Cross-sectional and traditional longitudinal study designs, which conduct assessments at one point or across several time points (usually 2-5 surveys months to years apart), have been widely used in the field of mental health epidemiology [1,2]. These methods serve well to provide detailed information on a population’s mental health at a given time and present important insights into epidemiological patterns (eg, prevalence, incidence, and persistence rates). They are also able to identify who within the population under study is more at risk of having mental health problems by assessing risk and protective factors that are static in the middle to long term, such as sociodemographic characteristics, personality traits, or childhood experiences. However, it is known that mental health outcomes are complex and key symptoms and risk factors such as stress, affect, sleep, and suicidal ideation may fluctuate in a matter of hours or even minutes [3-7]. Thus, it is important to study not just who is at risk but also when and under what daily life conditions individuals may be more vulnerable in the short term. This has prompted a diversification in how mental health is studied by introducing high-resolution designs [8]. Ecological momentary assessment (EMA; also referred to as “experience sampling method” [ESM]) is a type of intensive longitudinal study that collects survey data several times a day over multiple days [9], providing information on variables as they fluctuate and unfold in the context of daily life [10]. These data allow for the development of explanatory and predictive models at both an individual and group level [8] and enable individualized and contextualized interventions at critical moments when the risk of mental health problems may be high [3,11,12]. To date, these studies have typically relied on small sample sizes using nonrepresentative sampling designs [7,13], leaving important questions about EMA data representativeness and reliability unaddressed.

Although methodological guidelines have emerged in recent years [7,14,15], there is a large heterogeneity of study designs as well as an underreporting of methodological details and data quality parameters [16,17]. Hence, to avoid methodological anarchy, it is important that studies provide a thorough description of the methodology and basic information about EMA data representativeness in terms of degree and determinants of participation and compliance [16,18]. Providing this information is fundamental for ensuring the validity, reliability, and generalizability of study findings and improving the overall quality of the research. Furthermore, there needs to be an evaluation of the reliability of the measures as well as the participants to assess whether the collected data are robust enough to provide solid results. Assessment of the reliability of the measures in EMA data provides information on how reliable scales are to capture differences between and within individuals [14,19]. Unreliable answers are those that have been provided without paying sufficient attention to the questions, what has been commonly described as “careless responding” [20-22], and can be evaluated in 2 ways. A priori items can be used that either explicitly ask participants whether they have paid attention while answering the assessments or test the participants’ level of attention [23,24]. Alternatively, post hoc analyses have been proposed to detect possible careless responding by looking at parameters such as duration of response or variance in responses [20,21,24].

It has been well established that the university years are a sensitive period for the onset and persistence of mental health problems [25-27], and there has been a growing interest in the use of EMA among university students [28-30]. To our knowledge, no large-scale epidemiological study has provided an in-depth analysis of the representativeness and reliability of EMA data among university students. In this paper, we present data collected as part of the Promoting Mental Health Among University Students (PROMES-U) project, in which undergraduate students from 5 public universities in Spain were invited to participate in a web-based survey and subsequent 15-day EMA study [31]. This study addresses this gap in the literature by assessing EMA data quality in complementary ways. First, we estimate the participation rate and mean compliance and identify predictors of individual-level participation and compliance. Second, we provide between- and within-person reliability scores for measures of negative affect (NA) and positive affect (PA). Third, we build on previous work to identify potential careless responding [20,21].

The PROMES-U project is a prospective observational multicenter cohort study implementing 2 web-based self-reported surveys (one at baseline and one at the 12-month follow-up) and a 15-day EMA study and is part of the World Health Organization World Mental Health International College Student Initiative [32]. The project’s target population consists of undergraduate students from 5 public universities in Spain: University of the Balearic Islands, Jaume I University, Miguel Hernández University, Pompeu Fabra University, and University of Zaragoza. All Spanish-speaking students aged ≥18 years enrolled in any of the participating universities were eligible to participate. The 15-day EMA study immediately followed the baseline web-based survey (April 2022-June 2022) with asynchronous and continuous enrollment and is the focus of this report.

For further details regarding the PROMES-U project, its registration can be consulted on the internet [33].

Recruitment for the baseline web survey was conducted through mass emailing using university administrative lists (census sampling), with 1 to 3 reminder emails (in 3 of the 5 participating universities and in some colleges of a fourth one) as well as on-campus project information sessions, classroom announcements, links on the universities’ websites, poster campaigns, and social media posts on the universities’ accounts. A total of 2427 eligible students completed the baseline survey (operationalized as having provided basic sociodemographic variables, >50% of the required questions, and the mental health section of the survey; Multimedia Appendix 1).

Recruitment for the 15-day EMA study was done using quota sampling based on 30-day mental disorder screens in the baseline web survey. Students were hierarchically assigned to 1 of 4 mutually exclusive subsamples. The first subsample was composed of students who had reported passive or active suicidal ideation in the previous 30 days on the Columbia-Suicide Severity Rating Scale [34]. The second subsample consisted of students without 30-day suicidal ideation but with a positive screen for harmful alcohol use. This was assessed using core items of the Alcohol Use Disorders Identification Test scale (ie, frequency of consumption, quantity, and frequency of binge drinking episodes), which ranged from 0 (never) to 4 (4 or more times a week). Those who scored ≥4 in the sum of items screened positive for harmful alcohol use [35]. Students in the third subsample did not report 30-day suicidal ideation or harmful alcohol use but presented core symptoms of depression or anxiety in the previous 30 days through questions from the Composite International Diagnostic Interview screening scales [36]. Core symptoms of depression were “feeling sad or depressed,” “feeling discouraged about how things were going in their life,” and “taking little or no interest or pleasure in things,” which were operationalized as indicating “most of the time” or more. Core anxiety symptoms were “feeling worried or anxious,” “worrying about a number of different aspects in your life, such as your work, family, health, or finances,” “feeling more worried than other people in your same situation,” and “worrying excessively or too much,” which were operationalized as indicating “most of the time” or more. Participants in the third subsample needed to present at least one of the aforementioned key symptoms of depression or anxiety. Finally, the fourth subsample (control) was composed of students who did not meet any of the aforementioned criteria.

To be invited to the EMA study, students had to complete the baseline web-based survey and provide answers for the 30-day mental disorder screens required to enable subsample assignation during quota sampling. After providing informed consent, participants were given instructions on how to participate in the study through an explainer video and link to the study’s website, as well as information on how to download and register in the ExpiWell smartphone-based app available for Android and iOS.

A total of 1259 eligible students were invited, of whom 782 (62.1%) participated: 167 (21.4%) from the suicidal ideation subsample, 234 (29.9%) from the alcohol use subsample, 206 (26.3%) from the anxiety or depression subsample, and 175 (22.4%) from the control subsample. Multimedia Appendix 1 provides more information regarding the stages of EMA study recruitment.

Almost all participants (717/782, 91.7%) began the EMA study the same day they completed the baseline web-based survey, and 99.2% (776/782) began within a week of completing the baseline survey. The length of the EMA study was suggested by stakeholders in focus groups that were carried out during the design of the project [31] and by students who participated in a pilot study and falls within what is average for EMA studies in youth [18,37].

The 15-day EMA study was conducted following the baseline survey and assessed relevant constructs related to stress sensitivity (ie, affect reactivity) and mental health problems among university students. Measures were selected based on an extensive literature review by our research team and were adapted to our study’s objectives using semirandom time intervals and recall periods to decrease participant burden. Participants received push notifications on their smartphones at a random time within 2-hour time intervals in the morning (8-10 AM), midday (noon-2 PM), afternoon (4-6 PM), and evening (8-10 PM). A reminder notification was sent after 30 minutes. The median response time was 15 minutes after receiving the prompt. The momentary assessment was marked as missed if the participant did not complete the assessment within the 2-hour window.

On days 1 and 15, the instrument evaluated 2-week major depressive disorder using the 9-item Patient Health Questionnaire [38], 2-week generalized anxiety disorder using the 7-item Generalized Anxiety Disorder scale [39], and 2-week mental well-being using the Short Warwick-Edinburgh Mental Wellbeing Scale [40].

On days 2 to 15, participants were asked to complete 4 momentary assessments each day. Each assessment included twelve 7-point Likert-type scales (from not at all to a lot) that consisted of an introductory phrase (“Right now, I feel...”) followed by very short descriptions of momentary states. They were used to assess stress levels (“...stress”) from the ESM item repository [41] and energy levels (“...tired, with little energy”) and perceived concentration capacity (“...having trouble concentrating”) from the 9-item Patient Health Questionnaire [38].

Affect was assessed using 9 of the described 7-point Likert-type scales based on the 2-axis circumplex model of affect [42] (ie, high or low, and positive or negative valence). All items assessing affect were taken from validated scales, namely, the PHQ-9 [38], the 7-item Generalized Anxiety Disorder scale [39], the Short Warwick-Edinburgh Mental Wellbeing Scale [43], and the ESM item repository [41]. In total, 5 items were used to measure PA (ie, feeling “happy,” “interest in doing things,” “relaxed, “optimistic,” or “pleasure in doing things”) and 4 to measure NA (ie, feeling “nervous,” “upset,” “depressed,” or “worried”). PA and NA scores for each individual momentary assessment were calculated as the person-specific mean of the corresponding items.

Finally, 3 additional multiple-response items taken from the ESM item repository assessed momentary social context (company), physical context (location), and activity [41]. Morning and evening assessments contained additional questions regarding the previous night’s sleep duration and quality, as well as alcohol use, type of stressful events, and suicidal ideation within the previous 24 hours [41,44-47]. More information regarding the EMA measures used in this study is provided in Multimedia Appendix 2.

Explanatory variables for partic9ipation and compliance were collected in the baseline web survey, which was developed by the World Mental Health International College Student Initiative consortium and implemented using the Qualtrics survey platform (Qualtrics International Inc) [31,48]. In this survey, we included four groups of explanatory variables: (1) sociodemographic and college-related variables, (2) childhood and adolescence adverse experiences, (3) stressful events in the previous 12 months, and (4) mental disorder screens for the previous year. Multimedia Appendix 3 provides more information regarding the web-based survey measures.

Sociodemographic and college-related variables included sex, nationality, age, and field of study. Childhood and adolescence adverse experiences before the age of 18 years were assessed using 19 items adapted from several questionnaires. Items that assessed parental psychopathology (ie, parent with any serious mental or emotional problems, substance abuse, suicidal thoughts and behaviors or death by suicide, and criminal activities or interpersonal violence) were taken from the Adverse Childhood Experiences Study survey [49]. Items measuring neglect and sexual and emotional abuse were from the Childhood Trauma Questionnaire–Short Form [50]. Physical abuse was reported using items from the Childhood Trauma Questionnaire–Short Form and the Army Study to Assess Risk and Resilience in Servicemembers New Soldier Study survey [51]. Bully victimization (ie, direct verbal or physical bullying as well as indirect bullying and cyberbullying) was measured using items of the Youth Risk Behavior Survey [52]. Finally, dating violence was measured using items from the 2016 to 2017 Healthy Minds Study [53].

Past-year stressful events were assessed using 11 dichotomous items based on the Army Study to Assess Risk and Resilience in Servicemembers survey [54] that included life-threatening illness or injury of a friend or family member; death of a friend or family member; breakup with a romantic partner; cheating of a romantic partner; serious betrayal by someone close; serious ongoing arguments or breakup with friends or family members; and having had a life-threatening accident or illness, been physically assaulted, experienced sexual harassment, been sexually assaulted or raped, or had a serious legal problem (ie, any serious legal problem or problems with the police).

We evaluated major depressive disorder and generalized anxiety in the previous 12 months using the Composite International Diagnostic Interview screening scales [36]. We evaluated alcohol use disorder in the previous 12 months using the Alcohol Use Disorders Identification Test [55]. We assessed suicidal thoughts and behaviors in the previous 12 months using an adapted version of the Columbia-Suicide Severity Rating Scale [34,56].

Ethics approval was provided by the Parc de Salut Mar Clinical Research Ethical Committee (protocol 2020/9198/I). The project complies with the principles established by national and international regulations, including the Declaration of Helsinki. Explicit informed consent was obtained at the beginning of the web-based surveys, and a separate consent form was provided at the end of the web-based survey for participation in the EMA study. Data were deidentified before all analyses through pseudoanonymization using encrypted identifiers to ensure privacy, and neither the study investigators nor the individuals conducting the analyses had access to the personal data. Participants who completed at least 80% of the momentary assessments as well as the mental health screener scales of days 1 and 15 received a monetary incentive of 30€ (US $32.03). Participants were informed in advance of the requirements to receive the incentive and were not informed explicitly when they qualified for it, nor did the app show any information on compliance other than which assessments had been completed.

A total of 1259 baseline survey participants were invited to take part in the EMA study, of whom 1126 (89.4%) provided informed consent, and 782 (62.1% of those invited; 69.4% of those providing informed consent) participated (Multimedia Appendix 1). Table 1 shows the distribution of sociodemographic and college-related characteristics of the target populations and of the sample across the various phases of recruitment for the EMA study up until EMA participation. Compared to the target populations, EMA participants were more likely to be women (619/782, 79.2% vs 39,214/69,805, 56.2%-753,749/1,338,304, 56.3%), younger students (577/782, 73.8% vs 724,853/1,338,304, 54.2%-43,664/69,805, 62.6%), and science (94/782, 12% vs 3633/69,805, 5.2%-84,750/1,338,304, 6.3%) and health sciences (219/782, 28% vs 258,967/1,338,304, 19.4%-14,316/69,805, 20.5%) students.

These observed differences in EMA sample composition compared to the target population are mainly due to a different likelihood of participation in the baseline survey and, to a far lesser extent, to a different likelihood of EMA participation among those invited from the baseline survey.

The EMA study’s participation rate was 62.1% (782/1259). Mean compliance was 76.9% (SD 27.7%; range 1.9%-100%), with a median of 89.3% and a mode of 96.4% (Figure 1). A total of 82.5% (645/782) of the participants completed at least one assessment on the final day.

Multimedia Appendix 6 shows the associations between participation and compliance and sociodemographic and college-related characteristics, childhood and adolescence adverse experiences before the age of 18 years, stressful experiences, mental disorder screens, and suicidal thoughts and behaviors in the previous 12 months.

Female individuals had significantly higher odds of participation (OR 1.41, 95% CI 1.06-1.87), whereas those who were aged ≥30 years (OR 0.20, 95% CI 0.08-1.06), had had a death of a friend or family member in the previous year (OR 0.73, 95% CI 0.57-0.94), or had had a suicide attempt in the previous year (OR 0.26, 95% CI 0.10-0.64) had significantly lower odds of participation.

Compliance was significantly lower among non-Spanish participants (exponential of β=0.94, 95% CI 0.90-0.98), older age groups (exponential of β=0.88, 95% CI 0.82-0.94 for those aged 26-29 years; exponential of β=0.86, 95% CI 0.76-0.96 for those aged ≥30 years), and students of social and juridical sciences (exponential of β=0.92, 95% CI 0.89-0.95) than among arts and humanities students. Compliance was significantly lower among those who had experienced any of the childhood and adolescence adverse experiences under study except for parental psychopathology (exponential of β range 0.87-0.98), especially among those who experienced neglect (exponential of β=0.92, 95% CI 0.89-0.94) or sexual abuse (exponential of β=0.87, 95% CI 0.82-0.92). In total, 45% (5/11) of the stressful experiences in the previous 12 months under study were negatively associated with compliance (exponential of β range 0.80-0.97), especially sexual assault or rape (exponential of β=0.80, 95% CI 0.74-0.86). Compliance was higher among those with any stressful event in the previous 12 months (exponential of β=1.03, 95% CI 1.01-1.06) and those with exactly one stressful experience in the previous 12 months (exponential of β=1.09, 95% CI 1.05-1.13, compared to none). Regarding adverse mental health, compliance was lowest among those with a positive screen for alcohol use disorder in the previous 12 months (exponential of β=0.89, 95% CI 0.85-0.93) but also significantly lower among those with positive screens for major depression in the previous 12 months (exponential of β=0.95, 95% CI 0.93-0.97), generalized anxiety (exponential of β=0.95, 95% CI 0.92-0.97), and suicidal ideation (exponential of β=0.97, 95% CI 0.95-0.99). Compliance gradually decreased throughout the 15-day EMA study in a linear fashion both in the total sample and in each separate subsample (Multimedia Appendix 7).

Figure 2 presents PA and NA scores in all assessments for a random selection of 7 individuals within each subsample. Important differences in PA and NA scores across individuals were observed. For example, when looking at NA, some individuals presented very little intraindividual variability (eg, alc_508, anxdep_597, and cont_524), and others had important fluctuations from one assessment to the next (eg, sui_360 and anxdep_293; Figures 2A and 2B).

The total ICCs for mean PA and NA were 0.54 and 0.52, respectively. The ICC for both PA and NA was 0.50 in the subsample of participants with mental health problems, meaning that, on average, between- and within-person variance were very similar (Table 2), in line with the differences in variability observed in Figure 2. In the control subsample, the ICCs for PA and NA were slightly higher and lower (ICC=0.54 and 0.46, respectively). Overall affect variance (both between and within persons) was greater among those in the adverse mental health subsamples (Table 2).

As seen in Table 3, between-person reliability was high for both PA and NA (ie, random-effects reliability; R_kRn=0.99). Within-person reliability was lower but still acceptable (R_cn=0.48 and 0.59 for PA and NA, respectively). Within-person reliability was slightly higher on average among those with mental health problems. Within-person reliabilities were consistently higher for NA than for PA. Within-person reliability scores were highest when including all measured items both for PA and NA.

Multimedia Appendix 8 shows individual Cronbach α reliability coefficients by participant, which range from negative values to 0.97 for PA and 0.99 for NA. As expected, individuals that only answered 1 assessment (8/781, 1%) did not provide enough information to have reliability outcomes. Among the rest, there were 3.7% (19/782) of individuals with negative values for individual NA or PA reliability. Of all participants, 86.8% (679/782) and 66.8% (522/782) had moderate to substantial individual reliability (ie, raw Cronbach α>0.60) for PA and NA, respectively.

Participants that appeared to have very low NA variability in Figure 2B (eg, anxdep_597 and cont_524) had the lowest within-person reliability for NA (Multimedia Appendix 8). Furthermore, participant alc_508, who presented practically no variation in their mean level of reported NA, had no value for raw Cronbach α (Multimedia Appendix 8). Generally, participants with very low individual reliability scores presented either very low compliance rates or low variability in their answers.

Different thresholds were evaluated for each of the 3 careless responding parameters (ie, time per item, SD of item responses in each assessment, and percentage of items of each assessment that fell at the mode of that assessment).

The distribution of assessments according to time per item (Figure 3A) was sharply skewed to the right, with a median at 3.9 seconds per item. Figure 4 shows that, in assessments with a time per item of ≤1 second (r=−0.04), the relationship between feeling “anxious” and feeling “relaxed” flattens, thus conveying that responses below this threshold are likely careless. A total of 0.13% (44/33,626) of all completed assessments were below this threshold.

Figure 3B shows that the distribution of the SDs of the item responses in each assessment was normal, with a group of outliers that had an SD of 0 (indicating no variability in item responses). The relationship between psychometric antonyms shifted in those assessments at an SD of ≤1 and became bluntly positively correlated at an SD of ≤0.5 and ≤0.25 (r=0.94 and 1.00, respectively), which comprised 10.1%, 1.2%, and 0.7% of all answered assessments, respectively (Figure 5).

Figure 3C shows the distribution of the percentage of individual responses within a given assessment that fall at the mode of that assessment. This distribution also had a right skew. The relationship between “anxious” and “relaxed” items flattened in assessments with ≥60% of items with answers at the mode (r=0.16) and became sharply positive when ≥80% of the answers fell at the mode (r=0.82), which comprised 4.4% and 1.5% of all answered assessments, respectively (Figure 6).

Although the relationship between the psychometric antonyms flattened when the SD was of ≤1 and when ≥60% of items with answers fell at the mode, most of the answers that were below and above these thresholds were not outliers in the distribution (Figures 3B and 3C) and were theoretically plausible responses. Thus, we decided that only assessments with no variance in responses whatsoever (ie, SD of 0 or 100% of items at the mode) should be considered careless responses. Of all the answered assessments, 0.77% (261/33,626) had no variation in responses.

Considering the aforementioned thresholds, a total of 0.86% (291/33,626) of all completed assessments were flagged as having been responded carelessly. There was little overlap between careless responses identified through no variation and those identified through a time per item of ≤1 second (Figure S3A in Multimedia Appendix 9).

When looking at how careless responses clustered within individuals, 8.8% (69/782) of participants had at least one assessment flagged as careless, 0.8% (6/782) had more than a third of their answers flagged as careless, and 0.26% (2/782) had at least half of their answers flagged as careless (Multimedia Appendix 8). Furthermore, half of all careless responses clustered around 8 participants.

To evaluate whether careless responses could be related to monetary incentives, we looked at the relationship between the number of careless responses and compliance by performing a logistic regression dichotomizing compliance at the incentive threshold (ie, 80%). We found that there were no differences in the percentage of careless responses among those who did and did not receive an incentive (OR 0.99, 95% CI 0.96-1.02; Multimedia Appendix 10).

Figure 7 shows the number of assessments needed to have a stable estimate of the mean and variability of PA and NA scores. The mean PA and NA scores reached an ICC of 0.9 at approximately 4 assessments, clearly stabilizing at approximately 12 assessments, whereas approximately 25 were needed to be able to capture variability well. There was a total of 3.7% (29/782) of the participants who had <25 valid assessments (ie, completed and not flagged as carelessly responded) according to the considered parameters. In total, 6.4% (50/782) of the participants had very low or null individual reliability (raw Cronbach α<0.11) for either PA or NA.

In total, 17.5% (137/782) of the participants were identified as potential careless responders (Figure S3B in Multimedia Appendix 9).

We investigated EMA data quality in a large sample of university students that included participants who screened positive for mental health problems (607/782, 77.6%) as well as students who did not (175/782, 22.4%) by using state-of-the-art data-driven analyses to assess participation, compliance, and reliability indicators. A total of 1259 students were invited to participate in the EMA study, with a participation of 782 (62.1%) and mean compliance of 76.9% (SD 27.7%). We found that participants in our EMA study were representative of the invited population, whereas compliance was slightly different across groups of explanatory variables. The data were highly reliable to account for between-person differences in NA and PA and fair to moderate to study differences within each participant. The findings suggest that 0.86% (291/33,624) of all completed assessments were responded carelessly and that 17.5% (137/782) of the participants were potential careless responders.

Participation and its predictors are rarely investigated in EMA studies given that such analyses require having information on those who refuse to participate. This demands epidemiological study designs that include careful tracking of potential participants across recruitment phases as well as probability sampling techniques. By linking our EMA study to a large cohort study, we were able to investigate and predict EMA participation and compliance, including a wide range of relevant candidate predictor variables for these outcomes. We found that men had significantly lower rates of participation, which is a tendency across scientific studies [65]. Interestingly, the only mental health outcome that was significantly associated with lower participation was having had a suicide attempt in the previous year. This could be due to multiple reasons outside the scope of this study that could affect the desire or capacity to participate in an intensive longitudinal study [66]. It is possible that students that have attempted suicide in the past year have some with the highest levels of psychological pain and stress. The only stressful event related to lower participation was having had a friend or family member die in the previous year. Mourning is a process that is also highly distressing psychologically [67] and is more common than having recent suicide attempts, thus having potentially a higher impact on overall EMA study participation.

Mean compliance was found to be similar to what other studies have reported [16,68], including a pattern of declining compliance as the days passed [16,24,69,70]. Although we cannot empirically confirm that compliance may have been positively impacted by monetary incentives, there seems to be a relationship between the 2; we did observe an inflection point around the 80% mark of compliance, and the literature has also found that studies with monetary incentives tend to have better compliance rates [16,68]. Concerning predictors of compliance, we found that almost all childhood adversities and a significant number of recent stressful events were associated with lower compliance. However, it is important to stress that effect sizes on compliance were very low and the differences across groups were generally small. Compliance was lowest among participants that had been sexually abused during their childhood or adolescence and those who had been sexually assaulted or raped in the previous year. Childhood and recent sexual abuse have been widely identified as being associated with grave emotional distress [71-73]. Concerning mental health outcomes, positive screening for alcohol use disorder was the strongest mental health predictor for low compliance. This finding aligns with the literature, which argues that activities or behaviors such as drinking alcohol and experiencing stress, which draw attention away from participating, could result in lower compliance [66,74]. Lower compliance rates among those who have lived through extremely stressful events and those who have poor mental health can also be related to how these experiences or states can impact cognition [66].

When testing reliability, we found that between-person reliability for both PA and NA was excellent (R_kRn=0.99), whereas within-person reliability was lower (R_cn=0.48 and 0.59 for NA and PA, respectively). EMA studies typically do not report reliability coefficients; however, when they do, within-person reliability is lower than between-person reliability, with similar results to ours [19,75,76]. This is to be expected because within-person variability is also generally smaller than variability between individuals [77]. Although it is important to evaluate between- and within-person reliability in each study as they will vary from sample to sample, they are not typically reported in EMA studies [75]. We did find 3 EMA studies that reported affect reliability scores, all of which presented very high between-person reliability scores for PA and NA, ranging from 0.96 to 1.00, and lower within-person reliability scores. This is to be expected because within-person variability is also generally smaller than variability between individuals [77]. In total, 2 of the aforementioned EMA studies calculated within-person reliability in the same way as we did and reported very similar coefficients, ranging from 0.50 to 0.71 [19,76].

Next, we attempted to identify careless responses, finding that <1% of assessments (291/33,624, 0.86%) had been answered carelessly, whereas 1 in every 6 participants in our sample was a potential careless responder. This step, though crucial for future analyses, is typically overlooked in EMA research. Potential careless responses were evaluated building on research in this area [21] (refer to the Statistical Analysis subsection in the Methods section).

When implementing the proposed method, we realized that focusing strictly on shifts in the relationship between psychometric antonyms would lead to discarding many plausible responses. For example, it is reasonable for a participant to provide similar ratings across items measuring affect if those ratings are near the middle of the scale. This issue was also raised in the study by Jaso et al [21]. Thus, we decided to take a more conservative approach, which led to simplifying the analysis from using 3 parameters to just using 2, namely, low duration and no variability in responses (ie, straight-line responding). Interestingly, these are the 2 most common parameters mentioned in the literature [20-22]. Although the overall number of careless responses in our study was negligible, it is still crucial to identify them because they tend to cluster around a small number of participants, which in turn allows us to spot careless responders.

Assessments flagged as being answered carelessly will be considered “missed” in future studies using these data. This, in turn, raises the important question as to how missing data can hamper data quality and interpretation of EMA results [66,70], especially as we have seen that missed assessments do not occur at random and may be influenced by multiple factors. Future analyses could address this issue using full information maximum likelihood [14], imputation techniques, or dynamic structural equation modeling [78].

We believe that the parameters we used to identify potential careless responders (ie, minimum number of completed assessments and individual reliability scores) can provide a stepping stone for future studies as these parameters are data driven and grounded in previous literature [20,21,24]. Determining the minimum number of assessments needed to have a stable estimate of the key measures’ mean and variability may be particularly relevant as it is common practice in EMA studies to consider an arbitrary threshold of minimum responses [14,66]. This method was also adapted from the work by Jaso et al [21]. However, they used rank-order correlation, whereas we used the ICC as we considered it to be more statistically sound given that it also accounts for agreement between the measures. We found that a minimum of 25 valid assessments (ie, 25/56, 45% compliance in the case of our study) were needed to have a stable estimate of the mean and variability of affect.

When looking at individual reliability of PA and NA scores, we observed that most participants had moderate to substantial reliability (ie, raw Cronbach α>0.60) for both PA and NA. However, some individuals had very low or even negative values, especially for NA. This mainly happened for two reasons: (1) they had low or negative interitem covariance that conformed a construct or (2) the responses to these items very seldom varied across the 56 assessments.

When the variability of the items is small, it becomes mathematically challenging to assess reliability as it is difficult to determine whether the responses truly covary (ie, have internal consistency) [19,75,77,79]. This phenomenon has been previously described in the literature [75]. Therefore, the internal consistency reliability of students with lower variability in their affective states may be penalized even if the responses are plausible. For this reason, we decided to set a low threshold of individual reliability for careless responders, flagging participants with a very low individual reliability (<0.11; “virtually no reliability”) [62]. These potentially careless responders will not be excluded from the sample but flagged for sensitivity analyses in future studies.

This study has several limitations that should be noted. First, although we conducted a pilot study to ensure that the software and recruitment flow were effective, technological issues such as bugs and connectivity problems could have affected participation and compliance, although reported incidences were minimal. Second, our EMA sample is not fully representative of the target population due to relatively low baseline survey participation and due to quota sampling. Nevertheless, we have detailed mental health information and basic sociodemographic data that can help mitigate this lack of representativeness and restore it for key variables through weighting procedures. Third, although we used sound methods to identify careless responders and responses, we cannot externally validate these findings as being truly indicative of careless response behavior. In any case, we will conduct sensitivity analyses in future studies to assess the impact of excluding these participants. Finally, it is important to consider that our conclusions are specific to the context of our sample and study design and may not be generalizable to other contexts.

The increase in the use of EMA in the mental health epidemiology field demands standardized ways to assess data quality to provide robust conclusions to advance the field. Our data are highly reliable to account for between-person differences in affect and fair to moderate study differences within each participant. Careless responses in our sample were minimal, and 1 out of 6 participants was found to be a potential careless responder (Textbox 1).