Magnetic resonance imaging and computed tomography as tools for the investigation of sperm whale (Physeter macrocephalus) teeth and eye
2017

Kristian Aage
Olsen Alstrup
Ole Lajord Munk
Trine Hammer Jensen
Lasse Fast Jensen
Abdi Hedayat
Brian Hansen
Magnetic resonance imaging and computed tomography as tools for the investigation of sperm whale (Physeter macrocephalus) teeth and eye

Acta Vet Scand
5938201710.1186/s13028-017-0307-yRESEARCHImaging techniquesComputer tomographyMagnetic resonanceSperm whalesEyesTeeth
Background: Scanning techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) are useful tools in veterinary and human medicine. Here we demonstrate the usefulness of these techniques in the study of the anatomy of wild marine mammals as part of a necropsy. MRI and CT scans of sperm whale teeth (n = 4) were performed. The methods were compared and further compared to current standard methods for evaluation of tooth layering. For MRI a zero echo time sequence was used, as previously done for imaging of intact human teeth. For CT two different clinical scanners were used.Results:The three scanners did not provide sufficient information to allow age estimation, but both MRI and CT provided anatomical information about the tooth cortex and medulla without the need for sectioning the teeth. MRI scanning was also employed for visualizing the vascularization of an intact eye from one of the stranded sperm whale.Conclusions: Clearly, MRI was useful for investigation of the retinal vasculation, but optimum results would require well-preserved tissue. It was not possible to estimate age based on CT scans of tooth growth lines. Further research is needed to clarify the usability of MRI and CT as tools for marine mammal research when samples need to remain intact or when a spatial (three dimensional) arrangement of features needs to be determined.

Background

Traditionally, anatomical and pathological knowledge in cetacean research is obtained by dissection of stranded individuals followed by e.g. bacteriological, virological, morphological or histological examinations [1][2][3]. However, modern scanning techniques have much to offer in this research field-at least as a supplement to dissection: first, modern scanners offer the possibility of noninvasive imaging of organs providing access to data about organ structure in situ, e.g. vasculature of soft tissue organs such as the eye, heart or kidney. Second, many museums find preservation of the intact organ desirable, which can be compromised by traditional examination techniques, in particular necropsy. In toothed whales, for instance, age is estimated by counting growth layers in the teeth after cutting them into halves, which makes the teeth less suited for later display. Non-invasive analyses are also preferable when dealing with samples originating from rare or extinct species. Third, modern imaging techniques and computer aided data visualization offer a better three-dimensional understanding of the organ morphology than simple dissection, which is a benefit e.g. for bone examination in whales [4]. Today, threedimensional imaging techniques can provide anatomical information more quickly and in a non-invasive way, as described for terrestrial vertebrates by Lauridsen et al. [5].

Both magnetic resonance imaging (MRI) and computed tomography (CT) provide detailed images with millimeter spatial resolution for routine scans and cellular level resolution is attainable when imaging samples [6][7][8][9]. In MRI a constant external magnetic field sets up a magnetization in the subject or sample being imaged. This magnetization may be manipulated with radiofrequency pulses to induce a signal in a detector coil. During the scan the magnetization is encoded in frequency and phase by additional time-dependent magnetic fields (called gradients). This encoding makes it possible to reconstruct an image from the signal received in the coil. MRI offers very good soft tissue contrast, but MRI in bony structures is often not feasible due to the very rapid signal decay in these crystalline structures. In comparison, medical CT scanners consist of a rotating X-ray source, which is rigidly linked to an X-ray detector located on the other side of the target [10]. The target is exposed to a fan-shaped X-ray beam, while being translated through the scanner. The CT detectors measure the X-ray transmission through the target at all angles, which allows reconstruction of a three-dimensional density image. The resulting CT image is linearly rescaled into CT numbers in Hounsfield units.

CT is a fast and useful technique for visualizing the normal and pathological anatomy of organs, which has been particularly well-suited for imaging of mineral or crystalline structures such as bones and teeth. For clinical use, contrast-enhanced CT protocols with intravenously or orally administrated iodine-based contrast agents are routinely used to improve tissue differentiation and delineation of anatomic structures, and for ex vivo imaging, iodine staining of soft tissue specimens has recently been shown to improve visualization using diffusible iodine-based contrast-enhanced computed tomography (diceCT) [11]. Both MRI and CT are routinely used in human and veterinary medicine, and they are also useful tools in preclinical and clinical research [12][13][14].

Recent technological developments have improved both resolution and versatility of MRI and CT scanning techniques. For instance, using ultra-short echo time (UTE) sequences [15,16] and more recently, zero echo time (ZTE) sequences [17][18][19] allow MRI to be used on bony structures as well. Thus, Weiger et al. [20] showed that the ZTE technique provides a good delineation of mineralized dentine and enamel layers in human teeth. Likewise, recent developments in CT scanning and reconstruction techniques have increased the resolution significantly.

In cetaceans, scanning techniques have proved useful in anatomical examinations and have been applied in studying e.g. head anatomy of Cuvier's beaked whale (Ziphius cavirostris) [21][22][23] and sperm whale (Physeter macrocephalus) [24], brain anatomy of the white whale (Delphinapterus leucas) [25] and dwarf sperm whale (Kogia simus) [26] and in discovering a sensory organ related to lunge feeding in rorqual whales (Balaenopteridae), a technique used to engulf large quantities of concentrated prey [27]. However, scanning techniques are not applied on a routine basis in cetacean research due to the difficulties of getting access to fresh tissue or organ samples as well as logistical challenges and the sheer size of many cetacean species.

In this study the usage of modern MRI and CT scanning techniques in anatomical examinations of sperm whales was evaluated for two challenging samples: thick dense teeth with fine inner structures and an eye with detailed soft-tissue structures. Specifically, we investigate whether MRI and CT scanning of sperm whale teeth are suitable methods for age estimation by identifying tooth growth lines [28] and resolving details of anatomy, including measurement of dentin and enamel thickness. In addition, the anatomical details of retina vasculation in situ and sclera structure in the sperm whale eye were studied by MR imaging and compared to similar scans acquired in an isolated pig eye. This demonstrated possibilities and limitations of using modern clinical and preclinical imaging techniques in cetacean research, for which samples are often not in an ideal state due to various stages of decomposition.

Methods

Teeth ( Fig. 1) were sampled from a whale bone collection belonging to the Natural History Museum of Denmark, Copenhagen, Denmark. The teeth (n = 4) were from stranded male sperm whales stranded in 1996 (MCE1654; Rømø, Denmark), 1997 (MCE1652; Rømø, Denmark) and 2014 (MCE1644 and MCE1645; Henne Strand, Denmark). Due to the limited gantry size only teeth from MCE1652 (tooth A) and MCE1654 (tooth B) were MRI Fig. 1 The two whale teeth (tooth a MCE1652 and tooth b MCE1654) used for evaluation of the use of ZTE MRI in whale age estimation scanned, while all four teeth were CT scanned. In 2014, the eye was taken from a stranded whale (MCE1645) a few hours after death at Henne Strand, Denmark. The whale was still alive when it stranded, and the eye was immediately preserved in 4% buffered formaldehyde post mortem. After 2 weeks, the eye was transferred to a 99% ethanol solution until scanning was performed. Similarly, a pig eye was sampled from a female 40 kg domestic pig (Sus scrofa domesticus) after euthanasia as part of a different research experiment (approved by The Danish Animal Experiments Inspectorate). The pig eye was processed in the same way as the whale eye.

CT of the whale teeth was performed on two clinical CT scanners: Siemens Biograph 64 Truepoint PET/CT and Siemens Somatom Definition Flash. These or similar modern scanners are widely available. Images of the teeth were comparable for the two scanner systems (Figs. 2 and 3), but the scan settings and reconstruction parameters were important for the quality of the images and visualization of the inner structures of the teeth. We aimed at using parameters for high-resolution imaging. Details of the scan and reconstruction parameters are provided below. The eyes were not CT scanned because good soft tissue CT imaging requires the use of contrast agent, which is only possible for in vivo studies. Thus, MRI is more suitable for this purpose.

MRI of teeth and eyes was performed on a Bruker Biospec 9.4T animal system. Scan details are provided below. Anatomical scans of the eye were performed along three orthogonal directions to ensure a detailed mapping of the anatomical features. For mapping the vasculature of the eye, including the retina we used a fast gradient echo scan which to our experience is very sensitive to the vasculature in this type of sample where blood was not washed out prior to preservation. After scanning, vascular segments in each image plane were segmented out by hand and processed in Matlab (The Mathworks, Natick, MA, USA). In this manner, mapping of the eye vasculature was possible.

MRI method details

The eye anatomy was imaged using a 3D MDEFT sequence. Resolution in the pig eye scans was 120 µm × 120 µm × 250 µm, echo time: 3.5 ms, repetition time: 4.2 s. In the whale eye the resolution was 466 µm × 466 µm × 700 µm.

Teeth were imaged using a 40 mm quadrature coil and a ZTE sequence. For tooth A the scan parameters were: repetition time: 1.92 ms, flip angle: 5°, Bandwidth: 300 kHz, isotropic resolution of 234 µm, 1000 averages were acquired resulting in a total scan time of approximately 21 h. Tooth B was scanned with scan parameters: repetition time 8 ms, flip angle: 10°, Bandwidth: 313 kHz, isotropic resolution of 234 µm, 250 averages were acquired resulting in a total scan time of approximately 22 h. The ZTE sequence does not allow selective excitation and therefore everything within the coil's field of view contributes to the signal. The edges of the coil field of view are seen in Figs. 4 and 5 as a region where the image fades out. In each image, image contrast has been adjusted to improve visibility of the described features. In all cases contrast was adjusted evenly over the whole image. Remaining image background has been masked for presentation.

CT method details

Tooth images were imaged on two CT systems using the following scan-and reconstruction parameters.

Results

CT scans of whale teeth are shown in Fig. 2 (tooth A and tooth B) and Fig. 3 (tooth B). The CT images from two different clinical CT scanners are shown-the slices are similar, but not exactly from the same plane. The differences between the images are mainly due to different scan settings and reconstruction parameters rather than the CT scanner hardware. The data shown in Fig. 3 was acquired at higher resolution and is consequently more noisy than the data shown in Fig. 2. Higher resolution improves visualization of the fine V-shaped pattern in the tooth, whereas larger structures such as inner holes and different densities of enamel and marrow are clearly visualized at both resolutions. However, only parts of the V-shaped structures can be visualized and the total number of layers cannot be determined from these images. CT scans of the two other teeth gave similar results (data not shown).

The axial ZTE MRI image of tooth A in Fig. 4 (upper left corner) shows concentric ring-like structures. When shown with a sagittal slice orientation (Fig. 4, upper right corner and lower row) V-shaped patterns are seen. Examples of the ZTE scans of tooth B are shown in Fig. 5.

Here the contrast delineates the border between cortex and medulla.

Anatomical MRI scans of the pig eye and the significantly larger whale eye is shown in Fig. 6. In contrast to the pig eye, the deterioration was evident in the whale eye. Furthermore, the whale bulbus is seen to be encapsulated by a very large sclera (and remnants of retractor muscles, which were gently tried off-dissected prior to scanning). Neither of these features were prominent in the pig eye. While the pig eye scan very clearly shows optic nerve, lens, lens suspension, iris and cornea, the whale eye scan only shows optic nerve, lens and iris. In fact, the whale eye lacks major internal structures, e.g. the whale lens lack the suspension (ciliary muscles) seen in the pig eye. The whale eye vasculature of the retina was mapped as shown in Fig. 7, which shows an example of a raw image and a montage showing isolated vascular components in each image plane. All blood vessels radiate from the central retina, which is in agreement with position of a central artery and vein, both of which enter the eye together with the optic nerve in the highly vascularized rete ophthalmica.

Discussion

Although adult sperm whales cannot be scanned in commercial scanners, we found that single organ MRI and CT scannings can be suitable tools to gain knowledge of their anatomy. The MRI ZTE sequences and the two different CT scanners showed markedly different performance in elucidating detailed tooth structure. Layering was successfully resolved with MRI in tooth A but was insufficient for age estimation. The concentric appearance of the layers in the axial plane and their V-shaped arrangement in the sagittal plane indicate that the resolved structures are layers in the tooth's cone-shaped growth zones. In the images of tooth B (sagittal plane), the V-shape of these layers is readily seen along with the distinct border between tooth enamel and marrow (see Fig. 1). In the ZTE scans of tooth B (Fig. 5) this border between enamel and tooth marrow was clearly separated which might be useful for non-destructive studies of variation in enamel thickness in archived samples. This is an underexplored field of marine mammals, though enamel thickness is considered to be crucial for tooth strength in humans and therefore under investigation in dental medicine [29]. Layering, however, could not be resolved in tooth B possibly due to the sample drying out during storage in the museum. In the human teeth study [20] the teeth were stored in water to prevent drying prior to ZTE scans. Future studies might benefit from storing the samples in water for an extended period prior to ZTE imaging. In the case of fresh samples storing the teeth in saline immediately after extraction until imaging might be advantageous. On the CT scans, the border between enamel and marrow was easily seen. The V-shaped layering was resolved but not with sufficient detail to allow age estimation. Higher resolution helps to resolve the layering, but at very high resolutions the noise increasingly becomes a problem. New iterative image reconstruction algorithms are likely to produce images with less noise, but these algorithms were not available for the scanners applied in the present study. In this study, we have not tried high quality scanners, such as micro-CT, since we as well as many other research groups do not have access to such equipment. Therefore, we can not rule out if such scanners would have given images with better contrast and resolution.

The anatomical scans allow qualitative comparison between the whale eye and the terrestic porcine eye. All identified structures (and their size and shapes) were similar to what has already been reported for sperm whale eyes by Bjergager et al. [30] and in agreement with the general cetacean eye described by Mass and Supin [31]. In the pig eye, the scans clearly show the optic nerve, lens, lens suspension, iris and cornea. The whale eye was seen to lack internal structures beyond a lens. Fig. 3 CT image of tooth B using Biograph 64 and the scan-and reconstruction parameters in the CT method details. The slice is similar (but not exactly the same) as in Fig. 2. This image has smaller slice thickness and the filter was not as smooth as in Fig. 2. Thus, this image has more noise but higher spatial resolution, which improves visualization of the fine V-shaped pattern. Still only parts of the V-shaped structures can be visualized and the total number of layers cannot be determined from these images

The optic nerve is clearly visible, as previously described [30]. The findings of this somewhat superficial comparison of the two eyes are in agreement with what is known about the whale eye anatomy and its use as primarily a light detector without the need for focusing ability [31]. The whale eye is thought to be retractable to withstand the high pressures in the deep sea [30]. The anatomical scans of the whale eye show considerable deterioration of the sample even though the sample was extracted and preserved shortly after the time of death. Even though the pig and whale eyes were treated similarly, degradation was much more pronounced in the latter. This is probably because the large size of the eye, and the whale's thick sclera, which are both factors that would slow down the diffusion of formalin into the eye and therefore allow more time for sample degradation. It is also possible that the whale died a few hours earlier than the estimated time of death described herein. Consequently, the sample state made it impossible to visualize e.g. the structure of the sclera in this specimen by MRI. However, even in this state the eye vascular network can be visualized as shown in the 2D visualization in Fig. 7. In the present study, we chose to represent the vasculature as a montage of 2D segmentations, but a three dimensional (3D) reconstruction of the vascularization is entirely possible with the acquired data. In more well preserved samples 3D renderings of the nerves, vasculature and connective tissue would be possible with nerve fiber pathways traced by diffusion tensor tractography [32]. In this manner, the MRI would provide a digital

Conclusions

The use of modern scanning techniques in marine mammal research offers detailed studies of anatomical structures in situ that can increase our knowledge of e.g. anatomical adaptations to an aquatic lifestyle. Even though age could not be estimated based on tooth growing lines, the scanning techniques provided insight into tooth anatomy without the need to cut into the teeth which can be highly desirable for e.g. museum specimens. However, ZTE MRI techniques are continuously being improved and may soon provide sufficient resolution for age estimation. It was possible to get anatomical details of retina vasculation in situ with MRI. However, in soft tissues the state of the sample is the determining factor for successful use of high resolution MRI scanning in marine biology.

Biograph 64 :
64Protocol name: ExtrUHR, 120 kV, 248 eff. mAs, rotation time: 1.0 s, Pitch factor: 0.9, convolution kernel: U70u, slice thickness: 1.0 mm. Siemens Somatom Definition Flash: Protocol name: Inner Ear UHR: 120 kV, 195 eff. mAs, rotation time: 1.0 s, Pitch factor: 0.8, convolution kernel I: 50 s, slice thickness: 2.0 mm.

Fig. 2
2CT images of both teeth (tooth A and B) using Somatom Definition Flash and the scan-and reconstruction parameters in the CT method details. Coarse inner structures such as small inner holes and different densities of enamel and marrow are clearly visualized. Parts of the V-shaped structures can be seen

Fig. 4
4Examples of ZTE images in tooth A (see Fig. 1) along various axis (axial and sagittal). Axial at top left. Three sagittal slice planes are shown. Note that layering is visible in all examples, but it is not possible to determinate the exact numbers

Fig. 5
5Examples of ZTE images in tooth B (see Fig. 1) along various axis (axial and sagittal). Axial at top left. Three sagittal slice planes are shown. Here, the layered structure is visible as well as the border between marrow and enamel Fig. 6 Left pig eye scan showing optic nerve, lens, lens suspension, iris and cornea. Right whale eye scan showing optic nerve, lens, and lack of internal structures in the eye. Deterioration of the cornea is evident in whale eye dissection of the eye while leaving the sample intact.Modern scanning techniques could offer methods for non-destructive investigation of the musculature allowing such retraction mechanism.
AcknowledgementsWe thank Mia Hougaard Christiansen, Rikke Nielsen, and Niels Jorgen Rosenkrants, Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark for scanning and reconstructing the whale teeth on the Siemens Somatom Definition Flash CT scanner.Competing interestsThe authors declare that they have no competing interests.Availability of data and materialsThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.FundingExternal funding was not used in this study. The authors performed the scans in their spare time.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Submit your next manuscript to BioMed Central and we will help you at every step:
Post-mortem investigations on cetaceans found stranded on the coasts of Italy between. G Diguardo, U Agrimi, L Morelli, G Cardeti, G Terracciano, S Kennedy, Vet Rec. 136Diguardo G, Agrimi U, Morelli L, Cardeti G, Terracciano G, Kennedy S. Post-mortem investigations on cetaceans found stranded on the coasts of Italy between 1990 and 1993. Vet Rec. 1995;136:439-42.

Ocular anatomy, ganglion cell distribution and retinal resolution of a killer whale (Orcinus orca). A M Mass, A Y Supin, A V Abramov, L M Mukhametov, E I Rozanova, 10.1159/000341949Brain Behav Evol. 81Mass AM, Supin AY, Abramov AV, Mukhametov LM, Rozanova EI. Ocular anatomy, ganglion cell distribution and retinal resolu- tion of a killer whale (Orcinus orca). Brain Behav Evol. 2013;81:1-11. doi:10.1159/000341949.

Morphometry, gross morphology and available histopathology in North Atlantic right whale (Eubalaene glacialis) mortalities (1970-2002). M J Moore, A R Knowlton, S D Kraus, W A Mclellan, R K Bonde, J Cetac Res Managem. 6Moore MJ, Knowlton AR, Kraus SD, McLellan WA, Bonde RK. Morphom- etry, gross morphology and available histopathology in North Atlantic right whale (Eubalaene glacialis) mortalities (1970-2002). J Cetac Res Managem. 2004;6:199-214.

Left an example of the visualization of the eye vasculature (elongated dark structures in the central bright area) of the whale. Right the vascuFig. 7 Left an example of the visualization of the eye vasculature (elongated dark structures in the central bright area) of the whale. Right the vascu-

Necropsy report of a fin whale (Balaenoptera physalus) stranded in Denmark in 2010. Ako Alstrup, A Hedayat, T H Jensen, A S Hammer, O L Munk, H E Jensen, Aquat Mammals. 39Alstrup AKO, Hedayat A, Jensen TH, Hammer AS, Munk OL, Jensen HE. Necropsy report of a fin whale (Balaenoptera physalus) stranded in Den- mark in 2010. Aquat Mammals. 2013;39:385-8.

Inside out: modern imaging techniques to reveal animal anatomy. H Lauridsen, K Hansen, T Wang, P Agger, J L Andersen, P S Knudsen, 10.1371/journal.pone.0017879PLoS ONE. 617879Lauridsen H, Hansen K, Wang T, Agger P, Andersen JL, Knudsen PS, et al. Inside out: modern imaging techniques to reveal animal anatomy. PLoS ONE. 2011;6:e17879. doi:10.1371/journal.pone.0017879.

Magnetic resonance microscopy of mammalian neurons. J J Flint, C H Lee, B Hansen, M Fey, D Schmidig, J D Bui, 10.1016/j.Neuroimage.2009.03.009NeuroImage. 46Flint JJ, Lee CH, Hansen B, Fey M, Schmidig D, Bui JD, et al. Magnetic reso- nance microscopy of mammalian neurons. NeuroImage. 2009;46:1037- 40. doi:10.1016/j.Neuroimage.2009.03.009.

Cellular-level diffusion tensor microscopy and fiber tracking in mammalian nervous tissue with direct histological correlation. NeuroImage. J J Flint, B Hansen, M Fey, D Schmidig, M A King, P Vestergaard-Poulsen, 10.1016/j.neuroimage.2010.04.03152Flint JJ, Hansen B, Fey M, Schmidig D, King MA, Vestergaard-Poulsen P, et al. Cellular-level diffusion tensor microscopy and fiber tracking in mammalian nervous tissue with direct histological correlation. NeuroIm- age. 2010;52:556-61. doi:10.1016/j.neuroimage.2010.04.031.

Diffusion tensor microscopy in human nervous tissue with quantitative correlation based on direct histological comparison. B Hansen, J J Flint, C Heon-Lee, Fey M Vincent, F King, M A , 10.1016/j.neuroimage.2011.04.052NeuroImage. 57Hansen B, Flint JJ, Heon-Lee C, Fey M, Vincent F, King MA, et al. Diffusion tensor microscopy in human nervous tissue with quantitative correlation based on direct histological comparison. NeuroImage. 2011;57:1458-65. doi:10.1016/j.neuroimage.2011.04.052.

Magnetic resonance microscopy of human and porcine neurons and cellular processes. J J Flint, B Hansen, S Portnoy, C H Lee, M A King, M Fey, 10.1016/j.neuroimage.2012.01.050NeuroImage. 60Flint JJ, Hansen B, Portnoy S, Lee CH, King MA, Fey M, et al. Magnetic reso- nance microscopy of human and porcine neurons and cellular processes. NeuroImage. 2012;60:1404-11. doi:10.1016/j.neuroimage.2012.01.050.

Principles of CT and CT technology. L W Goldman, 10.2967/jnmt.107.042978J Nucl Med Technol. 35Goldman LW. Principles of CT and CT technology. J Nucl Med Technol. 2007;35:115-28. doi:10.2967/jnmt.107.042978.

Diffusible iodinebased contrastenhanced computed tomography (diceCT): an emerging tool for rapid, high-resolution, 3D imaging of metazoan soft tissues. P Gignac, N J Kley, J A Clarke, M W Colbert, A C Morhardt, D Cerio, J Anat. 228Gignac P, Kley NJ, Clarke JA, Colbert MW, Morhardt AC, Cerio D, et al. Dif- fusible iodinebased contrastenhanced computed tomography (diceCT): an emerging tool for rapid, high-resolution, 3D imaging of metazoan soft tissues. J Anat. 2016;228:889-909.

W R Hendee, E R Ritenour, Medical imaging physics. HobokenWiley4th edHendee WR, Ritenour ER. Medical imaging physics. 4th ed. Hoboken: Wiley; 2002.

Handbook of MRI pulse sequences. M A Bernstein, K F King, Z J Zhou, Academic PressCambridgeBernstein MA, King KF, Zhou ZJ. Handbook of MRI pulse sequences. Cambridge: Academic Press; 2004.

Magnetic resonance imaging: physical principles and sequence design. R W Brown, Ycn Cheng, E M Haacke, M R Thompson, R Venkatesan, WileyCambridge2nd edBrown RW, Cheng YCN, Haacke EM, Thompson MR, Venkatesan R. Mag- netic resonance imaging: physical principles and sequence design. 2nd ed. Cambridge: Wiley; 2014.

Boron-11 imaging with a threedimensional reconstruction method. G H Glover, J M Pauly, K M Bradshaw, J Magn Reson Imag. 2Glover GH, Pauly JM, Bradshaw KM. Boron-11 imaging with a three- dimensional reconstruction method. J Magn Reson Imag. 1992;2:47-52.

Three-dimensional radial ultrashort echo-time imaging with T2 adapted sampling. J Rahmer, P Bornert, J Groen, C Bos, 10.1002/mrm.20868Magn Reson Med. 55Rahmer J, Bornert P, Groen J, Bos C. Three-dimensional radial ultrashort echo-time imaging with T2 adapted sampling. Magn Reson Med. 2006;55:1075-82. doi:10.1002/mrm.20868.

Fast imaging in liquids and solids with the back-projection low angle shot (BLAST) technique. Magn Reson Imag. S Hafner, 12Hafner S. Fast imaging in liquids and solids with the back-projection low angle shot (BLAST) technique. Magn Reson Imag. 1994;12:1047-51.

Ultra-fast imaging using low flip angles and FIDs. D P Madio, I J Lowe, Magn Reson Med. 34Madio DP, Lowe IJ. Ultra-fast imaging using low flip angles and FIDs. Magn Reson Med. 1995;34:525-9.

MRI with zero echo time: hard versus sweep pulse excitation. M Weiger, K P Pruessmann, F Hennel, 10.1002/mrm.22799doi:10.1002/ mrm.22799Magn Reson Med. 66Weiger M, Pruessmann KP, Hennel F. MRI with zero echo time: hard versus sweep pulse excitation. Magn Reson Med. 2011;66:379-89. doi:10.1002/ mrm.22799.

High-resolution ZTE imaging of human teeth. M Weiger, K P Pruessmann, A K Bracher, S Köhler, V Lehmann, U Wolfram, 10.1002/nbm.2783NMR Biomed. 25Weiger M, Pruessmann KP, Bracher AK, Köhler S, Lehmann V, Wolfram U, et al. High-resolution ZTE imaging of human teeth. NMR Biomed. 2012;25:1144-51. doi:10.1002/nbm.2783.

Cuvier's beaked whale (Ziphius cavirostris) head tissues: physical properties and CT imaging. M S Soldevilla, M F Mckenny, S M Wiggins, R E Shadwick, T W Cranford, J A Hildebrand, 10.1242/jeb.01624J Exp Biol. 208Soldevilla MS, McKenny MF, Wiggins SM, Shadwick RE, Cranford TW, Hildebrand JA. Cuvier's beaked whale (Ziphius cavirostris) head tissues: physical properties and CT imaging. J Exp Biol. 2005;208:2319-32. doi:10.1242/jeb.01624.

Acoustic pathways revealed: simulated sound transmission and reception in Cuvier's beaked whale (Ziphius cavirostris). T W Cranford, P Krysl, J A Hildebrand, 10.1088/1748-3182/3/1/016001Bioinspir Biomim. Cranford TW, Krysl P, Hildebrand JA. Acoustic pathways revealed: simu- lated sound transmission and reception in Cuvier's beaked whale (Ziphius cavirostris). Bioinspir Biomim. 2008. doi:10.1088/1748-3182/3/1/016001.

Anatomic geometry of sound transmission and reception in Cuvier's beaked whale (Ziphius cavirostris). T W Cranford, M F Mckenny, M S Soldevilla, S M Wiggins, J A Goldbogen, R E Shadwick, 10.1002/ar.20652Anat Rec. 291Cranford TW, McKenny MF, Soldevilla MS, Wiggins SM, Goldbogen JA, Shadwick RE, et al. Anatomic geometry of sound transmission and recep- tion in Cuvier's beaked whale (Ziphius cavirostris). Anat Rec. 2008;291:353- 78. doi:10.1002/ar.20652.

The sperm whale's nose: sexual selection on a grand scale?. T W Cranford, 10.1111/j.1748-7692.1999.tb00882.xMar Mammal Sci. 15Cranford TW. The sperm whale's nose: sexual selection on a grand scale? Mar Mammal Sci. 1999;15:1133-57. doi:10.1111/j.1748-7692.1999. tb00882.x.

Anatomy and three-dimensional reconstructions of the brain of the white whale (Delphinapterus leucas) from magnetic resonance images. L Marino, T L Murphy, A L Deweerd, J A Morris, A J Fobbs, N Humblot, Marino L, Murphy TL, Deweerd AL, Morris JA, Fobbs AJ, Humblot N, et al. Anatomy and three-dimensional reconstructions of the brain of the white whale (Delphinapterus leucas) from magnetic resonance images.

. Anat Rec. 262Anat Rec. 2001;262:429-39.

Magnetic resonance images of the brain of a dwarf sperm whale (Kogia simus). L Marino, K Sudheimer, D A Pabst, W A Mclellan, J I Johnson, J Anat. 203Marino L, Sudheimer K, Pabst DA, McLellan WA, Johnson JI. Magnetic resonance images of the brain of a dwarf sperm whale (Kogia simus). J Anat. 2003;203:57-76.

Discovery of a sensory organ that coordinates lunge feeding in rorqual whales. N D Pyenson, J A Goldbogen, A W Vogl, G Szathmary, R L Drake, R E Shadwick, 10.1038/nature11135Nature. 485Pyenson ND, Goldbogen JA, Vogl AW, Szathmary G, Drake RL, Shadwick RE. Discovery of a sensory organ that coordinates lunge feeding in rorqual whales. Nature. 2012;485:498-501. doi:10.1038/nature11135.

A method of preparing sperm whale teeth for age determination. J M Bow, C Purday, Nature. 210Bow JM, Purday C. A method of preparing sperm whale teeth for age determination. Nature. 1966;210:437-8.

The histological features and physical properties of eroded dental hard tissues. C Ganss, A Lussi, N Schlueter, 10.1159/000359939)Monogr Oral Sci. 25Ganss C, Lussi A, Schlueter N. The histological features and physical prop- erties of eroded dental hard tissues. Monogr Oral Sci. 2014;25:99-107. doi:10.1159/000359939).

Anatomy of the eye of the sperm whale. P Bjerager, S Heegaard, J Togaard, Physeter macrocephalus L.). Aquat Mamm. 29Bjerager P, Heegaard S, Togaard J. Anatomy of the eye of the sperm whale (Physeter macrocephalus L.). Aquat Mamm. 2003;29:31-6.

Adaptive features of aquatic mammals' eye. A M Mass, A Y Supin, 10.1002/ar.20529Anat Rec (Hoboken). 290Mass AM, Supin AY. Adaptive features of aquatic mammals' eye. Anat Rec (Hoboken). 2007;290:701-5. doi:10.1002/ar.20529.

In vivo fiber tractography using DT-MRI data. P J Basser, S Pajevic, C Pierpaoli, J Duda, A Aldroubi, Magn Reson Med. 44Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vivo fiber tractogra- phy using DT-MRI data. Magn Reson Med. 2000;44:625-32.


Breakfast and Energy Drink Consumption in Secondary School Children: Breakfast Omission, in Isolation or in Combination with Frequent Energy Drink Use, is Associated with Stress, Anxiety, and Depression Cross-Sectionally, but not at 6-Month Follow-Up
published: 09 February 2016 Published: 09 February 2016

Leigh Gibson
Ulrich Schweiger
Michael Smith
Gareth Richards
Andrew P Smith

University of Roehampton
UK

Lübeck University Medical School
Germany

School of Psychology
Centre for Occupational and Health Psychology
Northumbria University
UK

Cardiff University
CardiffUK

Breakfast and Energy Drink Consumption in Secondary School Children: Breakfast Omission, in Isolation or in Combination with Frequent Energy Drink Use, is Associated with Stress, Anxiety, and Depression Cross-Sectionally, but not at 6-Month Follow-Up
published: 09 February 2016 Published: 09 February 201610.3389/fpsyg.2016.00106Received: 10 November 2015 Accepted: 19 January 2016ORIGINAL RESEARCH Edited by: Reviewed by: *Correspondence: Gareth Richards email@example.com Specialty section: This article was submitted to Eating Behavior, a section of the journal Frontiers in Psychology Citation: Richards G and Smith AP (2016) Breakfast and Energy Drink Consumption in Secondary School Children: Breakfast Omission, in Isolation or in Combination with Frequent Energy Drink Use, is Associated with Stress, Anxiety, and Depression Cross-Sectionally, but not at 6-Month Follow-Up. Front. Psychol. 7:106.adolescent behavioranxietybreakfastcaffeinedepressionenergy drinksmental healthstress
A considerable amount of research suggests that breakfast omission and the frequent use of caffeinated energy drinks may be associated with undesirable effects, and particularly so in children and adolescents. The current paper presents cross-sectional and longitudinal data from the Cornish Academies Project to investigate the effects of consuming energy drinks and missing breakfast on stress, anxiety, and depression in a cohort of secondary school children from the South West of England. Questionnaires were administered at two time-points (spaced 6 months apart) to collect information relating to diet and lifestyle over the previous 6 months. Demographic and school data were acquired through the School Information Management System, and single-item measures of stress, anxiety, and depression were administered at the second timepoint only. Associations between breakfast and energy drink consumption and stress, anxiety, and depression were investigated, and a multivariate approach was taken so that additional variance from diet, demography, and lifestyle could be controlled for statistically. Cross-sectional analyses showed that breakfast omission was consistently associated with negative outcomes, and that this was largely observed for both those who frequently consumed energy drinks and those who did not. However, cross-lag analyses showed that neither breakfast omission or energy drink consumption, alone or in combination, was predictive of stress, anxiety, or depression at 6-month followup. This suggests that associations between breakfast and mental health may be bi-directional rather than breakfast being the causal factor.

INTRODUCTION

Energy drinks are highly caffeinated soft drinks that are purported to increase performance and endurance (Meadows-Oliver and Ryan-Krause, 2007). Concern has been raised due to such products sometimes being marketed as functional foods or dietary supplements, bypassing legislation regarding caffeine content (McCusker et al., 2006;Pomeranz, 2012), and therefore having the potential to put users at risk of intoxication (Reissig et al., 2009;Seifert et al., 2011). What is particularly alarming is the use of energy drinks by young consumers, with the Committee on Nutrition and the Council on Sports Medicine andFitness (2011, p. 1182) claiming that "caffeine and other stimulant substances contained in energy drinks have no place in the diet of children and adolescents."

As caffeine appears to be the main active ingredient in energy drinks (McLellan and Lieberman, 2012), it is important to consider its effects on mood and behavior. If consumed in moderation, it appears not to induce serious adverse health effects in adults (Nawrot et al., 2003) or children (Mandel, 2002;Higdon and Frei, 2006), though it has been advised for those highly sensitive not to consume >400 mg/d (Nawrot et al., 2003). Caffeine use does however appear to have important implications in the school environment. Its high consumption has, for instance, been associated with difficulty sleeping and morning tiredness (Orbeta et al., 2006), falling asleep at school (Calamaro et al., 2009), anger , violence and conduct disorder (Kristjánsson et al., 2013), and low academic attainment . When specifically investigating energy drinks it may therefore be beneficial to take caffeine consumption into account, otherwise the effects could be wrongly ascribed.

The frequency and amount of consumption of many food and drink products is known to be heavily inter-correlated (Wiles et al., 2009), and there is evidence to suggest that the effects of energy drinks may be best understood in combination with other aspects of diet. , for instance, reported a combined effect, in which energy drink consumption and breakfast omission were together predictive of the acute occurrence of detentions in secondary school children. Breakfast consumption is itself known to be associated with a number of cognitive benefits to children and adolescents (see Hoyland et al., 2009, for a review). Though some studies (e.g., Dickie and Bender, 1982;Cromer et al., 1990;López et al., 1993) have found no cognitive benefit of breakfast compared to its omission, others have reported a number of advantages, such as improvements in attention (Wesnes et al., 2003;Ingwersen et al., 2007), short-term memory (Smith et al., 1994;Vaisman et al., 1996;Benton and Parker, 1998;Wesnes et al., 2003), long-term memory (Chandler et al., 1995), creativity (Wyon et al., 1997), arithmetic (Powell et al., 1998), mood (Smith et al., 1999), and behavior (Bro et al., 1994). Due to such observations, the regular consumption of breakfast is generally considered to be of importance, and particularly so in relation to children and adolescents. However, it should also be noted that a recent review by Brown et al. (2013) found evidence to suggest that the scientific literature relating to the proposed effect of diet on obesity suffers from distortions caused by research lacking probative value, and biased research reporting. It may therefore be that similar biases exist in relation to the literature on associations between breakfast and other outcomes, such as cognitive performance, memory, and behavior.

The current study aimed to examine associations between missing breakfast and consuming energy drinks, and selfreported stress, anxiety and depression in secondary school children. A multivariate approach to analysis was adopted so that variance from other aspects of diet, demography, and lifestyle could be accounted for. Combined effects of energy drinks and breakfast were also investigated, and it was hypothesized that breakfast omission in combination with frequent energy drink use would be the strongest predictor of undesirable outcomes.

MATERIALS and METHODS

Participants

Data presented in the current paper came from the Cornish Academies Project, a longitudinal study of diet in secondary school children that consisted of two cross-sections. Data at Time 1 (T1; December 2012) were collected 6 months prior to Time 2 (T2; June 2013). However, as the stress, anxiety, and depression variables were only collected at T2, data from T1 are only used here when presenting cross-lag analyses. 3071 pupils were asked to take part in the research at T1, and 2610 (85%) agreed. At T2, the cohort consisted of 3323 pupils (Academy 1 N = 971, Academy 2 N = 1375, Academy 3 N = 977), 2307 of which completed the questionnaires, providing a response rate of 88.4% (slightly higher than the 77.8% reported at T1). The sample consisted of 48.5% males and 51.5% females, an age range of 11-17 (M = 13.6, SD = 1.49) was observed, and similar percentages of participants came from each year group of secondary education (Year 7 = 18.8%, Year 8 = 19.7%, Year 9 = 20.3%, Year 10 = 20.2%, Year 11 = 21%). A relatively high proportion of pupils (29.2%) had a special educational needs (SEN) status, 13.1% were eligible to receive free school meals (FSM), and almost all pupils were White (97.2%), spoke English as their first language (98.3%), and were not looked after by a non-parental guardian (99.4%).

Apparatus/Materials

The Diet and Behaviour Scale (DABS;  was used as a measure of dietary intake of common foods and drinks. The questionnaire focuses on functional foods, and foods and drinks that may have effects on psychological outcomes (examples include breakfast, energy drinks, coffee, tea, cola, chewing gum, fruit, and vegetables). Eighteen questions assess the frequency of dietary intake on a five-point scale [1 = never, 2 = once a month, 3 = once or twice a week, 4 = most days (3-6), 5 = every day], and 11 items measure the amount of consumption. Through factor analyzing the whole scale,  found the measure to be associated with a four-factor structure labeled 'Junk Food, ' 'Caffeinated Soft Drinks/Gum, ' 'Healthy Foods, ' and 'Hot caffeinated Beverages.' Subscales derived from the Junk Food factor (comprised of items measuring both the frequency and amount of consumption of chocolate and crisps, and items measuring the frequency of consumption of sweets and chips) and the Healthy Foods factor (comprised of items measuring both the frequency and amount of consumption of fruit and vegetables) were used as control variables in the current study.

Alongside the DABS, participants were administered a questionnaire to record the frequency by which they took part in exercise (mildly energetic, moderately energetic, and vigorous), on a four-point scale (1 = three times a week or more, 2 = once or twice a week, 3 = about once to three times a month, 4 = never/hardly ever). These three items were factor analyzed to produce a single factor solution (the details of which can be found in , which was used as a control variable in the current study in order to avoid unnecessarily compromising the statistical power of multivariate analyses. In addition, participants also reported the number of hours for which they normally slept each night.

Questions were administered to measure self-assessed mental health at T2 only. Participants were asked to specify how frequently they had felt stressed, anxious, and depressed during the previous 6 months. Answers to these questions were provided on a five-point scale (1 = not at all, 2 = rarely, 3 = sometimes, 4 = frequently, 5 = very frequently). Single items were used because they have been shown to be valid and reliable, and are less time-consuming to administer than multi-item measures (Williams and Smith, 2012). These particular items were taken from the Wellbeing Process Questionnaire (WPQ; Williams, 2014), and have been validated against full-length scales.

Design and Procedure

Teachers administered the questionnaires to pupils at their schools. Demographic information was then obtained through the School Information Management System and stored in a confidential database at Cardiff University. This information included sex, age, school attended, year group, SEN status, and the eligibility/ineligibility to receive FSM (the latter being used as an indication of socioeconomic status; Shuttleworth, 1995).

All questionnaire and demographic data were anonymized prior to being merged into a single database. Cardiff University's School of Psychology Ethics Committee granted the study ethical clearance, informed consent was acquired from all participants (as well as from their parents), and the research was conducted in accordance with the Declaration of Helsinki.

Statistical Analysis

Both the predictor variables and the outcomes were measured using Likert response format items. There has been considerable debate (Carifio and Perla, 2007) about whether these can be considered as continuous variables (multi-item scales may be considered continuous but single items usually are not) and a conservative approach conceptualizes the Likert response type single items as ordered categorical variables (Jamieson, 2004). The distribution across the categories also means that it is appropriate to reduce the problems of unequal or small sample sizes by dichotomizing the scores using median splits. This approach has been adopted in other studies from the Cornish Academies Project and was continued here.

The predictor variables (breakfast and energy drink consumption) and outcomes (stress, anxiety, and depression) were dichotomized, and relationships with dietary, demographic, and lifestyle covariates were investigated using Chi-square and between subjects t-tests. Cross-sectional relationships between the frequency of breakfast and energy drink consumption and stress, anxiety, and depression were then investigated using binary logistic regression analysis (using enter method), so that aspects of demography (sex, school attended, school year, presence/absence of a SEN status, eligibility/ineligibility to receive FSM) and lifestyle (sleep hours, exercise frequency, and school attendance) could be controlled for statistically. In addition to this, as  observed caffeine consumption to be associated with mental health outcomes in this same sample of secondary school children, its weekly intake from cola, tea, and coffee were entered as covariates. Caffeine consumed from energy drinks was also entered when the indicator variable was breakfast (though not when the indicator variable was energy drinks). To control for other aspects of diet, the DABS subscales for Junk Food and Healthy Foods were also used; however, the subscales for Caffeinated Soft Drinks/Gum and Hot Caffeinated Beverages were not entered due to them being comprised of caffeinated products (energy drinks and cola in the case of the former, tea, and coffee in the case of the latter).

In order to further examine the nature of the relationships observed, the frequency of breakfast and energy drink consumption variables were combined so that all four possible groupings of frequent/infrequent intake could be investigated in relation to the dichotomous mental health outcomes using binary logistic regression analysis. Because consuming energy drinks less than once a week and eating breakfast every day were considered to be the healthiest dietary practices, the frequent breakfast/infrequent energy drinks condition was chosen as the comparison group. In addition to this, the odds ratios and 95% confidence intervals were compared across the other three conditions; if the confidence intervals were found not to overlap, then the difference between groups was considered to be significant.

All dietary, demographic, and lifestyle data were available from two cross-sections (spaced 6 months apart), although information relating to stress, anxiety, and depression were only collected at the latter. Though this meant that changes in mental health status could not be investigated, cross-lag analyses were conducted to determine whether breakfast and energy drink consumption (both in isolation and in combination) were predictive of stress, anxiety, and depression levels at 6month follow-up. These analyses therefore utilized the same procedures as the cross-sectional analyses, except that in this case the predictor variables and covariates came from T1 rather than T2. All data analysis was conducted using IBM SPSS Statistics Version 20. For details of all predictor variables and covariates used in the multivariate analyses, see Table 1.

RESULTS

Descriptive statistics relating to variance in demography and lifestyle for this sample have been reported in . In addition, frequency data for stress, anxiety, and depression are available in . This latter study observed a large amount of variability in mental health status. Though the most commonly reported frequency for experiencing depression was 'not at all' (36.1%), anxiety was most commonly reported as occurring 'rarely' (38.2%), and stress was most commonly reported as occurring 'sometimes' (37.4%). Mental health problems that were experienced 'very frequently' were relatively uncommon (stress = 8.5%, anxiety = 4.6%, depression = 4.8%).

Associations Between Covariates, Predictor Variables, and Outcomes

Associations Between Covariates and Predictor Variables

In order to assess relationships between the predictor variables and covariates, the single item DABS questions for frequency of consumption of breakfast and energy drinks were recoded into dichotomous variables; breakfast was coded as 'every day' vs.

'not every day' (answer 5 vs. answers 1, 2, 3, and 4), and energy drinks was coded as 'once a week or more' vs. 'less than once a week' (answers 3, 4, and 5 vs. answers 1 and 2). Relatively equal numbers were present for participants who ate breakfast every day (1108; 48%) and those who did not eat breakfast every day (1198; 52%); for energy drinks, the majority of participants used the products less than once per week (1728; 75.4%), with relatively few using them once a week or more (563; 24.6%). These variables were then investigated in relation to categorical covariates using Chi-square tests, and in relation to continuous covariates using between-subjects t-tests. Those who consumed breakfast every day were more likely to be male, χ 2 (1, N = 2092) = 39.749, p < 0.001, to be ineligible for FSM, χ 2 (1, N = 2251) = 11.701, p = 0.001, and to not have a SEN status, though the latter effect was only marginally significant, χ 2 (1, N = 2274) = 3.058, p = 0.08. Although breakfast consumption did not differ across the three schools investigated, χ 2 (2, N = 2306) = 1.264, p = 0.531, a significant effect was observed regarding the year group attended, χ 2 (4, N = 2250) = 21.007, p < 0.001. This reflected a significant linear trend in which the likelihood of eating breakfast every day decreased throughout secondary education, χ 2 (1, N = 2250) = 18.176, p < 0.001.

Compared to those who did not eat breakfast every day, those who did slept for longer, t(2190.355) = 12.041, p < 0.001, exercised more frequently, t(2139) = 2.57, p = 0.01, and achieved higher school attendance, t(2232.478) = 7.019, p < 0.001. Though not associated with Junk Food, t(2200) = 0.993, p = 0.321, those who ate breakfast every day achieved higher Healthy Foods scores, t(2217) = 5.837, p < 0.001, and consumed less caffeine from energy drinks, t(2093.156) = -6.819, p < 0.001, cola, t(2243.738) = -4.991, p < 0.001, and coffee, t(2206.026) = -2.927, p = 0.003. However, no difference was observed regarding the amount of caffeine consumed from tea, t(2265) = 0.19, p = 0.85.

Drinking energy drinks once a week or more was significantly associated with being male, χ 2 (1, N = 2078) = 56.833, p < 0.001, having a SEN status, χ 2 (1, N = 2259) = 42.149, p < 0.001, and being eligible for FSM, χ 2 (1, N = 2237) = 35.713, p < 0.001. School year was also associated, χ 2 (4, N = 2236) = 29.816, p < 0.001, reflecting a significant linear relationship in which energy drink consumption increased throughout secondary education, χ 2 (1, N = 2236) = 25.957, p < 0.001. Although more frequent energy drink users than expected came from Academy 3 (adjusted residual = 2), no overall significant difference between the schools was observed, χ 2 (2, N = 2291) = 4.563, p = 0.102.

Compared to those who consumed energy drinks less than once a week, those who consumed them once a week or more slept for fewer hours per night, t(770.321) = -8.547, p < 0.001, achieved lower school attendance, t(815.279) = -6.604, p < 0.001, and exercised more frequently, t(2130) = 1.933, p = 0.053 (though the latter effect was only marginally significant). In addition to this, frequent energy drink users achieved higher Junk Food scores, t(2196) = 8.619, p < 0.001, lower Healthy Foods scores, t(2206) = -4.675, p < 0.001, and consumed more caffeine from energy drinks, t(563.169) = 23.054, p < 0.001, cola, t(699.209) = 7.751, p < 0.001, and coffee, t(656.091) = 5.949, p < 0.001. As with breakfast consumption, there was no association between energy drink use and the amount of caffeine consumed from tea, t(2253) = 0.448, p = 0.654.

Associations Between Covariates and Outcome Variables

The single item measures of stress, anxiety, and depression were recoded into dichotomous variables, with those who answered with 1 or 2 ('never' or 'rarely' experienced stress, anxiety, or depression) being placed into the above average mental health group, and those who answered with 3, 4, or 5 ('sometimes, ' 'frequently, ' or 'very frequently' experienced stress, anxiety or depression) comprising the below average mental health group.

Compared to the low stress group, the high stress group achieved marginally lower Junk Food scores, t(2158) = 1.949, p = 0.051, though there was no difference regarding Healthy Foods, t(2176) = 1.072, p = 0.284. The low anxiety group on the other hand achieved higher Healthy Foods scores, t(2167) = 2.373, p = 0.018, but there was no difference for Junk Food, t(1902.825) = 1.321, p = 0.187. There was also a marginally significant trend for those in the low depression group to consume more Junk Food, t(1449.921) = 1.796, p = 0.073, though no effect on Healthy Foods was observed, t(2165) = 0.591, p = 0.554.  have already reported associations between the dichotomous stress, anxiety, and depression outcomes and caffeine consumed from energy drinks, cola, coffee, and tea in this sample. At the univariate level the study found caffeine intake from coffee to be positively associated with stress, anxiety, and depression (though the effects appeared to be due to coffee consumption being a strong predictor of overall caffeine intake), and caffeine consumed from cola to be associated with low stress (potentially reflecting a coping strategy).

Associations between the dichotomous stress, anxiety, and depression variables and demographic and lifestyle covariates used in the current study have been reported by . At the univariate level, their analysis found female gender, low sleep hours, infrequent exercise, and low school attendance to be predictive of high levels of stress, anxiety, and depression. Although no differences were detected regarding the schools attended, stress, anxiety, and depression were each found to increase throughout secondary education. In addition to this, those with a SEN status were more likely to report high levels of depression, and those eligible for FSM were more likely to report low anxiety.

Cross-Sectional Associations Between Breakfast and Energy Drink Consumption, and Stress, Anxiety, and Depression

Breakfast (every day vs. not every day) and energy drinks (once a week or more vs. less than once a week) were each entered separately (i.e., they were not entered into the same model) into binary logistic regression analyses upon the dichotomous stress, anxiety, and depression outcomes, along with dietary, demographic, and lifestyle covariates. Not eating breakfast every day was associated with high stress, OR = In order to investigate their potential combined effects, the dichotomous breakfast and energy drink consumption variables were combined into the following four groups: (1) breakfast every day/energy drinks less than once a week (N = 919; 40.1%), (2) breakfast every day/energy drinks once a week or more (N = 184; 8%), (3) breakfast not every day/energy drinks less than once a week (N = 809; 35.3%), (4) breakfast not every day/energy drinks once a week or more (N = 378; 16.5%). For ease of reporting, the frequency of breakfast and energy drink consumption will both henceforth be referred to as 'frequent' or 'infrequent'. Binary logistic regression analyses were conducted, and the frequent breakfast/infrequent energy drinks group was set as the control. The same covariates as entered in the analyses of energy drink consumption in isolation were once again used here.

The effect of breakfast and energy drinks groups was significant in relation to each of the outcome variables: stress, Wald = 8.228, p = 0.042; anxiety, Wald = 8.791, p = 0.032; depression, Wald = 15.17, p = 0.002. High stress levels were significantly associated with being a member of the infrequent breakfast/frequent energy drinks condition, OR = 1.513, 95% CI [1.072, 2.135], p = 0.018, whereas high anxiety was associated with the infrequent breakfast/infrequent energy drinks condition, OR = 1.317, 95% CI [1.037, 1.671], p = 0.024. High levels of depression, on the other hand, were associated with both groups that did not consume breakfast every day: infrequent breakfast/infrequent energy drinks, OR = 1.587, 95% CI [1.24, 2.032], p < 0.001; infrequent breakfast/frequent energy drinks, OR = 1.581, 95% CI [1.127, 2.218], p = 0.008. For visual representations of the odds ratios and 95% confidence intervals for the cross-sectional multivariate level combined effects of breakfast and energy drinks on stress, anxiety and depression, see

Cross-Lag Associations Between Breakfast and Energy Drink Consumption, and Stress, Anxiety, and Depression

In order to determine whether the frequency of breakfast and energy drink consumption was predictive of stress, anxiety, and depression at 6-month follow-up, a cross-lag analysis was conducted. For this, the same predictor variables and covariates as used in the cross-sectional analysis were used, except that they came from T1 rather than T2. Breakfast consumption at T1 was not associated with any of the outcome measures at T2: stress, OR = 1.

DISCUSSION

Due to reports in the literature associating breakfast omission and energy drink consumption with undesirable outcomes, the current study aimed to investigate these dietary variables in relation to self-reported stress, anxiety, and depression in a large cohort of secondary school children from the South West of England. In addition to this, as  reported that a combination of breakfast omission and energy drink use was predictive of the acute occurrence of behavioral sanctions, the combined effects of these variables were also investigated. It was hypothesized that a combination of breakfast omission and frequent energy drink consumption would be the strongest predictor of high stress, anxiety, and depression levels.

Effects of Breakfast and Energy Drinks on Stress, Anxiety, and Depression

At the cross-sectional level, eating breakfast every day was found to be predictive of low stress, anxiety, and depression. These effects generally reflected those already reported in the literature (e.g., Smith, 1998;O'Sullivan et al., 2009). Energy drink consumption on the other hand, was not associated with any of the mental health outcomes. This was somewhat surprising considering that a number of studies have previously reported positive relationships between energy drink use and stress (Hofmeister et al., 2010;Pettit and DeBarr, 2011), anxiety (Hofmeister et al., 2010;Stasio et al., 2011;Trapp et al., 2014), and depression (Azagba et al., 2014). However, it should also be noted that a number of such studies (e.g., Hofmeister et al., 2010;Arria et al., 2011;Trapp et al., 2014) have provided mixed results. Combined effects of breakfast and energy drinks were observed in relation to stress, anxiety, and depression. In line with the hypothesis, high stress was associated with the infrequent breakfast/frequent energy drinks condition. However, high anxiety was associated with the infrequent breakfast/infrequent energy drinks condition, and high depression was associated with both groups that did not consume breakfast every day. Taken together, these findings suggest that breakfast omission is consistently associated with negative mental health outcomes, and that such effects can generally be observed in those who frequently consume energy drinks as well as those who do not. However, when investigating the predictive value of breakfast omission and energy drink consumption, both in isolation and in combination, on stress, anxiety, and depression at 6-month follow-up, no significant effects emerged. This therefore suggests that the relationships observed at the cross-sectional level are unlikely to be casual in nature and there may be bi-directional mechanisms involved, with mental health also influencing whether or not breakfast is consumed.

Methodological Limitations and Directions for Future Research

Although the current study has a number of strengths (e.g., large sample size, multivariate approach to analysis), several limitations should be acknowledged. Considering both the minimum and average age of the participants, it is possible that some will not have fully understood the difference between each of the mental health problems in a way that they could tell to what degree they had suffered them over the previous 6 months. Due to this, the results of the study should be interpreted tentatively. In addition to this, the sample population was not entirely representative of the schools from which it came (see , and was also somewhat homogeneous, being comprised mainly of White schoolchildren from a specific geographical location. Though the methodology used attempted to control for these factors statistically, future research should aim to investigate similar dietary effects in more representative populations in order to increase the generalizability of findings.

Single items were also used to measure the mental health outcomes. These single items have been shown to be highly correlated with full length scales in studies of working age populations and university students. However, we do not know whether this is also true of adolescents and it may be the case that use of single items in this group requires examples of what is meant by stress, anxiety, and depression. Another issue incurred by the current study is that information relating to mental health was only collected at one time-point. This meant that, although cross-lag analyses could be conducted, changes in stress, anxiety, and depression could not be investigated. The cross-sectional analyses presented therefore incur the problem of reverse causation, and so, cause and effect cannot be determined. For instance, though breakfast omission may increase stress, anxiety, and depression, suffering from such conditions may also lead to sleep disturbances, reducing the likelihood of eating breakfast the following morning. Though the crosslag analyses provided evidence to suggest that associations between the dietary variables examined in this study and stress, anxiety, and depression were unlikely to be causal, they were themselves confounded by the fact that the two cross-sections were collected only 6 months apart. There is therefore a need for further longitudinal and intervention studies to better determine the nature of the effects so far observed.

CONCLUSION

The current study has provided evidence to suggest that high stress, anxiety, and depression levels in adolescents are associated with breakfast omission, and that such associations with energy drink use may be more variable. However, all significant effects observed were at the cross-sectional level, with cross-lag analyses finding these dietary patterns not to be predictive of mental health outcomes at 6-month follow-up. Nevertheless, given that the findings may still have implications regarding school policy, it is considered important that the combined effects of breakfast and energy drinks are investigated further, and in relation to other important outcomes such as academic attainment and school attendance.

AUTHOR CONTRIBUTIONS

AS conceived and designed the study, and GR analyzed and interpreted the data. GR also drafted the original manuscript, and AP revised it for important intellectual content. Both authors approved the final version of the manuscript for publication, and agree to be accountable for all aspects of the work in ensuring that questions related to accuracy and integrity are appropriately investigated and resolved.

FIGURE 1 |
1Likelihood of reporting high stress as a function of breakfast and energy drink consumption combinations.

Figures 1-3, respectively.

FIGURE 2 |
2Likelihood of reporting high anxiety as a function of breakfast and energy drink consumption combinations.

FIGURE 3 |
3Likelihood of reporting high depression as a function of breakfast and energy drink consumption combinations.

TABLE 1 |
1Predictor variables and covariates entered into cross-sectional and cross-lag binary logistic regression analyses upon the outcomes of stress, anxiety, and depression.Predictor Breakfast Categorical Frequency of breakfast consumption derived from DABS single item: every day vs. not every day Variables Energy drinks Categorical Frequency of energy drink consumption derived from DABS single item: less than once a week vs. once a week or more Breakfast and energy drink combinations Categorical All four combinations of frequent/infrequent breakfast and energy drink consumption derived from the above two variables Predictor variables were entered individually (i.e., in the absence of other predictor variables). All covariates were entered into each analysis except where noted. * Caffeine from energy drinks was only entered as a covariate when the predictor variable was breakfast consumption.Variable
Type
Description

Dietary
Junk food
Continuous
DABS subscale score

Covariates
Healthy foods
Continuous
DABS subscale score

Weekly caffeine from energy
drinks  *

Continuous
Caffeine consumed specifically from energy drinks; derived from DABS single item

Weekly caffeine from cola
Continuous
Caffeine consumed specifically from cola; derived from DABS single item

Weekly caffeine from tea
Continuous
Caffeine consumed specifically from tea; derived from DABS single item

Weekly caffeine from coffee
Continuous
Caffeine consumed specifically from coffee; derived from DABS single item

Demographic
School
Categorical
School attended (Academy 1, Academy 2, or Academy 3)

Covariates
School year
Categorical
Year group attended (Year 7, 8, 9, 10, or 11)

Sex
Categorical
Male or female

Special educational needs
(SEN)

Categorical
Presence or absence of a SEN status

Free school meals (FSM)
Categorical
Eligibility or ineligibility to receive FSM

Lifestyle
Sleep
Continuous
Average number of sleep hours achieved per night

Covariates
Exercise
Continuous
Factor score derived from three single items (frequency of mild, moderate, and vigorous exercise)

School attendance
Continuous
School attendance percentage

1.324, 95% CI [1.064, 1.647], p = 0.012, anxiety, OR = 1.35, 95% CI [1.088, 1.674], p = 0.006, and depression, OR = 1.515, 95% CI [1.212, 1.894], p < 0.001, though no significant effects were observed relating to energy drinks: stress, OR = 1.13, 95% CI [0.863,
1.479], p = 0.375; anxiety, OR = 0.94, 95% CI [0.72, 1.227],
p = 0.648; depression, OR = 1.131, 95% CI [0.863, 1.482],
p = 0.373.

Frontiers in Psychology | www.frontiersin.org
February 2016 | Volume 7 | Article 106
FUNDINGThe authors are grateful to The Waterloo Foundation for providing a grant (grant number 503692) to fund the current research; the funders had no influence on the study design, analysis, or decision to publish this manuscript. Neither of the authors have any financial relationships with entities that could be perceived to influence (or that give the appearance of potentially influencing) the work reported in this manuscript.
Energy drink consumption and increased risk for alcohol dependence. A M Arria, K M Caldeira, S J Kasperski, K B Vincent, R R Griffiths, K E Grady, 10.1111/j.1530-0277.2010.01352.xAlcohol. Clin. Exp. Res. 35Arria, A. M., Caldeira, K. M., Kasperski, S. J., Vincent, K. B., Griffiths, R. R., and O'Grady, K. E. (2011). Energy drink consumption and increased risk for alcohol dependence. Alcohol. Clin. Exp. Res. 35, 365-375. doi: 10.1111/j.1530- 0277.2010.01352.x

An emerging adolescent health risk: caffeinated energy drink consumption patterns among high school students. S Azagba, D Langille, Asbridge , M , 10.1016/j.ypmed.2014.01.019Prev. Med. 62Azagba, S., Langille, D., and Asbridge, M. (2014). An emerging adolescent health risk: caffeinated energy drink consumption patterns among high school students. Prev. Med. 62, 54-59. doi: 10.1016/j.ypmed.2014.01.019

Breakfast, blood glucose, and cognition. D Benton, P Y Parker, Am. J. Clin. Nutr. 67Benton, D., and Parker, P. Y. (1998). Breakfast, blood glucose, and cognition. Am. J. Clin. Nutr. 67, 772S-778S.

The effects of an in-class breakfast program on attendance and on-task behavior of high school students. R T Bro, L Shank, R Williams, T F Mclaughlin, 10.1300/j019v16n03_01Child Fam. Behav. Ther. 16Bro, R. T., Shank, L., Williams, R., and McLaughlin, T. F. (1994). The effects of an in-class breakfast program on attendance and on-task behavior of high school students. Child Fam. Behav. Ther. 16, 1-8. doi: 10.1300/j019v16n03_01

Belief beyond the evidence: using the proposed effect of breakfast on obesity to show 2 practices that distort scientific evidence. A W Brown, M M Bohan Brown, Allison , D B , 10.3945/ajcn.113.064410Am. J. Clin. Nutr. 98Brown, A. W., Bohan Brown, M. M., and Allison, D. B. (2013). Belief beyond the evidence: using the proposed effect of breakfast on obesity to show 2 practices that distort scientific evidence. Am. J. Clin. Nutr. 98, 1298-1308. doi: 10.3945/ajcn.113.064410

Adolescents living the 24/7 lifestyle: effects of caffeine and technology on sleep duration and daytime functioning. C J Calamaro, T B A Mason, S J Ratcliffe, 10.1542/peds.2008-3641Pediatrics. 123Calamaro, C. J., Mason, T. B. A., and Ratcliffe, S. J. (2009). Adolescents living the 24/7 lifestyle: effects of caffeine and technology on sleep duration and daytime functioning. Pediatrics 123, e1005-10. doi: 10.1542/peds.2008-3641

Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes. J Carifio, Perla , R J , 10.3844/jssp.2007.106.116J. Soc. Sci. 3Carifio, J., and Perla, R. J. (2007). Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes. J. Soc. Sci. 3, 106-116. doi: 10.3844/jssp.2007.106.116

School breakfast improves verbal fluency in undernourished Jamaican children. A M Chandler, S P Walker, K Connolly, S M Grantham-Mcgregor, J. Nutr. 125Chandler, A. M., Walker, S. P., Connolly, K., and Grantham-McGregor, S. M. (1995). School breakfast improves verbal fluency in undernourished Jamaican children. J. Nutr. 125, 894-900.

Sports drinks and energy drinks for children and adolescents: are they appropriate?. 10.1542/peds.2011-0965Sports Medicine and Fitness. 127PediatricsCommittee on Nutrition and the Council on Sports Medicine and Fitness (2011). Sports drinks and energy drinks for children and adolescents: are they appropriate? Pediatrics 127, 1182-1189. doi: 10.1542/peds.2011-0965

The school breakfast program and cognition in adolescents. B A Cromer, K J Tarnowski, A M Stein, P Harton, D J Thornton, 10.1097/00004703-199012000-00003J. Dev. Behav. Pediatr. 11Cromer, B. A., Tarnowski, K. J., Stein, A. M., Harton, P., and Thornton, D. J. (1990). The school breakfast program and cognition in adolescents. J. Dev. Behav. Pediatr. 11, 295-300. doi: 10.1097/00004703-199012000- 00003

Breakfast and performance in school children. N H Dickie, A E Bender, 10.1079/BJN19820134Br. J. Nutr. 48Dickie, N. H., and Bender, A. E. (1982). Breakfast and performance in school children. Br. J. Nutr. 48, 483-496. doi: 10.1079/BJN19820134

Coffee and health: a review of recent human research. J V Higdon, B Frei, 10.1080/10408390500400009Crit. Rev. Food Sci. Nutr. 46Higdon, J. V., and Frei, B. (2006). Coffee and health: a review of recent human research. Crit. Rev. Food Sci. Nutr. 46, 101-123. doi: 10.1080/10408390500400009

Over-thecounter stimulant, depressant, and nootropic use by veterinary students. E H Hofmeister, J L Muilenburg, L Kogan, S M Elrod, 10.3138/jvme.37.4.403J. Vet. Med. Educ. 37Hofmeister, E. H., Muilenburg, J. L., Kogan, L., and Elrod, S. M. (2010). Over-the- counter stimulant, depressant, and nootropic use by veterinary students. J. Vet. Med. Educ. 37, 403-416. doi: 10.3138/jvme.37.4.403

A systematic review of the effect of breakfast on the cognitive performance of children and adolescents. A Hoyland, L Dye, C L Lawton, 10.1017/s0954422409990175Nutr. Res. Rev. 22Hoyland, A., Dye, L., and Lawton, C. L. (2009). A systematic review of the effect of breakfast on the cognitive performance of children and adolescents. Nutr. Res. Rev. 22, 220-243. doi: 10.1017/s0954422409990175

A low glycaemic index breakfast cereal preferentially prevents children's cognitive performance from declining throughout the morning. J Ingwersen, M A Defeyter, D O Kennedy, K A Wesnes, A B Scholey, 10.1016/j.appet.2006.06.009Appetite. 49Ingwersen, J., Defeyter, M. A., Kennedy, D. O., Wesnes, K. A., and Scholey, A. B. (2007). A low glycaemic index breakfast cereal preferentially prevents children's cognitive performance from declining throughout the morning. Appetite 49, 240-244. doi: 10.1016/j.appet.2006.06.009

Adolescent substance use, sleep, and academic achievement: evidence of harm due to caffeine. J E James, A L Kristjánsson, I D Sigfúsdóttir, 10.1016/j.adolescence.2010.09.006J. Adolesc. 34James, J. E., Kristjánsson, A. L., and Sigfúsdóttir, I. D. (2011). Adolescent substance use, sleep, and academic achievement: evidence of harm due to caffeine. J. Adolesc. 34, 665-673. doi: 10.1016/j.adolescence.2010.09.006

Likert scales: how to (ab)use them. S Jamieson, 10.1111/j.1365-2929.2004.02012.xMed. Educ. 38Jamieson, S. (2004). Likert scales: how to (ab)use them. Med. Educ. 38, 1217-1218. doi: 10.1111/j.1365-2929.2004.02012.x

Adolescent caffeine consumption, daytime sleepiness, and anger. A L Kristjánsson, I D Sigfúsdóttir, J P Allegrante, James , J E , 10.1089/jcr.2011.0008J. Caffeine Res. 1Kristjánsson, A. L., Sigfúsdóttir, I. D., Allegrante, J. P., and James, J. E. (2011). Adolescent caffeine consumption, daytime sleepiness, and anger. J. Caffeine Res. 1, 75-82. doi: 10.1089/jcr.2011.0008

Adolescent caffeine consumption and self-reported violence and conduct disorder. A L Kristjánsson, I D Sigfúsdóttir, S S Frost, James , J E , 10.1007/s10964-013-9917-5J. Youth Adolesc. 42Kristjánsson, A. L., Sigfúsdóttir, I. D., Frost, S. S., and James, J. E. (2013). Adolescent caffeine consumption and self-reported violence and conduct disorder. J. Youth Adolesc. 42, 1053-1062. doi: 10.1007/s10964-013-9917-5

Breakfast omission and cognitive performance of normal, wasted and stunted schoolchildren. I López, I De Andraca, C G Perales, E Heresi, M Castillo, M Colombo, Eur. J. Clin. Nutr. 47López, I., de Andraca, I., Perales, C. G., Heresi, E., Castillo, M., and Colombo, M. (1993). Breakfast omission and cognitive performance of normal, wasted and stunted schoolchildren. Eur. J. Clin. Nutr. 47, 533-542.

Update on caffeine consumption, disposition and action. H G Mandel, 10.1016/s0278-6915(02)00093-5Food Chem. Toxicol. 40Mandel, H. G. (2002). Update on caffeine consumption, disposition and action. Food Chem. Toxicol. 40, 1231-1234. doi: 10.1016/s0278-6915(02)00093-5

Caffeine content of energy drinks, carbonated sodas, and other beverages. R R Mccusker, B A Goldberger, E J Cone, 10.1093/jat/30.2.112J. Anal. Toxicol. 30McCusker, R. R., Goldberger, B. A., and Cone, E. J. (2006). Caffeine content of energy drinks, carbonated sodas, and other beverages. J. Anal. Toxicol. 30, 112-114. doi: 10.1093/jat/30.2.112

Do energy drinks contain active components other than caffeine?. T M Mclellan, H R Lieberman, M Meadows-Oliver, P Ryan-Krause, 10.1111/j.1753-4887.2012.00525doi: 10.1016/j. pedhc.2007.08.005J. Pediatr. Health Care. 70Nutr. Rev.McLellan, T. M., and Lieberman, H. R. (2012). Do energy drinks contain active components other than caffeine? Nutr. Rev. 70, 730-744. doi: 10.1111/j.1753- 4887.2012.00525.x Meadows-Oliver, M., and Ryan-Krause, P. (2007). Powering up with sports and energy drinks. J. Pediatr. Health Care 21, 413-416. doi: 10.1016/j. pedhc.2007.08.005

Effects of caffeine on human health. P Nawrot, S Jordan, J Eastwood, J Rotstein, A Hugenholtz, M Feeley, 10.1080/0265203021000007840Food Addit. Contam. 20Nawrot, P., Jordan, S., Eastwood, J., Rotstein, J., Hugenholtz, A., and Feeley, M. (2003). Effects of caffeine on human health. Food Addit. Contam. 20, 1-30. doi: 10.1080/0265203021000007840

High caffeine intake in adolescents: associations with difficulty sleeping and feeling tired in the morning. R L Orbeta, M D Overpeck, D Ramcharran, M D Kogan, R Ledsky, 10.1016/j.jadohealth.2005.05.014J. Adolesc. Health. 38Orbeta, R. L., Overpeck, M. D., Ramcharran, D., Kogan, M. D., and Ledsky, R. (2006). High caffeine intake in adolescents: associations with difficulty sleeping and feeling tired in the morning. J. Adolesc. Health 38, 451-453. doi: 10.1016/j.jadohealth.2005.05.014

A good-quality breakfast is associated with better mental health in adolescence. T A O'sullivan, M Robinson, G E Kendall, M Miller, P Jacoby, S R Silburn, 10.1017/S1368980008003935Public Health Nutr. 12O'Sullivan, T. A., Robinson, M., Kendall, G. E., Miller, M., Jacoby, P., Silburn, S. R., et al. (2009). A good-quality breakfast is associated with better mental health in adolescence. Public Health Nutr. 12, 249-258. doi: 10.1017/S1368980008003935

Perceived stress, energy drink consumption, and academic performance among college students. M L Pettit, K A Debarr, 10.1080/07448481.2010.510163J. Am. Coll. Health. 59Pettit, M. L., and DeBarr, K. A. (2011). Perceived stress, energy drink consumption, and academic performance among college students. J. Am. Coll. Health 59, 335-341. doi: 10.1080/07448481.2010.510163

Advanced policy options to regulate sugar-sweetened beverages to support public health. J L Pomeranz, 10.1057/jphp.2011.46J. Public Health Policy. 33Pomeranz, J. L. (2012). Advanced policy options to regulate sugar-sweetened beverages to support public health. J. Public Health Policy 33, 75-88. doi: 10.1057/jphp.2011.46

Nutrition and education: a randomized trial of the effects of breakfast in rural primary school children. C A Powell, S P Walker, S M Chang, S M Grantham-Mcgregor, Am. J. Clin. Nutr. 68Powell, C. A., Walker, S. P., Chang, S. M., and Grantham-McGregor, S. M. (1998). Nutrition and education: a randomized trial of the effects of breakfast in rural primary school children. Am. J. Clin. Nutr. 68, 873-879.

Caffeinated energy drinks: a growing problem. C J Reissig, E C Strain, R R Griffiths, 10.1016/j.drugalcdep.2008.08.001Drug Alcohol Depend. 99Reissig, C. J., Strain, E. C., and Griffiths, R. R. (2009). Caffeinated energy drinks: a growing problem. Drug Alcohol Depend. 99, 1-10. doi: 10.1016/j.drugalcdep.2008.08.001

Acute effects of energy drinks on behavioural sanctions in secondary school children: a preliminary study. G Richards, N Millward, P Evans, J Rogers, A P Smith, 10.5539/jfr.v4n4p1J. Food Res. 4Richards, G., Millward, N., Evans, P., Rogers, J., and Smith, A. P. (2015a). Acute effects of energy drinks on behavioural sanctions in secondary school children: a preliminary study. J. Food Res. 4, 1-9. doi: 10.5539/jfr.v4n4p1

The diet and behaviour scale (DABS): testing a new measure of food and drink consumption in a cohort of secondary school children from the south west of England. G Richards, A Malthouse, A P Smith, 10.5539/jfr.v4n3p148J. Food Res. 4Richards, G., Malthouse, A., and Smith, A. P. (2015b). The diet and behaviour scale (DABS): testing a new measure of food and drink consumption in a cohort of secondary school children from the south west of England. J. Food Res. 4, 148-161. doi: 10.5539/jfr.v4n3p148

Caffeine consumption and selfassessed stress, anxiety, and depression in secondary school children. G Richards, A P Smith, 10.1177/0269881115612404J. Psychopharmacol. 29Richards, G., and Smith, A. P. (2015a). Caffeine consumption and self- assessed stress, anxiety, and depression in secondary school children. J. Psychopharmacol. 29, 1236-1247. doi: 10.1177/0269881115612404

Risk factors for, and effects of, stress, anxiety, and depression in adolescents. G Richards, A P Smith, 10.9734/BJESBS/2015/19501Br. J. Educ. Soc. Behav. Sci. 10Richards, G., and Smith, A. P. (2015b). Risk factors for, and effects of, stress, anxiety, and depression in adolescents. Br. J. Educ. Soc. Behav. Sci. 10, 1-10. doi: 10.9734/BJESBS/2015/19501

Health effects of energy drinks on children, adolescents, and young adults. S M Seifert, J L Schaechter, E R Hershorin, S E Lipschultz, 10.1542/peds.2009-3592Pediatrics. 127Seifert, S. M., Schaechter, J. L., Hershorin, E. R., and Lipschultz, S. E. (2011). Health effects of energy drinks on children, adolescents, and young adults. Pediatrics 127, 511-528. doi: 10.1542/peds.2009-3592

The relationship between social deprivation, as measured by individual free school meal eligibility, and educational attainment at GCSE in Northern Ireland: a preliminary investigation. I Shuttleworth, 10.1080/0141192950210404Br. Educ. Res. J. 21Shuttleworth, I. (1995). The relationship between social deprivation, as measured by individual free school meal eligibility, and educational attainment at GCSE in Northern Ireland: a preliminary investigation. Br. Educ. Res. J. 21, 487-504. doi: 10.1080/0141192950210404

Breakfast and mental health. A P Smith, 10.3109/09637489809089415Int. J. Food Sci. Nutr. 49Smith, A. P. (1998). Breakfast and mental health. Int. J. Food Sci. Nutr. 49, 397-402. doi: 10.3109/09637489809089415

Breakfast cereal and caffeinated coffee: effects on working memory, attention, mood, and cardiovascular function. A P Smith, R Clark, J Gallagher, 10.1016/s0031-9384(99)00025-6Physiol. Behav. 67Smith, A. P., Clark, R., and Gallagher, J. (1999). Breakfast cereal and caffeinated coffee: effects on working memory, attention, mood, and cardiovascular function. Physiol. Behav. 67, 9-17. doi: 10.1016/s0031-9384(99)00025-6

Effects of breakfast and caffeine on cognitive performance, mood and cardiovascular functioning. A P Smith, A M Kendrick, A L Maben, J Salmon, 10.1006/appe.1994.1004Appetite. 22Smith, A. P., Kendrick, A. M., Maben, A. L., and Salmon, J. (1994). Effects of breakfast and caffeine on cognitive performance, mood and cardiovascular functioning. Appetite 22, 39-55. doi: 10.1006/appe.1994.1004

Revving up and staying up: energy drink use associated with anxiety and sleep quality in a college sample. M J Stasio, K Curry, A L Wagener, D M Glassman, College Student J. 45738Stasio, M. J., Curry, K., Wagener, A. L., and Glassman, D. M. (2011). Revving up and staying up: energy drink use associated with anxiety and sleep quality in a college sample. College Student J. 45, 738.

Energy drink consumption is associated with anxiety in Australian young adult males. G S A Trapp, K Allen, T A O'sullivan, M Robinson, P Jacoby, W H Oddy, 10.1002/da.22175Depress. Anxiety. 31Trapp, G. S. A., Allen, K., O'Sullivan, T. A., Robinson, M., Jacoby, P., and Oddy, W. H. (2014). Energy drink consumption is associated with anxiety in Australian young adult males. Depress. Anxiety 31, 420-428. doi: 10.1002/da.22175

Effect of breakfast timing on the cognitive functions of elementary school students. N Vaisman, H Voet, A Akivis, E Vakil, 10.1001/archpedi.1996.02170350091016Arch. Pediatr. Adolesc. Med. 150Vaisman, N., Voet, H., Akivis, A., and Vakil, E. (1996). Effect of breakfast timing on the cognitive functions of elementary school students. Arch. Pediatr. Adolesc. Med. 150, 1089-1092. doi: 10.1001/archpedi.1996.02170350091016

Breakfast reduces declines in attention and memory over the morning in schoolchildren. K A Wesnes, C Pincock, D Richardson, G Helm, S Hails, 10.1016/j.appet.2003.08.009Appetite. 41Wesnes, K. A., Pincock, C., Richardson, D., Helm, G., and Hails, S. (2003). Breakfast reduces declines in attention and memory over the morning in schoolchildren. Appetite 41, 329-331. doi: 10.1016/j.appet.2003.08.009

Junk food' diet and childhood behavioural problems: results from the ALSPAC cohort. N J Wiles, K Northstone, P Emmett, Lewis , G , 10.1038/sj.ejcn.1602967J. Clin. Nutr. 63Wiles, N. J., Northstone, K., Emmett, P., and Lewis, G. (2009). 'Junk food' diet and childhood behavioural problems: results from the ALSPAC cohort. J. Clin. Nutr. 63, 491-498. doi: 10.1038/sj.ejcn.1602967

Researching and Developing Mental Health and Well-Being Assessment Tools for Supporting Employers and Employees in Wales. G Williams, CardiffCardiff UniversityPh.D. thesisWilliams, G. (2014). Researching and Developing Mental Health and Well-Being Assessment Tools for Supporting Employers and Employees in Wales. Ph.D. thesis, Cardiff University, Cardiff.

A holistic approach to stress and well-being. Part 6: the wellbeing process questionnaire (WPQ Short Form). G Williams, A P Smith, Occupat. Health Work. 9Williams, G., and Smith, A. P. (2012). A holistic approach to stress and well-being. Part 6: the wellbeing process questionnaire (WPQ Short Form). Occupat. Health Work 9, 29-31.

An experimental study of the effects of energy intake at breakfast on the test performance of 10-year-old children in school. D P Wyon, L Abrahamsson, M Järtelius, R J Fletcher, 10.3109/09637489709006958Int. J. Food Sci. Nutr. 48Wyon, D. P., Abrahamsson, L., Järtelius, M., and Fletcher, R. J. (1997). An experimental study of the effects of energy intake at breakfast on the test performance of 10-year-old children in school. Int. J. Food Sci. Nutr. 48, 5-12. doi: 10.3109/09637489709006958