Temporal relationship between daily pain and actigraphy sleep patterns in pediatric sickle cell disease

Fisher, Karin; Laikin, Andrea; Sharp, Katianne M. Howard; Criddle, Catherine A.; Palermo, Tonya M.; Karlson, Cynthia

doi:10.1007/s10865-018-9918-7

Cited by 33 publications

(32 citation statements)

References 28 publications

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“…38 They also may have disturbed sleep or sleep-disordered breathing. [39][40][41][42][43][44] Fatigue may be severe, with severe anemia, and correlates substantially not only with pain but with sleep quality, anxiety, depression, and stress 45 and neurocognitive function and decreased quality of life. 46 Poor interpersonal relationships at work have been reported to be a larger contributor to unemployment than physical health in individuals with SCD.…”

Section: Discussionmentioning

confidence: 99%

Indirect Economic Burden of Sickle Cell Disease

et al. 2021

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This study aimed to quantify the indirect costs of sickle cell disease in the United States.Methods: Adult patients from a sickle cell disease clinic at an urban academic healthcare system completed an adapted Institute for Medical Technology Assessment Productivity Cost Questionnaire related to the impact of their disorder on absenteeism, presenteeism, ability to contribute through unpaid work outside of employment, and other aspects of life. Additional data were collected from patient records about each participant's genotype, total hemoglobin level, and pain level.Results: Of the 192 individuals, 187 who completed the survey reported experiencing vaso-occlusive crisis pain events during the last year that negatively affected their productivity at work and in daily roles. Three-fourths of respondents reported impairment in their ability to complete everyday tasks, such as caring for children, running errands, doing housework, shopping for groceries, and volunteer (unpaid) work. Only 30% of respondents reported being employed or self-employed. Of those employed, estimated costs of absenteeism and presenteeism attributable to pain events averaged $15 103 per person annually. Estimated total annual losses in unpaid work productivity averaged $3 145 862 for the study respondents and another $2 870 652 for their caregivers.Conclusions: Sickle cell disease affected the work productivity, nonwork productivity, and the daily lives of adults seen with the disorder in an academic medical center.

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Section: Discussionmentioning

confidence: 99%

Indirect Economic Burden of Sickle Cell Disease

et al. 2021

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“…Overall, there were small-to-moderate associations between pain frequency/intensity and internalizing symptoms. Studies using youth self-report of internalizing symptoms reported small-to-moderate correlations with youth-reported pain frequency (Fisher et al, 2018;Karlson et al, 2017). Studies utilizing caregiver-reports of internalizing symptoms and pain frequency reported conflicting findings, with differences possibly related to the age range of study samples.…”

Section: Pain Frequency/intensity and Internalizing Symptomsmentioning

confidence: 99%

“…Five studies (17%) assessed internalizing symptoms, three of them utilizing a caregiver-report measure (Missouri Children's Behavior Checklist Internalizing scale) and two utilizing a youth-report measure (Revised Children's Anxiety and Depression Scale total score). Of the studies that looked at the relationship between pain and internalizing symptoms, the percentage of youth with clinically significant cut-off scores on internalizing measures ranged from 17 to 36% (Fisher et al, 2018;Karlson et al, 2017;Thompson, Gil, Burbach, Keith, & Kinney, 1993). Eight studies (28%) examined positive and negative affects, with all utilizing youth-report measures (Positive and Negative Affect Scale-Children was the most common) and two also using caregiver-report.…”

Section: Internalizing and Affective Symptomsmentioning

confidence: 99%

“…One study with children and adolescents found that those with more frequent pain episodes had higher levels of internalizing symptoms (Gil, Williams, Thompson, & Kinney, 1991), while another cross-sectional study focused on younger children ages 7-12 years did not find pain frequency to be a significant predictor of internalizing symptoms (Thompson et al, 1993). There was only one study that examined the association between pain intensity and internalizing symptoms, and it reported a small correlation (Fisher et al, 2018). Given that internalizing symptoms are usually conceptualized as a mix of depressive and anxiety symptoms, it is not surprising that these findings seem consistent with the aforementioned findings across depressive and anxiety symptoms.…”

Section: Pain Frequency/intensity and Internalizing Symptomsmentioning

confidence: 99%

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Systematic Review: Pain and Emotional Functioning in Pediatric Sickle Cell Disease

Reader

Rockman

Okonak

et al. 2019

J Clin Psychol Med Settings

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The objective of this systematic review was to assess the relationship between pain (frequency/intensity/duration, impairment, coping) and emotional functioning in pediatric Sickle Cell Disease, and evaluate the state of the literature. Studies were included if they met each of the following criteria: (a) primarily pediatric sample of youth or young adults up to age 21 years with SCD, (b) examined emotional functioning including anxiety and/or depressive and/or internalizing symptoms, and/or affect, (c) examined pain intensity/frequency/duration and/or pain-related impairment and/or pain coping as it relates to emotional functioning, as defined above. Using the established guidelines for systematic reviews, we searched PsycINFO, PubMED, and CINAHL databases for studies published through June 2018. Screening resulted in 33 studies meeting inclusion criteria. Study data were extracted and evaluated for scientific merit, resulting in four studies being removed. 29 studies were included in the final synthesis. Studies provide strongest evidence of a relationship between increased pain frequency and higher depressive and anxiety symptoms. There are moderate-to-strong associations between pain-related impairment and depressive symptoms, and small-to-strong associations between pain-related impairment and anxiety. When examining pain-coping strategies, maladaptive cognitive strategies show the strongest association with emotional functioning. There is a need for more adequately powered, prospective studies based on theoretical frameworks in order to advance our understanding of the relationship between pain and emotional functioning in pediatric SCD.

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“…In our mobile application study, >80% of the reports of aura were followed by pain. In another study (39), the sleep patterns of pediatric SCD subjects were monitored over 2 weeks using a wrist-mounted actigraphy device, and these were found to be associated with the occurrence of next-day pain recorded by daily pain diary. Taken together, these exciting recent developments point to the future possibility of embedding an ML-based model in a low-cost wearable system, consisting of a wristband paired with mobile phone, to assist clinicians in managing long-term therapy for SCD patients.…”

Section: Implications For Home-based Clinical Applicationmentioning

confidence: 99%

Identifying Elevated Risk for Future Pain Crises in Sickle-Cell Disease Using Photoplethysmogram Patterns Measured During Sleep: A Machine Learning Approach

Chalacheva

Rosen

et al. 2021

Front. Digit. Health

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Transient increases in peripheral vasoconstriction frequently occur in obstructive sleep apnea and periodic leg movement disorder, both of which are common in sickle cell disease (SCD). These events reduce microvascular blood flow and increase the likelihood of triggering painful vaso-occlusive crises (VOC) that are the hallmark of SCD. We recently reported a significant association between the magnitude of vasoconstriction, inferred from the finger photoplethysmogram (PPG) during sleep, and the frequency of future VOC in 212 children with SCD. In this study, we present an improved predictive model of VOC frequency by employing a two-level stacking machine learning (ML) model that incorporates detailed features extracted from the PPG signals in the same database. The first level contains seven different base ML algorithms predicting each subject's pain category based on the input PPG characteristics and other clinical information, while the second level is a meta model which uses the inputs to the first-level model along with the outputs of the base models to produce the final prediction. Model performance in predicting future VOC was significantly higher than in predicting VOC prior to each sleep study (F1-score of 0.43 vs. 0.35, p-value <0.0001), consistent with our hypothesis of a causal relationship between vasoconstriction and future pain incidence, rather than past pain leading to greater propensity for vasoconstriction. The model also performed much better than our previous conventional statistical model (F1 = 0.33), as well as all other algorithms that used only the base-models for predicting VOC without the second tier meta model. The modest F1 score of the present predictive model was due in part to the relatively small database with substantial imbalance (176:36) between low-pain and high-pain subjects, as well as other factors not captured by the sleep data alone. This report represents the first attempt ever to use non-invasive finger PPG measurements during sleep and a ML-based approach to predict increased propensity for VOC crises in SCD. The promising results suggest the future possibility of embedding an improved version of this model in a low-cost wearable system to assist clinicians in managing long-term therapy for SCD patients.

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Temporal relationship between daily pain and actigraphy sleep patterns in pediatric sickle cell disease

Cited by 33 publications

References 28 publications

Indirect Economic Burden of Sickle Cell Disease

Indirect Economic Burden of Sickle Cell Disease

Systematic Review: Pain and Emotional Functioning in Pediatric Sickle Cell Disease

Identifying Elevated Risk for Future Pain Crises in Sickle-Cell Disease Using Photoplethysmogram Patterns Measured During Sleep: A Machine Learning Approach

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