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References

Linnemann, A., Wenzel, M., Grammes, J., Kubiak, T., & Nater, U. M. (2018). Music Listening and Stress in

Daily Life—a Matter of Timing. International Journal of Behavioral Medicine, 25(2), 223–230.

https://doi-org.lopes.idm.oclc.org/10.1007/s12529-017-9697-5

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Music Listening and Stress in Daily Life—a Matter of Timing

Purpose: Despite increasing evidence suggesting that music listening in daily life has stress-reducing

effects, studies mostly rely on subjective, retrospective data on music listening. Thus, the temporal

dynamics underlying the stress-reducing effect of music listening remain unclear. Therefore, we

aimed to examine the temporal dynamics of the associations between stress and music listening by

assessing subjective and objective data on music in daily life.Design: An exploratory Ambulatory

Assessment study examining a total of 60 participants (37 women), aged 18 to 34 years

(<italic>M</italic> = 22.4 years, SD = 3.5) was conducted.Methods: For 1 week, participants

answered questions on music listening and stress six times per day via an electronic diary device,

which additionally objectively sampled the exact time point of music listening and its duration.Results:

Self-reports on mere music listening were associated with lower stress reports, whereas objectively

assessed data was not. However, concerning duration of music listening, both subjective and

objective data on music listening showed associations between a minimum of 20 min of music

listening and lower stress reports. Concerning the latency, objective data on music listening revealed

that the association between stress reports and music listening occurs in a time-delayed

manner.Conclusions: Although the study design does not allow for causal inferences, substantial

associations among subjectively and objectively assessed data on music listening were found to

differentially affect the experience of stress after music listening. In particular, when focusing on the

temporal dynamics, objectively assessed data allowed for a more fine-grained analysis. In

consequence, subjectively and objectively reported data on music listening should be assessed

jointly when investigating effects of music listening on health. Experimental research with rigorous

methodological control is required in order to corroborate our findings in a laboratory setting.

Ambulatory assessment; Duration; Health; Music listening; Stress; Temporal dynamics

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Music Listening and Stress in Daily Life—A Matter of Timing

Music listening in daily life may reduce stress [[ 1] ]. Most of the evidence on this stress-reducing

effect was gathered in quasi-experimental studies [[2] ]. However, findings from these studies are

quite heterogeneous, as they differ in terms of experimental design, music selection, and

participants, making comparisons across studies difficult. Furthermore, in such quasi-experimental

studies, participants are most often investigated only once, in one artificial setting (e.g., before

surgery, before a standardized stress test). Thus, it remains unclear whether these findings can be

generalized. Thus, research into the effects of music listening in various situations of daily life (in

contrast to one artificial experimental situation) is warranted. To address this shortcoming,

Ambulatory Assessment [[3] ]—as a complementary tool to laboratory research—enables

psychological phenomena to be studied in an ecologically valid way [[4] ].

Ambulatory research investigating the stress-reducing effects of music listening in daily life is still in

its infancy. Some of the available studies focused on the effects of music listening on emotion

regulation in daily life [[5] -[7] ]. In this regard, Juslin et al. [[5] ] found that participants reported more

positive emotions after having listened to music. The very few studies investigating associations

between music listening and stress in daily life found that music listening does not reduce stress per

se [[8] , [9] ]. Rather, situational factors may moderate this relationship: Music was only associated

with stress reduction when other individuals were present while listening to music [[9] ] or when

music was specifically listened to for relaxation purposes [[8] ]. Taken together, these studies

illustrate the importance of capturing situational factors using Ambulatory Assessment to gain a

deeper understanding of when and why music listening reduces stress.

However, as promising as these results might be, there are at least two shortcomings that deserve

critical discussion: ( 1) Previous studies have relied on subjective music listening data only, and (2)

there is a lack of studies investigating the temporal dynamics of the beneficial effects of music

listening. Regarding the first shortcoming, most research using Ambulatory Assessment has been

built around self-reports of music listening behavior, with participants being asked to indicate whether

they had listened to music since the last data entry [e.g., [5] , [10] ]. However, such data may be

susceptible to retrospective memory biases [[5] ]. Consequently, Juslin et al. [[5] ] called for studies

which objectively sample the music individuals listen to. To the best of our knowledge, to date only

one study has objectively assessed participants’ music-listening behavior [[6] ]. In this study,

participants were prompted to answer questions regarding emotion regulation strategies and music

listening whenever they started listening to music on their smartphone. The authors found that music

listening was effectively used to regulate emotions. However, given the limited evidence of one single

study, more research is clearly needed to test whether objectively assessed music listening behavior

can reproduce the stress-reducing effects of music listening found in studies using self-report

measures only.

The second shortcoming in the literature on music-induced stress reduction is that it is unclear how

long participants must listen to music in order for its beneficial effects to unfold (duration of music

listening) and how long it takes for music to exert stress-reducing effects (latency). This information is

necessary in order to shed light on the temporal dynamics underlying the effects of music listening on

stress. In experimental studies, the time intervals of music listening vary from study to study,

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spanning periods, for instance, from 10 min [[11] ] to 45 min [[12] ] and even more than 3 h [[13] ].

However, there is no recommendation on how long music should be listened to in order to exert

health-beneficial effects.

Concerning the latency of these beneficial effects in daily life, most previous studies used

retrospective self-reports on music listening, and only a small number of studies simultaneously

assessed either music listening and mood [[7] , [14] ] or current and past music listening [[4] , [15] ].

However, interpretations might vary depending on whether current or past music listening is reported.

When current music listening is assessed, it is possible to make predictions about acute effects of

music listening on stress (as both music listening and stress are assessed at the same moment).

However, the assessment of past music listening captures time-delayed effects of music listening on

stress, as music listening and stress are not assessed simultaneously. It would be intriguing to study

whether the beneficial effect of music listening varies depending on whether acute effects (e.g.,

current music listening) or time-delayed effects (e.g., past music listening) are assessed. Moreover,

regarding time-delayed effects, knowledge on the latency of the stress-reducing effects of music

listening is completely lacking. Thus, the optimal time lag between music listening and the

assessment of stress remains unexplored. For example, Linnemann et al. [[8] ] reported that it was

not possible in their study to determine how much time elapsed between music listening and the

assessment of stress. However, to design specific music listening interventions for stress reduction

purposes in daily life, research is needed to clarify the temporal dynamics underlying the

associations between music listening and stress.

The Present Study

Taken together, several problems remain unresolved in the literature on the relationship between

music listening and stress. A critical methodological limitation inherent in most studies set in daily life

is that they rely on (often retrospective) self-reports on music listening, which might be subject to

memory bias. Therefore, an objective assessment of music listening behavior is warranted, which

captures when individuals listen to music and for how long. This should enable researchers to

investigate (a) which duration of music listening is necessary for stress reduction and (b) whether

music listening has acute and/or time-delayed effects on stress. We therefore conducted an

Ambulatory Assessment study to pursue the following two aims: First, we investigated whether there

was still an association between self-reported stress levels and music listening when music listening

was objectively tracked. Second, we explored the temporal dynamics underlying the association

between music listening and stress in terms of duration (that is, the duration of music listening that is

necessary to be associated with beneficial effects) and latency (that is, how long it takes for music to

be associated with beneficial effects).

Method

Participants

We conducted a dual-site exploratory Ambulatory Assessment study with a total of N = 63

undergraduate students (study site 1: n = 37; study site 2: n = 26). Data from three participants

(n = 30 single observations) were excluded because they completed fewer than 33% of the total

signals [[16] ]. Thus, the final sample consisted of 60 participants (37 women), aged 18 to 34 years

(M = 22.4 years, SD = 3.5). Inclusion criteria were sufficient mastery of the German language and the

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ability to operate a smartphone. Participants were excluded if they indicated that they were pregnant,

currently breastfeeding, or suffering from a mental disorder. The study protocol was approved by the

local ethics committees at both study sites.

Procedure

Informed consent was obtained from all individual participants included in the study. Basic

demographic characteristics and self-reports on health using the Patient Health Questionnaire [[17] ]

were given from all participants. Then, participants received instructions on how to use the electronic

diary device (iPod touch at study site 1; department- or participant-owned smartphones at study site

2). Since participants were instructed to listen to music only using the study device via the application

“Simple Last.fm Scrobbler” (The SLS Team, 2016), the music files to which they intended to listen

during the ensuing week were uploaded onto the electronic diary device. Then, the use of the

application was explained. The “Simple Last.fm Scrobbler” application automatically logged the exact

time point of music listening for any song that was listened to for at least half the duration of the

track. The collected data on music listening for each participant were saved on the Last.fm servers

(Last.fm Limited, London, UK). Starting from the next day, for a total of six (study site 2) or seven

(study site 1) consecutive days, participants received six signals over a time window of 12 h,

beginning at 10.00. Upon each signal, participants were asked to complete items concerning stress,

mood, and music listening behavior, among others. Following recommendations of Hektner, Schmidt,

and Csikszentmihalyi [[18] ], this time window was divided into six blocks of 2 h, with the condition

that consecutive signals were at least 30 min apart. If participants failed to respond to a signal or did

not fully complete data entry, they were reminded twice with further signals. Participants were also

able to postpone the signal if they were unable to complete the scheduled data entry at the current

moment. Additionally, directly after waking up, participants completed a questionnaire on their

electronic diary device, which included items on sleep quality, mood, and stress. However, data from

this assessment are not included in the current analyses as no items on music listening were

presented at this point. After completion of data collection, participants returned to the lab and were

debriefed and reimbursed for their participation (either 20 euros or research credits).

A key advantage of Ambulatory Assessment methodology is its high external and ecological validity

[[19] ], as processes are investigated in their daily life [[3] ]. The internal validity of this approach is

limited, though, given the lack of rigorous experimental control, which renders causal inferences

difficult. Ambulatory Assessment, thus, serves as a methodological approach that complements

experimental studies. It opens up opportunities to assess ecologically valid data and allows for the

identification of meaningful associative patterns.

Participants at study site 1 received an iPod touch with the pre-installed application “iDialog Pad” (G.

Mutz, University of Cologne, Germany), which was used for presenting the items. The data were

stored locally on the iPod touch and exported upon completion of the Ambulatory Assessment period

when participants returned to the lab. Participants at study site 2 either received an Android-based

smartphone with Android OS 5.0.1 (Google, Mountain View, CA, USA) or used their own Android-

based smartphones. The data were collected via the movisensXS experience sampling application,

version 0.8.4203 (movisens GmbH, Karlsruhe, Germany), which was downloaded and installed either

on the personal or the department-owned smartphone at the beginning of the study. The data were

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stored locally and uploaded to a secure server when the smartphone was connected to the internet.

The administration of the items was comparable when either using the iPod or the smartphone.

Ambulatory Assessment Measures

Perceived Stress

To limit the burden on the participants, perceived stress (M = 1.17, SD = 1.02) was assessed using

one item (“At this moment, I feel stressed,” 5-point scale ranging from 0 = not at all to 4 = very much;

[[20] ]).

Self-Reported Music Listening

For each signal, participants were asked whether they were currently listening to music (yes vs. no)

and, if not, whether they had listened to music since the last signal (yes vs. no). Participants also

indicated the duration of music listening on a 4-point scale: < 5 min, 5-20 min, 21-45 min, > 45 min.

Objectively Assessed Music Listening

The “Simple Last.fm Scrobbler” application collected information for each track (artist, title, date and

time at the start of the track).

Analytic Approach

To test for associations between self-reported music listening and stress, we created the

dichotomous variable self-reported music listening, which was coded “1” if participants either

indicated currently listening to music at the time of the signal or having listened to music since the

last signal. It was coded “0” if they had not listened to music. To test for the latency with which self-

reported music listening and stress might be correlated, we created another variable, time lag, which

compared whether participants were currently listening to music ( 1) or had listened to music since

the last signal (0) according to self-reports. This enabled an assessment of whether self-reported

music was associated acute and/or delayed with stress.

In line with the variables on self-reported music listening, we created a binary variable, with current

music listening or music listening since the last signal coded as “1” and no music listening in this time

frame coded as “0,” respectively. Since Last.fm also stores exact dates and times, we generated the

duration of music listening in this time frame (M = 36.7 min, SD = 33.9) and the time lag between the

current signal and the most recent track (M = 46.5, SD = 44.6) as two continuous variables. This

enabled us to quantify and directly test associations among the amount of music listening, the

latency of music listening relative to the assessment of stress, and stress reports.

Since multiple data entries (Level 1) are nested within participants (Level 2), we computed two-level

models with random intercepts in Stata 14 (Stata Corporation, College Station, TX, USA) and

investigated within-person processes. Continuous independent variables on Level 1 were person-

mean centered, and those on Level 2 were grand-mean centered [[21] ]. We did not enter the

averaged continuous Level-1 independent variables as a measure of between-subject processes,

since we were primarily interested in within-subject processes only and thus wished to keep the

model as parsimonious as possible [[22] ]. Categorical variables were dummy-coded (reference of

subjectively assessed duration: “< 5 min”).

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We computed separate multilevel models for self-reported and objective music listening predictors to

increase statistical power, since self-reported and objectively assessed data on music listening did

not always converge. We first fitted the unconditional model, which revealed that 68.3% of the total

variance in stress was attributable to within-person variability (ICC (type 1) = 0.317). Then, in a first

step, we entered music listening as Level-1 predictor to test its association with stress. In the second

step, we included the variables assessing duration of music listening and the time lag between the

last track that was listened to and the current signal. Besides time, all analyses controlled for study

site and gender given the outlined methodological idiosyncrasies between the study sites and the

unequal distribution of gender across the study sites (χ ( 1) = 2.37, p = .124, Cramérs V = .20). We

additionally included the lag-1 serial autocorrelation, that is the stress level reported at the last

signal.1 [ 1] Finally, we did not include random effects of the Level-1 predictors, because their

inclusion did not improve model fit (χ (3) = 4.35, p = .226 for the model with the subjective data and

χ (3) = 2.76, p = .430 for the model with the objective data).

As there are no widely accepted recommendations for computing statistical power in multilevel

models, we opted for the general recommendation of having at least 50 participants to obtain

acceptable estimates of standard errors of predictors [[23] ]. P values of < 0.050 were considered

significant. All tests were two-tailed.

Results

Compliance with the Ambulatory Assessment protocol was good [[24] ], with participants completing

approximately 39 signals on average (83.3% compliance). To test whether the compliance

deteriorated over the course of the study, we computed a multilevel logistic regression, in which the

outcome was whether a participant responded to a presented signal (“1”) or not (“0”). Day of study

and the signal number of a given day were included as predictors. According to this model,

compliance did not deteriorate over the day (OR = 0.96, z = − 1.52, p = .129), but did deteriorate over

the course of the study (OR = 0.89, z = −2.86, p = .004).

Descriptive Statistics

We compared the frequencies of self-reported and objectively assessed music listening episodes.

Participants subjectively reported 692 episodes (38.5%) of music listening (either current or past

music listening). However, the “Simple Last.fm Scrobbler” application registered only 486 episodes

(26.9%) of music listening: Participants did not report 81 music listening episodes (4.6%) that were

registered via Last.fm. At the same time, participants indicated 287 times (16.2%) that they were

listening to music which was not registered via Last.fm. This indicates that participants possibly used

other sources for music listening as well. Importantly, however, a chi-square test revealed a

statistically significant relationship between self-reported and objectively assessed music listening,

χ ( 1) = 555.5, p < .001, Cramérs V = .56, which corresponds to a large effect size per convention

(Cohen, 1988). The large effect size is also illustrated by an OR = 17.2, which indicates that it was

17.2 times more likely that an episode of music listening was registered via Last.fm. Thus, although

there was a discrepancy between subjective and objective measures of music listening, the

association between the two measures was significantly large and substantive.

Self-Reported Vs. Objectively Tracked Music Listening

2

2

2

2

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As indicated in Table 1, the first step of the multilevel logistic regression using the self-report data on

music listening revealed a significant negative association between music listening and stress.

Participants indicated lower levels of stress when they reported current or past music listening

(M = 1.09, SE = 0.06) in comparison to no music listening (M = 1.19, SE = 0.06). However,

objectively assessed music listening via the “Simple Last.fm Scrobbler” application was not

significantly associated with stress. Participants indicated similar stress levels when they reported

current or past music listening (M = 1.13, SE = 0.07) in comparison to no music listening (M = 1.10,

SE = 0.06). Thus, although the subjective data replicated previous evidence that music listening is

associated with stress reduction, this association was not replicated using the objective measure of

music listening.

Temporal Dynamics of Stress-Reducing Effects of Music Listening

Both self-reported and objective measures of music listening duration were significantly associated

with stress (Table 1). Participants reported significantly higher stress levels when they reported past

music listening for less than 5 min (M = 1.15, SE = 0.09) or 5-20 min (M = 1.17, SE = 0.07)

compared to music listening for 21-45 min (M = 0.98, SE = 0.09) or more than 45 min (M = 0.85,

SE = 0.11). This was mirrored by the objective measure of duration, insofar as lower stress levels

were associated with increasing duration of music listening. Compared to the subjective measures,

the estimated marginal means showed that listening to music for less than 5 min was associated with

the highest stress levels (M = 1.19, SE = 0.08) compared to 5-20 min (M = 1.16, SE = 0.08),

21-45 min (M = 1.11, SE = 0.07), and 60 min (M = 1.03, SE = 0.08). However, the effect size was

larger for self-reported than for objectively assessed duration of music listening.

To test how long participants needed to listen to music in order to significantly detect an association

with stress, we built another multilevel model with stress as the outcome and extended the

categorical variable assessing duration by including data entries in which participants did not listen to

music as the base category. This analysis is based on self-reported music listening. This model

revealed that listening to music for less than 5 min, b = − 0.04, z = − 0.56, p = .577, and 5-20 min,

b = − 0.04, z = − 0.07, p = .945, was not associated with reduced stress levels significantly, in

contrast to listening for 21-45 min, b = − 0.18, z = − 2.44, p = .015, and more than 45 min, b = −0.34,

z = −3.59, p < .001. Thus, these results suggest that music listening begins to be associated with

reduced stress levels after around 20 min.

We also compared the descriptive statistics of the duration measures, which indicated that

participants seemed to underestimate how long they listened to music. The mean objectively

assessed duration of music listening was M = 24.5, SD = 19.5 when participants subjectively

reported that they had listened to music for less than 5 min. Likewise, the objectively assessed mean

duration was M = 33.2, SD = 28.3 when the self-reported duration was from 5 to 20 min, M = 46.9,

SD = 36.84 for a self-reported duration of 21 to 45 min, and M = 81.1, SD = 50.2 for a self-reported

duration of more than 45 min.

Next, we tested the latency of associations between stress and music listening. As a subjective

measure of the latency of this association, we compared whether self-reported music listening at the

current moment had a stronger association with reduced stress levels than having reported listening

M M

M

M

M M

M

M

M M

M M

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at a time point between the current and the previous signal. As indicated in Table 1, there was no

significant difference for the self-report measure, insofar as stress levels did not differ depending on

the time point of music listening. However, the objectively assessed time lag demonstrated a

significant negative relationship with subjective stress levels, insofar as decreasing levels of self-

reported stress were associated with an increasing time lag between the most recently played track

and the current signal. These results demonstrate that beneficial associations between music

listening and stress seem to occur in a time-delayed manner and not during the act of music listening

per se.

Discussion

The first aim of the present research was to investigate whether associations between music

listening and stress in daily life can be found when using either self-reported or objectively assessed

data on music listening. This was not the case: Whereas self-reports on mere music listening were

associated with lower stress reports, objectively assessed data on mere music listening were not

associated with reduced levels of stress.

Although the self-reported and objective measures of music listening were largely concordant, there

were notable discrepancies. On the one hand, participants subjectively reported more music listening

episodes than were objectively sampled, and on the other hand, in some cases, they did not report

music listening subjectively even though the objectively assessed data indicated music listening. This

discrepancy might explain why only the subjective measure of music listening was negatively

associated with stress. There are some possible explanations for this discrepancy between

subjectively and objectively assessed data on music listening. First, it might be that participants

sometimes forgot to report music listening. Second, it is possible that music listening was objectively

recorded but participants did not actively listen to it (i.e., participants did not stop the music when

interrupted by another activity). Third, it could be the case that participants did not always use the

smartphone application for music listening, for example at a concert. Another potential explanation

for the different associative patterns of the subjective and objective assessment of music listening

and stress might lie in previous findings that music-induced emotions are a prerequisite for music to

exert beneficial effects [[25] ]: When reporting music listening subjectively, participants may think

about the music they listened to and thus engage with music on a cognitive and emotional level. For

objectively assessed music listening, it is not clear whether the music was consciously listened to.

This might explain why stress-reducing associations of music listening seem to be limited to music

that is subjectively reported. Another explanation might be related to potential placebo effects of

music listening as health-beneficial effects of music listening are frequently presented in the public

discourse. Thus, in this regard, the association between music listening and lower subjective stress

ratings might reflect shared method variance with participants relating music listening to beneficial

effects in terms of positive music-induced emotions and lower subjective stress levels. Furthermore,

as music is ubiquitous in daily life, it might not be possible to objectively assess all music listening

that occurs in the soundscape of participants. Thus, the results do not provide clear evidence that

subjectively reported data on music listening are better than objectively assessed data, as a certain

proportion of music listening was probably not captured by the application. Instead, we believe that

the results demonstrate that self-report measures of music listening are a reliable and valid method

of registering music listening in daily life, as they capture a broader range of situations in which

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individuals listen to music and reflect more conscious acts of music listening. However, future

research should explore new approaches to objectively assess music listening in additional

applications, for example by using the Electronically Activated Recorder [[26] ].

The second aim of the present study was to quantify how long individuals must listen to music in

order to experience lower levels of stress and to explore the latency of these associations between

music listening and stress. Concerning the duration of music listening, both subjectively and

objectively assessed data revealed that with longer durations of music listening, individuals reported

lower levels of stress, with a larger effect size for the subjective than for the objective assessment of

duration. Our data further show that it seems necessary to listen to music for at least 20 min to show

associations with stress. This association between duration and stress might explain why previous

studies investigating the stress-reducing effects yielded inconsistent findings [[ 1] ] as studies vary

hugely in terms of the duration for which music was employed. Thus, future research should

investigate the moderating influence of duration of music listening, e.g., by conducting a meta-

analysis or by experimentally manipulating the duration of music listening.

Besides duration, another important factor of music listening in our study was the time lag between

music listening and the assessment of stress. Whereas, on a subjective level, we only distinguished

momentary music listening from past music listening; a more fine-grained analysis of the exact time

lags between music listening and the assessment of stress revealed that stress levels decreased

with increasing time lags between the most recently played track and the current assessment of

stress. This finding does not indicate that the positive association between music listening and stress

disappears over time, but rather indicates that it increases. It might be that other processes, events,

and acts in the less-controlled context of daily life mask the acute short-term effects of music

listening on stress found in laboratory research [e.g., [27] ]. Therefore, it might take some time before

more persistent long-term associations between music listening and stress appear, similar to the

results found for duration.

Future Directions and Outlook

Findings from this exploratory study can lead to hitherto neglected methodological hypotheses on the

associations between music listening and stress. Thus, these findings should inspire future studies

on music listening and stress—both experimental and Ambulatory Assessment studies—in order to

gain more empirical evidence on methodological factors that may determine associations between

music listening and stress. Furthermore, in order to prevent shared method variance from influencing

the pattern of results, future studies should measure stress from a multimodal perspective by means

of psychobiological stress measures in addition to mere self-reports. In addition to these

methodological issues, the identification of confounders and moderators (e.g., personality factors)

and a more detailed analysis of situational factors (e.g., the experience of stress before listening to

music) are necessary in order to gain insights into both between-subject and within-subject

processes concerning the complex associations between music listening and stress in daily life.

Strengths and Limitations

There are several limitations of the present study that warrant attention. First, as is common for

intensive longitudinal data generated by Ambulatory Assessment, the correlational design cannot

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provide causal evidence for the effects of music listening on stress. Therefore, we cannot rule out

alternative explanations, for instance that music listening co-varies with other factors possibly

affecting stress. While our data cannot replace experimental studies on the stress-reducing effect of

music listening, our study does provide evidence of the potential of such data for explaining the role

of music listening on stress in naturalistic situations and confirms part of the experimental evidence

outlined in the introduction with ecologically valid data. Second, to limit participant burden and to

ensure compliance, we used short self-report scales, including a single-item measure of stress.

Despite their frequent use in Ambulatory Assessment, single-item approaches limit reliability and

predictive validity, especially of constructs that are multidimensional and broad in scope, such as

stress [[28] ]. Future research should assess stress more comprehensively. Furthermore, as stress is

a multidimensional phenomenon, future studies should additionally assess physiological markers of

stress to shed light on the mechanisms underlying the stress-reducing effect of music listening in

daily life. Third, the variance of stress level was low, similar to findings from previous studies [[9] ,

[10] ]. Future studies should assess the associations between music listening and stress among

different stressful situations, as was the case in a previous study in which students were investigated

repeatedly during different stressful times of the university term [[8] ]. Finally, the procedures at the

two study sites differed in some aspects, such as smartphone or iPod touch application and study

days, which may have affected the results. However, including study site as a control variable did not

reveal significant differences between the study sites in the variables of interest, which, in our view,

justifies the aggregation of the two samples.

Conclusions

Our findings suggest that only subjectively assessed data on mere music listening was associated

with lower subjective stress levels, possibly because the subjective reports captured more instances

of music listening besides the smartphone/iPod application and because the participants re-engaged

with the music on a cognitive and emotional level. However, when temporal dynamics of this

association are of interest, subjectively reported data on music listening should be complemented by

objective data on the exact time of music listening. Nevertheless, as music is ubiquitous in daily life, it

remains challenging to objectively assess all music listening that occurs.

Alexandra Linnemann and Mario Wenzel contributed equally to this work.

Acknowledgements

Open Access Funding provided by University of Vienna. This research was supported by the early-

stage researcher peer mentoring grant awarded to MW and AL by the Health Psychology section of

the German Psychological Association.

Compliance with Ethical Standards

This study has been approved by the respective local ethics committees of the study sites and has

therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration

of Helsinki and its later amendments.

All persons gave their informed consent prior to their inclusion in the study. There are no details in

this text that might disclose the identity of the subjects under study.

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Conflict of interest

All authors declare that they have no conflict of interest related to the study.

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Footnotes

1 Since participants reported lower levels of stress on weekends, we additionally included weekend

as a binary control variable. However, this inclusion did not change the presented results, that is

none of the presented significant results became insignificant and vice versa.

~~~~~~~~

By Alexandra Linnemann; Mario Wenzel; Jennifer Grammes; Thomas Kubiak and Urs M. Nater

International Journal of Behavioral Medicine is a copyright of Springer, 2018. All Rights Reserved.

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