We examined the utility of a cell phone text-messaging system to monitor the outcomes of 62 outpatients with BDI and BDII. The prospective nature of data collection allowed us to examine the proportion of time patients spent in states of mania/hypomania, depression, mixed state, or euthymia, and time trends in the trajectory of symptoms. The compliance rate for adherence to the text-messaging protocol was 75% over an average of 36 weeks, with 83% of responses obtained within 12 hours of the prompting message. Fewer than 4% of the text messages sent by patients contained incorrectly formatted responses, suggesting that patients had little difficulty understanding the SMS requirements. These results suggest that this novel mood monitoring method is readily adopted by patients with BDI and BDII.
The proportions of time that patients reported spending in depressive, manic, and euthymic states were similar to the proportions reported in other longitudinal studies of bipolar patients (11
). Patients reported that a larger percent of their time (47.7%) was spent in depressed states, whereas only 36.5% was spent in euthymic mood. To test the validity of these weekly mood ratings, we would need to compare the current results to those obtained from clinician-gathered mood ratings, which were not available in the present study.
Depression was a more prominent feature of BDI than BDII. Participants with BDI reported depressive symptoms 53.6% of the time, whereas participants with BDII reported depressive symptoms 35.2% of the time. Patients with BDI reported relatively stable levels of minor depression, whereas patients with BDII initially reported higher levels of depression that decreased to below the levels of depression reported by patients with BDI. Possibly, depression among patients with BDI in this sample was less treatment responsive than depression among patients with BDII.
Differences in the proportion of time spent in depressive and manic states between BDI and BDII patients have varied from study to study. Kupka et al. (12
) and Joffe et al. (19
) found no significant differences between patients with BDI and patients with BDII on proportion of time spent with depressive symptoms. In contrast, Judd et al. (11
) found that patients with BDI spent 30.6% of weeks with depressive symptoms, whereas patients with BDII spent 51.9% of weeks with depressive symptoms. In an 18-month study in Finland (21
), patients with BDII spent 58.0% of their time in states of depression compared to 41.7% among BDI patients. The variable results across studies may reflect different assessment methods and differences in the length of the retrospective or prospective reporting intervals. Weekly prospective monitoring may expose more subtle but pervasive subsyndromal depressive symptoms among BDI patients that are less apparent among BDII patients.
Although men began with higher initial depression scores than women, the two genders were nearly identical in levels of depression by the end of the study. In contrast, mania scores among women were initially higher than mania scores among men, but decreased over time to levels below the manic or hypomanic threshold (6 or above). In contrast, in a three-year follow-up of 19 men and 37 women with bipolar disorder, Christensen et al. (22
) found that women had more depressive episodes than men, and men had more manic episodes than women. Future studies should examine whether gender differences remain robust once differences in age at onset, rapid cycling status, and pharmacological treatments are statistically controlled.
The group differences in depression scores at the end of the study (mean 6.6 ± 7.8 on the QIDS-SR for patients with BDII, compared to 8.3 ± 7.3 for patients with BDI), although statistically significant, may not be clinically significant given that both scores fell within the QIDS range reflecting ‘moderate depression’. Minor week-to-week fluctuations in QIDS or ASRM scores will not always signal the need for modifications of the treatment regimen, unless such minor changes have historically heralded the development of fully syndromal recurrences in that patient.
Perhaps the most significant barrier to conclusive interpretation of these results is lack of standardization of the pharmacological and psychological treatments. Patients were followed naturalistically, and could be enrolled in any treatment modality. As a consequence, the course patterns we observed cannot be exclusively attributed to the effects of gender or BDI/BDII status. It is also possible that the present results simply reflect differences in reporting behavior between patients with BDI and BDII, or between men and women. Future research should explore moderators of illness course as measured using the SMS, such as comorbid diagnoses, treatment regimen or modality, illness severity, or age at onset.
The findings are also limited by the choice of the ASRM, which is designed as a categorical measure of mania and has not been validated as a continuous measure. There are no brief, self-report mania scales that differentiate between mania and hypomania. Neither the ASRM nor the QIDS is intended as a diagnostic measure; hence, scores above 6 should not necessarily be construed as meeting DSM-IV criteria for a manic, hypomanic, or major depressive episode. Because both measures had identical cutoff scores, it was possible to consider each measure categorically as a guide to the presence or absence of mania or depression. However, each measure is scaled in a different metric, so it was not possible to determine the temporal relationships between fluctuations between the two scales or to measure patients’ cycling between the two mood states.
Whereas data were collected on a weekly basis, the SMS monitoring technique does not entirely solve the problem of retrospective recall. Patients were prompted to describe their mood over the past seven days. A truly contemporaneous measure would ask patients to enter their mood at the very moment they are prompted. Whereas ecological momentary assessment techniques (6
) may reduce recall bias, they may introduce other problems. Notably, prompting frequently enough to establish daily or even within-day fluctuations could feel laborious or intrusive to some patients.
The SMS methodology is best suited to studies of illness course and, potentially, treatment outcome, particularly if its validity relative to face-to-face measures of symptom course can be established. In the future, a parallel effort should be undertaken to improve the SMS technology for clinical purposes. In addition to alerting treatment providers to the patient’s mood shifts in ‘real time’, it may be possible to customize each system for each patient, allowing for the monitoring of variables such as hours slept, quality of sleep, levels of stress, medication adherence, exercise, and food intake. Correlations within each individual between these variables and mood change could then be determined.
The finding that the SMS was readily adopted by a clinical population and resulted in findings similar to those from prior longitudinal studies suggests that this technology could be developed both as a research and everyday clinical tool. The convergence of outcome measures will make much more feasible the comparison of data from clinical trials with data from ordinary practice. In bipolar disorder, short-term mood instability is pervasive and could be a much more sensitive measure of outcome than, for example, the frequency of syndromal illness episodes. The technology also appears to be empowering for its users and has the potential to reduce the need for more frequent face-to-face interviewing. The resource implications in terms of clinical time and travel could be considerable. We predict that text-messaging technology will have an important future in the clinical care of patients with bipolar disorder.