Ruscitto, C., and Ogden, J. (in press) The impact of an implementation intention to improve meal times and reduce jet lag in long-haul cabin crew



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Ruscitto, C., and Ogden, J. (in press) The impact of an implementation intention to improve meal times and reduce jet lag in long-haul cabin crew. Psychology and Health

The impact of an implementation intention to improve meal times and reduce jet lag in long-haul cabin crew

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
The impact of an implementation intention to improve meal times and reduce jet lag in long-haul cabin crew

Objective: Jet lag is common place amongst long haul cabin crew. Timed food has been shown to reset the circadian rhythm in rodents. Implementation intentions have been used to change eating behaviour. Meal times could therefore be used as a countermeasure to reduce jet lag and improve alertness in long-haul cabin crew through forming an implementation intention to improve the regularity of meals on days off.

Design: 60 long-haul crew took part in a randomized controlled trial, with two conditions: forming an implementation intention to eat regular meals on days off versus no implementation intention. Pre-intervention measurements were taken at baseline (before a long-haul trip) and post-intervention measures were taken on the first and second days off post-trip.

Main outcome measures: Subjective jet lag (unidimensional and multidimensional) and objective alertness (Psychomotor Vigilance Task (PVT)).

Results: Mixed ANOVA showed a significant condition x time interaction for unidimensional jet lag but not for multidimensional jet lag and objective alertness. In particular, the formation of an implementation intention to alter meal times resulted in a reduction of unidimensional jet lag. Conclusion: Implementation intentions can be used to alleviate jet lag in long-haul crew through promoting a change in meal times.

Keywords: long-haul cabin crew; jet lag; meal times; implementation intentions; peripheral clocks; PVT

Introduction


Long-haul cabin crew are exposed to circadian disruption which is caused by the inability of the body clock (suprachiasmatic nuclei, SCN), located in the hypothalamus, to adjust to local time after rapid travel across time zones. As a result, whilst abroad and especially on their return home, crew may suffer from several symptoms such as jet lag, fatigue, impaired sleep, cognitive performance, moodiness and loss of appetite (Suvanto et al., 1993; Lowden & Akerstedt, 1998; 1999; Sharma & Schrivastava, 2004; Atkinson, 2013). Traditionally, jet lag countermeasures aim to speed up circadian adaption to local time by using bright light as the central clock is particularly sensitive to light (Arendt, 2009). There is also evidence that artificial melatonin (0.5-5mg at 24-hour intervals before bedtime) can reduce jet lag symptoms by facilitating circadian re-entrainment (Arendt, Stone and Skene, 2000). However, in the UK, flight crew are not permitted to use melatonin due to its sedative effects and cabin crew are subject to severe restrictions (e.g. avoidance 12 hours before reporting for a duty and during time off overseas) (CAA, 2011). A further problem is that both methods require the supervision by a health care professional and effective use of melatonin necessitates knowledge of the phase of the internal clock (Arendt, 2009). Treating symptoms such as sleep difficulties by following sleep hygiene measures (e.g. sleep in a quiet and dark room, avoiding caffeine four hours before bedtime) can promote alertness but they do not boost circadian adaptation (McCallum, Sanquist, Mitler, & Krueger, 2003). In addition, retaining home-base sleep hours during layovers may alleviate jet lag during layovers but not at home (Lowden & Akerstedt, 1998). Circadian adaptation to the home time zone is particularly important to crew for fitting in with home life and ultimately for their wellbeing (Henderson & Burt, 1998; Eriksen, 2006).

An additional countermeasure that has received some interest over recent years is the role of diet and meal times in resetting the body clock. For example, the Argonne Diet was used successfully to reduce jet lag symptoms in military personnel (Reynolds & Montgomery, 2002). The diet consists of timing food in terms of a combination of fasting and feasting days and consumption of protein and carbohydrates before travel to reduce jet lag symptoms (Reynolds & Montgomery, 2002). Military personnel deployed across several time zones who adopted this diet reported significantly less jet lag during their trip and after their return home when compared to non-dieters (Reynolds & Montgomery, 2002). Krauchi and colleagues (2002) compared the effects of a single morning and evening carbohydrate-rich meal for three days and found that morning intake of this meal was able to advance the BCT and heart rate rhythms under controlled constant routine conditions. Moreover, Schoeller and colleagues (1997) found that a delay in the timing of three daily meals by 6.5 h was able to shift the diurnal rhythm of plasma leptin by five to seven hours without changing the light or sleep cycles. more recently a study showed that late eaters lost less weight than early eaters, regardless of energy intake, expenditure, dietary composition, circadian preference and sleep times (Garaulet et al., 2013). These studies showed that meal times affect wellbeing by altering the experience of jet lag and metabolic responses

The evidence of a relationship between timed food and changes to the circadian system in humans is limited but there is much of evidence that timed food can reset circadian rhythms in rodents (Johnston, 2013). To simplify, peripheral clocks exist in various organs (e.g. liver, lungs, and stomach) and these respond to feeding times whilst the central clock (SCN) responds to light. Evidence suggests that in rodents, food timing restriction during the day (e.g. 3 to 6 hours) shifts rodents’ behaviour from nocturnal to diurnal inverting the phase of clock gene rhythms in peripheral tissues (e.g. liver, kidney, lungs and heart) (Shibata, Tahara, & Hirao, 2010). The implication is that eating out of phase with the light-dark (LD) cycle can cause the circadian system (SCN and peripheral clocks) to be out of synchrony and exacerbate jet lag symptoms in humans (Reddy et al., 2005). A major problem with animal research is translating its evidence to humans. In an attempt to make data more relevant to human patterns, researchers have manipulated mealtime combinations (breakfast, lunch and dinner) and found that three meals a day fixed the phase of peripheral clocks in mice according to meal interval (Kuroda et al., 2012). Further, longer fasting between lunch and dinner was able to anticipate peripheral clock phase. To reduce this effect, dinner was divided into two small meals at 19:00 h and 23:00 h which caused the timing of the peripheral clocks to return to normal. For humans, the implication is that meal times in conflict with the LD cycle could potentially lead to the disorganisation of central (affected by light) and peripheral clocks, further exacerbating jet lag symptoms.

It is well documented that meal times in shift workers and long-haul cabin crew are altered due to their irregular working hours and disrupted body clock. For example, despite the potential for disruption during days off in home time zone, there is evidence that long-haul crew prefer to adapt to local time even during short layovers as staying on the home time zone interferes with leisure and eating opportunities (Lowden & Akerstedt, 1998). However, altered meal times following frequent return long-haul trips can affect how much it is eaten, meal responses and metabolic health. For example, in a forced desynchrony study, Heath and colleagues (2012) found that severely sleep-deprived subjects (4-hour sleep opportunity per 24 hours) ate more snacks between meals during the ‘biological day’ (circadian peak) than moderately sleep-deprived subjects (6-hour sleep opportunity per 24 hours). Further, Waterhouse et al. (2000; 2004; 2005) found that shifted meal times following time-zone transitions affected the subjective responses of food intake (e.g. hunger, appetite and satiety) such that appreciation of food was altered. The health implications of eating out of phase (during circadian night) have been shown by several studies (Van Cauter et al., 1989; Hampton et al., 1996; Ribeiro et al., 1998). Recently, Buxton et al. (2012) showed that three weeks of sleep restriction (5.6 hours per 24-h period) with concurrent circadian disruption (28-hour circadian days – reflecting 4 hours of jet lag accumulating each day) altered postprandial glucose levels, considered pre-diabetic as a result of insufficient insulin after a meal.

Taken together, evidence suggests that meal times are important for the general wellbeing of cabin crew and potentially for circadian adaption during days off. Whilst there is strong evidence that timed meals can alter the circadian rhythm of animals, to date, evidence that changing dietary patterns can reduce jet lag in humans is limited to few studies mentioned above (Shoeller et al., 1997; Reynolds & Montgomery, 2002; Krauchi et al., 2002). Furthermore, how such changes can be achieved also remains unexplored. Therefore, this study will test the ability of regular meals to improve jet lag symptoms during days off by using implementation intentions. Improving eating behaviour (e.g. reduce fat intake, increase fruit and vegetable consumption) usually requires the use of tailored interventions, however, research has found that forming implementation intentions (e.g. Armitage, 2004, Kellar & Abraham, 2005; Gollwitzer & Sheeran, 2006) is also effective for producing dietary changes. Implementation intentions are if-then plans that translate intention into action by stating where and when to implement the desired behaviour. Specifying the ‘where and when’ of an action creates a mental link between the critical situation of the intention (e.g. at 9:00 h) and the indented behaviour (e.g. breakfast) that translate it into action. The mental representation of the if- part of the plan becomes more activated and accessible thus leading to an automated response (Gollwitzer, 1993). These processes are effective in overcoming self-regulatory problems such as forgetting to act, failing to prioritise the goal due to other situational demands or negative states that contribute to the failure of intention to translate into behaviour (Gollwitzer & Sheeran, 2006). In the context of food consumption, implementation intentions have been shown to effectively improve an individual’s ability to eat a healthy diet (Verplanken & Faes, 1999; Armitage, 2004, 2007; Chapman et al., 2009; Adriaanse et al., 2011). In laboratory experiments, implementation intentions usually take the if- then format (e.g. if it is Monday, at 9:00, then I will eat breakfast). However, in the field, most studies use the ‘global’ approach (Armitage 2004, 2007) instructing participants they are free to formulate their plans paying attention to the situation. This strategy is more sensitive to the lifestyle of individuals and has been shown to be effective in generating dietary change in the field. Further, the advantages of this type of intervention, for the cabin crew sample in particular, are that administration/application is large-scale and it does not require the presence of a health professional.

Previous research indicates that circadian disruption can result in jet lag and a reduction in alertness in long haul cabin crew. Research also shows that timed meals can reduce circadian disruption, yet to date most of this research has used rodents and all has taken place in the laboratory. Furthermore, a number of studies highlight how implementation intentions can be used to change eating behaviour. The aim of the present study was to combine these areas of study and to evaluate whether an implementation intention intervention can be used to reduce the degree of jet lag experienced by long haul cabin crew and improve alertness via the alteration of their meal times after a long haul trip. To this end, the present study aimed to take an effect which has been predominantly tested in the laboratory out into the field and to evaluate the role of meal timing on jet lag in an occupational human sample whilst they were experiencing jet lag as part of their normal working lives. Therefore whilst the study lacks some of the controlled nature of a laboratory experiment it benefits from a stronger element of ecological validity. It was hypothesized that a simple meal plan formulated at baseline (Time 1 = the day before a long-haul trip) to eat regular meals on days off, based on implementation intentions, would significantly reduce subjective jet lag (uni-dimensional and multidimensional measures) and improve objective alertness (measured by a Psychomotor Vigilance Task (PVT)) on the participants’ days off (Time 2, Time 3)


Method

Design

The present study used a randomized controlled design with two conditions: Intervention (forming an implementation intention to eat regular meals on days off) vs Control (no implementation intention) to examine the impact of a meal planning intervention on jet lag. Pre-intervention measurements were carried out at Time 1 (baseline = day before a long-haul trip, e.g. London-New York-London) and post-intervention measures were taken back in the home time zone, at Time 2 (post-trip = day off 1) and Time 3 (post-trip = day off 2).


Participants


In a meta-analysis Gollwitzer and Sheeran (2006, p. 69), concluded that ‘implementation intentions had a positive effect of medium to large magnitude (d = .65) on goal attainment’. A priori power calculations showed that 30 participants in each condition (control and intervention) was sufficient to detect a medium to large effect size (d = .65, power level = .80, probability level = .05). Although the experiment was carried out in the field (e.g. repeated measures were distributed to participants to fill out in their own time), the date and time of completion of the baseline measures (online) and the PVT (iPhone application) were recorded and verified against the trip description given by participants at baseline (see Procedure). Accordingly, the sample size was calculated in line with Gollwitzer and Sheeran (2006) and set at 30 per condition. Sixty two participants enrolled and completed baseline measures. At baseline, 61 participants returned the jet lag diaries. Of these, 60 jet lag diaries were returned (response rate = 96.8) at Times 2 and 3. For the PVT data, of 62 participants who enrolled in the study, 60 completed the PVT at baseline (one dropout and one iPhone ‘app’ problem) and Times 1 and 2. However, of these, 3 PVT results were discarded as individual reaction times per task were consistently between 500 ms and 1 second indicating lapses and missed responses. The final sample for the analysis of the jet lag measures consisted of 60 participants whereas for the PVT results, the sample consisted of 57 participants (response rate = 91.9%). It is important to note, however, that the invitation email to participate was sent to approximately ten thousand long-haul cabin crew. Whilst non-respondent information is not available (data protection), the implications of a low response rate are considered in the discussion section.
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).


Intervention


At baseline (e.g. day before the trip), participants followed an online link to complete the baseline measures. At this stage, participants were randomly allocated to the intervention and control groups using a basic JavaScript random number generator function. Those allocated to the intervention group were asked to form implementation intentions about eating regular meals on days off following their trip. Instructions were given aimed at formulating detailed meal plans: “There is some evidence that eating 3 regular meals a day may help people recover after a long-haul flight. We want you to eat 3 regular meals (Breakfast, Lunch and Dinner) during your days off. To this end, you are free to choose, how to do this. However, we want you to formulate your plans in as much details as possible. For example, (a) when and (b) where you will have Breakfast, Lunch and Dinner. Here is some space for you to write your plan: …….”. Separate sections were provided for each day off. These instructions were adapted from Armitage’s study (2007) which did not contain the ‘if-then part’ structure but emphasised ‘where’ and ‘when’ the goal will be achieved (e.g. breakfast) in order to create an association between a specific situation and a desired behavioural response. To this end, space was provided for participants to write their detailed plans for each day off.

Measures


Participants completed the following measures to assess their profile characteristics and the outcome variables at different time points during the study. Reliability was assessed where appropriate using Cronbach’s alphas.

Profile characteristics

The following profile characteristics were assessed:



Demographics. Participants described their age, nationality, living status, whether they had a supervisory role, whether they worked FT or PT, years of flying long haul. In addition they completed the 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 (GMT) were classified as day flights. Any flights with a duty falling between 02.00 h and 5.99 h decimal time were classified as night flights (duty and flight time limitations scheme, Civil Aviation Authority, 2004). In addition, participants described the direction of the flight and their direction preference in terms of easier flying (fewer let lag symptoms), the time change at destination, the days off around their trip and the season in which the return trip took place.

Work Preparation Strategies. To control for differences in sleeping and eating patterns participants rated statements relating to sleep strategies (9 items, e.g. avoiding using sleeping pills, napping before a night flight (local time), avoiding using alcohol as a sleeping aid, ensuring the bedroom is quiet, cool and dark, staying on home time during a layover of 48 hours or less with a time change of +/-3 hours or less, staying on home time during a layover of more than 48 hours or more with a time change of +/- 4 or more, and eating strategies (5 items e.g. avoiding caffeine before sleep, having 3 balanced meals a day, avoiding eating less than 1 hour before bed, eating at regular meal times (home time), interrupting sleep to eat at regular meal times, (adapted from Henderson & Burt, 1998). Each statement is rated on a 5-point-scale ranging from never (1) to always (5).

Outcome Measures

The following outcome measures were assessed at baseline and follow up (Time 2 and Time 3)



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) which consisted of 14 items relating to jet lag (1 item), fatigue (1 item), sleep performance (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 1: ; Time 2: ; Time 3: ). Higher scores indicated greater symptoms of jet lag.

Objective alertness. The Psychomotor Vigilance Task (PVT, Dinges & Powell, 1985), a reaction time test, was used as an objective measure of alertness as it is sensitive to time of day (circadian misalignment), sleep deprivation and time on task (fatigue) and it has been widely used in circadian and sleep research (Basner, Mollicone and Dinges, 2011). The 10-minute PVT has been shown to be a valid and sensitive test of alertness and vigilance in several experimental (Jewett, Dijk, Kronauer & Dinges, 1999; Van Dongen, Maislin, Mullington & Dinges, 2003; Graw, Krauchi, Knoblauch, Wirz-Justice & Cajochen, 2004) clinical (e.g. Kribbs et al., 1993) and operational settings (e.g. Neri et al., 2002). In addition, its reliability is high (test-retest reliability above .80, Dorrian, Rogers, Dinges & Kushida, 2005) and performance is unaffected by practice (Dinges et al., 1997). Because the 10-minutes PVT is impractical in the field (length of time and administration in the laboratory), shorter tasks (3- and 5-minute PVT) administered by hand-held devices (e.g. portable computer programs and smartphone applications) have been validated in the field (Roach, Dawson & Lamond, 2006; Basner et al., 2011; Roach, Petrilli, Dawson & Lamond , 2012; Gartenberg, Forest, & Therrien, 2012). For the present study a three minute PVT was used to objectively measure alertness. For the present study it was administered via an iPhone/iPad application (‘sleep-2-Peak’) (Proactive life LLC; Gartenberg et al., 2012). The PVT requires responding to a visual stimulus (in this case, an image of the sun) as soon as it appears on the screen by touching the stimulus with the index finger of the dominant hand. The task lasts three minutes (a 6-trial PVT equals one minute in the app) and participants were instructed to complete the task at the end of the day after the last meal and soon after completing the second part of the jet lag questionnaire at Time 1 (baseline), Time 2 (Day off 1) and Time 3 (Day off 2).

Raw RTs have reduced power as they are affected by heteroscedasticity, skewness and outliers and are not suitable for ANOVAs (Whelan, 2008). Transforming RTs to speed (e.g. the reciprocal of latency) normalises the distribution by decreasing the contribution of very long lapses (e.g. RTs ≥ 500 ms) or very fast RTs (e.g. contribution of false starts) and ensures good power (Whelan, 2008). Therefore a reciprocal transformation was applied to raw RTs and the mean of the reciprocal reaction times or response speed (1/RTs) (1/ms) for each task was used in the analysis. In the present app false starts were identified as -1.000, missed responses as RTs 1.000. Genuine RTs have a minimum value of at least 100ms (Luce, 1986). 

Typically, a PVT lasts 10-minute and it is administered in the lab. The 10-minute PVT is considered impractical in the field. 3-minute PVTs have been validated and found to discriminate between sleep deprived and alert subjects (Basner et al., 2011). Effects sizes for PVT outcome measures (medium to large) were larger for the 10-min PVT than the 3-minute PVT. However, when compared to the 70% decrease in time duration, the loss of 22.7% in effect size was considered acceptable (Basner et al., 2011). Overall, there were fewer lapses in the 3-minute PVT than the 10-min PVT but when the threshold was lowered from 500 ms to 355 ms, results showed no differences in sensitivity to sleep loss between the 10-minute and the 3-minute PVT.  

Procedure


Following ethical approval, participants were recruited by email. The airline emailing system was used. This ensured that only long-haul crew were targeted. Prospective participants were sent a data pack containing the study protocol/checklist, a jet lag diary, PVT instructions and a pre-stamped envelope to return the completed diary. Once participants received the pack, a meeting at the airline offices or a telephone call was then set up between the investigator and the prospective participant to go through the protocol of the study in fine detail. To fulfil the inclusion/exclusion criteria (see earlier sections), prospective participants were asked about any medication taken as well as any underlying conditions that may affect sleep. Informed consent and completion of baseline measures was done online. As a precaution, participants were asked questions again online about taking melatonin/medication and underlying illnesses. Participants were unable to continue with the online ‘baseline’ 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. Demographic data and trip information before the trip were also collected online. The jet lag diary at T2 and T3 was completed in paper form and sent back to the researchers by post. All measures were completed after dinner except for the sleep questions of the jet lag questionnaire which were completed approximately 30 minutes after rising. Reminders by text and email were sent in the morning and evening to minimise the occurrence of missing data.

The PVT application and website used for the baseline survey recorded the date and times of completion of the ‘baseline’ survey and PVT. These were used to match the participants to their planned trip and verify their identification and completion of their trip.





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