- Email: [email protected]
Loneliness trajectories
Journal of Adolescence 36 (2013) 1247–1249
Contents lists available at ScienceDirect
Journal of Adolescence journal homepage: www.elsevier.com/locate/jado
Editorial
Loneliness trajectories a b s t r a c t Keywords Loneliness Adolescence Developmental trajectories
This special section brings together five studies using group-based modeling to capture developmental trajectories of loneliness from age 7 through age 20. Together, the findings from these studies provide further evidence that developmental trajectories of loneliness are likely not best understood at a continuum but reflect distinct subpopulations that differ both where they start out and how they change over time in terms of mean levels of loneliness. Furthermore, adolescents who show chronically high loneliness or increasing loneliness over time exhibit poorer psychological and physical health, including greater incidence of depressive symptoms and more frequent suicide attempts. The findings from these studies also suggest that individuals experiencing increases in loneliness with age fare worse as well in terms of both physical and psychological health. Ó 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.
Loneliness is often conceptualized as a subjective state of distress between actual and perceived relationships (Peplau & Perlman, 1982). Experiences of loneliness have major consequences for physical and psychological health (Hawkley & Cacioppo, 2010). Importantly, levels of loneliness tend to change with age and peak during adolescence (Heinrich & Gullone, 2006). Recent empirical findings indicate, however, that not all children follow the same age-related course of loneliness. In other words, there may be distinct age-related trajectories of loneliness. These research findings are, however, sparse and limited to changes from third to fifth grade (Jobe-Shields, Cohen, & Parra, 2011) and a sample of Latino adolescents during the first two years of high school (Benner, 2011). What is needed are comprehensive studies that investigate these developmental trajectories across a wider age range in order to comprehensively assess changes in loneliness from childhood through adolescence and into early adulthood and their implications for well-being and health. In this special section we bring together empirical findings from five original empirical studies. The findings from these studies provide further insight into distinct patterns of change in loneliness from childhood through early adulthood, how these patterns are a function of earlier experiences, and how they predict future behavioral functioning. We felt it was important to bring these papers together in a special section because group-based modeling approaches have been criticized for their difficulty to replicate across different samples (Bauer, 2007). Thus, it is our hope that having these findings together in one special section will provide a comprehensive picture of changes in loneliness during the first decades of life. In terms of integrating the findings from these 5 studies, we also want to pose a few notes of caution; the analytic toolboxes differed a bit across papers. Some may be quite obvious (differences in terms of measurement of loneliness) but other ones are not (or appear to be under the radar when people are integrating this work). Three of the five papers use Growth Mixture Modeling (GMM; Harris, Qualter, & Robinson, 2013; Ladd & Ettekal, 2013; Qualter et al., 2013) whereas two papers use Latent Class Growth Analysis (LCGA; Schinka, van Dulmen, Mata, Bossarte, & Swahn, 2013; Vanhalst, Goossens, Luyckx, Scholte, & Engels, 2013). This is an important difference because GMM relaxes the within-group variability assumption – assuming that the within-group variability is normally distributed – whereas LCGA does not. On the one hand it may be appealing to relax the within-group variability in these group-based models. Why not model variability around the mean, because one would expect there to be variability. On the other hand, GMM are more computer-intensive and assume the within-group variability is normally distributed, which may or may not be the case. We do not want to pose that LCGA are more correct/appropriate than GMM (or vice versa). What is important to note is that researchers make trade-offs in choosing one analytic technique over a different one. 0140-1971/$ – see front matter Ó 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.adolescence.2013.08.001
1248
M.H.M. van Dulmen, L. Goossens / Journal of Adolescence 36 (2013) 1247–1249
Despite the differences in analytic approach, it is remarkable how similar the results are across these five studies, spanning from age 7 to age 20 (see Table 1). In each of the studies, the largest proportion of individuals report stable low levels of loneliness across time. Furthermore, all 5 studies report a trajectory of individuals with decreasing levels of loneliness across time. Furthermore, 4 of the 5 studies report also a group of children/adolescents who report stable high levels of loneliness across time (Ladd, Qualter, Vanhalst, and Schinka). One possibility that the Harris et al. study only identified two classes is that this study had – compared to the other 4 studies – a relatively small sample size. Together, the findings from these studies provide further evidence that developmental trajectories of loneliness are likely not best understood as a continuum but reflect distinct subpopulations that differ both where they start out and how they change over time in terms of mean levels of loneliness. It is important to warn against the danger of reification of the trajectory groups found across the various studies. These groups are statistically derived and represent abstract and approximate trajectories that have to be interpreted, in the ideal world, within a coherent theoretical framework. In various domains, such as alcohol use and conduct disorders, four trajectories are frequently found, that represent a consistently “low” group, a “decrease” group, an “increase” group, and a consistently “high” group. These four-trajectory groups tend to emerge across samples that vary considerably in participants’ age at baseline, measurement frequency, and total duration of the study. This phenomenon, which is as yet poorly understood, has brought some authors to pay particular attention to those trajectory groups that deviate from the ubiquitous fourtrajectory pattern (i.e., high, decrease, increase, and low; Sher, Jackson, & Steinley, 2006). One should also realize that all trajectory approaches are based on the assumption that standard self-report measures of loneliness are sensitive to within-person change. This assumption can be tested empirically in diary studies using brief versions of standard instruments, but no such study has yet been conducted for loneliness scales (see Cranford et al., 2006, for such a diary study on self-reported mood). In short, the exact meaning and significance of the loneliness trajectory groups will become clear gradually, as future work will test all the basic assumptions of the statistical techniques employed and clarify the dependability of the results obtained with these techniques. Pending such research, we can focus on the antecedents and consequences of membership in the different trajectory groups, as these findings can also clarify, to a certain extent, the relevance of the findings obtained. As regards consequences, the studies in this special section clearly demonstrate that adolescents who show chronically high loneliness or increasing loneliness over time exhibit poorer psychological and physical health (Harris et al., 2013; Qualter et al., 2013), including greater incidence of depressive symptoms (Harris et al., 2013; Ladd & Ettekal, 2013; Qualter et al., 2013; Schinka et al., 2013; Vanhalst et al., 2013) and more frequent suicide attempts (Schinka et al., 2013). Furthermore, individuals experiencing increases in loneliness with age fare worse as well in terms of both physical (Qualter et al., 2013) and psychological health (Qualter et al., 2013; Schinka et al., 2013; Vanhalst et al., 2013). The papers in this special section provide a coherent picture of changes in loneliness during childhood and adolescence, as well as their antecedents and consequences. Furthermore, the papers in this special section are accompanied by a commentary that addresses conceptual issues in the field of loneliness (Laursen & Hartl, 2013). In particular, Laursen and Hartl Table 1 Overview sample and methodological characteristics loneliness trajectory manuscripts. Authors
Sample
Loneliness measure
Assessments
Classes (%)
Harris et al.
England N ¼ 209 51% Male United States N ¼ 478 50% Female
Loneliness and Aloneness Scale for Children and Adolescents (Goossens et al., 2009) peer-related loneliness sub-scale Three items (Ladd, Kochenderfer, & Coleman, 1996) from the Loneliness and Social Satisfaction Questionnaire (Cassidy & Asher, 1992)
Age 8–11 3 Assessments, 18 months apart Age 12–18 7 Assessments, annually
Schinka et al.
United States N ¼ 832 51% Female
Sixteen items from the Loneliness and Social Satisfaction Questionnaire (Cassidy & Asher, 1992)
Age 9–15 3 Assessments, Ages 9, 11, and 15
Qualter et al.
England N ¼ 586 50% Female
Loneliness and Aloneness Scale for Children and Adolescents (Goossens et al., 2009) peer-related loneliness sub-scale
Age 7–17 6 Assessments, every two years
Vanhalst et al.
Netherlands N ¼ 389 53% Male
Loneliness and Aloneness Scale for Children and Adolescents (Goossens et al., 2009) peer-related loneliness sub-scale
Age 15–20 5 Assessments, annually
Two classes Stable low (52%) Relatively high, reducing (48%) Five classes No loneliness (18.9%) Low (19.5%) Declining moderate (41.6%) Declining (6.2%) Stable high (13.7%) Five classes Stable low (49.1%) Decreasing (10.7%) Moderate increasing (31.6%) High increasing (4.5%) Chronic high (4.1%) Four classes Low stable (37%) Moderate decliners (23%) Moderate increasers (18%) Stable high (22%) Five classes Stable low (65%) Low increasing (17%) Moderate decreasing (8%) High decreasing (6%) Chronically high (3%)
Ladd & Ettekal
Note. LCGA ¼ Latent Class Growth Analysis; GMM ¼ Growth Mixture Modeling.
M.H.M. van Dulmen, L. Goossens / Journal of Adolescence 36 (2013) 1247–1249
1249
(2013) provide a new developmental perspective on how to conceptualize loneliness in terms of perceived social isolation. We hope that these 6 scholarly works encourage and foster continued endeavors on understanding the antecedents and consequences of loneliness during adolescence. References Bauer, D. J. (2007). Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research, 42, 757–786. Benner, A. D. (2011). Latino adolescents’ loneliness, academic performance, and the buffering nature of friendships. Journal of Youth and Adolescence, 40, 556–567. Cassidy, J., & Asher, S. R. (1992). Loneliness and peer relations in young children. Child Development, 63, 350–365. Cranford, J. A., Shrout, P. E., Ida, M., Rafaeli, E., Yip, T., & Bolger, N. (2006). A procedure for evaluating sensitivity to within-person change: can mood measures in diary studies detect changes reliably? Personality and Social Psychology Bulletin, 32, 917–929. Goossens, L., Lasgaard, M., Luyckx, K., Vanhalst, J., Mathias, S., & Masy, E. (2009). Loneliness and solitude in adolescence: a confirmatory factor analysis of alternative models. Personality and Individual Differences, 47, 890–894. Harris, R. A., Qualter, P., & Robinson, S. J. (2013). Loneliness trajectories from middle childhood to pre-adolescence: impact on perceived health and sleep disturbance. Journal of Adolescence. Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: a theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40, 218–227. Heinrich, L. M., & Gullone, E. (2006). The clinical significance of loneliness. Clinical Psychology Review, 26, 695–718. Jobe-Shields, L., Cohen, R., & Parra, G. R. (2011). Patterns of change in children’s loneliness: trajectories from third through fifth grades. Merrill-Palmer Quarterly, 57, 25–47. Ladd, G. W., & Ettekal, I. (2013). Peer-related loneliness across early to late adolescence: normative trends, intra-individual trajectories, and links with depressive symptoms. Journal of Adolescence. Ladd, G. W., Kochenderfer, B. J., & Coleman, C. C. (1996). Friendship quality as a predictor of young children’s early school adjustment. Child Development, 67, 1103–1118. Laursen, B., & Hartl, A. C. (2013). Understanding loneliness during adolescence: developmental changes that increase the risk of perceived social isolation. Journal of Adolescence. Peplau, L. A., & Perlman, D. (1982). Loneliness: A sourcebook of current theory, research, and therapy. New York: Wiley. Qualter, P., Brown, S. L., Rotenberg, K. J., Vanhalst, J., Harris, R. A., Goossens, L., et al. (2013). Trajectories of loneliness during childhood and adolescence: predictors and health outcomes. Journal of Adolescence. Schinka, K. C., van Dulmen, M. H. M., Mata, A. D., Bossarte, R., & Swahn, M. H. (2013). Psychosocial predictors and outcomes of loneliness trajectories from childhood to early adolescence. Journal of Adolescence. Sher, K. J., Jackson, K. M., & Steinley, D. (2006). Alcohol use trajectories and the ubiquitous cat’s cradle: cause for concern? Journal of Abnormal Psychology, 120, 322–335. Vanhalst, J., Goossens, L., Luyckx, K., Scholte, R. H. J., & Engels, R. C. M. E. (2013). The development of loneliness from mid- to late adolescence: trajectory classes, personality traits, and psychosocial functioning. Journal of Adolescence.
Manfred H.M. van Dulmen* Kent State University, Department of Psychology, PO Box 5190, Kent, OH 44242, USA Luc Goossens KU Leuven – University of Leuven, Belgium Corresponding author. Tel.: þ1 330 672 2504. E-mail address: [email protected]
Contents lists available at ScienceDirect
Journal of Adolescence journal homepage: www.elsevier.com/locate/jado
Editorial
Loneliness trajectories a b s t r a c t Keywords Loneliness Adolescence Developmental trajectories
This special section brings together five studies using group-based modeling to capture developmental trajectories of loneliness from age 7 through age 20. Together, the findings from these studies provide further evidence that developmental trajectories of loneliness are likely not best understood at a continuum but reflect distinct subpopulations that differ both where they start out and how they change over time in terms of mean levels of loneliness. Furthermore, adolescents who show chronically high loneliness or increasing loneliness over time exhibit poorer psychological and physical health, including greater incidence of depressive symptoms and more frequent suicide attempts. The findings from these studies also suggest that individuals experiencing increases in loneliness with age fare worse as well in terms of both physical and psychological health. Ó 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.
Loneliness is often conceptualized as a subjective state of distress between actual and perceived relationships (Peplau & Perlman, 1982). Experiences of loneliness have major consequences for physical and psychological health (Hawkley & Cacioppo, 2010). Importantly, levels of loneliness tend to change with age and peak during adolescence (Heinrich & Gullone, 2006). Recent empirical findings indicate, however, that not all children follow the same age-related course of loneliness. In other words, there may be distinct age-related trajectories of loneliness. These research findings are, however, sparse and limited to changes from third to fifth grade (Jobe-Shields, Cohen, & Parra, 2011) and a sample of Latino adolescents during the first two years of high school (Benner, 2011). What is needed are comprehensive studies that investigate these developmental trajectories across a wider age range in order to comprehensively assess changes in loneliness from childhood through adolescence and into early adulthood and their implications for well-being and health. In this special section we bring together empirical findings from five original empirical studies. The findings from these studies provide further insight into distinct patterns of change in loneliness from childhood through early adulthood, how these patterns are a function of earlier experiences, and how they predict future behavioral functioning. We felt it was important to bring these papers together in a special section because group-based modeling approaches have been criticized for their difficulty to replicate across different samples (Bauer, 2007). Thus, it is our hope that having these findings together in one special section will provide a comprehensive picture of changes in loneliness during the first decades of life. In terms of integrating the findings from these 5 studies, we also want to pose a few notes of caution; the analytic toolboxes differed a bit across papers. Some may be quite obvious (differences in terms of measurement of loneliness) but other ones are not (or appear to be under the radar when people are integrating this work). Three of the five papers use Growth Mixture Modeling (GMM; Harris, Qualter, & Robinson, 2013; Ladd & Ettekal, 2013; Qualter et al., 2013) whereas two papers use Latent Class Growth Analysis (LCGA; Schinka, van Dulmen, Mata, Bossarte, & Swahn, 2013; Vanhalst, Goossens, Luyckx, Scholte, & Engels, 2013). This is an important difference because GMM relaxes the within-group variability assumption – assuming that the within-group variability is normally distributed – whereas LCGA does not. On the one hand it may be appealing to relax the within-group variability in these group-based models. Why not model variability around the mean, because one would expect there to be variability. On the other hand, GMM are more computer-intensive and assume the within-group variability is normally distributed, which may or may not be the case. We do not want to pose that LCGA are more correct/appropriate than GMM (or vice versa). What is important to note is that researchers make trade-offs in choosing one analytic technique over a different one. 0140-1971/$ – see front matter Ó 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.adolescence.2013.08.001
1248
M.H.M. van Dulmen, L. Goossens / Journal of Adolescence 36 (2013) 1247–1249
Despite the differences in analytic approach, it is remarkable how similar the results are across these five studies, spanning from age 7 to age 20 (see Table 1). In each of the studies, the largest proportion of individuals report stable low levels of loneliness across time. Furthermore, all 5 studies report a trajectory of individuals with decreasing levels of loneliness across time. Furthermore, 4 of the 5 studies report also a group of children/adolescents who report stable high levels of loneliness across time (Ladd, Qualter, Vanhalst, and Schinka). One possibility that the Harris et al. study only identified two classes is that this study had – compared to the other 4 studies – a relatively small sample size. Together, the findings from these studies provide further evidence that developmental trajectories of loneliness are likely not best understood as a continuum but reflect distinct subpopulations that differ both where they start out and how they change over time in terms of mean levels of loneliness. It is important to warn against the danger of reification of the trajectory groups found across the various studies. These groups are statistically derived and represent abstract and approximate trajectories that have to be interpreted, in the ideal world, within a coherent theoretical framework. In various domains, such as alcohol use and conduct disorders, four trajectories are frequently found, that represent a consistently “low” group, a “decrease” group, an “increase” group, and a consistently “high” group. These four-trajectory groups tend to emerge across samples that vary considerably in participants’ age at baseline, measurement frequency, and total duration of the study. This phenomenon, which is as yet poorly understood, has brought some authors to pay particular attention to those trajectory groups that deviate from the ubiquitous fourtrajectory pattern (i.e., high, decrease, increase, and low; Sher, Jackson, & Steinley, 2006). One should also realize that all trajectory approaches are based on the assumption that standard self-report measures of loneliness are sensitive to within-person change. This assumption can be tested empirically in diary studies using brief versions of standard instruments, but no such study has yet been conducted for loneliness scales (see Cranford et al., 2006, for such a diary study on self-reported mood). In short, the exact meaning and significance of the loneliness trajectory groups will become clear gradually, as future work will test all the basic assumptions of the statistical techniques employed and clarify the dependability of the results obtained with these techniques. Pending such research, we can focus on the antecedents and consequences of membership in the different trajectory groups, as these findings can also clarify, to a certain extent, the relevance of the findings obtained. As regards consequences, the studies in this special section clearly demonstrate that adolescents who show chronically high loneliness or increasing loneliness over time exhibit poorer psychological and physical health (Harris et al., 2013; Qualter et al., 2013), including greater incidence of depressive symptoms (Harris et al., 2013; Ladd & Ettekal, 2013; Qualter et al., 2013; Schinka et al., 2013; Vanhalst et al., 2013) and more frequent suicide attempts (Schinka et al., 2013). Furthermore, individuals experiencing increases in loneliness with age fare worse as well in terms of both physical (Qualter et al., 2013) and psychological health (Qualter et al., 2013; Schinka et al., 2013; Vanhalst et al., 2013). The papers in this special section provide a coherent picture of changes in loneliness during childhood and adolescence, as well as their antecedents and consequences. Furthermore, the papers in this special section are accompanied by a commentary that addresses conceptual issues in the field of loneliness (Laursen & Hartl, 2013). In particular, Laursen and Hartl Table 1 Overview sample and methodological characteristics loneliness trajectory manuscripts. Authors
Sample
Loneliness measure
Assessments
Classes (%)
Harris et al.
England N ¼ 209 51% Male United States N ¼ 478 50% Female
Loneliness and Aloneness Scale for Children and Adolescents (Goossens et al., 2009) peer-related loneliness sub-scale Three items (Ladd, Kochenderfer, & Coleman, 1996) from the Loneliness and Social Satisfaction Questionnaire (Cassidy & Asher, 1992)
Age 8–11 3 Assessments, 18 months apart Age 12–18 7 Assessments, annually
Schinka et al.
United States N ¼ 832 51% Female
Sixteen items from the Loneliness and Social Satisfaction Questionnaire (Cassidy & Asher, 1992)
Age 9–15 3 Assessments, Ages 9, 11, and 15
Qualter et al.
England N ¼ 586 50% Female
Loneliness and Aloneness Scale for Children and Adolescents (Goossens et al., 2009) peer-related loneliness sub-scale
Age 7–17 6 Assessments, every two years
Vanhalst et al.
Netherlands N ¼ 389 53% Male
Loneliness and Aloneness Scale for Children and Adolescents (Goossens et al., 2009) peer-related loneliness sub-scale
Age 15–20 5 Assessments, annually
Two classes Stable low (52%) Relatively high, reducing (48%) Five classes No loneliness (18.9%) Low (19.5%) Declining moderate (41.6%) Declining (6.2%) Stable high (13.7%) Five classes Stable low (49.1%) Decreasing (10.7%) Moderate increasing (31.6%) High increasing (4.5%) Chronic high (4.1%) Four classes Low stable (37%) Moderate decliners (23%) Moderate increasers (18%) Stable high (22%) Five classes Stable low (65%) Low increasing (17%) Moderate decreasing (8%) High decreasing (6%) Chronically high (3%)
Ladd & Ettekal
Note. LCGA ¼ Latent Class Growth Analysis; GMM ¼ Growth Mixture Modeling.
M.H.M. van Dulmen, L. Goossens / Journal of Adolescence 36 (2013) 1247–1249
1249
(2013) provide a new developmental perspective on how to conceptualize loneliness in terms of perceived social isolation. We hope that these 6 scholarly works encourage and foster continued endeavors on understanding the antecedents and consequences of loneliness during adolescence. References Bauer, D. J. (2007). Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research, 42, 757–786. Benner, A. D. (2011). Latino adolescents’ loneliness, academic performance, and the buffering nature of friendships. Journal of Youth and Adolescence, 40, 556–567. Cassidy, J., & Asher, S. R. (1992). Loneliness and peer relations in young children. Child Development, 63, 350–365. Cranford, J. A., Shrout, P. E., Ida, M., Rafaeli, E., Yip, T., & Bolger, N. (2006). A procedure for evaluating sensitivity to within-person change: can mood measures in diary studies detect changes reliably? Personality and Social Psychology Bulletin, 32, 917–929. Goossens, L., Lasgaard, M., Luyckx, K., Vanhalst, J., Mathias, S., & Masy, E. (2009). Loneliness and solitude in adolescence: a confirmatory factor analysis of alternative models. Personality and Individual Differences, 47, 890–894. Harris, R. A., Qualter, P., & Robinson, S. J. (2013). Loneliness trajectories from middle childhood to pre-adolescence: impact on perceived health and sleep disturbance. Journal of Adolescence. Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: a theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40, 218–227. Heinrich, L. M., & Gullone, E. (2006). The clinical significance of loneliness. Clinical Psychology Review, 26, 695–718. Jobe-Shields, L., Cohen, R., & Parra, G. R. (2011). Patterns of change in children’s loneliness: trajectories from third through fifth grades. Merrill-Palmer Quarterly, 57, 25–47. Ladd, G. W., & Ettekal, I. (2013). Peer-related loneliness across early to late adolescence: normative trends, intra-individual trajectories, and links with depressive symptoms. Journal of Adolescence. Ladd, G. W., Kochenderfer, B. J., & Coleman, C. C. (1996). Friendship quality as a predictor of young children’s early school adjustment. Child Development, 67, 1103–1118. Laursen, B., & Hartl, A. C. (2013). Understanding loneliness during adolescence: developmental changes that increase the risk of perceived social isolation. Journal of Adolescence. Peplau, L. A., & Perlman, D. (1982). Loneliness: A sourcebook of current theory, research, and therapy. New York: Wiley. Qualter, P., Brown, S. L., Rotenberg, K. J., Vanhalst, J., Harris, R. A., Goossens, L., et al. (2013). Trajectories of loneliness during childhood and adolescence: predictors and health outcomes. Journal of Adolescence. Schinka, K. C., van Dulmen, M. H. M., Mata, A. D., Bossarte, R., & Swahn, M. H. (2013). Psychosocial predictors and outcomes of loneliness trajectories from childhood to early adolescence. Journal of Adolescence. Sher, K. J., Jackson, K. M., & Steinley, D. (2006). Alcohol use trajectories and the ubiquitous cat’s cradle: cause for concern? Journal of Abnormal Psychology, 120, 322–335. Vanhalst, J., Goossens, L., Luyckx, K., Scholte, R. H. J., & Engels, R. C. M. E. (2013). The development of loneliness from mid- to late adolescence: trajectory classes, personality traits, and psychosocial functioning. Journal of Adolescence.
Manfred H.M. van Dulmen* Kent State University, Department of Psychology, PO Box 5190, Kent, OH 44242, USA Luc Goossens KU Leuven – University of Leuven, Belgium Corresponding author. Tel.: þ1 330 672 2504. E-mail address: [email protected]