A daily diary investigation of the effects of work stress on exercise intention realisation: Can...

Daily diary investigation of work stress and exercise

Payne, N., Jones, F. & Harris, P.R. (2010). A daily diary

investigation of the effects of work stress on exercise intention

implementation: Can planning overcome the disruptive effects of work?

Psychology and Health, 25, 1, 111-129. DOI:10.1080/08870440903337622

A Daily Diary Investigation of the Impact of Work Stress on Exercise

Intention Realisation: Can Planning Overcome the Disruptive Influence

of Work?

Nicola Payne

Middlesex University

Fiona Jones

University of Leeds

Peter R. Harris

University of Sheffield

Nicola Payne, School of Health and Social Sciences, Psychology

Department, Middlesex University; Fiona Jones, Institute of

Psychological Sciences, University of Leeds; Peter R. Harris,

Department of Psychology, University of Sheffield.

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This project was supported by a grant from the U.K. Economic and

Social Research Council.

Correspondence concerning this article should be addressed to

Nicola Payne, Psychology Department, Middlesex University, Hendon NW4

4BT. E-mail: [email protected]

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mailto:[email protected]


Abstract

Using the theoretical context of the Theory of Planned Behaviour,

this study examined whether work has a disruptive influence on

people’s ability to carry out their daily intentions to exercise, and

whether daily planning helps overcome this. A daily questionnaire was

completed by 42 employees for 14 days. A brief daily planning

intervention was administered to half of the employees. Multilevel

modelling was used to analyse the data. The moderating effects of

daily perceived behavioural control (PBC), job demands and work-

related anxiety and depression on the relationship between intention

to exercise and subsequent behaviour were investigated, as well as

the impact of the intervention. Intention and PBC predicted exercise.

Job demands appeared to disrupt people’s ability to carry out their

daily exercise intentions. Contrary to expectation, people in the no

intervention group were more likely to exercise. Furthermore, on low-

demand days they were most successful in realising their exercise

intentions (when they intended to exercise for longer), whereas

people in the intervention group, on high-demand days were least

successful in realising their exercise intentions. The intervention

may have operated contrary to expectation by drawing attention to

potential failure.

Key words: physical exercise, theory of planned behaviour,

implementation intentions, planning, job demands, multilevel modeling

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A Daily Diary Investigation of the Impact of Work Stress on Exercise

Intention Realisation: Can Planning Overcome the Disruptive Influence

of Work?

Research increasingly highlights the potential role that

individuals have to play in influencing their own health. Research

linking diseases such as coronary heart disease to unhealthy

behaviours, such as lack of physical exercise is abundant (e.g. Blair

et al., 1989). However, many people find it difficult to engage in

healthy behaviours. Social cognition models help explain the

processes involved in initiating and implementing health behaviours.

According to a number of these theories, including Protection

Motivation Theory (Rogers, 1983), the Theory of Reasoned Action

(Fishbein & Ajzen, 1975) and the Theory of Planned Behaviour (TPB;

Ajzen, 1988, 1991), intention is the key determinant of behaviour.

However, reviews and meta-analyses show that intention only explains

moderate proportions of variance in behaviour (e.g. Armitage &

Conner, 2001; Sheeran, 2002; Sutton, 1998), including exercise

behaviour (e.g. Blue, 1995; Hagger, Chatzisarantis, & Biddle, 2002;

Hausenblas, Carron, & Mack, 1997).

Perceived behavioural control (PBC; i.e. how easy or difficult

it is to perform

a behaviour) is proposed as a further determinant of behaviour in the

TPB (Ajzen, 1988,

1991). PBC may directly aid the prediction of behaviour (Ajzen, 1991)

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or may act as a moderator of the intention–behaviour relationship.

PBC has sometimes been found to add relatively little to the

prediction of behaviour (e.g. on average, it explained 2% of the

variance in behaviour in a meta-analysis; Armitage & Conner, 2001).

The evidence for an interaction between intentions and PBC is

conflicting (e.g. 9 out of 19 studies in a meta-analysis reported a

significant interaction effect; Armitage & Conner, 2001).

Sheeran (2002) suggested that intenders who do not act (i.e.

people who intend to perform a specific behaviour but subsequently do

not) and non-intenders who do act are responsible for the gap between

intentions and behaviour. However, research on exercise rarely finds

many non-intenders who subsequently exercise (e.g. Godin, Shephard, &

Colantonio, 1986; Rhodes, Courneya, & Jones, 2003). Thus, intenders

who fail to act have become the focus of much research. Rhodes et al.

(2003) found that this group exhibited less positive attitudes and

decreased PBC over exercise compared to intenders who succeeded in

exercising. However, Godin et al. (1986) found few differences

between the cognitive profiles of the two groups. Overall, evidence

suggests that PBC may not be sufficient to account for the gap

between intentions and behaviour.

A limitation of much research into the TPB is that the gap

between measurements of intentions and behaviour is frequently long.

Therefore, intentions may fail because they change over time (i.e.

lack of temporal stability). Sheeran and Abraham (2003) found that

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temporal stability moderated the relationship between exercise

intentions and behaviour and mediated the impact of other moderators,

such as past behaviour and anticipated regret. Thus, temporal

stability appears to be a key to translate intentions into behaviour.

Fishbein and Ajzen (1975) suggested that measurements of intention

and behaviour should be close together in time and Courneya and

McAulay (1993) found that this led to larger exercise intention–

behaviour correlations. Yet, ignoring the issue of temporal stability

remains a limitation of much TPB research. Therefore, the present

study used daily measurements of intentions and behaviour.

Working Life

Godin et al. (1986) suggested that intenders who do not act may

experience significant social and environmental constraints that

restrict them from engaging in healthier behaviour. Much research

focuses on the constraints produced by working life and has found

that high levels of job demands, work-related hassles and perceived

work stress are linked to reduced exercise (e.g. Cohen, Schwartz,

Bromet, & Parkinson, 1991; Hellerstedt & Jeffery, 1997; Heslop et

al., 2001; Johansson, Johnson, & Hall, 1991; Ng & Jeffery, 2003;

Payne, Jones, & Harris, 2002; Weidner, Boughal, Connor, Pieper, &

Mendell, 1997), although some studies do not support this link (e.g.

Landsbergis, Schnall, Deitz, Warren, & Pickering, 1998; Steptoe,

Lipsey, & Wardle, 1998). Payne et al. (2002) found that the presence

of job demands as well as low-exercise PBC appeared to impede

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people’s ability to carry out their intentions to exercise and

differentiated intenders who succeeded in exercising from intenders

who failed. Such demands may not be completely accounted for by PBC

because they may reflect actual control and/or unexpected demands.

The concept of job demands only encompasses a limited aspect of

working life. Work stress has been linked to anxiety, depression and

negative moods, which may spill over into home life and have an

impact on health behaviours (e.g. Jones, O’Connor, Conner, McMillan,

& Ferguson, 2007). These negative affects have been associated with a

lack of exercise in a number of studies (Allgo¨ wer, Wardle, &

Steptoe, 2001; Anton & Miller, 2005; Ezoe & Morimoto, 1994; Farmer et

al., 1988) and positive affect has been linked to increased exercise

(Griffin, Friend, Eitel, & Lobel, 1993). Warr (1990) suggested that

context-specific measures of affect may be useful for occupational

research, and yet research rarely examines the impact of work-related

affect on exercise. In addition, the TPB has been criticised for

excluding affective processes (e.g. Conner & Armitage, 1998).

Attitudes towards behaviour have an affective component and research

has investigated the emotional appraisal of intentions to exercise

(e.g. Mohiyeddini, Pauli, & Bauer, 2009). However, the more general

impact of affect is rarely examined.

In summary, PBC, the variable suggested by the TPB to help

people convert intentions into behaviour, may not be sufficient to

account for why some people intend to exercise but fail to act.

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Various factors such as temporal stability may facilitate intention

realisation but social and environmental constraints, such as the

demands and negative affectivity produced by working life, may be

disruptive. The present study examined this potentially disruptive

influence of daily work factors on daily intention realisation.

In order to successfully carry out intentions to exercise,

individuals may have to overcome constraints produced by working life

and making plans, including contingency plans, may help do this.

Planning

Gollwitzer (1990, 1993, 1999) proposed that forming

implementation intentions, which involve planning where and when to

perform a specific behaviour, may help bridge the gap between

intentions and behaviour, promoting behavioural enactment rather than

intention formation. Implementation intentions form a mental link

between specific behavioural acts and specific situations, so that

when these situational cues are encountered, the behaviour is

elicited automatically. According to a number of meta-analyses (e.g.

Gollwitzer & Sheeran, 2006), participants who form implementation

intentions are significantly more likely to carry out a variety of

behaviours, including exercise (e.g. Milne, Orbell, & Sheeran, 2002;

Prestwich, Lawton, & Conner, 2003).

Schwarzer’s (1992) Health Action Process Approach is one of only

a few theories to take account of planning. Schwarzer proposed that

action planning (i.e. planning when, where and how to exercise,

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similar to implementation intentions) and coping planning (i.e.

anticipating barriers to exercise and planning how to overcome them)

are involved in translating intentions into behaviour. Sniehotta,

Scholz, and Schwarzer (2006) found that cardiac patients who made

action and coping plans were significantly more successful in

exercising than patients who made action plans only or a no

intervention control group. Lippke, Ziegelmann, and Schwarzer (2004)

obtained similar findings in orthopaedic patients. In both studies,

the interventions were quite complex (e.g. Sniehotta et al. (2006)

used one-to-one training) making them potentially demanding in terms

of organizational resources such as time and finances. Scholz, Schuz,

Ziegelmann, Lippke, and Schwarzer (2008) also found that coping

planning significantly predicted increased exercise and that both

action and coping planning moderated the relationship between

intention and behaviour, such that intenders who made plans were more

likely to successfully realize their intentions.

Since many complex health behaviours, such as exercise, are

performed each day (or at least more than once a week), it may prove

problematic to ask people to make plans only once and assume that

this will remain constant across all days. In addition, the context

in which a person exists (e.g. the conditions of their job) may

change from day to day. If such contextual factors affect behaviour,

even making plans may not be enough for successful behavioural

enactment (Cox, 1997). Thus, the present study examined whether a

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daily planning intervention focusing on work helps people carry out

their daily intentions to exercise and helps overcome the potentially

disruptive influence of work. In contrast to much previous research,

this intervention was self-administered, brief and relatively

undemanding, thus appropriate for daily use.

The Present Study

In summary, the aim of the present study was to examine the

disruptive influence of work on people’s ability to carry out daily

intentions to exercise and whether a simple daily planning

intervention could overcome this. Thus, there were two core research

questions:

(1) Are individuals less likely to realise their intentions to

exercise on days when they experience higher levels of job demands

and negative affect (i.e. work-related anxiety and depression)?

(2) Does making daily plans help people to carry out their daily

intentions to exercise and, in particular, does it overcome the

potentially disruptive influence of work?

Planning included a combination of action

planning/implementation intentions (i.e. deciding when, where and how

to exercise each day) and coping planning (i.e. considering potential

barriers and how these might be overcome each day).

The study used the TPB as a basis for investigating the proximal

predictors of behaviour and so the direct and moderating effects of

PBC were included. Thus this study aimed to examine whether the

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direct and moderating effects of work factors and of planning exert

an influence over and above the effects of PBC.

The present study utilised daily diaries. This should eliminate

much of the limitation of temporal stability found in previous

research, since measures of intention and behaviour are close

together in time and thus intention is likely to remain stable.

Measures of behaviour based on retrospective reports over long

periods inherently suffer from problems of recall, which may be

overcome by more proximal daily measures (Bolger, Davis, & Rafaeli,

2003). Daily measures of exercise are found to be as reliable as

direct, objective measures taken using continuous heart rate and

motion monitors (e.g. Taylor et al., 1984). Furthermore, daily

diaries provide rich data, which can more accurately reflect the

complexity of intention–behaviour relationships and dynamic

moderators of this relationship. Although several diary studies have

investigated stress and eating (e.g. Conner, Fitter, & Fletcher,

1999; Jones et al., 2007; Newman, O’Connor, & Conner, 2007; O’Connor,

Jones, Conner, McMillan, & Ferguson, 2008; Stone & Brownell, 1994;

Wolff, Crosby, Roberts, &Wittrock, 2000), daily diaries are rarely

used to examine work stress and exercise or to investigate the impact

of planning on intention realisation.

In addition, data will be analysed using multilevel random

coefficient modeling (MRCM), which is widely regarded as the

appropriate method of analysis for daily diary data (Nezlek, 2001;

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Nezlek & Zyzniewski, 1998; Raudenbush & Bryk, 2002; Snijders &

Bosker, 1999). Much previous diary research in this area (e.g. Conner

et al., 1999; Stone & Brownell, 1994) conducted ordinary-least-

squares multiple regression analysis and it is only relatively

recently that similar research has begun to employ MRCM (e.g. Jones

et al., 2007; Newman et al., 2007; O’Connor, et al., 2008).

Method

Sample

Participants were UK employees of a company involved in the

design, marketing and sales of computer hardware and software to

businesses. Employees involved at each stage from design to sales

participated in the study and jobs ranged from administrative to

management positions. Volunteers were recruited via an internal

global e-mail requesting participants who were currently exercising

but sometimes found it a struggle to maintain. This instruction aimed

to ensure that only those who intended to exercise (and therefore

were appropriate for a planning intervention) were included.

Sixty-one employees enquired about participating and, of these,

19 withdrew when given details of the study and 42 went on to begin

completion of the daily diaries for 14 days. The use of time lags

between intention and behaviour (i.e. exercise intention was recorded

on one day and behaviour was recorded the following day) meant that

for each participant there were 13 days of useable data. Although

there were a possible 546 days of useable data (i.e. 42 participants

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for 13 days), four participants failed to complete one or more days.

One of these participants accounted for more than half of the missing

days and so his/her data was not analysed. This left 41 participants

and 539 days of data in total. Participants worked on 348 (64%) of

these days.

Of the 41 employees, 18 (45%) were female. Twenty-one percent

were aged 16–24 years, 41% were aged 25–34 years, 17% were aged 35–44

years, 19% were aged 45–54 years and 2% were aged 55 years or older.

The Diary

Exercise

Respondents were asked ‘What forms of exercise did you do today

and how long did you devote to each?’ Exercise was defined in the

questionnaire as ‘taking part in purposeful activity which increases

the heart rate and produces at least a light sweat and is often

structured and pursued for health and fitness benefits. Do not

include walking, nightclub dancing, swimming with the kids, etc.,

unless these activities fulfil the above definition’. All forms of

exercise reported were in the moderate-strenuous category (Godin &

Shephard, 1985). Participants responded in hours or minutes. The

amounts of time spent on each form of exercise (all converted to

hours) were summed to form a measure of the total number of hours of

exercise per day.

Work day

Participants were asked if they had worked that day.

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Work-related affect

This was measured by six items from Warr (1990). Participants

were asked: ‘For how much of your time today has your job made you

feel depressed, gloomy, miserable, tense, uneasy and worried’.

Responses were on a six-point scale ranging from 1 (never) to 6 (all

the time). Mean scores across the first three items were computed to

give a total measure of depression (between participants alpha 0.87)

and mean scores across the last three items were computed to give a

total measure of anxiety (between participants alpha .0.87). High

scores denote high anxiety and high depression.

Job demands

This was measured by an 11-item scale adapted from Karasek

(1985). The items began with the stem ‘Today my job involved . . .

’. For example, ‘Today my job involved working fast’ and ‘Today my

job involved being asked to do an excessive amount of work’. Items

were accompanied by a five-point response scale ranging from 1

(strongly disagree) to 5 (strongly agree). Mean scores across the 11

items were computed to give a total measure of job demands (between

participants alpha .0.93). A high score denotes high levels of job

demands.

PBC over exercise the following day

PBC over exercise was measured using three items adapted from

Sparks, Guthrie, and Shepherd (1997): ‘For me to exercise tomorrow

would be. . . .’, 1 (very difficult) to 7 (very easy); ‘I am

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confident that I can exercise tomorrow if I wanted to’, 1 (not at all

confident) to 7 (very confident) and ‘How much personal control do

you feel you have over whether you exercise tomorrow?’, 1 (no

control) to 7 (complete control). A mean score across the three items

was computed to give a total measure of PBC (between participants

alpha .0.89). A high score denotes high PBC.

Exercise intention the following day

This was measured using a single item corresponding to the

measure of behaviour: ‘What forms of exercise do you intend to do

tomorrow and how long will you devote to each?’ Participants

responded in hours or minutes. The amounts of time spent on each form

of exercise (all converted to hours) were summed to form a measure of

the total number of hours participants intended to exercise the

following day.

Planning

Random allocation was used to place participants in the

intervention and no intervention conditions. This was achieved by

assigning a number to each participant, writing these numbers on

slips of paper and allocating the first half drawn at random to the

intervention group. Twenty-one participants received a diary with the

addition of items related to planning. These items attempted to help

participants overcome factors during the day that could disrupt their

intentions, and thus these additional items were only relevant on

days on which participants intended to exercise. After the item,

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‘What forms of exercise do you intend to do tomorrow and how long

will you devote to each?’, the following paragraph was added to the

daily diary for the intervention group:

Try to think about when you will be able to make time for

exercise tomorrow. For example, if you are working tomorrow,

when can you fit it in around your busy working day, what

facilities would be most convenient and when during the day do

you feel most up to exercising?

This was followed by open-ended questions asking participants:

‘When will you exercise?’, ‘Where will you exercise?’, ‘What

preparations will you need to make?’, ‘What might get in the way?’

and ‘How might you overcome these things?’ An example response to

each of these questions was provided to assist participants in

forming their own plans.

Procedure

Participants who expressed an interest in the research were sent

information about it by email.

As preference for completion mode may affect compliance and response

rates (Green, Rafaeli, Bolger, Shrout, & Reis, 2006), participants

were given the option of completing electronic or paper and pencil

diaries. Twenty-two participants (52%) opted for electronic diaries.

For 14 days they were sent a diary each evening at approximately

18:00 hours and asked to return it once completed. The 20

participants who opted for paper copies were sent a pack of 14

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diaries by post to their home address and asked to return seven by

post at the end of each week using the stamped addressed envelopes

provided. Green et al. (2006) suggested that providing explicit

directions to participants promotes compliance. Thus, the importance

of completing the diaries every evening was emphasised. The research

conforms to the ethical guidelines of the British Psychological

Society and was approved by a University ethics committee.

Data Analyses

The study yielded multilevel data such that daily observations

(level 1) are nested within each person (level 2). Consequently,

there is non-independence of observations, because multiple data

points are collected from the same people and thus ordinary-least-

squares multiple or logistic regression analysis is inappropriate.

MRCM should be conducted instead (Nezlek, 2001; Nezlek & Zyzniewski,

1998; Raudenbush & Bryk, 2002; Snijders & Bosker, 1999). MRCM enables

examination of within person relationships amongst level 1 variables

(e.g. on days when people have higher levels of job demands are they

less likely to carry out their daily exercise intentions than on days

when they have lower levels of job demands?). MRCM can also

incorporate level 2 variables (e.g. Are people more likely to carry

out their daily exercise intentions in the intervention group than in

the no intervention group?). As previous research in this area (e.g.

Hellerstedt & Jeffery, 1997; Heslop et al., 2001; Johansson et al.,

1991; Payne et al., 2002; Weidner, et al., 1997) has investigated

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between-person relationships (e.g. are people with higher levels of

job demands less likely to carry out their exercise intentions than

people with lower levels of job demands?), this article focuses on

within-person relationships.

Analyses were conducted using the programme HLM6 (Raudenbush,

Bryk, Cheun, & Congdon, 2004). Unlike OLS analyses, in MRCM different

centring options are available at each level of analysis. In all

analyses, continuous level 1 variables were group – (i.e. person)

mean centered and categorical level 1 and level 2 variables were

zero-centred to aid the interpretation of coefficients (Nezlek, 2001;

Nezlek & Zyzniewski, 1998).

MRCM also separates true and random error. In all analyses, the

reliability of the estimates of random error terms was examined.

Where a random error term of a coefficient could not be reliably

estimated (i.e. p > 0.20; Harris, Daniels, & Briner, 2003), it was

fixed and thus was non-randomly varying (Nezlek, 2001; Nezlek &

Zyzniewski, 1998).

Results

On average, participants succeeded in exercising on 57% of the

days they intended to exercise. Eight participants (19%) succeeded in

exercising on one-third or fewer of intended days, but 18

participants (44%) succeeded in exercising on more than two-thirds of

intended days (with four succeeding on all the intended days).

There were 196 days on which participants did not intend to

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exercise and did not exercise. Thirty-nine participants had at least

1 such day and on average people had 5 such days. There were 22 days

on which participants did not intend to exercise but did exercise.

Eighteen participants had at least 1 such day and on average people

had only 1 such day. There were 191 days on which participants

intended to exercise and succeeded. Forty participants had at least 1

such day and on average people had 5 such days. There were 130 days

on which participants intended to exercise and failed. Thirty-seven

participants had at least 1 such day and on average people had 3 such

days.

Means and standard deviations for all continuous measures were

calculated (intention to exercise M = 0.58 h, SD = 0.69; PBC M =

4.46, SD = 1.59; job demands M = 3.05,

SD = 0.79; anxiety M = 0.86, SD = 1.05; depression M = 0.56, SD =

0.85).

Initial analyses were conducted to check that randomisation was

successful. Ordinary least-squares between-participants analyses

showed that there was no difference between the intervention and no

intervention groups in terms of participant age t (39). = 0.81, p =

0.42 or sex Chi 2 (1, N = 41) = 0.35, p = 0.55 and no difference in

exercise behaviour reported on the first day of diary completion

before the first presentation of the intervention t (39) = 0.97, p =

0.34.

There was also no age difference (t (39) = 0.92, p = 0.36) or

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sex difference

(Chi 2 (1,N = 41). 2.61, p = .11) between participants completing

pencil and paper diaries and participants completing electronic

diaries. To further ensure that there was no influence of the mode of

diary completion, MRCM was used to examine whether diary mode

influenced exercise behaviour, intention and PBC. Thus three models

were examined, one with each of these three variables as outcomes.

Diary mode was entered as a level 2 predictor. The level 1 (within

person) model in each analysis is described by the following

equation:

yij = 0j + rij

where yij is daily exercise (or exercise intention or PBC) for

person j on day i, 0j is a random coefficient representing the mean of

y for person j across i days, and rij is the error associated with each

daily measure.

In MRCM, the coefficients from one level are passed on to the

next. The level-2 (between person) model is described by the

equation:

0j = 00 + 01 (group) + u0j

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where 00 is the grand mean of the person level means from the

day level model (i.e. 0j) and u0j is the error of 0j. Where 01 is

significant, this indicates that diary mode had a significant effect.

There was no significant effect of diary mode on behaviour (00 =

-.41, SE = .14, p = .004; 01 = .03, SE = .09, p = .75), intention (00

= .73, SE = .09, p < .001; 01 = -.10, SE = .06, p = .07) or PBC (00 =

13.18, SE = .67, p < .001; 01 = .11, SE = .42, p = .78).

The next analyses examined the main research questions. The

level 1 analyses addressed question 1, i.e. whether work has a

disruptive influence on people’s ability to carry out their daily

intentions to exercise, over and above the effects of PBC. The level

2 analyses addressed question 2, i.e. whether the intervention helps

people carry out their daily intentions to exercise and whether it

helps overcome the potentially disruptive influence of work on daily

intention realisation.

The distribution of exercise behaviour was negatively skewed,

such that on 60% of the days participants did not exercise, on 29% of

the days they exercised for 60 min or less, on 9% of the days they

exercised for between 61 and 120 min and on 2% of the days they

exercised for more than 120 min. Therefore, exercise was dichotomised

into days when people exercised and days when they did not exercise.

Since the exercise outcome was dichotomous, the level 1 model is a

Bernoulli model. Thus, the probability of exercise was investigated.

Three models were tested, one for each of the work-related

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variables. Job demands were included in model 1, work-related anxiety

in model 2 and work-related depression in model 3. Thus, three level

1 models addressed the first research question: Does work have a

disruptive influence on people’s ability to carry out their daily

intentions to exercise? In each of the level 1 models, the work-

related variable was included alongside the proximal predictors of

behaviour proposed by the TPB (1-day lagged, i.e. measured the

previous day, intention, PBC and intention_ PBC). Potential work-

related moderators of the intention-behaviour relationship were of

particular interest in order to confirm whether work has a disruptive

influence on people’s ability to carry out their daily intentions to

exercise. To investigate moderation, interaction terms, which were

created between 1-day lagged intention and each of the moderators,

were included in each model. In addition, controls were introduced

for exercise measured the previous day (i.e. 1-day lagged exercise)

and days of the week (i.e. six dummy variables). Since the dummy

variables were not significant in any analyses, they were excluded.

The level 1 model in each analysis is described by the following

equation:

prob(yij = 1 ij) = φij

ηij = log (φij) / (1- φij)

ηij = 0j + 1j (1-day lagged exercise) + 2j (1-day lagged

intention) + 3j (1-day lagged PBC) + 4j (1-day lagged intention x 1-

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day lagged PBC) + 5j (work-related moderator) + 6j (1-day lagged

intention x work-related moderator)

1j is a slope representing the within person relationship

between 1-day lagged exercise and the log odds of exercise, 2j is a

slope representing the within person relationship between intention

to exercise and the log odds of exercise, 3j is a slope representing

the within person relationship between PBC and the log odds of

exercise, 4j is a slope representing the within person relationship

between the intention x PBC interaction term and the log odds of

exercise (i.e. the moderating effect of PBC), 5j is a slope

representing the within person relationship between a work related

predictor (e.g. job demands) and the log odds of exercise, and 6j is

a slope representing the within person relationship between the

intention x work related predictor interaction term and the log odds

of exercise (i.e. the moderating effect of the work related

predictor).

Three level 2 models, one for each of the work-related

variables, addressed the second research question: Does planning help

people carry out their daily intentions to exercise and overcome the

potentially disruptive influence of work on daily intention

realisation? To do this, the intervention (i.e. group) was entered as

a level 2 predictor. It was decided to include cross-level

interactions with all the level 1 predictors. This examined whether

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within-person relationships between the level 1 predictors and the

log odds of exercise vary as a function of the intervention (i.e.

group).

The level 2 model in each analysis is described by the following

equation:

0j = 00 + 01 (group) + u0j

1j = 10 + 11 (group) + u1j

2j = 20 +21 (group) + u2j

3j = 30 + 31 (group) + u3j

4j = 40 + 41 (group) + u4j

5j = 50 + 51 (group) + u5j

6j = 60 + 61 (group) + u6j

Where 10, 20, 30, 40, 50 or 60 are significant, this indicates a

significant within person relationship between the level-1 predictor

and the log odds of exercise, (e.g. in the case of 60 this would

indicate that the relationship between intention and the log odds of

exercise is moderated by job demands in model 1, work-related anxiety

in model 2 and work-related depression in model 3). Where 11, 21, 31,

41, 51 or 61 are significant, this indicates that the within person

relationship between the level-1 predictor and the log odds of

exercise is moderated by the intervention (e.g. in the case of 21

this would indicate that the relationship between intention and the

24


log odds of exercise is moderated by the intervention, i.e. the

intervention is having an impact on people’s ability to implement

their intentions) and where 01 is significant, this indicates that

the intervention has a direct effect on the log odds of exercise.

These analyses, beginning with model 1 which includes job demands,

are presented in Table 1.

In all three models, the log odds of daily exercise were

primarily predicted by intention to engage in an increased amount of

exercise. Increased PBC and increased exercise the previous day (i.e.

1-day lagged exercise) were also significant predictors, but the

intention x PBC interaction was not.

There was a significant main effect of the intervention (i.e.

group) but in the opposite direction to that predicted, i.e. the log

odds of daily exercise were higher for people in the no intervention

group. There were also several significant interactions. Interactions

were interpreted by plotting them using software developed by

Preacher, Curran, and Bauer (2006). Group was found to interact with

1-day lagged exercise such that in the no intervention group an

increased amount of exercise the previous day increased the log odds

of exercise the following day, but in the intervention group an

increased amount of exercise the previous day decreased the log odds

of exercise the following day. Group was also found to interact with

PBC such that higher PBC increased the log odds of daily exercise for

people in the no intervention group only.

25


The only significant effect of a work-related variable was for

job demands in model 1. There was no evidence of a main effect but

there was a significant interaction, i.e. job demands acted as a

moderator of the relationship between intention and the log odds of

exercise, such that people were less likely to carry out their

intentions to exercise on days with higher levels of job demands than

on days with lower levels of job demands. In addition, the intention

x job demands x group interaction term was significant. This more

complex interaction is shown in Figure 1. Lines are plotted at high

and low levels of job demands for the intervention and no

intervention groups. The figure shows that people in the no

intervention group on low-demand days were most successful in

carrying out their intentions to exercise (when they intended to

exercise for longer) and people in the intervention group on high-

demand days were least likely to carry out their intentions to

exercise.

Discussion

The purpose of the present study was twofold; first, to examine

whether daily experiences relating to work have a disruptive

influence on people’s ability to carry out their daily intentions to

exercise and second, to examine whether daily planning helps people

carry out their daily intentions to exercise and whether it helps

overcome the potentially disruptive influence of work. Only job

demands (i.e. not work-related affect) had a disruptive influence on

26


people’s ability to carry out their daily exercise intentions. Thus,

the context of peoples’ lives, in particular working life, may have

an impact on health behaviour, an effect not accounted for by the

TPB. However, there was no evidence that the planning intervention in

the present study helped people implement their intentions or

overcome the disruptive influence of work. In fact, the intervention

appeared to be counterproductive. The log odds of daily exercise were

lower for people in the intervention group and even having high PBC

was not useful (in terms of increasing the log odds of exercise) for

people in the intervention group. A significant interaction between

intention, job demands and the intervention also showed that people

in the no intervention group on low demand days were most successful

in carrying out their intentions to exercise (when they intended to

exercise for longer), whereas people in the intervention group on

high demand days were least likely to carry out their intentions to

exercise, i.e. the impact of the intervention coupled with high daily

job demands appeared to be detrimental to intention realisation,

since an increase in intention did not lead to an increase in the log

odds of exercise.

Compared to much previous research (e.g. Armitage & Conner,

2001; Sheeran, 2002; Sutton, 1998), intention was particularly highly

predictive of behaviour. This may be a result of improvements in the

methodology of the present study, specifically the close proximity of

the measurements of intention and behaviour (Ajzen & Fishbein, 1980;

27


Courneya & McAulay, 1993) and the use of corresponding measures of

intention and behaviour (Ajzen, 1988). However, there was a

significant proportion of days (24%) on which people intended to

exercise but failed and 90% of people had at least 1 such day,

supporting previous research (Godin et al., 1986; Rhodes et al.,

2003). PBC did not appear to help people implement their intentions,

since there was no evidence of a moderating effect, but PBC had a

significant direct effect on behaviour, consistent with the TPB

(Ajzen, 1988, 1991).

Working Life

On days when people had lower levels of job demands, they were

more likely to carry out their intentions to exercise than on days

when they had higher levels of job demands, supporting Payne et al.

(2002). Thus, people did not appear to anticipate their level of job

demands for the following day and adjust their intentions

accordingly. Therefore, the relationship between job demands and the

realisation of intentions is likely to be due to unexpected job

demands. It may be of value for further research to distinguish

between the impact of expected and unexpected job demands. An

alternative explanation may be that people were overly optimistic

when forming their intentions and were liable to the planning fallacy

(Kahneman & Tversky, 1979). In other words, even though people may

have been aware that their intentions on previous days were disrupted

by job demands and thus were overly optimistic, they believed that

28


their current intentions were realistic. Negative affect related to

work had no impact, suggesting that perception and appraisal of

stress may not be important. However, levels of negative affect were

generally very low.

Planning

The intervention in the present study did not help people carry

out their intentions but instead appeared to be counterproductive.

The intervention had a direct negative impact on behaviour and got in

the way of PBC aiding behaviour. It did not have an influence on

translating intentions into behaviour generally, but it had a

negative impact on translating intentions into behaviour on days with

higher levels of job demands. Whilst interventions focused on action

and coping planning have been found to be effective in previous

research (e.g. Lippke et al., 2004; Sniehotta et al., 2006), asking

people to consider things that might get in the way of exercise and

how these might be overcome could have operated contrary to

expectation in the present study by drawing attention to potential

failure. This is likely to be particularly problematic on days with

high job demands, when presumably there are more things that might

get in the way. It is not possible to know the extent to which

participants in the present study were influenced by this focus,

especially when interventions in previous studies included a similar

focus on ‘barriers’ or ‘obstacles’ and yet were successful. However,

Cervone (1989) provided evidence to support this explanation. He

29


found that dwelling on factors that could impair performance on a

cognitive task diminished judgments of perceived self-efficacy on the

task and also impaired task persistence. Hallam and Petosa (1998)

similarly suggested that once people are faced with real barriers to

exercise, they may reevaluate their ability to overcome these

barriers. Budden and Sagarin (2007) found that people who formed

implementation intentions exercised significantly less than people

who did not. They suggested that this may be explained by the

influence of an individual difference variable (socially prescribed

perfectionism), which may have been overrepresented in their sample.

Research by Powers, Koestner, and Topciu (2005) suggested that for

socially prescribed perfectionists, implementation intentions may

lead to self-criticism and impede goal progress because they focus on

failed performance rather than on achieving success. This factor may

also have had an influence in the present study. Thus, focusing on

factors that may support performance may be a better method of

intervention. This could be related to ideas raised by Prospect

Theory, i.e., that the way a message is framed influences how people

respond to the message (Tversky & Kahneman, 1981). Indeed, exercise

promotion messages framed in terms of gains (i.e. benefits of

exercising) rather than losses (i.e. risks of not exercising) have

been more successful in promoting exercise (Jones, Sinclair, &

Courneya, 2003; Robberson & Rogers, 1988).

It is also possible that the intervention in the present study

30


was too brief to enable individuals to make sufficiently detailed and

comprehensive plans. This brevity was a deliberate attempt to make

the intervention relatively undemanding and appropriate for daily

use. However, in previous research, interventions often involve one-

to-one training or the provision of more complex written

instructions, which is more likely to ensure that participants are

guided to make clear, precise and realistic plans.

In addition to this, it is possible that the no intervention

condition in the present study acted as an intervention in itself. In

other words, responding to the question ‘what forms of exercise do

you intend to do tomorrow and how long will you devote to each?’ may

involve more than responding to a question such as ‘do you intend to

exercise tomorrow?’, perhaps involving at least some rudimentary form

of planning but without the focus on potential barriers as in the

intervention condition.

Limitations

The use of paper and pencil diaries, which are returned at the

end of each week, is common in similar research (e.g. Jones et al.,

2007; O’Connor et al., 2008). However, it is not possible to check

whether participants completing diaries via this mode comply with

instructions, particularly with regard to the timing of diary

completion. Green et al. (2006) found that in general, compliance and

resulting data quality were not affected by mode of completion (i.e.

paper and pencil vs. electronic). We also found no differences

31


between participants completing diaries via the different modes.

Green et al. (2006) also found that participants only expressed

negative opinions about completing electronic diaries. Therefore, it

may be important to offer paper and pencil as a mode of completion in

order to promote compliance. We also offered very specific

instructions about completing the diaries in the evening in a further

effort to promote compliance. Of course, evening completion may lead

to exercise impacting on the recall of job demands and work-related

affect. However, if this were the case, one would expect a positive

relationship between exercise intention implementation and job

demands (Taylor, 2000) and not the negative one found in the present

study.

Diary studies often involve small samples and this limits the

generalisability of the findings. In addition, in the present study,

the number of level 1 and level 2 units (i.e. the number of days per

participant and the number of participants) was relatively small.

Since a large number of predictors (including complex interaction

terms) were included in each model, the results should be viewed with

some caution. However, the number of units was adequate for

conducting MRCM (Raudenbush & Bryk, 2002).

The measure of intention used in the present study was a

frequency measure rather than a measure of intention strength, such

as ‘I intend to exercise next week’, 1 (strongly disagree) to 5

(strongly agree). The choice of measure was based on the

32


recommendation of Courneya (1994) who found that the use of

continuous-open or continuous-closed scales for both intention and

behaviour was the most satisfactory solution to obtain corresponding

measures of intention and behaviour. However, this led to a lack of

correspondence with the measure of PBC strength.

Conclusion

In the present study, only job demands were found to disrupt

daily intention realisation. Since this study is only focused on the

context of work, it would be useful for further research to examine

and compare the various contexts of peoples’ lives in more detail in

order to tease apart the various influences on exercise. Further

research could also investigate other work stressors, as well as

examining both negative and positive affect in more detail in order

to obtain a more complete picture of working life. Since we spend

much of our waking lives at work, it might be expected that work

would influence exercise. However, the workplace provides a

convenient forum for health behaviour change interventions. It may

prove beneficial for employers to introduce physical exercise as

advocated stress management training (Long, 1993), which may reduce

levels of stress and anxiety at work and in life outside work (e.g.

Taylor, 2000). However, since the planning intervention in the

present study did not help people overcome this and may be

counterproductive, it may be beneficial for future interventions to

focus on helping people make positive plans to actively support

33


exercise within the work context, rather than make more negatively

focused plans concerning potential barriers caused by work. It will

also be important to ensure that such interventions strike a balance

between ensuring that they are sufficient to help people plan

adequately, while being cost effective.

34


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Table 1

Summary of Three Multilevel Random Coefficient Models Predicting the

Log Odds of Exercise (within person N = 539, between person N = 41)

Predictor Coeff SE OR CI

Model 1

Intercept 00 -.08 .15 .93 .69, 1.25

Group 01 -.24* .10 .79 .65, .95

Lagged

exercise

10 .59* .26 1.81 1.08,

3.04

Lagged ex. x

group

11 -.60**

*

.18 .55 .39, .79

Intention 20 1.50**

*

.37 4.46 2.17,

9.16

Int. x group 21 .06 .24 1.07 .67, 1.70

PBC 30 .18*** .04 1.20 1.10,

1.311

PBC x group 31 -.12** .03 .92 .86, .97

Intention x

PBC

40 .05 .05 1.06 .95, 1.17

Int. x PBC x

group

41 -.07 .04 .91 .84, .99

Demands 50 -.01 .01 .99 .97, 1.02

45


Demands x

group

51 -.01 .01 .99 .98, 1.01

Int. x

demands

60 -.10** .01 1.04 1.01,

1.06

Int. x dem. x

group

61 .09** .01 .99 .95, .99

table continues

46



Model 2

Intercept 00 -.04 .15 .96 .72, 1.29

Group 01 -.28** .10 .75 .63, .91

Lagged

exercise

10 .49* .26 1.64 .99, 2.72

Lagged ex. x

group

11 -.51** .18 .60 .43, .85

Intention 20 1.18**

*

.34 3.26 1.67,

6.35

Int. x group 21 .27 .22 1.30 .84, 2.02

PBC 30 .17*** .04 1.18 1.90,

1.30

PBC x group 31 -.07* .03 .93 .88, .99

Intention x

PBC

40 .005 .05 1.005 .92, 1.10

Int. PBC x

group

41 -.04 .04 .96 .90, 1.03

Anxiety 50 -.11 .19 .89 .62, 1.29

Anxiety x

group

51 .14 .12 1.15 .91, 1.45

Int. x

anxiety

60 -.12 .21 1.13 .75, 1.70

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Int. x anx. x

group

61 -.03 .18 .97 .69, 1.38

table continues

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Model 3

Intercept 00 -.01 .15 .99 .74, 1.33

Group 01 -.30** .10 .74 .61, .90

Lagged

exercise

10 .50* .26 1.65 .99, 2.74

Lagged ex. x

group

11 -.51** .17 .60 .43, .85

Intention 20 1.08** .34 2.94 1.51,

5.71

Int. x group 21 .37 .22 1.45 .94, 2.25

PBC 30 .17*** .04 1.18 1.09,

1.29

PBC x group 31 -.07* .03 .93 .88, .99

Intention x

PBC

40 -.02 .05 .98 .89, 1.08

Int. x PBC x

group

41 -.03 .04 .97 .91, 1.04

Depression 50 -.29 .25 1.34 .83, 2.16

Depression x

group

51 -.02 .15 .98 .74, 1.31

Int. x

depression

60 -.53 .28 1.70 .97, 2.97

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Int. x dep. x

group

61 -.37 .22 .69 .45, 1.06

Note. OR = odds ratio, CI = confidence interval.

*p < .05. **p < .01. ***p < .001.

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Figure caption

Figure 1. The interaction between intention to exercise, job demands

and the intervention predicting the log odds of exercise

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-2

-1

0

1

2

3

4

5

6

7

0 0.5 1 1.5 2 2.5 3Intention to exercise (hrs)

Log odds of exercise

Control group, lowdem andsControl group, highdem andsIntervention group, lowdem andsIntervention group, highdem ands

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A daily diary investigation of the effects of work stress on exercise intention realisation: Can...

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