Electronic monitoring of self-reported mood: the return of the subjective?
© The Author(s) 2016
Received: 25 September 2016
Accepted: 19 November 2016
Published: 29 November 2016
This narrative review describes recent developments in the use of technology for utilizing the self-monitoring of mood, provides some relevant background, and suggests some promising directions. Subjective experience of mood is one of the valuable sources of information about the state of an integrated mind/brain system. During the past century, psychiatry and psychology moved away from subjectivity, emphasizing external observation, precise measurement, and laboratory techniques. This shift, however, provided only a limited improvement in the understanding of mood disorders, and it appears that self-monitoring of mood has the potential to enrich our knowledge, particularly when combined with the advances in technology. Modern technology, with its ability to transfer information from the individual directly to the researcher via electronic applications, enables us now to study mood regulation more thoroughly. Frequent subjective ratings can be helpful in identifying individualized treatment with effective mood stabilizers and recognizing subtypes of mood disorders. The variability of subjective ratings may also help us estimate the increased risk of recurrence and guide a tailored treatment.
KeywordsMood disorders Apps Episode prediction Youth Mood stabilizers Treatment
Subjective experience of mood is one of the valuable sources of information about the state of the mind/brain function. While building the foundations of psychiatry during the nineteenth century, clinicians paid considerable attention to the subjective feelings of patients. Kraepelin, for instance, put emphasis on the subjective experience of patients when describing diagnostic entities (Kraepelin and Diefendorf 1902).
The twentieth century psychiatry and psychology attempted to move away from the subjective experience, questioning their validity, especially under the influence of behaviorism. Researchers tended to neglect subjective experience. Introspection was considered mostly unreliable and scientifically unsatisfactory. Psychologists in particular long viewed self-reports with marked suspicion (Clark and Watson 1991). In addition, there was a tendency to accept self-reports only if some physiological measurements authenticated them.
Researchers in both fields emphasized external observation, laboratory techniques, and precise measurement. But despite a century limited predominantly to such objective strategies, the yield in terms of understanding the nature of mood disorders has been limited. Even after a century of neurobiological research, no single psychobiological measurement has emerged that expresses the activity of these complex regulations in an integrated way altogether as well as the subjective experience of the individual can do. While the search for the biological basis of mood disorders continues and expands to new areas, there has recently been some renaissance of interest in better utilizing the subjective experiences of the patients.
With the emergence of mood stabilizers, and when behaviorism gradually declined as a leading force, a new paradigm started to take place, in which mood—the color of the subjective experience—is seen as a rich source of information with a rather complex regulation. Mood regulation can be conceptualized as a buffering system, allowing flexible responses that enable us to adapt to an ever-changing range of stimuli (Ortiz et al. 2015). Given the complex control of many brain functions, subjective experience of mood as a single expression of integrated functions has a special value.
Emotions exert a profound influence on human life and human behavior, and markedly alter even major economic decisions people make (Lerner et al. 2015). The research interest in mood disorders expanded by recognizing the importance of the subjective experience and the individual’s moods, to enrich the information obtained from objective measurements.
Psychological studies of mood ratings
Emotions are usually viewed as fundamental, distinctly subjective affective states of shorter duration, accompanied by bodily expressions and autonomic changes. Ordinarily, negative emotions (such as sadness, fear, anger, or disgust) comprise only a small fraction of everyday affective experience in controls. Moods are usually defined as affective states that may last from several hours to several days and are strongly influenced by external events by factors such as stress, social activity, and exercise (Powers et al. 2015), as well as from endogenous cycles or rhythms (Powers et al. 2015; Murray et al. 2009). It is mood, rather than emotions, that provides a better understanding of everyday experience.
Psychologists have been carefully investigating serial subjective assessments of mood states and have discovered interesting correlations (Larsen and Ketelaar 1991). David Watson, in particular, examined systematically short-term mood fluctuations and their relationship to temperament and synthesized a vast body of knowledge (Watson 2000). These observations have been generated mostly on cohorts without a diagnosed psychiatric illness, but can be considered extendable to many patients with mood disorders treated on an outpatient basis. We extract some of the conclusions that may be useful to psychiatric observations.
In psychological research it has been shown that the affective experiences of an individual can be subsumed under two general dimensions: negative and positive mood states (Watson 2000). However, these relevant concepts have not yet had much impact on psychiatric research of mood disorders. Negative mood states involve the experience of negative emotions and poor self-concept. It subsumes a variety of negative emotions including anger, contempt, disgust, guilt, and fear. Positive mood states, as a characteristic that describes how we experience positive emotions and interact with others, is characterized by enthusiasm, alertness, energetic, and active traits. These two dimensions are nearly independent of each other; it is possible for someone to be high in both positive and negative traits; or high in one and low in the other; or low in both (Watson 2000). These traits have been found to be moderately stable over time and across situations.
Within these two dimensions, distinct affective experiences are meaningfully intercorrelated. For instance, neuroticism and extraversion traits strongly correlate with individual differences in negative and positive emotional experience (Lyubomirsky et al. 2005). Negative affect, a highly non-specific dimension that is common to many types of psychopathology, correlates moderately with neuroticism. Negative mood states are much more responsive to ongoing stress and current life crises; in contrast, positive mood states are strongly associated with social interaction and physical activity (Watson 1988, 2000).
In clinical practice, the primary tasks are to identify the illness early and treat it effectively. To start with, self-monitoring of mood can be helpful in clinical practice when evaluating whether a given treatment is useful. In addition, daily ratings and their appropriate analysis, in combination with objective data, will allow, for example, to differentiate among offsprings of bipolar parents those who will stay well and those who have a high risk of developing a mood disorder; to select those patients who will be likely stabilized on lithium versus those who require neuroleptics or lamotrigine; and to identify time periods when a patient is at a high risk of recurrence (Ortiz et al. 2016).
Identifying predisposed youth
Minor mood disorders (depression NOS, cyclothymia, dysthymia), along with childhood sleep and anxiety disorders precede and predict the onset of diagnosable major mood disorders (Grof et al. 2009; Duffy et al. 2013). Adolescents often experience subthreshold psychiatric symptoms—sadness, irritability, anxiety, elation, energy changes, and sleep problems—even years before a recognizable mood disorder (Duffy 2014). These changes cannot be reliably captured retrospectively and benefit from daily monitoring prospectively. One such prospective mood monitoring study that is underway is the True Colors study (Bonsall et al. 2012, 2015). This study uses mobile phone and web-based technology to collect self-reported health measures on a daily and weekly basis. This monitoring has been well accepted by patients, particularly the younger age group who are increasingly familiar with widespread mobile devices and social media. The investigators expect to find a significant difference in mood stability profiles between high-risk offspring and controls. Further, in those high-risk offspring with evidence of mood instability, they expect a higher risk of new onsets of diagnosable mood episodes over the two years of study. A similar, cross-sectional study suggests that mood instability may be a prodromal phenotype for BD in the offspring of parents with bipolar disorder compared to controls (Birmaher et al. 2013; Howes et al. 2011). If young people who are at high risk of developing serious and persistent mood disorders could be identified earlier this would enable them to receive appropriate, timely treatment, and reduce the associated morbidity and mortality, given that the risks of complicating addictions, school drop-out, and suicide are all greater early in the illness.
Contributing to the choice of an effective stabilizer
On the other hand, patients with bipolar spectrum disease who fail to respond to lithium and require stabilization with neuroleptics or lamotrigine, rarely show days fully free of symptoms for any length of time. Some deviation from the midline is usually present most of the time.
Furthermore, the patients who benefit most from lamotrigine often find it difficult to express their daily mood by marking a single point. Their symptoms frequently show “ultra-rapid” cycling, changing several times a day, and would need several points in a day to capture the changes. In addition, these patients very often recognize and comment on life events and external circumstances triggering significant changes in their mood.
Double-blind clinical trials comparing the three groups of stabilizers head-to-head and identifying responders have not been carried out and are unlike to take place, because of the cost and lack of interest. Fortunately, we can utilize systematic clinical observations of response as they markedly improve clinical practice.
Another interesting approach, similar to the one we are currently undertaking, includes the analysis of early response of mood instability to mood stabilizers as a new test paradigm that incorporates daily ratings and non-linear analyses (Harrison et al. 2016; Holmes et al. 2011).
Forecasting risk of recurrences in mood disorders
Mood variability patterns are different among patients with different diagnoses (Grof et al. 1994; Katerndahl et al. 2007). Several studies have shown that high levels of mood regularity (low variability, in other words) are characteristic of disease states particularly in patients with major depression (Katerndahl and Wang 2007), posttraumatic stress disorder (Cowdry et al. 1991), or panic disorder (Katerndahl and Wang 2007).
In bipolar disorder, up until recently, the emphasis has been placed on acute episodes of depression or mania, underestimating the subtleties of mood changes in-between episodes as a key feature of the disorder. We have shown different patterns of mood variability between healthy controls and euthymic bipolar patients in our previous studies: essentially, healthy controls showed higher mood variability levels, whereas stable bipolar patients showed low mood variability, in keeping with a less flexible system (Ortiz et al. 2015). These data suggest that the variability of subjective ratings may help us estimate the increased risk of recurrence and guide an effective, individually tailored treatment. A new protocol (Ortiz et al. 2016), currently underway, aims to use non-linear techniques to study mood variability in patients with mood disorders to predict high-risk times for the onset of episodes.
Other studies, also in bipolar disorder, have used a combination of actigraphy and self-assessment questionnaires a combination of actigraphy and self-assessment questionnaires sent by text messaging (Novak et al. 2014). Although it is unclear whether these self-assessment questions were previously validated, this is another example of the utility of subjective measurements and remote monitoring to establish whether a patient is at risk for relapse.
This type of approach could be related to better clinical monitoring, aimed at intermittent treatment, with few side effects and optimal recovery. Moreover, this approach can be particularly valuable to adolescents at risk—either because of a strong family history or previous episodes and because mobile technology and social media are part of their lives.
However, one important limitation of using self-ratings and self-monitoring is the severity of mood disorder. Neither manic nor severely depressed patients are capable of providing valid self-ratings of their mood states.
Electronic mood monitoring and utilization of apps
One of the first efforts to include a more comprehensive, electronic self-mood monitoring, medication compliance, and sleep was Chronorecord (Bauer et al. 2005, 2008; 2012; Whybrow et al. 2003). Using this approach in large, international studies, the authors have productively analyzed many aspects of bipolar disorder, ranging from clinical course (Bauer et al. 2006, 2007, 2010, 2011a, b, 2012; Rasgon et al. 2005), treatment adherence (Bauer et al. 2010, 2013a, b, c; Adli et al. 2005), sleep (Bauer et al. 2006, 2008, 2009), among others (Conell et al. 2016; Bauer et al. 2005, 2006, 2009b, c; Glenn et al. 2006). Other efforts have included the use of life charts by the Stanley Foundation Network (Leverich et al. 2001). More recently, as previously mentioned, the Oxford group has also developed the “True Colors” system, which also monitors mood remotely (Holmes et al. 2011; Miklowitz et al. 2012; Moore et al. 2014). True Colors provides a way of visualizing mood data over time. The data are made available to the patient, which allows the patient to be more aware of their mood variability, activity, and sleep. It allows them to monitor how much they are helped by a new medication or a particular psychological treatment. At the same time, creating an automatic system that picks up early signs of deterioration from self-reports is quite advantageous and will allow to study the specific effects of a drug. In this particular case, the OxLith trial (Saunders et al. 2016), is a high intensity RCT which seeks to discover new insights into lithium’s mechanism of action and eventually generate new leads for drug discovery.
Modern technology with its ability to transfer information from the individual directly to the researcher via electronic applications (“apps”) has moved the investigations further ahead. They make it relatively easy to collect the subjective rating of symptoms daily and submit them directly to automated analysis so that warning signs can then be communicated directly to the treatment team. Over 165,000 health apps are now directly available to patients; mental health is the largest group of apps for a specific disease state, larger than cardiology, endocrine, or other disorders (Torous et al. 2016). Patients are already bringing apps, sleep-tracking devices and activity monitoring devices to psychiatrists to share their data; however, research is needed to determine consistent means for evaluating the performance of apps. Currently, as a result of the dearth of information on regulatory practices for apps, several authors have developed standardized frameworks for their clinical use (BinDhim et al. 2015; Lewis and Wyatt 2014; Donker et al. 2013).
Mental health apps have the potential to be useful tools to complement clinical practice, but the majority of those currently available lack scientific evidence about their efficacy (Marley and Farooq 2015). In addition, they have a few challenges, including concerns about how the data will be used by the app service, lack of medical involvement in app development, patient confidentiality issues, clinical risk emerging from the use of apps, and lack of evidence-based practice recommendations. Moreover, there are societal and ethical implications related to the utilization of these for both medical and non-medical purposes (Glenn and Monteith 2014).
Researchers from the Copenhagen Affective Disorders Research Center developed a smartphone-based system which included daily subjective assessments of activity in BD patients, as well as a feedback loop between patients and clinicians. The system proved to be highly useful by patients, with high rates of self-assessment adherence (Bardram et al. 2013). The authors found that the severity of depression and mania ratings correlated with smartphone-generated data, including physical activity (Faurholt-Jepsen et al. 2014) and voice features (Faurholt-Jepsen et al. 2016). However, mood monitoring combined with a feedback loop system to clinicians did not result in decreased depressive or manic symptoms (Faurholt-Jepsen et al. 2014).
These findings are in keeping with the fact that mobile technology is well suited for augmenting the capability of the psychiatrist to deliver high-quality care (Hsin et al. 2016), and that both sides of the equation are needed in order for this enterprise to be successful (Spaniel et al. 2015). For this augmentation to work well, the apps should be embedded in clinical care, developed in close partnership with clinicians and patients, and combined with those aspects of the system that we know already work well.
Technology can support and enhance clinical practice (Torous and Baker 2016), for example, by incorporating data from the app and physiological data into electronic medical records. This method can allow a prompt detection of mood episodes (Grof et al. 1993), and possibly allow intermittent treatment, with lithium salts, for example, in those patients with a classical type of bipolar illness, which will decrease side effects and improve compliance. In the long term, these approaches could decrease rates of admission and suicide and will overall improve the quality of life of patients with mood disorders.
In this context, apps are becoming a technological advance enabling us to study mood regulation and to treat mood disorders better and more thoroughly.
Subjective experience of mood is one of the valuable sources of information about the state of an integrated mind/brain system. During the past century, psychiatry and psychology moved away from subjectivity, emphasizing external observation, precise measurement, and laboratory techniques. This shift, however, provided only a limited improvement in the understanding of mood disorders, and it appears that self-monitoring of mood has the potential to enrich our knowledge, particularly when combined with the advances in technology.
We have described three examples in which frequent monitoring of subjective mood ratings are being utilized in an attempt to identify the offsprings of bipolar parents at high risk of becoming later ill, to forecast the time of future episodes of illness and to select the optimal mood stabilizer for the patient. With the advantages of modern technologies, adopting an approach that combines subjective mood ratings and objective data will allow us to estimate the risk of recurrence and to guide an effective and tailored treatment for patients with mood disorders.
AO and PG equally contributed to this manuscript. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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- Adli M, Whybrow PC, Grof P, Rasgon N, Gyulai L, Baethge C, et al. Use of polypharmacy and self-reported mood in outpatients with bipolar disorder. Int J Psychiatry Clin Pract. 2005;9(4):251–6.View ArticlePubMedGoogle Scholar
- Bardram J, Frost M, Szanto K, Faurholt-Jepsen M, Vinberg M, Kessing LV, editors. Designing mobile health technology for bipolar disorder: a field trial of the MONARCA system. In: Proceedings of the SIGCHI conference on human factors in computing systems; 2013.
- Bauer M, Rasgon N, Grof P, Gyulai L, Glenn T, Whybrow PC. Does the use of an automated tool for self-reporting mood by patients with bipolar disorder bias the collected data? MedGenMed. 2005a;7(3):21.PubMedPubMed CentralGoogle Scholar
- Bauer M, Rasgon N, Grof P, Altshuler L, Gyulai L, Lapp M, et al. Mood changes related to antidepressants: a longitudinal study of patients with bipolar disorder in a naturalistic setting. Psychiatry Res. 2005b;133(1):73–80.View ArticlePubMedGoogle Scholar
- Bauer M, Grof P, Rasgon NL, Marsh W, Munoz RA, Sagduyu K, et al. Self-reported data from patients with bipolar disorder: impact on minimum episode length for hypomania. J Affect Disord. 2006a;96(1–2):101–5.View ArticlePubMedGoogle Scholar
- Bauer M, Grof P, Rasgon N, Bschor T, Glenn T, Whybrow PC. Temporal relation between sleep and mood in patients with bipolar disorder. Bipolar Disord. 2006b;8(2):160–7.View ArticlePubMedGoogle Scholar
- Bauer M, Rasgon N, Grof P, Glenn T, Lapp M, Marsh W, et al. Do antidepressants influence mood patterns? A naturalistic study in bipolar disorder. Eur Psychiatry. 2006c;21(4):262–9.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Grof P, Pfennig A, Rasgon NL, Marsh W, et al. Self-reported data from patients with bipolar disorder: frequency of brief depression. J Affect Disord. 2007;101(1–3):227–33.View ArticlePubMedGoogle Scholar
- Bauer M, Wilson T, Neuhaus K, Sasse J, Pfennig A, Lewitzka U, et al. Self-reporting software for bipolar disorder: validation of ChronoRecord by patients with mania. Psychiatry Res. 2008a;159(3):359–66.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Whybrow PC, Grof P, Rasgon N, Alda M, et al. Changes in self-reported sleep duration predict mood changes in bipolar disorder. Psychol Med. 2008b;38(7):1069–71.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Grof P, Rasgon N, Alda M, Marsh W, et al. Comparison of sleep/wake parameters for self-monitoring bipolar disorder. J Affect Disord. 2009a;116(3):170–5.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Grof P, Rasgon NL, Marsh W, Sagduyu K, et al. Relationship between adjunctive medications for anxiety and time spent ill in patients with bipolar disorder. Int J Psychiatry Clin Pract. 2009b;13(1):70–7.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Grof P, Rasgon NL, Marsh W, Sagduyu K, et al. Relationship among latitude, climate, season and self-reported mood in bipolar disorder. J Affect Disord. 2009c;116(1–2):152–7.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Grof P, Schmid R, Pfennig A, Whybrow PC. Subsyndromal mood symptoms: a useful concept for maintenance studies of bipolar disorder? Psychopathology. 2010a;43(1):1–7.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Grof P, Marsh W, Sagduyu K, Alda M, et al. The association between concurrent psychotropic medications and self-reported adherence with taking a mood stabilizer in bipolar disorder. Hum Psychopharmacol. 2010b;25(1):47–54.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Alda M, Grof P, Sagduyu K, Bauer R, et al. Comparison of pre-episode and pre-remission states using mood ratings from patients with bipolar disorder. Pharmacopsychiatry. 2011a;44(Suppl 1):S49–53.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Rasgon N, Marsh W, Sagduyu K, Grof P, et al. Decreasing the minimum length criterion for an episode of hypomania: evaluation using self-reported data from patients with bipolar disorder. Eur Arch Psychiatry Clin Neurosci. 2011b;261(5):341–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Bauer M, Glenn T, Keil M, Bauer R, Marsh W, Grof P, et al. Brief depressive symptoms in patients with bipolar disorder: analysis of long-term self-reported data. Aust N Z J Psychiatry. 2012;46(11):1068–78.View ArticlePubMedGoogle Scholar
- Bauer M, Glenn T, Alda M, Sagduyu K, Marsh W, Grof P, et al. Regularity in daily mood stabilizer dosage taken by patients with bipolar disorder. Pharmacopsychiatry. 2013a;46(5):163–8.View ArticlePubMedGoogle Scholar
- Bauer R, Glenn T, Alda M, Sagduyu K, Marsh W, Grof P, et al. Antidepressant dosage taken by patients with bipolar disorder: factors associated with irregularity. Int J Bipolar Disord. 2013b;1:26.View ArticlePubMedPubMed CentralGoogle Scholar
- Bauer M, Glenn T, Alda M, Sagduyu K, Marsh W, Grof P, et al. Drug treatment patterns in bipolar disorder: analysis of long-term self-reported data. Int J Bipolar Disord. 2013c;1:5.View ArticlePubMedPubMed CentralGoogle Scholar
- BinDhim NF, Hawkey A, Trevena L. A systematic review of quality assessment methods for smartphone health apps. Telemed J E Health. 2015;21(2):97–104.View ArticlePubMedGoogle Scholar
- Birmaher B, Goldstein BI, Axelson DA, Monk K, Hickey MB, Fan J, et al. Mood lability among offspring of parents with bipolar disorder and community controls. Bipolar Disord. 2013;15(3):253–63.View ArticlePubMedPubMed CentralGoogle Scholar
- Bonsall MB, Wallace-Hadrill SM, Geddes JR, Goodwin GM, Holmes EA. Nonlinear time-series approaches in characterizing mood stability and mood instability in bipolar disorder. Proc Biol Sci. 2012;279(1730):916–24.View ArticlePubMedGoogle Scholar
- Bonsall MB, Geddes JR, Goodwin GM, Holmes EA. Bipolar disorder dynamics: affective instabilities, relaxation oscillations and noise. J R Soc Interface. 2015;12(112):20150670.View ArticlePubMedPubMed CentralGoogle Scholar
- Clark LA, Watson D. Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications. J Abnorm Psychol. 1991;100(3):316–36.View ArticlePubMedGoogle Scholar
- Conell J, Bauer R, Glenn T, Alda M, Ardau R, Baune BT, et al. Online information seeking by patients with bipolar disorder: results from an international multisite survey. Int J Bipolar Disord. 2016;4(1):17.View ArticlePubMedPubMed CentralGoogle Scholar
- Cowdry RW, Gardner DL, O’Leary KM, Leibenluft E, Rubinow DR. Mood variability: a study of four groups. Am J Psychiatry. 1991;148(11):1505–11.View ArticlePubMedGoogle Scholar
- Donker T, Petrie K, Proudfoot J, Clarke J, Birch MR, Christensen H. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res. 2013;15(11):e247.View ArticlePubMedPubMed CentralGoogle Scholar
- Duffy A. Toward a comprehensive clinical staging model for bipolar disorder: integrating the evidence. Can J Psychiatry. 2014;59(12):659–66.PubMedPubMed CentralGoogle Scholar
- Duffy A, Horrocks J, Doucette S, Keown-Stoneman C, McCloskey S, Grof P. Childhood anxiety: an early predictor of mood disorders in offspring of bipolar parents. J Affect Disord. 2013;150(2):363–9.View ArticlePubMedGoogle Scholar
- Faurholt-Jepsen M, Frost M, Vinberg M, Christensen EM, Bardram JE, Kessing LV. Smartphone data as objective measures of bipolar disorder symptoms. Psychiatry Res. 2014a;217(1–2):124–7.View ArticlePubMedGoogle Scholar
- Faurholt-Jepsen M, Vinberg M, Frost M, Christensen EM, Bardram J, Kessing LV. Daily electronic monitoring of subjective and objective measures of illness activity in bipolar disorder using smartphones—the MONARCA II trial protocol: a randomized controlled single-blind parallel-group trial. BMC Psychiatry. 2014b;14:309.View ArticlePubMedPubMed CentralGoogle Scholar
- Faurholt-Jepsen M, Busk J, Frost M, Vinberg M, Christensen EM, Winther O, et al. Voice analysis as an objective state marker in bipolar disorder. Transl Psychiatry. 2016;6:e856.View ArticlePubMedGoogle Scholar
- Glenn T, Monteith S. New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet. Curr Psychiatry Rep. 2014;16(12):523.View ArticlePubMedGoogle Scholar
- Glenn T, Whybrow PC, Rasgon N, Grof P, Alda M, Baethge C, et al. Approximate entropy of self-reported mood prior to episodes in bipolar disorder. Bipolar Disord. 2006;8(5 Pt 1):424–9.View ArticlePubMedGoogle Scholar
- Grof P. Selecting effective long-term treatment for bipolar patients: monotherapy and combinations. J Clin Psychiatry. 2003;64(Suppl 5):53–61.PubMedGoogle Scholar
- Grof P, Alda M, Grof E, Fox D, Cameron P. The challenge of predicting response to stabilising lithium treatment: the importance of patient selection. Br J Psychiatry Suppl. 1993;21:16–9.Google Scholar
- Grof P, Alda M, Grof E, Zvolsky P, Walsh M. Lithium response and genetics of affective disorders. J Affect Disord. 1994;32(2):85–95.View ArticlePubMedGoogle Scholar
- Grof P, Duffy A, Alda M, Hajek T. Lithium response across generations. Acta Psychiatr Scand. 2009;120(5):378–85.View ArticlePubMedGoogle Scholar
- Harrison PJ, Cipriani A, Harmer CJ, Nobre AC, Saunders K, Goodwin GM, et al. Innovative approaches to bipolar disorder and its treatment. Ann N Y Acad Sci. 2016;1366(1):76–89.View ArticlePubMedPubMed CentralGoogle Scholar
- Holmes EA, Deeprose C, Fairburn CG, Wallace-Hadrill SM, Bonsall MB, Geddes JR, et al. Mood stability versus mood instability in bipolar disorder: a possible role for emotional mental imagery. Behav Res Ther. 2011;49(10):707–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Howes OD, Lim S, Theologos G, Yung AR, Goodwin GM, McGuire P. A comprehensive review and model of putative prodromal features of bipolar affective disorder. Psychol Med. 2011;41(8):1567–77.View ArticlePubMedGoogle Scholar
- Hsin H, Torous J, Roberts L. An adjuvant role for mobile health in psychiatry. JAMA Psychiatry. 2016;73(2):103–4.View ArticlePubMedGoogle Scholar
- Katerndahl D, Wang CP. Dynamic covariation of symptoms of anxiety and depression among newly diagnosed patients with major depressive episode, panic disorder, and controls. Nonlinear Dyn Psychol Life Sci. 2007;11(3):349–65.Google Scholar
- Katerndahl D, Ferrer R, Best R, Wang CP. Dynamic patterns in mood among newly diagnosed patients with major depressive episode or panic disorder and normal controls. Prim Care Companion J Clin Psychiatry. 2007;9(3):183–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Kraepelin E, Diefendorf AR. Clinical psychiatry, vol. 11. New York: The Macmillan Company; 1902.Google Scholar
- Larsen RJ, Ketelaar T. Personality and susceptibility to positive and negative emotional states. J Pers Soc Psychol. 1991;61(1):132–40.View ArticlePubMedGoogle Scholar
- Lerner JS, Li Y, Valdesolo P, Kassam KS. Emotion and decision making. Annu Rev Psychol. 2015;66:799–823.View ArticlePubMedGoogle Scholar
- Leverich GS, Nolen WA, Rush AJ. The Stanley foundation bipolar treatment outcome network. J Affect Disord. 2001;67:33.View ArticlePubMedGoogle Scholar
- Lewis TL, Wyatt JC. mHealth and mobile medical Apps: a framework to assess risk and promote safer use. J Med Internet Res. 2014;16(9):e210.View ArticlePubMedPubMed CentralGoogle Scholar
- Lyubomirsky S, King L, Diener E. The benefits of frequent positive affect: does happiness lead to success? Psychol Bull. 2005;131(6):803–55.View ArticlePubMedGoogle Scholar
- Marley J, Farooq S. Mobile telephone apps in mental health practice: uses, opportunities and challenges. BJPsych Bull. 2015;39(6):288–90.View ArticlePubMedPubMed CentralGoogle Scholar
- Miklowitz DJ, Price J, Holmes EA, Rendell J, Bell S, Budge K, et al. Facilitated integrated mood management for adults with bipolar disorder. Bipolar Disord. 2012;14(2):185–97.View ArticlePubMedPubMed CentralGoogle Scholar
- Moore PJ, Little MA, McSharry PE, Goodwin GM, Geddes JR. Mood dynamics in bipolar disorder. Int J Bipolar Disord. 2014;2(1):11.View ArticlePubMedPubMed CentralGoogle Scholar
- Murray G, Nicholas CL, Kleiman J, Dwyer R, Carrington MJ, Allen NB, et al. Nature’s clocks and human mood: the circadian system modulates reward motivation. Emotion. 2009;9(5):705–16.View ArticlePubMedGoogle Scholar
- Novak D, Albert F, Spaniel F. Analysis of actigraph parameters for relapse prediction in bipolar disorder: a feasibility study. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:4972–5.PubMedGoogle Scholar
- Ortiz A, Bradler K, Heintze A, Grof P. Personalized medicine for episode prediction in bipolar disorder. 2016. Personal communication.
- Ortiz A, Bradler K, Garnham J, Slaney C, Alda M. Nonlinear dynamics of mood regulation in bipolar disorder. Bipolar Disord. 2015;17(2):139–49.View ArticlePubMedGoogle Scholar
- Powers MB, Asmundson GJ, Smits JA. Exercise for mood and anxiety disorders: the state-of-the science. Cogn Behav Ther. 2015;44(4):237–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Rasgon N, Bauer M, Grof P, Gyulai L, Elman S, Glenn T, et al. Sex-specific self-reported mood changes by patients with bipolar disorder. J Psychiatr Res. 2005;39(1):77–83.View ArticlePubMedGoogle Scholar
- Saunders K, Cipriani A, Rendell J, Attenburrow M, Nelissen N, Bilderbeck A, et al. Oxford Lithium Trial (OxLith) of the early affective, cognitive, neural and biochemical effects of lithium carbonate in bipolar disorder: study protocol for a randomised controlled trials. Trials. 2016;17(1):116.View ArticlePubMedPubMed CentralGoogle Scholar
- Spaniel F, Vohlidka P, Hrdlicka J, Kozeny J, Novak T, Motlova L, et al. ITAREPS: information technology aided relapse prevention programme in schizophrenia. Schizophr Res. 2008a;98(1–3):312–7.View ArticlePubMedGoogle Scholar
- Spaniel F, Vohlidka P, Kozeny J, Novak T, Hrdlicka J, Motlova L, et al. The Information Technology Aided Relapse Prevention Programme in Schizophrenia: an extension of a mirror-design follow-up. Int J Clin Pract. 2008b;62(12):1943–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Spaniel F, Novak T, Bankovska Motlova L, Capkova J, Slovakova A, Trancik P, et al. Psychiatrist’s adherence: a new factor in relapse prevention of schizophrenia. A randomized controlled study on relapse control through telemedicine system. J Psychiatr Ment Health Nurs. 2015;22(10):811–20.View ArticlePubMedGoogle Scholar
- Torous J, Baker JT. Why psychiatry needs data science and data science needs psychiatry: connecting with technology. JAMA Psychiatry. 2016;73(1):3–4.View ArticlePubMedGoogle Scholar
- Torous JB, Chan SR, Yellowlees PM, Boland R. To use or not? evaluating ASPECTS of smartphone apps and mobile technology for clinical care in psychiatry. J Clin Psychiatry. 2016;77(6):e734–8.View ArticlePubMedGoogle Scholar
- Watson D. Intraindividual and interindividual analyses of positive and negative affect: their relation to health complaints, perceived stress, and daily activities. J Pers Soc Psychol. 1988;54(6):1020–30.View ArticlePubMedGoogle Scholar
- Watson D. Mood and temperament. New York: Guilford Press; 2000.Google Scholar
- Whybrow PC, Grof P, Gyulai L, Rasgon N, Glenn T, Bauer M. The electronic assessment of the longitudinal course of bipolar disorder: the ChronoRecord software. Pharmacopsychiatry. 2003;36(Suppl 3):S244–9.PubMedGoogle Scholar