User perspectives on long-term remote active electronic self-monitoring of mood symptoms in bipolar spectrum disorders

BACKGROUND

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.

METHODS

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.

RESULTS

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.

LIMITATIONS

Responder bias may be present. Findings may not be generalisable to all research cohorts.

CONCLUSIONS

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.

Have I argued with my family this week?": What questions do those with lived experience choose to monitor their bipolar disorder?

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.

Large-scale roll out of electronic longitudinal mood-monitoring for research in affective disorders: Report from the UK bipolar disorder research network

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.

Experiences of remote mood and activity monitoring in bipolar disorder: A qualitative study

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.

Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder

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.

Is energy a stronger indicator of mood for those with bipolar disorder compared to those without bipolar disorder?

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.

Evidence of weekly cyclicity in mood and functional impairment in those with a bipolar disorder

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.