Given the recent and projected growth in the number of individuals who are older adults and centenarians, a primary challenge is the ability to provide optimal care for this segment of the population, 50% of whom have three or more chronic illnesses. One symptom that resonates across most chronic illnesses is pain. More than one half of Americans experience chronic pain associated with comorbidities and mortality (Grey, Schulman-Green, Knafl, & Reynolds, 2015). The risk of experiencing pain increases for females and older adults (Nahin, 2015). Although acute pain serves a biological purpose, for example to warn of impending tissue damage or protect an injury until healed, chronic pain serves no such purpose. This is reflected in the International Association for the Study of Pain definition of pain as “an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage” (Merskey et al., 1986, p. S217). Pain is debilitating and can either occur as a symptom of a chronic condition or a primary problem (Ersek, Turner, Cain, & Kemp, 2008). According to the Institute of Medicine (IOM) report on “Relieving Pain in America” (Pizzo, Clark, & Pokras, 2011), chronic pain is a public health epidemic affecting more than 116 million Americans and costing more than $600 billion per year in health care expenses and lost work productivity. Despite advances in conventional pharmacological and nonpharmacological treatments for pain that are informed by the current understanding of basic biological mechanisms of chronic pain, most individuals do not obtain adequate pain relief.
There are few effective pharmacological agents that can completely alleviate chronic pain in dosages that do not produce debilitating reductions in functional status or quality of life. There is also considerable risk for addiction to prescription pain medications, which is a growing problem in the United States. In addition to pain, these patients also experience significant negative physical, psychological, social, and emotional consequences, all of which can reduce quality of life (Pizzo et al., 2011). In conjunction with reduced quality of life, pain-related disability is the single biggest contributor to years lived with a disability (Murray et al., 2013).
As noted, older adults are particularly at risk for experiencing chronic pain (Patel, Guralnik, Dansie, & Turk, 2013), which has a significant impact not only on quality of life but on function and physical activity. Pain is frequently cited as a primary symptom underlying disability (Ettinger et al., 1994; Leveille, Fried, & Guralnik, 2002; Melzer, Gardener, & Guralnik, 2005), and as many as 50% of individuals with dementia experience pain. There are challenges to identification of pain among these individuals, particularly those with cognitive impairment (Corbett et al., 2016; Klapwijk, Caljouw, Pieper, van der Steen, & Achterberg, 2016). Pain among these individuals often presents as aggression, agitation, withdrawal, confusion, and impaired or worsening function (Corbett & Ballard, 2012; McAuliffe, Brown, & Fetherstonhaugh, 2012). Thus, the effects of a decline in cognitive function on the ability to self-report pain may render prevalence data uncertain (Patel et al., 2013). This commentary will explain the role omics has played in advancing the understanding of pain perception, pain interpretation, and the pathophysiology of chronic pain. Current technologies for omics research are introduced. Recommendations are made for applying omics and precision health to the science of pain self-management.
Mechanisms of Pain Are Multifactorial in Older Adults
Given the challenges in identification and management of pain in older adults, the association of omics with pain is particularly important for older adults. Prior work has shown that there is evidence to suggest that single nucleotide polymorphisms (SNPs) in multiple genes influence pain perception and interpretation. Genetic influences of pain contribute to the modulation of pain in the central nervous system (CNS) and periphery; SNPs in genes that participate in synaptic plasticity or the activation of spinal microglia have been associated with pain. Genetic variation can also influence nerve conduction and synaptic transmission, which could lead to altered pain sensation. To date, candidate gene analyses in pain research have focused mainly on 10 genes that were identified either in animal models or humans to be associated with pain (Belfer et al., 2013; Di Lorenzo et al., 2014; Mogil, 2012; Renn, Leitch, & Dorsey, 2009). These genes include: brain-derived neurotrophic factor (BDNF), FK506 binding protein 5 (FKBP5), neurotrophic receptor tyrosine kinase 1 (NTRK1), neurotrophic receptor tyrosine kinase 2 (NTRK2), neurotrophic receptor tyrosine kinase 3 (NTRK3), oxytocin receptor (OXTR), dopamine receptor D4 (DRD4), serotonin transporter 1 (SLC6A4), catechol-O-methyltransferase (COMT), and monoamine oxidase A (MAOA). Although replications of these findings are limited, a recent study noted that there was an association between pain and BDNF, FKBP5, NTRK2, NTRK3, and OXTR (Resnick et al., 2016). The neurotrophin BDNF has repeatedly been shown to be a potent modulator of pain processing in the CNS (Merighi et al., 2008). Noxious stimulation increases BDNF production in the spinal dorsal horn (SDH) (Coull et al., 2005) and brainstem (Renn, Lin, Thomas, & Dorsey, 2006) leading to hyperalgesia and the formation of mechanical allodynia.
In addition to the consideration of the relationship of specific genes, microRNAs (miRNAs) have been evaluated as contributing to the development and pathophysiology of chronic pain (Follert, Cremer, & Béclin, 2014; Sakai & Suzuki, 2014; Sun & Shi, 2015). The protein expressions of hundreds of genes are post transcriptionally regulated by a single type of miRNA in a sequence specific manner. miRNA expressions are globally changed in various pain states in the dorsal root ganglion, spinal cord, and brain regions, such as the limbic system and prefrontal cortex. Chronic pain arises from a variety of pathologies, such as damage to the somatosensory system, cancers, or musculoskeletal problems. The miRNA expression profiles are highly distinctive depending on the cause of the pain. miRNAs have repeatedly been associated with the pathogenesis of diseases such as osteoarthritis as well as the associated symptom of pain (Barter & Young, 2013; Papanagnou, Stivarou, & Tsironi, 2016; Yu, Chen, & Wang, 2011; Zhang, Lygrisse, & Wang, 2017). For example, expression changes in miRNA-146a among other miRNAs were associated with cartilage change as well as inflammation in osteoarthritis (Barter & Young, 2013; Yu et al., 2011; Zhang et al., 2017). In animal models, rats with osteoarthritis experiencing pain expressed lower levels of miRNA-146a in their dorsal root ganglion and SDH. In addition, the role of miRNA-146a in the clinical manifestations of pain in osteoarthritis was substantiated by the fact that miRNA-146a dampens the expression of a set of inflammatory molecules associated with pain perception in hymen glial cells and the expression of transient receptor potential cation channel subfamily V member 1 (TRPV-1), an ion channel (Premkumar & Sikand, 2008). This finding has important implications for the detection of pain among older adults and the development of analgesic agents or behavioral interventions to target this pain. Moreover, these findings provide critically important information toward the development of precision health.
Although self-management of chronic conditions such as pain is a complex construct, it is typically defined as an interactive and dynamic process that individuals or caregivers engage in daily to manage chronic conditions (Grey et al., 2015; Ruggiero et al., 1997). Self-management is a deliberative process with patients, either alone or with the help of caregivers, to assuming responsibility for their own health care and engaging a set of self-regulation skills that include goal setting, self-monitoring, and reflective thinking to manage change and improve chronic conditions (Ryan, 2009). Self-management also requires sufficient frontal lobe function and cognitive ability on the part of the older adult to establish goals and plan a program of care. In some situations, more involvement of the caregiver is needed to facilitate this process. For example, learning from a caregiver that an older individual previously liked to swim can be used to establish swimming as an intervention to manage pain associated with osteoarthritis.
Self-Management Strategies for Chronic Pain Relief
Given the lack of efficacy and burdensome, as well as addictive, side effects of pharmacological treatments for chronic pain, there have been several self-management interventions designed and tested to reduce or eliminate this symptom. Examples of self-management interventions include educational programs (e.g., coping, social support) delivered in person or via technology (Evers, Kraaimaat, Geenen, Jacobs, & Bijlsma, 2003), physical activity, exercise, relaxation, mindfulness, cognitive behavioral programs, coping/social support strategies, and others (Bruckenthal, Marino, & Snelling, 2016; Du et al., 2011; Kroon et al., 2014; Liu & Petrini, 2015; Park, McCaffrey, Newman, Liehr, & Ouslander, 2017; Reid et al., 2008).
Physical activity has been one of the most commonly used and effective interventions for managing pain and pain associated symptoms such as functional changes and depression among older adults. Some early evidence for the efficacy of physical activity and exercise to reduce pain was provided by pre-clinical rodent studies. Physical activity (timed treadmill training and voluntary wheel running) was shown to reverse peripheral nerve injury–induced neuropathic thermal hyperalgesia (i.e., heightened response to a stimulation that is normally painful) and mechanical allodynia (i.e., a noxious response to a normally innocuous stimulation) (Cobianchi, Casals-Diaz, Jaramillo, & Navarro, 2013; Cobianchi, Marinelli, Florenzano, Pavone, & Luvisetto, 2010; Sheahan, Copits, Golden, & Gereau, 2015; Stagg et al., 2011). Non-weight bearing exercise (e.g., swim therapy) has also been shown to reduce neuropathic pain in rats following peripheral nerve injury (Shen, Fox, & Cheng, 2013), and regular physical activity can prevent the development of chronic pain in rats (Grace et al., 2016; Sluka, O'Donnell, Danielson, & Rasmussen, 2013).
In humans, there have been a number of studies that have tested the use of physical activity as a self-management intervention to reduce chronic pain across a variety of clinical pain conditions alone and in combination with other self-management strategies (Arnstein & Herr, 2017; Daenen, Varkey, Kellmann, & Nijs, 2015). Physical activity has been shown to alleviate inflammatory and neuropathic pain conditions and chronic pain experienced by patients with musculoskeletal disorders (Courneya et al., 2013; Dixit, Maiya, & Shastry, 2014; Hurkmans, van der Giesen, Vliet Vlieland, Schoones, & Van den Ende, 2009; Wright & Sluka, 2001). Meta-analyses of physical activity to reduce pain can be found for several conditions (Devos-Comby, Cronan, & Roesch, 2006; Kelley, Kelley, Hootman, & Jones, 2011; Nguyen et al., 2017). Devos-Comby et al. (2006) examined physical activity with and without other self-management interventions to improve osteoarthritis outcomes including pain. Of the studies reviewed, 16 met inclusion criteria. The findings revealed only a modest effect of physical activity to improve pain, psychological well-being, and physical well-being. Among other conclusions, the authors speculated that small sample sizes and the need to intensify the physical activity intervention might explain the lack of a more robust response (Devos-Comby et al., 2006). In another meta-analysis of community delivered physical activity to improve pain and physical function in adults with osteoarthritis and other types of rheumatic disease, statistical analysis of 33 published studies showed a decrease in pain (Kelley et al., 2011). Of note, across these studies, treatment fidelity was not well controlled, and dose delivered versus dose received of physical activity were not comprehensively evaluated. Based on these findings and research to date, the dose of physical activity needed to achieve optimal pain relief cannot be established.
Other modalities such as ice, heat, and positioning have consistently been used to manage chronic pain (Hawamdeh et al., 2012; Iversen, 2012). Although there is some evidence for the effectiveness of these nonpharmacological treatments (Petrofsky, Laymon, Alshammari, Khowailed, & Lee, 2014), the evidence is limited and, as with physical activity, the mechanism of action is unknown.
Although the efficacy of physical activity and exercise to reduce chronic pain seems promising, there are questions regarding the mechanisms underlying pain reduction, particularly in older adults. An increasing number of studies point to a link between physical activity/exercise and the endogenous pain modulatory system (Ellingson, Shields, Stegner, & Cook, 2012; Geva & Defrin, 2013; Naugle, Ohlman, Naugle, Riley, & Keith, 2017). In brief, increased physical activity and exercise have been shown to increase the efficacy of the endogenous pain modulatory system, producing analgesia. To this point, a recent study showed that self-reported levels of exercise and total physical activity were related to the function of the endogenous pain modulatory system in healthy young and old adults (Naugle & Riley, 2014). Because older adults are less active, it may be that reduced physical activity is associated with a decline in endogenous analgesia, leading to increased chronic pain (Naugle et al., 2017). This hypothesis was tested in a recent study of older adults, which demonstrated that less sedentary time and increased light physical activity significantly increased pain inhibition (Naugle et al., 2017).
Precision Health Defined
Precision medicine, or health, can broadly be defined as the tailoring of prevention or treatment strategies to a person's individual characteristics (e.g., genomics, environment, lifestyle), rather than using clinical practices that are based on what works for the aggregate (Collins & Varmus, 2015). Nurse scientists are uniquely poised to use precision health methods to address symptoms such as chronic pain. The National Institute of Nursing Research (NINR) Extramural Program supports studies that aim to predict who is at risk for symptoms related to chronic conditions and those that test tailored interventions to improve symptoms. In the NINR Intramural Program, scientists use the National Institutes of Health Symptom Science Model (NIH/SSM) (Cashion & Grady, 2015) to guide symptom science and precision health studies aimed at addressing cancer treatment–related fatigue, mechanisms of oxidative stress in congenital myopathy, pain and related symptoms in digestive disorders, and cognitive dysfunction following traumatic brain injury (Cashion, Gill, Hawes, Henderson, & Saligan, 2016). Thus, nursing science has fully engaged and embraced the use of precision health to move symptom science forward, including studies of chronic pain.
According to the IOM committee report on the review of omics-based tests to predict patient outcomes in clinical trials (Micheel, Nass, & Omenn, 2012), the term omics refers to multiple molecular disciplines brought to bear to characterize various biological molecules, including protein, DNA, RNA, and metabolites, to generate high-dimensional, systems-level data. For example, genetics refers broadly to the investigation of DNA, whereas transcriptomics studies measure RNA levels to quantify gene expression. One could examine the DNA to identify SNPs that might alter gene expression. In this case, both DNA and RNA would be assayed. Or, a study might be performed to identify epigenetic changes to DNA that are known to alter gene expression. However, if single omics tests are conducted (e.g., SNP analysis), the ability to draw meaningful conclusions regarding the mechanistic influence of each SNP is lost when gene expression studies are not also included. The data generated from omics studies can then be used to either predict individual outcomes related to a specific intervention or susceptibility to develop a symptom or disease.
Currently Available Technologies for Omics Research
Although a comprehensive review of omics technologies is beyond the scope of the current commentary, the available technology platforms are briefly described below.
Next Generation Sequencing. Next generation sequencing (NGS) technologies can be used to examine a DNA, RNA, or protein sequence or conduct high-resolution mapping of 5′—C—phosphate—G—3′ (CpG) dinucleotide methylation of DNA, termed epigenomics (Conley et al., 2013; Lan et al., 2011; Metzker, 2010; Wickersham & Dorsey, 2017). For DNA and RNA, the sequencing is performed to identify a predetermined length of nucleotide sequence (typically 25 to 150 base pairs), each of which is termed a short-read. Prior genome sequencing technologies accomplished this with assays that identified one molecule at a time using a technology known as bi-directional Sanger sequencing (Katsanis & Katsanis, 2013). Advances in technology, however, have made it possible to investigate millions of nucleotides simultaneously (Conley et al., 2013). NGS has become more efficient, with corresponding increases in sequence output, as well as less expensive (Wetterstrand, 2016), and it is likely that a sub-$1,000 whole genome sequence will soon be possible.
Microarray and Other Array-Based Technologies. DNA microarrays can be used to conduct transcriptomic, epigenomic, proteomic, or genome-wide association studies (GWAS). Microarrays are typically fabricated on silicon, glass, or plastic substrates and have tens to hundreds of thousands of probe sets or antibodies designed to capture DNA, RNA, methylated CpG sites, or proteins (Heller, 2002). Unlike NGS, where each nucleotide or amino acid is directly assayed, array-based analysis depends on transforming an analog fluorescence signal to a value representing the relative abundance of a gene or transcript, a protein, or to call a SNP for GWAS studies (DNA).
Applying Omics to Self-Management of Pain in Older Adults
As the study of omics continues, it is anticipated that discoveries of biomarkers for pain and the associated underlying cause of pain will be identified. This discovery of pain biomarkers will facilitate the measurement of pain and matching of treatment modalities to the cause of pain, either through discovery of new targeted pharmacological agents or via testing behavioral interventions. The ability to objectively measure pain is particularly important for older adults with cognitive impairment who may be unable to report pain and/or provide information about the location of the pain to help determine the cause. Regarding the treatment of pain, omics can be used to objectively measure outcomes following pain interventions. For example, balneotherapy was noted to modify miRNA expression levels in older adults with osteoarthritis (Giannitti et al., 2017). Furthermore, this information can be used to help guide the development of new technologies or pharmacotherapeutic agents.
Role of Gerontological Nurse Scientists and Clinicians
Incorporation of Omics into Research and Clinical Practice
Nurse researchers and clinicians are well-positioned to identify clinical problems related to pain and pain management that can drive future research questions. Identification of treatment modalities that work clinically can be tested rigorously using omics techniques as possible bio-markers of pain. Further, nurse researchers at the bench can work with nurses in the clinical setting to translate findings to practice and drive new research in animal models. Examples of this might be the replication of dosing studies with animals performed on humans or vice versa.
Continued research is needed to incorporate omics findings into pain research and management in real world clinical settings. Although it is a challenge to conduct research in these settings, due to the heterogeneity of older individuals, multimorbidity, and the inability to control psychosocial factors such as motivation and resilience, the findings are important to moving the science of precision health forward.
- Arnstein, P. & Herr, K. (2017). Persistent pain management in older adults. Journal of Gerontological Nursing, 43(7), 20–31. doi:10.3928/00989134-20170419-01 [CrossRef]
- Barter, M.J. & Young, D.A. (2013). Epigenetic mechanisms and non-coding RNAs in osteoarthritis. Current Rheumatology Reports, 15, 353. doi:10.1007/s11926-013-0353-z [CrossRef]
- Belfer, I., Segall, S.K., Lariviere, W.R., Smith, S.B., Dai, F., Slade, G.D. & Diatchenko, L. (2013). Pain modality- and sex-specific effects of COMT genetic functional variants. Pain, 154, 1368–1376. doi:10.1016/j.pain.2013.04.028 [CrossRef]
- Bruckenthal, P., Marino, M.A. & Snelling, L. (2016). Complementary and integrative therapies for persistent pain management in older adults: A review. Journal of Gerontological Nursing, 42(12), 40–48. doi:10.3928/00989134-20161110-08 [CrossRef]
- Cashion, A.K., Gill, J., Hawes, R., Henderson, W.A. & Saligan, L. (2016). National Institutes of Health Symptom Science Model sheds light on patient symptoms. Nursing Outlook, 64, 499–506. doi:10.1016/j.outlook.2016.05.008 [CrossRef]
- Cashion, A.K. & Grady, P.A. (2015). The National Institutes of Health/National Institute of Nursing Research intramural research program and the development of the National Institutes of Health Symptom Science Model. Nursing Outlook, 63, 484–487. doi:10.1016/j.outlook.2015.03.001 [CrossRef]
- Cobianchi, S., Casals-Diaz, L., Jaramillo, J. & Navarro, X. (2013). Differential effects of activity dependent treatments on axonal regeneration and neuropathic pain after peripheral nerve injury. Experimental Neurology, 240, 157–167. doi:10.1016/j.expneurol.2012.11.023 [CrossRef]
- Cobianchi, S., Marinelli, S., Florenzano, F., Pavone, F. & Luvisetto, S. (2010). Short- but not long-lasting treadmill running reduces allodynia and improves functional recovery after peripheral nerve injury. Neuroscience, 168, 273–287. doi:10.1016/j.neuroscience.2010.03.035 [CrossRef]
- Collins, F.S. & Varmus, H. (2015). A new initiative on precision medicine. New England Journal of Medicine, 372, 793–795. doi:10.1056/NEJMp1500523 [CrossRef]
- Conley, Y.P., Biesecker, L.G., Gonsalves, S., Merkle, C.J., Kirk, M. & Aouizerat, B.E. (2013). Current and emerging technology approaches in genomics. Journal of Nursing Scholarship, 45, 5–14. doi:10.1111/jnu.12001 [CrossRef]
- Corbett, A. & Ballard, C. (2012). Antipsychotics and mortality in dementia. American Journal of Psychiatry, 169, 7–9. doi:10.1176/appi.ajp.2011.11101488 [CrossRef]
- Corbett, A., Nunez, K.-M., Smeaton, E., Testad, I., Thomas, A.J., Closs, S.J. & Lawrence, V. (2016). The landscape of pain management in people with dementia living in care homes: A mixed methods study. International Journal of Geriatric Psychiatry, 31, 1354–1370. doi:10.1002/gps.4445 [CrossRef]
- Coull, J.A.M., Beggs, S., Boudreau, D., Boivin, D., Tsuda, M., Inoue, K. & De Koninck, Y. (2005). BDNF from microglia causes the shift in neuronal anion gradient underlying neuropathic pain. Nature, 438, 1017–1021. doi:10.1038/nature04223 [CrossRef]
- Courneya, K.S., McKenzie, D.C., Mackey, J.R., Gelmon, K., Friedenreich, C.M., Yasui, Y. & Segal, R.J. (2013). Effects of exercise dose and type during breast cancer chemotherapy: Multicenter randomized trial. Journal of the National Cancer Institute, 105, 1821–1832. doi:10.1093/jnci/djt297 [CrossRef]
- Daenen, L., Varkey, E., Kellmann, M. & Nijs, J. (2015). Exercise, not to exercise, or how to exercise in patients with chronic pain? Applying science to practice. Clinical Journal of Pain, 31, 108–114. doi:10.1097/AJP.0000000000000099 [CrossRef]
- Devos-Comby, L., Cronan, T. & Roesch, S.C. (2006). Do exercise and self-management interventions benefit patients with osteoarthritis of the knee? A metaanalytic review. Journal of Rheumatology, 33, 744–756.
- Di Lorenzo, C., Daverio, A., Pasqualetti, P., Coppola, G., Giannoudas, I., Barone, Y. & Di Lorenzo, G. (2014). The upstream Variable Number Tandem Repeat polymorphism of the monoamine oxidase type A gene influences trigeminal pain-related evoked responses. European Journal of Neuroscience, 39, 501–507. doi:10.1111/ejn.12458 [CrossRef]
- Dixit, S., Maiya, A. & Shastry, B. (2014). Effect of aerobic exercise on quality of life in population with diabetic peripheral neuropathy in type 2 diabetes: A single blind, randomized controlled trial. Quality of Life Research, 23, 1629–1640. doi:10.1007/s11136-013-0602-7 [CrossRef]
- Du, S., Yuan, C., Xiao, X., Chu, J., Qiu, Y. & Qian, H. (2011). Self-management programs for chronic musculoskeletal pain conditions: A systematic review and meta-analysis. Patient Education and Counseling, 85, e299–e310. doi:10.1016/j.pec.2011.02.021 [CrossRef]
- Ellingson, L.D., Shields, M.R., Stegner, A.J. & Cook, D.B. (2012). Physical activity, sustained sedentary behavior, and pain modulation in women with fibromyalgia. Journal of Pain, 13, 195–206. doi:10.1016/j.jpain.2011.11.001 [CrossRef]
- Ersek, M., Turner, J.A., Cain, K.C. & Kemp, C.A. (2008). Results of a randomized controlled trial to examine the efficacy of a chronic pain self-management group for older adults. Pain, 138, 29–40. doi:10.1016/j.pain.2007.11.003 [CrossRef]
- Ettinger, W.H. Jr.. , Fried, L.P., Harris, T., Shemanski, L., Schulz, R. & Robbins, J. (1994). Self-reported causes of physical disability in older people: The cardiovascular health study. Journal of the American Geriatrics Society, 42, 1035–1044. doi:10.1111/j.1532-5415.1994.tb06206.x [CrossRef]
- Evers, A.W.M., Kraaimaat, F.W., Geenen, R., Jacobs, J.W.G. & Bijlsma, J.W.J. (2003). Pain coping and social support as predictors of long-term functional disability and pain in early rheumatoid arthritis. Behaviour Research and Therapy, 41, 1295–1310. doi:10.1016/S0005-7967(03)00036-6 [CrossRef]
- Follert, P., Cremer, H. & Béclin, C. (2014). MicroRNAs in brain development and function: A matter of flexibility and stability. Frontiers in Molecular Neuroscience, 7, 5. doi:10.3389/fnmol.2014.00005 [CrossRef]
- Geva, N. & Defrin, R. (2013). Enhanced pain modulation among tri-athletes: A possible explanation for their exceptional capabilities. Pain, 154, 2317–2323. doi:10.1016/j.pain.2013.06.031 [CrossRef]
- Giannitti, C., De Palma, A., Pascarelli, N.A., Cheleschi, S., Giordano, N., Galeazzi, M. & Fioravanti, A. (2017). Can balneotherapy modify microRNA expression levels in osteoarthritis? A comparative study in patients with knee osteoarthritis. International Journal of Biometeorology. Advance online publication. doi:10.1007/s00484-017-1420-3 [CrossRef]
- Grace, P.M., Fabisiak, T.J., Green-Fulgham, S.M., Anderson, N.D., Strand, K.A., Kwilasz, A.J. & Watkins, L.R. (2016). Prior voluntary wheel running attenuates neuropathic pain. Pain, 157, 2012–2023. doi:10.1097/j.pain.0000000000000607 [CrossRef]
- Grey, M., Schulman-Green, D., Knafl, K. & Reynolds, N.R. (2015). A revised Self- and Family Management Framework. Nursing Outlook, 63, 162–170. doi:10.1016/j.outlook.2014.10.003 [CrossRef]
- Hawamdeh, Z.M., Alshraideh, M.A., Al-Ajlouni, J.M., Salah, I.K., Holm, M.B. & Otom, A.H. (2012). Development of a decision support system to predict physicians' rehabilitation protocols for patients with knee osteoarthritis. International Journal of Rehabilitation Research, 35, 214–219. doi:10.1097/MRR.0b013e3283533766 [CrossRef]
- Heller, M.J. (2002). DNA microarray technology: Devices, systems, and applications. Annual Review of Biomedical Engineering, 4, 129–153. doi:10.1146/annurev.bioeng.4.020702.153438 [CrossRef]
- Hurkmans, E., van der Giesen, F.J., Vliet Vlieland, T.P., Schoones, J. & Van den Ende, E.C. (2009). Dynamic exercise programs (aerobic capacity and/or muscle strength training) in patients with rheumatoid arthritis. Cochrane Database of Systematic Reviews, 4, CD006853. doi:10.1002/14651858.CD006853.pub2 [CrossRef]
- Iversen, M.D. (2012). Rehabilitation interventions for pain and disability in osteoarthritis: A review of interventions including exercise, manual techniques, and assistive devices. Orthopedic Nursing, 31, 103–108. doi:10.1097/NOR.0b013e31824fce07 [CrossRef]
- Katsanis, S.H. & Katsanis, N. (2013). Molecular genetic testing and the future of clinical genomics. Nature Reviews Genetics, 14, 415–426. doi:10.1038/nrg3493 [CrossRef]
- Kelley, G.A., Kelley, K.S., Hootman, J.M. & Jones, D.L. (2011). Effects of community-deliverable exercise on pain and physical function in adults with arthritis and other rheumatic diseases: A meta-analysis. Arthritis Care & Research, 63, 79–93. doi:10.1002/acr.20347 [CrossRef]
- Klapwijk, M.S., Caljouw, M.A.A., Pieper, M.J.C., van der Steen, J.T. & Achterberg, W.P. (2016). Characteristics associated with quality of life in long-term care residents with dementia: A cross-sectional study. Dementia and Geriatric Cognitive Disorders, 42, 186–197. doi:10.1159/000448806 [CrossRef]
- Kroon, F.P.B., van der Burg, L.R.A., Buchbinder, R., Osborne, R.H., Johnston, R.V & Pitt, V. (2014). Self-management education programmes for osteoarthritis. Cochrane Database of Systematic Reviews, 1, CD008963. doi:10.1002/14651858.CD008963.pub2 [CrossRef]
- Lan, X., Adams, C., Landers, M., Dudas, M., Krissinger, D., Marnellos, G. & Jin, V.X. (2011). High resolution detection and analysis of CpG dinucleotides methylation using MBD-seq technology. PLoS ONE, 6, e22226. doi:10.1371/journal.pone.0022226 [CrossRef]
- Leveille, S.G., Fried, L. & Guralnik, J.M. (2002). Disabling symptoms: What do older women report?Journal of General Internal Medicine, 17, 766–773. doi:10.1046/j.1525-1497.2002.20229.x [CrossRef]
- Liu, Y. & Petrini, M.A. (2015). Effects of music therapy on pain, anxiety, and vital signs in patients after thoracic surgery. Complementary Therapies in Medicine, 23, 714–718. doi:10.1016/j.ctim.2015.08.002 [CrossRef]
- McAuliffe, L., Brown, D. & Fetherstonhaugh, D. (2012). Pain and dementia: An overview of the literature. International Journal of Older People Nursing, 7, 219–226. doi:10.1111/j.1748-3743.2012.00331.x [CrossRef]
- Melzer, D., Gardener, E. & Guralnik, J.M. (2005). Mobility disability in the middle-aged: Cross-sectional associations in the English Longitudinal Study of Ageing. Age and Ageing, 34, 594–602. doi:10.1093/ageing/afi188 [CrossRef]
- Merighi, A., Salio, C., Ghirri, A., Lossi, L., Ferrini, F., Betelli, C. & Bardoni, R. (2008). BDNF as a pain modulator. Progress in Neurobiology, 85, 297–317. doi:10.1016/j.pneurobio.2008.04.004 [CrossRef]
- Merskey, H., Bond, M.R., Bonica, J.J., Boyd, D.B., Carmon, A., Deathe, A.B. & Sunderland, S. (1986). Classification of chronic pain. Pain, 3(Suppl.), S1–S226.
- Metzker, M.L. (2010). Sequencing technologies—The next generation. Nature Reviews Genetics, 11, 31–46. doi:10.1038/nrg2626 [CrossRef]
- Micheel, C.M., Nass, S.J. & Omenn, G.S. (2012). Evolution of translational omics lessons learned and the path forward. Washington, DC: The National Academies Press. doi:10.17226/13297 [CrossRef]
- Mogil, J.S. (2012). Pain genetics: Past, present and future. Trends in Genetics, 28, 258–266. doi:10.1016/j.tig.2012.02.004 [CrossRef]
- Murray, C.J.L., Atkinson, C., Bhalla, K., Birbeck, G., Burstein, R., Chou, D. & Wulf, S. (2013). The state of US health, 1990–2010: Burden of diseases, injuries, and risk factors. JAMA, 310, 591–608. doi:10.1001/jama.2013.13805 [CrossRef]
- Nahin, R.L. (2015). Estimates of pain prevalence and severity in adults: United States, 2012. Journal of Pain, 16, 769–780. doi:10.1016/j.jpain.2015.05.002 [CrossRef]
- Naugle, K.M., Ohlman, T., Naugle, K.E., Riley, Z.A. & Keith, N.R. (2017). Physical activity behavior predicts endogenous pain modulation in older adults. Pain, 158, 383–390. doi:10.1097/j.pain.0000000000000769 [CrossRef]
- Naugle, K.M. & Riley, J.L. III. . (2014). Self-reported physical activity predicts pain inhibitory and facilitatory function. Medicine and Science in Sports and Exercise, 46, 622–629. doi:10.1249/MSS.0b013e3182a69cf1 [CrossRef]
- Nguyen, A.L., Lake, J.E., Reid, M.C., Glasner, S., Jenkins, J., Candelario, J. & Moore, A.A. (2017). Attitudes towards exercise among substance using older adults living with HIV and chronic pain. AIDS Care, 29, 1149–1152. doi:10.1080/09540121.2017.1325437 [CrossRef]
- Papanagnou, P., Stivarou, T. & Tsironi, M. (2016). The role of miRNAs in common inflammatory arthropathies: Osteoarthritis and gouty arthritis. Biomolecules, 6(4). doi:10.3390/biom6040044 [CrossRef]
- Park, J., McCaffrey, R., Newman, D., Liehr, P. & Ouslander, J.G. (2017). A pilot randomized controlled trial of the effects of chair yoga on pain and physical function among community-dwelling older adults with lower extremity osteoarthritis. Journal of the American Geriatrics Society, 65, 592–597. doi:10.1111/jgs.14717 [CrossRef]
- Patel, K.V., Guralnik, J.M., Dansie, E.J. & Turk, D.C. (2013). Prevalence and impact of pain among older adults in the United States: Findings from the 2011 National Health and Aging Trends Study. Pain, 154, 2649–2657. doi:10.1016/j.pain.2013.07.029 [CrossRef]
- Petrofsky, J., Laymon, M., Alshammari, F., Khowailed, I.A. & Lee, H. (2014). Use of ThermaCare heat wraps as an adjunct to physical therapy. International Journal of Therapy and Rehabilitation, 21, 427–433. doi:10.12968/ijtr.2014.21.9.427 [CrossRef]
- Pizzo, P.A., Clark, N.M. & Pokras, O.C. (2011). Relieving pain in America? A blueprint for transforming prevention, care, education, and research. Washington, DC: The National Academies Press.
- Premkumar, L.S. & Sikand, P. (2008). TRPV1: A target for next generation analgesics. Current Neuropharmacology, 6, 151–163. doi:10.2174/157015908784533888 [CrossRef]
- Reid, M.C., Papaleontiou, M., Ong, A., Breckman, R., Wethington, E. & Pillemer, K. (2008). Self-management strategies to reduce pain and improve function among older adults in community settings: A review of the evidence. Pain Medicine, 9, 409–424. doi:10.1111/j.1526-4637.2008.00428.x [CrossRef]
- Renn, C.L., Leitch, C.C. & Dorsey, S.G. (2009). In vivo evidence that truncated trkB.T1 participates in nociception. Molecular Pain, 5, 61. doi:10.1186/1744-8069-5-61 [CrossRef]
- Renn, C.L., Lin, L., Thomas, S. & Dorsey, S.G. (2006). Full-length tropomyosin-related kinase B expression in the brainstem in response to persistent inflammatory pain. Neuroreport, 17, 1175–1179. doi:10.1097/01.wnr.0000215771.61355.e1 [CrossRef]
- Resnick, B., Klinedinst, N.J., Yerges-Armstrong, L., Magaziner, J., Orwig, D., Hochberg, M.C. & Dorsey, S.G. (2016). Pain, genes, and function in the post-hip fracture period. Pain Management Nursing, 17, 181–196. doi:10.1016/j.pmn.2016.03.003 [CrossRef]
- Ruggiero, L., Glasgow, R.E., Dryfoos, J.M., Rossi, J.S., Prochaska, J.O., Orleans, C.T. & Johnson, S. (1997). Diabetes self-management: Self-reported recommendations and patterns in a large population. Diabetes Care, 20, 568–576. doi:10.2337/diacare.20.4.568 [CrossRef]
- Ryan, P. (2009). Integrated theory of health behavior change: Background and intervention development. Clinical Nurse Specialist, 23, 161–172. doi:10.1097/NUR.0b013e3181a42373 [CrossRef]
- Sakai, A. & Suzuki, H. (2014). Emerging roles of microRNAs in chronic pain. Neurochemistry International, 77, 58–67. doi:10.1016/j.neuint.2014.05.010 [CrossRef]
- Sheahan, T.D., Copits, B.A., Golden, J.P. & Gereau, R.W. (2015). Voluntary exercise training: Analysis of mice in uninjured, inflammatory, and nerve-injured pain states. PLoS One, 10, e0133191. doi:10.1371/journal.pone.0133191 [CrossRef]
- Shen, J., Fox, L.E. & Cheng, J. (2013). Swim therapy reduces mechanical allodynia and thermal hyperalgesia induced by chronic constriction nerve injury in rats. Pain Medicine, 14, 516–525. doi:10.1111/pme.12057 [CrossRef]
- Sluka, K.A., O'Donnell, J.M., Danielson, J. & Rasmussen, L.A. (2013). Regular physical activity prevents development of chronic pain and activation of central neurons. Journal of Applied Physiology, 114, 725–733. doi:10.1152/japplphysiol.01317.2012 [CrossRef]
- Stagg, N.J., Mata, H.P.H., Ibrahim, M.M., Henriksen, E.J., Porreca, F., Vanderah, T.W. & Malan, T.P. Jr. . (2011). Regular exercise reverses sensory hypersensitivity in a rat neuropathic pain model: Role of endogenous opioids. Anesthesiology, 114, 940–948. doi:10.1097/ALN.0b013e318210f880 [CrossRef]
- Sun, E. & Shi, Y. (2015). MicroRNAs: Small molecules with big roles in neurodevelopment and diseases. Experimental Neurology, 268, 46–53. doi:10.1016/j.expneurol.2014.08.005 [CrossRef]
- Wetterstrand, K.A. (2016). DNA sequencing costs: Data from the NHGRI Genome Sequencing Program. Retrieved from http://www.genome.gov/sequencingcostsdata
- Wickersham, K. & Dorsey, S. (2017). Integration of genomics in nursing research: An example. In Grady, P. & Hinshaw, A. (Eds.), Using nursing research to shape health policy (2nd ed., pp. 147–170). New York, NY: Springer.
- Wright, A. & Sluka, K. (2001). Nonpharmacological treatments for musculoskeletal pain. Clinical Journal of Pain, 17, 33–46. doi:10.1097/00002508-200103000-00006 [CrossRef]
- Yu, C., Chen, W.-P. & Wang, X.-H. (2011). MicroRNA in osteoarthritis. Journal of International Medical Research, 39, 1–9. doi:10.1177/147323001103900101 [CrossRef]
- Zhang, M., Lygrisse, K. & Wang, J. (2017). Role of MicroRNA in osteoarthritis. Journal of Arthritis. Advance online publication. doi:10.4172/2167-7921.1000239 [CrossRef]