Motivation and emotion/Book/2015/Mood variation over the week

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Mood variation over the week:
How does affect change throughout the week?

Overview[edit | edit source]

Think back over the past week and consider these questions: When were you in a bad mood? What day did you experience your best mood? Could you always explain why you were in a certain mood? Did your mood change over the week? Did the presence of others affect your mood? Can you even remember your moods, and if so, how accurate is that memory?

These are questions researchers have sought to answer, in attempt to understand and predict mood variation over the week. This chapter will discuss these questions, and hopefully provide you with a greater understanding of your own and others’ moods.

Brenda-Ann Spencer, a 16-year-old girl, was sentenced to over 30 years in prison after conducting the Cleveland Elementary School Shooting in San Diego. This massacre was the first of its kind in American history. Spencer killed two teachers and wounded several others, including children. When asked why she did it, she replied,
'I Don't Like Mondays' .

Click here to read more, or watch a brief documentary

Is it true Monday is the worst day of the week? Whether returning to school or work, Mondays mark the end of freedom and relaxation and return of obligations and deadlines. The ‘week’ is a culturally constructed time-measurement, imposing structure on work, recreation, and social activities. Since events associated with 'days of the week' often show a cyclical pattern, it is likely moods also conform to weekly rhythms. Research suggests two weekly mood patterns: Blue Monday Phenomenon (BMP), where Monday moods are worse than other weekdays; and Weekend Effects (WEs), where moods are more positive on weekends than weekdays (Stone, Schneider, & Harter, 2012; Ryan, Bernstein & Brown, 2010). This chapter will explore day of the week (DOW) mood variation and explain research discrepancies. Two theories have been proposed to explain DOW mood: Circaseptan cycles and Self-determination theory (SDT). This chapter will provide support and criticisms for each. Perfectionism, motivation and personality will also be discussed as mediators of mood variation. This chapter will conclude by highlighting implications of weekly mood patterns, and offer suggestions to enhance mood and wellbeing.

Why we don't like Mondays: True-or-false quiz[edit | edit source]

1 Incidence of heart attack is highest on Mondays

True
False

2 More suicides occur on Mondays compared to Saturdays

True
False

3 Majority of workers compensation claims are made on Mondays

True
False

4 Accident and Emergency Departments are busiest on Mondays

True
False

5 Stock market returns are lowest on Mondays

True
False

6 Google searchers containing negative emotions are greatest on Mondays

True
False


Defining and measuring mood[edit | edit source]

How do moods and emotions differ?[edit | edit source]

Figure 1. Often we can be in a mood and not know why

Moods and emotions are related yet distinct phenomenon (Beedie, Terry & Lane, 2005). These concepts diverge on several dimensions, with duration, intensity and cause most salient. Moods persist longer than emotions, and are less intense. Unlike emotions, moods are not caused by specific events, or directed at specific objects. For example, you are often aware of the event/stimuli producing fear and anger (emotions); whereas, the same causal attribution cannot be made for feeling moody, irritable or lethargic (as depicted in figure 1). It was also argued emotions energise and direct behaviour, while moods influence cognitions (Beedie et al., 2005). A phrase often appearing in literature on mood is affect. Affect is synonymous with mood, and these phrases will henceforth be used interchangeably.

Mood measurement scales[edit | edit source]

Mood is conceptualised as two factors: positive and negative affect. Alertness, enthusiasm and energy characterise positive affect (PA). Negative affect (NA) is the general dimension of subjective-distress (Watson, Clark & Tellegen, 1988). Table 1. highlights differences between high and low positive and negative affect. PA and NA alone account for half to three-quarters of mood variance (Watson & Clark, 1999).

Table 1.
Distinguishing between the affects
Positive Negative
High High energy, full concentration

and pleasurable engagement

Anger, contempt, disgust,

guilt and nervousness

Low Sadness and lethargy Calmness and serenity

Several scales assess self-reported mood. The most validated is the Positive and Negative Affect Schedule (PANAS). The PANAS treats PA and NA as independent constructs; therefore, low, non-significant correlations exist between dimensions (Watson & Clark, 1999). In the brief version, 20-items measure NA and PA. The extended form (PANAS-X) includes 60 items, and in addition to first-order dimensions (PA/NA), second-order individual-affects are included: fear, sadness, guilt, hostility (basic negative emotions); joviality, self-assurance, attentiveness (basic positive emotions); and serenity, shyness, fatigue, surprise (other affective states). Both long and short-forms boast high validity and reliability (Watson & Clark, 1999).

An alternative scale is the Mood Adjective Check List (MACL). The MACL argues 3 factors: hedonic tone (pleasure-displeasure), energetic arousal and tense arousal, contribute to the experience of mood. The MACL demonstrated adequate psychometric properties, and correlated positively with physiological measures of autonomic-arousal (Matthews, Jones & Chamberlain, 1990). The PANAS and MACL measure slightly different aspects of mood, introducing implications when conducting and interpreting research.

Click here to try the PANAS for yourself.

Empirical evidence for Blue Monday Phenomenon and Weekend Effects[edit | edit source]

University students[edit | edit source]

Students' mood variation reflects WEs, but not BMP. Rossi and Rossi (1977) noted positive moods were higher on Friday through Sunday, and negative moods lower on Saturday and Sunday, in undergraduate women. Csikszentmihalyi and Hunter (2003) noted that students were significantly happier on Saturdays compared to Mondays, Tuesdays or Wednesdays. Similar patterns of affect-variation were obtained in Harvey et al.’s (2015) study: PA was highest on Saturday, and declined on Monday through Thursday. While research shows Monday moods are no worse relative to other weekdays, the biggest decrease occurs from Sunday to Monday. Thus, relative to pleasant moods experienced on weekends, Mondays may be perceived as the worst day. One published study found no WEs (Clark & Watson, 1988). This study included a limited sample of 18 students, and data collection occurred during irregular school-schedules. Due to small sample-size this study may have had insufficient power , and results are unlikely to represent the general student-population. Perhaps unsurprisingly, it appears that students experience most positive moods on weekends.

Adults[edit | edit source]

Adults demonstrate WEs similar to students; however, the exact nature of the effect is unclear. Certain studies reveal moods peak on Fridays and Saturdays, and decline on Sundays (Larsen & Kasimatis, 1990; Reis, Sheldon, Gable, Roscoe, & Ryan, 2000), whilst others suggest moods are not higher on Fridays; rather, peak on Saturdays and Sundays (Egloff, Tausch, Kohlmann, & Krohne, 1995; Kennedy-Moore, Greenberg, Newman, & Stone, 1992; Ryan et al., 2010; Stone, Hedges, Neale, & Satin, 1985). There are several explanations for inconsistencies in research findings, which will be discussed later in this section. The main difference between student and adult samples is presence of BMP in the latter. Meta-analysis by Areni, Burger and Zlatevska (2011) showed a small-effect for BMP. Meta-analytic tests calculate effect-sizes based on mean-difference; thus, authors were unable to account for whole-week patterns of mood variation. The small effect for BMP may have reflected weekend-weekday differences. This was the case in Stone et al.’s (2012) study, where BMP was significant only when Friday was included in the week. If Friday was excluded, BMP dissipated.

Figure 2. Weekends provide time to relax from work and engage in fun activities

Stone et al. (2012) conducted the largest study to date assessing DOW mood. Authors used telephone-questionnaire data from a national survey of 340,000 Americans, and found strong support for WEs. Participants reported greater positive mood and less negative mood on Fridays and weekends. This pattern emerged regardless of gender, age, work or partner-status. Ryan et al. (2010) attributed WEs to individuals experiencing less positive and more negative moods when at work compared to not-working. If work indeed does underlie DOW mood variation, WEs should disappear for retired individuals.

Older adults/retirees[edit | edit source]

WEs are less pronounced but still apparent for retirees. While the weekend–weekday mood difference was stronger in the working population, it was also evident in retired individuals and those aged over 70 years (Stone et al., 2012). Results emphasise factors besides employment effect DOW mood. For example, WEs may result from the nature of weekend activities, or increased time with family (see Figure 2).

Explaining research inconsistencies[edit | edit source]

Research indicates WEs; however, findings are inconsistent. Inconsistencies can be attributed to research design (whether prospective or retrospective), association of work with weekdays, selection bias; cultural expectations, and the measure of affect itself (Areni et al., 2011). Mood variation was greater for retrospective mood, suggesting cultural expectations and stereotypes bias memory. Indeed, Stone et al. (1985) noted Monday moods were no different from other weekdays; however, 65% of participants remembered Monday as their worst mood-day. Areni and Burger (2008) suggested two cognitive biases maintain DOW stereotypes: accessibility of work and leisure-related activities in memory, and tendency to exaggerate event's influence on mood. People recall fun/exciting activities easier than mundane work-related tasks, and over-emphasise negative aspects of returning to work, failing to consider factors increasing mood. Thus, work and leisure are remembered as worse and better than reality.

Figure 3. Individuals working in hospitality are less likely to fit stereotypical mood patterns

The assumption work-days and weekdays overlap further explains discrepancies in research. For example, individuals in hospitality (working weekends with Mondays off) are less likely to conform to stereotypical patterns (see Figure 3). Likewise, parents or carers may report no WE, since weekends are not associated with free-time (Areni, 2008). In addition, socially-withdrawn individuals with high job-satisfaction may experience opposite patterns, that is, ‘weekend-blues’ (Areni et al., 2011). Non-random sampling introduces selection bias, and may be falsely interpreted as mood variation. Tumen and Zeydanli (2014) found that participants interviewed on Friday or Saturday reported higher job-satisfaction compared to a random population sample, and that those interviewed on Sunday and Monday reported lower happiness. Expectations further exaggerate DOW effects. As Croft and Walker (2001) noted, participants believing everyone experiences bad moods on Monday, did indeed experience lowest PA and highest NA on Monday.

Measurement scales influence observed mood patterns (Kennedy-Moore et al., 1992). The MACL and PANAS produced significantly different results in two participant and procedure-matched studies (Egloff et al., 1995). When using the PANAS, PA was low on weekends; when using the MACL, PA was high. Authors attributed differences to scales emphasising distinctive aspects of PA. The PANAS represents arousal/engagement, explaining why PA was low on weekends (as less active engagement is required for leisure than work). The MACL measures pleasantness, explaining why PA was higher on weekends. Neither scale is superior; however, differences must be recognised when interpreting results.

Theoretical explanation[edit | edit source]

Circaseptan cycle[edit | edit source]

Figure 4. Red blood cells fluctuate on a 7-day cycle

According to Circaseptan theory, weekly mood variation results from innate biochemical processes, which exist as adaptive responses to environmental stressors (Cornelissen et al., 2005). Several physiological processes vary over seven days, including electrolytes, hormones, immune response, body-temperature and red-blood-cells (see figure 4). These systems are thought to influence mood via central (neurotransmitter) or peripheral (glucose-metabolism) routes (Croft & Walker, 2001). For example, Fibiger, Singer, Miller, Armstrong and Datar (1984) observed associations between adrenaline and physical-fatigue, cortisol and alertness, and noradrenaline and dopamine with tenseness and irritability. Studies also demonstrate direct correlations between circadian-rhythm disruptions and mood-disorder severity (McClung, 2013). Larsen and Kasimatis (1990) superimposed a seven day sine wave on aggregated mood data, and found it accounted for 40% of daily mood variance.

Unlike other time-measurements (e.g., the 24-hour day or 365-day year) the seven day week does not correspond to orbital-rotation, rather, is an artificial social construction. Since seven day biological processes do not align with the calendar week, it is likely cultural and social forces influence mood fluctuation.

Self Determination Theory[edit | edit source]

SDT provides a psychosocial explanation for DOW mood variation. Introduced by Ryan and Deci (2000), SDT postulates three psychological needs are neccessary to maintain well-being. These are autonomy: ones actions are self-endorsed and self-congruent, relatedness: being close and connected with others, and (3) competence: effectively bringing about desired outcomes (Reis et al., 2000). Daily mood can be attributed to the degree these three needs are met. For example, autonomy was linked to greater PA, vitality, self-esteem and reduced NA (Baard, Deci & Ryan, 2004).

Figure 5. Weekends provide time with friends and family, fulfilling the need of relatedness

Weekday and weekend activities fulfil psychological needs differently. Individuals are more likely to experience autonomy and relatedness when activities and company are freely-chosen (Reis et al., 2000). Ryan et al. (2010) noted greater autonomy and 'closeness to others' accounted for increased mood on weekends. Work involves external-control, time-pressure and demands - undermining individual autonomy. Likewise, work provides fewer opportunities to meet relatedness needs, as individuals often work alone or with assigned colleagues. Relatedness refers to more than being around others; it is the necessity for meaningful, close relationships. Indeed, a large proportion of the WE was explained by time spent with friends/family. Halliwell and Wang (2014) noted the two additional hours spent socialising with friends/family on weekends raised happiness levels approximately 2% (see figure 5). Reis et al. (2000) noticed similar weekly patterns for autonomy, relatedness and mood. Competence did not display the same pattern, suggesting work and study provide contexts for meeting competence-needs (Reis et al., 2000; see Figure 6). Ryan et al. (2010) reported that the three SDT variables explained 82% and 83% of variance in the relationship between work and PA and NA, respectively.

Figure 6. Work fulfils our psychological need for competence

SDT highlights importance of free-time for satisfying psychological needs and enhancing mood. Although it appears that work negatively effects mood, Csikszentmihalyi and LeFevre (1989) noted majority of flow experiences occur while working. Flow is defined as opportunity to expand capabilities, learn and increase personal complexity, which occurs when a person perceives high opportunity for action, and high ability to act (Csikszentmihalyi & Hunter, 2003). It is characterised by task-absorption and loss of self-consciousness. When both skill and challenge are high, experience will be positive, regardless of whether the activity is work or leisure. Csikszentmihalyi and LeFevre (1989) suggested the obligatory nature of work overrides positive experiences. Work is often perceived as a tradeoff to acquire money or free-time. This contradicts economists' and psychologists' suggestions of work's purpose (i.e. to provide happiness; Areni & Burger, 2008). One limitation of SDT in explaining mood variation is the possibility of a bi-directional relationship, where mood fluctuations alter task-engagement and social-interaction. It is important future research explores this alternative model. Work-leisure balance is important to enhance mood and well-being; as the following quote by Shakespeare (cited in Weil & Weil, 2007, p. 165) depicts:

Individual differences[edit | edit source]

Perfectionism[edit | edit source]

Harvey et al. (2015) distinguished self-critical from personal-standards perfectionism, highlighting the former increased and the latter reduced negative emotions toward returning to school on Mondays. Self-critical perfectionism is a maladaptive form of evaluation, characterised by negative self-appraisals and fear of failure. This perfectionism fosters harsh performance-judgement and is associated with poor adjustment. Personal-standards perfectionism does not involve negative evaluations, and is associated with higher self-esteem, flow experiences and adaptive coping (Harvey et al., 2015). The opposing mood pattern observed between self-critical and personal-standard perfectionists was mediated by differences in academic-goal motivation.

Motivation[edit | edit source]

Figure 7. Autonomous motivation is important for optimising goal-achievement

According to Ryan and Deci’s (2000) taxonomy of motivation, several types of motivation exist, ranging from intrinsically to extrinsically-driven. Motives are distinguished based on reason for goal pursuance, and are categorised as autonomous or control-orientated. Autonomous includes intrinsic and identified-motivation, whereas controlled represents external-regulation and introjection (Harvey et al., 2015). Figure 8 presents descriptions of each motivation. Self-critics reported more controlled reasons for pursuing goals, i.e. they felt externally-compelled to perform, and viewed school as burdensome and pressuring. Contrastingly, personal-standard perfectionists reported predominately autonomous motivation, and did not view school unpleasantly (see figure 7). Harvey et al.’s (2015) study relied on self-report, and was correlational. Therefore, although significant relations exist between perfectionism, motivation and BMP, a causal link cannot be established.

Figure 8. Ryan & Deci's (2000) taxonomy of human motivation
Thought Bubble Comics.png

ACTIVITY: Think about it[edit | edit source]

Think back to previous personal goals. Which were based on autonomous motivation? Were any pursued due to external motives (such as pressure, money, grades or another reward)? As you remember setting (and possibly achieving) these goals, what emotions do you recall experiencing? What effect do you think these goals had on your mood?

As SDT suggests, it is likely more positive moods/emotions were associated with the autonomous goals, compared to those that were extrinsically-motivated.

Personality[edit | edit source]

One of the most studied personality dimensions is the introversion-extraversion distinction. Individual degree of introversion or extraversion effects the extent mood reflects a weekly-cycle (Larson & Kasimatis, 1990). These bipolar traits differ in sensation-seeking, i.e. extraverts approach arousing-situations/activities, whereas introverts are more arousal-avoidant. Introverts have higher baseline physiological-arousal and are more reactive to environmental-stimuli. To maintain low-arousal, introverts manage unpredictable, unfamiliar situations by conforming to a weekly mood-rhythm. Indeed, Larsen & Kasimatis (1990) found extraverts' mood was less entrained to a seven day cycle than introverts'. Limited research has been conducted for the role of personality in DOW mood variation. Since neuroticism and extraversion correlate positively with NA and PA (Illies & Judge, 2002), it is likely these factors influence mood.

Implications[edit | edit source]

Figure 9. Spending time with friends is important for enhancing mood throughout the week

Since psychological-need satisfaction predicts mood variation, workplaces offering autonomous decision-making and teamwork will produce more positive employees. This is important for individual-wellbeing and employers. George (1991) indicated workers in positive moods demonstrated more pro-social behaviours, e.g. efficient customer service and assisting colleagues. Since customer service correlates positively with sales performance, positive moods are essential for maximising profits. Additionally, when non-work environments are more conducive to PA, employees will be likely to take leave to increase mood (Ilies & Judge, 2002). Autonomous goal-setting further improves mood, particularly at the beginning of the week. It is important employers provide workers with intrinsic as opposed to external motives, governed by control. To read more on fostering intrinsic motivation in others read the 2013 Motivation & Emotion book chapter on intrinsic motivation, or watch this TED Talk.

Spending time with family and friends increases PA (Halliwell & Wang, 2013). Thus, it is important workers have sufficient free-time to socialise with significant others (see Figure 9). Individuals working weekends or late nights are at risk of relatedness need-deficiency, as time usually spent with family or friends is allocated to working. The more psychological needs are neglected, the more wellbeing and mood will suffer.



5 tips for improving mood[edit | edit source]

  1. Make time to see family and friends
  2. Set autonomous goals: i.e. those you pursue for fun and enjoyment
  3. Increase opportunity to experience flow by finding tasks that challenge your ability
  4. Focus on positive experiences work or school provide, such as delivering a sense of competence and expanding social networks
  5. Remember: Mondays are no worse than other weekdays, and only appear bad if we recall them so

Conclusion[edit | edit source]

Weekly mood patterns are apparent in students, working-adults and retirees. Effects are robust for increased positive and reduced negative moods on weekends (Ryan et al., 2010). According to SDT, mood variation results from differences between activities performed on weekends and weekdays, and the extent these meet our psychological needs. Freely-chosen activities (and company) promote better moods than tasks assigned by others, as they satisfy autonomy and relatedness. Findings discussed in this chapter provide several implications for everyday living. For example, promoting autonomy and relatedness in the workplace is important to stimulate positive moods, pro-social behaviour, and business prosperity. Individuals should strive to set autonomous-goals and make time to see family and friends throughout the week. Staying late in the office or skipping lunch with a friend may have detrimental effects on your mood and well-being.

See also[edit | edit source]

References[edit | edit source]

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Areni, C. S. (2008). (Tell me why) I don't like Mondays: Does an overvaluation of future discretionary time underlie reported weekly mood cycles?. Cognition & Emotion, 22(7), 1228-1252. DOI: 10.1080/02699930701686107

Areni, C. S., & Burger, M. (2008). Memories of “bad” days are more biased than memories of “good” days: past Saturdays vary, but past Mondays are always blue. Journal of Applied Social Psychology, 38(6), 1395-1415. DOI: 10.1111/j.1559-1816.2008.00353.x

Areni, C. S., Burger, M., & Zlatevska, N. (2011). Factors affecting the extent of Monday blues: evidence from a meta-analysis. Psychological Reports, 109(3), 723-733. DOI: 10.2466/13.20.PR0.109.6.723-733

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Tsoi, L. C. H., Ip, S. Y., & Poon, L. K. (2011). Monday syndrome: Using statistical and mathematical models to finetune services in an emergency department. Hong Kong Journal of Emergency Medicine, 18(3), 150. Retrieved from <http://search.informit.com.au/documentSummary;dn=090048445514767;res=IELHEA>

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Weil, H., & Weil, J. (2007). The first part of King Henry IV. Cambridge University Press.

External links[edit | edit source]

Moodlytics a free mood-tracking app: track your daily moods to see when and why you experience certain moods

MoodJam allows you to visualise and express your moods artistically through colour

Ted Talk on novel mood measurement: using Twitter to gain insight in population moods

Ted Talk on how to create a successful work-life balance