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ORIGINAL ARTICLE |
From the Departments of Community Medicine (G.A., H.T., J.U.) and Behavioral Sciences and Community Health (S.R.) and the General Clinical Research Center (E.B., A.W.), University of Connecticut School of Medicine, Farmington, Connecticut; Section of Allergy and Immunology (A.A.), University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania; and Department of Medicine (R.Z-W.), St. Francis Hospital and Medical Center, Hartford, Connecticut.
Address reprint requests to: Glenn Affleck, PhD, Department of Community Medicine, University of Connecticut Health Center, Farmington, CT 06030. Email: affleck{at}nso1.uchc.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONWITHIN-PERSON STUDIES OF MOOD...METHODS AND PROCEDURESRESULTSDISCUSSIONNOTESACKNOWLEDGMENTSREFERENCES |
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METHODS: Thrice-daily for 21 consecutive days, 48 adults with moderate to severe asthma entered information in palm-top computers about their mood and asthma symptoms. A multidimensional model of mood, ie, the mood circumplex, informed the assessment of mood arousal and mood pleasantness. At each observation, participants also recorded their PEFR with peak flow meters that stored blinded data. Albuterol doses were also monitored electronically. Before and after the 21-day study, spirometric measures of airways obstruction were taken under controlled conditions.
RESULTS: Random effects regression models revealed a significant, but weak, within-person relation between symptoms and PEFR. Changes in mood vectors with an arousal component were significantly related to PEFR changes, whereas changes in mood vectors with a pleasantness component tracked changes in asthma symptom reports, even after adjustment for contemporaneous PEFR and after controlling for time of day and albuterol dosing. Comparison of spirometric assessments with unsupervised PEFR suggested that part of the relation between mood arousal and PEFR may be attributable to the "effort-dependence" of peak flow self-monitoring.
CONCLUSIONS: Different dimensions of mood were associated with transitory changes in asthma symptoms and PEFR. This may be one reason why individuals with asthma misperceive the severity of their symptoms in relation to underlying airways obstruction.
Key Words: asthma • mood • peak flow • symptoms • diaries
Abbreviations: PEFR = peak expiratory flow rate, ELI = electronicinterviewer
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONWITHIN-PERSON STUDIES OF MOOD...METHODS AND PROCEDURESRESULTSDISCUSSIONNOTESACKNOWLEDGMENTSREFERENCES |
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The present study highlights the role that transitory changes in mood play in the fluctuation of airways obstruction and symptom reports in adults with asthma. Many investigations have documented significant associations of emotional states such as panic, fear, anxiety, arousal, and fatigue with asthma severity, whether measured by symptom reports or peak flow monitors (9). This literature, however, is limited to inferences that justifiably can be drawn from "between-person" research designs, namely, that individuals who are, for example, more anxious tend to experience more severe asthma symptoms. Few have examined whether changes in these variables covary within individuals over time; in other words, whether changes in anxious mood actually track changes in asthma severity in the course of daily life for any of the individuals under study. Such time-intensive "daily process" designs can provide fresh insights through their capacity to minimize retrospection errors in symptom and mood reporting, use subjects as their own controls, and establish sequential relations from the many comparisons available (10). Brown and Moskowitz (11) anticipate that this approach will advance behavioral and psychosomatic medicine through its ability to better capture the ebb and flow of the many psychological and somatic processes that exhibit rapid moments of change.
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WITHIN-PERSON STUDIES OF MOOD AND ASTHMA SEVERITY |
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TOPABSTRACTINTRODUCTIONWITHIN-PERSON STUDIES OF MOOD...METHODS AND PROCEDURESRESULTSDISCUSSIONNOTESACKNOWLEDGMENTSREFERENCES |
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Neither of these small sample studies explored the possibility that there may be different mood correlates of PEFR as opposed to symptom perceptions. There is now substantial literature linking affective states with both minor and chronic illness symptoms (14–16). Symptom exacerbations may have emotional consequences, but certain emotions may intensify symptoms by increasing attention toward the self. In particular, variations in sad (or happy) mood may trigger (or attenuate) both the process of "turning inward" (17, 18) as well as the amplification of physical symptoms (19). For these reasons, Pennebaker (20) concluded that symptom perceptions can have stronger relations with emotional states than with the underlying physiological processes that create symptoms.
We hypothesize that variations in happy and sad mood would be most likely to track changes in symptom reports and would do so even after adjusting this relation for concurrent PEFR. We conducted a preliminary study of this possibility with 21 asthmatic adults who rated their mood, provided PEFR readings, and chronicled their symptoms three times a day for 21 days (7). The data were pooled across persons and observations and analyzed at the within-person level by fixing the differences between participants in their mean levels of mood, PEFR, and symptoms. Rising PEFR scores were most highly related to declining fatigue and increasing liveliness. Improvements in symptoms, in contrast, were most likely to correspond with increasing happiness and decreasing sadness.
These findings stand as preliminary evidence that short-term fluctuations in symptom perceptions and bronchoconstriction may correlate with different transitory moods. However, we did not conduct the multivariate analyses required to test this hypothesis adequately. Nor did our "fixed effects" statistical analysis allow us to generalize our findings to the population from whom the sample was drawn (21). Time-varying covariates, such as time of day and medication use, were also not considered as possible confounds of within-person relations between mood, symptoms, and peak flow.
The present study extends and refines previous research on this topic in several ways. First, we studied a larger sample and conducted multilevel random effects analyses that do permit generalization to the sampled population. Second, we determined whether the relations between mood and asthma severity could be specious because they either share the same, but independent, circadian pattern or they are both affected by the use of as-needed (PRN) asthma medications. Changes in mood (22, 23) and in airway obstruction (13, 24) across the day have been documented. Albuterol dosing is associated with symptom aggravation and PEFR (25) and may precipitate mood changes.
A third key feature of the study is its reliance on a theory-derived multidimensional model of mood assessment. Two general models have guided the self-report measurement of mood: the specific affects approach and the dimensional approach (26). The first reflects "the belief that there are many different types of mood, each with different... characteristics and response patterns." The second model takes the position that "there are a few, usually two, "core" dimensions of mood; specific moods are thought to be combinations of [these] dimensions" (26). For the present study, we adopted the theory-driven dimensional approach expressed in the circumplex model of emotion first proposed by Russell (27) and elaborated by Larsen and Diener (28). This approach places self-ratings of mood in a two-dimensional circular space, which distinguishes states according to their degree of pleasantness (or hedonic tone) and degree of activation (or arousal tone). Thus, for example, anxious mood combines high unpleasantness and high arousal, whereas calm mood combines high pleasantness with low arousal. We adopted this model principally because it distinguishes between arousal states, which may be most likely to correlate with peak flow ratings, and hedonic states, which may figure most strongly in symptom reports (7).
Finally, a novel feature of this investigation is its use of electronic methods for field measurements of all study variables: mood, symptoms, peak expiratory flow, and beta-agonist use. These methods assured participants’ compliance with demanding schedules for self-reports of mood and symptoms; blinded participants to their peak flow records when describing symptoms; and measured medication use without relying on self-report. Taken together, these procedures mitigate a formidable threat to the validity of experience sampling studies.
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METHODS AND PROCEDURES |
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TOPABSTRACTINTRODUCTIONWITHIN-PERSON STUDIES OF MOOD...METHODS AND PROCEDURESRESULTSDISCUSSIONNOTESACKNOWLEDGMENTSREFERENCES |
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The remaining 48 patients (64.6% women; 81.3% white; 54.2% married) had a mean age of 42.1 years (SD = 14.8) and a mean of 14.4 years of formal education (SD = 2.1). The average participant had been diagnosed with asthma 22.8 years earlier (SD = 15.9). Since then, they reported having had a mean of 4.6 asthma-related hospitalizations (SD = 14.9) and 4.4 asthma-related emergency room visits (SD = 9.33). They recalled having missed a mean of 4.3 days of work during the past year (SD = 17.6) because of their asthma symptoms.
Electronic Interviews of Mood and Asthma Symptoms
For 21 consecutive days, participants carried a palm-top computer programmed as an electronic interviewer (ELI), which asked them about their current asthma symptoms and mood three times a day at randomly selected times—once each during the morning (between 9:45 AM and 11:15 AM), afternoon (between 2:45 PM and 4:15 PM), and evening (6:45 PM and 9:15 PM). The computer was a programmable battery-powered Psion Organizer II (Psion, Concord, MA) with dimensions of 1.4 cm x 7.8 cm x 2.9 cm and weight of 250 g. This device has amply demonstrated its feasibility and reliability in prospective daily studies of pain, mood, and fatigue (29, 30); drinking and smoking (31, 32); asthma symptoms (33); and sleep quality (30, 34).
Some procedures for the ELI protocol parallel those designed by Shiffman and colleagues (31) for their electronic diary studies of cigarette smokers. The data entry procedure for each ELI request proceeded from the user’s termination with a keystroke of an audible beep to her choice to answer the interview then 5 minutes later, or 15 minutes later. The auditory signal lasted 60 seconds; if not answered within this time, it was repeated 5 minutes later, and if not answered again, another 5 minutes later. Failure to answer this sequence of three requests for data produced a missing entry for that time period.
Interview questions were presented one at a time in a fixed order on a liquid crystal display (2 lines by 20 characters per line). Participants replied to each question by scrolling across fixed-response options with backward and forward arrows and then pressing an "enter" button to save the response and its time stamp on a EEPROM data pak (which could not be erased without exposing it deliberately to 30 minutes of ultraviolet light). The response option appearing first on the screen with each new question was randomized to minimize response set. The project’s research associate trained participants on a demonstration version of ELI and gave them a manual which reviewed its functions and features.
The mood interview.
The 16 items for the mood interview were drawn from the mood circumplex items supplied by Larsen and Diener (28). This model classifies affect as pleasant or unpleasant, as aroused or unaroused, or as a mixture of these dimensions. Figure 1 diagrams the resulting mood octants and the specific mood adjectives used to measure them.
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We confirmed the appropriateness of this scoring procedure by conducting principal components analyses of the disaggregated data set. The mood vectors in Figure 1, which are orthogonal to each other in theory, should have empirical separation as well. In other words, the four adjectives composing the pleasant/unpleasant vector should form a component separate from that for the four adjectives composing the aroused/unaroused vector. Similarly, the four adjectives composing the aroused unpleasant/unaraoused pleasant vector should form a component separate from that for the adjectives composing the aroused pleasant/unaroused unpleasant vector.
Two principal components analyses with varimax rotation confirmed these predictions. Each mood score was first centered around the person’s own mean to examine the within-person covariance structure free of between-person differences in mean levels. The covariance pattern among the eight adjectives included in the pleasant/unpleasant vector and the aroused/unaroused vector yielded two components with eigenvalues greater than 1.0 and accounting for 67.2% of the variance. Each adjective loaded highly on its predicted vector (>0.70 or <-0.80). The same was found for the eight adjectives contained in the other two orthogonal vectors. Two components with eigenvalues greater than 1.0 accounted for 67.1% of the variance, and each adjective loaded highly on its predicted vector (>0.80 or <-0.80).
The asthma symptom interview.
The ELI asked about four current asthma symptoms: coughing, wheezing, chest tightness, and shortness of breath. Each was rated on a 0–6 scale, anchored verbally at 0 = none, 2 = mild, 4 = moderate, and 6 = severe. Asthma symptom severity for that interview was scored as the sum across these ratings. Using the mean-centering procedure described previously, Cronbach 
internal consistency estimates for the asthma symptom severity composite were 0.94 for 79 for the morning reports, 0.96 for the afternoon reports, and 0.93 for the evening reports.
Electronic Assessments of PEFR
Participants used an electronic peak flowmeter (PeakLog, Medtrac Technologies, Inc., Lakewood, CO) to measure their airways obstruction. This instrument, which is 13 cm x 6 cm x 2.5 cm and weighs 138 g, measures airflow with a hot-wire anemometer. Accuracy was tested by the manufacturer using recommendations by the National Asthma Education Program (35). This involved injecting nine standardized wave forms into each of 10 devices five times using a computer-driven pulmonary wave form generator. All instruments were within 10% of the target values and were within 5% of each other. A built-in barometer allows for self-correction for fluctuations in barometric pressure. The device received FDA approval on November 1, 1994.
PEFR was recorded along with the time and date of measurement by the electronic peak flowmeter after each electronic interview. Participants were given standardized instructions and observed in the use of the device during prestudy training. After prompting by the electronic diary (with the completion of the symptom interview), patients were instructed to stand, take a deep breath, fill their lungs completely, place the mouthpiece in the mouth past the teeth with lips tightly closed around the mouthpiece, and blow as hard and fast as possible in a single exhalation. Three such expiratory maneuvers were obtained, and the highest PEFR was reserved for data analysis.
The validity of peak flow monitor actuations in the field can be strengthened by establishing their correlation with assessments under controlled conditions. At the beginning and end of the self-monitoring period, spirometry was performed on all participants according to criteria of the American Thoracic Society (36) and the scores averaged across time. Mean daily peak flow ratings were correlated with spirometric measures of PEFR (r = 0.81, p < .001) and FEV1 (r = 0.69, p < .001).
Electronic Recording of Beta-Agonist Use
The only short-acting beta-agonist allowed in the study was albuterol by metered dose inhalation, prescribed on an as-needed basis. The canister was housed in an MDI Chronolog (Medtrac Technologies, Inc., Lakewood, CO) which recorded time and date of each actuation. For our data analyses, we calculated the number of doses during each morning, afternoon, and evening period dictated by the electronic interviewer protocol.
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RESULTS |
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Between-Person Relations
Correlations between participants’ average values on these variables were calculated to examine between-person associations. Individuals with higher aggregate PEFR (transformed as the ratio between mean raw scores and those predicted by the subject’s weight, height, and age) reported more severe symptoms (r = -0.30, p < .05). As Table 1 indicates, mean PEFR was unrelated to mean mood vector scores. However, those with more severe asthma symptoms scored lower on the happy-sad mood vector, on the calm-anxious mood vector, and on the peppy-drowsy mood vector. After controlling for PEFR, mean asthma symptoms correlated significantly with all mood vector scores. Albuterol use was higher for those reporting more severe symptoms (r = 0.40, p < .001) but was unrelated to average peak flow ratings or to average mood scores.
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Within-person changes insymptoms significantly tracked changes in PEFR (b = -0.005, p < .001). Yet, only 3.7% of the within-person variance in symptoms could be explained by peak flow actuations at the time symptoms were reported.2 This suggests that much of the differences in the average person’s symptom reports from morning to afternoon and afternoon to evening each day are not because of changes in bronchoconstriction, at least as they are measured by PEFR in the field. This leaves substantial room for associations between mood and asthma symptoms when PEFR is controlled and the opportunity to examine symptom perceptions independent of an objective measure of bronchoconstriction.3
Within-Person Relations
Table 2 summarizes the within-person relations between the four mood vector scores and PEFR. Rising scores on the active-passive and the peppy-drowsy mood vectors were associated with increasing PEFR. When examined jointly as predictors of PEFR, each was also an independent correlate of PEFR changes (active-passive, b = 0.70, p < .05; peppy-drowsy, b = 1.28, p < .001). Together, these two mood vectors accounted for 4% of the within-person variance in PEFR.
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These analyses take account of all observations in the data set. But in so doing, they may miss effects that are revealed only when asthma symptoms or airways obstruction are most severe. To examine this possibility, we recomputed the analyses of relations of mood with asthma symptoms and PEFR by comparing mood reports at extreme observations. The first compared mood scores at moments when PEFR was relatively high for that individual (>1 SD above that person’s mean) with those when PEFR was relatively low (>1 SD below the mean). Only the peppy-tired mood vector was significantly different for these two sets of observations (b = 0.29, p < .05). The second analysis compared mood scores at moments when asthma symptoms were relatively severe and relatively mild. Only the happy-sad mood vector differed significantly between these two sets of observations (b = -0.30, p < .04). These two significant findings echo significant effects reported in Table 1 for the full spectrum of observations. However, two other significant effects appearing in Table 1 were missed by this analysis, suggesting that mood may play a more prominent role in explaining the total variation of PEFR and asthma symptoms across the day than it does in the ability to differentiate between daily episodes of comparatively severe vs mild symptomatology.
%Next, we examine three possible sources of confounding that might spuriously inflate the associations of mood with PEFR and symptoms: a shared time course across reporting occasions within the day; as-needed administration of albuterol; and the effort-dependence of unsupervised peak flow monitoring.
Time of Day Effects?
The relations summarized to this point could simply be due to sharing the same time course across daily observations. To address this possibility, each variable was modeled for within-person changes across the day. Asthma symptoms did not differ by time of day, but PEFR did. As the day progressed, PEFR declined (b = -5.94, p < .001).4 So, too, did the mood states that were significantly associated with PEFR (active-passive, b = -0.24, p < .001; peppy-drowsy, b = -0.47, p < .001). However, both mood dimensions remained significantly associated with PEFR when time of day was entered along with them in the model predicting PEFR, ruling out confounding by time of day.
Albuterol Use Effects?
Participants’ self-administration of albuterol doses during each observation period was unrelated to that observation’s PEFR, but more doses were administered during times when they reported more severe symptoms (b = 0.79, p < .001). Albuterol dosing explained 3.7% of the within-person variance in symptoms. Higher use of albuterol was also associated with lower scores on the active-passive mood dimension (b = -0.28, p < .05) and on the peppy-drowsy dimension (b = -0.43, p < .001). Inspection of the relations presented in Table 1 disclose only one relation that could be confounded by albuterol use, namely the association between symptoms and peppy-drowsy mood. However, even controlling for albuterol dosing, this relation remained statistically significant.
Differential Effort in Peak Flow Monitoring?
The specific moods associated with PEFR signal differences in arousal states. Thus, it could be argued that these relations are less a function of intrinsic relations between mood and PEFR than they are of the relations between mood and the effort required to use the PEFR monitor to measure bronchoconstriction accurately. For example, might the relation between drowsiness and peak flow be due to the effect of drowsiness on suboptimal effort with the peak flowmeter?
This is a challenging problem for field studies. To understand this possibility, we computed a crude index of each participant’s overall effort as the ratio of his or her mean PEFRs across the 21 days with the gold standard of average PEFRs measured before day 1 and after day 21 with supervised spirometric assessments. Higher ratios could be construed as consistent with a more accurate or effortful use of PEFR monitors at home.
This ratio did not correlate with the mean mood scores for either the active-passive vector (r = 0.05) or the peppy-drowsy vector (r = 0.14). However, it was significantly associated with the magnitude of the within-person relations between these moods and PEFR. This was revealed by a random effects regression analysis that examined the ratio’s ability to predict variation in the mood-PEFR slopes. Participants with higher ratios, who arguably were more effortful in their use of peak flow monitors, were those who exhibited weaker relations between PEFR and active-passive mood (b = -3.94, p < .05) and peppy-drowsy mood (b = -0.4.63, p < .05).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONWITHIN-PERSON STUDIES OF MOOD...METHODS AND PROCEDURESRESULTSDISCUSSIONNOTESACKNOWLEDGMENTSREFERENCES |
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Several features of our research may be responsible for these novel findings. These include a) a theory-driven assessment of mood that separates arousal from hedonic tone; b) a daily process methodology that minimizes retrospection error in symptom and mood reporting; c) use of electronic methods that blind subjects to peak flow monitoring results and encourage and assess compliance with symptom ratings, and d) emphasis on the within-person patterning of changes in mood, symptoms, and peak flow within and across days.
Although we measured mood, asthma symptoms, and PEFR three times a day, our analyses failed to establish temporal priority. Thus, we cannot tell whether these mood states preceded changes in PEFR or asthma symptoms or were engendered by them. Our decision to sample these processes three times a day was balanced against the burden of a 21 day self-monitoring period. It may be that readings need to be taken more frequently within the day to capture sequential relations. Alternatively, the relations we uncovered may arise from simultaneous processes or sequential processes that are so ephemeral that they would be virtually impossible to detect in field studies.
Mood and Asthma Symptoms
A substantial quantity of literature has elucidated connections between mood and the perception of illness symptoms. Dysphoric mood may be a consequence of symptom exacerbations, but it could also amplify symptoms through increasing self-focused attention. Whether they are a consequence or antecedent of symptom changes, fluctuating levels of pleasant/unpleasant mood should most likely track changes in subjective asthma symptoms. Our findings support this hypothesis. It was variations in happy-sad and calm-anxious moods that were independently related to the ebb and flow of symptoms. Most important, these findings persisted even after controlling for contemporaneous peak flow ratings. Thus, within the limits of the reliability and validity of unsupervised peak flow monitoring as a measure of airways obstruction, there seems to be a unique relation between these moods and the individual’s subjective experience of bronchoconstriction.
Mood and PEFR
Interpretation of the relations between mood states and PEFR is aided in part by having blinded study participants to their peak flow ratings. Although symptom reports covaried with mood vectors involving pleasantness, PEFR covaried with mood vectors involving arousal. This included variations in both active-passive and peppy-drowsy mood.
Straightforward interpretation of the mood-PEFR relations, however, is complicated by the effort-dependence of peak flow monitoring reliability. If arousal affects expiration effort, then our findings may reflect nothing more than the differential reliability of peak flow monitoring with and without optimal effort. Without having frequent supervised actuations as primary data, it is impossible to dismiss this possibility. We tried to develop an understanding of this problem by calculating a gold standard for each participant’s PEFR under maximum effort with controlled spirometry readings before and after the 21–day self-monitoring study. Comparing this against each participant’s aggregate PEFR across the 21–day period afforded us a crude indicator of his or her optimal effort with peak flow monitoring. Interestingly, this indicator did not correlate with participants’ aggregate scores on the two mood states that correlated with PEFR changes. However, it did affect the magnitude of the within-person relation between these mood states and changes in PEFR. Those whose peak flow monitoring was judged by this indicator to be more effortful exhibited weaker connections between PEFR and these moods. Thus, our findings concerning the relation between mood and PEFR may be due in part to differential effort in peak flow monitoring. To settle this question, future investigations should plan more frequent in-home supervised monitoring of airways obstruction.
These caveats notwithstanding, the present study’s findings that different moods figure in symptom perceptions as opposed to changes in airways obstruction establish emotions as one factor that may account for the inaccuracy of some asthma patients’ estimation of the severity of their illness. Between 4% and 5% of the within-person variation in symptoms and PEFR were explained by contemporaneous mood. On balance, this effect size was equivalent to that captured by the within-person relation between symptoms and PEFR and between albuterol use and symptoms. How much of the unexplained fluctuation in symptoms and PEFR is simply idiosyncratic or random, and how much might be because of predictable individual differences between asthma patients needs to be examined in future studies using larger samples to permit adequate statistical power to address this question.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONWITHIN-PERSON STUDIES OF MOOD...METHODS AND PROCEDURESRESULTSDISCUSSIONNOTESACKNOWLEDGMENTSREFERENCES |
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NOTES |
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TOPABSTRACTINTRODUCTIONWITHIN-PERSON STUDIES OF MOOD...METHODS AND PROCEDURESRESULTSDISCUSSIONNOTESACKNOWLEDGMENTSREFERENCES |
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The HLM estimates of within-person variance explained in these models should not be confused with those provided in OLS regression (39). These estimates refer to the ability of explanatory variables to reduce the random variance components of their respective equations.
A companion set of analyses examined the ability of mood to predict the next within-day observation for symptoms or peak flow, as well as the reverse sequence. Controlling for contemporaneous associations, there were no significant lagged relations for any of these analyses.
This finding may at odds with the common clinical observation that asthmatics’ bronchoconstriction is worse after awakening and better in the evening. The first PEFR in the present analysis was taken in the late morning each day. We had also measured PEFR one-half hour after awakening, but omitted these data from this study because mood was not measured on this occasion. These earlier morning PEF ratings were indeed lower than those in the evening.
Received for publication March 15, 1999.
Revision received July 19, 1999.
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TOPABSTRACTINTRODUCTIONWITHIN-PERSON STUDIES OF MOOD...METHODS AND PROCEDURESRESULTSDISCUSSIONNOTESACKNOWLEDGMENTSREFERENCES |
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