Persistence of sleep difficulties for over 16 years amongst 66,948 working-aged adults.

The objective was to investigate the persistence of sleep difficulties for over 16 years amongst a population of working age. In this prospective cohort study, a group-based trajectory analysis of repeated surveys amongst 66,948 employees in public sector (mean age 44.7 [SD 9.4] years, 80% women) was employed. The main outcome measure was sleep difficulties based on Jenkins Sleep Scale (JSS). Up to 70% of the respondents did not experience sleep difficulties whereas up to 4% reported high frequency of notable sleep difficulties through the entire 16-year follow-up. Heavy drinking predicted sleep difficulties (OR 2.3 95% CI 1.6 to 3.3) except for the respondents younger than 40 years. Smoking was associated with sleep difficulties amongst women younger than 40 years (OR 1.2, 95% CI 1.0 to 1.5). Obesity was associated with sleep difficulties amongst men (OR 1.9, 95% CI 1.4 to 2.7) and women (OR 1.2, 95% CI 1.1 to 1.3) of middle age and amongst women older than 50 (OR 1.5, 95% CI 1.2 to 1.8) years. Physical inactivity predicted sleep difficulties amongst older men (OR 1.3, 95% CI 1.1 to 1.6). In this working-age population, sleep difficulties showed a great persistence over time. In most of the groups, the level of sleep difficulties during the follow-up was almost solely dependent on the level of initial severity. Depending on sex and age, increasing sleep problems were sometimes associated with high alcohol consumption, smoking, obesity and physical inactivity, but the strength of these associations varied.

Introduction

The concept of “sleep difficulties” may include mild dissatisfaction in sleep duration or quality, as well as severe insomnia and other clinically significant sleep disorders. The prevalence of sleep difficulties varies from a few percent up to 60% amongst adults, depending on the study population and diagnostic criteria for sleep difficulties [1-8]. However, it is widely agreed that sleep difficulties are a significant problem associated with higher comorbidity and mortality and is the cause of substantial economic loss including costs of work disability [9, 10].

Sleep difficulties have been found to be associated with female gender, physical inactivity, excessive alcohol consumption and insufficient amount of sleep hours [3, 6, 11–18]. The persistence of sleep difficulties has previously been studied mostly amongst children and elderly [19-22]. Only a few studies, cross-sectional or with short-term follow-ups, focused on general or working-age population [2]. A study on a cohort of people of working-age stated that the prevalence of occasional insomnia-related symptoms could be around 40% to 45% and, on a population level, that estimate prevails over time [23]. A recent study among a general population has reported that insomnia might be a very persistent condition [24]. However, several important questions remain. For example, how persistent are sleep problems in a general population? Does a person usually experience them for a limited period of time, or are they likely to experience the same amount of sleep difficulties more or less permanently? Does a baseline severity of sleep difficulties pertain also in the future? What factors may affect the trajectories of sleep complaints over time?

To address these questions, the objectives of this study were to investigate a) if sleep difficulties persist or whether the severity of these difficulties are likely to change over time, b) if different subgroups with different trajectories of sleep difficulties could be defined; and c) what modifiable factors might be associated with the severity of sleep difficulties.

Methods

Participants were drawn from the Finnish Public Sector (FPS) cohort study of employees of 10 towns and 6 hospital districts. Data included responses to five questionnaire surveys administered to the FPS sub-cohorts in 4‐year intervals from 2000 to 2017 (average response rate 70%). For this study, the baseline was the response given in 2000 or in 2004. All the respondents have approved a written informed consent. The study did not include minors. The ethics committee of the Hospital District of Helsinki and Uusimaa approved the study plan and the informed consent form (registration number HUS/1210/2016).

S1 File contains a complete description of the dataset (in Finnish). The data are not publicly available due to legislative restrictions, as the data contains information that could compromise the privacy of the research participants. The restrictions upon the dataset were imposed by the data owner, Finnish Institute of Occupational Health. The deidentified data that support the findings of this study are available on reasonable request from the corresponding author MS or directly from the data owner, Finnish Institute of Occupational Health–principal investigator JE Jenni.Ervasti@ttl.fi.

Age, gender, body mass index (BMI), level of physical activity, alcohol consumption, and smoking status were measured at the time of the first response. Age was defined in full years. The BMI was defined as weight/height2 and dichotomized to indicate obesity if BMI ≥30 kg/m2. The level of physical activity was calculated from the survey responses, converted into metabolic equivalent of task (MET) and dichotomized based on the cohort’s quartiles as “low physical activity”–the lower quartile vs. others. Alcohol consumption was obtained from the survey and converted into g/week, and >210 g of pure alcohol per week was considered a cut-off for excess alcohol consumption (no/yes). Smoking status was dichotomized as current smoking yes/no.

The JSS is a four-item questionnaire to follow common sleep problems in clinical areas. Four items evaluated, in the last month, the difficulty to fall asleep, wake up at night, difficulty to stay asleep and wake up exhausted in the morning. Each item is rated on a Likert-like scale from zero to five, where zero is “never”, 1 is “1–3 days”, 2 is “4–7” days, 3 is “8–14 days”, 4 is “15–21 days” and 5 is “22–28 days”. The total score is a simple sum of all four items’ scores zero (no sleep problems) to 20 (most sleep problems). In order to include also incomplete responses to the JSS, in this study, a total score was substituted by the average score of answered items (0.0 to 5.0) using a method of person mean imputation [25]. The JSS has been one of the most commonly used questionnaires in epidemiological sleep studies 1–4. It has been found to be valid and reliable amongst patients with different health problems as well as in large non-clinical populations [26-29].

Statistical analysis

The estimates were reported as means and standard deviations or as absolute numbers and percentage when appropriate. Group-based trajectory modeling was used to investigate the developmental trajectory (a course of outcome over time) of the severity of sleep disorders measured by the JSS. This method is a form of finite mixture modeling for analyzing longitudinal repeated measures data [30-32]. While conventional statistics show a trajectory of average change of outcome over time, group-based trajectory modeling is able to distinguish and describe subpopulations (clusters) existing within a studied population. The trajectories of such subpopulations may differ substantially from each other and from the average trajectory of the entire population. In this study, the procedure consisted of the following steps:

Censored (known also as ‘regular’) normal modeling was used with minimum and maximum values set at the lowest and the highest possible JSS scores (0 to 5).

The studied population was divided into 6 gender-age groups: <40, 40–49, and 50+ for men and women.

The number of groups may be defined by the size of the data set measuring in two dimensions: the number of cases and the number of repeated measures. There are no common recommendations on the number of trajectory groups. In theory, it can be any number from one up to the number of cases. Previous research has suggested breaking a sample down below 300 cases may not add significant information. In this study, we pre-agreed that the smallest group should be around 3% of the entire sample or the size of the smallest group should be around 300 cases [33]. We also pre-agreed that the number of trajectory groups will be the same for all gender-age groups to ease the interpretation of the results. This way, six cluster groups were identified for each gender-age group. The goodness of model fit was judged by running the procedure several times with a number of subpopulations starting from one up to six. A cubic regression was applied.

The Bayesian Information Criterion (BIC), Akaike information criterion (AIC) and average posterior probability (APP) were used as criteria to confirm the goodness of fit.

Odds ratios (ORs) were used to describe the associations of risk factors and the probability of being classified into a particular cluster. The ORs were accompanied by their 95% confidence intervals (95% CIs). The analyses were performed using Stata/IC Statistical Software: Release 16. College Station (StataCorp LP, TX, USA). The additional Stata module ‘traj’ was required to conduct group-based trajectory analysis. The module is freely available for both SAS® and Stata software (Jones and Nagin 1999; 2013).

Results

Of the 66,948 respondents, 53,541 (80%) were women and 13,407 (20%) were men. The average age was 44.7 (SD 9.4) years. Excessive alcohol consumption was reported by 36%, smoking by 12%, low physical activity by 24%, and obesity by 42% of the respondents. Table 1 illustrates the path of defining the final set of six trajectory groups. The table shows that the goodness of fit increased with every step from one- to six-group model. Six-cluster models with cubic regression demonstrated good fit for each gender-age group.

Table 1

The goodness of fit of group-based trajectory analysis models.

Model	Smallest group size	BIC1	AIC2	APP3
Men <40 (n = 4,286)
1-cluster	100%	20,710	20,694	1.0
2-cluster	21%	19,224	19,192	0.87 to 0.94
3-cluster	10%	18,764	18,716	0.80 to 0.86
4-cluster	4%	18,632	18,569	0.76 to 0.82
5-cluster	4%	18,527	18,447	0.65 to 0.84
6-cluster	3%	18,469	18,374	0.65 to 0.78
Men 40–49 (n = 4,274)
1-cluster	100%	24,056	24,040	1.0
2-cluster	28%	22,008	21,976	0.90 to 0.95
3-cluster	14%	21,390	21,342	0.84 to 0.89
4-cluster	9%	21,190	21,126	0.79 to 0.88
5-cluster	6%	21,082	21,003	0.71 to 0.82
6-cluster	3%	20,997	20,902	0.71 to 0.78
Men > 49 (n = 4,847)
1-cluster	100%	28,438	28,423	1.0
2-cluster	34%	26,191	26,159	0.90 to 0.94
3-cluster	13%	25,520	25,471	0.85 to 0.89
4-cluster	8%	25,322	25,257	0.80 to 0.86
5-cluster	3%	25,279	25,198	0.75 to 0.84
6-cluster	3%	25,149	25,052	0.73 to 0.80
Women <40 (n = 17,751)
1-cluster	100%	93,373	93,353	1.0
2-cluster	26%	86,571	86,532	0.88 to 0.94
3-cluster	8%	85,010	84,951	0.80 to 0.87
4-cluster	3%	84,511	84,433	0.73 to 0.85
5-cluster	4%	84,099	84,002	0.65 to 0.83
6-cluster	2%	83,899	83,783	0.63 to 0.76
Women 40–49 (n = 18,010)
1-cluster	100%	113,267	113,248	1.0
2-cluster	32%	104,238	104,199	0.90 to 0.94
3-cluster	12%	101,825	101,766	0.84 to 0.88
4-cluster	6%	101,157	101,079	0.77 to 0.87
5-cluster	7%	100,687	100,589	0.67 to 0.84
6-cluster	3%	100,344	100,227	0.69 to 0.76
Women > 49 (n = 17,780)
1-cluster	100%	114,688	114,668	1.0
2-cluster	33%	105,830	105,791	0.90 to 0.94
3-cluster	13%	103,340	103,281	0.84 to 0.88
4-cluster	6%	102,599	102,521	0.80 to 0.85
5-cluster	6%	102,091	101,994	0.70 to 0.83
6-cluster	3%	101,782	101,666	0.70 to 0.83

The chosen models are shown in bold.

1 BIC = Bayesian Information Criterion

2 AIC = Akaike information criterion

3 APP = average posterior probability.

For each gender-age group, the identified trajectories followed a similar pattern (Fig 1).

Fig 1

Trajectories of the JSS by gender-age groups.

95% confidence limits are shown as dot-lines.” Two trajectories with least sleep problems at baseline without any substantial change during the follow-up accounted for 50% to 70% of the respondents. For further analysis, these two trajectories were combined into one cluster, which served as a reference cluster. Also, for each gender-age group, there was a cluster/trajectory with consistently high frequency of sleep difficulties (every night), which represented 2% to 4% of a particular group. Almost every group demonstrated two or three trajectories with either decreasing or increasing frequency of sleep difficulties.

As shown in Table 2, many risk factors were inconsistently associated with both sleep difficulties and good sleep. However, there were several statistically significant unequivocal associations. Except for men and women younger than 40 years, heavy drinking predicted either steadily high or increasing sleep difficulties–OR varied from 1.15 (95% CI 1.01 to 1.31) up to 2.30 (95% CI 1.62 to 3.27). Smoking was associated with worsening sleep difficulties amongst women younger than 40 years–(OR 1.23, 95% CI 1.04 to 1.46). Low physical activity predicted sleep difficulties amongst men older than 50 years (OR 1.29, 95% CI 1.07 to 1.56). Obesity was associated with sleep difficulties amongst men (OR 1.92, 95% CI 1.36 to 2.70) and women (OR up to 1.19, 95% CI 1.08 to 1.30) of middle age and amongst women older than 50 years (OR 1.47, 95% CI 1.22 to 1.77).

Table 2

The strength of prediction amongst modifiable risks and probability of being placed into a particular cluster.

Trajectories	Risk factors
	Heavy drinking			Smoking			Low physical inactivity			Obesity
	OR	95% CI		OR	95% CI		OR	95% CI		OR	95% CI
Men <40 years (Fig 1A)
Steadily average sleepers (1 n/w a)	1.27	1.11	1.45	1.02	0.85	1.23	1.16	0.97	1.40	1.19	1.04	1.35
Steadily worst sleepers (5–6 n/w)	1.98	1.37	2.85	1.51	0.96	2.36	1.82	1.19	2.79	1.43	1.00	2.05
Worsening sleepers (1 → 2–4 n/w)	1.06	0.85	1.32	1.24	0.92	1.66	1.54	1.16	2.04	0.78	0.62	0.98
Improving sleepers (2–4 → 1 n/w)	1.49	1.11	1.98	1.10	0.74	1.63	1.93	1.37	2.72	1.07	0.80	1.42
Men 40–49 years (Fig 1B)
Steadily average sleepers (2–4 n/w)	1.62	1.34	1.96	0.93	0.72	1.19	1.21	0.97	1.50	1.19	0.99	1.44
Steadily worst sleepers (5–6 n/w)	2.30	1.62	3.27	1.06	0.69	1.64	1.15	0.78	1.70	1.92	1.36	2.70
Worsening sleepers (<1 → >1 n/w)	1.03	0.89	1.21	1.03	0.84	1.26	1.14	0.95	1.37	1.02	0.87	1.19
Improving sleepers (2–4 → 1 n/w)	0.93	0.71	1.23	0.83	0.56	1.23	1.32	0.97	1.80	0.90	0.68	1.19
Men 50+ years (Fig 1C)
Steadily average sleepers (1 n/w)	1.15	1.01	1.31	0.83	0.68	1.00	1.15	1.00	1.33	0.94	0.83	1.07
Steadily bad sleepers (2–4 n/w)	1.12	0.94	1.33	1.04	0.81	1.33	1.29	1.07	1.56	0.93	0.78	1.11
Steadily worst sleepers (5–6 n/w)	1.04	0.78	1.39	1.11	0.75	1.65	1.17	0.86	1.60	0.99	0.74	1.32
Improving sleepers (5–6 → 1 n/w)	1.01	0.71	1.44	0.95	0.57	1.59	1.45	1.00	2.10	1.20	0.84	1.71
Women <40 years (Fig 1D)
Steadily worst sleepers (5–6 n/w)	1.10	0.88	1.39	1.05	0.74	1.49	1.51	1.17	1.96	1.31	1.05	1.64
Worsening sleepers (<1 to 2–4 n/w)	0.94	0.86	1.02	1.10	0.96	1.26	1.16	1.04	1.30	0.90	0.83	0.99
Worsening sleepers (1 to 2–4 n/w)	1.05	0.94	1.19	1.23	1.04	1.46	1.08	0.93	1.25	1.01	0.90	1.14
Improving sleepers (2–4 → 1 n/w)	1.25	1.12	1.39	1.03	0.87	1.22	1.17	1.02	1.33	1.19	1.07	1.32
Women 40–49 years (Fig 1E)
Steadily worst sleepers (5–6 n/w)	1.23	1.01	1.49	2.00	1.59	2.50	1.47	1.21	1.79	1.30	1.08	1.57
Worsening sleepers (<1 to 1 n/w)	0.97	0.89	1.05	1.08	0.97	1.20	1.07	0.98	1.16	0.95	0.88	1.03
Worsening sleepers (1 to 2–4 n/w)	1.15	1.03	1.27	1.31	1.15	1.50	1.16	1.04	1.29	1.02	0.92	1.13
Improving sleepers (2–4 → 1 n/w)	1.02	0.90	1.16	1.29	1.10	1.52	1.21	1.07	1.38	1.03	0.92	1.16
Women 50+ years (Fig 1F)
Steadily average sleepers (1 n/w)	0.97	0.90	1.06	0.87	0.76	0.99	1.12	1.03	1.21	0.97	0.90	1.05
Steadily bad sleepers (2–4 n/w)	1.11	1.01	1.23	0.99	0.85	1.16	1.24	1.12	1.36	1.19	1.08	1.30
Steadily worst sleepers (5–6 n/w)	1.29	1.06	1.57	1.28	0.96	1.71	1.27	1.04	1.54	1.47	1.22	1.77
Improving sleepers (2–4 → <1 n/w)	0.93	0.82	1.05	1.10	0.91	1.32	1.34	1.18	1.51	1.10	0.98	1.24

Two trajectories with the lowest baseline JSS scores were combined into one cluster (“steadily good sleepers”) and used as a reference. Significant results are shown in bold.

a Nights per week.

Discussion

This prospective survey-based cohort study investigated the persistence of sleep difficulties experienced by 67,000 employed people depending on age and gender during a 16-year follow-up. Additionally, the study evaluated the associations of four modifiable risks with different trajectories of changes in sleep, using the JSS. From 50% to 70% of the respondents in each group had no, or only mild sleep difficulties through the entire follow-up. There were several different trajectories (responsible for a small part of the studied cohort) showing increasing or decreasing frequency of sleep difficulties. In most of the groups, the severity of sleep difficulties remained unchanged during the follow-up and this severity was depending only on the initial level. Except for the respondents younger than 40 years, heavy drinking predicted either steadily worse or worsening sleep difficulties. Smoking was associated with worsening sleep difficulties amongst young women. Physical inactivity was associated with sleep difficulties amongst older men. Obesity was associated with sleep difficulties amongst middle-age respondents and amongst older women.

It is noteworthy that trajectory analysis provides only an approximation of changes in sleep patterns. The method shows probable trends in a particular population. In this study, the population was limited to employed people of working age–mostly between 40 and 50 years. The majority of the participants were women, which reflects the sex distribution in public sector employees in Finland. While this may limit the generalizability of our findings, the size of the studied cohort and the longitudinal design strengthen validity of the observed associations. Also, the study offered a sophisticated instrument–group-based trajectory analysis–to evaluate fluctuations within a cohort’s clusters, which with more basic approaches often remain undetected.

The observed associations between sleep difficulties and physical inactivity and excessive alcohol consumption were in line with previous reports [3, 6, 11–18]. Previous studies are comparable with the present study with reservations as they have usually approached the problem from a different point of view. Numerous cross-sectional and longitudinal studies have reported prevalence or risk factors of sleep disorders in different populations [1, 4, 5, 7, 34, 35]. The persistence of sleep difficulties has previously been studied mostly amongst children and adolescents and elderly [19-22]. The respective knowledge regarding general middle-age populations is scarce. A longitudinal study from United Kingdom amongst patients, who had been seen by general practitioners, reported persistence of insomnia in a one-year follow-up [2]. Also, a recent study from Canada reported the persistent nature of insomnia severity in a general population [24]. No longitudinal repeated measure study has focused on the associations between modifiable risks and sleep difficulties in a healthy population.

We observed that sleep difficulties were mostly very persistent. For most of the respondents, the severity of sleep difficulties was depending only on the baseline level of that severity. Even clusters with ascending or descending trajectories showed only mild changes in sleep difficulties. Trajectories with more substantial improvement seen in older persons of both genders might reflect the possible effect of retirement. One might expect much more fluctuation in such a long period of time. It is possible that this phenomenon might be related to the specifics of the study design–the repeated measures described the situation during a particular month at a few rare repeated measures. Hence, short-term variability in sleep difficulties might be undetected.

Further research on large cohorts and with long follow-ups with several repeated measures should be repeated in different populations and settings. Further research should involve different age groups, diverse socio-economic situations, countries and cultures.

Conclusions

In this working-age population, sleep difficulties showed a great persistence over time. In most of the groups, the level of sleep difficulties during the follow-up was almost solely dependent on the level of initial severity. Depending on sex and age, increasing sleep problems were sometimes associated with high alcohol consumption, smoking, obesity and physical inactivity, but the strength of these associations varied.

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13 Sep 2021

PONE-D-21-02910Persistence of sleep difficulties over 16 years amongst 66,948 working-aged adultsPLOS ONE

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Review Comments to the Author

This manuscript examined longitudinal sleep difficulties of adults in Finland. This is the first study to prospectively assess sleep and modifiable health factors in a cohort of adults longitudinally over 2.5 decades. The authors report that sleep difficulties are persistent across the 16 year assessment period. They found specific health factors to be associated with sleep depending upon characteristics of age and sex. A strength of the study is the longitudinal cohort design allowing for repeated measurement and within subjects comparison. The primary weakness of the manuscript are grammatical/spelling/punctuation errors throughout the document that should be addressed for clarity and to improve readability. Additionally, the manuscript would benefit from further description of the manner in which the analyses were conducted with this specific sample in addition to the general description of the analytic techniques that were used. To summarize, this is an interesting research question and a unique population that provides new insights into the relationships between sleep and modifiable health outcomes. Thank you for the opportunity to review this paper.

Abstract and Statement of Significance

1. There are several grammatical errors in the Abstract and the Statement of Significance (e.g. words such as “a” and “the” appear to be missing resulting in some incomplete sentences and impaired readability, the phrase “solely depending” should be changed to “Solely dependent”). These section would benefit from a more thorough review by the authors. The Statement of Significance repeats the last line of the Abstract verbatim and does not explain the significance of this work (e.g. how does this work expand on what is already known about sleep difficulties in adults?).

Introduction

2. There are grammatical errors throughout the Introduction section (as in the Abstract, words such as “a” and “the” appear to be missing; e.g. lines 76-77). Some sentences are awkwardly worded such that readability is impacted (e.g. line 77-78)

Methods

1. There are grammatical errors throughout the Methods section (as in the Abstract, words such as “upon”; e.g. line 122)

2. The Methods section would benefit from a review for errant punctuation and general grammatical/spelling errors (e.g. line 104-10).

3. There is no description of whether informed consent (or a waiver alternative) was obtained from participants.

4. How were physical activity and alcohol consumption dichotomous values determined? If this is based on established norms, citations should be included. Additionally, it might be more accurate to use different cut offs for excessive alcohol consumption based on participant characteristics (e.g. sex or BMI).

5. There is no citation or description or established psychometric properties of the Jenkins Sleep Scale, nor is there a citation for the method for handling missing data.

6. After introducing the JSS abbreviation, Continue to use it through out rather than switching back and forth between the abbreviation and spelling out the full name of the measure in the text.

7. What methods or variables were used to determine the six clusters? What do the clusters consist of? How are they similar or different?

Results

8. It is unclear to me how the cluster models/trajectories (shown in Table 1) relate to the risk factor analyses (shown in Table 2). Further description of the purpose of and relationship between these analyses in either the Methods/Statistical Analysis section or in the Results would be helpful. Discussion

Discussion

9. There are grammatical errors throughout the Discussion section (e.g. lines 166-167, line 174, line 175, line 177, lines 181-183, line 198).

10. Line 178 – The term “instrument” should be replaced with a more accurate term such as “technique,” as the analysis is not physical tool or device as is suggested by the term instrument.

11. Line 201 – change “may” to “should”

Tables and Figures

12. Table 1: the text “The chosen models are shown in bold” belongs in the footnote/caption rather than in the title.

13. Table 2: the text “Two trajectories with lowest baseline JSS scores were combined into one cluster (“steadily good sleepers”) and used as a reference” belongs in the footnote/caption rather than in the title. Additionally, highlighting significant findings in some manner (e.g. bolding text) would be helpful to the reader.

14. The line graph Figures are completely unreadable as the text is blurry.

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

22 Sep 2021

Responses to the comments made by the Editor

Comment 1

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf

and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf.

Response 1

We have changed the style of figure presentation in the text. Could it be possible to get more information what else should be done?

Comment 2

Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

If you are reporting a retrospective study of medical records or archived samples, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information.

Response 2

We have now added/modified the following text to the Methods section:

“All the respondents have approved a written informed consent. The study did not include minors. The ethics committee of the Hospital District of Helsinki and Uusimaa approved the study plan and the informed consent form (registration number HUS/1210/2016).”

Comment 3

We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

Response 3

The discrepancy has been corrected.

Comment 4

We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

Response 4

We have now added the following statement to the Methods section:

“The deidentified data that support the findings of this study are available on reasonable request from the corresponding author, MS. The data are not publicly available due to legislative restrictions, as the data contains information that could compromise the privacy of the research participants.”

Responses to comments made by the reviewer #1

Comment 1

Abstract and Statement of Significance

There are several grammatical errors in the Abstract and the Statement of Significance (e.g. words such as “a” and “the” appear to be missing resulting in some incomplete sentences and impaired readability, the phrase “solely depending” should be changed to “Solely dependent”). These section would benefit from a more thorough review by the authors. The Statement of Significance repeats the last line of the Abstract verbatim and does not explain the significance of this work (e.g. how does this work expand on what is already known about sleep difficulties in adults?).

Response 1

We have re-checked the language of Abstract and Statement of Significance and made several corrections.

Comment 2

Introduction

There are grammatical errors throughout the Introduction section (as in the Abstract, words such as “a” and “the” appear to be missing; e.g. lines 76-77). Some sentences are awkwardly worded such that readability is impacted (e.g. line 77-78)

Methods

There are grammatical errors throughout the Methods section (as in the Abstract, words such as “upon”; e.g. line 122). The Methods section would benefit from a review for errant punctuation and general grammatical/spelling errors (e.g. line 104-10).

Discussion

There are grammatical errors throughout the Discussion section (e.g. lines 166-167, line 174, line 175, line 177, lines 181-183, line 198).

Line 178 – The term “instrument” should be replaced with a more accurate term such as “technique,” as the analysis is not physical tool or device as is suggested by the term instrument.

Line 201 – change “may” to “should”

Response 2

We have re-checked the language and made several corrections.

Comment 3

Methods

There is no description of whether informed consent (or a waiver alternative) was obtained from participants.

Response 3

We have now added/modified the following text to the Methods section:

“All the respondents have approved a written informed consent. The study did not include minors. The ethics committee of the Hospital District of Helsinki and Uusimaa approved the study plan and the informed consent form (registration number HUS/1210/2016).”

Comment 4

Methods

How were physical activity and alcohol consumption dichotomous values determined? If this is based on established norms, citations should be included. Additionally, it might be more accurate to use different cut offs for excessive alcohol consumption based on participant characteristics (e.g. sex or BMI).

Response 4

As indicated in the Methods section:

“The level of physical activity was calculated from the survey responses, converted into metabolic equivalent of task (MET) and dichotomized based on the cohort’s quartiles as “low physical activity” – the lower quartile vs. others. Alcohol consumption was obtained from the survey and converted into g/week, and >210 g of pure alcohol per week was considered a cut-off for excess alcohol consumption (no/yes).”

Comment 5

Methods

There is no citation or description or established psychometric properties of the Jenkins Sleep Scale, nor is there a citation for the method for handling missing data.

Response 5

The following text has now been added to the Methods section:

“The JSS has been one of the most commonly used questionnaires in epidemiological sleep studies 1-4. It has bene found to be valid and reliable amongst patients with different health problems as well as in large non-clinical populations [25-28].”

We have now modified the following text to the Methods section adding a new reference:

“In order to include also incomplete responses to the JSS, in this study, a total score was substituted by the average score of answered items (0.0 to 5.0) using a method of person mean imputation [25].”

Heymans M, Eekhout I. Missing data in questionnaires. 2019 [cited September 22, 2021]. In: Applied missing data analysis with SPSS and (R) studio [Internet]. Amsterdam: Heymans and Eekhout, [cited September 22, 2021]. Available from: https://bookdown.org/mwheymans/bookmi/

Comment 6

Methods

After introducing the JSS abbreviation, continue to use it through out rather than switching back and forth between the abbreviation and spelling out the full name of the measure in the text.

Response 6

This has been corrected as suggested.

Comment 7

Methods

What methods or variables were used to determine the six clusters? What do the clusters consist of? How are they similar or different?

Response 7

We have now extended the text of the Methods as follows:

“The number of groups may be defined by the size of the data set measuring in two dimensions: the number of cases and the number of repeated measures. There are no common recommendations on the number of trajectory groups. In theory, it can be any number from one up to the number of cases. Previous research has suggested breaking a sample down below 300 cases may not add significant information. In this study, we pre-agreed that the smallest group should be around 3% of the entire sample or the size of the smallest group should be around 300 cases [33]. We also pre-agreed that the number of trajectory groups will be the same for all gender-age groups to ease the interpretation of the results. This way, six cluster groups were identified for each gender-age group.”

A new reference has been added:

Nagin D, Tremblay R. Developmental trajectory groups: fact or a useful statistical fiction? Criminology. 2005;43(4):873-904.

Comment 8

Results

It is unclear to me how the cluster models/trajectories (shown in Table 1) relate to the risk factor analyses (shown in Table 2). Further description of the purpose of and relationship between these analyses in either the Methods/Statistical Analysis section or in the Results would be helpful.

Response 8

We have now added the following text to the Results:

“Table 1 illustrates the path of defining the final set of six trajectory groups. The table shows that the goodness of fit increased with every step from one- to six-group model. Six-cluster models with cubic regression demonstrated good fit for each gender-age group.”

Comment 9

Tables and Figures

Table 1: the text “The chosen models are shown in bold” belongs in the footnote/caption rather than in the title.

Tables and Figures

Table 2: the text “Two trajectories with lowest baseline JSS scores were combined into one cluster (“steadily good sleepers”) and used as a reference” belongs in the footnote/caption rather than in the title. Additionally, highlighting significant findings in some manner (e.g. bolding text) would be helpful to the reader.

Response 9

The modifications have now been made as suggested.

Comment 10

Tables and Figures

The line graph Figures are completely unreadable as the text is blurry.

Response 10

Unfortunately, this is something coming from a journal submission system.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

21 Oct 2021

Persistence of sleep difficulties for over 16 years amongst 66,948 working-aged adults

PONE-D-21-02910R1

Dear Dr. Saltychev,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

As suggeste by a reviewer, in editing, the text "% confidence limits are shown as dot-lines" in the Results section in reference to the placement of Figure 1 appears to not have been removed with the rest of a phrase that was deleted from this revised version of the manuscript. This may need to be removed/clarified.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Federica Provini

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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Reviewer #1: I thank the authors for their attention to previous comments and for the opportunity to review this interesting research. In editing, the text "% confidence limits are shown as dot-lines" in the Results section in reference to the placement of Figure 1 appears to not have been removed with the rest of a phrase that was deleted from this revised version of the manuscript. This may need to be removed/clarified.

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Reviewer #1: No

9 Nov 2021

PONE-D-21-02910R1

Persistence of sleep difficulties for over 16 years amongst 66,948 working-aged adults

Dear Dr. Saltychev:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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