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Right arrow Sleep and Biological Rhythms
Psychosomatic Medicine 66:242-250 (2004)
© 2004 American Psychosomatic Society

ORIGINAL ARTICLES

Monitoring for Sleep-Related Threat: A Pilot Study of the Sleep Associated Monitoring Index (SAMI)

Christina Neitzert Semler, BA (Hons), MA, MEd, DPhil and Allison G. Harvey, BSc, MClin Psych, PhD

From the Department of Experimental Psychology, University of Oxford, England (C.N.S., A.G.H.); and Department of Psychiatry, University of Oxford, England (A.G.H.).

Address reprint requests to: Allison G. Harvey, BSc, MClin Psych, PhD, University of Oxford, Department of Experimental Psychology, South Parks Road, Oxford, OX1 3UD, England. Email: allison.harvey{at}psy.ox.ac.uk


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
OBJECTIVE: The aims of this pilot study were: 1) to establish the reliability and validity of a new self-report instrument designed to index monitoring for sleep-related threat; 2) to determine the presence of ten monitoring types proposed in a recent cognitive model of insomnia and to examine the relationship between monitoring for sleep-related threat and severity of sleep disturbance; and 3) to explore the association between monitoring and the established constructs of amplification and self-focus.

METHODS: Participants (N = 400) completed the Sleep Associated Monitoring Index (SAMI) and the Pittsburgh Sleep Quality Index (PSQI) (1). Based on the PSQI score, the sample was split into two groups to compare normal sleepers (NS) and individuals with a clinically significant sleep disturbance (CSSD). A subset of the sample completed the SAMI and a battery of questionnaires to examine convergent validity between monitoring, amplification, and self-focus.

RESULTS: Individuals in the CSSD group had higher SAMI scores than the NS group and the SAMI correlated positively with severity of sleep disturbance as indexed by the PSQI. A principal components analysis extracted 8 components accounting for 69% of the variance. The 30-item SAMI demonstrated high validity, consistency, and reliability. Scores on the SAMI were moderately positively correlated with scores on measures of amplification and self-focus.

CONCLUSIONS: Preliminary evidence suggests that the SAMI offers a valid and reliable instrument to index monitoring before and after treatment for sleep disturbance. The implications for the presence of monitoring for sleep-related threat in chronic insomnia are discussed.

Key Words: insomnia, • monitoring, • selective attention, • sleep.

Abbreviations: CSSD = clinically significant sleep disturbance;; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders (4th edition);; NS = normal sleepers.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
Monitoring refers to a tendency to selectively attend to salient internal or external threat cues (2,3). In the clinical psychology literature, attentional bias toward threat has been identified as key to the maintenance of a range of psychological disorders (4–9). Theoretical and empirical work has implicated monitoring or attentional bias in panic disorder (5, 10), hypochondriasis and reporting of somatic symptoms (11, 12), social phobia (13, 14) , specific phobia (15), eating disorders (16, 17), obsessive-compulsive disorder (18, 19), posttraumatic stress disorder (20, 21), generalized anxiety disorder (22, 23), and chronic pain (24).

The interest in a role for monitoring for sleep-related threat in the maintenance of sleep disturbances is relatively new, although early reports indicated that patients with insomnia "monitor their level of sleep readiness" (25). Several theoretical accounts have noted a potential role for monitoring by suggesting that people with insomnia are particularly vigilant of the time (26, 27) or stimuli in the environment (28–30) while in bed trying to get to sleep. Although scant research has been conducted to specifically examine the role of monitoring in sleep disorders, several investigators have pointed to its potential importance. Watts, Coyle, and East (31) identified ‘somatic preoccupations’ as one of 6 factors that explained a significant amount of the variance in sleep-interfering thoughts during the pre-sleep period. Wicklow and Espie (32) asked people with insomnia to verbalize their thoughts when they were trying to get to sleep on 3 consecutive nights. A factor analysis of the verbal recordings elucidated ‘environmental awareness’ and ‘monitoring of present state’ as key factors. Both are suggestive of monitoring, particularly as the latter factor comprised thoughts about bodily functions and sleep. In addition, Harvey (33) found that participants with insomnia were more likely than good sleepers to report focusing their attention on detecting body sensations associated with falling asleep and on monitoring the time and the environment for noise during the pre-sleep period. One study has suggested that individuals with insomnia may also engage in daytime monitoring. Marchini et al. (34) randomly paged individuals with insomnia and good sleepers throughout the day and asked them to immediately record their thoughts. The results indicated that throughout the day, people with insomnia were significantly more preoccupied with self-related thoughts than good sleepers.

On the basis of the research reviewed above, we have proposed a cognitive model of insomnia that emphasizes the importance of monitoring for sleep-related threat in fueling insomnia (35). Specifically, monitoring is proposed to contribute to a vicious cycle of worry about sleep, arousal, and distress. Based on clinical practice, several types of monitoring for sleep-related threat are proposed. Specifically, during the pre-sleep period between initial ‘lights out’ and falling asleep or when trying to get back to sleep after waking in the night, patients with insomnia monitor: 1) their body sensations for signs consistent with falling asleep; 2) their body sensations for signs inconsistent with falling asleep; 3) the environment for signs indicative of wakefulness; 4) the clock to see how long it is taking to fall asleep; and 5) the clock to calculate how much sleep will be obtained. On waking, patients with insomnia monitor: 6) their body sensations for signs of poor sleep and 7) the clock to calculate how many hours of sleep were obtained. During the day, patients with insomnia monitor: 8) their body sensations for signs of fatigue; 9) their performance and functioning; and 10) their mood. To give one example as to how monitoring might contribute to the maintenance of insomnia, if a patient with insomnia looks at the clock in the middle of the night they will tend to think ‘Oh no! It’s 3 AM! It’s too early to be awake. . .this is terrible, I’m never going to get back to sleep. . .how will I ever cope tomorrow?’. This worry will trigger anxiety and distress that, in turn, will make it difficult to fall back to sleep. (See reference 35 for further examples.) We have suggested that monitoring is a predominantly automatic cognitive process in that it consumes minimal attentional resources and can happen without conscious decision-making (35–37).

Recognition of the role of monitoring for sleep-related threat in the maintenance of sleep disorders is relatively new. To facilitate research into this new concept, the current study aimed to establish the initial psychometric properties of a new scale designed to index monitoring for sleep-related threat. A second aim of this study was to use statistical methods to determine the presence of the 10 monitoring types proposed by Harvey (35) and to determine the relationship between monitoring and severity of sleep disturbance. Based on findings of heightened monitoring in insomnia (31–34) and our model of the maintenance of insomnia (35), it was predicted that individuals with a clinically significant sleep disturbance would have higher SAMI scores (indicative of an increased tendency to monitor) relative to normal sleepers. This investigation also presented an opportunity to explore the associations between monitoring and 2 other attentional processes, amplification and self-focus. Amplification has been defined as "the tendency to experience somatic sensation as intense, noxious, and disturbing" (12). Self-focus has been defined as "an awareness of self-referent, internally generated information" (38). Monitoring, amplification, and self-focus can be theorized to share several similarities as each term refers to a form of attentional bias directed toward one’s internal state. However, the extent to which these terms refer to similar or different constructs has not, as yet, been subjected to empirical scrutiny. As such, an additional aim of the current study was to explore the associations between monitoring, amplification, and self-focus.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 APPENDIX
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
The original item pool was developed in 2 phases. In the first phase, we administered a detailed semi-structured interview to 32 people with DSM-IV–diagnosed primary insomnia and 38 good sleepers to probe for each of the proposed monitoring types (39). Based on the interview findings we developed a preliminary item list comprised of 77 items assessing monitoring for sleep-related threat. In the second phase of development, we tested the 77-item list among a sample of 143 university students. In this preliminary study, we included a comments section asking participants to respond with any additional sleep-related threat cues they typically attend to during the night when getting to sleep, on waking, or during the day. This elicited an additional 41 sleep-related items, resulting in a total of 118 items. Each item was scored on a 5-point Likert scale (response scale: 1 = not at all to 5 = all the time).

Participants and Measures
Participants included staff and students from 2 local universities (University of Oxford and Oxford Brookes University) and members of the local community. Flyers and questionnaire packages were distributed to personal mailboxes and handed out in the university cafeterias. The initial flyers advertised for participants "interested in sleep research" to respond by mail. A total of 274 participants responded by this method, comprising Subset 1. A further 126 individuals completed the SAMI as part of their involvement in a second study that advertised for people who "have trouble sleeping", comprising Subset 2. As a result of this recruitment method, the total sample (N = 400) included in the current study may not be representative of the general population. All participants completed the Sleep Associated Monitoring Index (SAMI) and the Pittsburgh Sleep Quality Index (PSQI) (1). The PSQI is a 19-item questionnaire that was included to assess for presence and severity of sleep disturbance. The PSQI has good internal consistency and test-retest reliability, ({alpha} = 0.83, r = 0.85) (1).

To assess test-retest reliability, the participants in Subset 1 (N = 274) were asked to complete the SAMI on a second occasion, between 1 and 8 weeks after they completed the first questionnaire; 158 individuals completed and returned the Time 2 questionnaire (response rate = 58%). As noted earlier, 126 participants (Subset 2) completed the SAMI as part of their involvement in another study. To assess convergent validity, Subset 2 also completed the Beck Depression Inventory (BDI) (40), the Spielberger State-Trait Anxiety Inventory (STAI) (41), the Penn State Worry Questionnaire (PSWQ) (42), the Somatosensory Amplification Scale (SSAS) (43), the Private subscale of the Self-Consciousness Scale (SCS-P) (44), and the Private subscale of the Body Consciousness Scale (BCS-P) (45). The BDI, STAI, and PSWQ were used to index depression, anxiety, and worry, respectively. The SSAS, SCS-P, and BCS-P have been found to reliably index vigilance for physical symptoms (46), dispositional self-focus (47), and dispositional focus on physical sensations (48), respectively.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 APPENDIX
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
Participant Characteristics
Total Sample
The total sample comprised 243 females and 157 males. The sample mean age was 22.7 (SD = 6.3) years and the sample mean score for the PSQI was 6.8 (SD = 4.0). A cutoff score of greater than 5 on the PSQI has been shown to identify a clinically significant sleep disturbance with 89.6% sensitivity and 86.5% specificity (1). Accordingly, to assess discriminant validity, the sample was split into 2 groups: those with a PSQI score greater than 5 were classified as people with a clinically significant sleep disturbance (CSSD; N = 222) and those with a PSQI score of less than or equal to 5 were considered normal sleepers (NS; N = 178). T test comparisons (with a Bonferroni correction of 0.05/4 = p < .01) showed that the CSSD group was significantly older than the NS group (CSSD: M = 23.9, SD = 7.8 years; NS: M = 21.3, SD = 3.2 years), t (398) = 4.51, p < .001, but {chi}-square analyses indicated no difference between the groups for gender distribution (CSSD: 132 females, 90 males; NS: 110 females, 68 males). Based on responses to questions on the PSQI, the CSSD group reported significantly longer mean sleep onset latency (CSSD: M = 46.2, SD = 31.0 minutes; NS: M = 14.1, SD = 9.7 minutes), t (398) = 14.59, p < .001, and significantly shorter mean total sleep time (CSSD: M = 6.4, SD = 1.2 hours; NS: M = 7.9, SD = 0.9 hours), t (398) = 14.69, p < .001, compared with the NS group. These results confirmed the assumption that the CSSD group was experiencing significant sleep disturbance.

Subset 1
Among the 274 individuals who responded to the initial recruitment drive, there were 171 females and 103 males. The mean age was 21.6 (SD = 4.2) years and the mean score for the PSQI was 5.2 (SD = 3.0). Among the 158 people who returned the Time 2 questionnaire, there were 92 females and 66 males. The mean age was 20.9 (SD = 3.0) years and the mean score for the PSQI was 5.7 (SD = 3.0).

Subset 2
Subset 2 comprised 71 females and 55 males. The subset mean age was 25.1 (SD = 9.0) years and the subset mean score for the PSQI was 10.5 (SD = 3.3). For the additional questionnaires, the mean scores were: 10.7 (SD = 7.1) for the BDI; 41.2 (SD = 12.1) for the STAI-S; 45.4 (SD = 11.5) for the STAI-T; 50.9 (SD = 13.7) for the PSWQ; 27.9 (SD = 6.2) for the SSAS; 25.1 (SD = 6.5) for the SCS-P; and 10.9 (SD = 3.6) for the BCS-P.

Factorial Validity
Basic Scale Data
Various item analysis strategies were used to eliminate inappropriate and redundant items. Based on previous guidelines (49–51), items were deleted if they demonstrated: 1) high skewness (> 1.5); 2) a correlation of less than 0.30 with the total SAMI score; 3) a correlation of less than 0.50 with the relevant subscale score; 4) a factor loading of less than 0.50 on the primary subscale; or 5) high crossloading (> 0.45) on more than one factor. In addition, in cases of similarity (where the correlation between items was greater than or equal to 0.80), items were examined for redundancy and the more clinically useful items were retained. Following these procedures, a total of 88 items were deleted. The final scale comprised 30 items, with 10 proposed subscales, and is presented in the Appendix. Five subscales assessed pre-sleep monitoring1 including: 1) body sensations consistent with falling asleep (items 12–15); 2) body sensations inconsistent with falling asleep (items 4–7); 3) the environment (items 8–9); 4) the clock (items 10–11); and 5) calculation of time (items 1–3). Two subscales assessed waking monitoring including: 6) body sensations (items 17–21) and 7) calculation of time (item 16). Three subscales assessed daytime monitoring including: 8) body sensations (items 22–26); 9) performance and functioning (items 27–29); and 10) mood (item 30).

Scale Validity
A principal components analysis was conducted to investigate the component structure of the 30 items of the SAMI. Bartlett’s test for sphericity was significant (p < .001) and the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.89, indicating that this analysis was appropriate. The Varimax rotation with Kaiser normalization converged in 7 iterations, yielding a solution with 8 components that accounted for 69% of the variance. All components had eigenvalues greater than 1.0. Table 1 shows the eigenvalues and percentage variance explained for each of the 8 derived components. The 8 components that emerged from the rotated matrix corresponded with the proposed subscales (35) with 2 exceptions: 1) the pre-sleep calculation of time subscale items loaded together with the waking calculation of time subscale items; and 2) the items from the performance and functioning subscale loaded together with the mood subscale. As a result, the SAMI subscales were adjusted so that items from the pre-sleep calculation of time and waking calculation of time subscales formed the ‘calculation of time’ subscale (items 1–3, 16) and items from the performance/functioning and mood subscales formed the ‘daytime monitoring of functioning’ subscale (items 27–30).


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  		TABLE 1. Eigenvalues, Percent of Variance Explained, and Cumulative Percent for Components Emerging From the Rotated Matrix
 
Item Validity
The factor loadings emerging from the rotated matrix for all items are shown in Table 2. The items demonstrated strong primary loadings (0.54 to 0.89) and minimal loadings on other components, suggesting 8 relatively distinct monitoring types. Pearson product-moment correlation coefficients were computed to evaluate associations between items. The mean correlation values were: body sensations consistent with falling asleep, r = 0.53; body sensations inconsistent with falling asleep, r = 0.45; environment, r = 0.74; clock, r = 0.77; calculation of time r = 0.63; waking body sensations, r = 0.52; daytime body sensations, r = 0.54; and functioning, r = 0.54. Due to the number of correlations, {alpha} was set to 0.01 to control for Type I errors associated with multiple comparisons. All correlations reached significance. Table 2 also presents the correlations between the items and their subscale totals (range: r = 0.71 to 0.94), and between the items and the overall SAMI total (range: r = 0.31 to 0.65). All correlations reached significance.


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  		TABLE 2. Item-subscale Correlations, Item-Total Correlations, and Factor Loadings From the Rotated Component Matrix for Individual Items of the Sleep Associated Monitoring Index (SAMI)
 
Scale Consistency and Reliability
Internal Consistency
Table 3 presents the means, standard deviations, and internal consistencies (Cronbach’s coefficient alpha) for each of the 8 subscales that emerged from the principal components analysis for the full sample (N = 400). Internal consistency refers to the degree to which the items of a scale are correlated with one another and provides an average value of the inter-item correlations (52). The internal consistencies for the subscales were high ({alpha} = 0.77 to 0.87; mean {alpha} = 0.83), and the internal consistency for the entire scale was also high ({alpha} = 0.91), demonstrating good scale coherence. Table 4 presents the correlations between the subscales, which were low to moderate (r = 0.09 to 0.60) suggesting that they each assess distinct aspects of monitoring. Due to the number of correlations, {alpha} was set to 0.01 to control for Type I errors associated with multiple comparisons. All but two of these correlations reached significance. Table 4 also presents the correlations between the subscales and the overall total, which were moderate to high (r = 0.50 to 0.80). All correlations between subscale scores and the total SAMI score reached significance.


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  		TABLE 3. Means, Standard Deviations, and Internal Consistencies (Cronbach’s {alpha}) for the Subscales of the Sleep Associated Monitoring Index
 

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  		TABLE 4. Intercorrelations Between Subscales and Total for the Sleep Associated Monitoring Index
 
Test-Retest Reliability
As described earlier, 158 participants from Subset 1 completed the SAMI on 2 separate occasions, between 1 week and 8 weeks apart. Pearson product-moment correlations were used to compare Time 1 and Time 2 scores with a Bonferroni correction (0.05/9 = p < .006) to control for multiple comparisons. The test-retest reliabilities for the subscales ranged from r = 0.63 to r = 0.77. The mean of these correlations was r = 0.72. The test-retest reliability for the total score was high, r = 0.82. All correlations reached significance.

External Validity
Discriminative Validity
T tests were conducted to determine whether the SAMI would effectively discriminate between individuals with a clinically significant sleep disturbance and normal sleepers (using the PSQI cutoff score) on the subscales and overall SAMI total scores.2 A Bonferroni correction (0.05/9 = p < .006) was applied to control for multiple comparisons. Table 5 presents the results. People in the CSSD group had significantly higher total SAMI scores than normal sleepers, and significantly higher scores than normal sleepers on 7 of the 8 SAMI scales (the exception was calculation of time).


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  		TABLE 5. Means, Standard Deviations, and T-Test Results for the Subscales of the Sleep Associated Monitoring Index (SAMI) for Individuals With a Clinically Significant Sleep Disturbance (CSSD) and Normal Sleepers (NS)
 
Convergent Validity
Comparison of the SAMI with other related scales was conducted with correlational analyses. A Bonferroni correction (0.05/8 = p < .006) was applied to control for multiple comparisons. To our knowledge, there are no questionnaires currently available to index monitoring for sleep-related threat. To examine the association between monitoring and severity of sleep disturbance, the total score on the SAMI was correlated with the global score on the PSQI in the total sample (N = 400). This correlation was moderate and positive (r = 0.46) and reached significance. As noted in the introduction, the associations between monitoring, amplification, and self-focus have not yet been examined empirically. To test these associations, the total score on the SAMI and the 8 subscale scores on the SAMI were correlated with the total scores on the SSAS, SCS-P, and the BCS-P. The results are presented in Table 6. As mentioned earlier, Subset 2 (N = 126) completed the SAMI along with the BDI, STAI, PSWQ, SSAS, SCS-P, and BCS-P. To examine potential relationships between monitoring, depression, and anxiety, the total score on the SAMI was correlated with the total scores on the BDI, STAI, and PSWQ. Moderate positive correlations were found for the BDI (r = 0.32), the STAI-S (r = 0.33), the STAI-T (r = 0.38), and the PSWQ (r = 0.36). All correlations reached significance.


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  		TABLE 6. Correlations Between the Subscale Scores and the Total Score of the Sleep Associated Monitoring Index (SAMI), the Private Subscale of the Self-Consciousness Scale (SCS-P), the Private subscale of the Body Consciousness Scale (BCS-P), and the Somatosensory Amplification Scale (SSAS)
 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
APPENDIX
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
The aims of the current study were threefold: 1) to establish the reliability and validity of the SAMI; 2) to determine the presence of the 10 types of monitoring for sleep-related threat proposed in a recent cognitive model of insomnia and to examine the relationship between monitoring and severity of sleep disturbance; and 3) to explore the relationships between monitoring and established measures of amplification and self-focus. In addressing the first aim, the SAMI and its subscales demonstrated high internal consistency and good test-retest reliability, and the 8 subscales accounted for more than two thirds of the variance. The correlations between the individual items, between the items and their subscale totals, and between the items and the overall SAMI total were moderate to high. These results suggest that the SAMI can be considered a reliable and valid instrument to index monitoring for sleep-related threat.

With respect to the second aim, the structure that emerged from the principal components analysis is consistent with the types of monitoring previously identified (35) with 2 exceptions. First, the items from the pre-sleep calculation of time and waking calculation of time subscales loaded together onto 1 component, which is not surprising given the obvious similarity between these 2 constructs. Second, the items from the performance/functioning and mood subscales were shown to load together onto 1 factor. All of the 30 SAMI items loaded strongly onto the primary component and minimally onto other components, suggesting a relatively pure solution with 8 distinct monitoring types. As all of the subscales extracted had eigenvalues greater than 1.0 and the cumulative proportion of variance explained by the final solution was high, all 8 subscales are likely to be important for a thorough assessment of monitoring in insomnia.

Regarding the relationship between monitoring and severity of sleep disturbance, the SAMI total score was significantly higher for individuals in the CSSD group compared with the NS group, based on the established cutoff score on the PSQI. In addition, people with CSSD showed significantly higher scores for 7 of the 8 subscales, the exception being calculation of time. Further, total scores on the SAMI were significantly positively correlated with total scores on the PSQI. These results are consistent with our prediction that people with a clinically significant sleep disturbance would show a stronger tendency to monitor for sleep-related threat, as indexed by the SAMI. In addition, the findings are consistent with research highlighting the presence of monitoring in insomnia at night and during the day (31–34) and with recent theoretical proposals (35).

The third aim of the study was to explore the relationships between monitoring and established measures of amplification and self-focus, which have not yet been subjected to empirical scrutiny. By definition, these concepts can be theorized to share several similarities given that each term refers to a form of attentional bias (12, 35, 38). The significant positive correlations between the total score on the SAMI and measures of vigilance for physical symptoms (the SSAS), dispositional self-focus (the SCS-P), and dispositional focus on physical sensations (the BCS-P), as well as the many significant positive correlations between the subscales of the SAMI and these three constructs are not surprising given the conceptual overlap with the various monitoring types. Interestingly, only calculation of time, which also did not discriminate individuals with clinically significant sleep disturbances from normal sleepers, did not correlate significantly with the SSAS, SCS-P, or the BCS-P. A noteworthy point is that amplification and self-focus are both measures of internal attention (12, 38). Future research should aim to compare the SAMI against measures of externally-focused attention as the monitoring construct includes some forms of attentional bias directed externally (eg, monitoring the clock).

Significant positive correlations were also noted between the SAMI and other scales including the BDI, STAI, PSWQ, SSAS, SCS-P, and BCS-P. Given that insomnia has been associated with anxiety and depression in several previous studies (53, 54, 55), the relationship of the SAMI to the BDI, STAI, and PSWQ were not unexpected. In addition, relationships between the SAMI and indices of anxiety and worry make sense conceptually. In previous studies monitoring for threat has been associated with increased detection and misinterpretation of irrelevant cues (3, 56, 57). On this basis, it has been suggested that in insomnia, monitoring for sleep-related threat triggers excessively worrisome negative thoughts and emotional distress, thereby perpetuating the sleep disturbance (35).

Several limitations of the current study merit consideration. The first 3 limitations relate to generalizability of this study to patients with insomnia, as we envisage that the SAMI will be most relevant to this clinical group. First, the majority of the people in the sample were drawn from a university-based population. While it was important to include normal sleepers to establish the association between the SAMI and severity of sleep disturbance, future investigations should be conducted with physician-referred individuals to ensure that the current findings generalize to a clinical insomnia population. Second, it should also be noted that the overall mean score of the PSQI in the sample was high (6.8) and may not be representative of a typical student/staff university population. Third, the distinction between people with a clinically significant sleep disturbance and normal sleepers relied solely on the PSQI cutoff score. While this cutoff shows high sensitivity and specificity for identifying clinically significant sleep disturbances (1), replication of the current study with a diagnostic interview that specifically assesses for each of the inclusion and exclusion criteria specified for a diagnosis of insomnia is necessary. Fourth, a related issue is that because the PSQI was the sole index of severity of sleep disturbance, participants were not screened for other comorbid sleep disorders, such as breathing-related sleep disorders or narcolepsy. Although monitoring for sleep-related threat is likely to play a role in perpetuating a variety of sleep disorders, future research with the SAMI should utilize objective measures such as wrist actigraphy or polysomnography to distinguish between these syndromes. Fifth, participants in this investigation were not assessed for psychiatric comorbidity. However, it should be noted that the distinction between primary (insomnia as the primary complaint) and secondary (a complaint of insomnia that is secondary to another comorbid disorder) insomnia has been challenged (58, 59). Even if this distinction is retained it is possible that monitoring for sleep-related threat may be just as relevant to secondary insomnia as to primary insomnia. Future studies should clarify this issue by assessing for comorbid psychological disorders. Sixth, the validity of the SAMI was tested in the current study through its correlation with self-report questionnaires. Fichten et al. (60) cite an unpublished study that shows high correlations between retrospective sleep estimates obtained from questionnaires and prospective self-reports of sleep estimates (r = 0.72 to 0.83) among 156 individuals with insomnia. These correlations suggest that retrospective reports constitute valid and reliable indicators of sleep estimates. Nonetheless, future research should compare the SAMI with objective assessments of sleep disturbance to confirm external validity. Finally, although the sample recruited for this study was large, replication is required to confirm the reliability and validity of the SAMI and the replicability of the factor structure reported here.

In summary, the present study provides initial support for the use of the SAMI as a valid and reliable instrument to index monitoring for sleep-related threat sleep disturbances. The factor structure of the SAMI supports previous research identifying the presence of monitoring at night and during the day (31–34) and is consistent with the proposal that several types of monitoring operate during the night and during the day to maintain chronic insomnia (35). The SAMI has potential for use in experimental studies relating to insomnia. Furthermore, if future research confirms our prediction that monitoring serves to maintain insomnia the SAMI will prove useful as an indicator of processes that need to be targeted during treatment and as a measure of treatment outcome.


   APPENDIX
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 APPENDIX
NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
Below is a list of things that people sometimes do or feel when falling asleep, waking up, or during the day. For each statement please CIRCLE THE BEST RESPONSE for how much you notice these things on a TYPICAL NIGHT or a TYPICAL DAY over the past month:


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  		SAMI
 
Note. The numbers appearing in parentheses after each item correspond to the subscales that emerged from the rotated matrix as follows: I = daytime monitoring for body sensations; II = calculation of time; III = waking monitoring for body sensations; IV = pre-sleep monitoring for body sensations consistent with falling asleep; V = daytime monitoring of functioning; VI = pre-sleep monitoring for body sensations inconsistent with falling asleep; VII = pre-sleep monitoring the clock; VIII = pre-sleep monitoring the environment.


   ACKNOWLEDGMENTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 NOTES
 ACKNOWLEDGMENTS
REFERENCES
 
This research was supported by funding from the Wellcome Trust and the Social Sciences and Humanities Research Council of Canada. The authors are grateful to Professor David Firth and Dr. Kathy Parkes for statistical advice.


   NOTES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 NOTES
ACKNOWLEDGMENTS
 REFERENCES
 
1 It is important to note that we are using the term "pre-sleep" here to refer to any period of wakefulness during the night including waking that occurs (a) during the initial pre-sleep period between lights out and falling asleep; (b) on waking in the middle of the night; and (c) on waking early in the morning. Back

2 To control for the age difference reported earlier, Analysis of Covariance (ANCOVA) was conducted on the subscale and overall SAMI total scores using age as the covariate. All reported differences between the CSSD and NS groups remained significant. Back

Received for publication January 13, 2003.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 

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