Moderating Role of Self-Regulation Difficulties in the Momentary Associations Between Depressive Symptoms and Participation Poststroke

Abstract

Importance: Because depressive symptoms are momentarily associated with lower levels of participation poststroke, it is crucial to investigate what moderates such associations to identify a potential intervention target to reduce the momentary links between depressive symptoms and participation poststroke. Self-regulation seems to be a potential moderator of such associations.

Objective: To investigate the extent to which difficulties in self-regulation moderate the momentary associations between depressive symptoms and participation poststroke.

Design: This study uses a real-time, repeated-measures design using smartphone-based ecological momentary assessment surveys five times a day for 10 days. We performed multilevel modeling to uncover the momentary associations among the study variables.

Setting: Community.

Participants: 39 people with stroke.

Outcomes and Measures: We obtained real-time data for difficulties in self-regulation (total, cognitive, behavioral, and emotion regulation), depressive symptoms, and participation in daily activities (performance in daily activities and satisfaction with performing daily activities).

Results: We included 1,612 survey responses in the analysis. Higher depressive symptoms were momentarily associated with lower levels of performance (β = −0.05 to −0.07, p < .001) and satisfaction (β = −0.04 to −0.06, p < .05), regardless of adjusting for self-regulation variables and other covariates. Difficulties in total self-regulation (β = −0.01, p < .001) and emotion regulation (β = −0.02, p < .001) magnified the negative associations between depressive symptoms and satisfaction with performing daily activities.

Conclusions and Relevance: Using self-regulation and emotion regulation strategies may be a target for just-in-time intervention for reducing the momentary associations between depressive symptoms and satisfaction with performing daily activities poststroke.

Plain-Language Summary: Depressive symptoms have immediate and real-time associations with lower levels of participation in daily activities in people with stroke. Self-regulation, including cognitive regulation (e.g., goal setting, planning strategies), behavioral regulation (e.g., controlling impulsive behaviors), and emotion regulation (e.g., managing negative feelings), may be an intervention target for reducing the immediate associations between depressive symptoms and lower levels of participation poststroke. Thus, we investigated whether difficulties in self-regulation variables magnify the negative real-time associations between depressive symptoms and participation poststroke. To obtain real-time data on difficulties in self-regulation variables, depressive symptoms, and participation (i.e., performance and satisfaction), we asked 39 community-dwelling people with stroke to answer smartphone surveys. The results showed that difficulties in total self-regulation and emotion regulation magnified the negative associations between depressive symptoms and satisfaction with performing daily activities. Our findings suggest that self-regulation and emotion regulation strategies may be a target for real-time intervention for reducing the momentary associations between depressive symptoms and satisfaction with performing daily activities poststroke.

The authors investigated whether difficulties in self-regulation magnify the negative real-time associations between depressive symptoms and participation in daily activities in people poststroke.

Depressive symptoms are a common sequel of stroke; approximately one-third of survivors of stroke suffer from poststroke depression (Hackett et al., 2005; Medeiros et al., 2020; Towfighi et al., 2017). Depressive symptoms are a key risk factor for restrictions in participation in daily activities after stroke (de Graaf et al., 2022; Ezekiel et al., 2019; Kapoor et al., 2017; Lee et al., 2023; Tse et al., 2017). Participation, which refers to involvement in a life situation, is a principal health outcome in stroke rehabilitation (World Health Organization, 2001). Given that ample evidence has demonstrated the associations of depressive symptoms with restrictions in participation poststroke (de Graaf et al., 2022; Ezekiel et al., 2019; Kapoor et al., 2017; Lee et al., 2023; Tse et al., 2017), it is essential to investigate what moderates such associations to design an effective intervention to reduce the adverse associations of depressive symptoms with participation poststroke.

Self-regulation can be a potential mechanism underpinning the associations of depressive symptoms with participation in daily activities after stroke. Self-regulation refers to a systematic and active process with conscious efforts to regulate cognition, behaviors, and emotions to establish goals within a specific environment (Zeidner et al., 2000). Depression has been considered a disorder of self-regulation in that cumulative experiences of self-regulation failures can manifest depressive symptoms (Strauman & Eddington, 2017). Moreover, self-regulation failures can be negatively associated with participation in daily activities (Cooper et al., 2015; Mol et al., 2023; Suchy et al., 2019). According to Carver and Scheier’s self-regulation of behavior model, self-regulation processes address the associations of depressive symptoms with poor performance of goal-directed activities (Carver & Scheier, 2001). In other words, it is plausible that the associations between depressive symptoms and participation in daily activities can differ by self-regulation capacity: People with more difficulties in self-regulation may have a stronger association between depressive symptoms and lower levels of participation in daily activities compared with people with less difficulties in self-regulation. Moderation analysis is a statistical technique that examines how one variable can influence the relationship between the two variables (a predictor and an outcome; Fairchild & McQuillin, 2010). It helps determine whether a moderating variable changes the strength or direction (or both) of the relationship between the two variables (Fairchild & McQuillin, 2010). Therefore, it is important to explore whether self-regulation moderates the associations between depressive symptoms and participation in daily activities in people with stroke.

To explore the moderating role of self-regulation in the associations between depressive symptoms and participation poststroke, the within-person variability of these constructs needs to be considered. To be specific, depressive symptoms (Bos et al., 2019) and self-regulation capacity (Baumeister et al., 1998) fluctuate over time within a person. Therefore, the momentary self-regulation capacity within a person may moderate the moment-to-moment associations between depressive symptoms and participation poststroke. Moment-to-moment (i.e., momentary) association refers to the relationship between variables that occur in real-time, which explains how variables are related to each other at the moment. Therefore, it is critical to understand the real-time, momentary associations among depressive symptoms, self-regulation, and participation poststroke. However, few studies have addressed whether self-regulation moderates the momentary associations between depressive symptoms and participation poststroke.

Ecological momentary assessment (EMA) is effective for obtaining real-time, repeated sampling of a person’s behaviors and emotions and capturing within-person dynamics in real-world environments (Shiffman et al., 2008). Smartphone-based EMA is useful for measuring moment-to-moment (momentary) associations within a person through smartphone surveys (Shiffman et al., 2008). In addition, EMA minimizes recall bias and maximizes ecological validity (Shiffman et al., 2008). A previous study encouraged using EMA as an assessment approach in the field of rehabilitation to measure the complex and dynamic nature of dysfunctions in real-time and real-world environments (McKeon et al., 2018).

Therefore, the objective of this study is to examine whether difficulties in self-regulation moderate the momentary associations between depressive symptoms and participation in daily activities after stroke, using EMA. We hypothesize that more difficulties in self-regulation will momentarily magnify the negative momentary associations of depressive symptoms with participation in daily activities.

Method

The study method and results follow the Strengthening the Reporting of Observational Studies in Epidemiology Statement (Von Elm et al., 2007).

Participant Recruitment

We recruited participants from the stroke registry, which is a single-hospital database of patients with stroke who have agreed to receive information regarding research projects, at the Washington University School of Medicine from December 2021 to January 2023. We modified inclusion and exclusion criteria used in previous EMA studies with people with stroke (Bui et al., 2022; Lau et al., 2023). We included people with mild stroke who were experiencing minimal impacts from motor impairments (Carlsson et al., 2003) so that they would be able to operate a smartphone. Similarly, we excluded people with visual difficulties (visual acuity and neglect), severe aphasia, and apraxia in the screening, because these conditions can affect the ability to use a smartphone.

The inclusion criteria were the following:

▪ Participants were community-dwelling people with mild stroke (with a National Institutes of Health Stroke Scale [NIHSS] score of 5 or less at their acute hospital stay).

▪ They received a diagnosis of stroke at least 6 mo before enrollment.

▪ They were at least 18 yr old.

▪ They were fluent in the English language.

▪ They had the ability to use smartphone apps.

The exclusion criteria were the following:

▪ previous or current neurological or psychiatric disorders

▪ an inability to provide reliable EMA responses because of communication difficulties

▪ a history of functional limitations before stroke (with a prestroke modified Rankin Scale score >2; Sulter et al., 1999)

▪ severe apraxia (with a score <6 on the Test for Upper Limb Apraxia; Vanbellingen et al., 2011)

▪ poststroke neglect (with a Star Cancellation Test score ≤ 44; Friedman, 1992)

▪ low vision (with a Lighthouse Near Visual Acuity Test score ≤ 20/100 corrected vision; Bailey & Lovie, 1976).

Participant Characteristics

A total of 39 participants were included in the analysis. The average age of the participants was 59.5 yr (SD = 0.49). Most of the participants were White (74.4%) and had infarction (76.9%). The average stroke severity at the onset of stroke, measured using the NIHSS, was 1.1 (range = 0–5), which reflects mild stroke. The average general cognition measured using the Montreal Cognitive Assessment (MoCA) was 27.8 out of 30. The details of the demographic and clinical characteristics of the participants are presented in Table A.1 in the Supplemental Material (available online with this article at https://research.aota.org/ajot).

Study Procedure

Participants had a laboratory visit before and after EMA monitoring. At the first laboratory visit, participants provided informed consent and completed laboratory-based assessments. A research staff member installed an EMA mobile application (the Participation in Everyday Life Survey app) (Jessup et al., 2012) on the participant’s own cell phone. Participants received training on how to use the EMA app and received the instruction manual. The instruction manual includes step-by-step guidance on how to use the EMA app, survey delivery schedules, emergency contact information, common questions and answers, and more. The EMA app gave prompts for surveys five times per day, at approximately 2.5-hr intervals randomly initiated from 8:30 a.m. to 8:50 p.m. for 10 days (a total of 50 surveys). The Washington University School of Medicine Institutional Review Board approved the study procedure and informed consent process (No. 202104024).

Measurements

Real-Time Participation in Daily Activities

We modified EMA items from previous studies to measure participation (Bui et al., 2022; Moore et al., 2017). We used two items for self-appraisal of participation in daily activities: performance and satisfaction (Table A.2). Response options were rated on a 5-point scale ranging from 0 (not at all) to 4 (very much). Higher scores indicate higher ratings of performance of daily activities and satisfaction with performance of daily activities.

Real-Time Difficulties in Self-Regulation

We selected EMA items from the Behavior Rating Inventory of Executive Function−Adult (BRIEF−A) to measure real-time self-regulation ability. The BRIEF−A has been applied to EMA (Williams et al., 2019). The BRIEF-A consists of nine subdomains that are categorized into three main domains: cognitive (i.e., metacognition index with five subdomains), behavioral (two subdomains), and emotion regulation (two subdomains; Donders & Strong, 2016). We extracted one item with the highest factor loading from each of the nine subdomains (nine EMA items in total) (Waid-Ebbs et al., 2012; Table A.2). We computed a total self-regulation score by summing all nine items. In addition to the total self-regulation, we summed up the items in each subdomain to generate cognitive, behavioral, and emotion regulation total scores. Response options were rated on a 5-point scale from 0 (not at all) to 4 (very much). Higher scores reflect more difficulties in self-regulation. Spearman’s ρ showed significant moderate to strong relationships between the responses of EMA items extracted from the BRIEF−A and the laboratory-based BRIEF−A scores (total self-regulation: r = .600, p < .001; cognitive regulation: r = .719, p < .001; behavioral regulation: r = .638, p < .001; and emotion regulation: r = .443, p = .005).

Real-Time Depressive Symptoms

We have modified the items of Patient Health Questionnaire-9 (PHQ-9) to develop EMA items to measure depressive symptoms. The PHQ-9 is a diagnostic and severity measure with adequate validity and reliability (Dajpratham et al., 2020; Kroenke & Spitzer, 2002; Kroenke et al., 2001). It consists of nine depressive symptoms defined in the fourth edition of Diagnostic and Statistical Manual of Mental Disorders (Kroenke & Spitzer, 2002; Kroenke et al., 2001). The EMA items included seven of those nine items (Table A.2): We excluded two PHQ-9 items (sleep difficulties and suicidal ideation). The item on sleep difficulties was not included, because it is inappropriate to ask this question five times within a day. Additionally, we chose not to include the item of suicidal ideation to prevent any potential risk of stimulating suicidal ideation by repeatedly asking this question throughout the EMA survey period. Response options were rated on a 5-point scale ranging from 0 (not at all) to 4 (very much). We summed up the seven items to derive a total depressive symptoms score. Higher scores indicated more severe depressive symptoms. Spearman’s ρ showed significant moderate relationships between seven items of the EMA PHQ-9 and laboratory-based PHQ-9 scores (r = .508, p = .001).

Statistical Analysis

Participants provided 1,687 (86.5% response rate) EMA responses in total. We excluded 75 (4.45%) responses from the analysis because of redundant responses within the same time frame (e.g., the previous survey was answered within the following survey’s time frame) or delayed responses beyond the EMA time frame. Both the redundant responses and the delayed responses happened when a participant completed the survey a long time after the survey was delivered. A total of 1,612 responses (82.7%) have been included in the analysis. We built multilevel models (MLMs) (Schwartz & Stone, 1998, 2007). We performed the ANOVA function in the CAR package in R to compare the model fit of MLMs using the Akaike information criterion. The significance level was set at .05. Statistical analyses were performed using the nlme package (Pinheiro et al., 2013) using R Studio (Version 2022.02.3 + 492, with the “Prairie Trillium” Release for Windows) running in R, Version 4.2.1. Detailed information about building MLMs is provided in Appendix A.1. in the Supplemental Material.

Multilevel Model Building

For the models with performance as an outcome, we first built the unconditional model without any independent variables (Model P0) to understand the proportions of variance in performance at each level. We included depressive symptoms as a predictor in Model P1. Then, we added self-regulation variables to Model P1 to generate individual models with difficulties in total self-regulation (Model P2), difficulties in cognitive regulation (Model P3), difficulties in emotion regulation (Model P4), and difficulties in behavioral regulation (Model P5), respectively. Last, to build the individual models with interactions, we added the interactions between depressive symptoms and the corresponding self-regulation variables—difficulties in total self-regulation (P2i), difficulties in cognitive regulation (P3i), difficulties in emotion regulation (P4i), and difficulties in behavioral regulation (P5i)—to each model from P2 to P5.

For the models with satisfaction as an outcome, we followed similar steps conducted in developing the models with performance:

▪ unconditional model (Model S0)

▪ model with depressive symptoms only (Model S1)

▪ individual models with both depressive symptoms and each self-regulation variable without interactions (Models S2, S3, S4, and S5)

▪ individual models with interactions of depressive symptoms with self-regulation variables (Models S2i, S3i, S4i, and S5i).

We included gender and MoCA total score to control for their effects on performance and satisfaction variables in all the models except for the unconditional models (Models P0 and S0).

Results

Real-Time Momentary Associations Between Self-Regulation Variables and Performance as an Outcome

In all models (Models P1–P5i), depressive symptoms had a significant negative momentary association with performance, controlling for other variables: A one-unit increase in depressive symptoms corresponded to a predicted decrease of 0.05 to 0.07 in performance (p < .05). The results of Models P2, P3, P4, and P5 indicated that difficulties in total self-regulation (β = −0.03, p < .001), cognitive regulation (β = −0.03, p = .003), and emotion regulation (β = −0.06, p = .006), but not behavioral regulation (β = −0.04, p = .108), had significant negative momentary associations with performance, controlling for depressive symptoms, gender, and general cognition. However, the fits of models with self-regulation variables (Models P2−P5) did not significantly differ from the model with depressive symptoms only (Model P1, p > .05). This result indicated that the inclusion of self-regulation variables did not sufficiently improve the fit of the models (Models P2–P5) compared with the model with depressive symptoms only (Model P1). This may indicate that the effects of self-regulation variables on performance are relatively marginal for improving the overall model fits. In models with interactions (Model P2i, P3i, P4i, and P5i), all interaction effects were insignificant. The fits of models with interactions (Models P2i, P3i, P4i, and P5i) were significantly poorer than the fits of models with self-regulation variables (Models P2, P3, P4, and P5; p < .05). Table 1, Table 2, and Table 3 provide more details of the results of the models.

Table 1.

Results of Multilevel Modeling of Difficulties in Total Self-Regulation With Performance

Variable	Model P0: Unconditional			Model P1: DS			Model P2: SR = Total SR			Model P2i: Total SR × DS Interaction
Variable	Est.	95% CI	p	Est.	95% CI	p	Est.	95% CI	p	Est.	95% CI	p
Fixed effects
Intercept	3.35	[3.15, 3.55]	<.001	1.12	[−1.17, 3.96]	.442	0.97	[−1.92, 3.87]	.510	0.95	[−1.94, 3.85]	.518
DS_within				−0.07	[−0.09, −0.05]	<.001	−0.05	[−0.07, −0.03]	<.001	−0.05	[−0.07, −0.03]	<.001
DS_between				−0.06	[−0.15, 0.02]	.124	−0.09	[−0.21, 0.02]	.117	−0.09	[−0.21, 0.02]	.119
SR_within							−0.03	[−0.05, −0.02]	<.001	−0.03	[−0.05, −0.01]	.001
SR_between							0.03	[−0.06, 0.13]	.488	0.03	[−0.06, 0.13]	.477
Gender_between				0.15	[−0.05, 0.36]	.129	0.17	[−0.04, 0.37]	.111	0.17	[−0.04, 0.37]	.112
MoCA_between				0.09	[−0.02, 0.19]	.107	0.09	[−0.02, 0.20]	.095	0.09	[−0.02, 0.20]	.093
SR × DS_within										−0.00	[−0.01, 0.00]	.409
Random effects
Within person (variance)	.40			.39			.39			.39
Between persons (variance)	.40			.32			.32			.32
ICC	.50			.45			.46			.46
R ² _marginal	.000			.139			.147			.147
R ² _conditional	.495			.527			.536			.536
AIC	3,241			3,202			3,203			3,215
Model fit and p	NA			P0 vs. P1, p <.001			P1 vs. P2, p = .209			P1 vs. P2i, p = .076; P2 vs. P2i, p = .002

Note. The Montreal Cognitive Assessment (MoCA) was used to measure general cognition. Gender was coded as 0 = male and 1 = female. AIC = Akaike information criterion; CI = confidence interval; DS = depressive symptoms; Est. = estimate; ICC = intraclass correlation; NA = not applicable; SR = self-regulation.

Table 2.

Results of Multilevel Modeling of Difficulties in Cognitive, Emotion, and Behavioral Regulation With Performance: Models P3, P4, and P5 (Models With No Interaction)

Variable	Model P3: SR = CR			Model P4: SR = ER			Model P5: SR = BR
Variable	Est.	95% CI	p	Est.	95% CI	p	Est.	95% CI	p
Fixed effects
Intercept	1.11	[−1.71, 3.94]	.439	1.54	[−1.44, 4.52]	.312	1.01	[−1.89, 3.90]	.495
DS_within	−0.06	[−0.08, −0.04]	<.001	−0.06	[−0.08, −0.04]	<.001	−0.07	[−0.09, −0.05]	<.001
DS_between	−0.13	[−0.26, 0.00]	.055	−0.03	[−0.14, 0.07]	.520	−0.07	[−0.15, 0.02]	.109
SR_within	−0.03	[−0.05, −0.01]	.003	−0.06	[−0.10, −0.02]	.006	−0.04	[−0.09, 0.01]	.108
SR_between	0.11	[−0.07, 0.29]	.214	−0.17	[−0.53, 0.19]	.349	0.06	[−0.15, 0.28]	.558
Gender_between	0.19	[−0.02, 0.40]	.070	0.15	[−0.06, 0.35]	.151	0.15	[−0.05, 0.36]	.144
MoCA_between	0.09	[−0.02, 0.19]	.105	0.07	[−0.04, 0.18]	.196	0.09	[−0.02, 0.20]	.100
Random effects
Within person (variance)	.39			.39			.39
Between persons (variance)	.31			.32			.33
ICC	.45			.45			.46
R ² _marginal	.155			.146			.143
R ² _conditional	.533			.532			.533
AIC	3,206			3,205			3,211
Model fit and p	P1 vs. P3, p = .958			P1 vs. P4, p = .572			P1 vs. P5, p = .073

Note. The Montreal Cognitive Assessment (MoCA) was used to measure general cognition. Gender was coded as 0 = male and 1 = female. AIC = Akaike information criterion; BR = difficulties in behavioral regulation; CI = confidence interval; CR = difficulties in cognitive regulation; DS = depressive symptoms; ER = difficulties in emotion regulation; Est. = estimate; ICC = intraclass correlation; SR = self-regulation.

Table 3.

Results of Multilevel Modeling of Difficulties in Cognitive, Emotion, and Behavioral Regulation With Performance: Models P3i, P4i, and P5i (Models With Interaction)

Fixed Variable	Model P3i: DS × CR Interaction			Model P4i: DS × ER Interaction			SR P5i: DS × BR Interaction
Fixed Variable	Est.	95% CI	p	Est.	95% CI	p	Est.	95% CI	p
Fixed effects
Intercept	1.10	[−1.72, 3.92]	.444	1.52	[−1.46, 4.51]	.316	1.01	[−1.89, 3.91]	.495
DS_within	−0.06	[−0.08, −0.04]	<.001	−0.05	[−0.07, −0.03]	<.001	−0.07	[−0.09, −0.05]	<.001
DS_between	−0.13	[−0.26, 0.00]	.056	−0.03	[−0.14, 0.07]	.528	−0.07	[−0.15, 0.02]	.109
SR_within	−0.03	[−0.05, −0.01]	.013	−0.05	[−0.10, −0.01]	.014	−0.04	[−0.09, 0.01]	.109
SR_between	0.11	[−0.07, 0.29]	.209	−0.03	[−0.05, −0.00]	.023	0.06	[−0.15, 0.28]	.559
Gender_between	0.19	[−0.02, 0.40]	.070	0.15	[−0.06, 0.35]	.152	0.15	[−0.05, 0.36]	.144
MoCA_between	0.09	[−0.02, 0.19]	.103	0.07	[−0.04, 0.18]	.193	0.09	[−0.02, 0.20]	.100
DS × SR_within	−0.00	[−0.01, 0.00]	.539	−0.00	[−0.01, 0.00]	.607	0.00	[−0.02, 0.02]	.948
Random effects
Within person (variance)	.39			.39			.39
Between persons (variance)	.31			.32			.33
ICC	.45			.45			.45
R ² _marginal	.155			.148			.143
R ² _conditional	.533			.534			.533
AIC	3,218			3,219			3,221
Model fit and p	P1 vs. P3i, p = .021; P3 vs. P3i, p = .002			P1 vs. P4i, p =.065; P4 vs. P4i, p = .007			P1 vs. P5i, p =.005; P5 vs. P5i, p = .006

Real-Time Momentary Associations Between Self-Regulation Variables and Satisfaction as an Outcome

The results of all models (Models S1−S5i) indicated that depressive symptoms had a significant negative momentary association with satisfaction: A one-unit increase in depressive symptoms produced a decrease in satisfaction (β = −0.04 to −0.06, p < .05). In the models with total self-regulation (Models S2 and S2i), more difficulties in total self-regulation had negative momentary associations with satisfaction (β = −0.03 in Model S2 and −0.02 in Model S2i, p < .05). In the model with interaction of depressive symptoms with difficulties in total self-regulation (Model S2i), the interaction effect was confirmed, indicating that more difficulties in total self-regulation momentarily exacerbated the association between depressive symptoms and satisfaction (β = −0.01, p < .001). The model fit indicated that Model S2i was significantly better than Model S1 (the model with depressive symptoms only) and Model S2 (the model without interaction; p < .05). The results are described in detail in Table 4 and Figure A.1.

Table 4.

Results of Multilevel Modeling of Difficulties in Total Self-Regulation With Satisfaction

Variable	Model S0: Unconditional			Model S1: DS			Model S2: SR = Total SR			Model S2i: Total SR × DS Interaction
Variable	Est.	95% CI	p	Est.	95% CI	p	Est.	95% CI	p	Est.	95% CI	p
Intercept	3.43	[3.22, 3.63]	<.001	0.37	[−2.51, 3.25]	.801	0.13	[−2.76, 3.01]	.931	0.04	[−2.85, 2.93]	.979
DS_within				−0.06	[−0.09, −0.03]	<.001	−0.04	[−0.07, −0.01]	.004	−0.04	[−0.07, −0.01]	.012
DS_between				−0.06	[−0.14, 0.02]	.161	−0.10	[−0.22, 0.02]	.088	−0.10	[−0.21, 0.02]	.095
SR_within							−0.03	[−0.05, −0.02]	<.001	−0.02	[−0.04, −0.01]	.001
SR_between							0.05	[−0.05, 0.15]	.302	0.05	[−0.05, 0.15]	.282
Gender_between				0.11	[−0.09, 0.32]	.277	0.13	[−0.08, 0.33]	.216	0.13	[−0.08, 0.33]	.226
MoCA_between				0.11	[0.01, 0.22]	.035	0.12	[0.02, 0.23]	.025	0.13	[0.02, 0.23]	.022
DS × SR_within										−0.01	[−0.01, −0.00]	<.001
Random effects
Within person (variance)	.30			.27			.27			.27
Between persons (variance)	.41			.33			.33			.33
ICC	.58			.56			.56			.56
R ² _marginal	.000			.156			.176			.182
R ² _conditional	.581			.627			.635			.641
AIC	2,721			2,635			2,630			2,626
Model fit and p	NA			S0 vs. S1, p < .001			S1 vs. S2, p = .011			S1 vs. S2i, p = .002; S2 vs. S2i, p = .012

Models S3, S4, and S5 showed that more difficulties in cognitive regulation (β = −0.03, p = .001) and difficulties in emotion regulation (β = −0.07, p < .001), but not difficulties in behavioral regulation (β = −0.04, p = .058), were momentarily associated with lower satisfaction, respectively. However, none of these models showed significantly better model fits compared with the model with depressive symptoms only (Model S1). In models with interactions (Models S3i, S4i, and S5i), all interaction effects were significant: More difficulties in cognitive regulation (β = −0.01, p = .002), emotion regulation (β = −0.02, p < .001), and behavioral regulation (β = −0.03, p < .001) momentarily worsened the association between depressive symptoms and satisfaction. However, the fit of Model S4i (the model with interaction of emotion regulation with depressive symptoms)—but not the fits of Models S3i and S5i—was only significantly better than the fits of its nested models (Model S1, the model with depressive symptoms; and Model S4, the model with emotion regulation without interactions; p < .05). The results are described in detail in Table 5, Table 6, and Figure A.1.

Table 5.

Results of Multilevel Modeling of Difficulties in Cognitive, Behavioral, and Emotion Regulation With Satisfaction: Models S3, S4, and S5 (Models With No Interaction)

Variable	Model S3: SR = CR			Model S4: SR = ER			Model S5: SR = BR
Variable	Est.	95% CI	p	Est.	95% CI	p	Est.	95% CI	p
Fixed effects
Intercept	0.30	[−2.49, 3.10]	.832	0.52	[−2.52, 3.55]	.740	0.29	[−2.63, 3.21]	.845
DS_within	−0.05	[−0.08, −0.02]	.001	−0.05	[−0.08, −0.02]	.001	−0.06	[−0.09, −0.03]	<.001
DS_between	−0.14	[−0.27, −0.01]	.035	−0.04	[−0.15, 0.06]	.402	−0.06	[−0.15, 0.02]	.133
SR_within	−0.03	[−0.05, −0.01]	.001	−0.07	[−0.11, −0.03]	<.001	−0.04	[−0.08, 0.00]	.058
SR_between	0.14	[−0.03, 0.32]	.108	−0.08	[−0.45, 0.29]	.669	0.07	[−0.14, 0.29]	.488
Gender_between	0.16	[−0.05, 0.37]	.132	0.11	[−0.10, 0.31]	.308	0.11	[−0.10, 0.32]	.290
MoCA_between	0.12	[0.01, 0.22]	.028	0.11	[−0.00, 0.22]	.054	0.12	[0.01, 0.22]	.034
Random effects
Within person (variance)	.27			.27			.27
Between persons (variance)	.32			.34			.34
ICC	.55			.57			.56
R ² _marginal	.187			.160			.162
R ² _conditional	.631			.635			.633
AIC	2,637			2,634			2,644
Model fit and p	S1 vs. S3, p = .328			S1 vs. S4, p = .058			S1 vs. S5, p = .111

Table 6.

Results of Multilevel Modeling of Difficulties in Cognitive, Behavioral, and Emotion Regulation With Satisfaction: Models S3i, S4i, and S5i (Models With Interaction)

Variable	Model S3i: DS × CR Interaction			Model S4i: DS × ER Interaction			SR S5i: DS × BR Interaction
Variable	Est.	95% CI	p	Est.	95% CI	p	Est.	95% CI	p
Intercept	0.25	[−2.55, 3.04]	.863	0.47	[−2.58, 3.51]	.764	0.22	[−2.70–3.14]	.883
DS_within	−0.05	[−0.08, −0.02]	.002	−0.05	[−0.08, −0.02]	.003	−0.05	[−0.08, −0.02]	.001
DS_between	−0.14	[−0.27, −0.01]	.036	−0.04	[−0.15, 0.06]	.410	−0.06	[−0.15, 0.02]	.141
SR_within	−0.02	[−0.04, 0.00]	.052	−0.05	[−0.09, −0.01]	.010	−0.04	[−0.08, 0.01]	.093
SR_between	0.15	[−0.03, 0.32]	.098	−0.07	[−0.44, 0.30]	.712	0.07	[−0.14, 0.29]	.490
Gender_between	0.16	[−0.55, 0.37]	.132	0.10	[−0.10, 0.31]	.319	0.11	[−0.10, 0.31]	.308
MoCA_between	0.12	[0.02, 0.22]	.025	0.11	[−0.00, 0.22]	.051	0.12	[0.01, 0.23]	.031
DS × SR_within	−0.01	[−0.01, −0.00]	.002	−0.02	[−0.03, −0.01]	<.001	−0.03	[−0.05, −0.02]	<.001
Random effects
Within person (variance)	.27			.27			.27
Between persons (variance)	.32			.34			.34
ICC	.55			.57			.57
R² _marginal	.190			.166			.167
R² _conditional	.633			.641			.638
AIC	2,640			2,628			2,639
Model fit and p	S1 vs. S3i, p = .676; S3 vs. S3i, p = .401			S1 vs. S4i, p = .005; S4 vs. S4i, p = .007			S1 vs. S5i, p = .511, S5 vs. S5i, p = .010

Note. The Montreal Cognitive Assessment (MoCA) was used to measure general cognition. Gender was coded as 0 = male and 1 = female. AIC = Akaike information criterion; BR = difficulties in behavioral regulation; CI = confidence interval; CR = difficulties in cognitive regulation; DS = depressive symptoms; ER = difficulties in emotion regulation, Est. = estimate; ICC = intraclass correlation; NA = not applicable; SR = self-regulation.

Discussion

The objective of this study was to investigate whether difficulties in self-regulation moderate the momentary associations between depressive symptoms and participation poststroke. The results indicated that depressive symptoms had adverse momentary associations with participation poststroke. Moreover, more difficulties in total self-regulation, with a specific emphasis on more difficulties in emotion regulation, aggravated the momentary associations between depressive symptoms and satisfaction with performing daily activities in people with stroke.

Previous studies using EMA have revealed that depressed feeling had momentary associations with participation in people with stroke (Bui et al., 2022; Lau et al., 2023). However, these studies have applied only one EMA statement: “Right now, I feel depressed” (Bui et al., 2022; Lau et al., 2023). Because depression is multifaceted, it is important to consider comprehensive depressive symptoms (Fried et al., 2014). This study has applied seven depressive symptoms that were extracted from the PHQ-9. The results of this study may provide a more comprehensive view of the real-time associations between depressive symptoms and participation poststroke.

The present findings indicated that difficulties in total self-regulation appear to be momentarily associated with lower levels of satisfaction with performing daily activities in people with stroke. This result is aligned with a previous study using EMA showing that higher self-regulation ability was positively associated with higher level of participation in physical exercise in young adults (Schöndube et al., 2017). In a similar vein, a cross-sectional study has indicated that self-regulation is positively associated with satisfaction with participation in people who have received rehabilitation services, including people with brain injuries (Mol et al., 2023). Several systematic reviews with meta-analysis have demonstrated the beneficial effects of self-regulation interventions on health behavior change such as facilitating physical activity across various populations (Hennessy et al., 2020; Maes & Karoly, 2005; Spring et al., 2021; Suls et al., 2020). Our findings expand the existing understanding of the benefits of applying self-regulation strategies to survivors of stroke by showing the momentary associations of difficulties in self-regulation with lower levels of satisfaction with performing daily activities poststroke.

The findings of this study indicated that difficulties in self-regulation moderate the momentary associations with depressive symptoms and satisfaction with performing daily activities in people with stroke: People with more difficulties in self-regulation have a stronger association between more severe depressive symptoms and lower levels of satisfaction with performing daily activities compared with people with less difficulties in self-regulation. Previous studies have demonstrated the relationship between self-regulation difficulties and depressive symptoms (Acuff et al., 2019; Dube et al., 2022; Letkiewicz et al., 2014; Strauman, 2017). Our results showed that self-regulation difficulties and depressive symptoms are not only related to each other but also have synergistic momentary effects on satisfaction with performance of daily activities in people with stroke. This result is aligned with a previous cross-sectional study that revealed that self-regulation mediated the association between depressive symptoms and independence in basic activities of daily living, measured using a modified Barthel Index in people with stroke (Kim & Park, 2015).

The present findings indicate that, of three types of self-regulation, difficulties in emotion regulation particularly exacerbated the momentary associations of depressive symptoms with satisfaction with performance of daily activities. Although previous studies have demonstrated the relationship between emotion regulation and depressive symptoms (Boemo et al., 2022) or participation in daily activities (Cooper et al., 2015; Suchy et al., 2019), none of these studies have investigated the moderating role of difficulties in emotion regulation on the momentary impacts of depressive symptoms on participation poststroke. Thus, the present findings may further clarify the moderating role of difficulties in emotion regulation and provide scientific evidence to use emotion regulation strategies to momentarily reduce depressive symptoms (Boemo et al., 2022) and, ultimately, to facilitate participation in daily activities after stroke. Self-regulation ability can be increased through intervention or practice (Muraven et al., 1999), and it has been widely used as an intervention means to facilitate healthy behaviors (Hennessy et al., 2020; Maes & Karoly, 2005; Spring et al., 2021; Suls et al., 2020). Our findings suggest the need for just-in-time interventions that target self-regulation strategies (e.g., setting a goal, planning) or emotion regulation strategies (e.g., cognitive reappraisal) to reduce the momentary associations between depressive symptoms and participation in daily activities after stroke.

Study Limitations

Although this study is clinically and scientifically meaningful, it still has limitations. First, previous longitudinal studies have proposed that self-regulation difficulties prospectively predict increases in depressive symptoms (Acuff et al., 2019; Letkiewicz et al., 2014); however, this study did not elaborate on causal explanations. Future studies need to investigate lagged associations to provide explanations for the causal relationships among the study variables. Moreover, this study included people with mild stroke (NIHSS ranging from 1 to 5) who were assessed during the acute hospital stay. Because recent statistics have demonstrated that the median NIHSS at hospital admission has decreased from 4 in 2005 to 3 in 2020 (Bernegger et al., 2022), focusing on mild stroke may not considerably limit the generalizability of the study. Future studies with people with more severe stroke may be helpful to generalize the findings of this study. In this study, we performed multiple analyses to uncover momentary associations among the study variables. Although all steps were necessary to address the research objectives, multiple analyses can increase the risk of Type 1 errors. Moreover, the nature of EMA, which involves repeated measures within a person, may mitigate the limitation of small sample size for within-person momentary associations, which was the primary focus of this study. However, future studies need to include a larger sample size to attain sufficient power for exploring between-person associations and reducing biases caused from Type 1 errors. Last, we have revised the standardized measures to develop EMA items. Although this is a common approach in EMA studies, we have provided statistical evidence on the associations between scores on the laboratory-based assessments and the corresponding EMA responses to demonstrate the reliability and validity of the EMA items. However, it is important to note that future studies are necessary to provide stronger evidence for reliable and valid EMA items. In addition, our study only included performance and satisfaction aspects of participation. Future studies that encompass different aspects of participation, such as frequency of participating activities and enjoyment of performing activities, could help generalize our findings to other aspects of participation.

Implications for Occupational Therapy Practice

We have explored the momentary interplay among self-regulation, depressive symptoms, and participation poststroke. Our findings indicate that occupational therapy practitioners may incorporate self-regulation strategies as a means of interventions to reduce the negative momentary associations between depressive symptoms and lower levels of participation poststroke. Furthermore, this study has incorporated digital technologies (i.e., smartphones), which may provide scientific evidence for developing digital technologies–based just-in-time rehabilitation and occupational therapy approaches in the home and community settings.

Conclusion

The findings of this study indicate that difficulties in self-regulation, with a specific emphasis on difficulties in emotion regulation, aggravated the negative, momentary associations between depressive symptoms and satisfaction with performing daily activities. This study indicates that self-regulation and emotion regulation strategies may be targets for just-in-time interventions to reduce the real-time, momentary associations between depressive symptoms and satisfaction, thereby ultimately increasing participation poststroke.

Supplemental Material

Supplemental Information

Supplementary material for Moderating Role of Self-Regulation Difficulties in the Momentary Associations Between Depressive Symptoms and Participation Poststroke

Supplementary material, sj-pdf-1-aot-10.5014_ajot.2024.050742.pdf for Moderating Role of Self-Regulation Difficulties in the Momentary Associations Between Depressive Symptoms and Participation Poststroke by Yejin Lee, Erin R. Foster, Carolyn Baum and Lisa T. Connor in The American Journal of Occupational Therapy

Footnotes

Acknowledgments

We express our gratitude to Yejin Lee’s dissertation committee members for their contribution to this work. We extend our appreciation to the Program in Occupational Therapy at Washington University School of Medicine for technical and financial support of this project. We thank the research assistants for their help in participant recruitment, and we thank the participants in the project for their contributions. This study and the analysis plan were not formally registered. The deidentified data used in this study are not available in a public archive. Analytic code used to conduct the analyses presented in this study are not available in a public archive. They may be available by e-mailing the corresponding author. Materials that were used to conduct the study are not publicly available.

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