life (QoL). Given the sample size, Propensity Score Models were used to ensure balanced baseline covariates. Mixed-effects models examined change over time. Results 24 participants completed the study (12 per arm). No significant between-group differences emerged in engagement. CLIMB participants engaged in a median of 8 sessions (IQR = 2), 2.8 h of SCT (IQR = 7.5), and 2710 EMAs; control participants engaged in a median of 9 sessions (IQR = 3) and 2.2 h of GCT (IQR = 7.9). As a group, participants showed significant improvements in social functioning (p = .046), with no between-group differences. Intent-to-treat analyses indicated greater improvements in QoL (p = .025) for the active control. Conclusions Delivering group-based mobile interventions to individuals with SSD is feasible. EMAs allow clinicians to maintain inter-session engagement, build participant self-awareness, and tailor treatment delivery. In this treatment model, whether SCT or GCT is more effective remains unclear. Further research will evaluate group-based mobile interventions to improve outcomes in SSD.
... 2,3 The accessibility of mobile apps makes it easy to report routine mental health ratings, such as for depression or other mood states, into ecological instantaneous assessment tools. [4][5][6][7][8][9] Existing studies have demonstrated that smartphones are a pervasive computing platform that provide a tremendous opportunity to automatically detect depression using collected sensory data. Further, they can be used for effective depression screenings. ...
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Purpose: Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices. Patients and methods: We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words. Results: Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642-0.895) and 0.85 (95% CI = 0.780-0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273-67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented. Conclusion: Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.
... Assessments done without paired brain signal and outside that window were excluded from analysis, resulting in a variable number of recordings across patients ( Table 1). The IMS is a validated tablet-based tool that assesses momentary mood symptoms (Nahum et al., 2017), correlates well with standardized selfreport measures of depression (PHQ-9) and anxiety (GAD-7), and further captures symptom fluctuations in-the-moment. Subjects rated their current emotional state using 12 pairs of words thought to represent extremes of depressive (item 1-7, Figure 1C, black words) and anxiety (items 8-12, Figure 1C, blue words) related dimensions. ...
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Objective: Anxiety and depression are prominent non-motor symptoms of Parkinson’s disease (PD), but their pathophysiology remains unclear. We sought to understand their neurophysiological correlates from chronic invasive recordings of the prefrontal cortex (PFC). Methods: We studied four patients undergoing deep brain stimulation (DBS) for their motor signs, who had comorbid mild to moderate anxiety and/or depressive symptoms. In addition to their basal ganglia leads, we placed a permanent prefrontal subdural 4-contact lead. These electrodes were attached to an investigational pulse generator with the capability to sense and store field potential signals, as well as deliver therapeutic neurostimulation. At regular intervals over 3–5 months, participants paired brief invasive neural recordings with self-ratings of symptoms related to depression and anxiety. Results: Mean age was 61 ± 7 years, mean disease duration was 11 ± 8 years and a mean Unified Parkinson’s Disease Rating Scale, with part III (UPDRS-III) off medication score of 37 ± 13. Mean Beck Depression Inventory (BDI) score was 14 ± 5 and Beck Anxiety Index was 16.5 ± 5. Prefrontal cortex spectral power in the beta band correlated with patient self-ratings of symptoms of depression and anxiety, with r -values between 0.31 and 0.48. Mood scores showed negative correlation with beta spectral power in lateral locations, and positive correlation with beta spectral power in a mesial recording location, consistent with the dichotomous organization of reward networks in PFC. Interpretation: These findings suggest a physiological basis for anxiety and depression in PD, which may be useful in the development of neurostimulation paradigms for these non-motor disease features.