Investigating Goal Difficulty and Motivational Quality as Moderators of the Association Between Sleep Quality and Goal Progress Open Access

Although people frequently set goals, making progress towards attaining a goal can oftentimes be difficult (Harkin et al., 2016). Indeed, research on new year’s resolutions finds that between 30 and 60 percent of people do not stick with their resolutions after just a couple of months (Ballard, 2024; Norcross et al., 1989; Oscarsson et al., 2020). It is well documented that individuals struggle to quit smoking, with some studies suggesting that approximately 75-93% of people fail to break the habit (Babb et al., 2017; Hughes & Callas, 2010). Additional findings also show that approximately 80% of individuals do not successfully accomplish a weight loss goal they set for themselves (Wing & Hill, 2001). Overall, these findings highlight a common paradox seen in goal pursuit research—that is, individuals setting a goal for themselves but struggling to make any type of progress towards achieving it. This lack of progress is often traced back to the obstacles an individual faces during goal pursuit.

A goal obstacle represents anything “that stands in the way of a goal that has been set” (Kreibich et al., 2020, p. 215). According to research by Hofmann et al. (2012), temptations are a prevalent obstacle during goal pursuit. For example, their research shows that approximately 49% of an individual’s daily desires can be classified as a temptation (Hofmann et al., 2012). These temptations were mainly confined to eating something unhealthy or initiating sleep when it may not be appropriate (e.g., in a work meeting; Hofmann et al., 2012). Beyond just experiencing temptations, individuals can also encounter additional goal obstacles, such as money or time constraints (Leduc-Cummings et al., 2022; Milyavskaya & Werner, 2021). For example, an individual may no longer be able to make sufficient progress on their goal of being more social with friends and family because they simply do not have the time or money to do so. People may also experience internal obstacles such as self-doubt or other negative emotions linked to the goal, which can result in procrastination and interfere with goal pursuit (Pollack & Herres, 2020; Pychyl & Sirois, 2016).

Obstacles may interfere with goal progress, but one aspect that might make the obstacles that people encounter even worse and contribute to a lack of goal progress is poor sleep quality. Indeed, both sleep quality and quantity have been linked to changes in cognitive and emotional processing of emotional and stressful stimuli (Vandekerckhove & Cluydts, 2010). After a night of poor sleep, people report feeling drowsy, more emotional, and less capable of making decisions or resisting impulses, which would make self-regulation and goal pursuit more difficult (Guarana et al., 2021; Hagger, 2014). Researchers have investigated—and demonstrated—the consequences of poor sleep quality on goal progress in a variety of life domains. For example, in an academic context, Flueckiger and colleagues (2014) had 72 Swiss undergraduate students complete evening assessments over a 32-day period regarding their sleep quality, physical activity, and academic goal progress during the day. The results from the study revealed that poor sleep quality—but not physical activity—predicted lower academic goal progress at the person- and daily-level (Flueckiger et al., 2014). Flueckiger and colleagues (2017) later attempted to replicate these findings in two additional longitudinal studies with Swiss undergraduate students. These follow-up studies used similar methods to their initial research, but they contained larger samples sizes and additional measures about emotions and snacking behaviours during the day (Flueckiger et al., 2017). The results from both studies revealed that after controlling for snacking behaviour and physical activity, poor sleep quality predicted lower academic goal progress at the person- and daily-level. It was also identified that students tended to report increased negative affect and decreased positive affect after a night of poor sleep quality, and these affective states were, in turn, related to lower academic goal progress.

The relation between sleep quality and goal progress has also been investigated in an occupational context. For example, researchers investigated whether poor sleep quality predicted the progress that United States Military members made on their work-related goals, such as maintaining a proper training regimen (Gunia et al., 2021). To get at this objective, the researchers had 663 military members report their sleep quality, impulse control and occupational goal progress at two different time points that were four months apart. The results were consistent with past research and suggested that military members who slept poorly tended to make less progress on their work-related goals, and reported an inability to manage their impulses. This was found both cross-sectionally (i.e., sleep at one time point related to outcomes at the same time point), and longitudinally (sleep at one time point predicted goal progress and impulse management months later). However, the researchers did not investigate whether this lack of impulse control was involved in the relation between poor sleep quality and lower goal progress.

The consequences of poor sleep quality on goal progress has also been investigated amongst individuals with a chronic health condition. For example, researchers investigated on the relation between sleep quality and goal progress amongst 50 women with fibromyalgia, which is a chronic disease characterized by immense pain distributed around the body (Affleck et al., 1998). Using a 30-day experience sampling procedure, it was identified that on days when the participants perceived that they slept poorly the night before, they tended to make lower progress on their health-related goals during the day (e.g., exercising; Affleck et al., 1998). This association, however, was not present for social goals, such as wanting to be more social with friends and family (Affleck et al., 1998).

Together, the available evidence suggests that poor sleep quality may hinder the progress people make on goals, but this may differ somewhat across goals in different areas of one’s life (e.g., health, social). After a poor night’s sleep, people may struggle to make progress on some goals but not others. Highlighting this possibility is Affleck and colleagues’ (1998) research showing that women with fibromyalgia struggled to make progress on health goals after a night of poor sleep quality, but their progress on social goals remained relatively unaffected. This inconsistent association may have occurred due to different characteristics of the health and social goals that were pursued. For example, maybe the health goals were perceived as more difficult than the social goals, making them more challenging to complete after a night of poor sleep quality. Or perhaps the participants did not have the same motivation for pursuing the health and social goals, which contributed to the inconsistent association found across these goal domains. Overall, these possibilities demonstrate that the association between poor sleep quality and lower goal progress may depend on certain characteristics of the goals that people are pursing, such as the difficulty of their goals or the motivation they have for achieving them.

Goal difficulty may play a role in the association between sleep quality and goal progress because it directs effort allocated towards goal pursuit. For example, as Locke and colleagues (1981) outline in their goal setting theory, a goal must be reasonably challenging for an individual to be motivated to work towards achieving it. This implies that more difficult—rather than easier—goals should be related to better goal progress (Locke et al., 1981). However, contrary to this notion, researchers have either failed to identify a relation between goal difficulty and personal goal progress (Koestner et al., 2002) or have found that higher goal difficulty is actually related to lower goal progress (Chevance et al., 2021; Werner et al., 2016). One reason that greater goal difficulty may be related to lower goal progress could be due to the way individuals allocate their effort to meet the demands of a task (Brehm & Self, 1989). For example, according to the motivational intensity theory, individuals put in the necessary effort to meet the demands of a task but can reach a ‘plateau’ and disengage from the task when it becomes overly difficult (Brehm & Self, 1989). Applying this concept to our research, we suspect that after a night of poor sleep quality individuals might reach this ‘plateau’ sooner, and struggle to make progress on their difficult goals. In other words, people may be less willing to put in a lot of effort when they are tired, and so are more likely to struggle with more difficult goals that require more effort.

Another aspect potentially playing a role in the association between sleep quality and goal progress is the level of want-to and have-to motivation an individual has for achieving their goals. Have-to motivation represents an individual’s desire to achieve a goal for the purpose of obtaining an external reward (e.g., money, positive peer evaluations) or to avoid feelings of shame if the goal is not achieved. Want-to motivation, on the other hand, represents an individual’s desire to work towards a goal because it aligns with their personal values, or they find the goal-related tasks fun and interesting (Milyavskaya et al., 2015). Past research has alternatively referred to these as autonomous (for want-to) and controlled (for have-to) goals, or has considered self-concordance as the extent to which a goal is relatively more want-to/autonomous compared to have-to/controlled (Deci & Ryan, 2000; Sheldon & Elliot, 1999; Werner & Milyavskaya, 2019),¹. Although these two forms of motivation are theoretically considered to be two poles of one construct, and sometimes combined into one variable, research typically finds them to be orthogonal and related to different outcomes (e.g., Koestner et al., 2008). In this paper we thus treat them as two different constructs, and are interested in the potential effects of each one. Research on these two forms of motivational quality has generally found that want-to motivation is related to better goal progress (Koestner et al., 2008). This, in part, might be explained by individuals with more want-to motivation tending to experience less obstacles when pursing their goals (Milyavskaya et al., 2015).

Individuals with more want-to (and less have-to) motivation might make better progress on their goals following a night of poor sleep quality. A reason for this possibility could be due to individuals with more want-to motivation tending to perceive that their goal pursuit is subjectively easier and less depleting (Inzlicht et al., 2014; Milyavskaya & Werner, 2021; Werner et al., 2016). Nonetheless, it is still possible that individuals with more have-to motivation might also make sufficient progress on their goals following a night of poor sleep quality. For example, Milyavskaya et al. (2015) found that individuals tend to put in more effort when pursuing have-to goals (compared to goals with less have-to motivation), and this increased effort is related to goal progress. Therefore, after experiencing a night of poor sleep quality, an individual pursuing a goal for external reasons (e.g., rewards, praise) might allocate additional effort to their goal, leading to goal progress.

In the present studies, we address whether the association between poor sleep quality and lower goal progress depends on the difficulty of the goals individuals are pursuing as well as the motivational quality for achieving their goals. These objectives are carried out in two longitudinal studies involving community adults (Study 1) and university students (Study 2). The first study uses morning and nighttime assessments to examine whether sleep quality from the night before is related to self-reported progress on multiple goals over the course of a single day. The second study builds on this by tracking sleep quality and perceived goal progress over the course of a five-day period. As is common in most research on personal (idiosyncratic) goals, we focus on people’s perceptions of their progress (see Smyth et al., 2023 for a discussion on the relations between subjective and objective measures of goal progress). The research questions, hypotheses, and analytical plans for both studies were pre-registered. These preregistrations can be found on OSF, along with all the materials, data, and code (https://osf.io/jv6ex).

In Study 1, participants completed a morning assessment where they listed up to five goals they planned to pursue that day and reported on the characteristics of each goal. They also reported their sleep quality from the night before. Then in an evening assessment on the same day participants reported the amount of progress they made on each of their goals. As in past research, we expected poorer subjective sleep quality to be related to lower goal progress (H1). We also expected that the positive relation between sleep quality and goal progress will be stronger for more difficult goals (compared to easier goals; H2). Finally, we explored whether have-to and want-to motivation moderate the association between subjective sleep quality and goal progress, such that sleep may impact progress differently depending on the motivational qualities of a given goal, but did not have specific hypotheses (RQ1-2).

The data used for Study 1 was originally collected in Summer 2023 for another research project investigating goal progress and daytime productivity (Gennara, 2023). The project gathered two samples from an online recruitment platform called CloudResearch^®. The first sample had 196 participants complete the morning assessment and 181 participants complete the evening assessment. The second sample had 222 participants complete the morning assessment and 187 participants complete the evening assessment. For the purposes of the current investigation, we combined the data from both samples.² The combined data contained a total of 436 participants. In line with our pre-registration, we excluded participants who did not complete the follow-up survey (n = 50), did not complete the goal measures (n = 15), and provided string responses (n = 2). Although not initially specified in our pre-registration, we also removed duplicate responses (n = 4) and participants who completed less than 10% of the survey (n = 2) or did not complete the sleep quality item (n = 2). After applying these exclusion criteria, we were left with 361 participants (more details about the data cleaning protocol are available on OSF at https://osf.io/jv6ex). The final sample had approximately the same number of males (48.8%) and females (51.2%). These participants were also predominately Caucasian (72.0%) and had an average age of 41.1 years (SD = 12.3).

All participants in both samples consented to participate in a study where they would get $2.00 (USD) to complete two surveys. These surveys focused on measuring their goal pursuit over the course of a single day. The first survey was administered in the morning and the second survey in the evening approximately eight hours later. Both assessments took place on Qualtrics (an online survey platform).

In the morning survey, participants were asked to list up to five goals they planned to pursue that day. Participants then used Likert scales ranging from 1 (strongly disagree) to 7 (strongly agree) to respond to items about characteristics of each goal.³ Of interest for the present study, difficulty of the goal and want-to and have-to motivation were each assessed using one item per goals: “It will be difficult for me to reach this goal”; “I am pursuing this goal because I genuinely want to (i.e., it is personally meaningful, connects to my broader values/goals, and/or is enjoyable)”; “I am pursuing this goal because I feel like I have to (i.e., because of pressure by other people/society, to avoid feelings of guilt/shame, and/or to obtain an external reward)”. Participants’ sleep quality from the previous night was also measured with a single item: “Last night, how would you rate your sleep quality overall?”), with response options ranging from 1 (very bad) to 4 (very good). This item was taken directly from the Pittsburgh Sleep Quality index and in past research has demonstrated a strong correlation with scores from the full 19-item sleep quality scale (r = .89; Buysse et al., 1989).

In the second survey later in the day, participants were reminded of the goals they listed in the morning assessment, and then were asked to report the amount of progress they made on each goal using a single item per goal (“I have made a lot of progress toward this goal”). Response options ranged from 1 (strongly disagree) to 7 (strongly agree). All study procedures were approved by the university’s research ethics board.

Three separate multi-level models with goals nested within participants were used to test whether the association between sleep quality and goal progress was moderated by goal difficulty, have-to motivation and want-to motivation. The reason for using this modeling approach was to account for the dependency in the data created by the participants being able to list multiple goals. All models were computed in R version 4.2.3 using restricted maximum likelihood estimation⁴. To understand differences at the goal-level, the predictor variable scores were person-mean centered. Although our key hypotheses and research questions focused on goal-specific (within-person) effects, we wanted to account for possible between-person differences in our analyses. Predictor variable scores (i.e., goal difficulty, want-to motivation, have-to motivation) were thus averaged across goals to get at between-participant effects. As sleep quality was only measured once (at the start of the day), it was only considered as a predictor at the between-participant level. The predictors entered into each model included sleep quality, as well as a person-mean centered and a grand-mean centered variable representing the main effects of the moderator (e.g., have-to motivation). Each model also included a between-participant and a cross-level interaction. Despite not being explicitly preregistered, we also specified the person-mean centered moderator as a random effect in each of our models following Heisig and Schaeffer’s (2019) recommendations that multi-level models investigating cross-level interactions should specify random effects to ensure accurate standard error estimates and p-values. In the models that included motivation, we controlled for the motivation type that was not the focal predictor of the analysis. For example, when conducting the analysis with want-to motivation as the focal predictor, we controlled for have-to motivation. This was due to inconsistencies in the literature regarding whether want-to and have-to motivation are on a continuum, or represent two separable types of motivation; in case of the latter, we wanted to capture their unique association with goal progress. Additional supplementary analyses (suggested by a reviewer) were conducted to examine (1) the curvilinear effects of goal difficulty and its interaction with sleep quality; (2) interactive effects between have-to and want-to motivation, and the three-way interaction with sleep quality. Results of these additional analyses can be found in online supplementary materials on OSF.

The participants reported planning to pursue an average of 4.1 goals (SD = 1.2) during the one-day study period. Some of these goals included: “walk two-miles”, “clean the office”, “practice the guitar” and “eat a healthy lunch”. Most of the sample also reported that their sleep quality the night before was either fairly good (54.8%) or very good (18.0%), whereas the remainder of the sample reported that their sleep quality the night before was either fairly bad (21.3%) or very bad (5.8%). It was also found that participants who slept poorly the night before, on average, tended to report lower want-to motivation and goal progress. At the goal-level, it was also identified that more difficult goals were related to lower goal progress. Goals with higher want-to motivation were also perceived as less difficult, whereas goals with higher have-to motivation were perceived as more difficult (see Table 1 for a full overview of the descriptive statistics and correlations).

A series of multi-level models with goals nested within participants were conducted to test our research objectives. First, a null model indicated that approximately 23% of the variability in goal progress was at the participant-level, and 77% of the variability was at the goal-level. Additional visual checks also revealed that the slopes and intercepts of our person-mean centered predictors (i.e., goal difficulty, want-to motivation, have-to motivation) and goal progress varied across the participants. Therefore, we specified random effects and random intercepts in each of our models that included a person-mean centered predictor.

The first model tested whether sleep quality on a given night was positively related to goal progress the following day. In line with H1, we identified that individuals who slept better the night before, on average, tended to report making more progress on their goals the next day, b = .58, SE = .09, 95% CI[.41, .74], p < .001.

Next, we examined whether the association between sleep quality and goal progress was moderated by the difficulty of the goals the participants were pursuing. As presented in Table 2, individuals who were pursuing more difficult goals, on average, tended to make significantly less goal progress. This association was also significant at the goal-level: people made less progress on goals that were more difficult, compared to their other (easier) goals. We also found that individuals who slept poorly the night before tended to make significantly lower goal progress after adjusting for the average difficulty of the goals they were pursuing. This association, however, was not moderated by goal difficulty at either the participant- or goal-level.⁵ Therefore, our second hypothesis specifying that goal difficulty would moderate the association between sleep quality and goal progress was not supported. We nonetheless performed an exploratory simple slopes analysis for the cross-level interaction and identified that the pattern in the data somewhat matched our hypothesis (see Figure 1). That is, the positive association between sleep quality and within-person goal progress was slightly stronger for more difficult goals (b = .61, SE = .12, p < .001) compared to their easier goals (b = .42, SE = .10, p < .001).

To examine the role of motivational quality (RQ1 and RQ2), we conducted two separate multi-level models. The first model focused on identifying whether have-to motivation moderated the association between sleep quality and goal progress after controlling for want-to motivation (see Table 2). The second model, on the other hand, examined whether want-to motivation moderated the association between sleep quality and goal progress after controlling for have-to motivation. Overall, the results from both models demonstrated that poor sleep quality the night before continued to be related to lower goal progress after controlling for have-to and want-to motivation. The results also indicated that average want-to motivation (but not average have-to motivation, and not goal-specific motivational quality), was related to better goal progress. Addressing RQs1-2, the association between sleep quality and goal progress was not moderated by have-to nor want-to motivation (at either the participant-or goal-level). Therefore, individuals who slept poorly the night before tended to make significantly lower goal progress regardless of the quality of the motivation they had for achieving their goals.

Although we did not initially specify it in our preregistration, we conducted separate Bayesian analyses to probe the results of the hypothesized main effects of sleep quality (H1) and interactions between sleep quality and our moderators (i.e., goal difficulty, have-to motivation, want-to motivation) at both the participant- and goal-level. Our process for each calculation was the same. First, we calculated the Bayesian Information Criterion (BIC) of a complete model containing the main effects (For H1) or the main effects and interactions (for H2 and RQs1-2) at both the participant- and goal-level (i.e., the BIC of each model specified in Table 2). We then compared the BIC of the complete model to the BIC of a reduced model excluding the effect or interaction of interest (∆BIC = |BIC_complete – BIC_reduced|). Finally, we converted the difference scores to a Bayes factor (BF); when the reduced model has a smaller value the BF represents the likelihood of the null hypothesis matching the data (BF₀₁); when the complete model has a smaller value it represents the likelihood of the key hypothesis matching the data (BF₁₀).⁶ In line with Raftery (1995) and Wagenmakers (2007), we deemed BFs larger than 3 as positive evidence, and BFs larger than 20 as strong evidence in favor of the simpler model. As shown in Table 3, The BF in support of the main effect of sleep quality (for H1) was exceedingly strong (12 million times more likely than the alternative). For the interactions, the BF₀₁ was above 20 for each one (and in most cases over 100), suggesting that the null (no interaction) is 100+ times more likely than the alternative hypothesis. These findings demonstrate that the effect of poor sleep quality on lower goal progress did not depend on goal difficulty or motivation, at neither the person nor goal level.

The goal of this study was to examine whether individuals who slept poorly the night before tended to make less progress on their goals the following day. We were also interested in learning whether this association depended on the difficulty of the goals they were pursuing, as well as the level of have-to and want-to motivation they had for achieving their goals. The results supported our first hypothesis, replicating past work showing that individuals who report poor sleep quality tend to make less progress on their goals the following day. We also found that the association between poor sleep quality and lower goal progress did not depend on goal difficulty or motivation quality. However, despite the interaction between sleep quality and goal difficulty not being statistically significant, follow-up probing suggested that the pattern in the data somewhat matched our second hypothesis. That is, the positive association between sleep quality and goal progress was slightly stronger for more difficult goals (compared to easier goals). Follow-up Bayesian analyses showed that the models without interactions fit the data much better, providing strong to overwhelming evidence that the effects of sleep quality on goal progress did not differ based on goal difficulty or motivation quality.

The aim of Study 2 was to examine the relation between sleep quality and goal progress across multiple days. University students completed a baseline assessment where they reported a personal goal they were currently pursing. They also indicated the perceived difficulty of the goal as well as the quality of their motivation for achieving it. We then tracked their sleep quality and goal progress for seven consecutive days. Based on the results from Study 1, we expected poorer subjective sleep quality to be related to lower goal progress the following day (H1). We also expected that the positive association between sleep quality and goal progress will be stronger for more difficult (compared to easier) goals (H2). Although we did not find support for this hypothesis (H2) in Study 1, we thought that it might be possible for this moderating effect to occur when sleep quality and goal progress are tracked over multiple days, rather than a single day. We also examined whether have-to and want-to motivation moderate the association between subjective sleep quality and goal progress (RQ1-2) but did not have directional hypotheses.

The data used for this study was originally collected in the Fall of 2023 as part of a larger study on goal pursuit, which included an experience sampling (up to 5 per day) and a daily diary component (Thorne et al., 2024). Participants were 179 university students who were recruited from the department of psychology’s participant pool. Participants who only completed the baseline survey (n = 2) and did not provide partial or complete responses for at least two survey days (n = 2) were excluded. Participants were also excluded for not completing any sleep quality (n = 5) or goal progress (n = 14) assessments. After data cleaning, we retained 156 participants for analyses (more details about the data cleaning protocol are available on OSF at https://osf.io/jv6ex) .⁷ The final sample was predominately female (76.3%), Caucasian (53.5%), and had an average age of 18.5 years (SD = 10.5).

All participants consented to participate in a study where they would receive up to 4% course credits for completing a baseline survey and seven-days of follow up surveys. The baseline survey asked participants to list a personal goal they planned to pursue over the next couple of months.⁸ Participants then rated the goal on a number of dimensions; for the purposes of the present study we were only interested in difficulty and motivation (the full list of measures administered is available on OSF). All items were rated using Likert scales ranging from 1 (strongly disagree) to 7 (strongly agree). One item assessed the perceived difficulty of the goal (“I think it will be difficult for me to reach this goal”). Goal motivation was assessed with three items measuring have-to motivation (e.g., “I am pursuing this goal because somebody else wants me to, or because I will get something from somebody if I do”) and three-items measuring want-to motivation (e.g., “I am pursuing this goal because I really believe that it is an important goal to have—I endorse it freely and value it wholeheartedly”; Koestner et al., 2015). Total scores were obtained for have-to (omega = .64) and want-to (omega = .62) motivation by separately averaging the items of each scale (Macdonald’s omega is the preferred measure of reliability for multidimensional scales; Flora, 2020; Hayes & Coutts, 2020).

On the first Monday after the baseline survey, participants began the seven-day experience sampling portion of the study, which included assessments of sleep quality and goal progress. On the first survey of each day participants reported how well they slept the night before (i.e., “Last night, how would you rate your sleep quality overall?”) using a scale ranging from 1 (very bad) to 4 (very good). Later in the evening (in the final survey of the day) the participants reported the progress they made on their goal (i.e., “How much progress did you make towards achieving this goal today?”) using a scale ranging from 1 (none/not at all) to 7 (a great extent).

Three separate multi-level models with days nested within participants were used to test whether the association between daily sleep quality and goal progress was moderated by goal difficulty and goal motivation (have-to, want-to). Each model was computed in R version 4.2.3 using Full Information Maximum Likelihood (FIML) estimation⁹. In each model, sleep quality was a level-1 (daily) predictor and was person-mean centered. Difficulty and motivation were level-2 (person-level) predictors and were grand-mean centered. In the goal motivation models, we controlled for the goal motivation type that was not the focal predictor of the analysis, which was the same method that we used in Study 1. We also specified sleep quality as a random effect in each model, and included a person average sleep quality score (grand-mean centered), as well as level-2 interactions between difficulty/motivation quality and sleep quality (to disaggregate the level-1 and level-2 effects). In all cases, we considered results to be statistically significant when p < .05. As in Study 1, results of supplementary analyses focusing in curvilinear effects of difficulty and interactive effects of motivational quality with sleep quality can be found on OSF.

The response rate was 70.7% over the seven-day period. Participants responded to an average of 6.4 (SD = 1.03) survey days, for a total of 539 days used in the analyses. Over the course the study period, the participants mainly reported pursuing academic goals (69.2%). Examples goals included: “achieve an 85% average”, “maintain a balanced work ethic at school” and “get on the Dean’s honours list”. On most survey assessments, ratings of the previous night’s sleep quality were fairly good (59.9%) followed by fairly bad (19.3%) and very good (17.8%). Reports of very bad sleep quality were infrequent (3%). As presented in Table 4, sleep quality was not related to goal progress at either the participant- or daily-level. Instead, more have-to motivation was related to better goal progress. Want-to motivation was related to lower perceived goal difficulty, whereas more have-to motivation was related to higher perceived goal difficulty.

A series of multi-level models were used to identify whether sleep quality was related to goal progress, and whether this association was moderated by goal difficulty and goal motivation (i.e., have-to, want-to). First, a null model was computed and indicated that approximately 46% of the variability in goal progress was at the person-level and 54% of the variability was at the daily-level. Subsequent results from an unadjusted model also revealed that sleep quality the night before was not related to goal progress at the daily-level (b = .06, SE = .14, 95% CI[-.21, .34], p = .64). Therefore, H1 was not supported. There was also no association of average (level-2) sleep with progress (b = .14, SE = .26, 95% CI[-.38, .66], p = .59). Despite these findings, we still conducted our planned moderation analyses to identify whether this association might have been present at certain levels of goal difficulty and goal motivation.¹⁰

Our first model examined whether the association between sleep quality and goal progress was moderated by goal difficulty. The results showed that sleep quality was not related to goal progress at either the participant- or daily-level (see Table 5). Goal difficulty, on average, was also not related to goal progress, and did not moderate the association between sleep quality at the daily-level (nor the participant-level) and goal progress. Therefore, H2 was not supported.

Next, we conducted two separate multi-level models to examine RQ1 and RQ2. The first model focused on identifying whether have-to motivation moderated the association between sleep quality and goal progress after controlling for want-to motivation. The second model focused on identifying whether want-to motivation moderated the association between sleep quality and goal progress after controlling for have-to motivation. The results showed that neither have-to nor want-to motivation moderated the association between sleep quality and goal progress (see Table 5). The results, however, consistently showed that students with more have-to and want-to motivation tended to make better goal progress.

Despite not being specified in our pre-registration, we conducted exploratory Bayesian analyses to understand the null association between sleep quality and goal progress, as well as the null interactions between sleep quality and the moderators (i.e., goal difficulty, have-to motivation, want-to motivation) at both the participant- and daily-level. The same analytic process outlined in Study 1 was used (see Study 1 results section for more details). The BIC of the complete models again represented the fit of the models outlined in Table 4. However, the complete model for the sleep quality main effects only included the participant- and daily-level sleep quality fixed effects, as well as a sleep quality random effect.

As shown in Table 3, BFs ranged from 8 to 21, representing a modest positive effect in favour of the null. Overall, these finding provide strong evidence that sleep quality is unrelated to goal progress, and modest evidence that the relation between sleep quality and progress does not depend on goal difficulty or motivation quality.

Additional exploratory analyses were conducted to examine effects of sleep quality on goal progress assessed 3 weeks and 3 months after the experience sampling part of the study. We thought that perhaps the effects of poor sleep are not felt immediately, but accumulate. Bivariate correlations showed no associations between sleep quality during the week of experience sampling (i.e., the average of all the daily sleep quality scores) and progress on the focal goal, or the average of all three goals, at either of the two follow-ups (rs ranging from .08 to .18, ps > .10). There were also no interactions with goal difficulty or motivation quality (see online supplemental material for all analyses).

The focus of this study was to track university students’ sleep quality and goal progress over multiple days to understand whether students who experienced poor sleep quality tended to make lower goal progress the following day. We were also interested in identifying whether this association depended on the difficulty of the goal the students were pursuing, as well as the level of have-to and want-to motivation they had for achieving their goal. The results of this study did not replicate our findings from Study 1, and instead, demonstrated that sleep quality the night before was unrelated to goal progress the following day. Additional exploratory analyses showed that sleep quality over the course of the week was not associated with progress over the course of the following weeks and months. We also identified that the association between sleep quality and goal progress did not depend on goal difficulty or motivation. Follow up Bayesian analyses provided strong support for the null hypothesis that sleep quality was unrelated goal progress, amd modest support that there was no moderation by goal difficulty or motivation quality. Nonetheless, we did identify that students who had generally higher levels of want-to and have-to motivation tended to make better goal progress.

In two repeated-measures studies, this paper examined whether the previously established association between poor sleep quality and lower next-day goal progress depends on the difficulty of the goals people are pursuing, as well as the underlying motivation they have for achieving their goals. Overall, the results demonstrated that participants in the adult community sample—but not in the university student sample—who slept poorly on a given night tended to report making less progress on their goals the following day. Across both studies we also showed that the association between sleep quality and perceived goal progress did not depend on the difficulty of the goals people were pursing or the motivation quality for achieving their goals, finding positive to strong support for the null hypothesis.

A central aspect of our research was to understand how perceptions of sleep quality are related to the progress that people make on their goals. Our results suggest that poor sleep quality may only be related with less daily goal progress amongst a community adult sample, but not in university students. This inconsistency comes as somewhat of a surprise considering that past research has consistently shown that restorative sleep is related to goal progress (Flueckiger et al., 2014). For example, researchers have identified that adults who experience a night of poor sleep quality often face difficulties progressing on important health (Affleck et al., 1998) and occupational goals (Gunia et al., 2021). Researchers have also consistently shown that university students who do not get quality sleep tend to make less progress on their academic goals the following day (Flueckiger et al., 2014, 2017).

A potential reason for the inconsistent relation between sleep quality and goal progress in our research could be due to the different types of goals that were pursued across our samples. In our first study the goals were mainly short-term daily tasks (e.g., responding to emails, doing laundry, cleaning the kitchen), as we only tracked goal pursuit over the course of a single day. In our second study, however, we asked students about longer-term goals, and tracked daily progress on those goals that would take months to complete (e.g., achieving an 80% average). Based on these differences, it is possible that the types of goals (i.e., short vs. long) played a role in the association between poor sleep quality and goal progress. For example, participants might have reported less progress after a night of poor sleep quality because the short-term goals they were pursuing were perceived as less important and could easily be put off until another day. In contrast, goal progress amongst university students might not have been hindered after a night of poor sleep because the types of long-term goals they were pursuing were seen as more important, and simply had to get done no matter how they felt when waking up.

Another reason for the inconsistent findings may lie in the methods we used to assess goal progress, which differed from past studies. Past research on sleep and goals in university students assessed goal pursuit in general. For example, Flueckiger and colleagues (2014, 2017) only administered a single item to their participants at the end of each day asking them to broadly rate the progress they made on their academic goals. This approach likely captured the progress the students made on several short-term academic tasks (e.g., completing course readings, watching lectures), which might be challenging to complete after a night of poor sleep quality. In contrast, we asked participants for a specific goal that they were pursuing over the course of the semester, and assessed progress on that goal. Perhaps students are still able to maintain focus on what’s most important despite a night of poor sleep, even as performance on some other tasks (and academic pursuit overall) suffers. Alternatively, perhaps students have more flexibility than working adults to shift their schedules to make up for potential deficits due to a poor night’s sleep. Future research is needed to better understand the potential reasons for the discrepancies.

Our second hypothesis specifying that the association between sleep quality and goal progress would depend on the difficulty of the goals people were pursuing was not supported. We initially expected that individuals who experience a night of poor sleep quality would make less progress on goals they perceived as more difficult, rather than goals they perceived as easier. The rationale behind this hypothesis partly came from studies suggesting that difficult personal goals are related to lower goal progress (Chevance et al., 2021; Werner et al., 2016), and we thought that this may be exacerbated after a night of poor sleep. In addition, according to motivational intensity theory, individuals put in the necessary effort to meet the demands of a task but can reach a ‘plateau’ and disengage from a task when it becomes overly difficult (Brehm & Self, 1989). Applying this concept to our research, we suspected that individuals might reach this plateau sooner after a night of poor sleep quality, and ultimately, struggle to make progress on their difficult goals. Our findings, however, ended up consistently showing that the association between sleep quality and daily goal progress did not depend on the difficulty of the goals people were pursuing. Follow-up Bayesian analyses in both studies also provided moderate to strong support for the null hypothesis specifying that goal difficulty did not moderate the association between sleep quality and goal progress. As such, any potential effects of sleep on progress occur independently of how difficult a goal may be.

As an exploratory aim, we investigated whether the association between sleep quality and next day goal progress depended on the want-to and have-to motivation an individual had for pursuing their goals. For both types of motivation quality, our findings consistently showed that goal motivation did not play a role in the association between sleep quality and goal progress. Indeed, in our first study, we identified that adults who experienced a night of poor sleep quality tended to make less progress on their goals the next day, regardless of the level of want-to or have-to motivation they had for achieving their goals. Follow up Bayesian analyses also provided strong support for the null hypothesis specifying that want-to and have-to motivation would not moderate the association between sleep quality and goal progress. Therefore, the consequences of a poor sleep quality on reduced goal progress appears to occur irrespective of goal motivation amongst adults pursuing several personal goals. The evidence in our second study was weaker, but Bayesian analyses still showed moderate support for null interactions.

Although not explicitly pre-registered as a hypothesis, our analyses showed that was that want-to motivation was consistently related to better goal progress, whereas have-to motivation was only related to better goal progress amongst university students, but not adults. Generally, want-to motivation has been touted as an optimal form of motivation due to it being consistently related to better goal progress (Koestner et al., 2008). The findings for have-to motivation, on the other hand, have been mixed (Koestner et al., 2008). For example, researchers have found that individuals pursuing have-to goals tend to put in more effort into their goal pursuit, but they typically experience more goal obstacles (Milyavskaya et al., 2015). This particular finding is relevant to the current investigation because in our second study we tracked the university students goal pursuit at the start of an academic semester. Therefore, have-to motivation may have been related to better goal progress due to the students being in the early stages of their academic goal pursuit, which may have been a time when they were not experiencing goal obstacles yet.

Although this study furthered our understanding of sleep quality and goal progress, several limitations of our research should be acknowledged. First, sleep quality was only measured via self-report, and only using one item. Although daily sleep diaries completed soon after waking up are the gold standard of assessing sleep quality (e.g., Carney et al., 2012), they frequently have multiple items. It is unclear how using only one item affects the measurement quality, although a recent study that examined the psychometric properties of a single face-valid item used to asses (past week) sleep quality finds strong correlations with more comprehensive self-report measures in a clinical populations (Snyder et al., 2018). Nevertheless, the use of an unvalidated self-reported single item in our study constitutes a limitation. It is also worth noting that even more comprehensive self-report measures of sleep quality do not always align with more objective assessments, and that there are many possible objective indices that assess different aspects of sleep quality, without a consensus regarding which one is best to use (Krystal & Edinger, 2008). Future researchers can incorporate multiple indices of sleep quality (subjective and objective, such as derived from actigraphy watches) into their investigations, which would provide a more nuanced prospective into sleep quality.

Similarly, we assessed subjective (perceived) goal progress, rather than objective progress. Although strongly correlated with objective indicators of progress, subjective measures of progress are distinct but arguably more relevant in the context of idiosyncratic goal pursuit (see Smyth et al., 2023). Second, this study mainly used single-item measures. Although single-item measures are commonly used in experience sampling studies to reduce participant burden, these items may have not captured the full complexity of the constructs under investigation (Allen et al., 2022). As such, researchers could address this limitation by incorporating more multi-item scales into their research if doing so would not place too much of a burden on participants. Third, have-to and want-to motivation in Study 2 were both measured with three-item scales that had low internal consistency. This low internal consistency may have occurred due to the scales only having a small number of items (Kruyen et al., 2013). Additionally, considering that the scales are each a composite of different types of motivation that are theoretically meant to form two different ‘poles’ of motivational quality (i.e., intrinsic, identified, and integrated for want-to motivation; extrinsic and introjected for have-to motivation; see Werner & Milyavskaya, 2019 for a review), some lack of coherence between the items may be expected. Additionally, goal difficulty and motivation quality were the only goal characteristics examined in both studies. Therefore, it would be valuable for researchers to examine whether the association between sleep quality and goal progress depends on other goal characteristics, such as the perceived importance of the goals people are pursuing or whether they are pursuing short-term (e.g., cleaning the apartment) versus long-term goals (e.g., losing 35lbs). Finally, our studies were not designed to explicitly compare working adult and college student populations. Future studies could examine our key questions using other study designs and populations (i.e., conduct conceptual replications) to ensure generalizability beyond the specific study design and populations used here.

Our research highlights how a night of poor sleep quality might only be determinantal to goal progress the following day amongst a sample of community adults pursuing short-term goals, but not amongst a sample of university students pursuing a long-term academic goal. Furthermore, the consequences of poor sleep quality amongst adults appears to occur regardless of the difficulty of the goals they are pursuing or the underlying motivation quality they have for achieving their goals. Future research, however, is necessary to clarify these findings, and possibly identify specific goals that university students struggle to make progress on after not getting quality sleep.

RS designed the research question with feedback from MM. TT and AG conceptualized the study design and oversaw data collection. RS conducted the analyses and wrote the manuscript, with edits and feedback from MM, TT, and AG.

This research was funded by a grant from SSHRC to MM. MM also received support from the Canadian Foundation of Innovation and the Ontario Research Fund for infrastructure that was used in this research.

We declare that we have no competing interests.

REB approval for these studies was obtained from Carleton University (#118953 for study 1; #118524 for study 2).

All the data used in this manuscript, along with the R scripts for analyses, can be found on OSF (https://osf.io/jv6ex)