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User feedback is crucial in the development of electronic self-monitoring tools for bipolar spectrum disorders (BSD). Previous studies have examined user experiences in small samples self-monitoring over relatively short time periods. We aimed to explore the experiences of a large sample of individuals with BSD engaged in long-term remote active electronic self-monitoring. An online survey, containing closed and open questions, was sent to participants with BSD enrolled on the Bipolar Disorder Research Network (BDRN) True Colours mood-monitoring system. Questions related to experiences of using True Colours, including viewing mood graphs, and sharing data with healthcare professionals (HCPs) and/or family/friends. Response rate was 62.7 % (n = 362). 88.4 % reported finding using True Colours helpful. Commonly reported benefits were having a visual record of mood changes, patterns/triggers and identifying early warning signs. Limitations included questions not being comprehensive or revealing anything new. One third had shared their graphs, with 89.9 % finding it helpful to share with HCPs and 78.7 % helpful to share with family/friends. Perceived benefits included aiding communication and limitations included lack of interest/understanding from others. Responder bias may be present. Findings may not be generalisable to all research cohorts. The majority of participants valued long-term self-monitoring. Personalisation and ease of use were important. A potential challenge is continued use when mood is long-term stable, highlighting the need for measures to be sensitive to small changes. Sharing self-monitoring data with HCPs may enhance communication of the lived experience of those with BSD. Future research should examine HCPs' perspectives. • Benefits and limitations of long-term remote digital self-monitoring were assessed. • Participants: individuals with Bipolar Spectrum Disorders (BSD; N = 362) • Majority valued the tool; personalisation and ease of use were important. • Sharing BSD self-monitoring mood data with others may enhance communication. • Limitations: responder and reporting bias, small sample responses, generalisability [ABSTRACT FROM AUTHOR]

Apps developed for bipolar disorder (BD) allow a sense of autonomy and self‐regulation whilst monitoring for early warning signs indicative of a relapse. C.A.L.M BD was specifically co‐designed and co‐produced with young people (16–25 years) living with BD, their family members, clinicians, researchers in human–computer interaction and an app development company to address the current lack of such technology for this age range. The prototype app tracked mood, and provided young people with the instant ability to monitor mood trends and use lifeline functionality when in crisis. The evaluation of the app demonstrated that it successfully engaged participants and provided insight to an effective way of iteratively designing an app. The future development of this app could improve outcomes for young people living with BD, however, needs to be evaluated rigorously. [ABSTRACT FROM AUTHOR]

In this study, a literature survey was conducted of research into the development and use of wearable devices and sensors in patients with depression. We collected 18 studies that had investigated wearable devices for assessment, monitoring, or prediction of depression. In this report, we examine the sensors of the various types of wearable devices (e.g., actigraphy units, wristbands, fitness trackers, and smartwatches) and parameters measured through sensors in people with depression. In addition, we discuss future trends, referring to research in other areas employing wearable devices, and suggest the challenges of using wearable devices in the field of depression. Real-time objective monitoring of symptoms and novel approaches for diagnosis and treatment using wearable devices will lead to changes in management of patients with depression. During the process, it is necessary to overcome several issues, including limited types of collected data, reliability, user adherence, and privacy concerns.

Background: Electronic self-report mood monitoring tools for individuals with bipolar disorder (BD) are rapidly emerging and predominately employ predefined symptom-based questions. Allowing individuals to additionally choose what they monitor in relation to their BD offers the unique opportunity to capture and gain a deeper insight into patient priorities in this context.Methods: In addition to monitoring mood symptoms with two standardised self-rated questionnaires, 308 individuals with BD participating in the Bipolar Disorder Research Network True Colours electronic mood-monitoring tool for research chose to create and complete additional personalised questions. A content analysis approach was used to analyse the content of these questions.Results: 35 categories were created based on the personalised questions with the most common being physical activity and exercise, anxiety and panic, sleep and coping/stress levels. The categories were grouped into six overarching themes 1) mental health; 2) behaviour and level of functioning; 3) physical wellbeing; 4) health behaviours; 5) active self-management; and, 6) interpersonal.Limitations: The average age of the sample was around 50 years meaning our findings may not be generalisable to younger individuals with BD.Conclusions: Aspects of BD important to patients in relation to longitudinal monitoring extend well beyond mood symptoms, highlighting the limitations of solely relying on standardised questions/mood rating scales based on symptoms primarily used for diagnosis. Additional symptoms and aspects of life not necessarily useful diagnostically for BD may be more important for individuals themselves to monitor and have more meaning in capturing their own experience of changes in BD severity. [ABSTRACT FROM AUTHOR]

The mental health care available for depression and anxiety has recently undergone a major technological revolution, with growing interest towards the potential of smartphone apps as a scalable tool to treat these conditions. Since the last comprehensive meta-analysis in 2019 established positive yet variable effects of apps on depressive and anxiety symptoms, more than 100 new randomized controlled trials (RCTs) have been carried out. We conducted an updated meta-analysis with the objectives of providing more precise estimates of effects, quantifying generalizability from this evidence base, and understanding whether major app and trial characteristics moderate effect sizes. We included 176 RCTs that aimed to treat depressive or anxiety symptoms. Apps had overall significant although small effects on symptoms of depression (N=33,567, g=0.28, p<0.001; number needed to treat, NNT=11.5) and generalized anxiety (N=22,394, g=0.26, p<0.001, NNT=12.4) as compared to control groups. These effects were robust at different follow-ups and after removing small sample and higher risk of bias trials. There was less variability in outcome scores at post-test in app compared to control conditions (ratio of variance, RoV=-0.14, 95% CI: -0.24 to -0.05 for depressive symptoms; RoV=-0.21, 95% CI: -0.31 to -0.12 for generalized anxiety symptoms). Effect sizes for depression were significantly larger when apps incorporated cognitive behavioral therapy (CBT) features or included chatbot technology. Effect sizes for anxiety were significantly larger when trials had generalized anxiety as a primary target and administered a CBT app or an app with mood monitoring features. We found evidence of moderate effects of apps on social anxiety (g=0.52) and obsessive-compulsive (g=0.51) symptoms, a small effect on post-traumatic stress symptoms (g=0.12), a large effect on acrophobia symptoms (g=0.90), and a non-significant negative effect on panic symptoms (g=-0.12), although these results should be considered with caution, because most trials had high risk of bias and were based on small sample sizes. We conclude that apps have overall small but significant effects on symptoms of depression and generalized anxiety, and that specific features of apps - such as CBT or mood monitoring features and chatbot technology - are associated with larger effect sizes., (© 2024 World Psychiatric Association.)

Background: Electronic longitudinal mood monitoring has been shown to be acceptable to patients with affective disorders within clinical settings, but its use in large-scale research has not yet been established.Methods: Using both postal and email invitations, we invited 4080 past research participants with affective disorders who were recruited into the Bipolar Disorder Research Network (BDRN) over a 10 year period to participate in online weekly mood monitoring. In addition, since January 2015 we have invited all newly recruited BDRN research participants to participate in mood monitoring at the point they were recruited into BDRN.Results: Online mood monitoring uptake among past participants was 20%, and among new participants to date was 46% with participants recruited over the last year most likely to register (61%). More than 90% mood monitoring participants engaged for at least one month, with mean engagement period greater than one year (58 weeks) and maximum engagement for longer than three years (165 weeks). There were no significant differences in the proportion of past and new BDRN participants providing data for at least 4 weeks (91%, 92% respectively), 3 months (78%, 82%), 6 months (65%, 54%) or one year (51%, 44%).Limitations: Our experiences with recruiting participants for electronic prospective mood monitoring may not necessarily generalise fully to research situations that are very different from those we describe.Conclusions: Large-scale electronic longitudinal mood monitoring in affective disorders for research purposes is feasible with uptake highest among newly recruited participants. [ABSTRACT FROM AUTHOR]

Background. Mobile mood-monitoring applications are increasingly used by mental health providers, widely advocated within research, and a potentially effective method to engage young people. However, little is known about their efficacy and usability in young populations. Method. A systematic review addressing three research questions focused on young people: (1) what are the psychometric properties of mobile mood-monitoring applications; (2) what is their usability; and (3) what are their positive and negative clinical impacts? Findings were synthesised narratively, study quality assessed and compared with evidence from adult studies. Results. We reviewed 25 articles. Studies on the psychometric properties of mobile mood-monitoring applications were sparse, but indicate questionable to excellent internal consistency, moderate concurrent validity and good usability. Participation rates ranged from 30% to 99% across studies, and appeared to be affected by methodological factors (e.g. payments) and individual characteristics (e.g. IQ score). Mobile mood-monitoring applications are positively perceived by youth, may reduce depressive symptoms by increasing emotional awareness, and could aid in the detection of mental health and substance use problems. There was very limited evidence on potential negative impacts. Conclusions. Evidence for the use of mood-monitoring applications in youth is promising but limited due to a lack of high-quality studies. Future work should explicate the effects of mobile mood-monitoring applications on effective selfregulation, clinical outcomes across disorders and young people's engagement with mental health services. Potential negative impacts in this population should also be investigated, as the adult literature suggests that application use could potentially increase negativity and depression symptoms. [ABSTRACT FROM AUTHOR]

Background Mobile technology enables high frequency mood monitoring and automated passive collection of data (e.g. actigraphy) from patients more efficiently and less intrusively than has previously been possible. Such techniques are increasingly being deployed in research and clinical settings however little is known about how such approaches are experienced by patients. Here, we explored the experiences of individuals with bipolar disorder engaging in a study involving mood and activity monitoring with a range of portable and wearable technologies. Method Patients were recruited from a wider sample of 50 individuals with Bipolar Disorder taking part in the Automated Monitoring of Symptom Severity (AMoSS) study in Oxford. A sub-set of 21 patients participated in a qualitative interview that followed a semi-structured approach. Results Monitoring was associated with benefits including increased illness insight, behavioural change. Concerns were raised about the potential preoccupation with, and paranoia about, monitoring. Patients emphasized the need for personalization, flexibility, and the importance of context, when monitoring mood. Conclusions Mobile and electronic health approaches have potential to lend new insights into mental health and transform healthcare. Capitalizing on the perceived utility of these approaches from the patients’ perspective, while addressing their concerns, will be essential for the promise of new technologies to be realised. [ABSTRACT FROM AUTHOR]

Background: Traditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose.Methods: We introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customised smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorders (BPD) and 51 Healthy control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools.Results: MZ correlated well (|R|>0.5,p<0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (|R|>0.3,p<0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires.Limitations: All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings.Conclusions: MZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice. [ABSTRACT FROM AUTHOR]

Various styles of communication can be used when working with people with mental health issues, and all formats can contribute to therapeutic goals. However, there is little in the literature about young people's use of pictorial text messages, or emojis, when they are experiencing mental distress. This article considers how young people in mental distress can send emojis in text messages to their parents or guardians to open up channels of communication, and how this can help improve their mental well-being. [ABSTRACT FROM AUTHOR]

Theory and research indicate that activity is fundamental to mood episodes in bipolar disorder (BD), yet researchers have not tested whether energy is more closely tethered to mood in BD compared to those without BD. Eighty-seven participants (13 with self-reported BD) completed 4396 energy and mood ratings through a mood-monitoring application. Mixed modeling analyses indicated that low energy, but not high energy, was related to mood within the BD group. Low energy could provide a strong and easily recognized indicator of negative mood states in persons with BD. [ABSTRACT FROM AUTHOR]

Abstract: A key characteristic of bipolar disorder is fluctuation in mood symptoms and functional capacity, yet assessment of bipolar symptomatology often relies heavily on interval measurement that is unable to capture the full range of daily symptom variability and severity. The current study provides a detailed analysis of the variability in mood symptoms, functional impairment and medication compliance in a large sample of individuals newly diagnosed with bipolar disorder. Individuals diagnosed with bipolar disorder in the previous 12 months (n=192) rated their mood, functional impairment, medication compliance and symptom triggers daily over 10 consecutive weeks. High mood, low mood and functional impairment were found to vary on a weekly cycle, independently of medication compliance. Low mood and functional impairment were worse on weekdays, particularly Mondays and Tuesdays, whereas mood was most elevated on Saturdays. Work-related stressors were the most common symptom triggers on weekdays, whereas sleep-related problems and positive social events were the most common triggers on weekends. This study provides evidence that individuals newly diagnosed with bipolar disorder experience fluctuations in mood and functioning that vary according to a weekly cycle. This finding has implications for the assessment and treatment of patients, and for future research. [Copyright &y& Elsevier]

In this study, a literature survey was conducted of research into the development and use of wearable devices and sensors in patients with depression. We collected 18 studies that had investigated wearable devices for assessment, monitoring, or prediction of depression. In this report, we examine the sensors of the various types of wearable devices (e.g., actigraphy units, wristbands, fitness trackers, and smartwatches) and parameters measured through sensors in people with depression. In addition, we discuss future trends, referring to research in other areas employing wearable devices, and suggest the challenges of using wearable devices in the field of depression. Real-time objective monitoring of symptoms and novel approaches for diagnosis and treatment using wearable devices will lead to changes in management of patients with depression. During the process, it is necessary to overcome several issues, including limited types of collected data, reliability, user adherence, and privacy concerns., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Lee, Kim, Park and Jeon.)

Background: Mobile health apps are increasingly available and used in a clinical context to monitor young people's mood and mental health. Despite the benefits of accessibility and cost-effectiveness, consumer engagement remains a hurdle for uptake and continued use. Hundreds of mood-monitoring apps are publicly available to young people on app stores; however, few studies have examined consumer perspectives. App store reviews held on Google and Apple platforms provide a large, rich source of naturally generated, publicly available user reviews. Although commercial developers use these data to modify and improve their apps, to date, there has been very little in-depth evaluation of app store user reviews within scientific research, and our current understanding of what makes apps engaging and valuable to young people is limited., Objective: This study aims to gain a better understanding of what app users consider useful to encourage frequent and prolonged use of mood-monitoring apps appropriate for young people., Methods: A systematic approach was applied to the selection of apps and reviews. We identified mood-monitoring apps (n=53) by a combination of automated application programming interface (API) methods. We only included apps appropriate for young people based on app store age categories (apps available to those younger than 18 years). We subsequently downloaded all available user reviews via API data scraping methods and selected a representative subsample of reviews (n=1803) for manual qualitative content analysis., Results: The qualitative content analysis revealed 8 main themes: accessibility (34%), flexibility (21%), recording and representation of mood (18%), user requests (17%), reflecting on mood (16%), technical features (16%), design (13%), and health promotion (11%). A total of 6 minor themes were also identified: notification and reminders; recommendation; privacy, security, and transparency; developer; adverts; and social/community., Conclusions: Users value mood-monitoring apps that can be personalized to their needs, have a simple and intuitive design, and allow accurate representation and review of complex and fluctuating moods. App store reviews are a valuable repository of user engagement feedback and provide a wealth of information about what users value in an app and what user needs are not being met. Users perceive mood-monitoring apps positively, but over 20% of reviews identified the need for improvement., (©Emily Widnall, Claire Ellen Grant, Tao Wang, Lauren Cross, Sumithra Velupillai, Angus Roberts, Robert Stewart, Emily Simonoff, Johnny Downs. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 10.10.2020.)

Aims and Method: True Colours is an automated symptom monitoring programme used by National Health Service psychiatric services. This study explored whether patients with unipolar treatment-resistant depression (TRD) found this a useful addition to their treatment regimes. Semi-structured qualitative interviews were conducted with 21 patients with TRD, who had engaged in True Colours monitoring as part of the Lithium versus Quetiapine in Depression study. A thematic analysis was used to assess participant experiences of the system., Results: Six main themes emerged from the data, the most notable indicating that mood monitoring increased patients' insight into their disorder, but that subsequent behaviour change was absent., Clinical Implications: Patients with TRD can benefit from mood monitoring via True Colours, making it a worthwhile addition to treatment. Further development of such systems and additional support may be required for patients with TRD to experience further benefits as reported by other patient groups.

The rapid advances and adoption of smartphone technology presents a novel opportunity for delivering mental health interventions on a population scale. Despite multi-sector investment along with wide-scale advertising and availability to the general population, the evidence supporting the use of smartphone apps in the treatment of depression has not been empirically evaluated. Thus, we conducted the first meta-analysis of smartphone apps for depressive symptoms. An electronic database search in May 2017 identified 18 eligible randomized controlled trials of 22 smartphone apps, with outcome data from 3,414 participants. Depressive symptoms were reduced significantly more from smartphone apps than control conditions (g=0.38, 95% CI: 0.24-0.52, p<0.001), with no evidence of publication bias. Smartphone interventions had a moderate positive effect in comparison to inactive controls (g=0.56, 95% CI: 0.38-0.74), but only a small effect in comparison to active control conditions (g=0.22, 95% CI: 0.10-0.33). Effects from smartphone-only interventions were greater than from interventions which incorporated other human/computerized aspects along the smartphone component, although the difference was not statistically significant. The studies of cognitive training apps had a significantly smaller effect size on depression outcomes (p=0.004) than those of apps focusing on mental health. The use of mood monitoring softwares, or interventions based on cognitive behavioral therapy, or apps incorporating aspects of mindfulness training, did not affect significantly study effect sizes. Overall, these results indicate that smartphone devices are a promising self-management tool for depression. Future research should aim to distil which aspects of these technologies produce beneficial effects, and for which populations., (© 2017 World Psychiatric Association.)

Background: We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables., Objective: The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety., Methods: We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable., Results: We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC., Conclusions: This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice., (©Athanasios Tsanas, Kate Saunders, Amy Bilderbeck, Niclas Palmius, Guy Goodwin, Maarten De Vos. Originally published in JMIR Mental Health (http://mental.jmir.org), 25.05.2017.)

Mood fluctuations are problematic in bipolar disorder. Current approaches to frequent monitoring of mood in bipolar disorder are paper diaries and electronic handheld devices. These approaches are limited in several ways, notably in the reliability of the data being collected which is often retrospectively reported. The experience sampling method offers a research paradigm which could be modified for use in clinical settings, to real time frequent mood monitoring. Mobile phone technology has also recently been developed to monitor weekly mood in a bipolar sample, demonstrating successful compliance rates. We propose the use of mobile phone technology as a novel method for frequently monitoring mood in bipolar disorder.