Physical activity,sleep,and C-reactive protein as markers of positive health in resilient older men

Abstract

This study explored whether physical activity and sleep, combined with the biomarker C-reactive protein, indexed positive health in older men. Many were former prisoners of war, with most remaining psychologically resilient and free of any psychiatric diagnoses. Activity and sleep were recorded through actigraphy in 120 veterans (86 resilient and 34 nonresilient) for 7 days. Resilient men had higher physical activity, significantly lower C-reactive protein levels, and 53 percent had lower cardiac-disease risk compared to nonresilient men. Sleep was adequate and not associated with C-reactive protein. Results suggest continued study is needed in actigraphy and C-reactive protein as means to index positive health.

Keywords

actigraphy C-reactive protein physical activity resilience sleep

According to the United Nations study on aging, older populations (aged 60 years or older) are “expected to more than double from 841 million people in 2013 to more than 2 billion in 2050” and will comprise 21 percent of the global populace (United Nations Department of Economic and Social Affairs (UN), 2013: xii). This older group tends to suffer from chronic illnesses such as diabetes, hypertension, and cardiovascular disease, which can negatively impact individual health and cause a burden to public health as a whole (Halter et al., 2014; Katz and Gilbert, 2008; Owen et al., 2010). In order to minimize these deleterious health outcomes, a broader definition of health has been established to include soundness of mind, body, and social well-being (World Health Organization (WHO), 1948). Positive health is now considered more than just the absence of illness and disease; rather, it relates to a wide variety of personal factors that contribute to a healthier and longer life including biological, subjective, and functional aspects (Seligman, 2008).

Research supports the concept that physical activity (PA) plays a large role in overall physical health. Long-term PA can reduce adiposity, improve insulin resistance, increase antioxidant defenses, modulate cardiovascular mortality, and improve endothelial function (Kasapis and Thompson, 2005). Inactivity is “the fourth leading risk factor for global mortality,” and reducing inactivity has become a public health priority worldwide (World Health Association, 2011: 1). Physical inactivity is also a risk factor for various undesired psychological issues including depressive symptoms, phobias, alcohol and drug dependence, as well as anxiety and mood disorders (Meng and D’Arcy, 2013). PA is also a modifiable and inexpensive element that has been associated with reduced risk of developing and slowing progression of Alzheimer’s disease and dementia (Brown et al., 2013; Ruthirakuhan et al., 2012).

The importance of sleep is widely recognized, and there is now a national objective to improve sleep in government programs such as Healthy People 2020 (United States Department of Health and Human Services, 2013). Sleep contributes to physical health in various ways, including undoing the damage of daily life stresses, promoting proper biological modulation of metabolic and endocrine function, stimulating both pro-inflammatory and anti-inflammatory cytokines, and prompting nocturnal waste removal from the brain (Frey et al., 2007; Hawkley et al., 2005; Spiegel et al., 1999; Xie et al., 2013). Abnormal sleep, in both chronic and acute situations, has been linked to such adverse physical conditions as coronary heart disease, hypertension, obesity, and all-cause mortality (Ayas et al., 2003; Gottlieb et al., 2006; Patel et al., 2004; Taheri et al., 2004). Poor sleep has also been associated with negative psychological aspects such as pain perception, posttraumatic stress disorder (PTSD), and major depressive disorder (Gulec et al., 2012; Pieh et al., 2011; Van Liempt, 2012).

An easily obtained, readily available biomarker may provide an objective, non-verbal indication that an individual may have a health risk (either mentally or physically). One such biomarker is C-reactive protein (CRP). It is a nonspecific, acute-phase response protein widely considered to be one of the most stable, accurate, and reliable markers of heightened systemic inflammation, which is related to a broad range of negative health events, morbidity, and mortality in older adults (Bassuk et al., 2004; Pepys and Hirschfield, 2003; Willerson and Ridker, 2004). CRP exhibits little variability within stable patients, appears comparable among healthy men and women, displays little circadian variability, and is not affected by short-term factors such as eating (Meier-Ewert et al., 2001; Mohsin and Ahmad, 2011; Rifai and Ridker, 2003). CRP values may possibly be more accurate at diagnosing inflammation and physical damage than plasma viscosity or erythrocyte sedimentation rate (Pepys and Hirschfield, 2003).

There has been some debate on the effect PA has on CRP. It has been reported that PA is inversely associated with levels of CRP even when adjusting for suspected confounders such as age, smoking, and body mass index (BMI) while other studies have not shown any significant relationship (Church et al., 2010; Ford, 2002; Kasapis and Thompson, 2005). In population studies of middle-aged men and women, CRP concentrations reduced with increasing levels of PA (Aronson et al., 2004). Exploration of CRP levels and PA in older populations is important as there is a greater likelihood of reduced PA and increased sedentary behavior due to age-related infirmities, putting the older population at a greater health risk (Milanovic et al., 2013).

A clear relationship between quantity and quality of sleep and CRP in healthy populations has not been established either. Some researchers have found that sleep quality was associated with CRP in males, and others have found the correlation in females only (Liu et al., 2014; Meier-Ewert et al., 2004; Miller et al., 2009). Grandner et al. (2013) reported that CRP has shown to be significantly elevated in long sleepers (>9 hours), but not in short sleepers (<5 hours), when adjusting for sleep and health disorders. It is important to note that current research on this relationship has largely been in those with sleep and medical comorbidities and not those reporting normal health.

CRP has also been found to be elevated in psychological conditions including acute stress, PTSD, and major depression and anxiety disorders (Hou and Baldwin, 2012; Raison et al., 2013; Spitzer et al., 2010; Toker et al., 2005; Vogelzangs et al., 2013). Liukkonen et al. (2011) found in males that the probability of CRP in the highest tertile (CRP > 3.0 mg/L) increases over twofold in those with symptoms of anxiety disorder, while Pitsavos et al. (2006) reported male participants in the upper tertile of the Spielberger State Anxiety Inventory (STAI) had 22 percent higher levels of CRP. Among females, the associations between CRP and psychological states have not been as consistent (Liukkonen et al., 2011; Pitsavos et al., 2006).

Other psychological constructs have also been linked to CRP levels. Burnout is a “chronic affective state comprised of emotional exhaustion, physical fatigue, and cognitive weariness” and has been associated with increased CRP in women (Toker et al., 2005). Shirom et al. (2010) define vigor as a “moderately aroused positive affect of physical strength, cognitive liveliness, and emotional energy.” They found high levels of vigor were associated with reduced levels of CRP in both men and women. Also Stephan et al. (2014) reported for every standard deviation (SD) decrease in self-perceived age, there was a decrease in CRP between 8 and 15 percent and that subjective age was a stronger predictor of CRP than was actual chronological age.

While CRP has previously been employed as an indicator of acute physical issues, its application across a broader spectrum of positive health concepts may be salient in the overall health of aging adults. CRP measurements correlate so highly with “severity, extent, and progression of many different pathologies,” that it has great potential use in the identification of patients who might be in need of further medical evaluation, more frequent or detailed monitoring, lifestyle risk education and modifications, or even pharmacological therapies for underlying causes of the increased CRP (Pepys and Hirschfield, 2003: 1811). Thus, this is a biomarker which could potentially index “tip-of-the-iceberg” underlying physical and/or psychological issues impacting an individual.

The impact of PTSD is of great concern as the United States has experienced a number of traumas at a national scale in recent years (9/11 terrorist attacks, Boston Marathon bombings, Iraq/Afghanistan wars, and major national disasters—Hurricane Katrina, etc.). According to Bonanno (2004), 50–60 percent of the US population has experienced a traumatic event, thus increasing the likelihood of developing PTSD and making them susceptible to various health risks (e.g. disorders of depression, anxiety, and substance abuse). However, only 5–10 percent of those individuals exposed to trauma are later diagnosed with PTSD. Therefore, a number of people are resilient to traumatic experiences and exhibit intact psychological functioning (Bonanno, 2004; Smith et al., 2010).

One such group who experienced extreme trauma is the Vietnam-era repatriated US prisoners of war (RPWs). These individuals suffered from years of wartime imprisonment, torture, and starvation; yet, a number of them were resilient to these experiences. Most of these same individual’s sleep quality improved significantly at the time of repatriation (ca 1973). Specifically, sleep issues before, during, and after captivity were assessed upon repatriation from a sample of 440 Vietnam RPWs by Segovia et al. (2013). In that study, psychiatric disorders were assessed at repatriation (ca 1973), and assessments were continued annually by a psychiatrist or clinical psychologist. Odds ratios examining the presence of sleep-disturbance symptoms (i.e. early morning awakenings, nightmares) showed “resilient” RPWs (i.e. those free of psychiatric diagnoses) reporting fewer sleep-disturbance symptoms compared to “nonresilient” RPWs before, during, and after captivity. Particularly interesting were the repatriates who seemed to have “bounced back” from the captivity experience. Those reporting fewer sleep complaints at repatriation were nearly 2½ times more likely to be resilient than the groups reporting sleep difficulties at repatriation. Reporting fewer sleep complaints, but not necessarily an absence of them before, during, and after the trauma could predict psychological and physical resilience over time (Segovia et al., 2013).

One valuable tool recognized by many to be both reliable and valid in sleep and PA research is wrist actigraphy (Mathie et al., 2004; Sadeh, 2011). Benefits of actigraphy include reasonable comparability to the gold standard sleep test of polysomnography (PSG), cost effectiveness, small size and light weight, practicality in participants’ natural environments, and allowance for long-term monitoring over multiple days and nights (De Souza et al., 2003; Martin and Hakim, 2011). Another added benefit is that actigraphy, unlike self-reported data, is not subject to recall or reporting bias, which could undermine the accuracy of activity classification.

Given the potential to prevent, reduce, or reverse damage from adverse conditions associated with poor PA or sleep quality, we sought to examine a cohort of older men using actigraphy to measure levels of PA and sleep and correlate these results with the CRP biomarker as a factor to measure positive health. Furthermore, since these men had been survivors of a long-term prisoner-of-war experience, we also looked at group differences of resiliency in their state of health.

Methods

Participants

Participants came from the Robert E. Mitchell Center for Prisoner of War Studies who have contributed to an annual voluntary medical and psychological follow-up program within the Department of Defense since 1973. The participants were a subsection of Vietnam-era repatriates (RPWs) from all branches of service, as well as a matched comparison group (CG) of combat veterans, who had been in similar combat but never captured/imprisoned. All ranged in age from 61 to 86 years (Mean = 73 years, SD = 5 years). This study included 138 participants (105 RPW and 33 CG) who came for medical follow-up at the center between May 2012 and June 2013. Of the original 138 participants, 120 were able to supply actigraphy data (88 RPW; 32 CG). Others were excluded due to technical problems with equipment or declination to participate. All have been evaluated psychiatrically at each medical follow-up over the past 40 years. Those who have been free of any psychiatric diagnoses over the past 40 years of medical follow-up were considered resilient (58 RPW; 28 CG); those who had been diagnosed with at least one psychiatric diagnosis were considered nonresilient (30 RPW; 4 CG) from their combat/imprisonment experiences. Details of this cohort have been published elsewhere (Segovia et al., 2013). This research was reviewed and approved by an Institutional Review Board, and all participants included in this report consented to be included in the study.

Actigraphy monitor

ActiGraph™ GT3X+ monitors (ActiGraph™, LLC, Pensacola, FL, USA) were used to gather PA and sleep data continuously from our participants. These actigraphy monitors are tri-axial accelerometers that identify movement in horizontal, vertical, and diagonal planes. They are enabled with 512 MB of memory to collect activity of movement, steps, kilocalories, and positional vectors at a rate of 30–100 times per second (30–100 Hz) and ambient light (lux) measurement at a 1 Hz rate. Segments of activity were divided based upon the following activity counts per minute (CPM) cut points established by the adult algorithm included in ActiGraph software: “Sedentary” = 0–100, “Lifestyle” = 101–759, “Light” = 760–1952, “Moderate” = 1953–5724, “Vigorous” = 5725–9498, and “Very Vigorous” = 9499-infinity. Participants wore the monitor on their non-dominant wrist continuously for 7 days and nights. The monitor was secured by a locking band around their wrist to prevent premature removal.

Sleep diary

A sleep diary designed by the American Academy of Sleep Medicine (n.d.) was used for 1 week by participants concurrently with actigraphy to augment the quantitative and qualitative sleep information. Information logged in the diary included when participants went to bed, went to sleep, took naps or prescribed medication, exercised, and drank caffeinated beverages or alcohol.

Procedures

At the medical follow-up, height and current weight were collected and BMI calculated for all participants. As part of the routine clinical practice, blood samples were obtained which included high-sensitivity CRP biomarker data. Also, self-reported data were collected by clinical staff on their self-reported sleep efficiency and self-reported sleep difficulties to compare later with nighttime actigraphy data. When the participant returned home, their actigraphy monitor turned “on” and collected sleep/awake tri-axial accelerometer activity for 7 days and nights. In addition, the participant maintained the 7-night sleep diary. The monitor and diary were mailed back to the research office afterward.

Data analysis

Data analysis was conducted between August and December 2013 using SPSS, version 19 (IBM SPSS, Chicago, IL, USA). Spearman correlational analyses were conducted to determine whether there were any significant relationships in actigraphy and CRP biomarker for the cohort. This was followed by Mann–Whitney U analysis between the two subgroups (resilient vs nonresilient). Of the 120 participants included in this study, we collected data on 110 (92%) for a full 7 days, 8 had 6 days, 1 had 5 days, and 1 had 4 days due to forgetting to wear the device or who took it off sooner for personal reasons. Therefore, to maintain statistical power of the sample, we used average daily actigraphy as a way to normalize and include those few who had less than 7 days of actigraphy in the analyses. Nighttime sedentary data were removed from total sedentary activity to determine daytime sedentary data. Sleep efficiency was calculated by the proprietary ActiGraph software, but was also compared with clinical and self-reported sleep efficiencies at the annual medical visit. As part of standard clinical practice in evaluating CRP levels and cardiac risk, the actigraphy data were then organized into tertiles based on CRP clinical cut points for levels of cardiac-disease risk: high risk >.3 mg/dL, medium risk .1–.3 mg/dL, and low risk <.1 mg/dL (Pearson et al., 2003).

Results

Table 1 provides the descriptive statistics and details the level of PA (from light to very vigorous), daytime sedentary, average amount of sleep per night, and sleep efficiency collected for the cohort and for the subgroups.The average age of the cohort was 73 years, average BMI was 28, and average level of CRP was .197 mg/dL. Alcohol use (moderate drinkers) and smoking (quit smoking years ago) were considered as constants in this study. The cohort was relatively active over the data collection period, particularly in the Lifestyle to Moderate PA levels. Their minutes of average daily activity (M = 699) exceeded their minutes of daytime sedentary activity (M = 286) by a ratio of greater than 2 to 1. Their average amount of sleep per night (M = 413) was almost 7 hours, at a sleep efficiency of 90 percent on average. The latter was confirmed with the sleep efficiency results collected at the medical follow-up. Spearman’s correlational analyses of the cohort did not reveal any significant relationships with sleep as measured by actigraphy and CRP.

Table 1.

Means, standard deviations (SD), percent statin use and sleep efficiencies of the cohort.

	All subjects (N = 120)		Resilient (N = 86)		Nonresilient (N = 34)
	Mean	±SD	Mean	±SD	Mean	±SD
Age (years)	73	5	73	5	73	5
C-reactive protein (mg/dL)	.197	.302	.19	.324	.213	.241
BMI	28	4	27	4	29	4
Statin use	61%		64%		53%
Activity
Average daily activity (kcal)	1982	682	2070	713	1760	546
Average daily no. of steps	10,598	7351	11,304	8484	8812	2209
Average daily activity (min)	699	110	716	108	657	104
Average daily lifestyle activity (min)	201	44	205	45	192	38
Average daily light activity (min)	307	55	309	53	304	61
Average daily moderate activity (min)	184	73	196	76	154	55
Average daily vigorous activity (min)	6	6	6	6	5	7
Average daily very vigorous activity (min)	1	4	1	5	1	3
Average daily daytime sedentary (min)	286	132	281	133	297	130
Sleep
Average daily sleep time (min)	413	74	414	74	409	75
Sleep efficiency—actigraphy	90%	5%	90%	4%	89%	6%
Sleep efficiency—interview	(N = 114)		(N = 82)		(N = 32)
Sleep efficiency—interview	93%	6%	93%	6%	92%	7%
Sleep efficiency—log	(N = 110)		(N = 82)		(N = 28)
Sleep efficiency—log	91%	8%	91%	7%	91%	9%

Mann–Whitney U tests were performed to assess any group differences between the resilient and nonresilient participants. As presented in Table 2, there were significant differences in levels of PA and the biomarker CRP. Specifically, the Mann–Whitney U analysis indicated the resilient group had more minutes of average daily PA, average daily kilocalories, and average daily number of steps taken, particularly at the average daily moderate activity level (i.e. 1953–5724) compared to the nonresilient group (refer to Table 2). Furthermore, the resilient group had significantly reduced levels of CRP (M = .190, SD = .324) compared to the nonresilient group (M = .213, SD = .241). The effect sizes for these statistically significant differences were considered small (refer to Table 2).

Table 2.

Nonparametric group differences, significance, confidence intervals (CI), and effect size (r) of the cohort.

	Mann–Whitney U (120) =	z =	Significance	95% confidence interval	Effect size r =
Age			p = .649	−2.00 to 3.00
C-reactive protein (mg/dL)	1119.50	−1.997	p = .046	.00 to .090	−.18
BMI			p = .140	−.40 to 2.60
Statin use			p = .267	.00 to .00
Activity
Average daily activity (kcal)	1812.00	2.038	p = .042	−567.89 to −11.94	.19
Average daily no. of steps	1866.00	2.35	p = .019	−2607.29 to −294.00	.21
Average daily activity (min)	1922.50	2.68	p = .007	−97.57 to −15.86	.25
Average daily lifestyle activity (min)			p = .197	−27.71 to 5.57
Average daily light activity (min)			p = .829	−26.89 to 20.57
Average daily moderate activity (min)	1922.50	2.68	p = .007	−67.57 to −10.14	.25
Average daily vigorous activity (min)			p = .437	−2.00 to .71
Average daily very vigorous activity (min)			p = .477	.00 to .00
Average daily daytime sedentary (min)			p = .242	−22.00 to 70.83
Sleep
Average daily sleep time (min)			p = .485	−37.15 to 19.13
Sleep efficiency—actigraphy			p = .231	−2.29 to .60
Sleep efficiency—interview			p = .337	−3.00 to 1.00
Sleep efficiency—log			p = .525	−1.86 to 3.59

r = .10 to .29 (small effect).

In analyzing the extremes in cardiac-disease risk (i.e. high risk vs low risk), results with sleep actigraphy and CRP were not significant. However, the results with daytime activity levels and CRP were significant. Specifically, Mann–Whitney U analyses revealed low cardiac risk participants (N = 57) were significantly more active compared to high cardiac risk participants (N = 18) in the average daily vigorous category (i.e. 5725–9498 counts), U (75) = 722.5, z = 2.60, p = .009, and a medium effect size r = .30. Moreover, in comparing the resilient versus the nonresilient, 53 percent of the resilient subjects were in the low-risk CRP category, χ² (2, N = 51) = 6.22, p = .045. Actigraphy, sleep diary, and interviews all reported good (89%–93%) sleep efficiency.

Discussion

The findings of this study suggest that the CRP level for this cohort of older men was a better index for PA than for sleep factors. While studies have shown an association between sleep and CRP concentration between some groups, it was problematic to make comparisons of CRP levels for poor versus adequate sleepers as our participants were all good sleepers for their age (Liu et al., 2014; Meier-Ewert et al., 2004; Miller et al., 2009). Grandner et al. (2013) found that participants with >9 hours or <5 hours of sleep per night had increased levels of CRP, but 40 years after repatriation from Vietnam prisons these men generally engaged in normal sleep (~90% efficacy). On average, they were asleep nearly 7 hours a night—standard sleep for their ages (Ram et al., 2010). Our participants also had a low frequency of night awakenings as well. Even nonresilient RPWs, in time, noted sleep quality improvement; thus, our sleep results were at maximum sleep quality at their age.

This negative finding between CRP levels and sleep quality was not completely unexpected. These same RPWs showed dispositional optimism (identified ca 1973) was a protective factor in confronting their imprisonment experiences and future psychiatric diagnoses (Segovia et al., 2012). Furthermore, Segovia et al. (2015) showed optimism continued to be a strong protective factor to their physical/mental health today, 40 years later.

While the data indicated that CRP levels lacked a significant association with sleep quality, they did suggest a relationship between CRP and level of average daytime activity. Those who were at low risk of cardiac disease (CRP < .1 mg/dL) demonstrated significantly more average daily vigorous activity compared to those at high risk (CRP > .3 mg/dL). Our results lend some support to previous reports which concluded that CRP concentrations are inversely related to PA (Ford, 2002; Kasapis and Thompson, 2005). Moreover, those identified as psychologically resilient from their former prisoner of war experiences had higher average daily moderate activity levels and comprised the majority (N = 51) of the low-risk group as well. This finding is in agreement with numerous previous reports that positive psychological states are associated with reduced levels of CRP (Hou and Baldwin, 2012; Raison et al., 2013; Spitzer et al., 2010; Toker et al., 2005; Vogelzangs et al., 2013).

Benefits of an active lifestyle and proper sleep can be far reaching and are likely to include reduced hospitalization, disability, dependence, and frailty for a significant and ever growing segment of the populous. Resilience has been noted to be a strong indication of physical and psychological health as well as a measure of successful aging (Tomas et al., 2012). Using this construct to address the ability to cope with trauma, negative life experiences, or the stressors of aging could influence positive health of the older population. Also, given that inflammation can be an influential risk factor for both physical and psychological morbidity in older adults, it is imperative that patients and providers alike consider these modifiable contributors to inflammatory levels (Kiecolt-Glaser et al., 2002; Zacho et al., 2010). To our knowledge, this is the first study to objectively investigate both activity and sleep across a spectrum in the older population to potentially identify modifiable risk factors that affect positive health. The benefits of PA and proper sleep on overall health have long been studied but rarely in conjunction with each other or along an activity continuum in the aged. The use of actigraphy enabled a data collection period of 7 days of 24-hour observation while in the participants’ natural environment. This allowed for a more true-to-life accounting of their behaviors, over a longer period of time, than would have been available in a laboratory setting.

Limitations

While actigraphy is a reasonable substitute for formal PSG, it has known limitations. Specifically, it has been reported that some devices and models may lack specificity in identifying wakefulness during motionless sleep periods (Sedah, 2011). Additionally, the study was limited by a cohort of aged Caucasian men from a high socioeconomic status; therefore, the results are not generalizable to the average US population. Also, a restricted definition for “resilience” was used that applied to the participants’ ability to maintain psychological health despite their unique experiences in war and combat. However, this terminology could still be appropriate for a broad range of individuals healthfully overcoming many of life’s challenges. Finally, the participants were 40 years post-trauma. Individuals examined more proximal to traumatic events may show greater differences in sleep disturbance resulting in CRP increases.

Conclusion

While the intended objective of documenting an association between sleep and CRP was not confirmed, the present research demonstrated possible associations for future inquiry between CRP and daytime activity as well as psychological resilience. As literature continues to suggest that CRP correlates with a myriad of health aspects, it makes it a worthwhile biomarker for the quest for positive health in older adults. The concept of health has broadened to encompass physical, mental, and social fitness and not just the “absence of disease or infirmity”; therefore, taking advantage of such a multi-faceted biomarker could give the aging, and those that treat and care for them, the ability to move past merely curing disease and onto enhancing quantity and quality of life (WHO, 1948). Our results in combination with previous data are important for population health, as they highlight the potential for CRP to act as an objective indicator of overall positive health, and to serve as both a catalyst and a target for treatment.

Future research should include participants who fit a wider range of demographic characteristics in order to increase generalizability and participants with known reduced sleep efficiency to compare with those with satisfactory sleep abilities. For example, sleep efficiency and CRP could be measured in active duty combatants post-deployment when the potential for physical and psychological trauma is more acute. The underlying mechanisms and directionality of CRP are not fully understood, and further investigation involving longitudinal data would pave the way for this biomarker’s use in diagnostics and intervention.

Footnotes

Acknowledgements

This research was sponsored by the U.S Office of Naval Research and the Bureau of Medicine and Surgery Clinical Naval Investigation Program. The Robert E. Mitchell Center for Prisoner of War Studies would like to take this opportunity to thank all of our repatriated prisoners of war (RPWs) for their participation in our studies, which makes our research possible. To all our RPWs, we thank you for your courage, honor, and commitment.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article was supported by a grant (ONR-FY13 N0001413AF00002) from the Office of Naval Research (ONR). The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the ONR or the Department of the Navy.

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