Ruscitto, C., and Ogden, J (in press). Predicting jet lag in long-haul cabin crew: The role of

illness cognitions and behaviour. Psychology and Health.

Predicting jet lag in long-haul cabin crew: The role of illness cognitions and behaviour

Cristina Ruscitto and Jane Ogden

School of Psychology, University of Surrey, UK

Address for correspondence:

Cristina Ruscitto (PhD)

School of Psychology,

University of Surrey,

Guildford,

GU2 7XH

UK

email: c.ruscitto@surrey.ac.uk

1

Predicting jet lag in long-haul cabin crew: The role of illness cognitions and behaviour

Objective: Established risk factors for jet lag are mostly physiological including circadian

preference, age, gender, the number of flight zones crossed and to some extent direction of

travel. Some research has also highlighted a role for psychosocial factors including sleep,

diet and ‘circadian’ health behaviours and illness cognitions although this remains relatively

untested. The aim of this study was to evaluate the role of sleep, diet and illness cognitions in

predicting perceived jet lag amongst long-haul crew.

Design: 60 long-haul crew took part in a longitudinal study. Profile characteristics (including

chronotype), preparation strategies (sleep, eating and ‘circadian’ behaviours) and illness

cognitions were measured at baseline (before a trip). Main outcome measures: Subjective

jet lag (unidimensional and multidimensional) was measured on the crews’ second day off

(post-trip).

Results: Hierarchical regression analyses showed that unidimensional jet lag was predicted

by the belief in a cyclical timeline whereas multidimensional jet lag was predicted by

multidimensional jet lag at baseline and to a lesser extent by identity. No role was found for

profile characteristics and preparation strategies.

Conclusion: Illness cognitions partly explain the experience of perceived jet lag in long-haul

cabin crew indicating that jet lag is in part a psycho-social construct, not just a biological one.

Keywords: Long-haul cabin crew; jet lag; sleep strategies; eating strategies; circadian

preference; illness cognitions

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Introduction

Long-haul flights are a common experience for cabin crew and can be associated with several

health problems. Cabin crew are faced with two challenges to their circadian alignment: jet

lag and shift-work. Jet lag is a general malaise caused by rapid travel across multiple time

zones which results in a desynchrony of an individual’s endogenous circadian rhythms (e.g.

sleep/wake cycle) and the external world (e.g. time cues in new time zone) (Atkinson, 2013).

Previous research has found a relationship between disrupted circadian rhythm following

transmeridian flying and impairment of physical and psychological health (Arendt, Stone &

Skene, 2000). Jet lag symptoms include disturbed night-time sleep, daytime fatigue, impaired

performance, moodiness, loss of appetite, and gastrointestinal problems (Waterhouse et al.,

2000, Sharma & Shrivastava, 2004).

The physiological component of jet lag has been relatively well-established including

interventions that target these factors (Arendt, Stone, & Skene, 2000; Arendt, 2009). Known

risk factors for jet lag include circadian preference (e.g. morningness-eveningness, which has

a genetic basis, Katzenberg et al., 1998), length of exposure to jet lag (e.g. years of flying)

and the number of time zones crossed (effects of light exposure) (Suvanto, Partinen, Harma,

& Ilmarinen, 1990; Cho, Ennaceur, Cole & Suh, 2000; Roach et al., 2002; Flower et al.,

2003; Monk, 2005). There is contrasting evidence as to whether increasing age is a risk factor

for jet lag (Sack et al., 2007). Some research found that older cabin crew experienced

increased perceived desynchronosis following 10-hour westward and eastward time zone

transition (Suvanto et al., 1990). This was supported by a study of simulated adjustment to jet

lag following a 6-hour schedule shift (Moline, Pollak, Monk, & Lester, 1992). With ageing

there is an advance phase of entrainment which results in shorter circadian phases, earlier

waking up times and shorter sleep durations (Mistlberger & Rusak, 2000) which may explain

the increased susceptibility to jet lag associated with age. In contrast, some research using

3

long-haul flight and cabin crew found older people coped better with readjustment to jet lag

(Suvanto, Harma, & Laitinen, 1993; Tresguerres et al., 2001). This finding seems to suggest

that the benefits of learning to deal with jet lag through experience may compensate

deteriorations in the body clock (Reilly, 2009). Although some sex differences in circadian

characteristics are documented (Wever, 1984), there is limited data suggesting sex

differences in jet lag (Sack et al., 2007). However, one problem is that women are under-

represented in circadian and sleep research (Santhi et al., 2016). A recent study of simulated

jet lag found that the circadian effect on performance was significantly higher in women than

in men such that women were more cognitively impaired during the early morning hours,

which coincides with the end of a night shift in the real world (Santhi et al., 2016). In

addition, evidence in the area of symptom perception suggests women tend to report a higher

number of symptoms than men (Michel, 2007). Gender as a potential risk factor for jet lag

needs further evaluation.

Among symptoms experienced, sleep loss, difficulty initiating and maintaining sleep

and daytime sleepiness are the major problems in civil aviation as a result of sleep and work

periods conflicting with circadian rhythms (Lowden & Akerstedt, 1999, Waterhouse, Reilly,

Atkinson & Edwards, 2007, Griffiths & Powell, 2012). For example, sleep quality, measured

subjectively and objectively (e.g. actigraphy), has been shown to be closely related to

perceived jet lag in the cabin crew population (Spencer & Montgomery, 1995; Lowden and

Akerstedt, 1998, 1999) and in occasional travellers (Waterhouse et al., 2000, 2002). Several

studies have also shown that night workers (including cabin crew) have altered melatonin

levels (Grajewsky et al., 2003; Burch et al., 2005) and a delayed circadian rhythm (Roach,

Rodgers, & Dawson, 2002; Papantoniou et al., 2014) due to exposure to light as well as sleep

loss (Deboer, Detari & Meijer, 2007). It is not surprising that interventions to alleviate jet lag

have largely focused on targeting physiological aspects of jet lag with varying degree of

4

success. Managing jet lag consists of two basic methods: i) reducing circadian misalignment

(promoting adaptation of the circadian rhythm to the light/dark cycle) and ii) treating the

symptoms (Arendt et al., 2000). The first method involves correctly timed light exposure and

the administration of melatonin or a combination of both used to improve sleep/alertness and

to shift the circadian rhythm in the desired direction (e.g. Revell et al., 2006). The second

method involves taking sleep aids. Whilst there is evidence that melatonin can reduce

circadian misalignment and improve subjective jet lag and sleep quality (Arendt, Aldhous &

Marks, 1986), melatonin is not recommended for cabin crew (avoidance at least 12 h before

duty, Civil Aviation Authority ([CAA], 2011) and there is evidence that it does not

consistently reduce jet lag in cabin crew (Petrie, Dawson, Thompson & Brook, 1993) due to

the inability to estimate circadian phase in this group (Arendt, 2009). Further, bright-light

therapy requires the intervention of a health care professional (Arendt, 2009) and crew’s use

of sleep aids (including zolpidem) is severely restricted, (Civil Aviation Authority, 2011).

Thus, research has looked at other ways to reduce circadian misalignment in cabin

crew. Lowden and Akerstedt (1998) found that retaining home base sleep hours reduced jet

lag in cabin crew during layover but not back home, indicating that reducing circadian

disruption by preserving home sleep times is insufficient in reducing jet lag in cabin crew in

the home time zone. However, to date, much of the advice to cabin crew regarding combating

jet lag and fatigue during a duty and at home is based on preserving sleep (McCallum et al.,

2003). Airlines in the UK are required to provide ‘fatigue management training’ during initial

and recurrent training (CAA, 2004; European Aviation Safety Agency [EASA], 2016)

because of operational safety implications. The fatigue countermeasures recommended

(EASA, 2016) are drawn upon research evidence and include: avoiding jet lag by staying on

home time on short layovers (Arendt, 2009), reducing sleep dept by scheduling naps before

night duties (Bonnet & Arand, 1994) and using good sleep habits (sleep hygiene) that

5

improve the sleep environment (Morin & Espie, 2003). The latter include sleeping in a dark,

cool and noise free environment and avoiding food and stimulants before sleep (Morin &

Espie, 2003). Although the above strategies are mainly aimed at reducing fatigue and sleep

debt in operational settings, research shows that fatigue and sleep are the biggest predictors of

jet lag (Lowden & Akerstedt, 1999; Waterhouse et al., 2000, 2002; Waterhouse, Reilly,

Atkinson, & Edwards, 2007) and insufficient sleep is linked to circadian rhythm disruption

(Deboer, Detari & Meijer, 2007; Möller-Levet et al., 2013, Ackermann et al., 2013). Thus,

whilst sleep behaviours may prevent fatigue by reducing sleep debt, they also have an impact

on circadian rhythm activity and jet lag by increasing the duration and severity of jet lag

symptoms (Waterhouse et al., 2007). Further, jet lag in cabin crew is compounded by factors

related to shift work. These include long duty hours, night flying, changing patterns of work

(different layover lengths and directions of travel) which may contribute to the disturbance of

the biological clock and the severity of jet lag (McCallum, Sanquist, Mitler, & Krueger,

2003; Caldwell, 2005).

More recently, attention has shifted to the circadian resetting properties of food timing

in animal studies which suggest that there may be a ‘feeding’ clock affected by eating

patterns (Stephan, 2002) which may help explain how disruption to dietary patterns

contribute to circadian misalignment and the high prevalence of metabolic syndrome amongst

air crew and shift workers (Chung, Son & Kim, 2011; Scheer, Hilton, Mantzoros, & Shea,

2009). However, the impact of eating behaviour on jet lag is limited. Evidence for the impact

of eating behaviours such as avoiding caffeine, alcohol and food before bed time, eating

carbohydrate-rich food in the evening on sleep quality is contrasting (Arendt, 2009;

Henderson & Burt, 1998; Irish et al., 2015). Further, Waterhouse et al. (2007) argued that

food intake behaviour has a weak circadian component and therefore adapts more quickly

following transmeridian travel than those behaviours with a larger endogenous component

6

such as mood, mental performance and sleep. In contrast, Scheer, Morris and Shea (2013)

demonstrated that subjective hunger had a large endogenous circadian rhythm in a forced

desynchrony study, indicating that food intake behaviour has a strong link with jet lag. A

meal plan that contained days of feasting and fasting as well as a combination of

carbohydrates and proteins before air travel was successful in reducing perceived jet lag and

its symptoms in military personnel (Reynolds & Montgomery, 2002). Further, a simple meal

plan to eat at regular time in the home time zone reduced jet lag in a sample of cabin crew

(Ruscitto & Ogden, 2017). Thus, whilst ‘what’ we eat may have limited impact on jet lag,

evidence suggests that ‘when’ we eat may have an important role in circadian adaptation and

jet lag (Reynolds & Montgomery, 2002; Ruscitto & Ogden, 2017).

Several studies have also shown a psycho-social component of jet lag (e.g. incidence

of depression, suicide, alcoholism, cabin crew work stress, Bor & Hubbard, 2006; Eriksen,

2006). However, there is limited information on how this aspect could further exacerbate the

experience of jet lag in cabin crew. In line with the Self-Regulatory Model (SRM, Leventhal

et al., 1980) and research using illness cognitions (Weinman, Petrie, Moss-Morris, & Horne,

1996; Moss-Morris et al., 2002) jet lag may also be related to sense making which would help

explain variation in symptom perception (Pennebaker, 1982). Thus, it may be that some

individuals are asymptomatic despite circadian misalignment (Benhaberou-Brun, Lambert, &

Dumont, 1999, Ruscitto, Ogden & Ellis, under review) or report more jet lag symptoms

relative to their circadian disruption. Using the Self-Regulatory Model (Leventhal et al.,

1980), variability in symptom perception may be explained by individuals having non-

specialised models about their condition, which vary according to the experience of the health

threat. As individuals’ perception about the disease is influenced by their interpretation of

socio-cultural knowledge of the illness (from significant others and authoritative sources such

as doctors), previous and current experience of the illness (symptomatic information), the

7

emphasis is on the changing nature of cognitions in response to the environment. Whilst jet

lag is not an illness, it is considered to be a ‘syndrome’ which may be chronic for those who

travel back and forth across multiple time zones like airline cabin crew (American Academy

of Sleep Medicine, 2005). According to SRM (Leventhal et al., 1980; Cameron & Leventhal,

2003), individuals consistently describe their illness around cognitive and emotional

representations of their condition. Applied to jet lag, these are: i) the identity (frequency and

prevalence of jet lag symptoms); the perceived cause (e.g. disruption to body clock); the time

line (e.g. jet lag lasts 1,2,3 days after the trip or beyond); the perceived consequences (e.g. the

impact on wellbeing, relationships); the cure/control of an illness (e.g. ‘I can deal with jet

lag’) and the emotional response (e.g. ‘my jet lag disrupts my personal/social relationships’,

Eriksen, 2006). Research using the IPQ (Illness Perception Questionnaire) has revealed that

these components are strongly interrelated. For example, individuals who have a strong

illness identity tend to view their illness as uncontrollable, chronic and with severe

consequences (Hagger & Orbell, 2003). Furthermore, there is a strong link between illness

cognitions and a number of outcomes in chronic illness, including quality of life and self-

management behaviours (Hagger & Orbell, 2003).

Taken together, evidence from observational and interventional studies suggests that

focusing on the physiological component of jet lag may only partly explain jet lag in cabin

crew with limited scope for helping this community (e.g. melatonin and light therapy). To

date, although it is acknowledged that jet lag has a psycho-social component, the role of

illness cognitions in predicting jet lag has not been investigated. Further, although long-haul

cabin crew receive training on preventative strategies aimed at reducing fatigue and jet lag

(CAA, 2004; EASA, 2016), the impact of such preparation strategies (sleep, diet and

‘circadian’ health behaviours) on the experience of jet lag in cabin crew (days off, post-trip)

needs further evaluation.

8

The aim of the present study was therefore to assess the contribution of profile

characteristics (including chronotype) preparation strategies (sleep, eating and ‘circadian’

health behaviours) and illness cognitions (before a trip) in predicting subjective jet lag

(unidimensional and multidimensional which includes sleep quality, mood, fatigue and

attitudes to meals) at follow up (post-trip), on crew’s second day off.

Methods

Design

The present study used a prospective design. Predictor variables (profile characteristics,

preparation strategies (sleep, diet and ‘circadian’) and illness cognitions) were taken at Time

(T) 1 (baseline = the day before a trip, e.g. London – Los Angeles - London). Outcome

variables (subjective jet lag unidimensional and multidimensional) were measured at T2

(crew’s second day off, post-trip).

Participants

Sixty long-haul crew took part in the study. Cabin crew may have a full time or part-time

contract and have different responsibilities onboard: Supervisory or non-supervisory. Long-

haul cabin crew cross several time zones on average once per week and have two to four days

off after each trip depending on the number of time zones crossed, length of trips and duty

times.

Inclusion criteria

Crew had to be potentially jet lagged. As a result, only crew with the following trips were

considered: ≥ 4 hours time change (Atkinson, 2013); duration of layover ≥ 48 hours (crew

may tend to stay on home time on night-stops so may not be jet lagged). According to the

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International Classification of Sleep Disorders-2 (American Academy of Sleep Medicine

2005, pp.130-131), the diagnostic criteria for Jet Lag Disorder are:

‘A. There is a complaint of insomnia or excessive daytime sleepiness associated with

transmeridian jet travel across at least two time zones.

B. There is associated impairment of daytime function, general malaise, or somatic

symptoms such as gastrointestinal disturbance within one to two days after travel.

C. The sleep disturbance is not better explained by another current sleep disorder, medical or

neurological disorder, mental disorder, medication use or substance use disorder.’

Exclusion criteria

i) Any crew taking melatonin (affects the circadian rhythm);

ii) Any crew with underlying medical conditions that affect sleep (e.g. chronic fatigue

syndrome, depression, seasonal affective disorder, anorexia);

iii) Any crew with another current sleep disorder.

Taking any medications that may affect sleep: anti-arrhythmic (heart rhythm problems), beta

blockers (high blood pressure, angina), Clonidine (for high blood pressure, smoking cessation

and other health problems, corticosteroids (inflammation, asthma), diuretics (high blood

pressure), nicotine replacement products, sedating antihistamines (for cold and allergy

symptoms e.g. Benadryl). As per CAA regulations (2011), crew cannot take sleeping

medication (including zolpidem) at least 12 h before duty and they have to be free of any

adverse effects before duty. Sleep aids outside of these restrictions are recommended for

short-term only. The majority of the sample (88.3%) reported avoiding the use of sleeping

pills before bed time (always and often) compared to 5% (3 participants) who ‘never’ avoid

their use.

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Procedure

Following ethical approval, participants were sent a recruitment email with details on how to

take part. Participation was voluntary and anonymous. Informed consent and completion of

the profile, work preparation and illness cognition measures was done online. The online

survey contained questions about taking melatonin and underlying illnesses that may cause

sleep disturbance. Participants were unable to continue with the survey in case of a ‘yes’

answer to both questions and they were offered the option to get in touch with the researcher

for further information. Prospective participants taking any other medication were asked to

contact the investigators before deciding to take part as some medications may affect sleep

quality. Outcome measures were completed on paper. In order to make jet lag, fatigue and

sleep more distinct for participants and avoid issues of construct validity, the following

information was given to participants in the in the Participant Information Sheet: ‘When

answering questions about jet lag, it may help to have a definition of jet lag and some of its

symptoms as although related, they are different: Jet Lag: When the body clock (e.g.

sleep/wake; feeding/fasting) is out of sync with the light/dark cycle in a new environment as

a result of flying across multiple time zones. As the body may not adjust quickly to this rapid

change, some symptoms may be experienced. Fatigue: Described as deterioration of

performance, lack of energy or immune activation (e.g. adaptive response to infections). It is

the signal from the body that you should stop what you are doing (physical, mental activity or

being awake). Sleepiness: The drive for sleep. It is a physiological need like hunger and

thirst. It may be affected by several factors including increasing time since sleep, disturbed

sleep or jet lag’.

Measures

Participants completed the following key predictor variables at baseline (profile

characteristics, flight characteristics, preparation strategies and illness cognitions) and the

11

outcome variables at two time points during the study: T1: Baseline and T2: crew’s second

day off post-trip. Reliability was assessed where appropriate using Cronbach’s alphas.

Predictor variables

The following profile characteristics were assessed:

Profile characteristics. Participants described their age, whether they any children, their

nationality, living status, whether they had a supervisory role, whether they worked full-time

(FT) or part-time (PT), years of flying long haul. In addition they completed the

Morningness-Eveningness Questionnaire (MEQ) to assess their chronotype

(Horne & Ostberg, 1976).

Flight characteristics. Trip schedules are only planned, as per crew’s roster at baseline. These

times were used to calculate trip length and to categorise flights into ‘night’ and ‘day’. Flights

that departed at 6.00 h or after and arrived at destination (abroad or UK) before 24:00 h

(Greenwich Mean Time, GMT) were classified as day flights. Any flights with a duty falling

between 00.00 h and 5.99 h decimal time were classified as night flights (duty and flight time

limitations scheme, CAA, 2004). In addition, participants described the direction of the flight

and the time change at destination, the days off around their trip, the length of the layover and

flight duties (inbound and outbound) and the season in which the return trip took place (see

Appendix B). The above variables represent the influence of flight/work factors on jet lag and

their inclusion help control their impact on the illness cognition – health behaviours – jet lag

relationship.

Preparation strategies

Participants rated their sleep, diet and ‘circadian’ health behaviours in preparation for

a trip (Appendix B). Pre-flight measures that relate to good sleep habits (e.g. sleep hygiene)

included: ensuring the bedroom is quiet, cool and dark, avoiding using sleeping pills,

avoiding using alcohol as a sleeping aid and avoiding eating less than one hour before bed

12

(Henderson & Burt, 1998; Morin & Espie, 2003). Sleep behaviours also included strategic

napping before an outbound and homeward night-flight to minimise the effects of sleep loss

(Rosekind, Boyd, Gregory, Glotzbach & Blank, 2002). Behavioural statements known in the

literature to help manage disruption of the body clock associated with crossing time zones

included: ‘staying on home time’ during a layover of 48 hours or less with a time change of

+/- three hours and ‘staying on home time’ during layovers of 48 hours or more with a time

change of +/- four hours or more (Arendt et al., 2000; Flower, 2001). The distinction between

layover length and time change reflects the finding that cabin crew are more likely to adapt

on longer layovers with a larger time change than shorter layovers even though this strategy

may be more disruptive for jet lag symptoms post-trip (Lowden & Akerstedt, 1998). Eating

strategies aimed at minimising disruption to scheduled meals included: having 3 balanced

meals a day, eating at regular meal times (home time), interrupting sleep to eat at regular

meal times (home time) (Henderson & Burt, 1998). Some statements relate to general health

behaviours (e.g. sleep hygiene and eating behaviours) whilst others are more specific (e.g.

strategic napping before night-flights and ‘staying on home time’ before two types of

layovers). In addition, all behaviours relate to pre-flight strategies (e.g. before an outbound

and/or an inbound flight) except for items 7 and 8 which refer to ‘layover’ strategies (e.g.

‘staying on home’ time during a layover). All health behaviours are therefore considered

baseline predictor measures (the day before a trip) as they assessed participants’ planned

behaviour before trips. Each statement is rated on a 5-point-scale ranging from never (1) to

always (5). For descriptive purposes only, the proportion of participants who used strategies

‘often’ and ‘always’ were calculated (sum of frequency scores divided by 60).

Illness cognitions. The participants completed the Revised Illness Perception Questionnaire

(IPQ-R) by Moss-Morris et al. (2002). This was adapted to measure illness perceptions of jet

lag (Appendix B). The IPQ-R evaluates nine dimensions from Leventhal and colleagues’

13

Self-Regulatory Model (1980). The first part was concerned with the assessment of the

identity of jet lag (the number of symptoms endorsed by the participants). 17 symptoms were

included based on the literature (Spitzer et al., 1999; Waterhouse et al., 2007; American

Academy of Sleep Medicine, 2005). Participants indicated whether or not they had

experienced each symptom as part of their jet lag. The sum of the ‘yes’ answers represented

the identity score.

The second part measured seven dimensions: Timeline (6 items,

acuteness/chronicity of jet lag); Consequences (6 items, effects of jet

lag on an individual’s lifestyle, health and well- being); Personal Control (6 items,

self management of jet lag); Treatment Control (5 items, treatment

management of jet lag); Illness Coherence (5 items, personal understanding of jet

lag); Timeline cyclical (5 items, evolution of jet lag); Emotional Representations

(6 items, emotional reaction to jet lag).

The third part consisted of items that measured the causes of jet lag with 18 possible

causal attributions within three categories: Psycho-behavioural (8 items,

environmental (4 items, and biological (4 items, All responses were

given on a 5-point scale (strongly disagree = 1, disagree = 2, neither agree nor disagree = 3,

agree = 4 and strongly agree = 5). The items in each subscale were summed after reverse

scoring and the mean was calculated. High scores indicated stronger beliefs about jet lag’s

chronicity, cyclical course, personal influence, treatment possibilities, perceived

understanding and emotional impact of jet lag as well as stronger beliefs about the causal

attributions of jet lag.

Outcome Measures

The following outcome measures were assessed at baseline (T1: The day before a trip) and

post-trip (T2: Crew’s second day off).

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Unidimensional jet lag. This was assessed using the single jet lag item of The Liverpool Jet

Lag Questionnaire (Waterhouse et al., 2000).

Multidimensional jet lag. This was assessed using an amended version of the Liverpool Jet

Lag Questionnaire (Waterhouse et al., 2000, Appendix B) which consisted of 14 items

relating to jet lag (1 item), fatigue (1 item), sleep quality (4 items), mood/cognitive

performance (3 items), attitudes to meals (3 items), bowel consistency (1 item) and sleepiness

after dinner (1 item). This was computed to create a total score (Time 2: ). Higher

scores indicated greater symptoms of jet lag. The jet lag questionnaire was revised to address

the following issues: i) Respondents were asked to assess their perception of jet lag and

symptoms on a Likert scale (1= not at all to 5 = very much) instead of by visual analogue

scale (-5 = less; 0 = normal; +5 = more) thus eliminating comparing jet lag levels to ‘normal’.

The implication is that as cabin crew may be chronically jet lagged, any notion of normality

may be compromised; ii) Respondents were asked to assess how much jet lag and related

symptoms they had once (in the evening, after dinner) instead of five times a day as the focus

was on the overall perception of jet lag as opposed to its changeability during the day. The

reduction in the number of items was also to encourage participation and iii) The assessment

of sleepiness after dinner was added: ‘How sleepy are you right now?’ (1 = not at all to 5 =

very much).

Data Analysis

SPSS 21 was used to conduct data analysis. Prior to analysis, the data were assessed for

accuracy of data entry, missing values, outliers and assumptions of normality. The data were

then analysed in the following ways: (i) to describe participants’ predictor variables (profile

characteristics, flight characteristics, preparation strategies and illness cognitions) (ii) to

assess the role of predictor variables in predicting key outcomes (unidimensional and

15

multidimensional jet lag) using part correlations for screening followed by hierarchical

regression analyses.

A Bonferroni correction may be employed to adjust for multiple comparisons in a set

of correlation coefficients to avoid the risk of type I error. However, given the number of

variables in each of the two correlation matrices (47) the adjusted p-value of .000 (p

= .05/2209 (number of correlation coefficients)) was deemed to be too conservative and

would lead to an increase in Type II errors (Field, 2013). As an alternative significance

testing tool, Field (2013) suggests assessing the effect size of a correlation coefficient and the

confidence levels which represent the likely size of the population effect (ranges within

which the population value is likely to lie). To this end, correlations would be considered

significant if they represented a medium effect size (r = > .30, Cohen, 1988).

The assumptions of multiple regression analyses were checked by verifying the

variance inflation factors (VIFs) were smaller than 10 and tolerance values were greater

than.10 (multicollinearity check), there were no cases with standard residuals greater than 3

or less than -3, Mahalanobis and Cook’s distance values were within range (outliers’ check),

residuals’ histogram, normal probability plots and scatter plots (linearity and

homoscedasticity’s check) (Tabachnick & Fidell, 2007).

Results

Predictor variables

Profile characteristics, flight characteristics, preparation strategies and illness cognitions for

the sample are shown in Tables 1 and 2 here.

-Insert tables 1 and 2 about here -

In terms of profile characteristics, the majority of participants were female, British, living

with a partner and no children and were neither evening nor morning types. The average age

16

was 42. Most participants did not have a supervisory role, were part-time and had been flying

in the long-haul only fleet for 15 years. In terms of flight characteristics (Table 2), the

frequency of night-time flights was higher for the inbound sectors (back home) than for the

outbound sectors (to another time zone). Further, outbound flight duty times were longer than

inbound flight duty times reflecting the fact that most crew had had a trip towards the west

(e.g. USA) with a time change that ranged between – 8 hours and + 11 hours (4 – 11 hours,

absolute measure). The majority of the participants had three to four days off before and three

days off after their trip and conducted the study in the autumn/winter time (November-

March). In terms of preparation strategies, the most frequently used (‘often’ and ‘always’

answers) sleep hygiene strategies were ‘avoiding using sleeping pills’ (88.3%) and ‘having a

quiet bedroom’ (81.7%), followed by ‘avoiding alcohol’ (76.7%), ‘caffeine within four

hours’ (60%) and ‘eating less than an hour’ (51.7%) before going to bed. The least

frequently used strategies were: ensuring ‘the bedroom is cool’ (40%) and ‘dark’ (16.7%).

Scheduled napping was used markedly more frequently before an outbound flight (88.3%)

than a homeward night-flight (15%). Further, crew used the strategy of ‘staying on home

time’ on shorter trips more frequently (58.3%) than they did on longer trips (30%). Finally, a

relatively large proportion of participants reported having ‘three balanced meals a

day’(43.3%) and ‘eating at regular mealtimes’ (36.7%) whereas the least used strategy was

‘interrupting sleep to eat at regular meal times’(3.3%). In terms of cabin crew’s perceptions

of jet lag, overall, crew showed strong beliefs about the identity of jet lag (M = 12.37, SD =

3.30) a good understanding of the condition (M = 3.77, SD = 0.68) and a positive belief that it

can be controlled by the individual (M = 3.56, SD = 0.68) and treatment (M = 3.42, SD =

0.72) despite its cyclical nature (M = 3.33, SD = 0.86). On average, crew perceived jet lag to

be temporary (M = 2.82, SD = 0.94) with minor consequences (M = 2.74, SD = 0.82) and

little emotional impact (M = 2.35, SD = 0.88). Finally, crew showed strong beliefs for all

17

three causes of jet lag: environmental (M = 3.49, SD = 0.60), biological (M = 3.47, SD =

0.46) and psycho-behavioural factors (M = 3.39, SD = 0.60).

The role of predictor variables in predicting outcomes (jet lag uni- and multidimensional)

at T2

The results were analysed to explore the best predictors (profile and flight characteristics,

preparation strategies, illness cognitions) of jet lag at T2: Crew’s second day off. Due to the

smaller sample size within this group (N = 60), initial partial correlations for screening

(controlling for jet lag at baseline) were carried out to assess which variables could be entered

into the hierarchical multiple regression analyses. Step one included the control variable of jet

lag at baseline. Step 2 included any significant profile, flight characteristics, preparation

strategies and illness cognitions.

Unidimensional jet lag. The results showed that time cyclical (r = .37, p < .01) chronotype (r

= - .31, p < .05) and outbound flight duty time (r = .28 , p < .05) were significantly correlated

with jet lag. A trend towards significance was noted between jet lag and the following

variables: inbound flight duty time (r = .25, p = .053) consequences (r = .23, p = .08) and

time change (r = .22, p = .09). Multidimensional jet lag. The results showed that identity (r

= .31, p < .05), time cyclical (r = .29, p < .05) and environmental causes (r = .29, p < .05)

were significantly correlated with jet lag. A trend towards significance was noted between jet

lag and for the following variables: biological causes (r = .25, p = .06), time change (r = .23,

p = .08), age (r = .22, p = .10) and chronotype (r = - .21, p = .11). None of the sleep, diet and

‘circadian’ health behaviours related to jet lag.

As not all significant variables can be entered in the model due to the small sample

size (Stevens, 1996), for the present study a maximum of three variables (baseline profile

18

characteristics, preparation strategies and illness cognitions; ratio of 20 participants per each

regressor) with the largest correlation coefficient (> .30) were entered into the hierarchical

regression analyses. This resulted in two predictors being selected for the hierarchical

regression analysing unidimensional jet lag at T2 and one predictor being selected for the

hierarchical regression analysing multidimensional jet lag at T2. The correlation coefficients

obtained (> .30) indicate a moderate correlation between the predictor variables selected and

jet lag (uni- and multidimensional; Cohen, 1988).

Linearity and Homoscedasticity. This assumption was checked by inspecting the scatter

plots of standardised residuals. For jet lag as unidimensional measure, the standardised

residuals were not randomly distributed suggesting this assumption was violated. As the

control variable of baseline jet lag did not have a normal distribution, a log transformation

was employed but the results revealed no change to the model fit. Bootstrapping (designed

for small samples) was chosen as it does not rely on assumptions of normality and

homoscedasticity and it reduces the impact of bias as it produces robust estimates (e.g.

significance value and confidence intervals) in a way that is unaffected by the distribution of

scores (Field, 2013). The process takes bootstrap samples (1000) from the original sample,

determines the parameters within each bootstrap sample and re-estimates them (e.g. standard

errors, the confidence intervals and significance value) for each predictor based on the

bootstrapped samples.

Predicting subjective unidimensional jet lag at T2

The first step included the control variable: jet lag (unidimensional) at baseline (T1). The

results (Table 3) showed that jet lag at T1 (β = .29, p < .05) accounted for 8% of the total

variance in jet lag scores (R2 = .08, Adj R2 = .07, F(1, 58) = 5.21, p < .05). The addition of

morningness-eveningness and time cyclical to the regression model explained an additional

19

17% of the variation in jet lag scores (R2 = .25, Adj R2 = .21, F(3, 56) = 6.18, p < .001) and

resulted in the loss of significance of jet lag at baseline. The only significant predictor of jet

lag at T2 was time cyclical (β = .30, p < .01). This indicated that higher perceived

unidimensional jet lag levels on crew’s second day off were best predicted by greater

negative perception about the cyclical time course of jet lag.

Predicting subjective multidimensional jet lag at T2

The first step included the control variable: jet lag (multidimensional) at baseline (T1). The

results (Table 4) showed jet lag at baseline (β = .56, p < .001) accounted for 31% of the total

variance (R2= .31, Adj R2 = .30, F(1, 58) = 26.52, p < .001. The addition of identity explained

an additional 7% of the variance in jet lag scores (R2= .38, Adj R2 = .36, F(2, 57) = 17.46, p

< .001) with jet lag at T1 (β = .51, p < .001) and identity (β =.26, p < .05) emerging as

significant predictors of jet lag. This meant that with all variables taken into account, an

increase in the multidimensional measure of jet lag on crew’s second day off was best

predicted by increased perceived jet lag at baseline. A strong belief about the identity of jet

lag predicted an increase in perceived jet lag on crew’s second day off. However, this

predictor was less significant.

-Insert tables 3 and 4 about here -

Discussion

The present study aimed to assess the role of profile and flight characteristics, preparation

strategies and illness cognitions in predicting jet lag on crew’s second recovery day post-trip

(T2).

The results showed no role for profile characteristics such as gender, age, years of

flying and onboard role (see Appendix A for a full list of predictor variables) in contrast with

previous findings (Suvanto et al., 1990; Suvanto et al., 1993; Cho et al., 2000; Santhi et al.,

20

2016). However, this may be due to differences in outcome measures used (objective vs.

subjective measures). For example, Cho et al. (2000) and Santhi et al. (2016) evaluated the

impact of circadian disruption on cognitive skills. Similarly, no role for chronotype was

found in the model predicting unidimensional jet lag. However, it is important to note that

eveningness was associated with increased unidimensional jet lag (part correlations). This

finding is in accordance with past research on social jet lag which found evening types may

be more susceptible to jet lag because their preference for late bed times and the need for

early rise, to fulfil social commitments, can cause a cumulative sleep debt in this group

(Volk, Dyroff, Georgi & Pflug, 1994; Taillard, Philip, & Bioulac, 1999). Further,

eveningness has been associated with poor health behaviours (Caci, Robert, & Boyer 2003).

For example, there is evidence that evening types eat later, have unhealthy eating habits,

increased stress hormones and BMI and more psychological problems compared to morning

types (e.g. Lucassen et al., 2013).

In terms of flight characteristics, only ‘outbound flight duty time’ emerged as a

correlate of unidimensional jet lag (inbound flight duty time only showed a trend toward

significance) highlighting the relationship between scheduling factors, such as long working

hours, and the experience of jet lag, already identified in the literature (McCallum, Sanquist,

Mitler, & Krueger, 2003; Caldwell, 2005). Interestingly, such relationship was not evident in

the context of multidimensional jet lag which includes fatigue and sleep quality ratings. This

is a surprising finding given that it was expected that long flight duration would relate

primarily to fatigue and sleep quality (Caldwell, 2005). However, one explanation may be

that long working hours conflict with regular rest/activity patterns altering circadian activity

(Möller-Levet et al., 2013, Ackermann et al., 2013) resulting in the relationship between

work factors and unidimensional jet lag. It is important to note that correlations between time

change at destination and jet lag uni- and multidimensional showed a trend towards

21

significance (r = .22, p = .09 and r = .23, p = .08). Despite the lack of significant results, it is

worth noting that the direction of the correlations is consistent with the finding that eastwards

travel (positive correlation) is associated with increased jet lag (Waterhouse et al., 2007). The

difficulty in advancing sleep (phase advance) to cope with the shortened day, following

eastward travel, is partly due to a phenomenon called directional asymmetry, the fact that the

endogenous circadian rhythm tends to run slow (free run by 24-26 h), therefore there is a

natural tendency to phase delay, associated with westward travel (Waterhouse et al., 2007).

However the tendency to phase advance or phase delay is also affected by chronotype and the

present results only showed a trend towards significance, thus caution should be used when

interpreting these results.

While it is surprising that known risk factors for jet lag such as the number of time

zones crossed (absolute time change) and direction of travel were not associated with jet lag,

this may be explained by a potential gap that may exist between circadian disruption and jet

lag. It is expected that light in a new time zone will facilitate a delay or advance of the

circadian rhythm (depending on direction of travel) (Arendt, 2009). Because of the short

length of trips, the circadian rhythm only partly adapts to local time as it shifts slowly in

response to new external cues (about one time zone per day, Graeber, 1994). At this time jet

lag symptoms may be elevated because crew’s habitual sleep, meal times are in conflict with

the new local time while the circadian rhythm is slowly adapting. The extent of the conflict is

likely to depend on the size of the time change (Arendt et al., 2000). Therefore, a

relationship between time change and jet lag may be more evident during layover than during

crew’s days off. In addition, this relationship could be influenced by past expectations

(Leventhal et al., 1980). This also highlights the issue that circadian disruption and jet lag

may not be related (Ruscitto, Ogden and Ellis, under review) and that symptom perception

does not simply reflect what goes on in the body (Pennebaker, 1982). For example, symptom

22

perception may be higher than the circadian phase change (partly adapted). This further

emphasises the psychological aspect of jet lag and the importance of cognitions.

Moreover, jet lag was not predicted by preparation strategies (sleep, diet and

‘circadian’ behaviours) at baseline which conflicts with previous research (Henderson &

Burt, 1998; Cho et al., 2000; Roach et al., 2002; Reynolds and Montgomery, 2002; Flower et

al., 2003; Monk, 2005; Ruscitto & Ogden, 2017). This may reflect different designs,

differences in the timing of follow ups, the differing levels of jet lag between study

populations or how preventative strategies have been measured or implemented (e.g.

Henderson & Burt, 1998; Rosekind et al., 2006). In addition, whilst most of the pre-travel

behaviours are more relevant for reducing fatigue and sleep dept (fatigue countermeasures,

CAA, 2004; EASA, 2016), which could partly explain the lack of correlation between sleep

strategies and perceived jet lag, it was surprising that ‘staying on home time’ was not

associated with jet lag as this strategy is more relevant to circadian rhythms. One explanation

may be that preserving home sleep patterns may have a positive effect on reducing perceived

jet lag during a layover (Lowden & Akerstedt, 1998) but perceived jet lag back home may be

best explained by psycho-social factors.

Indeed, in terms of illness cognitions, the results showed that the belief about the

cyclical nature of symptoms of jet lag was the biggest predictor of subjective unidimensional

jet lag on crew’s second recovery day. This finding is consistent with previous results about

the changeable nature of jet lag symptoms during the course of a day and across days in long-

haul cabin crew (Lowden & Akerstedt, 1998; 1999) and occasional travellers (Waterhouse et

al., 2000). The belief that jet lag symptoms go in cycles and get better and worse within a

day also reflects the cyclical nature of the circadian rhythm (24 h cycle, Arendt et al., 2000)

where jet lag is notably worse at night during ‘circadian low’ (peak time of melatonin and

body core temperature minimum, Arendt et al., 2000). Interestingly, the finding that

23

multidimensional jet lag was best predicted by multidimensional jet lag at baseline, indicated

the impact of chronicity on such condition. These results are also consistent with past

research which found that long-haul cabin crew are affected by repeated circadian

desynchrony and chronic jet lag (Cho et al., 2000; McCallum et al., 2003). For example,

Reynolds and Montgomery (2002) found that prior jet lag (past history and jet lag after

deployment) increased the odds of reported jet lag after the return (odds ratio = 4.25; p = .01)

in a sample of military personnel.

Despite chronicity (baseline jet lag) explaining most of the variance in

multidimensional jet lag scores, a strong sense of identity was also found to be a predictor of

multidimensional jet lag. Thus, overall, the impact of illness cognitions on unidimensional

(time cyclical) and multidimensional (identity) jet lag is consistent with the major tenet of the

SRM (Leventhal et al., 1980) of a causal relationship between illness cognitions and

outcomes and reflects research across a wide range of conditions such as coronary heart

disease, chronic fatigue syndrome (CFS), human immunodeficiency syndrome (HIV),

diabetes and cancer (Hagger & Orbell, 2003). It also reflects the wider position in Health

Psychology that illness is not just the result of pathological processes but it can be explained

in a meaningful way by psycho-social factors (Conner & Norman, 2005). For example, after

adjusting for unidimensional jet lag at baseline, only ‘time cyclical’ emerged as a predictor of

increased unidimensional jet lag post-trip. These results indicate that while chronotype may

be linked to jet lag, when illness cognitions were taken into consideration (time cyclical

entered in the model) a belief in the cyclical timeline of the condition was the only significant

predictor of jet lag, further demonstrating the significance of psycho-social factors in

explaining unidimensional jet lag over and above physiological factors (Conner & Norman,

2005).

24

However, the lack of a role for preparation strategies (sleep, eating and ‘circadian’

health behaviours) in the present study highlights a problem for the SRM (Leventhal et al.,

1980) in terms of the proposed illness cognitions-coping-outcome relationship. A key aspect

of the SRM is that coping mediates the relationship between illness cognitions and outcomes

(e.g. CFS, diabetes, cardiovascular disease, Carver, Scheider, & Weintraub, 1989; Hagger &

Orbell, 2003; Carver & Connor-Smith, 2010). However, it may be that jet lag affects

individuals in different ways. This is also likely to change from trip to trip. Thus, strategies

may be more successful if they are tailored to suit personality (e.g. chronotype).

A number of potential limitations should be noted. Firstly, the sample size is

relatively small which prevented the inclusion of other predictors (e.g. environmental causes

for multidimensional jet lag and duty times for unidimensional jet lag) in the hierarchical

regression analyses. Secondly, we did not measure actual use of strategies which may help

explain the lack of influence of coping on outcome as proposed by the SRM (Leventhal et al.,

1980). Thirdly, the Work Preparation Strategies questionnaire could be improved by

assessing strategies according to stage of travel: pre-outbound flight, during a layover and

pre-inbound flight reflecting the need for different coping strategies to deal with these stages.

In addition, subjective methods could be supplemented by objective methods (e.g.

actigraphy) which help would evaluate crew compliance with planned preparation strategies.

This is important to evaluate more accurately the impact of countermeasures for jet lag.

Further, light is the biggest re-setter of the body clock and exposure to light at the

wrong circadian time disrupts the body clock with implications for jet lag (Arendt, 2009). In

the present study, light was not assessed directly, only indirectly through the variation in day

length between summer and winter (e.g. Suvanto et al., 1993), night flying (e.g. night light

exposure) and time change. The lack of significant results may therefore be attributable to

lack of sensitivity of these ‘surrogate’ measures (e.g. time change only showed a trend

25

towards significance), highlighting the importance of measuring light directly (e.g. actiwatch

light).

Finally, the illness cognition assessment is more comprehensive (e.g. more items)

than the sleep, diet and circadian health behaviour assessment which could explain why more

significant correlations were found with illness cognitions.

In conclusion, the present study demonstrated that after controlling for baseline jet

lag, illness cognitions, specifically, beliefs relating to time cyclical and identity explain a

small amount of the variance in uni- and multidimensional subjective jet lag (ΔR2 17% and

7% respectively) in long-haul cabin crew. To this end, although much research has

emphasised the biological component of jet lag with a focus on chronotype and melatonin,

this study also highlights a key role for sense making. Accordingly, reflecting much research

utilising the SRM (Leventhal et al., 1980), the finding from this study indicate that in line

with a wide of range of chronic conditions, jet lag is also influenced by illness cognitions and

can similarly be considered a psycho-social phenomenon. Because illness cognitions

influence the way individuals cope with a condition, a belief that jet lag is cyclical, which

reflects the nature of the circadian rhythm, may reduce the use of jet lag countermeasures by

emphasising the ‘uncontrollable’ nature of jet lag (e.g. ‘I believe it gets better and worse’).

For example, the present study found a low uptake of preparation strategies such as ‘staying

on home time’ on longer trips and some sleep hygiene strategies (‘ensuring the bedroom is

cool and dark’). Therefore, at practical level, long-haul cabin crew may benefit from

awareness that pessimistic illness cognitions can impact on their ability to cope with jet lag.

Because of the small sample size, methodological limitations and small amount of the

variance in jet lag explained by illness cognitions, it is important to emphasise that this study

represents a primary exploration of the impact of illness cognitions on jet lag. Nevertheless,

the current research suggests that illness cognitions play a role in the ratings of jet lag on

26

recovery days with potential scope for alleviating jet lag through changing pessimistic

cognitions. This may be achieved by reframing the way in which jet lag is conceptualised and

acknowledging it as a psycho-social construct which in turn means it could be modifiable

through changes in behaviours and cognitions, rather than through more physiological

approaches such as medication.

Aviation terms used

Outbound flight: Flight to another time zone

Inbound flight: Flight back home

Trip: Time away from home including the outbound and inbound flight

Layover: A period of time off spent in another time zone

Day(s) off: Time off in the home time zone

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Table 1. Participants’ profile characteristics (N = 60).  n % M SD Range Age 41.95 9.91 20 - 60Gender Female 49 (81.70) Male 11 (18.30)Children No 35 (58.3) Yes 25 (41.7)Nationality UK 49 (81.70) Other 11 (18.30)Living status Live with partner/spouse 40 (66.70) Live alone 20 (33.30)Smoke No 54 (90.00) Yes 6 (10.00)Role Main Crew 37 (61.70) Supervisory 23 (38.30)Type of Contract Part-time 34 (56.70) Full-time 26 (43.30)Length of service 15.02 8.75 0.6 - 40Chronotypea 53.53 10.60 33 - 75 Definitely Morning Type 5 ( 8.30) Moderately Morning Type 15 (25.00) Neither Type 30 (50.00) Moderately Evening Type 10 (16.70) Definitely Evening Type 0 ( 0.00)

aMornigness-Evenigness questionnaire, Horne & Östberg, 1976

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Table 2. Participants’ trip characteristics (N = 60).n % M SD Range

Time change (range -8 -+11) -2.77 (6.24) -8 - 11Time change absolute (range 4 - 11) 6.76 (1.49) 4 - 11Direction     East 16 (26.70) West 44 (73.30) Days OFF before trip    

2 20 (33.30) 3 18 (30.00) 4 6 (10.00)

5-10 16 (26.70) Days OFF after trip    

2 8 (13.30) 3 37 (61.70) 4 12 (20.00)

5-10 3 ( 5.00) Season     Autumn/Winter 48 (80.00) Spring/Summer 12 (20.00) Trip length 3.72 (0.98) 3 - 8

Outbound flight duty time 12.26 (1.17) 9.80 -14.75

Inbound flight duty time 11.62 (1.80) 8 - 15.42Outbound night/day     Daytime 24 (40.00)  Night-time 36 (60.00)Inbound night/day     Daytime 1 ( 1.70)  Night-time 59 (98.30)

Note. Time is represented as decimal hour

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Table 3. Predictors of subjective jet lag as unidimensional measure at T2, with 95% bias corrected and accelerated confidence intervals. Confidence intervals and standard errors based on 1000 bootstrap samples (N = 60)

                     

Outcome Variable T2 N Predictor Variable b 95% CI SE β R2 ΔR2

       

Jet lag unidimensional 60 Step 1   .08

    Constant 1.96 1.43, 2.50 0.25    Jet Lag T1 0.33 0.09, 0.60 0.13 .29*    Step 2   .25 .17    Constant   2.14 0.12, 4.07 1.07    Jet Lag T1 0.19 -0.02, 0.44 0.13 .17    M-Ea -0.02 -0.05, 0.01 0.01 -.22    Time cyclical 0.38 0.11, 0.66 0.14 .30**                     

Note. ΔF = 6.19, p < .01 for Step 2. * p < .05.** p < .01.aMornigness-Evenigness questionnaire, Horne & Östberg, 1976

Table 4. Predictors of subjective jet lag as multidimensional measure at T2 (N = 60)                     Outcome Variable T2 N Predictor Variable b 95% CI SE β R2 ΔR2

       Jet lag multidimensional 60 Step 1   .31    Constant 1.45 0.95, 1.94 0.25

    Jet Lag T1 0.54 0.33, 0.75 0.11 .56***

    Step 2   .38 .07    Constant   0.98 0.37 1.59 0.33    Jet Lag T1 0.49 0.28, 0.70 0.11 .51***    Identity 0.05 0.01, 0.09 0.02 .26*       

Note. ΔF = 6.07, p < .05 for Step 2. * p < .05. *** p < .001.

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APPENDIX 1

Profile characteristics Scoring

Age 20-60

Gender (0-1)

Nationality (UK-Other) (0-1)

Marital status (Live alone-With partner) (0-1)

Children (No-Yes) (0-1)

Smoke (No-Yes) (0-1)

Role (Crew-Manager) (0-1)

Contract (Part-time-Full-time) (0-1)

Service length (Years) (.06-40)

Morningness-Eveningness (MEQ) (16-86)

Flight characteristics

Days off before trip (2; 3; 4; 5-10) (1-4)

Direction (West-East) (0-1)

Outbound (to a new time zone) flight duty time (9.08-14.75)

Inbound (back home) flight duty time (8-15.42)

Outbound flight – day/night time (0-1)

Inbound flight – day/night time (0-1)

Time change (-8 - +11)

Time change (Absolute measure) (4-11)

Layover length (Days) (3-8)

Days off after trip (2; 3; 4; 5-10) (1-4)

Season (Winter-Summer) (0-1)

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APPENDIX 2

WORKING PREPARATION STRATEGIES QUESTIONNAIRE

Listed below are some questions about work preparation strategies regarding sleep the night before a flight, napping and eating strategies. Please choose an appropriate answer (Never to Always). In general, before you go on a long-haul flight, how often do you...........?

Never

Rarely Sometimes

Often Always

1 Ensure your bedroom is cool?

2 Ensure your bedroom is quiet?

3 Avoid using sleeping pills

4 Avoid using alcohol as a sleeping aid?

5 Nap1 before an outbound night-flight (local time)?

6 Nap1 before a homeward night-flight?(local time)

7 Stay on home time during a layover of 48 hours or less with a time change of +/-3 hours or less?

8 Stay on home time during a layover of more than 48 hours or more with a time change of +/- 4 or more?

9 Ensure your bedroom is dark?

10

Avoid caffeine within 4 hours before bed?

11

Interrupt sleep to eat at regular meal times (home time)?

12

Eat at regular meal times (home time)?

13

Avoid eating less than an hour before going to bed?

14

Have 3 balanced meals a day?

1. No more than 45 minutes

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JET LAG PERCEPTION QUESTIONNAIRE

YOUR VIEWS ABOUT YOUR JET LAG

Definition of Jet Lag: When the body clock (e.g. sleep/wake; feeding/fasting) is out of sync with the light/dark cycle as a result of flying across multiple time zones. As the body may not adjust quickly to this rapid change, some symptoms may be experienced (Jet lag Questionnaire).

Listed below are a number of symptoms that you may or may not have experienced as part ofjet lag. Please indicate by ticking Yes or No, whether you have experienced any of these symptoms.

I have experienced this symptom since my jet lag

Yes No

1 Difficulty falling asleep2 Waking up frequently during the night3 Waking up too early4 Not feeling alert 30 minutes after waking5 Poor quality sleep6 Daytime sleepiness7 Fatigue8 Low appetite9 Irritability10 Inability to concentrate11 Frequent urination12 Moodiness13 Headaches14 Upset stomach15 Confusion16 Constipation17 Loose bowel

We are interested in your own personal views of how you now see your jet lag.Please indicate how much you agree or disagree with the following statements about your illness by ticking the appropriate box.

VIEWS ABOUT YOUR JET LAGSTRONGLYDISAGREE DISAGREE

NEITHERAGREE

NORDISAGREE

AGREESTRONGLY

AGREE

IP1 My jet lag will last a short time after my return flight

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IP2 My jet lag is likely to be chronic rather than temporary

IP3 My jet lag will last beyond my days off

IP4 This jet lag will pass quickly

IP5 I expect to be jet lagged beyond my days off

IP6 My jet lag will improve in time

IP7The symptoms of my jet lag change a great deal from

day to dayIP8 My symptoms come and go in cycles

IP9 My jet lag is very unpredictable

IP10I go through cycles in which my jet lag gets better and

worseIP11 My jet lag is a serious condition

IP12 My jet lag has major consequences on my life

IP13 My jet lag does not have much effect on my life

IP14 My jet lag strongly affects the way others see me

IP15 My jet lag has serious financial consequences

IP16My jet lag causes difficulties for those who are close to

meIP17

There is a lot which I can do to control my symptoms

IP18What I do can determine whether my jet lag gets better

or worseIP19 The course of my jet lag depends on me

IP20 Nothing I do will affect my jet lag

IP21 I have the power to influence my jet lag

IP22My actions will have no affect on the outcome of my jet

lag

IP23There is very little that can be done to improve my jet

lagIP24 My actions will be effective in curing my jet lag

IP25The negative effects of my jet lag can be prevented

(avoided) by my actionsIP26 My actions can control my jet lag

IP27 There is nothing which can help my jet lag

IP28 The symptoms of my jet lag are puzzling to me

IP29 My jet lag is a mystery to me

IP30 I don’t understand my jet lag

IP31 My jet lag doesn’t make any sense to me

IP32 I have a clear picture or understanding of my jet lag

IP33 I get depressed when I think about my jet lag

IP34 When I think about my jet lag I get upset

IP35 My jet lag makes me feel angry

IP36 My jet lag does not worry me

IP37 Having this jet lag makes me feel anxious

IP38 My jet lag makes me feel afraid

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CAUSES OF JET LAG

We are interested in what you consider may be the cause of jet lag. As people are very different, there is no correct answer for this question. We are most interested in your own views about the factors that may cause jet lag rather than what others including doctors or family may have suggested to you. Below is a list of possible causes for jet lag. Please indicate how much you agree or disagree that they were causes for you by ticking the appropriate box.

POSSIBLE CAUSES STRONGLYDISAGREE

DISAGREE NEITHERAGREE NORDISAGREE

AGREE STRONGLYAGREE

IP1 Stress or worry

IP2 Hereditary

IP3 Effects of light exposure/avoidance

IP4 Diet or eating habits

IP5 Chance or bad luck

IP6 Unadjusted Body Clock

IP7 Aircraft environment (air conditioning)

IP8 My own behaviour

IP9 My mental attitude e.g. thinking about life negatively

IP10 Family problems or worries

IP11 Overwork

IP12 My emotional state e.g. feeling down, lonely, anxious, empty

IP13 Ageing

IP14 Alcohol

IP15 Smoking

IP16 Whole Body Vibration (onboard)

IP17 My personality

IP18 Acoustical noise (onboard)

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JET LAG QUESTIONNAIRE

We are interested in your assessment of Jet Lag overall and Jet Lag symptoms today. For each question please indicate the answer that most closely applies to you (1= not at all; to 5= very much). Please answer questions about your previous night’s sleep 30 minutes after rising and questions about fatigue, mood, attitudes to meal, bowel activity and sleepiness in the evening, after the last meal:

Please complete the following questions about 30 minutes after getting up

2. LAST NIGHT'S SLEEP.

a. How easily did you get to sleep?

Not at all Very easily1 2 3 4 5

b. How well did you sleep?

Not at all Very well1 2 3 4 5

c. Did you have any waking episodes?

Not at all Very much1 2 3 4 5

d. How alert did you feel 30 minutes after rising?

Not at all Very alert1 2 3 4 5

Please complete the following questions after the last meal of the day

1. JET-LAG:How much jet-lag did you have today?

Not at all Very much1 2 3 4 5

3. FATIGUE:In general, how fatigued were you today?

Not at all Very much1 2 3 4 5

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4. MEALS.

a. How hungry did you feel before your meals?

Not at all Very much1 2 3 4 5

b. How palatable (appetising) were your meals?

Not at all Very much1 2 3 4 5

c. After your meals, how bloated did you feel?

Not at all Very much1 2 3 4 5

5. MENTAL PERFORMANCE AND MOOD.

a. How well were you able to concentrate today?

Not at all Very much1 2 3 4 5

b. How motivated did you feel today?

Not at all Very much1 2 3 4 5

c. How irritable did you feel today?

Not at all Very much1 2 3 4 5

6. BOWEL ACTIVITY TODAY.

a. Was the consistency normal?

Not at all Very much1 2 3 4 5

7. SLEEPINESS. How sleepy are you right now?

Not at all Very much1 2 3 4 5

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