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BMI, Psychosocial Correlates, Pain and Activities of Daily Living in Sickle Cell Disease Patients

Kaufman, Kelli, MSa; Chin, Shao-Hua, BSa; Kahathuduwa, Chanaka, MBBS, MPhila,,b,,c; Wood, Mary, MAd; Feliu, Miriam, PsyDd; Hill, LaBarron, PhDd; Barker, Camela, MAe; Reif, Rosellen, MSd; Keys, Abigail, PsyDd; Edwards, Christopher L., PhDd; Binks, Martin, PhDa

Progress in Preventive Medicine: June 2018 - Volume 3 - Issue 4 - p e0019
doi: 10.1097/pp9.0000000000000019
State of the Science

Pain, psychosocial issues and impaired activities of daily living (ADLs) are common in sickle cell disease (SCD). Improved longevity may be leading to increases in body mass index (BMI). We both describe an SCD clinic population and determine if BMI, psychosocial distress, and pain predict impairments in ADLs. Archival data from 252 adult African American SCD clinic patients were examined (ie gender, BMI, SCD type, psychosocial status via Symptom Checklist-90-Revised; pain via Short-form McGill Pain Questionnaire: Sensory Pain, Affective Pain (AP), Present Pain Intensity and ADLs. In total, 44% of the sample was overweight (BMI ≥ 25 kg/m2). Depression, anxiety, and Symptom Checklist-90-Revised Global Severity Index were in the clinical range. Patients with HbSβ+-thalassemia and hemoglobin SC disease had higher BMIs than those with HbSS (P < 0.001). AP and age-predicted impaired ADLs. In an OW/OB subsample, AP, age, and SCD severity predicted impaired ADLs. The consistent relationship of AP to ADL impairment suggests targeting AP via behavioral pain management may improve functioning.

aDepartment of Nutritional Sciences, Texas Tech University, Lubbock, TX

bDepartment of Human Development and Family Studies, Texas Tech University, Lubbock, TX

cDepartment of Physiology, Faculty of Medicine, University of Peradeniya, Sri Lanka

dDepartment of Psychiatry, Duke University Medical Center, Durham, NC

eB and D Behavioral Health, Inc.; Durham, NC.

Preliminary data were presented at the annual meeting of the Society of Behavioral Medicine 22 April 2015 to 25 April 25, San Antonio, TX, and the European Congress on Obesity 6 May 6-2015 to 9 May 2015, Prague, Czech Republic.

Published online 1 June 2018

Address reprint requests to Martin Binks, PhD, Department of Nutritional Sciences, Texas Tech University, Mail Stop 41270, Lubbock, TX 79409-1270. E-mail address: m.binks@ttu.edu (Binks M)

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

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Introduction

Sickle cell disease (SCD) is the most common genetic blood disorder in the United States and 1 of the most common worldwide.[1] According to the World Health Organization, there are over 332,000 conceptions affected by hemoglobin disorders annually. Sickle cell disorders account for about 275,000 of those conceptions.[1] Worldwide, people of sub-Saharan African descent show the highest prevalence of sickled hemoglobin; however, people whose lineage derives from the Western Hemisphere (South America, the Caribbean, and Central America), Saudi Arabia, India, and Mediterranean countries like Turkey, Greece, and Italy are also at risk.[2] In the United States, it is estimated that SCD is prevalent in over 89,000 Americans, including 1 of every 500 African Americans and 1 of every 36,000 Hispanic Americans.[3] In addition, approximately 2 million Americans are carriers.

Sickle cell disease occurs when the sickle cell gene has been inherited by both parents and passed to their offspring, resulting in alteration in the shape of red blood cells (RBCs) resulting in faulty oxygen transport and causing medical complications and tissue damage.[4] Although healthy RBCs are round, smooth, flexible, and can live up to 120 days, sickle cell RBCs, as a result of the hemoglobin mutation, are stiff and acquire a “C” shape in the deoxygenated state. These cells only live for 10–20 days and have difficulty moving effectively through blood vessels due to their abnormal shape.[5] The sickled shape leads to restricted movement of RBCs in blood vessels, which contributes to various medical complications of SCD including severe pain and tissue and end organ damage.

The severity/type of SCD is dependent on how many HbS genes are inherited (from 1 versus both parents) and if other hemoglobinopathies (ie, genetic defects in hemoglobin chains) are present. The 4 most common types of SCD are sickle cell anemia (HbSS), hemoglobin SC disease (HbSC), and 2 forms of hemoglobin S beta thalassemia (HbSβ+ and HbSβ0).[6] HbSS occurs when individuals inherit 1 sickle cell gene (S) from each parent. This form is commonly referred to as sickle cell anemia and is the most common type of SCD. HbSC is typically a milder form of SCD.[5] It occurs when a sickle cell gene (S) is inherited from 1 parent and an abnormal hemoglobin (C) gene is inherited from the other parent. Hemoglobin S beta thalassemia (HbSβ+ and HbSβ0) occurs when 1 sickle cell gene is inherited from 1 parent and 1 beta thalassemia gene is inherited from the other parent. Beta thalassemia causes insufficient hemoglobin production and results in hemoglobin deficiency in RBCs.[7] Overall, HbSS and HbSβ0-thalassemia forms are more severe.[6 , 8] Additionally, there are more rare forms of sickle cell disease including HbSD, HbSE, and HbSO, where “D,” “E,” and “O” are other abnormal hemoglobin types. Finally, sickle cell trait (SCT), denoted HbAS, occurs when 1 sickle cell gene is inherited from 1 parent and 1 normal hemoglobin gene (A) is inherited from the other parent.[5 , 6] It is not considered a form of sickle cell disease[9] as people with SCT do not typically experience signs and symptoms of sickle cell disease. However, they are considered “carriers” of the sickle cell gene[5] and thus their offspring is at higher risk if the other parent is also a carrier or has SCD. The hemoglobin S gene is inherited in an autosomal recessive manner, which means that there is a 1 in 4 chance for offspring to develop SCD if both parents have the SCT.[6]

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Diagnosis, treatment, and course

In developed countries, people with SCD are living longer due to early diagnosis and improved management.[6 , 10] However, the life expectancy of individuals with SCD in the United States is still much lower compared with the average American life span (39 years compared with the average American lifespan of 79 years).[11–13] In the United States overall, longevity of people with sickle cell disease has shifted toward a longer average life span in the period from 1974 through 2006 and deaths of children aged 1–4 has dropped from 13% to 2%.[12] These gains are notable; however, only 10% of all SCD cases in the world are located in developed regions[10]; thus worldwide, progress can at times seem negligible. Estimates in Africa reveal 50–80% of infants with SCD die before the age of 5 years,[14] highlighting the need for a worldwide effort to provide resources that support early diagnosis (and subsequent improved prognosis) for infants in developing regions.[5]

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Associated medical conditions

People with SCD typically experience many medical issues throughout their life span. Pain is the most common symptom of SCD and is a result of vaso-occlusion by sickled cells. Vaso-occlusion occurs when sickled red blood cells stick to blood vessel walls and obstruct proper movement of oxygen to tissues in the body. Due to hypoxia (low oxygen levels in blood), body tissues become damaged resulting most commonly in pain, stroke, delayed growth, later onset of sexual maturation, pulmonary issues, and necrosis in the hip and shoulder area.[9 , 15] Acute pain episodes are commonly referred to as vaso-occlusive crises. Other common complications include acute chest syndrome, acute stroke, priapism, hepatobiliary complications such as gallstones, splenic sequestration and acute renal failure, avascular necrosis, pulmonary hypertension, renal complications such as chronic kidney disease, ophthalmologic complications such as proliferative sickle retinopathy and vitreous hemorrhage, leg ulcers, and stuttering or recurrent priapism.[11]

Medical comorbidities may vary in terms of presentation and severity depending on the type of SCD. People with HbSS have the most severe symptoms associated with SCD, followed by HbSβ0.[16] HbSS and HbSβ0-thalassemia forms are at higher risk for acute chest syndrome and pain crises compared with HbSC and HbSβ+-thalassemia forms.[17 , 18] Although people with the HbSC type typically have milder symptoms compared with individuals with HbSS, they have a greater risk of experiencing “thromboembolic complications, retinopathy, and renal papillary necrosis.”[19] In terms of survival, individuals with HbSS have a much lower median lifespan when compared with people with HbSC; a difference of almost 20 years.[20] Individuals with HbSS type also have the highest rate of cerebrovascular complications resulting from restricted cerebral blood flow due to hemoglobin “sickling.”[21] Clinical manifestations of SCD vary both across individuals and also within individuals over time, and although rare, some may be asymptomatic at times, although this is rare.[9]

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Psychosocial conditions and their management

SCD frequently (and intermittently) results in impairments in performing normal daily activities and also participating fully in activities with the patient’s family, friends, and others around them. Common psychosocial considerations in SCD include coping with medical complications and prognosis of SCD, diminished quality of life, depression, mood changes, anxiety, social withdrawal, loneliness, insomnia, daily activity and role limitations, psychosocial sequela related to neurocognitive impairment, insomnia, and analgesic dependence.[11 , 22 , 23] Health-related quality of life is lower in SCD compared with the general population.[24] Attending social functions, work, and school on a regular basis can be difficult for this population due to medical complications including but not limited to acute pain crises. Prolonged functional and medical difficulty over time contributes to depression and anxiety in children and adults with SCD. Perhaps most salient are issues with pain and its impact on psychosocial functioning and overall quality of life throughout the life span.

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Pain and sickle cell disease

Pain is the primary defining complication associated with SCD. Pain leads to frequent emergency department visits and hospitalizations and is considered a primary contributor to decreased quality of life among SCD patients.[25 , 26]

Pain assessment typically targets 2 broad categories (1) Acute Pain: Pain that is of relatively brief duration; (2) Chronic Pain: Pain that persists for more than 3 months. Chronic pain can be further categorized according to the origin of pain. For example, neuropathic pain, described as pain caused by a lesion or disease of the somatosensory nervous system[27] and nociceptive pain, which is typically derived from nonneural tissue damage. It was once thought that sickle cell pain was strictly nociceptive; however, recent data have shown neuropathic pain is present in people with SCD.[28 , 29]

Among the most frequent causes of pain in SCD is vaso-occlusion,[9] an unpredictable event that occurs when sickled RBCs block blood vessels resulting in ischemic tissue damage, inflammation, and pain.[30 , 31] Specifically, tissue injury resulting from vaso-occlusion produces chemicals that are released around a nerve area called the primary afferent nociceptor, resulting in a feeling of subjective pain in that area.[28] Pain attributed to SCD can be experienced anywhere in the body; the limbs and back are most frequently affected.[30] Acute pain events are typically described as “pain episodes” or “crises,” which are highly variable in terms of frequency and duration and are the main cause of hospital admissions in this population. Studies have found a range of frequencies of pain episodes in SCD ranging from 1 in 14 days to as much as 50% of days.[32 , 33]

Pain episodes may also be precipitated by factors other than vaso-occlusive events; these may include specific factors related to the type of SCD, physical or emotional stress, dehydration, extreme temperatures, and nutritional status.[9 , 31 , 34] Type of SCD is a predictor of both pain severity and episodes of pain.[17] People with HbSS type or HbSβ0- thalassemia, for example, generally have more pain episodes compared with people with HbSC type or HbSβ+-thalassemia.[16 , 17] Managing pain in people with SCD is challenging as crises are difficult to anticipate and there is a mixed presentation of both acute and chronic course,[11] but beyond acute pain crises, the ongoing presence of chronic pain in this population is associated with psychosocial and quality of life impairment.

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Chronic pain in SCD

Chronic pain has until recently received little attention in individuals with SCD as traditionally there has been a greater focus on the deleterious effects of acute pain crises. However, with an observed rise in chronic pain reports over the last 20 years, there is a need for more research in the area.[35 , 36] According to a review of literature by Taylor et al.,[36] people with SCD had chronic pain 13–50% of the days they participated in studies and most commonly felt their chronic pain in the hip and back. Contrary to acute crises, chronic pain does not typically warrant hospitalization unless a patient is experiencing an acute crisis while still experiencing persistent pain.[37] The impact of chronic pain often extends beyond physical effects of acute pain to include the taxing emotional and psychological effect of experiencing high intensity pain over many months. Psychosocial and psychological complications (eg, depression, anxiety, and decreased quality of life) are often the result of withstanding mental exhaustion due to coping with pain and thus further psychological treatment may be indicated.[15]

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Pain and activities of daily living

Pain influences mobility and the ability to participate in normal daily activities that other people take for granted such as walking from their car, bathing, housework, and so forth. These behaviors are collectively termed activities of daily living (ADLs). There are different types of ADLs, including physical (eg, dressing, bathing, eating), cognitive (eg, remembering to take medications, playing games), complex ADLs (shopping, cooking), and combination ADLs, which involve an integration of all types of ADLs. In addition to documented impairment in ADLs associated with chronic pain, research has also shown that pain is related to impaired ADLs in many other chronic diseases such as cancer[38] chronic low back pain[39] and fibromyalgia[40] and chronic pain disorders in general.[41]

There is limited research demonstrating decreased ADLs specifically in SCD; however, there are some studies in SCD populations that have shown that pain is associated with reduced physical activity and ADLs.[42 , 43] Gil et al.[44], assessed pain coping strategies in 79 SCD patients and found reduced ADLs (ie, household activity, work activity, and social activity during pain episodes). In a cross-sectional study, Anie et al. assessed items relating to pain and ADLs taken from a broader health-related quality of life measure (SF-36) in a sample of 96 adult patients with SCD. They found that patients with SCD had significantly more role limitations due to physical problems and overall lower health-related quality of life compared with a comparable sample drawn from the general population of adults. In addition, pain was significantly associated with the role limitations particularly in the “physical function” domain.

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Nutritional status and weight in SCD

Underweight and SCD

Weight status categorized by body mass index (BMI), allows for classification of relative health risks (eg, cardiovascular disease, hypertension, cancer, diabetes mellitus, gallbladder disease, endocrine disturbances, osteoarthritis, and pulmonary diseases) associated with weight in the general population.[45] Historically, patients with SCD (especially those with HbSS) are known to be or be at risk for underweight.[46] Thus, the most common nutritional recommendation made in SCD patient populations was designed to offset fears of under nutrition and unintentional weight loss; namely to consume excess calories and increase macronutrient intake.[47] Although a review of the literature suggests a relative lack of empirical evidence upon which to base these recommendations, nutrition has nonetheless been identified as a crucial feature of SCD since the 1980s.[48 , 49]

The most popular hypothesis to explain underweight in SCD is that SCD causes the body to be in a hypermetabolic state, which increases energy demand and can lead to an undernourished state if not offset by increased nutrient consumption. Studies have recorded high resting energy expenditure in people with SCD, and high myocardial energy requirements and an increase in proinflammatory cytokine production.[50 , 51] One possible explanation for the hypermetabolic state lies in the shortened RBC life span in SCD. RBCs destruct more often in SCD, which leads to an increase in erythropoiesis to maintain homeostasis. Erythropoiesis, or the production of RBCs, consumes protein in the process and depletes energy to do so, thus creating a hypermetabolic state. Another equally plausible explanation for hypermetabolism causing underweight is that the body is compensating for tissue hypoxia. Low oxygen levels produce tachycardia with reduced appetite and greater muscle and organ stress as the hematological system attempts to compensate by transporting more oxygen to tissues and cells collectively contributing to poor nutrition and underweight over time. Changes in nutrient intake[48 , 52–54] due to pain and other comorbid conditions has also been considered as contributory but support for these hypotheses is limited.

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Obesity and SCD

Relatively recent advances in medicine (eg, treatments such as hydroxyurea and frequent blood transfusions), especially in developed countries such as the United States have resulted in increased life expectancy[11] and increasing BMI in SCD populations.[54] This may be particularly relevant to the issue of pain in SCD as in non-SCD populations an association between pain and obesity has been documented.[55–60]

The combination of increasing obesity and associated pain, and obesity-related psychosocial distress coupled with the already significant SCD-related pain and psychosocial issues could prove to be of particular importance in managing patients with SCD. Thus, our goals in this study are 2-fold: In study 1 (Descriptive Comparisons), we described important characteristics of our SCD clinic population including age, BMI, depression, anxiety, overall psychological distress, social support (ie, psychosocial considerations), pain, and ADLs. We conducted comparisons on variables of interest according to gender and considered the relationship of weight status to all variables of interest by conducting comparisons across BMI categories (underweight, normal weight, overweight, and obese). In addition, we examined differences among types of SCD and variables of interest through comparisons across 5 SCD type categories. Next, in study 2 (predicting ADLs), based on our previous work in non-SCD obese populations showing that BMI and pain are related to reduced ADLs,[60] we predicted that a similar relationship (pain and BMI predicting greater impairment in ADLs) would be apparent in this SCD clinic population (whole sample). Thus, we examined whether BMI, pain, and psychosocial correlates predicted impairments in total and physical ADLs. In addition, following our initial analysis, additional a priori hypotheses were developed to examine the relationship of SCD severity on ADLs with a separate analysis that include a dichotomized categorical variable, SCD severity (severe versus mild), as a predictor of ADLs. Finally, given the potential unique qualities of the overweight and obese subsample, the above analyses were repeated using only the overweight/obese subsample.

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Methods

Design and participants

A cross-sectional, archival data analysis of baseline data from the first 2-hundred-fifty 2 consecutive adult African American patients (2002–2010) participating in an ongoing longitudinal study at Duke Comprehensive Sickle Cell Disease Center was conducted. Deidentified data relevant to the current study were analyzed at Texas Tech University. The current archival data review was granted exempt status by Texas Tech University; Protection of Human Subjects Committee, Lubbock, TX, on January 27, 2015 (Protocol # 504986). Participants in the larger ongoing study provided informed consent before baseline data collection. The original data collection was approved by the Duke University Medical Center, Institutional Review Board. Exclusion criteria for the original data collection included (1) inability to read or understand the written instructions of the study; (2) unwillingness or inability to commit to the time required to complete the Longitudinal Exploration of Medical and Psychosocial Factors in Sickle Cell Disease (LEMPFSCD); (3) Active acute pain or other medical crisis at the time of their visit to the clinic; (4) Diagnosis other than SCD.

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Methodological overview

The current data analysis was divided into 2 studies:

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Study 1: Descriptive comparisons

We first considered the whole sample according to descriptive variables of interest (detailed below). We then did comparisons according to gender, BMI category, and SCD type.

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Study 2: Predicting ADLs

Predictive modeling was undertaken to determine the relationship of age; BMI; Symptom Checklist-90-Revised (SCL-90-R) depression, anxiety and Global Severity Index (GSI); Alford Edwards Social Support Inventory (AESSI); and pain as measured by Short-form McGill Pain Questionnaire (SF-MPQ) variables [ie, sensory pain, SP; affective pain, AP; and present pain intensity, Present Pain Index (PPI)] to each of physical and total ADLs for the whole sample and with an OW/OB only subsample.

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Measures

Medical record review

Age and gender.

Adult (18 years plus) male and female participants included.

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Weight, height, and BMI.

Measured weight and height were obtained by clinic personnel at the time of the patient’s clinic visit and BMI (kg/m2) calculated. BMI was classified as: (1) underweight < 18.5 kg/m2; (2) normal weight 18.5–24.9 kg/m2; (3) overweight 25–29.9 kg/m2; or (4) obese ≥ 30.0 kg/m2.[45]

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SCD type.

SCD type included the following: (1) HbSS; (2) HbSC; (3) HbSβ+-thalassemia; (4) HbSβ0-thalassemia; and (5) Other (other variants of hemoglobin disorders). For the purpose of predictive analyses performed in study 2, “SCD Severity” was categorized as (1) severe, (HbSS and HbSβ0-thalassemia types); and (2) mild, (HbSC and HbSβ+-thalassemia types).

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LEMPFSCD

Select variables from the LEMPFSCD, a 700-item self-report inventory that assesses demographics, pain, and psychiatric, behavioral, and social functioning using 8 validated, content-driven instruments, plus original items created specifically for the survey were utilized:

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Symptom Checklist-90-Revised (SCL-90-R).

The SCL-90-R[61] is a 90-item checklist that assesses the existence and degree of multiple psychological symptoms on a 5-item rating scale from 0 “not at all” to 4 “extremely.” Subscales of the SCL-90-R are categorized into 9 primary symptom dimensions and 3 general indices. The following were used: depression, anxiety, and GSI, which measures overall psychological distress. Subscale internal consistency ranges from 0.77 to 0.90. Cronbach’s alpha for GSI have been reported as ranging from 0.96 to 0.97. Standard scoring procedures convert SCL-90-R raw scores to T-scores with a T-score above 50 indicating a “clinical” range for the domain being measured.

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Alford Edwards Social Support Inventory.

The AESSI[62 , 63] is a validated self-report measure used to assess present satisfaction with 4 theory-driven components of social support (Emotional, Instrumental, Informational, Comparison) from 5 primary sources (Spouse/Significant Other, Friends, Family, Co-workers, and Other Social Contacts). Subjects completing the inventory circled a number from 0 (none of the time) to 4 (all of the time) for each of the 24 questions. Lower scores represent lower levels of support. Internal consistency is reasonable (Cronbach’s alpha, 0.93). The Total Support Index is a sum of item scores and is used to determine overall social support in this study.

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Short-form McGill Pain Questionnaire (SF-MPQ).

The SF-MPQ[64] is a well-validated and widely used abbreviated version of the validated MPQ and takes 2 to 5 minutes to complete. It consists of 15 descriptors (11 sensory, 4 affective) that are rated according to intensity on a 4 point scale: (0 = none, 1 = mild, 2 = moderate, 3 = severe). The sum of these ratings is used to form 3 validated pain rating scales; sensory pain (SP; throbbing, shooting, stabbing, cramping, aching, and tender; maximum score is 33), affective pain (AP; emotional aspects of pain experience; tiring-exhausting, sickening, fearful, and cruel-punishing. Maximum score is 12), and total pain. In addition, a visual analog scale (VAS; 100 mm line, no pain to worst possible pain) and the PPI (1 mild, 2 discomforting, 3 distressing, 4 horrible, and 5 excruciating) from the standard McGill Pain Questionnaire are included.

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ADLs.

Our measure of ADLs is a composite measure that was created for the purposes of this study to assess impairments in 35 common daily activities. It asks patients to indicate if they have difficulty performing activities in the context of their pain by endorsing “Yes” or “No” for each of 35 activities. Items are then categorized based on each subcategory of ADLs (ie, physical, cognitive, complex, combination) and totaled forming 4 subscales plus a fifth cumulative total ADLs category. Higher numbers of ADLs indicate a greater number of impaired ADLs endorsed. Each ADL category is described as follows:

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Physical ADLs.

Basic activities like eating, dressing, bathing, brushing ones teeth, and mobility.

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Cognitive ADLs.

Tasks that require thinking and/or problem solving aspects of daily function (eg, remembering to take medicines, remembering appointments, being capable of playing cards or games).

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Complex ADLs.

“Instrumental functioning” that requires complex integration of physical and cognitive factors in their performance. Some examples include shopping, cooking, and managing one’s finances.

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Combination ADLs.

Derived variable that combines both cognitive and complex ADLs.

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Total ADLs.

Summary score of all ADLs included with a maximum of 35.

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Statistical analysis

Values are given as mean ± SD unless otherwise indicated. For all comparisons of means an a priori alpha level of 0.05 was selected. Where appropriate, Bonferroni correction was used to control for multiple comparisons. For stepwise regression modeling, inclusion/exclusion criteria were 0.05 and 1.00, respectively, for all analyses. The following statistical analyses were performed using Statistical Package for Social Sciences (SPSS) Version 22.0 for Windows (IBM Corp, Armonk, N.Y.).

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Study 1: Descriptive comparisons

Analysis 1.

Descriptive statistics using the whole sample (N = 252) were calculated (mean + SD) for the following demographic, medical, and psychosocial variables: gender; age; BMI; SCL-90-R depression, anxiety and GSI; AESSI; SF-MPQ SP, AP, and PPI; and ADLs including physical ADLs, cognitive ADLs, complex ADLs, combination ADLs, and total ADLs.

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Analysis 2.

To determine the relationship of gender to the variables of interest, 1-way analysis of variance (ANOVA) (dependent variable, DV = gender) was used to compare the means for age; BMI; SCL-90-R depression, anxiety and GSI; AESSI; SF-MPQ SP, AP, and PPI; and ADLs including physical ADLs, cognitive ADLs, complex ADLs, combination ADLs, and total ADLs in the whole sample.

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Analysis 3.

One-way ANOVA was also used to examine the dependent variable BMI category comparing underweight (UW), normal weight (NW), overweight (OW), and obese (OB), for the following independent variables using the whole sample: age; BMI; SCL-90-R depression, anxiety and GSI; social support; SF-MPQ SP, AP, and PPI; and ADLs (ie, physical ADLs, cognitive ADLs, complex ADLs, combination ADLs, and total ADLs).

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Analysis 4.

To examine the association of sickle cell type to the independent variables of interest, 1-way ANOVA (DV = SCD type) was used to compare the means for age; BMI; SCL-90-R depression, anxiety, and GSI; AESSI; SF-MPQ SP, AP, and PPI; and ADLs including physical ADLs, cognitive ADLs, complex ADLs, combination ADLs, and total ADLs in the whole sample.

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Study 2: Predicting ADLs

Predictive modeling was used to examine the relationships of independent variables of interest to each of physical and total ADLs in both the whole sample and the OW/OB subsample.

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Whole sample.
Analysis 1.

Multiple regression analysis using stepwise linear modeling procedures was performed and included the following predictor variables: age; BMI; SCL-90-R depression, anxiety and GSI; SF-MPQ SP, AP, and PPI, which were used to predict each of the dependent variables physical and total ADLs in separate analyses.

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Analysis 2.

Multiple regression analysis was completed using stepwise linear modeling procedures to determine if the independent variables age; BMI; SCL-90-R depression, anxiety, and GSI; SF-MPQ SP, AP, PPI and SCD severity predicted each of physical and total ADLs in separate analyses.

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Overweight/obese subsample.
Analysis 3.

Multiple regression analysis using stepwise linear modeling procedures was conducted to determine if age; BMI; SCL-90-R depression, anxiety, and GSI; SF-MPQ SP, AP, and PPI predicted the each of the dependent variables physical and total ADLs in separate analyses.

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Analysis 4.

Multiple regression analysis using stepwise linear modeling procedures was conducted to determine if age; BMI; SCL-90-R depression, anxiety, and GSI; SF-MPQ SP, AP, PPI; and SCD severity predicted each of physical and total ADLs.

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Results

Study 1: Descriptive comparisons

This cross-sectional sample included a total of 252 adult participants aged 18–85 years. Gender was relatively balanced with 46% being male and 54% being female. The mean age was 32.93 ± 12.39 years, and the mean BMI was 25.76 ± 6.64 kg/m2 (overweight range). Means for SCL-90-R variables including depression (55.71 ± 12.54) anxiety (52.69 ± 13.43) and GSI (56.63 ± 13.11) were all in the clinical range indicating that on average the participants in our study were experiencing clinically significant levels of depression, anxiety, and overall psychological distress. The mean for total AESSI (54.65 ± 19.63) was in the average range that lies between the subjective “anchors” of receiving social support “some of the time” and “most of the time.” Mean scores on the PPI subscale fell between the subjective “anchors” distressing and horrible (3.56 ± 1.37) indicating a moderate-to-severe degree of pain. In addition, the average sensory pain score was indicative of moderate intensity (16.61 ± 7.35) and affective pain scores were mild to moderate in intensity (5.94 ± 3.35). On average, patients reported the following number of impairments in ADLs associated with pain episodes: 7.42 ± 5.51 physical ADLs, 3.34 ± 2.52 cognitive ADLs, 2.24 ± 1.66 complex ADLs, 5.56 ± 3.59 combination ADLs, and 12.89 ± 8.28 total ADLs.

Using the whole sample, 1-way ANOVA by gender was conducted on the following variables of interest: age, BMI, psychosocial variables, pain, and ADLs. There was a significant difference in BMI, F(1,236) = 7.871, P = 0.005, with females (n = 129) having a higher BMI (26.9 ± 7.2 kg/m2 overweight range) compared with males (n = 109) whose BMI was in the normal weight range (24.5 ± 5.7 kg/ m2). A significant gender difference was noted in complex ADLs, F(1,151) = 7.121, P = 0.008, with females (n = 77) reporting greater impairment in complex ADLs as compared with their male counterparts (n = 76, 1.9 ± 1.6). All other variables of interest were not statistically different by gender (P > 0.05).

One way ANOVA (DV = BMI category) to examine the variables of interest according to weight status in the whole sample (Table 1) was performed. Of those with available BMI data (n = 238), 50% were in the NW range, 6% were in the UW range, leaving a combined 44% of the sample in the OW (24%) and OB (20%) categories F(3,234) = 339.04, P < 0.001. In addition, there was a significant difference in complex ADLs across BMI categories, F(3,144) = 3.224, P = 0.024. Post hoc analyses using the Bonferroni correction indicated that the average number of impairments in complex ADLs were significantly lower in the NW compared with OW (P = 0.021) category. Age was not statistically different according to BMI category, F(3,235) = 2.584, P = 0.054. However, the order of the means which showed a progressive trend (increase) in age as BMI category moves from UW to OB. BMI category comparisons for all other variables of interest were not statistically different (P > 0.05; Table 1).

Table 1

Table 1

The whole sample was also used for 1-way ANOVAs for the dependent variable SCD type (Table 2). Prevalence of SCD types in our sample was 60% HbSS, 26% HbSC, 10% HbSβ+, 2% HbSβ0, and 2% other variant SCD types. As predicted, BMI was significantly different according to SCD type, F(4,233) = 10.50, P < 0.001. Comparisons using the Bonferroni correction indicated that types HbSβ+ thalassemia and HbSC had significantly higher BMIs compared with those with HbSS type, P < 0.001 but were not significantly different than each other. No other significant relationships of SCD type and BMI were noted. Furthermore, while an omnibus ANOVA test showed that cognitive ADLs and combination ADLs were statistically different among sickle cell types, F(4,149) = 2.462, P = 0.048 and F(4,149) = 2.452, P = 0.048, respectively, Bonferroni corrected comparisons showed no significant differences by SCD type (adjusted P > 0.05). All other variables of interest compared according to sickle cell type were not statistically different (P > 0.05; Table 2).

Table 2

Table 2

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Study 2: Predicting ADLs

Multiple regression analyses were used throughout study 2, first on the whole sample (N = 252) and subsequently on an OW/OB subsample (n = 104). First, we considered the following predictors as they related to total and physical ADLs in each sample: age; BMI; SCL-90-R depression, anxiety and GSI; SF-MPQ SP, AP, and PPI. Second, we added SCD severity (severe versus mild) to the predictive model in addition to those variables already included.

In the whole sample AP and age predicted total ADLs with greater AP predicting increased impairment in total ADLs, and greater age predicting lower impairment in total ADLs. Combined, AP and age explained 23.0% of the total variance, R2 = 0.230, F(2, 90) = 13.418, P < 0.001, with age only contributing an additional 3.6% to the model (Table 3). AP also predicted physical ADLs with greater AP predicting increased impairment in physical ADLs and accounting for 16.1% of the total variance, R2 = 0.161, F(1, 90) = 17.298, P < 0.001 (Table 3). Further analysis included SCD severity in addition to the predictor variables age, BMI, depression, anxiety, GSI, SP, AP, and PPI, predict total ADLs. For total ADLs, SCD severity did not enhance the predictive model with AP and age predicting total ADLs (greater AP predicting increased impairment in total ADLs and greater age predicting lower impairment in total ADLs). Combined, they explained 27.2% of the total variance, R2 = 0.272, F(2, 88) = 16.404, P < 0.001, with age contributing an additional 5.1% to the model (Table 3). Similarly, for physical ADLs, the addition of SCD severity did not enhance the predictive models, and AP and age continued to predict physical ADLs (greater AP predicting increased impairment in physical ADLs and greater age predicting lower impairment in physical ADLs). Combined, AP and age explained 22.0% of the total variance, R2 = 0.220, F (2, 87) = 12.294, P < 0.001, with age contributing an additional 4.3% to the model (Table 3).

Table 3

Table 3

In the OW/OB subsample, the following predictors were first included in the model to predict total ADLs in this subsample: age, BMI, SCL-90-R depression, anxiety, GSI, SP, AP, and PPI. AP significantly predicted total ADLs with greater AP predicting increased impairment in total ADLs and AP accounting for 28.4% of the total variance, R2 = 0.284, F (1, 40) = 15.870, P < 0.001 (Table 4). For physical ADLs, AP predicted physical ADLs with greater AP predicting increased impairment in physical ADLs and accounting for 23.7% of the total variance, R2 = 0.237, F (1, 39) = 12.139, P = 0.001 (Table 4). Further analysis that included SCD severity in the model in addition to the predictor variables age, BMI, SCL-90-R depression, anxiety, GSI, SP, AP, and PPI showed that AP, age, and SCD severity predicted total ADLs with greater AP predicting increased impairment in total ADLs, greater age predicting lower impairment in total ADLs, and greater SCD severity predicting increased impairment in total ADLs. Combined, AP, age, and SCD severity explained 50.6% of the total variance, R2 = 0.506, F (3, 36) = 12.283, P < 0.001 (Table 4). Moreover, this model revealed that the mean impairment in total ADLs in the severe types of SCD was significantly greater than the mean impairment in total ADLs in the mild types of SCD when controlled for age and AP in the OW and OB subsample (B = 4.73, SE = 2.09, t = 2.265, P = 0.030). Finally, when adding SCD severity to the model to predict physical ADLs, AP, age, and SCD severity predicted physical ADLs with greater AP predicting increased impairment in physical ADLs, greater age predicting lower impairment in physical ADLs, and greater SCD severity predicting increased impairment in physical ADLs. Combined, AP, age, and SCD severity explained 45.5% of the total variance, R2 = 0.455, F (3, 35) = 9.734, P < 0.001 (Table 4). Mean impairment of physical ADLs in the severe types of SCD was also significantly greater than the mean impairment in physical ADLs in the mild types of SCD when controlled for age and AP in the OW and OB subsample (B = 4.07, SE = 1.48, t = 2.75, P = 0.009).

Table 4

Table 4

As shown in Tables 3, 4, adjusted R2 values (which indicate the strengths of the models) including SCD severity were greater than those models that did not include SCD severity. Furthermore, adjusted R2 values of the models constructed including the data of only the OW and OB individuals were greater than their parallel models that were constructed including the whole sample. As a result, the highest adjusted R2 values were observed in the models constructed within the OW and OB subpopulation, including the age, AP, and SCD severity as predictors of the physical and total ADLs (adjusted R2 = 0.408 and 0.465, respectively).

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Discussion

This study provides an overview of key characteristics (demographics, psychosocial correlates, BMI, pain, impairments in activities of daily living, prevalence of SCD types, and relationships among these) of an adult African American clinic population of people with SCD. It also determines if several of these factors may influence impairment in ADLs in this clinical sample of SCD patients. Given the general lack of research involving this population, our study provides both confirmation of findings of other studies and novel insights into some emerging considerations in the SCD population.

In our sample, on average, patients were young adults (33 years), which is comparable with other studies of adult SCD populations.[44 , 65] When comparing our sample according to BMI category, we found that age increased sequentially from UW through OB. Although this finding failed to achieve significance (P = 0.054), the observed consistent linear trend in the means is nonetheless interesting. This trend toward increasing age at higher BMIs is suggestive of a similar relationship to that which is commonly observed in the general population.[66] Further research with larger sample size may further elucidate this potential relationship. Contrary to the findings of Platt et al.,[20] age was not associated with SCD type in our sample.

Of particular interest in our sample is the finding that our clinic patients were on average in the OW range. Historically, people with SCD have struggled with maintaining weight and have been typically underweight due to elevated metabolism needed to compensate for increased disposal of sickled RBCs and recycling new RBCs into the bloodstream. The finding of OW in this study extends prior preliminary findings drawn from a substantially smaller subset of the same clinic sample[54] and 1 other small very recent study of 100 SCD clinic patients who also found mean BMI in the OW (26.3 kg/m2) range.[67] Taken together, these findings are suggestive of excess weight being an important consideration in SCD patients. However, it is also important to note that in the context of a SCD population, little to no evidence is available to fully understand if BMI in the OW and/or OB range confers the same level of health risk as seen in the general obese population. First, in African Americans in the general population, considerable evidence suggests that health risk and mortality is lower at higher BMI as compared with White populations.[68 , 69] In a study of more than 1 million U.S. adults, Calle et al.[69] found that African American men and women had lower risks of death compared with white males and females when adjusting for age, education level, physical activity, and select food consumption. Another consideration may relate to the fact that SCD populations are often times very medically compromised and experience frequent and intensive medical intervention. One study of 16,812 adult patients in an intensive care setting examined the relationship between BMI and mortality 30 days and 1 year after admission into the ICU. The authors reported that the patients in OW and OB ranges had lower mortality risks at 30 days as compared with those in the UW range (who had between 40 and 50% higher risk for mortality at both time points).[70] This, juxtaposed against the fact that obesity still may have a negative impact at higher BMI (severe) even in African American patients, highlights the need for more research to fully delineate these relationships and understand what degree of obesity should be considered appropriate to be treated in the SCD population.

When compared by gender, females had significantly higher BMIs relative to males. This is consistent with the BMI distribution of the general African American population.[71] When comparing men and women on ADLs, we found that women had significantly greater impairments in performing complex ADLs as compared with men. One likely explanation might be found in the known relationship of BMI to ADLs. BMI has been shown to predict ADLs[72 , 73]; therefore, the reason for greater impairment in complex ADLs may simply be due to higher BMI among our female participants.

We also found that complex ADLs differed significantly across BMI categories such that NW individuals were less likely to experience difficulties with complex ADLs compared with OW individuals. This is not surprising, given that BMI has been shown to predict overall ADLs. However, it is somewhat surprising that when considering all other ADL variables (physical ADLs, cognitive ADLs, combination ADLs, total ADLs), no significant differences were seen according to BMI category. Furthermore, it is surprising that the relationship in NW versus OW individuals on complex ADLs did not extend to the OB group. Further examination of the relationship among BMI categories and all types of ADLs, using larger sample sizes, may further elucidate these relationships.

In terms of psychosocial variables, our sample was on average reporting scores in the clinical range for depression, anxiety, and general psychological distress. This was expected as chronic diseases including SCD are known to be associated with elevated levels of depression, anxiety, and general psychological distress.[40 , 74 , 75] There were no significant differences noted according to gender, BMI category, and SCD type. However, it is notable that means were consistently in the clinical range or approaching the clinical range across all subgroups. In terms of social support, our sample reported scores in the average range across all comparisons. Given the important role that social support plays in the patient’s ability to manage complex medical and psychosocial issues associated with SCD,[76] this finding is encouraging. In addition, this is important because, in several studies of SCD patients, social support has be shown to be related to better health outcomes[77 , 78] and predictive of overall self-care.[77]

As expected, the majority (60%) of patients in our cohort had HbSS, the most common type of SCD.[11] Our findings are consistent with other studies of SCD clinic populations in this regard.[44 , 46 , 79] When comparing our variables of interest in the whole sample by SCD type, HbSβ+ and HbSC types were associated with higher BMIs compared with HbSS type. These results are consistent with Chawla et al.[46], who also found that types HbSβ+ and HbSC had higher BMIs as compared with HbSS and types HbSβ0 genotypes. One possible explanation for these findings is that people with mild forms of SCD are more likely to be negatively impacted by the obesogenic environment (ie, abundant highly palatable and calorically dense foods and lack of opportunity for physical activity) than are those with more severe types of SCD. The rationale for this conjecture is that people with the more severe forms of SCD have larger amounts of sickled hemoglobin, which causes the body to replenish RBCs more frequently. To compensate for the increase in RBC turnover, the body increases metabolism. This higher metabolism has been documented in patients with sickle cell anemia.[52] Mild forms of SCD, however, do not have similarly high levels of sickled hemoglobin and therefore have lower metabolic utilization of consumed calories leading them to have BMIs approaching that of the general population. Another possible explanation lies in the changes in dietary patterns of people with SCD during a pain crisis. Pells et al.[54] found that 87% of their SCD clinic patients reported eating less during pain episodes. Given the frequency of pain episodes in people with HbSS, and the associated reductions in caloric consumption, it is plausible that over periods of many years and frequent pain episodes, this could lead lower BMI in this group. More research is needed to confirm this conjecture.

It is well known that pain is a substantial issue in this population; therefore, elevated pain levels were expected and found. Our sample reported moderate pain intensity in the sensory domain, mild-to-moderate pain intensity in the affective domain and moderately high present pain intensity levels. Sensory pain is thought to measure the physical sensation of pain, whereas affective pain is thought to measure the emotional reaction to pain. Reported pain did not differ by gender, BMI category, or SCD type in our sample. While not a novel finding, the overall presence of pain in our sample highlights the need to assess for and provide assistance with enhancing SCD patients’ pain coping strategies that may help decrease the impact that pain has on functioning and overall quality of life in this population.[80]

The finding that on average, in our study, patients were reporting impairments in over one-third of the total ADLs that were possible is noteworthy. This indicates that activities that are often taken for granted in the general population such as brushing one’s teeth, bathing, being able to dress without assistance, shopping, and cooking to name a few, are at times extremely difficult for people with SCD. This can have a substantial and meaningful negative impact on overall quality of life.[81] Providing further context, Covinsky et al.[82] examined functional limitations and pain in the context of age. According to the authors, participants with pain had functional ability that was comparable with those who were 2 to 3 decades older when compared with those without any pain. If we were to extrapolate these findings to our sample, the average young adult with SCD would have functional ability comparable with that of a 50–60 year old.

The final goal of our study was to better understand the association of our variables of interest with ADLs. Specifically, predictive modeling was undertaken to determine if age; BMI; SCL-90-R depression, anxiety and GSI; SF-MPQ SP, AP, PPI, and SCD severity were predictive of impairment in each of physical and total ADLs. To summarize, AP was predictive of both total and physical ADLs in every model with higher AP consistently predicting greater impairment in ADLs. Furthermore, this finding was consistent across both the whole sample and the OW/OB subsample. Affective pain is thought to measure the emotional response to pain such as the feelings of fear and distress that pain will not go away in the future. It has been further suggested that AP can also be indicative of long-term effects of persistent pain also known as “suffering.”[83] Based on these factors taken together, 1 might consider that fear of future pain or severe chronic pain may lead to avoidance of ADLs in an attempt to forestall the feared triggering or intensification of pain. Further support for the notion of chronic pain negatively influencing ADLs has been demonstrated in other chronic diseases including cancer,[84] obesity,[73 , 85] and fibromyalgia,[40] which may help to inform optimal strategies for targeting this important issue in the SCD population.

The apparently unique contribution of affective pain to ADLs in our SCD clinic population holds importance in informing treatment. It could be argued that rather than the often exclusive focus on pharmacological pain management, more programs should include interventions that target the affective component of pain (ie, multidisciplinary behavioral pain management programs). This may increase patients’ ability to perform normal day-to-day activities and improve their overall quality of life.

It was surprising that BMI did not predict ADLs in any of our analyses, given the relationship of obesity to ADLs in the general population.[72 , 73] These findings are suggestive of the fact that increased BMI may be a less salient issue in SCD in relation to overall functioning in comparison with other potential contributors, especially pain. It is possible that to the extent that pain influences ADL impairment in this population, it masks the influence of BMI seen in non-SCD populations. More research is needed to better understand these relationships in larger SCD cohorts.

It is notable that when age was added to AP in 3 out of the 4 predictive analyses (whole sample), it actually contributed in such a way that older age predicted less impairment. Although counterintuitive, the relatively small contribution to the total variance make this finding a curiosity, but perhaps not a substantial focus in interpreting our results.

Finally, to better understand the relationships among our variables of interest and ADLs in the context of overweight, we repeated our predictive models using only the subsample that was overweight or obese and found that these models were stronger in predicting the total and physical ADLs than the models constructed including the whole sample. Of particular interest, we found that SCD severity, in addition to AP and age, was predictive of total and physical ADLs. When considering total ADLs and physical ADLs, the findings of increased affective pain predicting increased impairment is consistent with previous analyses. Furthermore, the finding that more severe types of SCD predicted greater impairments in total and physical ADLs is consistent with expectations. People with types HbSS and HbSβ0 thalassemia are known to have more severe complications associated with their disease such as higher incidences of acute chest syndrome and pain crises, which lead to greater risks for early death.[16] Our finding of more severe types having greater impairment in ADLs in an overweight/obese sample, therefore, supports the negative impact that more severe types of SCD may have on these patients. Thus, it is important to consider SCD type when monitoring and treating patients with SCD who are in the overweight or obese range. Future research is needed to determine the impact SCD severity may have in relation to quality of life and the risk for obesity-related comorbidities in the overweight and obese SCD population.

There are several limitations to this study. As a cross-sectional design, the generalizability of our results is limited, and we cannot infer causality. In addition, based on the nature of the data collection (consecutive patients attending clinic visits), competing demands of the clinical appointment at times led to missing data, which restricted the sample sizes for specific analyses. There is no reason to believe these were systematic in nature. However, low sample sizes can negatively affect power and the ability to detect significant differences among groups. At times, due to overall prevalence in the available population and or missing data, small numbers of subjects in specific subgroups were used in comparisons. Nonetheless, at this stage, in our understanding of this understudied population, it is important to provide a preliminary examination of these albeit relatively small subsamples. Another limitation is that much of our pain and psychosocial data were obtained via self-report. Potential confounding variables not controlled for in our study include measures of the ability to cope, which may moderate the relationship of pain with ADL impairment. Also, a detailed accounting of other comorbid medical conditions in addition to SCD would assist us in determining their role relative to psychosocial factors, pain, BMI, and ADLs in SCD.

Our study has notable strengths. This is a novel and emerging field of inquiry. Thus, there is a paucity of research that considers obesity in the context of SCD. To our knowledge, this is the first study to look at age, BMI, pain, and psychosocial variables in the context of ADLs. In addition, pain, depression, anxiety, and GSI were assessed using validated questionnaires, and measured height and weight were used to calculate BMI. This study utilizes data from a large comprehensive assessment battery, which provides extensive information and allows us to examine a wide range of variables across several relevant domains of functioning. In addition, patients were assessed in a clinical SCD setting, which provides a valuable context that is readily generalizable to other clinical settings. Our relatively large sample (in the context of existing studies) represented all BMI categories, and both males and females were well represented.

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Conclusions

In conclusion, these findings point to the relevance of monitoring lifestyle-related medical and psychosocial conditions in this population, so that treatments can target these issues appropriately in the context of comprehensive SCD care. To better understand the relationship of obesity and SCD, future studies are needed in the obese SCD population. In addition, more research is needed to further delineate the role affective pain plays in ADLs so that we may better inform and test novel treatment approaches that may provide optimal pain relief, improved functionality, and result in improved quality of life.

Given the prevalence of psychosocial correlates in the clinical range, health care providers need to be more aware of depression, anxiety, and generalized psychosocial distress in this population and would be well advised to monitor these over time. Future research needs to be directed toward better understanding the interrelationships between physical health, psychosocial distress, and overall quality of life. In addition, no clinical recommendations can be made regarding BMIs in the overweight and obese range based on our results. Future research is needed to better understand the potential positive and/or negative impact(s) that overweight and obesity may have in this population. Finally, given the consistent relationship of affective pain and activities of daily living, future interventions should specifically target the affective component of pain (ie, multidisciplinary behavioral pain management programs). This may increase patients’ ability to perform normal day-to-day activities and improve this emotionally driven aspect of pain.

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Disclosure

The authors have no financial interest to declare in relation to the content of this article. The Article Processing Charge was paid for by Progress in Preventive Medicine at the discretion of the Editor-in-Chief.

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References

[1]. Modell B, Darlison MGlobal epidemiology of haemoglobin disorders and derived service indicators. Bull World Health Organ. 2008;86:480–487.
[2]. Data & Statistics. Centers for Disease Control and Prevention. Available at http://www.cdc.gov/ncbddd/sicklecell/data.html. Accessed August 1, 2014.
[3]. Brousseau DC, Panepinto JA, Nimmer M, et alThe number of people with sickle-cell disease in the United States: national and state estimates. Am J Hematol. 2010;85:77–78.
[4]. Martin EAConcise Medical Dictionary. 2010.New York, NY: Oxford University Press;
[5]. Apanah S, Rizzolo DSickle cell disease: taking a multidisciplinary approach. JAAPA. 2013;26:28–33.
[6]. Wilson RE, Krishnamurti L, Kamat DManagement of sickle cell disease in primary care. Clin Pediatr (Phila). 2003;42:753–761.
[7]. World Health Organization. Sickle-cell disease and other haemoglobin disorders. Available at http://www.who.int/mediacentre/factsheets/fs308/en/#. Accessed August 1, 2014.
[8]. Meier ER, Miller JLSickle cell disease in children. Drugs. 2012;72:895–906.
[9]. Creary M, Williamson D, Kulkarni RSickle cell disease: current activities, public health implications, and future directions. J Womens Health (Larchmt). 2007;16:575–582.
[10]. Aygun B, Odame IA global perspective on sickle cell disease. Pediatr Blood Cancer. 2012;59:386–390.
[11]. Yawn BP, Buchanan GR, Afenyi-Annan AN, et alManagement of sickle cell disease: summary of the 2014 evidence-based report by expert panel members. JAMA. 2014;312:1033–1048.
[12]. Hassell KLPopulation estimates of sickle cell disease in the U.S. Am J Prev Med. 2010;38:S512–S521.
[13]. Life expectancy Data by country. Global health observatory data repository. World Health Organization web site. Available at http://apps.who.int/gho/data/node.main.688. Accessed January 12, 2015.
[14]. Weatherall D, Akinyanju O, Fucharoen S, et alJamison DT, Breman JG, Measham ARInherited disorders of hemoglobin. In: Disease Control Priorities in Developing Countries. 2006.New York, NY: Oxford University Press and the World Bank;
[15]. Edwards CL, Scales MT, Loughlin C, et alA brief review of the pathophysiology, associated pain, and psychosocial issues in sickle cell disease. Int J Behav Med. 2005;12:171–179.
[16]. Ashley-Koch A, Yang Q, Olney RSSickle hemoglobin (HbS) allele and sickle cell disease: a HuGE review. Am J Epidemiol. 2000;151:839–845.
[17]. Platt OS, Thorington BD, Brambilla DJ, et alPain in sickle cell disease. Rates and risk factors. N Engl J Med. 1991;325:11–16.
[18]. Castro O, Brambilla DJ, Thorington B, et alThe acute chest syndrome in sickle cell disease: incidence and risk factors. The Cooperative Study of Sickle Cell Disease. Blood. 1994;84:643–649.
[19]. Ballas SK, Lewis CN, Noone AM, et alClinical, hematological, and biochemical features of Hb SC disease. Am J Hematol. 1982;13:37–51.
[20]. Platt OS, Brambilla DJ, Rosse WF, et alMortality in sickle cell disease. Life expectancy and risk factors for early death. N Engl J Med. 1994;330:1639–1644.
[21]. Ohene-Frempong K, Weiner SJ, Sleeper LA, et alCerebrovascular accidents in sickle cell disease: rates and risk factors. Blood. 1998;91:288–294.
[22]. Edwards LY, Edwards CLPsychosocial treatments in pain management of sickle cell disease. J Natl Med Assoc. 2010;102:1084–1094.
[23]. Anie KAPsychological complications in sickle cell disease. Br J Haematol. 2005;129:723–729.
[24]. McClish DK, Penberthy LT, Bovbjerg VE, et alHealth related quality of life in sickle cell patients: the PiSCES project. Health Qual Life Outcomes. 2005;3:50.
[25]. Rees DC, Olujohungbe AD, Parker NE, et alGuidelines for the management of the acute painful crisis in sickle cell disease. Br J Haematol. 2003;120:744–752.
[26]. Steiner CA, Miller JLSickle cell disease patients in U.S. hospitals, 2004: statistical brief #21. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. 2006.Rockville MD: Agency for Healthcare Research and Quality;
[27]. Merskey H, Bogduk NPart III: Pain Terms, A Current List with Definitions and Notes on Usage. Classification of Chronic Pain, 209–214. 1994.Seattle, WA: IASP Press;
[28]. Wang ZJ, Wilkie DJ, Molokie RNeurobiological mechanisms of pain in sickle cell disease. Hematology Am Soc Hematol Educ Program. 2010;2010:403–408.
[29]. U.S. Department of Health and Human Services. Evidence-based management of sickle cell disease. 2014. Available at http://www.nhlbi.nih.gov/health-pro/guidelines/sickle-cell-disease-guidelines. Accessed 21 September 2014.
[30]. Brousse V, Makani J, Rees DCManagement of sickle cell disease in the community. BMJ. 2014;348:g1765.
[31]. Wood AJ, Steinberg MHManagement of sickle cell disease. New Engl J Med. 1999;340:1021–1030.
[32]. Fuggle P, Shand PA, Gill LJ, et alPain, quality of life, and coping in sickle cell disease. Arch Dis Child. 1996;75:199–203.
[33]. Smith WR, Penberthy LT, Bovbjerg VE, et alDaily assessment of pain in adults with sickle cell disease. Ann Intern Med. 2008;148:94–101.
[34]. Waters J, Thomas VPain from sickle-cell crisis. Nurs Times. 1995;91:29–31.
[35]. Edwards C, Whitfield K, Sudhakar S, et alParental substance abuse, reports of chronic pain and coping in adult patients with sickle cell disease. J Natl Med Assoc. 2006;98:420–428.
[36]. Taylor LE, Stotts NA, Humphreys J, et alA review of the literature on the multiple dimensions of chronic pain in adults with sickle cell disease. J Pain Symptom Manage. 2010;40:416–435.
[37]. Ballas SK, Gupta K, Adams-Graves PSickle cell pain: a critical reappraisal. Blood. 2012;120:3647–3656.
[38]. Te Boveldt N, Vernooij-Dassen M, Burger N, et alPain and its interference with daily activities in medical oncology outpatients. Pain Physician. 2013;16:379–389.
[39]. Siddiqui IJ, Gerrard PBChronic low back pain: implications for disability and dysfunction in the United States. Am J Phys Med Rehabil. 2014:a23.
[40]. Hughes LPhysical and psychological variables that influence pain in patients with fibromyalgia. Orthop Nurs. 2006;25:112–119; quiz 120.
[41]. Björnsdóttir SV, Jónsson SH, Valdimarsdóttir UAFunctional limitations and physical symptoms of individuals with chronic pain. Scand J Rheumatol. 2013;42:59–70.
[42]. Jacob E, Miaskowski C, Savedra M, et alChanges in sleep, food intake, and activity levels during acute painful episodes in children with sickle cell disease. J Pediatr Nurs. 2006;21:23–34.
[43]. Gil KM, Abrams MR, Phillips G, et alSickle cell disease pain: relation of coping strategies to adjustment. J Consult Clin Psychol. 1989;57:725–731.
[44]. Anie KA, Steptoe A, Bevan DHSickle cell disease: pain, coping and quality of life in a study of adults in the UK. Br J Health Psychol. 2002;7:331–344.
[45]. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. 2000.Geneva, Switzerland: World Health Organization;
[46]. Chawla A, Sprinz PG, Welch J, et alWeight status of children with sickle cell disease. Pediatrics. 2013;131:e1168–e1173.
[47]. Nelms M, Sucher KP, Lacey K, et alDiseases of the hematological system. Nutrition Therapy & Pathophysiology. 2011:2nd ed. Belmont, CA: Wadsworth; 588–591.
[48]. Hyacinth HI, Gee BE, Hibbert JMThe role of nutrition in sickle cell disease. Nutr Metab Insights. 2010;3:57–67.
[49]. Okam MM, Shaykevich S, Ebert BL, et alNational trends in hospitalizations for sickle cell disease in the United States following the FDA approval of hydroxyurea, 1998-2008. Med Care. 2014;52:612–618.
[50]. Hibbert JM, Creary MS, Gee BE, et alErythropoiesis and myocardial energy requirements contribute to the hypermetabolism of childhood sickle cell anemia. J Pediatr Gastroenterol Nutr. 2006;43:680–687.
[51]. Akohoue SA, Shankar S, Milne GL, et alEnergy expenditure, inflammation, and oxidative stress in steady-state adolescents with sickle cell anemia. Pediatr Res. 2007;61:233–238.
[52]. Hyacinth HI, Adekeye OA, Yilgwan CSMalnutrition in sickle cell anemia: implications for infection, growth, and maturation. J Soc Behav Health Sci. 2013;7:23–34.
[53]. Singhal A, Parker S, Linsell L, et alEnergy intake and resting metabolic rate in preschool Jamaican children with homozygous sickle cell disease. Am J Clin Nutr. 2002;75:1093–1097.
[54]. Pells JJ, Presnell KE, Edwards CL, et alModerate chronic pain, weight and dietary intake in African-American adult patients with sickle cell disease. J Natl Med Assoc. 2005;97:1622–1629.
[55]. Fowler-Brown A, Wee CC, Marcantonio E, et alThe mediating effect of chronic pain on the relationship between obesity and physical function and disability in older adults. J Am Geriatr Soc. 2013;61:2079–2086.
[56]. Barofsky I, Fontaine KR, Cheskin LJPain in the obese: impact on health-related quality-of-life. Ann Behav Med. 1997;19:408–410.
[57]. Stone AA, Broderick JEObesity and pain are associated in the United States. Obesity (Silver Spring). 2012;20:1491–1495.
[58]. Ladabaum U, Mannalithara A, Myer PA, et alObesity, abdominal obesity, physical activity, and caloric intake in US adults: 1988 to 2010. Am J Med. 2014;127:717–727 e712.
[59]. Mitchell JA, Bottai M, Park Y, et alA prospective study of sedentary behavior and changes in the body mass index distribution. Med Sci Sports Exerc. 2014;46:2244–2252.
[60]. Wachholtz A, Binks M, Eisenson H, et alDoes pain predict interference with daily functioning and weight loss in an obese residential treatment-seeking population? Int J Behav Med. 2010;17:118–124.
[61]. Derogatis LSCL-90-R Administration, Scoring and Procedures Manual—II. 1983.Towson, MD: Clinical Psychometric Research;
[62]. Alford S, Shaw L, Robinson E, et alToward an empirically-based measure of social support: the AESSI. Ann Behav Med. 2002:S089.
[63]. Edwards C, Williams V, Robinson EThe Alford-Edwards Social Support Inventory: internal consistency reliability. 2006; Paper presented at: Poster presented at: Society of Behavioral Medicine’s 27th Annual Meeting. San Francisco, CA.
[64]. Melzack RThe short-form McGill Pain Questionnaire. Pain. 1987;30:191–197.
[65]. Osuobeni EP, Okpala I, Williamson TH, et alHeight, weight, body mass index and ocular biometry in patients with sickle cell disease. Ophthalmic Physiol Opt. 2009;29:189–198.
[66]. Meeuwsen S, Horgan GW, Elia MThe relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr. 2010;29:560–566.
[67]. Farooqui MW, Hussain N, Malik J, et alPrevalence of obesity in sickle cell patients. Blood. 2014;124:4932.
[68]. Stevens J, Plankey MW, Williamson DF, et alThe body mass index-mortality relationship in white and African American women. Obes Res. 1998;6:268–277.
[69]. Calle EE, Thun MJ, Petrelli JM, et alBody-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med. 1999;341:1097–1105.
[70]. Abhyankar S, Leishear K, Callaghan FM, et alLower short- and long-term mortality associated with overweight and obesity in a large cohort study of adult intensive care unit patients. Crit Care. 2012;16:R235.
[71]. National Center for Health S. Health, United States. Health, United States, 2013: With Special Feature on Prescription Drugs. 2014.Hyattsville, MD: National Center for Health Statistics;
[72]. den Ouden ME, Schuurmans MJ, Mueller-Schotte S, et alDomains contributing to disability in activities of daily living. J Am Med Dir Assoc. 2013;14:18–24.
[73]. Backholer K, Wong E, Freak-Poli R, et alIncreasing body weight and risk of limitations in activities of daily living: a systematic review and meta-analysis. Obes Rev. 2012;13:456–468.
[74]. Levenson JL, McClish DK, Dahman BA, et alDepression and anxiety in adults with sickle cell disease: the PiSCES project. Psychosom Med. 2008;70:192–196.
[75]. Harris A, Parker N, Barker CAdults with sickle cell disease: psychological impact and. Psychol Health Med. 1998;3:171.
[76]. Weisberg D, Balf-Soran G, Becker W, et al“I’m talking about pain”: sickle cell disease patients with extremely high hospital use. J Hosp Med. 2013;8:42–46.
[77]. Matthie N, Jenerette C, McMillan SRole of self-care in sickle cell disease. Pain Manag Nurs. 2015;16:257–266.
[78]. Imhonde HO, Ndom RJE, Ehon ASocial-support, self-esteem and depression as determinants of quality of life among sickle cell patients. IFE PsychologIA. 2013;21:101–113.
[79]. Mitchell MJ, Carpenter GJ, Crosby LE, et alGrowth status in children and adolescents with sickle cell disease. Pediatr Hematol Oncol. 2009;26:202–215.
[80]. Gil KM, Carson JW, Porter LS, et alDaily stress and mood and their association with pain, health-care use, and school activity in adolescents with sickle cell disease. J Pediatr Psychol. 2003;28:363–373.
[81]. Bhagat VM, Baviskar SR, Mudey AB, et alPoor health related quality of life among patients of sickle cell disease. Indian J Palliat Care. 2014;20:107–111.
[82]. Covinsky KE, Lindquist K, Dunlop DD, et alPain, functional limitations, and aging. J Am Geriatr Soc. 2009;57:1556–1561.
[83]. Price DDPsychological and neural mechanisms of the affective dimension of pain. Science. 2000;288:1769–1772.
[84]. Wedding U, Röhrig B, Klippstein A, et alAge, severe comorbidity and functional impairment independently contribute to poor survival in cancer patients. J Cancer Res Clin Oncol. 2007;133:945–950.
[85]. Wei L, Wu BRacial and ethnic differences in obesity and overweight as predictors of the onset of functional impairment. J Am Geriatr Soc. 2014;62:61–70.
Keywords:

Sickle cell disease; Body mass index; Pain; Psychosocial functioning; Activities of daily living

Copyright © 2018 The Authors. Published by Wolters Kluwer Health Inc., on behalf of the European Society of Preventive Medicine.