literature review about time management

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Does time management work? A meta-analysis

1 Concordia University, Sir George Williams Campus, Montreal, Quebec, Canada

Aïda Faber

2 FSA Ulaval, Laval University, Quebec City, Quebec, Canada

Alexandra Panaccio

Associated data.

All relevant data are within the manuscript and its Supporting Information files.

Does time management work? We conducted a meta-analysis to assess the impact of time management on performance and well-being. Results show that time management is moderately related to job performance, academic achievement, and wellbeing. Time management also shows a moderate, negative relationship with distress. Interestingly, individual differences and contextual factors have a much weaker association with time management, with the notable exception of conscientiousness. The extremely weak correlation with gender was unexpected: women seem to manage time better than men, but the difference is very slight. Further, we found that the link between time management and job performance seems to increase over the years: time management is more likely to get people a positive performance review at work today than in the early 1990s. The link between time management and gender, too, seems to intensify: women’s time management scores have been on the rise for the past few decades. We also note that time management seems to enhance wellbeing—in particular, life satisfaction—to a greater extent than it does performance. This challenges the common perception that time management first and foremost enhances work performance, and that wellbeing is simply a byproduct.

Introduction

Stand-up comedian George Carlin once quipped that in the future a “time machine will be built, but no one will have time to use it” [ 1 ]. Portentously, booksellers now carry one-minute bedtime stories for time-starved parents [ 2 ] and people increasingly speed-watch videos and speed-listen to audio books [ 3 – 5 ]. These behaviors are symptomatic of an increasingly harried society suffering from chronic time poverty [ 6 ]. Work is intensifying—in 1965 about 50% of workers took breaks; in 2003, less than 2% [ 7 ]. Leisure, too, is intensifying: people strive to consume music, social media, vacations, and other leisure activities ever more efficiently [ 8 – 11 ].

In this frantic context, time management is often touted as a panacea for time pressure. Media outlets routinely extol the virtues of time management. Employers, educators, parents, and politicians exhort employees, students, children, and citizens to embrace more efficient ways to use time [ 12 – 16 ]. In light of this, it is not surprising that from 1960 to 2008 the frequency of books mentioning time management shot up by more than 2,700% [ 17 ].

Time management is defined as “a form of decision making used by individuals to structure, protect, and adapt their time to changing conditions” [ 18 ]. This means time management, as it is generally portrayed in the literature, comprises three components: structuring, protecting, and adapting time. Well-established time management measures reflect these concepts. Structuring time, for instance, is captured in such items as “Do you have a daily routine which you follow?” and “Do your main activities during the day fit together in a structured way?” [ 19 ]. Protecting time is reflected in items such as “Do you often find yourself doing things which interfere with your schoolwork simply because you hate to say ‘No’ to people?” [ 20 ]. And adapting time to changing conditions is seen in such items as “Uses waiting time” and “Evaluates daily schedule” [ 21 ].

Research has, furthermore, addressed several important aspects of time management, such as its relationship with work-life balance [ 22 ], whether gender differences in time management ability develop in early childhood [ 23 ], and whether organizations that encourage employees to manage their time experience less stress and turnover [ 24 ]. Despite the phenomenal popularity of this topic, however, academic research has yet to address some fundamental questions [ 25 – 27 ].

A critical gap in time management research is the question of whether time management works [ 28 , 29 ]. For instance, studies on the relationship between time management and job performance reveal mixed findings [ 30 , 31 ]. Furthermore, scholars’ attempts to synthesize the literature have so far been qualitative, precluding a quantitative overall assessment [ 18 , 32 , 33 ]. To tackle this gap in our understanding of time management, we conducted a meta-analysis. In addressing the question of whether time management works, we first clarify the criteria for effectiveness. In line with previous reviews, we find that virtually all studies focus on two broad outcomes: performance and wellbeing [ 32 ].

Overall, results suggest that time management enhances job performance, academic achievement, and wellbeing. Interestingly, individual differences (e.g., gender, age) and contextual factors (e.g., job autonomy, workload) were much less related to time management ability, with the notable exception of personality and, in particular, conscientiousness. Furthermore, the link between time management and job performance seems to grow stronger over the years, perhaps reflecting the growing need to manage time in increasingly autonomous and flexible jobs [ 34 – 37 ].

Overall, our findings provide academics, policymakers, and the general audience with better information to assess the value of time management. This information is all the more useful amid the growing doubts about the effectiveness of time management [ 38 ]. We elaborate on the contributions and implications of our findings in the discussion section.

What does it mean to say that time management works?

In the din of current debates over productivity, reduced workweeks, and flexible hours, time management comes to the fore as a major talking point. Given its popularity, it would seem rather pointless to question its effectiveness. Indeed, time management’s effectiveness is often taken for granted, presumably because time management offers a seemingly logical solution to a lifestyle that increasingly requires coordination and prioritization skills [ 39 , 40 ].

Yet, popular media outlets increasingly voice concern and frustration over time management, reflecting at least part of the population’s growing disenchantment [ 38 ]. This questioning of time management practices is becoming more common among academics as well [ 41 ]. As some have noted, the issue is not just whether time management works. Rather, the question is whether the techniques championed by time management gurus can be actually counterproductive or even harmful [ 26 , 42 ]. Other scholars have raised concerns that time management may foster an individualistic, quantitative, profit-oriented view of time that perpetuates social inequalities [ 43 , 44 ]. For instance, time management manuals beguile readers with promises of boundless productivity that may not be accessible to women, whose disproportionate share in care work, such as tending to young children, may not fit with typically male-oriented time management advice [ 45 ]. Similarly, bestselling time management books at times offer advice that reinforce global inequities. Some manuals, for instance, recommend delegating trivial tasks to private virtual assistants, who often work out of developing countries for measly wages [ 46 ]. Furthermore, time management manuals often ascribe a financial value to time—the most famous time management adage is that time is money. But recent studies show that thinking of time as money leads to a slew of negative outcomes, including time pressure, stress, impatience, inability to enjoy the moment, unwillingness to help others, and less concern with the environment [ 47 – 51 ]. What’s more, the pressure induced by thinking of time as money may ultimately undermine psychological and physical health [ 52 ].

Concerns over ethics and safety notwithstanding, a more prosaic question researchers have grappled with is whether time management works. Countless general-audience books and training programs have claimed that time management improves people’s lives in many ways, such as boosting performance at work [ 53 – 55 ]. Initial academic forays into addressing this question challenged those claims: time management didn’t seem to improve job performance [ 29 , 30 ]. Studies used a variety of research approaches, running the gamut from lab experiments, field experiments, longitudinal studies, and cross-sectional surveys to experience sampling [ 28 , 56 – 58 ]. Such studies occasionally did find an association between time management and performance, but only in highly motivated workers [ 59 ]; instances establishing a more straightforward link with performance were comparatively rare [ 31 ]. Summarizing these insights, reviews of the literature concluded that the link between time management and job performance is unclear; the link with wellbeing, however, seemed more compelling although not conclusive [ 18 , 32 ].

It is interesting to note that scholars often assess the effectiveness time management by its ability to influence some aspect of performance, wellbeing, or both. In other words, the question of whether time management works comes down to asking whether time management influences performance and wellbeing. The link between time management and performance at work can be traced historically to scientific management [ 60 ]. Nevertheless, even though modern time management can be traced to scientific management in male-dominated work settings, a feminist reading of time management history reveals that our modern idea of time management also descends from female time management thinkers of the same era, such as Lillian Gilbreth, who wrote treatises on efficient household management [ 43 , 61 , 62 ]. As the link between work output and time efficiency became clearer, industrialists went to great lengths to encourage workers to use their time more rationally [ 63 – 65 ]. Over time, people have internalized a duty to be productive and now see time management as a personal responsibility at work [ 43 , 66 , 67 ]. The link between time management and academic performance can be traced to schools’ historical emphasis on punctuality and timeliness. In more recent decades, however, homework expectations have soared [ 68 ] and parents, especially well-educated ones, have been spending more time preparing children for increasingly competitive college admissions [ 69 , 70 ]. In this context, time management is seen as a necessary skill for students to thrive in an increasingly cut-throat academic world. Finally, the link between time management and wellbeing harks back to ancient scholars, who emphasized that organizing one’s time was necessary to a life well-lived [ 71 , 72 ]. More recently, empirical studies in the 1980s examined the effect of time management on depressive symptoms that often plague unemployed people [ 19 , 73 ]. Subsequent studies surmised that the effective use of time might prevent a host of ills, such as work-life conflict and job stress [ 22 , 74 ].

Overall, then, various studies have looked into the effectiveness of time management. Yet, individual studies remain narrow in scope and reviews of the literature offer only a qualitative—and often inconclusive—assessment. To provide a more quantifiable answer to the question of whether time management works, we performed a meta-analysis, the methods of which we outline in what follows.

Literature search and inclusion criteria

We performed a comprehensive search using the keywords “time management” across the EBSCO databases Academic Search Complete , Business Source Complete , Computers & Applied Sciences Complete , Gender Studies Database , MEDLINE , Psychology and Behavioral Sciences Collection , PsycINFO , SocINDEX , and Education Source . The search had no restrictions regarding country and year of publication and included peer-reviewed articles up to 2019. To enhance comprehensiveness, we also ran a forward search on the three main time management measures: the Time Management Behavior Scale [ 21 ], the Time Structure Questionnaire [ 19 ], and the Time Management Questionnaire [ 20 ]. (A forward search tracks all the papers that have cited a particular work. In our case the forward search located all the papers citing the three time management scales available on Web of Science .)

Time management measures typically capture three aspects of time management: structuring, protecting, and adapting time to changing conditions. Structuring refers to how people map their activities to time using a schedule, a planner, or other devices that represent time in a systematic way [ 75 – 77 ]. Protecting refers to how people set boundaries around their time to repel intruders [ 78 , 79 ]. Examples include people saying no to time-consuming requests from colleagues or friends as well as turning off one’s work phone during family dinners. Finally, adapting one’s time to changing conditions means, simply put, to be responsive and flexible with one’s time structure [ 80 , 81 ]. Furthermore, time management measures typically probe behaviors related to these three dimensions (e.g., using a schedule to structure one’s day, making use of downtime), although they sometimes also capture people’s attitudes (e.g., whether people feel in control of their time).

As shown in Fig 1 , the initial search yielded 10,933 hits, excluding duplicates.

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The search included no terms other than “time management” to afford the broadest possible coverage of time management correlates. Nevertheless, as shown in Table 1 , we focused exclusively on quantitative, empirical studies of time management in non-clinical samples. Successive rounds of screening, first by assessing paper titles and abstracts and then by perusing full-text articles, whittled down the number of eligible studies to 158 (see Fig 1 ).

Inclusion Criteria	Exclusion Criteria
Study must contain a quantitative measure of time management (e.g., scale, survey, questionnaire) and/or feature a time management experiment with at least one control group	Qualitative approaches (e.g., interviews, case studies)
Construct must be related to time management, such as time structure, time planning, scheduling, time management behaviors, time management practice, time management skills, and attitudes toward time management	Time-use studies (e.g., national time-use surveys, individual-level time-tracking studies), time perception studies, studies on non-personal time management (e.g., real-time management in supply chains), and time management studies focusing mainly on clinical samples (e.g., with chronic pain or ADHD)
Study must be about time management in relation to other variables (e.g., life satisfaction, stress, academic achievement)	Studies focusing exclusively on time management (e.g., factor analyses)

Data extraction and coding

We extracted eligible effect sizes from the final pool of studies; effect sizes were mostly based on means and correlations. In our initial data extraction, we coded time management correlates using the exact variable names found in each paper. For instance, “work-life imbalance” was initially coded in those exact terms, rather than “work-life conflict.” Virtually all time management correlates we extracted fell under the category of performance and/or wellbeing. This pattern tallies with previous reviews of the literature [ 18 , 32 ]. A sizable number of variables also fell under the category of individual differences and contextual factors, such as age, personality, and job autonomy. After careful assessment of the extracted variables, we developed a coding scheme using a nested structure shown in Table 2 .

Performance				Wellbeing		Individual Differences
Professional Setting		Academic Setting		Positive (wellbeing)	Negative (distress)	Demographics	Personality	Attributes and Attitudes	Contextual Factors
				Job Satisfaction	Emotional Exhaustion	Age	Agreeableness	Internal Locus of Control	Job Autonomy
Job performance	Creativity	GPA	Procrastination (reverse coded)	Life Satisfaction	Stress	Gender	Extraversion	Type A	Role Overload
	Helping Behavior	Standardized Tests	Motivation	Mental Health (positive)	Work-life Conflict	Education	Conscientiousness	Self-esteem	Time Management Training
	Job Involvement	Test Scores		Optimism	Anxiety	Number of Children	Neuroticism	Protestant Work Ethic
	Procrastination (reverse coded)			Physical health (positive)	Depression	Marital Status	Openness	Multitasking
	Motivation			Positive affect	Psychological Distress			Cognitive Ability
	Proactiveness			Self-actualization	Hopelessness			Hours Worked
				Sense of purpose	Boredom
				Wellbeing	Negative Affect
					Worry
					Physical Distress

Aeon and Aguinis suggested that time management influences performance, although the strength of that relationship may depend on how performance is defined [ 18 ]. Specifically, they proposed that time management may have a stronger impact on behaviors conducive to performance (e.g., motivation, proactiveness) compared to assessments of performance (e.g., supervisor rankings). For this reason, we distinguish between results- and behavior-based performance in our coding scheme, both in professional and academic settings. Furthermore, wellbeing indicators can be positive (e.g., life satisfaction) or negative (e.g., anxiety). We expect time management to influence these variables in opposite ways; it would thus make little sense to analyze them jointly. Accordingly, we differentiate between wellbeing (positive) and distress (negative).

In our second round of coding, we used the scheme shown in Table 2 to cluster together kindred variables. For instance, we grouped “work-life imbalance,” “work-life conflict” and “work-family conflict” under an overarching “work-life conflict” category. The authors reviewed each variable code and resolved rare discrepancies to ultimately agree on all coded variables. Note that certain variables, such as self-actualization, covered only one study (i.e., one effect size). While one or two effect sizes is not enough to conduct a meta-analysis, they can nonetheless be grouped with other effect sizes belonging to the same category (e.g., self-actualization and sense of purpose belong the broader category of overall wellbeing). For this reason, we included variables with one or two effect sizes for comprehensiveness.

Meta-analytic procedures

We conducted all meta-analyses following the variables and cluster of variables outlined in Table 2 . We opted to run all analyses with a random effects model. The alternative—a fixed effects model—assumes that all studies share a common true effect size (i.e., linking time management and a given outcome) which they approximate. This assumption is unrealistic because it implies that the factors influencing the effect size are the same in all studies [ 83 ]. In other words, a fixed effects model assumes that the factors affecting time management are similar across all studies—the fallacy underlying this assumption was the main theme of Aeon and Aguinis’s review [ 18 ]. To perform our analyses, we used Comprehensive Meta-Analysis v.3 [ 84 ], a program considered highly reliable and valid in various systematic assessments [ 85 , 86 ].

Meta-analyses do not typically perform calculations on correlations (e.g., Pearson’s r). Instead, we transformed correlations into Fisher’s z scales [ 83 ]. The transformation was done with z = 0.5 × ln ( 1 + r 1 − r ) , where r represents the correlation extracted from each individual study. The variance of Fisher’s Z was calculated as V z = 1 n − 3 where n corresponds to the study’s sample size; the standard error of Fisher’s Z was calculated as S E z = V z .

In many cases, studies reported how variables correlated with an overall time management score. In some cases, however, studies reported only correlations with discrete time management subscales (e.g., short-range planning, attitudes toward time, use of time management tools), leaving out the overall effect. In such cases, we averaged out the effect sizes of the subscales to compute a summary effect [ 83 ]. This was necessary not only because meta-analyses admit only one effect size per study, but also because our focus is on time management as a whole rather than on subscales. Similarly, when we analyzed the link between time management and a high-level cluster of variables (e.g., overall wellbeing rather than specific variables such as life satisfaction), there were studies with more than one relevant outcome (e.g., a study that captured both life satisfaction and job satisfaction). Again, because meta-analyses allow for only one effect size (i.e., variable) per study, we used the mean of different variables to compute an overall effect sizes in studies that featured more than one outcome [ 83 ].

Overall description of the literature

We analyzed 158 studies for a total number of 490 effect sizes. 21 studies explored performance in a professional context, 76 performance in an academic context, 30 investigated wellbeing (positive), and 58 distress. Interestingly, studies did not systematically report individual differences, as evidenced by the fact that only 21 studies reported correlations with age, and only between 10 and 15 studies measured personality (depending on the personality trait). Studies that measured contextual factors were fewer still—between 3 and 7 (depending on the contextual factor). These figures fit with Aeon and Aguinis’s observation that the time management literature often overlooks internal and external factors that can influence the way people manage time [ 18 ].

With one exception, we found no papers fitting our inclusion criteria before the mid-1980s. Publication trends also indicate an uptick in time management studies around the turn of the millennium, with an even higher number around the 2010s. This trend is consistent with the one Shipp and Cole identified, revealing a surge in time-related papers in organizational behavior around the end of the 1980s [ 87 ].

It is also interesting to note that the first modern time management books came out in the early 1970s, including the The Time Trap (1972), by Alec MacKenzie and How to Get Control of your Time and your Life (1973), by Alan Lakein. These books inspired early modern time management research [ 21 , 58 , 88 ]. It is thus very likely that the impetus for modern time management research came from popular practitioner manuals.

To assess potential bias in our sample of studies, we computed different estimates of publication bias (see Table 3 ). Overall, publication bias remains relatively low (see funnel plots in S1). Publication bias occurs when there is a bias against nonsignificant or even negative results because such results are seen as unsurprising and not counterintuitive. In this case, however, the fact that time management is generally expected to lead to positive outcomes offers an incentive to publish nonsignificant or negative results, which would be counterintuitive [ 89 ]. By the same token, the fact that some people feel that time management is ineffective [ 38 ] provides an incentive to publish papers that link time management with positive outcomes. In other words, opposite social expectations surrounding time management might reduce publication bias.

Job performance	Academic achievement	Wellbeing	Distress
344	2,735	6,496	9,333
75	309	339	364
B(0) = 2.76	B(0) = 1.18	B(0) = 0.31	B(0) = -1.18
CI (95%) = (-.77; 6.28)	CI (95%) = (-.36; 2.72)	CI (95%) = (-.4.08; 4.69)	CI (95%) = (-.3.31; 0.94)
> .05	> .05	> .05	> .05
1 study missing	0 studies missing	0 studies missing	14 studies missing
New effect size = .188	0 studies missing	0 studies missing	New effect size = .283

Finally, we note that the link between time management and virtually all outcomes studied is highly heterogeneous (as measured, for instance, by Cochran’s Q and Higgins & Thompson’s I 2 ; see tables below). This high level of heterogeneity suggests that future research should pay more attention to moderating factors (e.g., individual differences).

Time management and performance in professional settings

Overall, time management has a moderate impact on performance at work, with correlations hovering around r = .25. We distinguish between results-based and behavior-based performance. The former measures performance as an outcome (e.g., performance appraisals by supervisors) whereas the latter measures performance as behavioral contributions (e.g., motivation, job involvement). Time management seems related to both types of performance. Although the effect size for results-based performance is lower than that of behavior-based performance, moderation analysis reveals the difference is not significant (p > .05), challenging Aeon and Aguinis’s conclusions [ 18 ].

Interestingly, the link between time management and performance displays much less heterogeneity (see Q and I 2 statistics in Table 4 ) than the link between time management and other outcomes (see tables below). The studies we summarize in Table 4 include both experimental and non-experimental designs; they also use different time management measures. As such, we can discount, to a certain extent, the effect of methodological diversity. We can perhaps explain the lower heterogeneity by the fact that when people hold a full-time job, they usually are at a relatively stable stage in life. In school, by contrast, a constellation of factors (e.g., financial stability and marital status, to name a few) conspire to affect time management outcomes. Furthermore, work contexts are a typically more closed system than life in general. For this reason, fewer factors stand to disrupt the link between time management and job performance than that between time management and, say, life satisfaction. Corroborating this, note how, in Table 6 below, the link between time management and job satisfaction ( I 2 = 58.70) is much less heterogeneous than the one between time management and life satisfaction ( I 2 = 95.45).

Variable	k	N	r	95% CI	Q(df)		(SE)
	21	3,990	0.259	0.197–0.318	77.32 (20)	0.016	0.007	74.13
	13	2,532	0.221	0.144–0.295	44.19 (12)	0.015	0.009	72.84
	13	2,474	0.297	0.225–0.365	40.56 (12)	0.013	0.008	70.41
Creativity	1	213	0.460	0.347–0.560	-	-	-	-
Helping behavior	1	254	0.160	0.038–0.278	-	-	-	-
Job involvement	4	617	0.207	0.129–0.282	2.99 (3)	0	0.006	0
Procrastination (reverse coded)	2	198	0.374	0.166–0.550	1.61 (1)	0.012	0.046	37.92
Motivation	4	711	0.352	0.226–0.467	10.12 (3)	0.014	0.016	70.37
Proactiveness	3	813	0.267	0.121–0.401	8.81 (2)	0.014	0.018	77.30

* p < .05

** p < .01

*** p < .001.

k = number of studies related to the variable | N = total sample size related to the variable.

r = effect size of the correlation between time management and the variable | 95% CI = confidence interval of the effect size.

Q = Cochran’s Q, a measure of between-study heterogeneity | τ 2 = measure of between-study variance | I 2 = alternative measure of between-study heterogeneity.

Variable	k	N	r	95% CI	Q(df)		(SE)
	30	9,905	0.313	0.244–0.380	395.83 (29)	0.040	0.014	92.67
Job satisfaction	11	2,856	0.248	0.189–0.305	24.21 (10)	0.006	0.005	58.70
Life satisfaction	9	2,855	0.426	0.273–0.558	175.86 (8)	0.068	0.038	95.45
Mental health (positive)	2	473	0.556	0.349–0.711	7.56 (1)	0.031	0.051	86.77
Optimism	2	330	0.305	0.108–0.479	3.44 (1)	0.016	0.032	70.94
Physical health (positive)	2	567	0.293	-0.002–0.542	13.07 (1)	0.045	0.068	92.35
Positive affect	5	2,725	0.280	0.186–0.368	18.73 (4)	0.010	0.010	78.65
Self-actualization	1	336	0.280	0.178–0.376	-	-	-	-
Sense of purpose	1	529	0.351	0.274–0.424	-	-	-	-
Wellbeing	5	1,447	0.219	0.092–0.338	22.86 (4)	0.018	0.016	82.50

Moreover, we note that the relationship between time management and job performance (see Fig 2 ) significantly increases over the years ( B = .0106, p < .01, Q model = 8.52(1), Q residual = 15.54(9), I 2 = 42.08, R 2 analog = .75).

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Time management and performance in academic settings

Overall, the effect of time management on performance seems to be slightly higher in academic settings compared to work settings, although the magnitude of the effect remains moderate (see Table 5 ). Here again, we distinguish between results- and behavior-based performance. Time management’s impact on behavior-based performance seems much higher than on results-based performance—a much wider difference than the one we observed in professional settings. This suggests than results-based performance in academic settings depends less on time management than results-based performance in professional settings. This means that time management is more likely to get people a good performance review at work than a strong GPA in school.

Variable	k	N	R	95% CI	Q(df)		(SE)
	76	30,605	0.262	0.223–0.300	916.31 (75)	0.029	0.007	91.81
	63	27,225	0.196	0.160–0.232	535.28 (62)	0.018	0.005	88.41
GPA	57	24,270	0.213	0.178–0.247	384.48 (56)	0.014	0.004	85.43
Standardized Tests	7	6,270	0.011	-0.053–0.094	33.35 (6)	0.007	0.006	82.01
Test Scores	3	603	0.228	0.151–0.303	1.21 (2)	0	0.005	0
	28	8,186	0.430	0.365–0.490	310.83 (27)	0.037	0.013	91.31
Procrastination (reverse coded)	14	3,558	0.490	0.399–0.572	136.62 (13)	0.040	0.020	90.48
Motivation	17	5,805	0.381	0.302–0.454	178.85 (16)	0.031	0.013	91.05

In particular, time management seems to be much more negatively related to procrastination in school than at work. Although we cannot establish causation in all studies, we note that some of them featured experimental designs that established a causal effect of time management on reducing procrastination [ 90 ].

Interestingly, time management was linked to all types of results-based performance except for standardized tests. This is perhaps due to the fact that standardized tests tap more into fluid intelligence, a measure of intelligence independent of acquired knowledge [ 91 ]. GPA and regular exam scores, in contrast, tap more into crystallized intelligence, which depends mostly on accumulated knowledge. Time management can thus assist students in organizing their time to acquire the knowledge necessary to ace a regular exam; for standardized exams that depend less on knowledge and more on intelligence, however, time management may be less helpful. Evidence from other studies bears this out: middle school students’ IQ predicts standardized achievement tests scores better than self-control while self-control predicts report card grades better than IQ [ 92 ]. (For our purposes, we can use self-control as a very rough proxy for time management.) Relatedly, we found no significant relationship between time management and cognitive ability in our meta-analysis (see Table 8 ).

Variable	k	N	r	95% CI	Q(df)		(SE)

Age	21	7,579	0.032	-0.013–0.076	70.42 (20)	0.007	0.004	71.60
Age (excluding children)	19	6,811	0.048	0.010–0.086	40.71 (18)	0.004	0.002	55.79
Gender	37	16,044	-0.087	-0.129 \| -0.045	232.40 (36)	0.013	0.005	84.51
Education	3	808	0.019	-0.050–0.088	0.304 (2)	0	0.005	0
Number of children	3	961	0.027	-0.037–0.090	0.247 (2)	0	0.004	0
Marital status	3	980	0.015	-0.048–0.078	0.548 (2)	0	0.003	0

Agreeableness	10	4,562	0.169	0.091–0.244	57.85 (9)	0.013	0.008	84.43
Extraversion	13	5,345	0.102	0.039–0.164	59.05 (12)	0.010	0.006	79.67
Conscientiousness	15	5,159	0.451	0.326–0.561	367.16 (14)	0.079	0.041	96.18
Neuroticism	14	5,222	-0.151	-0.229 \| -0.072	94.61 (13)	0.018	0.010	86.26
Openness	11	4,793	0.141	0.037–0.243	124.17 (10)	0.028	0.016	91.94

Internal locus of control	3	579	0.346	0.269–0.419	2.16 (2)	0	0.006	7.39
Type A	7	2,388	0.110	0.017–0.202	31.05 (6)	0.013	0.09	80.67
Self-esteem	3	947	0.346	0.225–0.456	8.19 (2)	0.010	0.014	75.58
Protestant Work Ethic	3	998	0.026	-0.036–0.088	0.240 (2)	0	0.003	0
Multitasking	5	932	-0.088	-0.164 \| -0.010	5.53 (4)	0.002	0.006	27.66
Cognitive ability	3	1,484	0.015	-0.064–0.094	4.36 (2)	0.003	0.005	54.11
Hours spent studying	6	3,184	0.137	0.036–0.235	30.08 (5)	0.012	0.011	83.37
Hours spent working	8	3,682	-0.042	-0.159–0.076	64.87 (7)	0.023	0.019	89.21

Job autonomy	4	751	0.101	-0.060–0.256	8.38 (3)	0.016	0.022	64.23
Role overload	7	1,187	-0.146	-0.284 \| - 0.003	26.59 (6)	0.025	0.023	77.43
Time management training	3	846	0.173	0.031–0.309	5.92 (2)	0.010	0.016	66.62

a Female = 1; Male = 2.

b Single = 1; Married = 2.

Time management and wellbeing

On the whole, time management has a slightly stronger impact on wellbeing than on performance. This is unexpected, considering how the dominant discourse points to time management as a skill for professional career development. Of course, the dominant discourse also frames time management as necessary for wellbeing and stress reduction, but to a much lesser extent. Our finding that time management has a stronger influence on wellbeing in no way negates the importance of time management as a work skill. Rather, this finding challenges the intuitive notion that time management is more effective for work than for other life domains. As further evidence, notice how in Table 6 the effect of time management on life satisfaction is 72% stronger than that on job satisfaction.

Time management and distress

Time management seems to allay various forms of distress, although to a lesser extent than it enhances wellbeing. The alleviating effect on psychological distress is particularly strong ( r = -0.358; see Table 7 ).

Variable	k	N	r	95% CI	Q(df)		(SE)
	58	15,387	-0.222	-0.273 \| -0.170	611.57 (57)	0.038	0.010	90.68
	26	5,621	-0.225	-0.295 \| -0.153	184.49 (25)	0.031	0.012	86.44
Emotional exhaustion	3	213	-0.260	-0.338 \| -0.179	1.86 (2)	0	0.006	0
Stress	17	3,367	-0.286	-0.390 \| -0.176	163.84 (16)	0.05	0.024	90.23
Work-life conflict	9	2,812	-0.163	-0.277 \| -0.043	82.11 (8)	0.031	0.018	90.25
	34	10,100	-0.254	-0.315 \| -0.190	350.58 (33)	0.034	0.012	90.85
Anxiety	16	6,648	-0.181	-0.255 \| -0.105	140.28 (15)	0.021	0.011	89.30
Depression	2	625	-0.226	-0.375 \| -0.065	-	-	-	-
Psychological distress	10	2,196	-0.358	-0.447 \| -0.263	52.98 (9)	0.023	0.014	83.01
Hopelessness	2	565	-0.218	-0.296 \| -0.138	-	-	-	-
Boredom	5	1,248	-0.310	-0.507 \| -0.081	69.68 (4)	0.070	0.055	94.26
Negative affect	4	2,393	-0.232	-0.451 \| 0.014	70.74 (3)	0.061	0.061	95.75
Worry	3	291	-0.191	-0.355 \| -0.016	3.98 (2)	0.012	0.025	49.77
	7	2,067	-0.204	-0.264 \| -0.142	11.52 (6)	0.003	0.004	47.93

That time management has a weaker effect on distress should not be surprising. First, wellbeing and distress are not two poles on opposite ends of a spectrum. Although related, wellbeing and distress are distinct [ 93 ]. Thus, there is no reason to expect time management to have a symmetrical effect on wellbeing and distress. Second, and relatedly, the factors that influence wellbeing and distress are also distinct. Specifically, self-efficacy (i.e., seeing oneself as capable) is a distinct predictor of wellbeing while neuroticism and life events in general are distinct predictors of distress [ 94 ]. It stands to reason that time management can enhance self-efficacy. (Or, alternatively, that people high in self-efficacy would be more likely to engage in time management, although experimental evidence suggests that time management training makes people feel more in control of their time [ 89 ]; it is thus plausible that time management may have a causal effect on self-efficacy. Relatedly, note how time management ability is strongly related to internal locus of control in Table 8 ) In contrast, time management can do considerably less in the way of tackling neuroticism and dampening the emotional impact of tragic life events. In other words, the factors that affect wellbeing may be much more within the purview of time management than the factors that affect distress. For this reason, time management may be less effective in alleviating distress than in improving wellbeing.

Time management and individual differences

Time management is, overall, less related to individual differences than to other variables.

Age, for instance, hardly correlates with time management (with a relatively high consistency between studies, I 2 = 55.79, see Table 8 above).

Similarly, gender only tenuously correlates with time management, although in the expected direction: women seem to have stronger time management abilities than men. The very weak association with gender ( r = -0.087) is particularly surprising given women’s well-documented superior self-regulation skills [ 95 ]. That being said, women’s time management abilities seem to grow stronger over the years ( N = 37, B = -.0049, p < .05, Q model = 3.89(1), Q residual = 218.42(35), I 2 = 83.98, R 2 analog = .03; also see Fig 3 below). More realistically, this increase may not be due to women’s time management abilities getting stronger per se but, rather, to the fact that women now have more freedom to manage their time [ 96 ].

An external file that holds a picture, illustration, etc.
Object name is pone.0245066.g003.jpg

Other demographic indicators, such as education and number of children, were nonsignificant. Similarly, the relationships between time management and personal attributes and attitudes were either weak or nonsignificant, save for two notable exceptions. First, the link between time management and internal locus of control (i.e., the extent to which people perceive they’re in control of their lives) is quite substantial. This is not surprising, because time management presupposes that people believe they can change their lives. Alternatively, it may be that time management helps people strengthen their internal locus of control, as experimental evidence suggests [ 89 ]. Second, the link between time management and self-esteem is equally substantial. Here again, one can make the argument either way: people with high self-esteem might be confident enough to manage their time or, conversely, time management may boost self-esteem. The two options are not mutually exclusive: people with internal loci of control and high self-esteem levels can feel even more in control of their lives and better about themselves through time management.

We also note a very weak but statistically significant negative association between time management and multitasking. It has almost become commonsense that multitasking does not lead to performance [ 97 ]. As a result, people with stronger time management skills might deliberately steer clear of this notoriously ineffective strategy.

In addition, time management was mildly related to hours spent studying but not hours spent working. (These variables cover only student samples working part- or full-time and thus do not apply to non-student populations.) This is consistent with time-use studies revealing that teenagers and young adults spend less time working and more time studying [ 98 ]. Students who manage their time likely have well-defined intentions, and trends suggest those intentions will target education over work because, it is hoped, education offers larger payoffs over the long-term [ 99 ].

In terms of contextual factors, time management does not correlate significantly with job autonomy. This is surprising, as we expected autonomy to be a prerequisite for time management (i.e., you can’t manage time if you don’t have the freedom to). Nevertheless, qualitative studies have shown how even in environments that afford little autonomy (e.g., restaurants), workers can carve out pockets of time freedom to momentarily cut loose [ 100 ]. Thus, time management behaviors may flourish even in the most stymying settings. In addition, the fact that time management is associated with less role overload and previous attendance of time management training programs makes sense: time management can mitigate the effect of heavy workloads and time management training, presumably, improves time management skills.

Finally, time management is linked to all personality traits. Moreover, previous reviews of the literature have commented on the link between time management and conscientiousness in particular [ 32 ]. What our study reveals is the substantial magnitude of the effect ( r = 0.451). The relationship is not surprising: conscientiousness entails orderliness and organization, which overlap significantly with time management. That time management correlates so strongly with personality (and so little with other individual differences) lends credence to the dispositional view of time management [ 101 – 103 ]. However, this finding should not be taken to mean that time management is a highly inheritable, fixed ability. Having a “you either have it or you don’t” view of time management is not only counterproductive [ 104 ] but also runs counter to evidence showing that time management training does, in fact, help people manage their time better.

Does time management work? It seems so. Time management has a moderate influence on job performance, academic achievement, and wellbeing. These three outcomes play an important role in people’s lives. Doing a good job at work, getting top grades in school, and nurturing psychological wellbeing contribute to a life well lived. Widespread exhortations to get better at time management are thus not unfounded: the importance of time management is hard to overstate.

Contributions

Beyond answering the question of whether time management works, this study contributes to the literature in three major ways. First, we quantify the impact of time management on several outcomes. We thus not only address the question of whether time management works, but also, and importantly, gauge to what extent time management works. Indeed, our meta-analysis covers 53,957 participants, which allows for a much more precise, quantified assessment of time management effectiveness compared to qualitative reviews.

Second, this meta-analysis systematically assesses relationships between time management and a host of individual differences and contextual factors. This helps us draw a more accurate portrait of potential antecedents of higher (or lower) scores on time management measures.

Third, our findings challenge intuitive ideas concerning what time management is for. Specifically, we found that time management enhances wellbeing—and in particular life satisfaction—to a greater extent than it does various types of performance. This runs against the popular belief that time management primarily helps people perform better and that wellbeing is simply a byproduct of better performance. Of course, it may be that wellbeing gains, even if higher than performance gains, hinge on performance; that is to say, people may need to perform better as a prerequisite to feeling happier. But this argument doesn’t jibe with experiments showing that even in the absence of performance gains, time management interventions do increase wellbeing [ 89 ]. This argument also founders in the face of evidence linking time management with wellbeing among the unemployed [ 105 ], unemployment being an environment where performance plays a negligible role, if any. As such, this meta-analysis lends support to definitions of time management that are not work- or performance-centric.

Future research and limitations

This meta-analysis questions whether time management should be seen chiefly as a performance device. Our questioning is neither novel nor subversive: historically people have managed time for other reasons than efficiency, such as spiritual devotion and philosophical contemplation [ 72 , 106 , 107 ]. It is only with relatively recent events, such as the Industrial Revolution and waves of corporate downsizing, that time management has become synonymous with productivity [ 43 , 65 ]. We hope future research will widen its scope and look more into outcomes other than performance, such as developing a sense of meaning in life [ 108 ]. One of the earliest time management studies, for instance, explored how time management relates to having a sense of purpose [ 73 ]. However, very few studies followed suit since. Time management thus stands to become a richer, more inclusive research area by investigating a wider array of outcomes.

In addition, despite the encouraging findings of this meta-analysis we must refrain from seeing time management as a panacea. Though time management can make people’s lives better, it is not clear how easy it is for people to learn how to manage their time adequately. More importantly, being “good” at time management is often a function of income, education, and various types of privilege [ 42 , 43 , 46 , 109 ]. The hackneyed maxim that “you have as many hours in a day as Beyoncé,” for instance, blames people for their “poor” time management in pointing out that successful people have just as much time but still manage to get ahead. Yet this ill-conceived maxim glosses over the fact that Beyoncé and her ilk do, in a sense, have more hours in a day than average people who can’t afford a nanny, chauffeur, in-house chefs, and a bevy of personal assistants. Future research should thus look into ways to make time management more accessible.

Furthermore, this meta-analysis rests on the assumption that time management training programs do enhance people’s time management skills. Previous reviews have noted the opacity surrounding time management interventions—studies often don’t explain what, exactly, is taught in time management training seminars [ 18 ]. As a result, comparing the effect of different interventions might come down to comparing apples and oranges. (This might partly account for the high heterogeneity between studies.) We hope that our definition of time management will spur future research into crafting more consistent, valid, and generalizable interventions that will allow for more meaningful comparisons.

Finally, most time management studies are cross-sectional. Yet it is very likely that the effect of time management compounds over time. If time management can help students get better grades, for instance, those grades can lead to better jobs down the line [ 110 ]. Crucially, learning a skill takes time, and if time management helps people make the time to learn a skill, then time management stands to dramatically enrich people’s lives. For this reason, longitudinal studies can track different cohorts to see how time management affects people’s lives over time. We expect that developing time management skills early on in life can create a compound effect whereby people acquire a variety of other skills thanks to their ability to make time.

Overall, this study offers the most comprehensive, precise, and fine-grained assessment of time management to date. We address the longstanding debate over whether time management influences job performance in revealing a positive, albeit moderate effect. Interestingly, we found that time management impacts wellbeing—and in particular life satisfaction—to a greater extent than performance. That means time management may be primarily a wellbeing enhancer, rather than a performance booster. Furthermore, individual and external factors played a minor role in time management, although this does not necessarily mean that time management’s effectiveness is universal. Rather, we need more research that focuses on the internal and external variables that affect time management outcomes. We hope this study will tantalize future research and guide practitioners in their attempt to make better use of their time.

Supporting information

S1 checklist, acknowledgments.

We would like to take this opportunity to acknowledge our colleagues for their invaluable help: Mengchan Gao, Talha Aziz, Elizabeth Eley, Robert Nason, Andrew Ryder, Tracy Hecht, and Caroline Aubé.

Funding Statement

The authors received no specific funding for this work.

Data Availability

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Personnel Review

ISSN : 0048-3486

Article publication date: 13 February 2007

The purpose of this article is to provide an overview for those interested in the current state‐of‐the‐art in time management research.

Design/methodology/approach

This review includes 32 empirical studies on time management conducted between 1982 and 2004.

The review demonstrates that time management behaviours relate positively to perceived control of time, job satisfaction, and health, and negatively to stress. The relationship with work and academic performance is not clear. Time management training seems to enhance time management skills, but this does not automatically transfer to better performance.

Research limitations/implications

The reviewed research displays several limitations. First, time management has been defined and operationalised in a variety of ways. Some instruments were not reliable or valid, which could account for unstable findings. Second, many of the studies were based on cross‐sectional surveys and used self‐reports only. Third, very little attention was given to job and organizational factors. There is a need for more rigorous research into the mechanisms of time management and the factors that contribute to its effectiveness. The ways in which stable time management behaviours can be established also deserves further investigation.

Practical implications

This review makes clear which effects may be expected of time management, which aspects may be most useful for which individuals, and which work characteristics would enhance or hinder positive effects. Its outcomes may help to develop more effective time management practices.

Originality/value

This review is the first to offer an overview of empirical research on time management. Both practice and scientific research may benefit from the description of previous attempts to measure and test the popular notions of time management.

Time measurement
Job satisfaction
Performance management

Claessens, B.J.C. , van Eerde, W. , Rutte, C.G. and Roe, R.A. (2007), "A review of the time management literature", Personnel Review , Vol. 36 No. 2, pp. 255-276. https://doi.org/10.1108/00483480710726136

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Research Article

Does time management work? A meta-analysis

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Concordia University, Sir George Williams Campus, Montreal, Quebec, Canada

Roles Methodology, Validation

Affiliation FSA Ulaval, Laval University, Quebec City, Quebec, Canada

Roles Validation, Writing – review & editing

Brad Aeon,
Aïda Faber,
Alexandra Panaccio

Published: January 11, 2021
https://doi.org/10.1371/journal.pone.0245066
Reader Comments

Citation: Aeon B, Faber A, Panaccio A (2021) Does time management work? A meta-analysis. PLoS ONE 16(1): e0245066. https://doi.org/10.1371/journal.pone.0245066

Editor: Juan-Carlos Pérez-González, Universidad Nacional de Educacion a Distancia (UNED), SPAIN

Received: October 27, 2020; Accepted: December 21, 2020; Published: January 11, 2021

Copyright: © 2021 Aeon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist

Introduction

What does it mean to say that time management works?

Literature search and inclusion criteria

As shown in Fig 1 , the initial search yielded 10,933 hits, excluding duplicates.

PPT PowerPoint slide
PNG larger image
TIFF original image

https://doi.org/10.1371/journal.pone.0245066.g001

https://doi.org/10.1371/journal.pone.0245066.t001

Data extraction and coding

https://doi.org/10.1371/journal.pone.0245066.t002

Meta-analytic procedures

Overall description of the literature

https://doi.org/10.1371/journal.pone.0245066.t003

Time management and performance in professional settings

https://doi.org/10.1371/journal.pone.0245066.t004

https://doi.org/10.1371/journal.pone.0245066.g002

Time management and performance in academic settings

https://doi.org/10.1371/journal.pone.0245066.t005

Time management and wellbeing

https://doi.org/10.1371/journal.pone.0245066.t006

Time management and distress

https://doi.org/10.1371/journal.pone.0245066.t007

https://doi.org/10.1371/journal.pone.0245066.t008

Time management and individual differences

Time management is, overall, less related to individual differences than to other variables.

Age, for instance, hardly correlates with time management (with a relatively high consistency between studies, I 2 = 55.79, see Table 8 above).

https://doi.org/10.1371/journal.pone.0245066.g003

Contributions

Future research and limitations

Supporting information

S1 checklist. prisma 2009 checklist..

https://doi.org/10.1371/journal.pone.0245066.s001

S1 File. Funnel plots.

https://doi.org/10.1371/journal.pone.0245066.s002

S2 File. Dataset.

https://doi.org/10.1371/journal.pone.0245066.s003

Acknowledgments

We would like to take this opportunity to acknowledge our colleagues for their invaluable help: Mengchan Gao, Talha Aziz, Elizabeth Eley, Robert Nason, Andrew Ryder, Tracy Hecht, and Caroline Aubé.

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During the last two decades, there has been a growing recognition of the importance of time in the organizational literature. According to [44] Orlikowsky and Yates (2002), the temporal dimension of work has become more important because of expanding global competition and increased demands for immediate availability of products and services. [19] Garhammer (2002) has pointed at the increased pace of life shown in doing things faster (acceleration), contracting time expenditure (e.g. eat faster, sleep less), and compressing actions (making a phone call while having lunch). Other studies have examined the perception of time in organizational contexts (e.g. [46] Palmer and Schoorman, 1999) and the experience of time pressure among employees (e.g. [25] Jackson and Martin, 1996; [40] Major et al. , 2002; [63] Teuchmann et al. , 1999).

The increasing salience of time is reflected in theoretical as well as practical publications. A number of authors discussed the need for better incorporating time in theoretical models and research designs (e.g. [3] Ancona et al. , 2001; [20] George and Jones, 2000; [71] Wright, 2002). Others focused on the ways in which people in organizations manage their time, and on ways in which these efforts can be improved (e.g. [36] Macan, 1994). In this article we will address time from the second perspective, and review the empirical studies on time management. More specifically, we will review definitions of time management, discuss methods for studying time management, summarize empirical findings on time management and the use and effectiveness of time management methods, identify gaps in the current research literature, and give suggestions for future research.

The interest in time management is by no means new. The problem of how to manage time was already discussed in the 1950s and 1960s, and several authors proposed methods on how to handle time issues on the job (e.g. [13] Drucker, 1967; [33] Lakein, 1973; [39] Mackenzie, 1972; [41] McCay, 1959). They suggested simple remedies such as writing work plans down on paper (so-called "to-do lists") in order to increase job performance. At the same time, some authors (e.g. [13] Drucker, 1967) recognized that planning tasks and activities does not always lead to the completion of planned work, especially when time pressure is high.

[41] McCay (1959) developed a concept...

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What Do We Know About Time Management? A Review of the Literature and a Psychometric Critique of Instruments Assessing Time Management

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T1 - A review of the time management literature

AU - Claessens, B.J.C.

AU - Eerde, van, W.

AU - Rutte, C.G.

AU - Roe, R.A.

N2 - Purpose – The purpose of this article is to provide an overview for those interested in the current state-of-the-art in time management research. Design/methodology/approach – This review includes 32 empirical studies on time management conducted between 1982 and 2004. Findings – The review demonstrates that time management behaviours relate positively to perceived control of time, job satisfaction, and health, and negatively to stress. The relationship with work and academic performance is not clear. Time management training seems to enhance time management skills, but this does not automatically transfer to better performance. Research limitations/implications – The reviewed research displays several limitations. First, time management has been defined and operationalised in a variety of ways. Some instruments were not reliable or valid, which could account for unstable findings. Second, many of the studies were based on cross-sectional surveys and used self-reports only. Third, very little attention was given to job and organizational factors. There is a need for more rigorous research into the mechanisms of time management and the factors that contribute to its effectiveness. The ways in which stable time management behaviours can be established also deserves further investigation. Practical implications – This review makes clear which effects may be expected of time management, which aspects may be most useful for which individuals, and which work characteristics would enhance or hinder positive effects. Its outcomes may help to develop more effective time management practices. Originality/value – This review is the first to offer an overview of empirical research on time management. Both practice and scientific research may benefit from the description of previous attempts to measure and test the popular notions of time management

AB - Purpose – The purpose of this article is to provide an overview for those interested in the current state-of-the-art in time management research. Design/methodology/approach – This review includes 32 empirical studies on time management conducted between 1982 and 2004. Findings – The review demonstrates that time management behaviours relate positively to perceived control of time, job satisfaction, and health, and negatively to stress. The relationship with work and academic performance is not clear. Time management training seems to enhance time management skills, but this does not automatically transfer to better performance. Research limitations/implications – The reviewed research displays several limitations. First, time management has been defined and operationalised in a variety of ways. Some instruments were not reliable or valid, which could account for unstable findings. Second, many of the studies were based on cross-sectional surveys and used self-reports only. Third, very little attention was given to job and organizational factors. There is a need for more rigorous research into the mechanisms of time management and the factors that contribute to its effectiveness. The ways in which stable time management behaviours can be established also deserves further investigation. Practical implications – This review makes clear which effects may be expected of time management, which aspects may be most useful for which individuals, and which work characteristics would enhance or hinder positive effects. Its outcomes may help to develop more effective time management practices. Originality/value – This review is the first to offer an overview of empirical research on time management. Both practice and scientific research may benefit from the description of previous attempts to measure and test the popular notions of time management

U2 - 10.1108/00483480710726136

DO - 10.1108/00483480710726136

M3 - Article

SN - 0048-3486

JO - Personnel Review

JF - Personnel Review

DOI: 10.5772/37248
Corpus ID: 96423292

What Do We Know About Time Management? A Review of the Literature and a Psychometric Critique of Instruments Assessing Time Management

L. Hellsten
Published 23 March 2012

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Time management has helped people organize their professional lives for centuries. The existing literature, however, reveals mixed findings and lack of clarity as to whether, when, how, and why time management leads to critical outcomes such as well-being and job performance. Furthermore, insights relevant to time management are scattered across various disciplines, including sociology, psychology, and behavioral economics. We address both issues by synthesizing and integrating insightful elements from various fields and domains into three novel perspectives on time management. First, we draw on the sociology of time to describe two key concepts: time structures and time norms. We illustrate how time structures and time norms operate at the team, organizational, and national levels of analysis in influencing time management outcomes. Second, we draw on the psychology of time to show how individual differences including time-related beliefs, attitudes, and preferences affect the way people manage time and, consequently, time management outcomes. Third, we rely on the behavioral economics literature to describe how cognitive biases influence individual time management decisions. Integrating insights from a diverse set of fields results in a better understanding of past research and allows us to reinterpret conflicting results prevalent in the time management literature. Finally, we offer directions for future research and discuss implications for how organizations and individuals can implement interventions resulting in a stronger and positive relationship between time management and desirable outcomes.

Kaye Enrique

Ida Sabelis

Abstract The topic of time management inevitably emerges when researching concepts of time in organizations. And, as we live in a society where we deal with organizations all the time and in a variety of forms, we are continually confronted with the way in which time is managed in an'organizational'way. This article departs from a critical overview of time-management literature and confronts the assumptions therein with the experiences of a (female) chief executive, managing her time.

jude tamukong

Shahabuddin Mughal

To survive and succeed in the competent world in today's increasingly hostile and fast-moving business environment, organizations have to manage time efficiently. Employee motivation and satisfaction are the prime aspects of all the organizations nowadays. Proper management of time plays a vital role in motivating the employees and thus improving the performance of the organization. The innovation based organization with the effective use of time management lead towards business growth, enhanced organizational performance and helps in increasing employee's comfort level. This research study is intended to assess the level of time management in public and private sector organizations and then to find the impact of time management on employee satisfaction and the overall performance of the organization. Close ended questionnaires were administered from 260 male and female faculty members and students of public and private universities of Hyderabad and Jamshoro districts. Data was analyzed through independent sample T-test and correlation. The results of the test indicated that employees of both type of organizations act on time management almost equally. The result also indicated that females are more time conscious as compared to

Kaveh Farrokh - Langara College

ronald dulay

Evgeny Osin

In this review questions such as " What is a good use of time? " , " How can one achieve satisfaction with their time? " and " How can one's relationship with time contribute to their well-being? " are raised and discussed with regard to empirical research on various aspects of positive psychology of time. This paper differs from traditional approach to thinking about time in organisations in three substantial ways. Firstly, it reviews the existing empirical research on time use, focusing on the implications of this research for organizations and individuals. Secondly, it highlights the limitations of believing that time is infinitely stretchable and defined good time use as one that results in increased well-being, rather than productivity at the expense of well-being. Thirdly, although the workplace is in the centre of the paper, we view time use from a broader perspective of life and work-leisure balance. A range of evidence is considered, based on both objective and subjective time use studies, suggesting specific measures to increase well-being through time use, first of all, at workplace, but also touching on other domains, such as media, leisure, etc. Based on Self-Determination Theory, we argue that good time use results from choosing activities that help people to satisfy their basic needs and are directed at intrinsic goals (helping other people, establishing relationships, developing and growing as a person, maintaining health and balance in one's life). A pathway to increase basic need satisfaction and, as a result, happiness associated with good time use, is by supporting autonomy: giving people more opportunities for choosing and working towards goals that are self-congruent and intrinsic, benefitting both themselves and societies. Keywords: time management, time use, satisfaction with time use, time use and well-being, positive psychology of time, time affluence, balanced time perspective

Irina Melinte

INTRODUCTIONTime management has increasingly become an issue of crucial relevance.Time needs to be viewed as a complex mathematical value and not as a simple linear graphic, hence the current debate of whether it is better to follow classical methods to speed up the pace as the fast hare or the alternative view of actually slowing down the rhythm like the wise tortoise. The current paper aims at developing, besides the classical and the alternative views regarding time management, a third category of methods that focus on the individual, personal perception of time."Do you begin each day with a planner brimming with goals and to-dos that are important to you, or are you handcuffed by poorly planned days that result in nothing done by day's end? Plan and achieve." This is a constant slogan that we hear daily in all personal development and time management seminaries. But is it all that easy as they say?In today's hectic life style, it is becoming increasingly diffic...

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Time Management Is About More Than Life Hacks

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Your productivity hinges on these three skills.

There is certainly no shortage of advice — books and blogs, hacks and apps — all created to boost time management with a bevy of ready-to-apply tools. Yet, the frustrating reality for individuals trying to improve their time management is that tools alone won’t work. You have to develop your time management skills in three key areas: awareness, arrangement, and adaptation. The author offers evidence-based tactics to improve in all three areas.

Project creep, slipping deadlines, and a to-do list that seems to get longer each day — these experiences are all too common in both life and work. With the New Year’s resolution season upon us, many people are boldly trying to fulfill goals to “manage time better,” “be more productive,” and “focus on what matters.” Development goals like these are indeed important to career success. Look no further than large-scale surveys that routinely find time management skills among the most desired workforce skills, but at the same time among the rarest skills to find.

Erich C. Dierdorff is a professor of management and entrepreneurship at the Richard H. Driehaus College of Business at DePaul University and is currently an associate editor at Personnel Psychology.

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Recent literature on student time management: current practice and trends in research

Introduction

Guides and advice for students and teachers

“Time management is the skill which above all others can make the difference between graduating and drop out.” - Pickford & Brown, 2006, 47.

“Time management is arguably one of two of the most basic study skills, the other one being reading.” – Fry, 1997, 2.

These quotes exemplify the widespread recognition amongst teaching and learning professionals of the fundamental role this learning area plays in successful study. Despite this, there is little published work dedicated to time management for students in UK Higher Education Institutions. Besides a very few books providing specific guidance, advice is found in books on general study practices for various student communities (including research, mature and part-time students) and business manuals for those in employment. In terms of research, there is a considerable body of work on procrastination, mostly from a psychological stance. In addition there is unpublished educational research in doctoral theses and dissertations (mostly conducted with US students), including studies of the effects of interventions, impact of poor time management on cheating, and time management issues for at risk students.

This paper offers a brief survey of both current practice in advice on time management and trends in research. Its scope is restricted to works published after 1990. Titles were obtained through database searches including OCLC WorldCat and ArticleFirst, and the British Library’s Integrated Catalogue. Publications aimed at both students and instructors, and in monograph and journal article formats are reviewed. Full details of both the texts mentioned and other useful texts can be found in the annotated bibliographies linked to here . This section will also include publications aimed at those in employment, which feature useful strategies that might be adapted for students.

Advice for students on time management is found in two types of literature: guides to study practices generally, and more specific guides to time management practices.

There are few single topic guides on time management for students, and (surprisingly considering increases in non-traditional student entrants and student employment) none published in recent years. Fry’s Manage Your Time (1997) is typical of those that do exist, offering a targeted guide to organisation, planning and motivation for students. The book uses case studies to illustrate the kind of time management problems that students may face. It takes a self-reflective approach with self-assessment questionnaires throughout, and provides tools like examples and proforma for planning. Underwood’s Study Time Management (1997) imports ideas from business into HE-level study. Although framed as a guide to time management, the book in fact includes tips for a comprehensive list of effective study practices including working in groups, note-making, writing assignments and exam preparation. Bowes’ Time Management For Students: How To Survive In A World Where There Never Seems To Be Enough Time (1996) is apt for use directly by students, or as a mediated resource for teaching staff, as a photocopiable text with exercises and resources for students to take a self-reflexive approach to time management.

In an article directed at geography students, Kneale (1997) points out that if study time is used effectively, time is freed up for leisure. ‘Maximising play time: time management for Geography students’ illustrates how to adapt generic time management advice to the specific situation of students in particular disciplines, building in sections on the difference between managing time during field work, normal study time, and dissertations.

Basic advice on time management is found in most general guides to study practices. Especially useful is Race’s How to Study (2003), which has relevant chapters on ‘Managing your time’ and ‘Getting started’. Practical advice is given in the form of tips which place particular emphasis on the need to focus on active learning practices (in Race’s term, “high learning pay-off” (20)). Each tip could be easily used as the basis for a learning activity. Northedge’s The Good Study Guide (2005) has a chapter on ‘Taking control of your studies’ which includes advice on taking responsibility and keeping motivated, as well as the more obvious planning time and getting organised.

Guides which are aimed at particular student groups have more targeted suggestions for managing time. For instance, students undertaking lengthy research projects may have particular issues with project management and maintaining motivation over a long period of single subject study. Hunt’s Your Research Project (2005) is aimed at Final Year undergraduates and Masters level postgraduates and includes advice on project management and personal time management. Wisker’s The Postgraduate Research Handbook (2001) has advice specific to postgraduates on achieving a balance, managing time and tasks and getting organised.

Guides for mature students like Rickards’s How to Win as a Mature Student (1992) include (along with the usual advice on managing time and organisation) sections on stress and managing crises. Part-time students are often also mature and share many of the same concerns. Gatrell’s Managing Part-Time Study (2006) has advice on managing studies, sustaining motivation, prioritising competing demands on available time and anticipating challenges.

There are a number of books which focus on specific aspects of time management, or offer particular types of strategy for managing time; the former frequently provide psychological approaches while the latter often offer ‘life-coaching’ advice. Most of these are aimed at a general readership rather than just students. Two books aimed at students are Fry’s Get Organized (2004) which focuses on organisation and includes a chapter on dealing with crises. Sapadin et al’s Beat Procrastination and Make the Grade (1999) identifies six styles of procrastination and offers strategies for each, specifically aimed at students.

Texts aimed at teaching staff which include information on student time management include Pickford & Brown (2006), aimed particularly at new lecturers. They make the point that students sometimes regard time management as an ontological property of some students, rather than a practice which can be learned, and note that because of this attitude, expecting students to learn good time management behaviour by modelling (like expecting them to model their academic writing on the academic texts they read) is not effective: rather assessed tasks may be needed to overcome such preconceptions. Advice on working with procrastinators is covered in a comprehensive volume edited by Schouwenburg et al, Counselling the Procrastinator in Academic Settings (2004). Emerging from an academic conference with the same title, the book includes a variety of papers concerning approaches to working with procrastinators in educational settings including universities.

[Full details of articles mentioned in this section are in the annotated bibliographies linked to here .]

Procrastination dominates trends for research conducted on time management for university students. There is a large and well-established corpus of research into this topic, mostly taking a psychological approach. This will be the subject of a separate review (to follow).

Other research has examined behaviours and considered the effectiveness of strategies in relation to outcomes. Kearns & Gardiner (2007), for instance, examine time management behaviours in University staff and students in relation to stress and effectiveness. They conclude that “a clear sense of career purpose” is the most important factor in a hierarchy of behaviours. Trueman & Hartley (1996) conducted an analysis of time management behaviours in a large cohort of first year Psychology students. They conclude that, while female students report better time management than males, and mature students better than younger students, there is no significant co-relation with academic results.

A common behavioural issue is that students typically claim that they know about time management strategies (such as planning and organisation), but then fail to use them. In two key papers, König & Kleinmann use behavioural decision theory to explain this phenomenon. In a 2005 paper, they examine the phenomenon of ‘deadline rush’, using a mathematical model to show that while decisions about time planning are based on economically logical models, consequent inconsistency of action demonstrates that students fail to internalise the predicted outcomes of the models they use. In a 2007 paper, they examine the ‘discounted utility’ decisions taken by students, showing through two experimental studies that work on tasks with smaller but sooner outcomes is typically prioritised over tasks with larger but later outcomes.

It has been shown in a number of studies that students are more open to skills training which is embedded in subject modules. This propensity has to be balanced against resistance to any significant increase in teaching workloads. A study by Adamson et al (2004) considered the effectiveness of time management instruction using a minimal teaching intervention. First year health science students were surveyed to discover their attitudes towards and practices in time management. They were then given access to a self-directed training package, and surveyed again five weeks later. No significant progress was found, and reasons for this are discussed along with the implications for training interventions. The authors conclude that although all students would probably benefit from time management training, it may be more economical to focus interventions on identifying and supporting those who are most in need of help. An example of this in practice is provided by McFadden (1992). A study was conducted of time management skills among 143 undergraduate business students. The results were used to develop monitoring tools to assess students’ effective use of time and the variables that affected this process.

Papers also report on the development and effectiveness of particular teaching strategies. For instance, Sweidel (1996) considers the use of study portfolios to record and reflect upon study practices generally including time management, while Finn & Crook (2003) report on an online project (STARS) to provide advice on research skills including time management. Ho (2003) looks more closely at individual relationships between supervisor and supervisee in final year undergraduate English projects and how these affect the student’s response to the supervisor’s presentation of time management training.

Specific student groups have their own needs and issues when managing their time. For instance, mature students may be used to managing their time independently (unlike younger students) but may also have more commitments to manage. Blaxter (1994) looks at the time management practices of mature students studying part-time, and balancing study with other commitments. Edwards’ study of Mature Women Students (2003) collates experiences from this group in HE. The discussion examines the impact on family life of trying to balance home and study commitments.

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Preferences for Neurodevelopmental Follow-Up Care for Children: A Discrete Choice Experiment

Original Research Article
Open access
Published: 29 August 2024

Cite this article

You have full access to this open access article

Pakhi Sharma ORCID: orcid.org/0000-0002-3309-0933 1 ,
Sanjeewa Kularatna ORCID: orcid.org/0000-0001-5650-154X 1 , 2 ,
Bridget Abell ORCID: orcid.org/0000-0002-1324-4536 1 ,
Steven M. McPhail ORCID: orcid.org/0000-0002-1463-662X 1 , 3 &
Sameera Senanayake ORCID: orcid.org/0000-0002-5606-2046 1 , 2

Introduction

Identifying and addressing neurodevelopmental delays in children can be challenging for families and the healthcare system. Delays in accessing services and early interventions are common. The design and delivery of these services, and associated outcomes for children, may be improved if service provision aligns with families’ needs and preferences for receiving care. The aim of this study is to identify families’ preferences for neurodevelopmental follow-up care for children using an established methodology.

We used a discrete choice experiment (DCE) to elicit families’ preferences. We collected data from families and caregivers of children with neurodevelopmental needs. The DCE process included four stages. In stage 1, we identified attributes and levels to be included in the DCE using literature review, interviews, and expert advice. The finalised attributes were location, mode of follow-up, out-of-pocket cost per visit, mental health counselling for parents, receiving educational information, managing appointments, and waiting time. In stage 2, we generated choice tasks that contained two alternatives and a ‘neither’ option for respondents to choose from, using a Bayesian d- efficient design. These choice tasks were compiled in a survey that also included demographic questions. We conducted pre- and pilot tests to ensure the functionality of the survey and obtain priors. In stage 3, the DCE survey was administered online. We received 301 responses. In stage 4, the analysis was conducted using a latent class model. Additionally, we estimated the relative importance of attributes and performed a scenario analysis.

Two latent classes were observed. More families with full-time employees, higher incomes, postgraduate degrees, and those living in metropolitan areas were in class 1 compared with class 2. Class 1 families preferred accessing local public health clinics, face-to-face follow-up, paying AUD100 to AUD500, mental health support, group educational activities, health service-initiated appointments, and waiting < 3 months. Class 2 families disliked city hospitals when compared with private, preferred paying AUD100 or no cost, and had similar preferences regarding mental health support and wait times as class 1. However, no significant differences were noted in follow-up modality, receiving educational information, and appointment management. The relative importance estimation suggested that location was most important for class 1 (28%), whereas for class 2, cost accounted for nearly half of the importance when selecting an alternative. The expected uptake of follow-up care, estimated under three different hypothetical scenarios, may increase by approximately 24% for class 2 if an ‘ideal’ scenario taking into account preferences was implemented.

This study offers insights into aspects that may be prioritised by health services and policymakers to improve the design and delivery of neurodevelopmental follow-up care for children. The findings may enhance the organisation and functioning of existing care programmes; and therefore, improve the long-term outcomes of children with neurodevelopmental needs and their families.

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Avoid common mistakes on your manuscript.

Family preferences and barriers may impact broader health service design of neurodevelopmental follow-up care for children; however, there is a gap in eliciting families’ preference, particularly regarding health service design and delivery perspective.
A latent class analysis revealed two classes, with a higher proportion of families with full-time employees, a higher income, and metropolitan residents belonging to class 1 compared with class 2. Most of the decision making in class 1 was driven by the location of follow-up care services, while that in class 2 was influenced by cost of services.
A scenario analysis noted about 24% increase in the uptake of follow-up care when families’ ‘ideal’ preferences were considered.

1 Introduction

Neurodevelopmental disorders are a leading cause of morbidity impacting children and their families as well as healthcare and education systems [ 1 ]. These consist of a group of disorders, including diagnoses of intellectual disorders (ID), communication disorders, autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), learning disorders (LD), motor disorders, and tic disorders [ 2 ]. In existing literature, few studies have measured the prevalence of neurodevelopmental disorders. While there are estimates for some individual diagnoses, collective prevalence estimates are lacking. A national survey in the United States estimated the weighted prevalence of ADHD, ASD, ID, and LD to be 8.5%, 2.9%, 1.4%, and 6.4%, respectively, in 2019–2020 [ 3 ]. These estimates are comparable to reports from other high-income countries; for example, the Australian Institute of Health and Welfare reported the prevalence of ADHD in Australian children to be 8.2% [ 4 ].

In this manuscript, ‘neurodevelopmental follow-up care’ refers to the care provided to children at risk of or diagnosed with neurodevelopmental disorder or delay and their families, to monitor the developmental outcomes and provide suitable support and services [ 5 , 6 ]. Effective follow-up care programmes are likely to require sustained engagement with healthcare services. To promote sustained engagement with services, follow-up care programmes ought to be designed considering the preferences and needs of families.

Despite the importance of including family preferences in the design and delivery of neurodevelopmental care programmes, this topic has received little attention in research to date. Previous neurodevelopment care-related studies have primarily focussed on diagnosis-specific issues without consideration of how family preferences and challenges may impact broader health service design [ 7 , 8 , 9 , 10 ]. For example, waiting time has frequently been reported as a major barrier to accessing developmental services for children [ 11 , 12 , 13 ]. Another important healthcare-service-related factor for families is the cost associated with neurodevelopmental assessments, therapies, medications, as well as travelling or accommodation for accessing services [ 11 , 14 ]. A lack of educational resources [ 11 , 15 ] and mental health support for parents [ 16 ] have also been noted. These known factors are likely to be priority considerations for families of children with neurodevelopmental needs, with implication for the design and improvement of health services.

Preferences can be identified using several methods. Previous studies in this clinical area have elicited preferences using interviews, focus groups, and surveys [ 17 , 18 , 19 , 20 ]; however, quantifying weighted preference of attributes (features) cannot be captured using these methods. Additionally, the design and analysis associated with these methods may not be robust enough to disentangle preferences for the purpose of health service design. Discrete choice experiments (DCE) are an increasingly used preference method that can quantify people’s preferences where individuals are presented with several hypothetical health scenarios (choice tasks), each containing several alternatives (options) with different attributes for the individuals to choose from [ 21 ]. A recent systematic review by our research team indicated that DCE is a suitable method for studies related to neurodevelopmental follow-up care [ 22 ]. However, limitations were noted in the methodology of the studies included in the review, such as not providing reasons for forced choices, not conducting pilot tests, and using numerous attributes or choice sets, which made it cognitively burdensome for respondents.

Given the methodological limitations and the lack of eliciting preferences in this field, particularly from the perspective of health service design and delivery, this study aimed to identify families’ preferences for neurodevelopmental follow-up care for children in Australia using a DCE.

This study was approved by the Human Research Ethics Committee, Queensland University of Technology (approval number: 6468). Written consent was obtained from all the participants. The DCE design and analysis were reported based on the checklist and reports of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Conjoint Analysis Task Forces (Table 1 of Online Resource 1, see electronic supplementary material [ESM]) [ 23 , 24 , 25 ]. Recent guidance published by the International Academy of Health Preference Research (IAHPR) was also used to ensure that the methodological approaches aligned with current best practices in health preference research [ 26 , 27 ].

A DCE has several stages that makes it a robust technique for eliciting preferences. These stages broadly include attribute identification and selection, experimental design strategy, final survey administration, and econometric model and data analysis [ 28 , 29 , 30 , 31 ]. Several components within these stages were specifically adopted for our DCE, and this process is outlined in the flowchart in Fig. 1 .

Flowchart of discrete choice experiment study process. MNL multinomial logit, MMNL mixed multinomial logit, LCM latent class model, AIC/N Akaike information criterion/number of observations, LL Log likelihood function

2.1 Stage 1: Attribute Identification and Selection

It is typically recommended to conduct qualitative groundwork to support the identification and selection of attributes [ 23 , 32 , 33 ]. We conducted a systematic review and in-depth interviews to explore a broad range of attributes, followed by expert panel discussions to narrow down the attribute list for inclusion in the DCE. These are explained in detail below.

2.1.1 Systematic Review

We undertook a systematic review to identify a list of attributes. We systematically searched PubMed, Embase, CINAHL, Scopus, and Google Advance databases. After the title and abstract ( n = 6618 papers) and full-text ( n = 16 papers) screening, we obtained a comprehensive list of attributes ( n = 102 attributes, Table 2 of Online Resource 1, see ESM) from international literature. This review is published elsewhere [ 22 ].

2.1.2 Interviews

We interviewed families of children with neurodevelopmental delays to obtain their first-hand experiences regarding follow-up care. Due to the sensitive nature of the condition, recruiting a large number of participants was challenging [ 34 , 35 ]. Moreover, we faced challenges due to attempted imposter participation after recruiting via open postings on social media platforms associated with a neurodevelopment organisation [ 36 ]. This initially caused disruption and delays in the study, and we chose not to use any data collected from the attempted imposter participants. We promptly implemented strategies to prevent such occurrences in subsequent recruitment with a stricter screening process and recruitment via a closed social media page affiliated with the same organisation. In doing so, we were able to recruit two participants for in-depth interviews that provided valuable insights into the topic. Given time and resource limitations we were not able to recruit additional participants. Content analysis of the interview transcripts by PS (and confirmed by BA) identified 14 attributes/themes which aligned with the attributes identified in our systematic review (Table 3 , Online Resource 1, see ESM).

2.1.3 Expert Panel

Limitations, including the cognitive burden on respondents and sample size constraints, prevented the inclusion of all identified possible attributes via the preliminary qualitative work [ 37 ]. Typically, five to seven attributes are ideal for a DCE [ 38 , 39 ]. Consequently, before the expert panel discussion, a team of health economists and a qualitative research expert on neurodevelopment-related care (SK, BA and PS) reviewed the attributes identified from the review and interviews to prioritise their inclusion based on their relevance to the research question and contextual importance in Australia. Irrelevant attributes were excluded, and similar attributes were combined, reducing the list to 11 attributes. (Table 4 , Online Resource 1, ESM).

This was followed by expert panel discussions to further narrow the attribute list. The expert panel ( n = 5) included health economists and qualitative research experts with extensive experience in conducting interviews with a similar cohort, that is, families of children needing neurodevelopment-related care. This exercise was conducted individually with each expert. The 11 attributes were presented to the experts, who discussed and ranked the attributes in the order of their importance, relevance to neurodevelopmental follow-up care for children, and suitability to our research question. Then, all experts gathered to finalise the attributes to be included in the DCE, aiming to reach a consensus via unanimity, ensuring no objections persisted. At this point, seven attributes were finalised. This exercise also included defining the levels of attributes. The final list of attributes and levels and their descriptions are provided in Table 1 .

2.2 Stage 2: Experimental Design Strategy

This stage includes the construction of choice tasks using a suitable experimental design. A choice task includes hypothetical scenarios, each containing a certain number of alternatives [ 21 ]. Those alternatives will comprise the finalised attributes and levels from Stage 1, allowing individuals to choose from them. This is followed by pre-testing and pilot testing of the design to check the functionality of the survey and to obtain suitable priors for the final DCE, respectively.

2.2.1 Constructing Choice Tasks

We included two unlabelled alternatives (Follow-up care A and Follow-up care B) and a ‘neither’ alternative. In this case, neither indicates that respondents do not prefer any of the provided options (A and B), but it does not necessarily mean that they do not prefer follow-up care at all. We employed a dual response approach where respondents were first given three options, ‘A’, ‘B’ and ‘neither’ (unconditional response). If they selected ‘neither,’ they were presented with a forced choice between ‘A’ and ‘B’ (conditional response). The inclusion of a ‘neither’ option was to make the choice tasks more realistic, as in the real world, there may be many scenarios where people do not prefer any available choices. To prevent bias due to the placement order of the alternatives, the position of the ‘neither’ alternative in the choice task was varied randomly.

The below formulae were used to estimate the total number of profiles and pairwise choice tasks, respectively:

Where P is the possible number of profiles and M1, M2, M3 are the number of attributes corresponding to the number of levels (L1, L2, L3) for different attributes [ 40 ]. Given three attributes with three levels (L1 = 3, M1 = 3), two attributes with two levels (L2 = 2, M2 = 2), and two attributes with five levels (L3 = 5, M3 = 2), this would result in 2700 (3 3 × 2 2 × 5 2 ) possible profiles and 3,643,650 combinations of pairwise choice tasks [2700 (2700 – 1)/2]. A full factorial design (a design encompassing all possible combinations of attribute levels) was not possible, so we used a fractional factorial design (a subset of the full design). A d- efficient design was generated using NGENE software [ 41 ].

To estimate the minimum number of choice tasks required for our design, we used the formula:

Where S min is the minimum number of choice tasks, K is the total number of parameters to estimate, and A is the number of alternatives [ 40 ]. According to this, our minimum requirement was eight choice tasks. Usually, this estimate is multiplied by two or three to ensure an adequate sample size for the study. We tested different numbers of choice tasks including 20, 30, 40, 50, and 60 and compared the normalised d- error of each design (Fig. 1 of Online Resource 2, see ESM). Even though the normalised d- error reduced with the increase in number of choice tasks, the design with 30 choice tasks was selected considering it had near-best balance. A design achieves balance when each level of an attribute occurs an equal number of times [ 23 ]. While there is limited existing literature on determining the number of choice tasks per person, eight to 16 tasks are usually considered suitable [ 23 ]. For large designs, it is recommended to use ‘blocks’, which divides the main design into groups to ease the cognitive burden on participants [ 42 ]. Therefore, the 30 choice tasks were divided into three blocks of ten. In the absence of prior information on the coefficients of different attributes, small positive or negative priors (non-informative priors) were used. The design codes are provided in Fig. 2 of Online Resource 2 (see ESM). An example choice task is given in Fig. 2 .

Example of choice set

2.2.2 Pre-Testing

We conducted two think-aloud exercises with ten randomly selected choice tasks from the design [ 43 ]. Participants of the two think-aloud exercises were parents from the general population ( n = 10 and n = 8, respectively). The participants were presented with one choice task at a time and asked to verbalise their thought process in selecting an option from ‘A’, ‘B’, or ‘neither’. They were also requested to indicate if they had problems understanding specific attributes or levels. Their feedback was used to refine the comprehension of choice tasks, including restructuring wording for specific attributes, clarifying the meaning of some levels, providing examples alongside levels for better understanding, and improving the hypothetical scenario provided. Participants also noted that the choice tasks were easy to understand, with seven attributes not overwhelming and ten choice tasks not overly burdensome. After addressing these adjustments, we proceeded with the first pilot test.

2.2.3 Pilot-Testing

The survey was administered to families of children aged below five years, not specifically targeting those with neurodevelopmental disorders. This broader approach was considered to engage a larger audience and obtain a comprehensive perspective. We used PureProfile, an online survey panel, to administer our survey. The survey had four parts: eligibility questions, ten DCE choice tasks, demographic details, and questions related to the comprehensibility of the survey. In addition to these ten choice tasks per participant, a repeated choice task was also included to assess the internal reliability and consistency of responses, creating 11 DCE choice tasks presented to each participant. We received 53 responses. After analysing the data using multinomial logit (MNL) model, using NLOGIT software [ 44 ] (analysis codes are provided in Fig. 3 of Online Resource 2, see ESM), we found that the cost attribute was dominant as its parameter estimate was higher than the rest of the attributes by a large margin. However, about 26% of the respondents, identified through demographic questions, were families or caregivers of children with neurodevelopmental disorders. In this subgroup, cost was not a dominant attribute. Because of this divergent finding, we decided to focus on this population and conducted another pilot study.

For the second pilot, we used the same experimental design as the first pilot and recruited families of children aged under five years with neurodevelopmental delays via PureProfile. The survey was administered, and we received 48 responses. After analysing the data using the MNL model, we found no issues regarding the dominance of any attributes. We used the parameter estimates obtained from this pilot as priors in the main DCE.

2.3 Stage 3: Final DCE Survey

For the final survey, choice sets were generated again in NGENE using priors obtained from the second pilot. We used a Bayesian d- efficient design optimised using a swapped algorithm with two Gauss draws. We compared this design to the second pilot survey design using the ‘eval’ function in NGENE (second pilot d- error = 0.43), and the efficiency of the Bayesian design was improved by 9% (Bayesian d- error = 0.39). The codes are provided in Fig. 4 of Online Resource 2 (see ESM). Bayesian efficient designs are known to enhance the reliability of parameter estimates and reduce sample size requirements [ 45 ]. After a design was produced, the survey was administered. The eligible survey participants were families or caregivers of children < 5 years of age with neurodevelopmental needs. They were recruited via PureProfile. Full DCE survey, participant information sheet, and more information about PureProfile is provided in Online Resource 3 in the ESM.

We used S-estimates to determine our sample size. The S-estimate is the minimum sample size required for a parameter, with Sb mean estimates incorporating the full range of Bayesian priors for a comprehensive interpretation [ 46 ]. In our study, Sb mean estimates indicated a minimum sample size of 11,772, which was impractical. Therefore, we opted for the next largest Sb mean estimate of 105. With a three-block design, our target sample size was approximately 315 participants. We ultimately collected 301 responses from the final DCE survey. More details and explanations on the design, priors, and sample size can be found in Online Resource 4 (see ESM).

2.4 Stage 4: Econometric Model and Data Analysis

A panel mixed multinomial logit (MMNL) model was used to identify the overall preferences at a population level; however, this output was presented in the supplementary information and not in the main analysis (Table 5 , Online Resource 1, ESM). A panel latent class model (LCM) was used and presented as the main analysis due to its ability to account for preference heterogeneity and identify preferences within different groups of the sample [ 47 ]. In this study, we aimed to determine if decisions can be linked to certain demographic groups. By identifying 'latent' or ‘hidden’ groups with distinct preferences and needs, neurodevelopmental care services can be tailored to meet the specific requirements of each group, which may ensure that access to care is provided to maximum families and their children. Moreover, we considered the theoretical interpretability of different models and decided to use panel LCM due to its clearer understanding and potential implications.

Since LCM is probabilistic, respondents belong to a group up to a certain probability and can be allocated the group they are most likely to be a member of [ 40 ]. Posterior probabilities were estimated to describe the differences in sociodemographic characteristics across groups. For each respondent, the posterior probability that they belong to each latent class was calculated and they were assigned to the class with the highest probability. Class membership was determined solely from the estimated preferences from the LCM [ 48 ]. The panel LCM codes are provided in Fig. 5 of Online Resource 2 and information on model fits are provided in Table 6 of Online Resource 1 (see ESM).

The below utility was estimated:

Where V is the observable relative utility and is a function of constant β 0 and 16 parameters measuring the effect of neurodevelopmental follow-up care attributes described in Table 1 .

Descriptive statistics were used to describe demographic characteristics of the overall sample and each latent class. The DCE data were categorical, therefore they were dummy coded. We conducted a scenario analysis (NLOGIT codes provided in Fig. 6 of Online Resource 2, ESM). By controlling attribute levels within hypothetical scenarios, researchers may assess respondents' sensitivity to variations, providing insights into potential policy outcomes, and informing decision-making processes [ 49 , 50 ]. Moreover, to evaluate the extent to which each attribute contributes to the overall utility of follow-up care, we calculated the relative importance of attributes within each class [ 51 ]. To estimate the relative importance of each attribute, the difference between the highest and lowest coefficient of each attribute was divided by the sum of these differences across all attributes [ 48 ].

We performed Monte Carlo simulation to address the uncertainty associated with the parameters used in estimating the relative importance of attributes. For this purpose, a probability distribution was assigned to each parameter, reflecting the uncertainty surrounding its value [ 52 ]. This study used normal distributions (mean and standard error) to represent parameter estimates for each attribute level. Our Monte Carlo simulations consisted of 1000 iterations, whereby values were randomly sampled from the parameter distributions to generate simulated observations. These simulated observations were then used to estimate the mean and 95% confidence intervals of the relative importance of attributes in each class.

All analyses were done using NLOGIT Version 6 software [ 44 ] and R Version 1.3.1093 [ 53 ]. The output from the unconditional dataset was reported as the main result. Due to the small and incomparable sample size, the output from the conditional dataset is presented in Table 7 of Online Resource 1 in the ESM.

3.1 Respondents’ Characteristics

Demographic details of the respondents are presented in Table 2 . Overall, there were more female respondents than male (56.4% and 42.5%, respectively). Most respondents had completed a higher education qualification, including both undergraduate and postgraduate degrees. Over half of the respondents had full-time jobs and earned 60,000–120,000 Australian dollars (AUD). Approximately 64% of the respondents lived in metropolitan cities, and some lived in regional (27%) and rural (7%) areas. The median age of family members was 35 years. Most respondents’ children were diagnosed with ADHD (34%) or ASD (19%).

3.2 DCE Results

3.2.1 latent class analysis.

The results of the panel LCM are provided in Table 3 . We tested the model with two and three classes, and ultimately selected the model with two classes because of its enhanced comprehensibility and clearer interpretability. The probability of individuals being in class 1 is more (63%) than them being in class 2 (37%).

Class 1 had more families with postgraduate degrees, full-time employees, and more individuals with higher incomes living in metropolitan areas when compared with class 2. Conversely, class 2 had more families with undergraduate degrees, part-time or casual employees, slightly lower income, and higher representation of individuals from regional areas when compared with class 1. Both groups showed similar age distributions and types of neurodevelopmental disorders, with most respondents' children diagnosed with ADHD and ASD. However, class 2 had a higher proportion of ADHD and ASD diagnoses, while class 1 had a higher representation of learning disabilities, intellectual disability, and conduct disorder.

The alternative specific constant (ASC) reveals that preferences for follow-up care options A and B were positive in class 1, indicating a tendency for individuals in this class to choose these options over the reference option (neither). On the contrary, preferences were negative in class 2, suggesting a preference against options A and B compared with the reference option. There was a significant heterogeneity regarding these preferences among respondents.

In class 1, local public health community clinics were preferred (0.97, 95% CI 0.45–1.50) when compared with private services. A similar trend was observed in class 2; however, it was not statistically significant. Moreover, city-based hospitals were disliked by families in class 2 (− 0.21, 95% CI − 0.44 to 0.02). Regarding modality of follow-up care, families from class 1 preferred face-to-face follow-up sessions (0.23, 95% CI 0.08–0.38); however, no significant difference was observed in preferences regarding this attribute in class 2. Regarding out-of-pocket costs, even though both classes 1 and 2 preferred to pay at least AUD100, the cost was more crucial in the decision-making process for families in class 2 (2.72, 95% CI 2.27–3.16) compared with those in class 1 (0.50, 95% CI 0.13–0.85). Both class 1 and class 2 preferred having mental health support provided to them (class 1: 0.13, 95% CI 0.01–0.25 and class 2: 0.43, 95% CI 0.23–0.64). While class 1 preferred group activities (0.27, 95% CI 0.06–0.48) and online learning (0.22, 95% CI 0.005–0.43) as ways of receiving educational information, there was no significant difference in the preferences regarding this attribute in class 2. Class 1 preferred health service providers initiating reminders and scheduling appointments for them (0.33, 95% CI 0.21–0.46), whereas class 2 was found to be indifferent to this aspect. Lastly, both classes preferred waiting <3 months for follow-up appointments compared with a waiting time of around 12 months.

3.2.2 Relative Importance

The relative importance of all attributes was estimated for each class and is reported in Table 4 . In class 1, the most important attribute was location, driving 28% of the decision making, followed by cost (26%) and waiting time (16%). In class 2, cost accounted for nearly half of the importance when selecting an alternative (49%).

3.2.3 Scenario Analysis

The scenarios were discussed and finalised by the research team considering their practicality in the real world and alignment with the research question. The scenarios and analysis results are presented in Table 5 . The probabilities of uptake for three hypothetical scenarios were compared with the probability of uptake for the base scenario (with reference levels of all attributes). For the overall sample, the base scenario showed uptake probability of 88.7%. For scenario 1, the probability of uptake increased to 98.6%, making it the most preferred or an ‘ideal’ follow-up care scenario. The probabilities of uptake for scenario 2 and scenario 3 are 88.9% and 96.8%, respectively. While there was not a considerable difference in preferences among all scenarios in class 1, class 2 showed a significantly higher preference for scenario 1 (99%) and a lower preference for scenario 2 (66%).

4 Discussion

The primary aim of this study was to identify families’ preferences for neurodevelopmental follow-up care for children. Overall, the findings suggested that both class 1 and class 2 families preferred accessing local public health community clinics and disliked city-based hospitals. Both classes favoured receiving mental health support and waiting < 3 months. Even though both classes preferred paying AUD100, the next preferred choice for class 1 was AUD500, whereas class 2 preferred no cost. While class 1 liked face-to-face follow-up, group activities for receiving educational information, and health service providers initiating reminders and scheduling appointments for them, no significant differences were observed in preferences regarding these attributes in class 2. The heterogeneity of follow-up care preferences may be of value to policy makers and health services officials that may facilitate care management service improvements for children and families.

Families in both classes preferred local public health community clinics, but this preference was not statistically significant in class 2. There is limited published literature on public care services, particularly for neurodevelopmental follow-up care for children. Some available services, such as Child Development Services in the state of Queensland, Australia, offer formal evaluation and diagnosis of neurodevelopmental delays, which are crucial for obtaining referrals for early interventions [ 54 ]. In a general healthcare context, these facilities may be located closer to home, which may save travel time and improve access, especially in rural or regional areas, and sometimes offer more services compared with private care [ 55 , 56 ]. The cost of private services is relatively high; however, waiting time may be less and some families may be able to pay for private services to bypass public waiting lists. City-based hospitals may not be preferred due to their centralised nature, leading to extra travel time for patients, particularly if the families are from rural or regional areas [ 57 ]. Since there is a higher representation of families from regional areas in class 2, this may explain why they strongly dislike city-based hospitals. Additionally, a higher proportion of lower-income families in class 2 may contribute to their significant dislike, as increased travel time and costs pose a greater burden.

Families in both classes preferred face-to-face follow-up, although this was not statistically significant in class 2. This was somewhat contradictory, as recently, an increased trend of virtual care has been noted due to enhanced convenience and reduced waiting times [ 58 , 59 , 60 ]. Additionally, recent reports from the United States recommend integrating a telehealth model in neurodevelopmental assessments [ 61 , 62 ]. There may be several potential barriers however, including technical issues, difficulty in ensuring confidentiality, limited control over distractions at home, difficulties in administering standardised testing virtually, and access to technology and electronic devices. However, virtual care may be further explored for those services where specialists do not need to physically examine the child. Even though it may improve access and efficiency in follow-up care, facilitating quicker connections for patients needing support and services, further investigation is therefore still required about its implementation to meet both family and heath service needs.

It is likely that the presence of higher-income individuals and more full-time employees in class 1 meant that cost was not the driving factor of decision making in this group. They preferred to pay at least AUD100 or AUD500 for their child’s treatment. Interestingly, the preference for AUD1500 over no cost in class 1 may be perceived as receiving more service benefits and better quality. However, the coefficient for AUD0 was statistically insignificant, so a definitive conclusion should not be made. However, for class 2, which was associated with comparatively lower-income individuals with more part-time or casual jobs, cost was the most crucial deciding factor (nearly 50% relative importance for this attribute). They preferred to pay AUD100 followed by paying no cost. Other studies have shown cost as a crucial factor in decision making [ 63 , 64 , 65 , 66 ], especially for a lower-income group as they often encounter significant financial burden [ 67 , 68 ]. In Australia, the National Disability Insurance Scheme also financially supports those who qualify, but the qualification process may be time consuming and difficult to access for some individuals [ 69 ].

Families of both classes preferred mental health support. Mental health for families may be a lower priority when they are concerned about their children. Previous studies have indicated that parents of children with neurodevelopmental needs often experience poor wellbeing, anxiety, and depression [ 70 , 71 , 72 , 73 ]. Despite this information, very few neurodevelopmental follow-up services have included mental health support for families as a crucial consideration. For example, a cardiac neurodevelopmental programme in Canada counselled 100% of the families in that programme on neurodevelopmental outcomes [ 74 ]. Moreover, studies have reported that financial stress may also negatively influence mental health and wellbeing [ 75 , 76 ], which may be the reason that mental health support was relatively more important for class 2 (more low-income families), but the least important attribute for class 1 (more high-income families). This may inform decision makers to prioritise families’ mental health to reduce burden, improving their resilience related to the neurodevelopmental outcomes of their children.

Providing appropriate educational resources to families regarding neurodevelopmental delays and problems specific to their children is important. Even though class 2 families did not think this attribute was important, class 1 families considered group activities such as playgroups or peer support classes and online learning material helpful. Prior evidence shows that playgroups or peer support activities have been beneficial for families as they offer opportunities to connect with others with similar experiences, feel accepted, enhance their knowledge and skills, as well as provide an environment for child development, socialising, and enjoyment [ 77 , 78 ]. Online learning material or training for families may also be valuable as it may make them self-reliant and skilled at addressing parenting stress or issues with their children, especially if they do not have the time to attend traditional classes [ 79 , 80 ]. Preliminary evidence from a study in Canada indicated high parent satisfaction and improved parenting confidence, parenting skills, and child behaviour with the development of an online training programme that included educational modules and videoconference sessions with a therapist [ 81 ]. On the contrary, educational information provided face-to-face via a professional was not as effective, possibly due to its higher cost or the time commitment required, with sessions typically lasting 45–90 minutes [ 79 , 82 ]. Moreover, studies have shown that using a phone application can be a useful tool for educational information purposes and accessing support related to neurodevelopmental follow-up care [ 83 , 84 ]. It may be interpreted for class 2, which had more families with a lower income, that these educational information methods were not deemed necessary as they may have an additional cost.

In terms of managing appointments, it had no significant effect on class 2 families, but class 1 families preferred health service providers initiating reminders and scheduling appointments for them. A recent study reported that when health service providers scheduled appointments for neurodevelopmental evaluation, adherence was higher compared with when families scheduled appointment themselves [ 85 ]. This may lead to more effective and quick formal evaluations for children, identifying delays earlier and providing appropriate treatments, thus facilitating better patient-centred care. Our interviews confirm that implementing such systems could serve as a safety net. However, flexibility should also be provided based on the availability of family members, hence forming a hybrid care management system.

Waiting time was crucial for both classes. In healthcare, this issue has persisted where people often wait excessively to access services. This may be detrimental in the case of neurodevelopmental issues, as by the time families seek assistance for their child, the issue may have already progressed, with considerable literature highlighting this concern [ 86 , 87 , 88 , 89 , 90 , 91 ]. The latest findings from the Sydney Child Neurodevelopment Research Registry suggest that the average age that caregivers identified developmental concerns was 3 years of age, but the average age for receiving a developmental assessment was about 6 years [ 92 ]. These waiting delays may challenge implementing early intervention and support policies that depend on timely developmental assessments. This is further confirmed by one of our interview participants, who reported waiting for 3 years for their child's follow-up visit. During this waiting period, they experienced anxiety due to uncertainty about their child's condition and the lack of access to suitable treatment or support.

The scenario analysis suggests that there may be a likely impact of different scenarios on programme uptake. It shows the importance of considering various system-level aspects to optimise the effectiveness of neurodevelopmental follow-up care for children and families. Even though the different scenarios did not make notable differences in preference for class 1, the significant uptake increase observed in the first scenario—nearly 10% for the overall sample and nearly 24% for class 2—indicates that future programmes may be designed to account for the preferences identified in that scenario. This may include policies designed to reduce out-of-pocket costs, improve access to local public health community clinics, minimise waiting times, provide families online learning material, promote mental health counselling, and enhance coordination between healthcare providers and families. Other studies have conducted scenario analysis including DCEs regarding maternal health, doctor attendance, vaccination preferences, hypertension management, and environmental health fields [ 49 , 50 , 93 , 94 , 95 , 96 ], consequently informing policy simulations.

4.1 Further Steps

This study has a range of implications. The findings may be useful for Child Health Services and Child Development Services to enhance their neurodevelopmental follow-up care services. Moreover, health insurance policymakers may use these findings to step up to cover the costs of more services, including providing education and mental health support to families. Methodology-wise, DCEs may be conducted considering different perspectives (e.g., taxpayers’ perspective). Since taxpayers fund these services as a society, eliciting their preferences regarding the structure and delivery of care may also be an essential voice. The findings may also be integrated with economic evaluations to predict service uptake or cost effectiveness of neurodevelopmental care services. Furthermore, virtual care solutions may be explored to enhance service accessibility and reduce waiting times. Implementing such strategies may ensure practical use of healthcare resources and improved overall patient experiences. While including children's preferences was outside the scope of this study, it is a crucial area for future research, as also suggested by another study [ 97 ].

4.2 Strengths and Limitations

While this may not be considered as a strength or a limitation, it is worth noting that most of the sample in this study were better educated than the general population, which might imply that they may be more informed and proactive in seeking neurodevelopmental care for their children; this may have influenced preferences and decision-making processes.

It can be challenging to conduct DCEs in neurodevelopment-related contexts. Generating an apt design that focuses on the specific population and is methodologically correct remains imperative. Previous DCEs conducted in this context have evident limitations in their designs. For instance, absence of justification for forced choices (i.e., respondents not having the freedom to opt out if they were not interested in an option [ 98 , 99 ]); using many attributes (e.g., n = 20) and choice tasks (e.g., n = 30) [ 100 , 101 , 102 ]; and not conducting pilot tests [ 10 , 103 ]. These shortcomings were addressed in our study. The inclusion of a ‘neither’ option made the choice task realistic without forcing a participant to choose from the given choices. Our pre-test determined cognitively appropriate numbers of attributes, levels, and choice tasks to ensure that they do not cause a cognitive burden on respondents. The pilot studies informed us about the availability of the target population, due to which the priors we obtained may be comparatively efficient. Several trials in designing the DCE were useful for various reasons, including gaining an understanding of the sample size, obtaining priors, finalising the structure or function of the choice tasks and the overall DCE survey.

Despite several strengths of this study, there were some limitations. Firstly, the qualitative attribute development phase; even though our literature review and expert advice were appropriate for the qualitative groundwork for the DCE, we experienced challenges with recruitment as we encountered a case of participant fraud in online focus groups and interviews. Although it interrupted the study, we identified it early and implemented strategies to prevent this in further recruitment. Moreover, we identified ‘red flags’ for identifying imposter participants in the future [ 36 ]. Secondly, we reported the unconditional dataset as the main output. Even though combining the unconditional and conditional datasets increases the dataset's quality, the combined behavioural model would imply something completely different compared with the unconditional one, which is the true choice of the sample. Thirdly, another limitation of our study is the complexity of the ‘location’ attribute, which may encompass more than just the geographical aspect of healthcare services (e.g., the type of provider). In another instance, the implication of travel time may vary depending on the respondent's location as ‘local’ clinics are often presumed to be nearby for all respondents, while ‘city-based’ hospitals may be more accessible for urban respondents than for those in rural areas. This variability could influence preferences based on individual travel constraints and convenience. Moreover, different levels of the location attribute might imply differences in service quality, and some respondents may have ideological preferences for public versus private providers. Future studies could benefit from disentangling these factors to provide clearer insights into preferences related to healthcare service locations. Fourthly, in terms of analysis, we considered conducting a sub-analysis for various neurodevelopmental disorders, but the sample size (overall and of the two classes) for each diagnostic group were insufficient. Additionally, we did not use a statistical significance test to assess differences between classes as the primary goal of our study was to provide a descriptive overview of the characteristics of the identified latent classes. Given the exploratory nature of our analysis, we aimed to offer an understanding of the profiles within each class rather than to test for statistical differences. Furthermore, the use of statistical significance tests may not be appropriate in this context due to the assumptions required by these tests, which may not be met by the latent class analysis-derived classes. Therefore, we chose to present the descriptive characteristics to highlight the practical relevance and implications of our findings. Lastly, this study represents the combined preferences for all neurodevelopmental disorders or delays. The scope of this study was limited to providing an overview of preferences for informing a healthcare service design perspective rather than individual diagnosis. Given the diversity within neurodevelopmental disorders and individual circumstances, preferences may vary widely among consumers within the same diagnostic category and across different diagnostic groups. Moreover, health system designs internationally have not typically established different health systems and services for individual diagnostic groups, but rather aim to provide comprehensive services that cater to a spectrum of at-risk children regardless of whether they have specific diagnoses upon entering services. Our analysis also supports this information as the sociodemographic characteristic 'types of neurodevelopmental disorders' did not show any evidence suggesting that families of children with different conditions preferred different service models. We have tried to be inclusive of a range of diagnoses and disorders in this study. Therefore, the findings arising from this study should not be interpreted as representing the preferences of all families who may access neurodevelopmental follow-up care.

5 Conclusion

The design and delivery of neurodevelopmental care services for children have room for improvement. We conducted a DCE that identified families’ preferences for neurodevelopmental follow-up care for their children. The findings suggest that health services may benefit from prioritising aspects including making local public health community clinics more accessible, reducing waiting times and costs, providing mental health and educational resources to family members, and organising better care coordination for follow-up visits. Implementing these evidence-based findings may support resource allocation and contribute to improved outcomes for children with neurodevelopmental needs and their families.

Data Availability

The datasets generated and/or analysed during the current study are not publicly available due to privacy considerations as they may contain potentially identifiable information about participants but are available from the corresponding author on reasonable request.

Code Availability

Relevant codes are provided as supplementary material.

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Open Access funding enabled and organized by CAUL and its Member Institutions. This work is part of a programme of research funded through a Medical Research Future Fund (MRFF) Congenital Heart Disease Grant (ARGCHDG0035) 2020-2024. This funder did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Pakhi Sharma: Conceptualization, Data curation, Methodology, Formal analysis, Investigation, Software, Writing—Original Draft, Writing—Review & Editing. Sanjeewa Kularatna: Conceptualization, Data curation, Methodology, Validation, Resources, Writing—Review & Editing, Supervision, Project administration, Funding acquisition. Bridget Abell: Conceptualization, Data curation, Methodology, Investigation, Formal analysis, Validation, Resources, Writing—Review & Editing, Supervision. Steven M. McPhail: Conceptualization, Methodology, Validation, Resources, Writing—Review & Editing, Supervision, Project administration, Funding acquisition. Sameera Senanayake: Conceptualization, Data curation, Methodology, Investigation, Formal analysis, Validation, Resources, Software, Writing—Review & Editing, Supervision.

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Sharma, P., Kularatna, S., Abell, B. et al. Preferences for Neurodevelopmental Follow-Up Care for Children: A Discrete Choice Experiment. Patient (2024). https://doi.org/10.1007/s40271-024-00717-3

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DOI : https://doi.org/10.1007/s40271-024-00717-3

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A systematic review of the key predictors of progression and mortality of rheumatoid arthritis-associated interstitial lung disease.

1. Introduction

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Akiyama et al., 2016 [ ]	Case/control retrospective (2008–2014)	395	69 (20.3)	78 (19.7)	6 (1.5)
Franzen et al., 2016 [ ]	Observational retrospective (2013–2015)	33	17 (51)		6 (22)
Md Yusof et al., 2017 [ ]	Observational retrospective (2004–2015)	56	32 (57)	20 (36)	14 (25)	9 (16)
Mochizuki et al., 2018 [ ]	Observational retrospective, 47.8 months	131
Kim et al., 2010 [ ]	Observational retrospective (2001–2008)	82	59 (72)	20 (24)		8 (9.7)
Zamora-Legoff et al., 2016 [ ]	Observational retrospective (1998–2014)	167	105 (63)	89 (53)	33 (19)
Dawson et al., 2015 [ ]	Observational prospective, 2 years	29	10 (34)		10 (35)	4 (14)
Solomon et al., 2015 [ ]	Observational retrospective (1995–2013)	137	87 (64)	108 (79)		54 (39)
Dixon et al., 2010 [ ]	Observational registry-based cohort study, 3.8 years	14.113	10799 (76)			160 (1.13)
Wolfe et al., 2007 [ ]	Case/control prospective, 3.6 years	17498				100 (27)
Kurata et al., 2019 [ ]	Observational retrospective (2008–2017)	49	14 (35)	6 (12)
Chen et al., 2020 [ ]	Observational retrospective (2008–2017)	241	88 (36)	66 (27)		39 (16)
Hyldgaard et al., 2017 [ ]	Case/control prospective (2004–2015)	679				26 (3.8)
Koduri et al., 2010 [ ]	Prospective cohort study (1986–1998)	52	19 (36)			39 (75)
Tsuchiya et al., 2011 [ ]	Observational retrospective (1996–2006)	144	54 (37.5)	7 (5)		71 (49)
Song et al., 2012 [ ]	Observational prospective (2002–2011)	51			21 (41)
Izuka et al., 2021 [ ]	Observational retrospective (2007–2019)	165	73 (44)	70 (42)	30 (18)	13 (8)
Nurmi et al., 2017 [ ]	Observational retrospective (2000–2014)	59	30 (51)	31 (52)	24 (41)	27 (46)
Oh et al., 2022 [ ]	Observational retrospective (1999–2015)	144	63 (44)	53 (37)		44 (30)
Hozumi et al., 2022 [ ]	Observational retrospective (2007–2019)	58	39 (67)	34 (59)		43 (74)
Cano-Jiménez et al., 2021 [ ]	Observational retrospective (2013–2018)	106	63 (60)	55(61)	53 (50)	18 (17)
Jacob et al., 2018 [ ]	Observational retrospective (1995–2015)	90	65 (72)
Kelly et al., 2014 [ ]	Observational retrospective (1987–2012)	230	135 (59)	103 (65)		90 (57)
Ng et al., 2022 [ ]	Observational retrospective (1997–2013)	214				48 (22)
Li et al., 2019 [ ]	Observational retrospective (2008–2017)	278	106 (38)	91 (33)	83 (29)	53 (69)
Juge et al., 2023 [ ]	Observational retrospective (2013–2018)	4330
Brooks et al., 2022 [ ]	Prospective cohort study, 2 years	227	192 (85)	147 (65)		108 (47)
Rojas-Serrano et al., 2022 [ ]	Observational prospective	37
Kim et al., 2020 [ ]	Observational retrospective (1995–2018)	84	37 (44)	34 (40)		33 (39)
Lee et al., 2016 [ ]	Retrospective cohort study	62		62 (100)
Avouac et al.2020 [ ]	Observational prospective study	147	52 (35)	21 (14)	7 (4.7)
Ito et al., 2017 [ ]	Observational retrospective (2007–2016)	65				16 (24)
Font et al., 2017 [ ]	Longitudinal prospective (2007–2017)	37	26(72)	24(66)		7 (19)
Nieto et al., 2021 [ ]	Longitudinal prospective (2005–2018)	47	25 (54)	26 (55)	10 (21)	16 (34)
Yang et al., 2019 [ ]	Longitudinal prospective (1991–2011)	77	4 (5)	32 (48)		27 (36)
Mena-Vázquez et al., 2024 [ ]	Observational prospective (2015–2023)	148	13 (18)	46 (66)	21 (30)	1 (1.4)
Kim et al., 2024 [ ]	Observational retrospective (1995–2018)	313	139 (45)	202 (65)	125 (40)
Chen et al., 2022 [ ]	Observational prospective, 5 years	60		3 (16)	19 (49)	4 (7)
Kelly et al., 2021 [ ]	Observational retrospective (1990–2015)	290	174 (60)	200 (69)	75 (28)
Venerito et al., 2022 [ ]	Observational retrospective (2021–2022)	30		18 (60)	13 (43)
Yamakawa et al., 2019 [ ]	Observational retrospective (2012–2017)	96	45 (47)	21 (20)	11 (11)	25 (26)
Ekici et al., 2021 [ ]	Observational retrospective (2010–2018)	156	67 (42)	74 (47)		40 (26)
Kakutani et al., 2020 [ ]	Observational retrospective (2009–2014)	261/2702	120 (46)	120 (46)		19 (7)
Wang et al., 2019 [ ]	Observational retrospective (2016–2019)	96	9 (20)	18 (40)	25 (56)	4 (9)
Tanaka et al., 2021 [ ]	Observational retrospective (2010–2019)	125	59 (50)	32 (25)		37 (29)
Kwon et al., 2022 [ ]	Observational retrospective (2016–2022)	310			87 (28)	89 (29)
Kang et al., 2020 [ ]	Retrospective cohort (2006–2015)	1999	759 (38)		415 (21)
Farquhar et al., 2024 [ ]	Observational retrospective (2006–2008, 2011–2013)	100	64 (64)	38 (43)	68 (73)	26 (26)
Marcoux et al., 2023 [ ]	Observational prospective (2015–2018)	181	112 (70)	66 (73)		39 (24)
Tyker et al., 2021 [ ]	Observational retrospective (2006–2019)	70	45 (64)	47 (67)		29 (70)

Category	Potential Risk Factors	References	Effect Estimate
Demographic features	Age *	[ , , ]	OR 0.55–2.91, AUC 0.74
CDAI score *		[ ]	OR 4.7
Laboratory findings	ACPA **	[ ]	HR 3.94
	KL-6 *	[ , , , ]	OR 1.00–72.7
	MMP13 **	[ ]	AUC 0.71
	CXCL11/I-TAC **	[ ]	AUC 0.67
	MUC5B mutation **	[ ]	HR 2.30
Pulmonary function	FVC% pred **	[ ]	HR 3.42
	DLCO% pred **	[ ]	HR 1.72
Underlying radiological features	UIP pattern on HRCT *	[ , ]	OR 2.29–4.11
Pre-treatment	Corticosteroid *	[ ]	HR 15.0
	Nonbiologic DMARDs *	[ , ]	OR 1.75–.75
	Biologic DMARDs **	[ , ]	HR 0.44–2.33

Category	Potential Risk Factors	References	Effect Estimate
Demographic features	Age **	[ , , , , , , , , , , , ]	HR 1.04–4.8
	Male gender **	[ , , ]	HR 2.83–14.5
	Female gender **	[ ]	HR 3.6
	Smoking history **	[ , ]	HR 2.58–3.17
	Low socioeconomic status *	[ ]	HR 2.07
	The onset of ILD before RA onset	[ ]	HR 8.4
Disease activity	DAS28 score **	[ ]	HR 1.21–1.43
	CDAI score *	[ ]	HR 1.07
	MDHAQ score **	[ ]	HR 1.85
	Pain VAS *	[ ]	HR 1.01
	Patient global assessment **	[ ]	HR 1.16
Laboratory findings	RF *	[ , , ]	HR 1.00–2.08
	ESR **	[ , , ]	HR 1.01–1.15
	CRP **	[ ]	HR 1.12 (1.06–1.18)
	LDH *	[ ]	HR 1.05
	KL-6 **	[ , ]	HR 1.00–3.23
	IL-6 **	[ ]	HR 1.04
	SP-D *	[ ]	HR 1.0
Pulmonary function	DLCO % pred **	[ , , , ]	HR 0.97–1.77
	FVC % pred **	[ , , , , , ]	HR 0.97–4.43
	TLCO % pred **	[ ]	HR 0.97
	PaO /FiO *	[ ]	HR 0.94
Underlying radiological features	ILD extent **	[ , , , ]	HR 1.03–4.47
	UIP **	[ , , , , , , ]	HR 2.44–5.84
	Honeycombing *	[ ]	HR 2.49
	Radiomics *	[ ]	HR 9.35
	DAD *	[ ]	HR 2.88
	Emphysema *	[ , ]	HR 3.43–6.84
Comorbidities	COPD **	[ ]	HR 2.12
	Diabetes mellitus **	[ ]	HR 1.09
Pre-treatment	Corticosteroid **	[ ]	HR 1.09
	Nonbiologic DMARDs *	[ , ]	HR 0.16–5.53
	Biologic DMARDs **	[ ]	HR 0.44–2.33
Acute exacerbations of ILD **		[ ]	HR 1.12–3.19

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Click here to enlarge figure

Potential Risk Factors	Study	Effect Estimate
Age at ILD diagnosis **	44, 70	HR 2.18; OR 1.7
Male gender **	70	OR 2.2
Smoking history ***	37, 70	OR 1.7–6.13
DAS28 ***	37	OR 1.71
Arthritis onset **	44	HR 1.87
KL-6 **	74	HR 3.37
DLCO **	53	OR 3.02
UIP pattern **	53	OR 3.47
Combined pulmonary fibrosis and emphysema **	59	OR 6.12
Preexisting rheumatic airway disease **	95	OR 7.40

Prognostic Factor	References	Effect Estimate
Age **	[ , , , , , , , , , ]	HR 1.04–5.02
Male gender **	[ ]	OR 2.5–18.13
Female gender **	[ ]	HR 6.8
Smoking history *	[ ]	HR 1.06–3.89
Disease duration of RA **	[ ]	HR 1.3
ESR **	[ ]	HR 5.35
HAQ disability **	[ ]	OR 2.3
Steinbrocker class 3 or 4 **	[ ]	HR 2.1
FVC% pred *	[ ]	HR 2.52
DLCO ***	[ , ]	HR 0.85–0.97
TLCO **	[ ]	HR 0.98
Final oxygen saturation in the 6MWT **	[ ]	HR 0.62
UIP pattern **	[ , , , , ]	HR 2.3–10.3
Non-UIP pattern **	[ ]	HR 4.9
ILD extent ***	[ , ]	HR 2.40–9.01
Radiological honeycombing **	[ ]	HR 3.69
Combined pulmonary fibrosis and emphysema **	[ ]	HR 2.16
Pleural effusion **	[ ]	HR 14.4
Corticosteroid *	[ ]	HR 2.5
Immunosuppressive agents **	[ ]	HR 3.0
Withdrawal of MTX or LFN after ILD diagnosis **	[ ]	HR 2.18
Diagnostic delay of ILD **	[ ]	HR 1.11
PM *	[ ]	HR 1.67
History of acute ILD exacerbations ***	[ , ]	HR 2.42–6.48

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Groseanu, L.; Niță, C. A Systematic Review of the Key Predictors of Progression and Mortality of Rheumatoid Arthritis-Associated Interstitial Lung Disease. Diagnostics 2024 , 14 , 1890. https://doi.org/10.3390/diagnostics14171890

Groseanu L, Niță C. A Systematic Review of the Key Predictors of Progression and Mortality of Rheumatoid Arthritis-Associated Interstitial Lung Disease. Diagnostics . 2024; 14(17):1890. https://doi.org/10.3390/diagnostics14171890

Groseanu, Laura, and Cristina Niță. 2024. "A Systematic Review of the Key Predictors of Progression and Mortality of Rheumatoid Arthritis-Associated Interstitial Lung Disease" Diagnostics 14, no. 17: 1890. https://doi.org/10.3390/diagnostics14171890

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https://educationhub.blog.gov.uk/2024/08/20/gcse-results-day-2024-number-grading-system/

GCSE results day 2024: Everything you need to know including the number grading system

Thousands of students across the country will soon be finding out their GCSE results and thinking about the next steps in their education.

Here we explain everything you need to know about the big day, from when results day is, to the current 9-1 grading scale, to what your options are if your results aren’t what you’re expecting.

When is GCSE results day 2024?

GCSE results day will be taking place on Thursday the 22 August.

The results will be made available to schools on Wednesday and available to pick up from your school by 8am on Thursday morning.

Schools will issue their own instructions on how and when to collect your results.

When did we change to a number grading scale?

The shift to the numerical grading system was introduced in England in 2017 firstly in English language, English literature, and maths.

By 2020 all subjects were shifted to number grades. This means anyone with GCSE results from 2017-2020 will have a combination of both letters and numbers.

The numerical grading system was to signal more challenging GCSEs and to better differentiate between students’ abilities - particularly at higher grades between the A *-C grades. There only used to be 4 grades between A* and C, now with the numerical grading scale there are 6.

What do the number grades mean?

The grades are ranked from 1, the lowest, to 9, the highest.

The grades don’t exactly translate, but the two grading scales meet at three points as illustrated below.

The bottom of grade 7 is aligned with the bottom of grade A, while the bottom of grade 4 is aligned to the bottom of grade C.

Meanwhile, the bottom of grade 1 is aligned to the bottom of grade G.

What to do if your results weren’t what you were expecting?

If your results weren’t what you were expecting, firstly don’t panic. You have options.

First things first, speak to your school or college – they could be flexible on entry requirements if you’ve just missed your grades.

They’ll also be able to give you the best tailored advice on whether re-sitting while studying for your next qualifications is a possibility.

If you’re really unhappy with your results you can enter to resit all GCSE subjects in summer 2025. You can also take autumn exams in GCSE English language and maths.

Speak to your sixth form or college to decide when it’s the best time for you to resit a GCSE exam.

Look for other courses with different grade requirements

Entry requirements vary depending on the college and course. Ask your school for advice, and call your college or another one in your area to see if there’s a space on a course you’re interested in.

Consider an apprenticeship

Apprenticeships combine a practical training job with study too. They’re open to you if you’re 16 or over, living in England, and not in full time education.

As an apprentice you’ll be a paid employee, have the opportunity to work alongside experienced staff, gain job-specific skills, and get time set aside for training and study related to your role.

You can find out more about how to apply here .

Talk to a National Careers Service (NCS) adviser

The National Career Service is a free resource that can help you with your career planning. Give them a call to discuss potential routes into higher education, further education, or the workplace.

Whatever your results, if you want to find out more about all your education and training options, as well as get practical advice about your exam results, visit the  National Careers Service page  and Skills for Careers to explore your study and work choices.

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Does time management work? A meta-analysis

Aïda Faber

Alexandra Panaccio

Introduction

What does it mean to say that time management works?

Literature search and inclusion criteria

Data extraction and coding

Meta-analytic procedures

Overall description of the literature

Time management and performance in professional settings

Time management and performance in academic settings

Time management and wellbeing

Time management and distress

Time management and individual differences

Contributions

Future research and limitations

Supporting information

Funding Statement

Data Availability

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Does time management work? A meta-analysis

Introduction

What does it mean to say that time management works?

Literature search and inclusion criteria

Data extraction and coding

Meta-analytic procedures

Overall description of the literature

Time management and performance in professional settings

Time management and performance in academic settings

Time management and wellbeing

Time management and distress

Time management and individual differences

Contributions

Future research and limitations

Supporting information

S1 File. Funnel plots.

S2 File. Dataset.

Acknowledgments

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