The Scent of a Good Night’s Sleep: Olfactory Cues of a Romantic Partner Improve Sleep Efficiency

Abstract

Almost nothing is known about whether exposure to the scent of loved ones influences sleep. In the current study, 155 participants spent 2 nights with their partner’s scent and 2 nights with a control scent (in random order). Sleep was measured in two ways: sleep efficiency (via actigraphy) and perceived sleep quality (via self-report). Sleep efficiency was higher when participants were exposed to their partner’s scent. This increase occurred regardless of participants’ beliefs about the origin of the scent. Perceived sleep quality was higher when participants believed that they were smelling their partner’s scent. Exposure to a partner’s scent led sleep efficiency to increase by more than 2% on average, an improvement similar in magnitude to the effect of melatonin on sleep. The current work speaks to the critical role of olfaction in communication and reveals that social scents can impact sleep.

Keywords

olfaction social communication social support nonverbal communication health behaviors open data open materials preregistered

The scent of another person is emotionally evocative. It can spark sexual attraction, induce fear, and provide psychological comfort (de Groot, Semin, & Smeets, 2017; Gildersleeve, Haselton, Larson, & Pillsworth, 2012; McBurney, Shoup, & Streeter, 2006). Social scents also influence physiological processes such as hormone release, heart rate, and sweat production (Granqvist et al., 2019; Hofer, Collins, Whillans, & Chen, 2018; Maner & McNulty, 2013). In the current research, we examined whether the scent of a romantic partner can improve sleep quality.

The significance of social scents begins early in life. Newborn babies turn their heads toward their mother’s scent and are calmed by this scent (as evidenced by decreased movement and cortisol production; Nishitani et al., 2009; Rattaz, Goubet, & Bullinger, 2005). Whereas the primary attachment figure for most infants is their mother, the primary attachment figure for most adults is their romantic partner (DeLongis & Holtzman, 2005). Long-term romantic relationships have many positive health implications (for a review, see Burman & Margolin, 1992). Close contact with an attachment figure, such as a romantic partner, provides a sense of emotional security, promotes effective day-to-day functioning (e.g., Brennan & Shaver, 1995), and helps people to regulate (consciously and unconsciously) their physiology and psychology (Bodenmann, Meuwly, & Kayser, 2011; Ditzen et al., 2007). For example, research has shown that when couples are together, they report higher quality sleep than when they are physically separated (Diamond, Hicks, & Otter-Henderson, 2008).

However, periodic physical separation from romantic partners is inevitable, especially in our highly mobile contemporary society. Fortunately, romantic partners need not be physically present to provide security. Simply viewing a photograph of a romantic partner can be sufficient to buffer reactions to pain (Eisenberger et al., 2011; Younger, Aron, Parke, Chatterjee, & Mackey, 2010). Because a romantic partner’s scent can also serve as a cue to that person’s current or recent presence, we hypothesized that exposure to a romantic partner’s scent will help people regulate their psychology and physiology.

The Current Work

Research has demonstrated that exposure to a romantic partner’s scent can reduce stress and induce feelings of safety and security (Granqvist et al., 2019; Hofer et al., 2018; McBurney et al., 2006), which may have implications for sleep. Stress and vigilance are antithetical to the state of sleep, whereas feelings of safety and security are optimal for high-quality consolidated sleep (Troxel, 2010). Thus, in the current work, we turned to the question of whether exposure to a romantic partner’s scent can improve sleep.

In daily life, people often sleep with a romantic partner’s previously worn clothing while physically separated from their partner. In a sample of U.S. college students, more than 70% of women and 25% of men reported this behavior (McBurney et al., 2006). However, the effects of this common behavior on people’s sleep outcomes have not been systematically studied or quantified. The current work is, to our knowledge, the first attempt to examine whether sleeping with an article of clothing previously worn by a romantic partner improves sleep in adults. In addition, we explored whether conscious awareness of scent identity plays a role in whether sleep is affected by scent.

Participants slept with a shirt worn by their romantic partner for 2 nights and a control shirt for 2 nights (shirts were used as pillowcases). Sleep quality was assessed via a sleep watch (sleep efficiency) as well as via self-report (perceived sleep quality). We predicted that, compared with nights spent with a control scent, sleep efficiency and perceived sleep quality would be higher on nights spent with a partner’s scent.

These two hypotheses were examined using data from three related samples. Materials, data, and R code are available online (https://osf.io/mj9ux/). All three samples have associated preregistrations.¹ Data from the three samples were combined to increase statistical power.

Method

Participants

Couples were recruited for a study about interpersonal relationships at a large public university in North America in exchange for either pay (scent donors received 20 CAD; sleepers received 40 CAD) or course credit. Couples were eligible if they were in heterosexual long-term (3 months or more) romantic relationships and met basic screening criteria (e.g., no chronic medical conditions, the ability to smell, no sleep disorders; for full exclusion criteria, see Table S1 in the Supplemental Material available online). The final sample consisted of 155 participants with sleep data (25% male, 75% female; relationship length: M = 23.28 months, SD = 20.63; age: M = 20.75 years, SD = 3.24). Most participants identified as Asian (55%, including South Asian) or Caucasian (30%).

Procedure

The procedures were approved by our university’s behavioral research ethics board. All participants provided informed consent.

Scent donors

Scent donors first washed their bedsheets with unscented detergent and showered using unscented soap. They then wore a white cotton T-shirt under their clothes for 24 hr, refrained from using scented body products, and avoided odor-producing activities (i.e., exercise, sex, smoking, drinking alcohol, eating pungent foods; Hofer et al., 2018; Maner & McNulty, 2013). Shirts were returned within 5 hr of wearing, placed into sealed freezer bags, and stored at −20 °C (Lenochova, Roberts, & Havlicek, 2009).

Sleepers

Sleepers were invited for an initial lab session (taking place on a Monday), during which they received two shirts identical in appearance: their partner’s shirt and a control shirt (either an unworn shirt or a stranger’s shirt). For the following 4 nights, participants slept with one shirt placed over their pillow (shirt A on Monday and Tuesday, shirt B on Wednesday and Thursday; shirt order was random and double blind; Fig. 1). Participants laundered their bed linens with unscented detergent on Monday and Wednesday (before sleeping with each new shirt) and showered with unscented soap and shampoo before bed (unscented products were provided by us). To reduce sleep disturbances, we asked participants to refrain from drinking alcohol or caffeine after 2 p.m. Participants slept alone throughout the 4 nights of data collection.

Fig. 1.

Timeline of the sleep procedures.

Variables of primary conceptual interest

Sleep efficiency

A wrist-worn actigraphy monitor recorded participants’ sleep/wake intervals each night using epochs of 30 s in length (Philips Respironics Actiwatch 2 watches were used with Phillips Actiware 6 software). Participants were asked to set a marker (by pressing a button on the side of the watch) when they started trying to fall asleep in the evening and when they awoke in the morning. Total time in bed was defined as the period between the evening and morning marker. Two participants’ data did not have markers, and 3 further participants indicated that in specific instances, the markers they made were inaccurate. In these cases, self-reported sleep/wake times from participant’s daily diary reports were used. Sleep efficiency was calculated by dividing time asleep over total time in bed. This measure represents the proportion of time that participants spent asleep out of the total time they spent in bed attempting to sleep. Actigraphy monitors record motor activity and use an algorithm to distinguish sleep from wakefulness. Data from actigraphy monitors are well validated against polysomnography and are used extensively in sleep research (de Souza et al., 2003; Gordon, Mendes, & Prather, 2017).

Perceived sleep quality

Each morning, participants indicated what time they went to bed and what time they got up. They also answered the following two questions: “Last night, how would you rate your sleep quality overall?” and “How well rested do you feel this morning?” (1 = very bad/unrested, 7 = very good/rested; items modified from the Consensus Sleep Diary; Carney et al., 2012). These two items were averaged to form a measure of perceived sleep quality (all within-participants correlations were performed as described by Bakdash & Marusich, 2017), r = .53, p < .001, 95% confidence interval (CI) = [.46, .59]. One participant was given an incorrect version of the questions (referring to the previous month) and was excluded from analyses on perceived sleep quality.

Control variables

Perceived stress

Each evening, participants answered the following questions: “How stressful was your day today?” and “How stressful do you expect your day tomorrow will be?” (1 = very unstressful, 3 = fairly unstressful, 5 = fairly stressful, 7 = very stressful). These two items were averaged (r = .19, p < .001, 95% CI = [.10, .28]) to form a measure of perceived stress. Some participants wrote a response (e.g., “not very stressful”) rather than using the scale. Of the total 620 nights (4 nights × 155 participants), this occurred 4% of the time (25 times). On 19 of these occasions, the first author translated the response provided by the participant into a numeric scale response (e.g., “not very stressful” = 2).² The remaining 6 occasions could not easily be coded (e.g., “good”) and were removed from analyses that included perceived stress.

Weeknight

Night of the week was recorded (1 = Monday, 2 = Tuesday, 3 = Wednesday, 4 = Thursday). As the week progressed, people reported higher perceived sleep quality (Ms = 4.5, 4.6, 4.8, and 4.8, respectively, on the 7-point scale). Thus, to control for changes in perceived sleep quality over the week, we used weeknight as a linear time variable.

Additional measures

In the initial lab visit, all participants completed questionnaires assessing relationship quality (Perceived Relationship Quality Components Inventory; Fletcher, Simpson, & Thomas, 2000), attachment style (Adult Attachment Questionnaire; Simpson, Rholes, & Phillips, 1996), and their sex and relationship length.

Methods for each sample

A two-tailed paired-samples t test with 40 total participants and a medium effect size (d = 0.5) has a power of 87%. The effect size of 0.5 was chosen because a recent meta-analysis on common sleep aids reported effect sizes in the medium to large range (Buscemi et al., 2007). On the basis of this power analysis, we recruited three samples with 40 couples each. Initially, each sample was designed to address multiple research questions (described below): some that were similar across samples and some that were unique to that specific sample. In retrospect, we believe that the effect size used (based on the effect of pharmaceutical-grade sedatives on sleep) was overly optimistic. The magnitude of the effect we actually found was more similar to that of melatonin on sleep efficiency (Brzezinski et al., 2005). Because of concerns about analyses on individual samples being underpowered, we decided to combine data and focus on the overarching research questions that were similar across samples (demographics reported above were combined across samples and did not significantly differ between samples). Given a power of 80% and a two-tailed test, the combined sample (N = 155) allowed for detection of even a small effect (d = 0.23).

In the first sample, we examined whether female participants’ sleep improves with exposure to the scent of their partners, compared with no scent. Forty couples were recruited; women served as sleepers and men as scent donors. Women spent 2 nights with their partner’s shirt as a pillowcase and another 2 nights with a control (unworn) shirt as a pillowcase. In the second sample, we examined the same effect in a different group of women and additionally examined whether the effect extended to men. Forty couples were again recruited; however, in this sample, both members of the couple (women and men) served as scent donors and sleepers. In the third sample, we compared exposure to the scent of a partner with a different control scent, namely, the scent of a stranger. Forty couples were recruited; as in Sample 1, women served as sleepers and men as scent donors. In this sample, male scent donors provided two shirts (worn consecutively using the scent protocols outlined above). One shirt served as their female partner’s “partner” scent, and one shirt served as another couple’s “stranger” scent. Each female participant in Sample 3 spent 2 nights sleeping with her partner’s shirt and 2 nights with a stranger’s shirt.

During data collection for Sample 1, one relationship ended, and two couples failed to adhere to instructions not to sleep in the same bed. These unforeseen circumstances caused our final Sample 1 size to be 37 (fewer than the planned 40). To avoid this issue in Samples 2 and 3, we recruited participants until the sample of 40 analyzable women was reached (analyzable was defined as having at least 1 night of sleep-efficiency data for the partner condition and the control condition). This resulted in a total sample of 40 women and 38 men in Sample 2 and 40 women in Sample 3.

In total, 25 participants were excluded from Samples 2 and 3: 4 for sleeping in the same bed as their partner, 3 for being unsure which order they slept with shirts, 2 for switching experimental shirts with their partner (sleeping with their own scent instead of their partner’s scent), 2 for not adhering to preregistered cigarette and marijuana restrictions during scent collection, 5 for not fitting preregistered eligibility requirements regarding smoking and drug usage, 1 for being given incorrect materials by the experimenter, and 8 because of malfunctioning sleep watches.

In total, we analyzed data from 155 participants (Sample 1 = 37 women; Sample 2 = 40 women and 38 men; Sample 3 = 40 women). Descriptive and inferential statistics on data broken out by sample are displayed in Figures 2 and 3.

Fig. 2.

Mean sleep efficiency in each of the control and partner conditions of Samples 1 through 3, along with a forest plot showing the mean change between the conditions in each sample. The size of each square in the forest plot is proportional to the weight of that sample. The estimate for the fixed-effects model is also given. CI = confidence interval.

Fig. 3.

Mean perceived sleep quality in each of the control and partner conditions of Samples 1 through 3, along with a forest plot showing the mean change between the conditions in each sample. Mean perceived sleep quality was rated on a scale from 1 to 7, with 7 indicating highest sleep quality. The size of each square in the forest plot is proportional to the weight of that sample. The estimate for the fixed-effects model is also given. CI = confidence interval.

Results

Multilevel models on combined data

Data included within-participants measures of sleep across 4 nights. To account for the clustered nature of the data, we used multilevel models (MLMs). MLMs were estimated using R and the MLM package lme4 (Bates, Mächler, Bolker, & Walker, 2015; R Core Team, 2018). Repeated measures of sleep (Level 1) were nested within individuals (Level 2), and a random-slope model was used. Because couples were not allowed to share the same sleep environment, they were not expected to influence one another’s sleep. Thus, a couple level was not added to the model (intraclass correlation coefficients, or ICCs, were also rather low: sleep efficiency = .00; perceived sleep quality = .13). However, results of a three-level model that includes the couple level can be seen in the Supplemental Material and are very similar, with identical inferences, to the results reported here.

Initial MLM

The initial model measuring the effect of scent type (0 = control, 1 = partner) on sleep employed the following equations:

Level 1 : {sleep}_{i j} = b_{0 j} + b_{1 j} ({scent}_{i j}) + e_{i j}

Level 2 : b_{0 j} = γ_{00} + u_{0 j}

b_{1 j} = γ_{10} + u_{1 j}

Sleep efficiency

As predicted, mean sleep efficiency was higher on nights spent with a partner’s shirt than with a control shirt (M = 88.03%, SD = 6.50% and M = 85.35%, SD = 10.26%, respectively; d = 0.31,³ 95% CI = [0.09, 0.54]). The initial MLM (using the equations above) indicated that scent type was a significant predictor of sleep efficiency (b = 2.58, SE = 0.76, p < .001, 95% CI = [1.10, 4.05]).^4,5

To control for other pertinent variables, we computed an additional MLM that included several control variables as simultaneous predictors.⁶ Predictors added at Level 1 include daily perceived stress and weeknight. Weeknight (scored as 1 to 4 representing Monday through Thursday, respectively) was centered by subtracting by the mean (2.5). Perceived stress was cluster centered within participants. Predictors added at Level 2 were control scent, avoidant and ambivalent attachment, sex, relationship length, relationship quality, and order. All continuous control variables measured at the person level (Level 2) were centered around their grand mean (these were relationship quality, relationship length, avoidant attachment style, and ambivalent attachment style). The dummy-coded variable control scent indicates which control scent a participant slept with (unworn shirt = 0, stranger’s shirt = 1). The mean perceived stress level for each person across all 4 days was included as a measure of average perceived stress (Level 2) and grand-mean centered.

Next, nonsignificant predictors (using a relaxed threshold of p < .10) were removed to create a more parsimonious model. Results including all predictors simultaneously can be seen in the Supplemental Material (in these analyses, the relationship between scent and sleep efficiency was very similar, with identical inferences, to the relationship reported here). The final model (shown in Table 1) had two predictors, scent and sex, and employed the following equations:⁷

Level 1 : {sleep}_{i j} = b_{0 j} + b_{1 j} ({scent}_{i j}) + e_{i j}

Level 2 : b_{0 j} = γ_{00} + γ_{01} ({sex}_{j}) + u_{0 j}

b_{1 j} = γ_{10} + u_{1 j}

Table 1.

Results From Hierarchical Linear Models Predicting Sleep Efficiency From Scent and Sex

Fixed effect	b (SE)	p	95% CI
Scent	2.59 (0.76)	< .001	[1.09, 3.99]
Sex	−2.71 (1.11)	.016	[–0.49, –5.03]

Note: Participants = 155; nights = 610. Scent was coded 0 for control and 1 for partner; sex was coded 0 for female and 1 for male. Restricted maximum-likelihood (REML) estimation was used. Intraclass correlation coefficient = .20; random effects: intercept variance = 53.25, slope variance = 23.10. CI = confidence interval.

Results indicated that participants’ sex negatively predicts sleep, indicating that, on average, women have higher sleep efficiency than men. In addition, even after we controlled for a number of potentially related variables, scent type positively predicted sleep efficiency, indicating that sleeping with a partner’s scent leads to increased sleep efficiency.

Perceived sleep quality

Although perceived sleep quality was higher on nights spent with a partner’s shirt than with a control shirt (M = 4.74, SD = 0.85 and M = 4.60, SD = 0.87, respectively; d = 0.16, 95% CI = [−0.06, 0.38]), the initial MLM indicated that this difference was not statistically significant (b = 0.15, SE = 0.08, p = .074, 95% CI = [−0.01, 0.31]). Equations used for the initial MLM were identical to those used for sleep efficiency.

To control for other variables and detect potential moderators, we created an MLM predicting perceived sleep quality from a larger set of variables (identical to those described for sleep efficiency). The final parsimonious model (shown in Table 2) included three significant predictors (scent type, weeknight, and perceived stress) and employed the following equations:

\begin{matrix} Level 1 : {sleep}_{i j} = b_{0 j} + b_{1 j} ({scent}_{i j}) \\ + b_{2 j} (perceived {stress}_{i j}) + b_{3 j} ({weeknight}_{i j}) + e_{i j} \end{matrix}

Level 2 : b_{0 j} = γ_{00} + u_{0 j}

b_{1 j} = γ_{10} + u_{1 j}

b_{2 j} = γ_{20}

b_{3 j} = γ_{30}

Table 2.

Results From Hierarchical Linear Models Predicting Perceived Sleep Quality From Scent, Weeknight, and Perceived Stress

Fixed effect	b (SE)	p	95% CI
Scent	0.18 (0.08)	.030	[0.02, 0.33]
Weeknight	0.06 (0.04)	.078	[–0.01, 0.14]
Perceived stress	−0.19 (0.05)	< .001	[–0.27, −0.10]

Note: Participants = 154; nights = 605. Scent was coded 0 for unworn and 1 for partner; weeknight was coded 1 for Monday, 2 for Tuesday, 3 for Wednesday, and 4 for Thursday. Perceived stress was measured on a 7-point Likert-type scale. Restricted maximum-likelihood (REML) estimation was used. Intraclass correlation coefficient = .19; random effects: intercept variance = 0.31, slope variance = 0.14. CI = confidence interval.

Results indicated that on days when perceived stress was higher, perceived sleep quality was lower. In addition, when daily stress and weeknight were included in the model, exposure to a partner’s scent did significantly increase perceived sleep quality. We also tested whether a relationship existed between sleep efficiency and perceived sleep quality. These measures of sleep were weakly correlated (r = .11, p = .017, 95% CI = [.02, .20]).

Discussion

Exposure to the scent of a partner increased sleep efficiency, and this relationship remained significant even when we controlled for attachment style, relationship length and quality, stress level, day of the week, order of scent exposure, and type of control scent. Exposure to the scent of a partner did not significantly increase perceived sleep quality. However, when we added perceived stress and weeknight to the model, exposure to the scent of a partner did significantly increase perceived sleep quality.

Internal meta-analysis

The preceding analysis combined data collected across three samples. To ensure that this combination did not obscure differences across samples, we performed a second analysis on the same data, resulting in similar conclusions. A forest plot with results by sample appears in Figures 2 and 3. A fixed-effects internal meta-analysis (with mean effect sizes weighted by the inverse of their variance) confirmed that sleeping with a partner’s scent resulted in improved sleep efficiency (mean difference = 2.14, z = 2.74, p = .006, 95% CI = [0.61, 3.68]).^8,9 A second fixed-effects internal meta-analysis confirmed that the difference in perceived sleep quality was nonsignificant (mean difference = 0.14, z = 1.76, p = .078, 95% CI = [−0.02, 0.30]). These internal meta-analyses did not include covariates, and results are therefore comparable with the initial models described above.

Belief versus actual scent exposure

Were participants able to identify the scents to which they were exposed, and how did their beliefs interact with the effect of scent on sleep? To explore these questions, we created the variable belief. Each night, participants were coded as believing the scent was their partner’s (1) or not believing the scent was their partner’s (0). Participants were able to identity their partner’s scent at levels above chance (70% accuracy; Table 3).

Table 3.

Number of Nights During Which Participants Believed They Were Exposed or Not Exposed to Their Partners’ Scent

Actual exposure	Believed exposure		Total
Actual exposure	Partner’s scent	Not partner’s scent	Total
Partner’s scent	204	105	309
Control scent	78	232	310
Total	282	337	619

Sleep efficiency

To determine whether participants’ sleep efficiency was impacted by exposure to their partner’s scent, their belief about the scent to which they were exposed, or some interaction of these variables, we computed an MLM predicting sleep efficiency from scent type, belief, and the interaction of the two, employing the following equations:

\begin{matrix} Level 1 : {sleep}_{i j} = b_{0 j} + b_{1 j} ({scent}_{i j}) + b_{2 j} ({belief}_{i j}) \\ + b_{3 j} ({scent}_{i j}) \times ({belief}_{i j}) + e_{i j} \end{matrix}

Level 2 : b_{0 j} = γ_{00} + u_{0 j}

b_{1 j} = γ_{10} + u_{1 j}

b_{2 j} = γ_{20} + u_{2 j}

b_{3 j} = γ_{30}

Results indicated that the interaction between scent and belief was not significant (p = .73). When the interaction term was removed, scent (b = 2.42, SE = 0.79, p = .002, 95% CI = [0.89, 3.92]), but not belief (b = 0.45, SE = 0.79, p = .57, 95% CI = [−1.04, 1.99]), significantly predicted sleep efficiency. Therefore, results suggest that people who are actually exposed to their partner’s scent (regardless of whether they believed they were smelling their partner’s scent) experienced improved sleep efficiency.

Perceived sleep quality

A second analysis was computed predicting perceived sleep quality using the same model described above. Results indicated that the interaction between scent and belief was not significant (p = .74). When the interaction term was removed, belief (b = 0.26, SE = 0.10, p = .008, 95% CI = [0.07, 0.45]), but not scent type (b = 0.04, SE = 0.09, p = .67, 95% CI = [−0.14, 0.22]), significantly predicted perceived sleep quality.¹⁰ Therefore, our results suggest that people who believed that they were smelling their partner’s scent (regardless of whether they were actually exposed to their partner’s scent) experienced improved perceived sleep quality.

Discussion

Participants’ beliefs about the scent to which they were exposed did not influence their sleep efficiency (measured using actigraphy). However, participants’ beliefs about the scent to which they were exposed did predict their perceived sleep quality. Specifically, participants who thought they were sleeping with a partner’s scent experienced improved perceived sleep quality independently of the (nonsignificant) effect of actual scent exposure. These results are in line with previous research that indicates that people believe that their partner’s scent is calming (McBurney et al., 2006) as well as our own prior work suggesting that beliefs play a role in the effects of a partner’s scent on stress (Hofer et al., 2018).

General Discussion

In the present study, we examined whether mere exposure to the scent of a romantic partner can improve sleep. We found that exposure to the scent of a romantic partner overnight leads to improved sleep efficiency. Participants in our study experienced an average of more than 9 min of additional sleep per night when exposed to the scent of their partner, equating to more than 1 hr of additional sleep per week. This increase was achieved without participants spending any more time in bed. As shown in Figure 2, sleep efficiency increased by an average of 2.1%, an effect similar in magnitude to the effect of melatonin on sleep (2.2%; Brzezinski et al., 2005). This increase in sleep efficiency appears to occur outside of conscious awareness: Beliefs about the scent’s identity do not influence the positive impact of exposure to a partner’s scent on sleep efficiency. In other words, people sleep better when exposed to their partner’s scent regardless of whether they thought they were exposed to their partner’s scent.

Perceptions of sleep quality also improved with exposure to the scent of a romantic partner, but only when we controlled for other factors (e.g., daily levels of stress). When pitting scent type against beliefs about scent identity, we found that belief about scent identity emerged as the only significant predictor of perceived sleep quality. Thus, when people believe that they are smelling their partner’s scent, they also believe that they are sleeping better.

We found distinct effects of a partner’s scent on sleep efficiency (measured via actigraphy) and perceived sleep quality (measured via self-report). Indeed, sleep efficiency and sleep quality were only weakly correlated in our study and were affected differently by our manipulation, a difference that is consistent with findings of prior research (e.g., Lockley, Skene, & Arendt, 1999; Russell, Wearden, Fairclough, Emsley, & Kyle, 2016). Perceived sleep quality in our study was influenced by several psychological factors, including daily stress and belief about scent identity, whereas sleep efficiency was influenced by objective differences, such as the sex of the sleeper. Perceived sleep quality and sleep efficiency have both been associated with long-term health outcomes (Aziz et al., 2017; Thurston et al., 2017), yet they seem to overlap only weakly when measured in parallel. Thus, our recommendation is that future research continue to assess both measures of sleep.

Strangers’ scents may provide an interesting avenue for further research. When a stranger’s scent was used as the control odor (Sample 3), the contrasting effect of a partner’s scent on sleep efficiency was less pronounced than when a clean scent was used as the control odor (Samples 1 and 2). This suggests that, overall, the smell of a stranger may have a mildly positive effect on sleep relative to no scent. However, initial evidence (available in the Supplemental Material) also suggests that people’s responses to the stranger’s scent varied considerably. Interestingly, higher ratings of the pleasantness of the stranger’s scent were associated with reduced sleep efficiency. We hesitate to overinterpret this unexpected finding but note, more generally, that researchers should keep in mind that a stranger’s scent likely creates its own, potentially complex, manipulation. The design of the current study does not allow us to examine in a nuanced way how strangers’ scents may influence sleep quality, but this could be a fruitful direction for future research.

Individual differences (such as sex and relationship characteristics) are likely to moderate the way in which scent affects sleep. The current study was not designed to examine these possibilities; however, on an exploratory basis, the moderating effects of our control variables were examined (these results—available in the Supplemental Material—suggest that female sleepers may benefit more from exposure to their male partners’ scent than vice versa).

In today’s highly mobile society, separation from loved ones is quite common. During these separations, individuals are particularly vulnerable to sleep disturbances, and a behavioral intervention to improve sleep may be especially valuable. The negative effects from suboptimal sleep on health and well-being are substantial (Heslop, Smith, Metcalfe, Macleod, & Hart, 2002) and widespread (one in three people report recurring sleep irregularities; Ohayon, 2002). Learning how naturalistic behaviors—that may be as simple as sleeping with a loved one’s worn article of clothing—affect sleep may help to eventually uncover the makings of a good night’s sleep.

Supplemental Material

Hofer_OpenPracticesDisclosure_rev – Supplemental material for The Scent of a Good Night’s Sleep: Olfactory Cues of a Romantic Partner Improve Sleep Efficiency

Supplemental material, Hofer_OpenPracticesDisclosure_rev for The Scent of a Good Night’s Sleep: Olfactory Cues of a Romantic Partner Improve Sleep Efficiency by Marlise K. Hofer and Frances S. Chen in Psychological Science

Supplemental Material

Hofer_Supplemental_Material_rev – Supplemental material for The Scent of a Good Night’s Sleep: Olfactory Cues of a Romantic Partner Improve Sleep Efficiency

Supplemental material, Hofer_Supplemental_Material_rev for The Scent of a Good Night’s Sleep: Olfactory Cues of a Romantic Partner Improve Sleep Efficiency by Marlise K. Hofer and Frances S. Chen in Psychological Science

Footnotes

Transparency

Action Editor: James K. McNulty

Editor: D. Stephen Lindsay

Author Contributions

Both authors contributed to the study design. M. K. Hofer collected, analyzed, and interpreted the data and drafted the manuscript. F. S. Chen provided critical revisions to the manuscript. Both authors approved the final version of the manuscript for submission.

ORCID iD

Marlise K. Hofer

Supplemental Material

Additional supporting information can be found at

Notes

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