Running head: DAILY EVENTS AND WELL-BEING IN DEPRESSION 1
(In press, Clinical Psychological Science, 2020)
Daily life positive events predict well-being among depressed adults 10 years later
Vanessa Panaite1,2, Andrew R. Devendorf1,2, Todd B. Kashdan3, Jonathan Rottenberg2
1James A. Haley Veterans’ Hospital
2University of South Florida
3George Mason University
This article is in-press at Clinical Psychological Science
©Association for Psychological Science (APS), . This paper is not the copy of record
and may not exactly replicate the authoritative document published in the APS journal.
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The final article will be available, upon publication, at Clinical Psychological Science (doi to
Corresponding Author: Vanessa Panaite, PhD, Research Service, James A. Haley Veterans'
Hospital, 8900 Grand Oak Circle, 132B, Tampa, FL, 33637, USA. firstname.lastname@example.org
Word Count = 6696; Tables = 9
DAILY EVENTS AND WELL-BEING IN DEPRESSION 2
We know relatively little concerning the links between the events and emotions experienced in
daily life and long-term outcomes among people diagnosed with depression. Using daily diary
data from the Midlife Development in the United States (MIDUS), we examined how positive
daily life events and emotions influence long-term (10 years later) depression severity and well-
being. Participants met criteria for major depressive disorder (MDD; n=121) or reported no
depression (n=839) over the past 12-months. Participants reported positive events, socializing
activities, and negative and positive affect (NA, PA) for 8 consecutive days. Relative to non-
depressed adults, depressed adults reported fewer positive events (fewer positive interactions,
spending less time with others), lower PA, and higher NA. Among initially depressed adults,
higher baseline well-being was related to higher daily PA, lower NA, and fewer days of low
reported social time; higher daily PA and positive interactions predicted higher well-being 10
years later (N=77). Variations in day-to-day events and emotions among people with depression
may presage psychological functioning years later.
Keywords: daily diary, major depressive disorder, well-being, longitudinal methods, affect
DAILY EVENTS AND WELL-BEING IN DEPRESSION 3
Daily life positive events predict well-being among depressed adults 10 years later
Depression has been viewed as a chronic and recurrent condition with a poor prognosis
(see Monroe, Anderson, & Harkness, 2019; Rottenberg, Devendorf, Kashdan, Disabato, 2018 for
reviews). Now, depression is the leading source of personal and economic disability (WHO,
2018). To combat the burden of depression, most research has focused on reducing depression
symptoms (McKnight & Kashdan, 2009), perhaps with the assumption that symptoms correlate
with well-being and functioning. As a result, less research has focused on understanding good
outcomes after depression (e.g., presence of well-being; Rottenberg, Devendorf, Kashdan, &
Disabato, 2018). However, a comprehensive review found only moderate correlations of
depressive symptoms with well-being (e.g., presence of social relationships) and functional
outcomes (e.g., occupational functioning), suggesting that these may be somewhat independent
phenomena (McKnight & Kashdan, 2009). Neglecting measures of well-being may hamper our
ability to understand what predicts sustained recovery and well-being after depression
(Rottenberg, Devendorf, Panaite, Disabato, & Kashdan, 2019), as preliminary work shows that
elevated well-being may protect against future depression (Keyes, Dhingra, & Simoes, 2010).
This finding is key given that half of people with an initial depressive episode experience a
recurrence (Monroe, Anderson, & Harkness, in press).
Thus, it is important for depression research to specifically investigate predictors of good
outcomes like well-being (e.g., Cuijpers, 2019). In this study, we are interested in learning how
daily factors, such as positive events and affect, play a role in long-term well-being among
people with depression. As there is no universal definition as to what constitutes long-term, we
use “long-term” to denote follow-up data of at least one-year. Others have used similar timelines
(e.g., 7 months, Lemmens, et al., 2019; 18 months, Hunkeler et al., 2006).
DAILY EVENTS AND WELL-BEING IN DEPRESSION 4
Major Depression, Well-being, and Emotions
Why do some depressed adults achieve excellent long-term outcomes (Rottenberg,
Devendorf, Kashdan, & Disabato, 2018) and avoid a recurrent or chronic course of disorder
(Monroe & Harkness, 2011)? An initial investigation found that a substantial minority (nearly
10%) of adults with depression history went on to recover and achieve high levels of
psychological well-being at a 10-year follow-up (Rottenberg, Devendorf, Panaite, Disabato, &
Kashdan, 2019). This work also uncovered that people with depression vary in their global
reports of psychological well-being, and that these variations predicted long-term outcomes
(Rottenberg et al, 2019). A clear next step in this research program is to clarify what aspects of
well-being are important for long-term positive outcomes.
Well-being models propose several mechanisms that may impact long-term mental health
(e.g., sustained high functioning at least one year). Hedonic models of well-being emphasize the
presence of positive emotions (e.g., happiness) and sensations (e.g., pleasure), the absence of
negative emotions (e.g., sadness), and overall life satisfaction (Diener, 2000; Kahneman et al.
1999). Variables under the umbrella of well-being have been expanded to include optimal
psychosocial functioning as one cornerstone of mental health (i.e., eudaimonic model of well-
being; Disabato et al., 2016; Kashdan et al., 2008; Keyes & Annas, 2009). Conversely,
depression – an affective disorder characterized by low mood and anhedonia, often expressed
through withdrawal from activities – is associated with dysregulated hedonic and psychosocial
functioning. While hedonic and social deficits may appear separate, the capacity to feel pleasure
likely impacts social engagement (Rottenberg & Gotlib, 2004) and interest in other rewarding
activities (Lewinsohn & Graf, 1973). Consider, for example, how the symptom of anhedonia
may impact engagement in everyday activities (e.g., social engagement; work functioning).
DAILY EVENTS AND WELL-BEING IN DEPRESSION 5
Anhedonia is the diminished interest or pleasure in activities. About 70% of adults with a
depression diagnosis experience anhedonia (Shankman et al., 2014), and this symptom may
partially account for observations that people with depression often exhibit diminished positive
affect, arousal (e.g., Berenbaum & Oltmanns 1992, Rottenberg et al. 2002, Sloan et al. 2001; cf.
Dichter et al. 2004), and response to reward (Henriques & Davidson, 2000). Notably, the
deleterious role of anhedonia in response to positive (e.g., rewarding) contexts (e.g., receiving
good news) has been supported by theoretical (e.g., Rottenberg, 2005; see Watkins, Grimm,
Whitney, & Brown, 2005 for a review), clinical (e.g., Behavioral Activation therapy), and
experimental perspectives of depression (see meta-analysis by Bylsma et al, 2008).
For instance, self-reported anhedonia increased the risk for chronic depression over a 10-
year period (Moos & Cronkite, 1999) and a systematic review of laboratory studies found that
diminished affective processing and response to positive stimuli can have a detrimental role on
future depression (see Morris et al, 2009 for a review of longitudinal literature). Other work has
shown that people with depression who exhibited the lowest behavioral reactivity to an amusing
film evidenced worse depression severity one-year later (Rottenberg et al., 2002). This body of
work suggests that deficits in reward processing (e.g., anhedonia) are likely to play important
roles in long-term well-being of people with depression. Unfortunately, few studies have
empirically tested these questions using a longitudinal design.
Daily Diary Research and Depression
Daily diary research has added further insight into emotion dysregulation features that
delineate people with and without depression in daily life. Daily diary designs gather end-of-day
estimates from people about their daily activities and emotional experiences. This method has
been used extensively in depression research, traditionally to investigate relationships between a
DAILY EVENTS AND WELL-BEING IN DEPRESSION 6
person’s emotions and their number of experienced positive events (see e.g., Hammen & Glass,
1975; Larson, Raffaelli, Richards, Ham, & Jewell, 1990; Lewinsohn & Graf, 1973; Lewinsohn &
Libet, 1972; Nezlek, Hampton, & Shean, 2000). Findings have suggested that depressed persons
engage in fewer day-to-day positive activities than healthy persons (Lewinsohn & Graf, 1973),
despite observations showing that more engagement in day-to-day positive activities correlates
with better overall daily affect (Lewinsohn & Libet, 1972; Starr & Hershenberg, 2017) and
adjustment for people with depression (Nezlek & Gable, 2001). More recent studies using
experience sampling methods (ESM; which include multiple assessments per day) find that
depressed persons tend to report higher daily levels of NA and lower levels of PA, overall, and
fewer subjectively rated positive events of any kind (Bylsma, et al, 2011; Thompson et al, 2012).
In trying to understand the role of positive events and daily affect, among people with
depression, daily diary research has gleaned puzzling findings that diverge from conventional
expectations. Specifically, researchers hypothesized that people with depression show
diminished affective response to positive events. However, research has actually found that
depressed and non-depressed persons show similar reactions to intensely positive events
(Bylsma, et al, 2011; Thompson et al, 2012), and that reactions increase with the intensity of
positive events (Panaite, et al, 2018).
Depressed persons often even report a puzzling “mood brightening effect” (Bylsma et al,
2011), which is evidenced by larger decreases in NA in response to highly positive contexts
(among other signs of greater sensitivity to rewarding events; Steger & Kashdan, 2009). In a
subsequent analysis within a depressed group, the magnitude of the mood brightening effect was
positively correlated with the intensity of judgment of daily events as positive (Panaite et al,
2018). Taken together, this preliminary work suggests that, despite a low prevalence rate of
DAILY EVENTS AND WELL-BEING IN DEPRESSION 7
positive events, coupled with the experience of generally low PA and high NA, people with
depression are able to benefit at least momentarily when such events occur; evidenced by both
increases in PA and decreases in NA in response to positive events. Such variations in moment-
to-moment benefits may be relevant for long-term depression severity and well-being.
Daily Emotions, Positive Events, and Long-term Outcomes
Despite the value of daily diary designs, relatively little published work has investigated
the role of daily emotions and positive events in predicting long-term (e.g., >1 year) depression
symptoms and well-being. Relatedly, few studies have examined what types of daily positive
events influence these long-term depression outcomes. In our study, like in most ESM studies
(e.g., Bylsma, 2008; Thompson et al., 2012; Peeters et al, 2003), we define positive events by
using participant’s subjective event appraisals.
These positive events are especially relevant when considering behavioral theories of
depression (Lewinsohn & Libet, 1972; Lewinsohn, Sullivan, & Grosscup, 1980). Behavioral
models of depression posit that low levels of positive reinforcement (e.g., positive events) lead to
sustained periods of negative affect (e.g., sadness), which can further increase avoidance
behaviors (e.g., solitary activities like watching TV, sleeping). Additionally, BA models
acknowledge that the quality (intensity) and quantity of positive (e.g., rewarding) interactions
with the environment influence depression outcomes (e.g., Lewinsohn, Sullivan, & Grosscup,
1980). Consequently, behavioral treatments help clients develop activity schedules and goals to
increase the likelihood of positive interactions.
Therapists using behavioral treatments for depression note that “…particular activation
strategies that work for one person may not work for another person” (Dimidjian et al, 2014, p.
335). Indeed, some types of positive events may have a stronger relationship to depressed
DAILY EVENTS AND WELL-BEING IN DEPRESSION 8
individuals’ well-being and symptoms than others. Solitary leisure activities, for example, may
represent a common form of avoidance behavior among people with depression, and thus be
associated with lesser increases in well-being (e.g., Lewinsohn & Graf, 1973; Watson et al,
1992). Prior work found that persons who reported more positive social events experienced
higher well-being (Steger & Kashdan, 2009). Taken together, these findings suggest that it is
important to assess a range of events that may be relevant to long-term well-being among people
Unfortunately, relatively little work has investigated the role of daily experiences (events
and emotions) for the long-term well-being of people with depression. Studies of short-term
outcomes offer some precedents. For example, depressed persons showing higher NA decreases
and higher PA increases in response to daily positive events exhibited the most improvement in
depressive symptomatology at one a month follow up (Peeters, Berkhof, Rottenberg, &
Nicolson, 2010). Behavioral studies of depression highlight the importance of both quality
(intensity) as well as quantity of positive and reinforcing interactions with the environment for
depression course (e.g., Lewinsohn, Sullivan, & Grosscup, 1980). Importantly, in a 21-day daily
diary study, adjustment covaried more strongly with positive events for participants with
depression than they did for participants without depression, suggesting that positive events may
have a larger impact on adjustment among people with depression (Nezlek & Gable, 2011).
Finally, daily reward reports of PA in response to positive events predicted both fewer
depressive symptoms and decreased the likelihood of a recurrent MDD episode over a year later
(Wichers, Peeters, Geschwind, et al., 2010). The current work builds on these findings by
offering a daily diary assessment of emotions and positive events in both intrapersonal and social
contexts as they contribute to long-term outcomes in clinically depressed adults. We sought to
DAILY EVENTS AND WELL-BEING IN DEPRESSION 9
leverage the flexibility of a daily process design to capture daily affective processes (for a review
see Bylsma & Rottenberg, 2011) may contribute to long-term health.
This study examined the relationship between depression, positive events, and daily
emotions with two cross-sectional and one longitudinal aim:
1. Replicate cross-sectional findings of lower PA, higher NA, and generally lower rates of
reported positive events among people with depression relative to people without depression.
2. Given that well-being models and interpersonal theories of depression focus on the importance
of socializing (e.g., Disabato et al., 2016), we evaluated the role of positive social versus solitary
leisure events on daily well-being. Prior work specifically highlights ties between positive social
interactions and positive affect (e.g., Lewinsohn & Graf, 1973; Vittengl & Holt, 1998; Watson et
al, 1992). Thus, we hypothesized that interpersonal positive events would have a stronger
relationship with affect among depressed persons than would solitary positive events.
3. Evaluate whether daily positive events, PA, and NA predict long-term depression severity and
well-being – specifically, 10 years later. We hypothesized that daily interpersonal (relative to
solitary) positive events, more overall daily positive events, higher PA, and lower NA would
predict two outcomes 10 years later: clinical improvement (i.e., lower odds of meeting a
depression diagnosis) and well-being (i.e., autonomy, environmental mastery, personal growth,
positive relations with others, purpose in life, and self-acceptance, dispositional affect and life
Samples and procedures
DAILY EVENTS AND WELL-BEING IN DEPRESSION 10
Data for the current study were extracted from the main random digit dialing sample at
Waves 2 and Wave 3 of the Midlife Development in the United States study (2004–2006, 2013-
2014; MIDUS: http://midus.wisc.edu/scopeofstudy.php). At Wave 2, participants comprised of a
nationally representative, English-speaking, non-institutionalized sample between the ages of 35
and 84 (n = 2,257). Additionally, some participants completed both a 30-minute phone interview
and a battery of self-administered questionnaires. For this study, we focus on a subsample of
participants (n=960) who completed phone interviews for eight consecutive nights for the
National Study of Daily Experiences (Ryff & Almeida, 2017). Participants completed an average
of 7.4 (SD=1.2) daily interviews. Less than 3% of the sample completed fewer than 50% of the
Mental health diagnoses and severity. At both Waves, mental health disorders were
assessed with the Composite International Diagnostic Interview Short Form (CIDI-SF), which
was based on Diagnostic and Statistical Manual of Mental Disorders 3rd edition, revised. The
CIDI-SF assessed 12-month major depression, generalized anxiety disorder (GAD), and panic
disorder (PD). The CIDI-SF for major depression, GAD, and PD assessments have good
classification accuracy relative to the full CIDI instrument (93%, 99%, and 98%, respectively)
(Kessler et al., 1998).
Participants met depression criteria if they reported having a period of at least two weeks
(in the past 12 months) of either depressed mood or anhedonia most of the day or nearly every
day and endorsed 4 additional symptoms to qualify for a major depressive episode. The
sensitivity of CIDI-SF classification for major depression is 89.6%, with specificity of 93.9%
(Kessler et al., 1998). Depression symptom severity was calculated for those with a depression
DAILY EVENTS AND WELL-BEING IN DEPRESSION 11
diagnosis by totaling positive responses to CIDI-SF items. GAD and PD were also assessed with
the screening version of the World Health Organization’s (WHO) “Composite International
Diagnostic Interview”, Version 10 (CIDI) (WHO, 1990; Kessler et al., 1998). We created a
grouping variable for people without depression, GAD, and PD (non-disordered control = 0) and
people meeting depression criteria (depressed = 1) at both Waves.
Finally, a substance use screening test assessed possible substance use problems. The
screening was completed by participants’ yes/no self-report on four items inquiring about:
emotional or psychological problems from using alcohol, strong desire or urge to use, spending a
great deal of time using alcohol, and using more than usual to get the same effect (Selzer, 1971).
If participants answered “yes” to any of the four questions the screen would be deemed positive
for substance use over the past 12 months. Given that the screen cannot determine probable
substance use disorder, this information was presented in Table 1 but not used as an exclusion
Global well-being. At Waves 2 and 3, overall well-being was assessed as part of self-
administered questionnaires. A 42-item measure captured Ryff’s (1989) six dimensions of
psychological well-being (7 items per dimension): (1) autonomy (acting with a sense of volition
or willingness); (2) environmental mastery (self-direction and productivity); (3) personal growth
(continual self-improvement); (4) positive relations with others (the capacity to love and be
loved); (5) purpose in life (an overarching life aim); and (6) self-acceptance (positive self-
regard). All items were rated on a Likert Scale ranging from 1 (strongly agree) to 7 (strongly
Additionally, Diener’s (1984) tripartite model of subjective well-being was assessed. A
5-item measure of life satisfaction was measured on a Likert Scale ranging from 0 to 10 (items
DAILY EVENTS AND WELL-BEING IN DEPRESSION 12
addressed satisfaction with life overall, work, health, relationship with spouse/partner, and
relationship with children). Further, 30-day positive (e.g., cheerful, good spirts) and negative
affect (e.g., sad, nervous) were assessed, each with 6-items on a Likert scale ranging from 1 (all
of the time) to 5 (none of the time).
Scale responses were averaged to create each dimension’s observed scores. Therefore, to
assess global well-being at each Wave, each of the nine scores were standardized using z-scores
and summed together to create a composite score (negative affect was reverse scored). In the
current study, reliability of the global well-being scale was acceptable for Wave 2 (Cronbach’s α
= .88) and Wave 3 (Cronbach’s α = .88). Well-being exhibited moderate stability across the
Wave 2 and Wave 3, (r = .71) and a high two-way mixed effects ICC = .86.
Daily positive events. Nightly over 8 days, participants were queried by phone regarding
daily experiences, events, and activities.
Time use on leisure activities. Participants were asked how much time (captured in hours
and minutes) over the past 24 hours was spent engaging in leisure activities “Since this time
yesterday, how much time did you spend relaxing or doing leisure time activities?” If necessary,
the following clarification was used: “Leisure time activities means actively choosing to do
things for yourself. This may overlap with other categories, such as spending time with your
children.” For present purposes, we transformed the two variables (leisure hours and minutes)
into one variable capturing the entire time in minutes.
Time used socializing. Participants were asked whether the time they usually spend with
others decreased over the past 24 hours “Did you spend less time with people in your personal
life today compared to usual because of any problems with either your physical health, your
emotions, use of alcohol, some combination, or other?” This was a yes/no question.
DAILY EVENTS AND WELL-BEING IN DEPRESSION 13
Positive interactions. Participants were asked to respond to a yes/no question “did you
have an interaction with someone that most people would consider particularly positive (for
example, sharing a good laugh with someone, or having a good conversation) since (this time/
we spoke) yesterday?”
Number of positive events. To examine the positive events in respondents’ daily
experiences, respondents were asked questions regarding the most positive event that occurred in
the last 24 hours, the time the positive event occurred, where the event occurred (e.g., work,
home) and who else was involved in these positive events. This yielded a daily sum of relevant
Daily affect. Daily positive and negative affect were measured using an adapted
inventory of emotions from the Non-Specific Psychological Distress Scale and the Positive and
Negative Affect Schedule (Kessler et al., 2002; Mroczek & Kolarz, 1998; Watson, Clark, &
Tellegen, 1988). Respondents reported how often during the past day they experienced 13
different positive emotions (e.g., cheerful, happy, active) and 14 different negative emotions
(e.g., worthless, hopeless, angry) using a 5-point scale ranging from 0 (none of the time) to 4 (all
of the time). Items for each scale were summed and averaged for each day, with higher scores
reflecting higher positive and negative affect. Using two unconditional, intercept only models,
excellent reliability for negative affect (α = .95) and positive affect (α = .98) was estimated.
Given the clustered, non-independent nature of daily diary data, analyses were performed
using multi-level modeling (MLM) for continuous outcomes (i.e., daily affect) and Generalized
Estimating Equations (GEE) for dichotomous outcomes (i.e., occurrence of a positive event).
Analyses were performed in SPSS Version 24 (IBM, 2018). MLM can accommodate within
DAILY EVENTS AND WELL-BEING IN DEPRESSION 14
person clustering of days by accounting for non-independence of clustered data and estimating
variance at all levels (Nezlek, 2001). MLM analyses used an unstructured covariance matrix with
maximum likelihood estimation. GEE analyses were performed with an independent covariance
matrix. We corrected for multiple comparisons within each family of analyses using the
Benjamini/Hochberg technique (see Benjamini & Hochberg, 1995). Finally, an evaluation of a
correlation matrix showed correlations ranging from r = -.004 to r = +/-.57. As expected, one
correlation that exceeded .7 was between baseline and follow-up global well-being (r = .72).
Models predicting well-being were ran with and without baseline well-being.
Hypothesis 1, Concurrent model: Number of daily positive events and affect as a
function of depression status, depression severity, well-being. Using MLM, daily positive and
negative affect, number of positive activities, time spent with people in daily life, and time
performing leisure activities were modeled as a function of group membership (healthy or
depressed) by regressing each person i's outcome level onto a dummy variable indicating the
depression group membership (Depression statusti: 0=no depression; 1=MDD in the past 12
months). At Level-2, the Level-1 intercept was allowed to vary randomly across participants and
modeled as a function of individual differences in depressive diagnosis (i.e., MDD over the past
12 months), as shown in the Level-2 model equations, below. These models were repeated with
depression severity and well-being as predictors while restricting the sample to those with a 12-
month depression diagnosis. A final model tested whether occurrence of positive events
predicting daily NA and PA among those with a 12-month depression diagnosis.
Cross-sectional models testing group differences were first run without covariates and
then evaluated including baseline characteristic that were identified to vary across groups.
Level 1 Model:
DAILY EVENTS AND WELL-BEING IN DEPRESSION 15
Continuous outcometi= β0i + rti
Level 2 Model:
β0i=y00+ y01(Depression statusi)+u0i
Hypothesis 2, Concurrent model: Frequency of positive events as a function of
depression status, depression severity, well-being. Using GEE, we examined whether group
membership at baseline was associated with odds of a positive event occurring in daily life. GEE
is a general linear model used for clustered data which accounts for multiple incidents of daily
positive events over the course of the 8-day daily process study, adjusts for the within-subject
correlation present among repeated observations over time, and corrects for missing data by
weighting each individual’s data according to the number of available observations. Two more
models were evaluated with depression severity and well-being as predictors restricting the
sample to those with a 12-month depression diagnosis at baseline. Cross-sectional models testing
group differences were first run without covariates and then evaluated including baseline
characteristic that were identified to vary across groups.
The following set of equations summarize the basic models:
Mean response model: E (yij) = µij
is related to the predictor by a link function: g(µi) = Xi β
Hypothesis 3, Longitudinal model: Daily positive events and affect predicting
depression status, depression severity, and well-being 10 years later among those with a 12-
month depression diagnosis at baseline. These analyses followed a two-step procedure (for a
similar approach, see, e.g., Caminis, Henrich, Ruchkin, Schwab-Stone, and Martin, 2007;
Kuppens, Sheeber, Yap, et al, 2012). In the first step, estimates of person level average number
of daily positive events, NA, and PA were obtained from two-level (days nested in persons)
DAILY EVENTS AND WELL-BEING IN DEPRESSION 16
multilevel analyses performed separately for each variable from the daily diary data. At level 2
of the models, person-specific (random) intercept and slope values were estimated (intercept and
slope estimates were allowed to covary across participants.
In the next step, these estimates were used to predict depressive status at follow-up in
linear and logistic regression analyses. As noted, all analyses included the 77 participants with a
12-month depressive disorder at baseline and outcome data 10 years later. Longitudinal models
were first tested using the main predictors based on our hypothesis: daily diary affect and events.
Next, covariates were included based on known predictors of depression course: age, sex, and
education. Finally, baseline depression severity and baseline well-being were added to the
models as covariates.
Missing data and attrition
There was a 36% attrition rate at the 10-year follow-up. An analysis of baseline
differences between those with and without data at follow-up resulted in null findings; there were
no statistically significant differences in age, gender, baseline depression severity, or global well-
being (ps > .05) among those that did and did not complete Wave 3 data.
Demographics and clinical characteristics
Of those interviewed, 960 participants met our inclusion and exclusion criteria.
Participants met primary group membership as either non-depressed (no depression, anxiety,
panic disorders for the prior 12 months; n=839) or with a 12-month depression diagnosis
(n=121). On average, those with a depression diagnosis, relative to non-depressed, were younger
(Mdepressed = 52.48, SDdepressed = 10.73; Mnon-depressed = 58.56, SDnon-depressed = 12.51; p<.001) and
more likely to be women (Depressed: 74.4 % (n = 90); Non-depressed: 51.3% (n = 430);
DAILY EVENTS AND WELL-BEING IN DEPRESSION 17
p<.001); groups were indistinguishable based on educational attainment: Depressed participants:
8.3% some high school, 28.3% high school diploma/GED, 32.5% some college; 30.8% college
graduate or professional degree; Non-depressed: 4.3% some high school, 24.6% high school
diploma/GED, 29.1% some college; 41.1% college graduate or professional degree; p>.05.
Finally, depressed participants were more likely to screen positive for problematic alcohol use
over the past 12 months (14.5%; n = 17) versus 2.9% (n = 23 of the non-depressed); p<.001.
Among those meeting criteria for depression, 18.2% (n = 22) and 22.3% (n = 27) also reported
generalized anxiety and panic disorders, respectively. Group differences are presented in Table
S1. Age, sex, and positive alcohol screen were entered as control variables in the following
Baseline group differences in daily positive events and affect
First, we evaluated events and affect individually to understand the relationship between
group membership on each of the variables of interest. Groups did not differ on: total leisure
time spent daily (B = -6.41, SE = 10.63, t = -.60, p = .547). Persons with a 12-month major
depression diagnosis reported higher mean levels of NA (B = .27, SE = .02, t = 12.33, p < .001),
lower mean levels of PA (B = -.57, SE = .07, t = -8.57, p < .001), and fewer positive events on
average (B = -.13, SE = .06, t = -2.20, p = .028) in daily life, relative to healthy persons in the
prior 12 months. Those with a 12-month diagnosis of depression also had lower odds of a
positive interaction with someone in their personal life (B = -.24, SE = .11, Wald = 4.83, p =
.028, OR = .79) and higher odds of reporting having spent less time with people in their personal
life (B = 1.10, SE = .21, Wald = 26.77, p < .001, OR = 2.99) relative to healthy persons.
Next, using a GLM MANOVA, we evaluated the group membership effect on the daily
events and affect concurrently. The multivariate test indicated an overall group effect (F (6,928)
DAILY EVENTS AND WELL-BEING IN DEPRESSION 18
= 27.35, p < .001; Wilk's Λ = 0.850, partial η2 = .15). The tests of between subjects effects
indicated a group main effect on: average NA (F (1,933) = 154.51, p < .001; partial η2 = .14),
average PA (F (1,933) = 69.07, p < .001; partial η2 = .07), number of days when less time with
people was reported (F (1,933) = 24.61, p < .001; partial η2 = .03), and number of days when a
positive interaction was reported (F (1,933) = 6.34, p < .012; partial η2 = .01) were significant
when variables were entered concurrently. In summary, while groups did not differ on time spent
on leisure activities and number of positive events, depressed persons reported higher NA, lower
PA, spending less time with people, and fewer days with a positive interaction relative to
controls (ps < .042).
See Tables S2 for complete models.
Does depression severity and global well-being relate to daily positive events and affect among
persons with a depression diagnosis?
Among persons with a 12-month depression diagnosis, depression severity did not predict
daily positive events or affect (F (6,109) = .82, p = .56; Wilk's Λ = .957, partial η2 = .04) (see
Tables S3). However, those with higher global well-being reported lower daily NA (B = -.02, SE
= .01, t = -5.01, p < .001), higher daily PA (B = .06, SE = .01, t = 6.65, p < .001), and had lower
odds of spending less time with people (B = -.09, SE = .02, Wald = 16.75, p < .001, OR = .92).
The global well-being effect continued to be statistically significant in a multivariate model
(F (6,99) = 9.30, p < .001; Wilk's Λ = .639, partial η2 = .36). An investigation of between-
subjects effects showed that the same effects remained statistically significant: NA (F (1,104) =
25.71, p < .001; partial η2 = .20), PA (F (1,104) = 51.83, p < .001; partial η2 = .33), and fewer
days with less time with people (F (1,104) = 15.52, p < .001; partial η2 = .13). See Tables S4 for
full model results.1
Results remained unchanged after including age, gender, and alcohol screen score as covariates. Covariates were
chosen based on baseline group differences.
DAILY EVENTS AND WELL-BEING IN DEPRESSION 19
How do daily positive events relate to daily affect among persons with depression?
First, we ran a series of bivariate analyses among depressed participants. Results reflected
that less daily time spent with people was associated with higher NA (B = .51, SE = .05, t = 9.92,
p < .001), while positive interactions were associated with lower NA (B = -.07, SE = .03, t = -
2.27, p= .023). While on days people reported higher NA they also reported spending less time
with others, a greater variety of positive events were associated with daily PA levels. First,
mirroring NA findings, less time with others was associated with lower PA (B = -.76, SE = .07, t
= -10.60, p < .001) and positive interactions were associated with higher PA (B = .21, SE = .04, t
= 5.21, p < .001). Higher overall number of positive events (B = .14, SE = .02, t = 5.84, p < .001)
was associated with higher PA among depressed participants.
Next, we conducted two multivariate multilevel models to understand the effect of
various events on NA and PA separately. Our first model evaluated the role of different positive
events on daily PA. Taken together, less time with people and number of positive events
remained significant predictors of PA, such that less daily time spent with people was associated
with lower PA (B = -.71, SE = .07, t = -9.99, p < .001) and an increased number of positive
events was associated with more PA (B = .08, SE = .03, t = 2.33, p = .020). For NA, less daily
time spent with others was the only predictor of high NA among depressed people (B = .51, SE =
.05, t = 9.84, p < .001). Finally, neither leisure time, nor positive interactions had a statistically
significant relationship to PA or NA (ps > .031) (see Table 1).
How do daily positive events and affect relate to depressed persons’ status, severity, and
general well-being 10 years later?
Finally, among initially depressed persons, preliminary bivariate analyses suggested that
higher NA was associated with both higher depression severity (β = .33, t = 3.22, p = .002) and
DAILY EVENTS AND WELL-BEING IN DEPRESSION 20
higher odds of a depression diagnosis (B = 1.92, SE = .75, Wald = 6.61, p = .01, OR = 6.81) at
10-year follow-up. PA alone was not associated with depression severity or diagnosis 10 years
later. Nor were any of the positive events; for example, having a positive interaction (β = -.23, t
= -2.26, p = .027) was not related to lower depression severity, nor did it predict lower odds of a
depression diagnosis (B = -.22, SE = .11, Wald = 4.18, p = .04, OR = .80) at 10-year follow-up.
When predictors were entered concurrently, NA was no longer related to depression severity (β =
.33, t = 2.26, p = .027) and odds of a depression diagnosis (B = 2.22, SE = .99, Wald = 4.99, p =
.025, OR = 9.19) at the 10-year follow-up (see Tables 2 and 3 for full models).
Next, we evaluated the role of daily events and affect in follow-up global well-being
among depressed individuals. Increased average leisure time (β = -.33, t = -3.03, p = .003) over
the eight-day assessment period and higher daily NA (β = -.45, t = 4.28, p <.001) predicted lower
well-being at the 10-year follow-up. More days with a positive interaction (β = .33, t = 3.04, p =
.003), number of positive events (β = .30, t = 2.67, p = .009), and higher PA (β = .46, t = 4.45, p
<.001) individually predicted higher wellbeing at follow-up. Reporting more days with less
social time (β = .23, t = 2.04, p = .045) was not related to wellbeing 10 years later.
When predictors were included concurrently in our longitudinal analysis, higher average
PA (β = .30, t = 2.66, p = .010) was related to higher global well-being. Higher average NA (β =
-.32, t = -2.16, p = .034) was no longer related to follow-up well-being. When baseline well-
being was introduced in the model, NA and PA no longer predicted follow-up well-being (ps >
.017), but more frequent positive interactions became a predictor of higher well-being (β = .27, t
Introduction of age, gender, education, baseline depression severity as control variables that could possibly have a
role in depression outcomes did not change the results.
DAILY EVENTS AND WELL-BEING IN DEPRESSION 21
= 2.40, p = .017) along more days when less time with people was endorsed (β = .28, t = 2.80, p
= .007) (see Table 4 for complete model).
Why do some persons with depression achieve excellent long-term outcomes
(Rottenberg, Devendorf, Kashdan, & Disabato, 2018)? In the current study, we shed light on this
question with an eight-day daily process design, examining the relationship between daily well-
being (PA and NA), involvement in daily positive events, and long-term positive outcomes for
depression, including symptom reduction and elevated psychological well-being.
People with depression reported fewer positive opportunities, lower PA, and higher NA.
These findings replicated prior work using daily assessments of affect and positive events
(Bylsma et al, 2011; Thompson, et al., 2012) and in clinical self-reports from people with
depression (Lewinsohn & Libet, 1972). Moreover, cross-sectional well-being among people with
depression was positively associated with more daily time spent with others, which was in turn
associated with lower daily NA and higher daily PA. Interestingly, positive event domains (e.g.,
leisure time) were unrelated to NA and PA. This pattern may indicate that interpersonal events
may be more central to daily well-being in depression relative to other positive events (for more
see, Steger & Kashdan, 2010).
Positive event frequency was also cross-sectionally associated with higher daily PA, and,
to a lesser extent, lower NA. Prior work demonstrated that depressed patients’ engagement in a
greater number of positive events was associated with better overall daily affect characterized by
endorsement of more “happy” states and fewer “unhappy” states (Lewinsohn & Libet, 1972).
Some of the differential relationship between positive events and PA and NA may be a function
Introduction of age, gender, education, and baseline depression severity changed the results in that reporting
positive interactions no longer related to follow-up well-being (see Table 4).
DAILY EVENTS AND WELL-BEING IN DEPRESSION 22
of pleasantness intensity. For example, prior work suggests that the intensity of mood
brightening effects observed in prior studies (i.e., decreases in NA in response to positive events;
Bylsma et al, 2011; Thompson et al, 2012) may vary as a function of appraisals (Panaite et al,
2018). However, when clinically depressed, the frequency of positive events may be less
important to predicting how positive events felt than how positive events are appraised (Hammen
& Glass, 1975). Unfortunately, this study did not collect participants’ judgments of event
pleasantness or meaningfulness. Similarly, it is possible that NA may be more resistant to
change, requiring greater thresholds of manageability or internal controllability by depressed
compared to healthy adults (Peeters, Nicolson, Berkhof, Delespaul & deVries, 2003). To test
whether thresholds differ for which types of positive events elicit long-term benefits and which
types of negative events elicit long-term problems for depressed adults, future research will
require fine-grained subjective assessments (including meta-emotion and meta-cognition).
Our findings demonstrate the importance of daily affect for long-term outcomes in
depression. Notably, this is the first study to show that higher daily PA and positive events
predicted higher overall well-being 10-years later. Conversely, higher daily NA was related to
higher depression severity and lower well-being 10-years later. Daily life PA appeared to be
linked to benefits 10-years later among those with depression. Importantly, the role of positive
interactions remained a significant predictor of well-being at 10 years even after controlling for
global well-being at baseline. This finding supported our hypothesis that some daily events, in
the aggregate, can have a greater impact on depressed individuals’ long-term functioning. This
finding is commensurate with short-term observations of depression within a behavioral
framework, where scheduling activities that are high in reward potential are a means to increase
positive reinforcement (Dimidjian et al., 2014).
DAILY EVENTS AND WELL-BEING IN DEPRESSION 23
Our data potentially offer greater specificity than prior reports where overall well-being
at one time point was predictive of depression at a future time point (Keyes, Dhingra, &, Simoes,
2010; Wood & Joseph, 2010; Rottenberg et al., 2019). Notably, in our dataset, better hedonic
functioning captured through daily experiences of PA and positive events were related to more
benign depression outcomes over the long term. Although our study does not elucidate the
specific mechanisms of change, we know elsewhere that experience of positive affect facilitates
a variety of positive outcomes (see Lyubomirsky, King, & Diener, 2005 for a meta-analysis)
potentially by facilitating sociability, engaging in social interactions, and appraising these
activities as more rewarding (see Lyubomirsky, et al., 2005). Conversely, anhedonia is
implicated in a worse course of depression, as it has been shown to increase the risk of chronicity
over a 10-year period (Moos & Cronkite, 1999).
We report on clinically meaningful outcomes over a long follow up period. These study
features reinforce the clinical significance of these findings. Indeed, these findings bolster
approaches such as behavioral activation therapy (e.g., Hopko, Lejuez, Ruggiero, & Eifert, 2003)
and the development of skills such as savoring (e.g., Bryant & Veroff, 2017). Broadly, our
findings are in keeping with behavioral and interpersonal theories of depression. Behavioral
theories conceptualize depression within a person’s life context. Behavioral models of depression
predict that low levels of positive reinforcement can increase feelings of sadness, which can
further increase avoidance behaviors (e.g., solitary activities like watching TV, sleeping).
Behavioral avoidance tends to exacerbate depressed moods by decreasing social contact and
engagement in pleasurable activities (Dimidjian, Martell, Addis, & Herman-Dunn, 2008).
Depressed moods can also elicit support from others and our findings suggest that when this is
successful, long-term mood benefits emerge (Allen & Badcock, 2003; Rottenberg & Gross,
DAILY EVENTS AND WELL-BEING IN DEPRESSION 24
2004). The idea that sustaining engagement with significant others and in pleasurable activities
may lead to long term well-being is at the core of behavioral activation techniques in therapy.
A few limitations restrict the scope of our findings. For example, although sample size
was relatively high for a daily process study, we did not have a sufficient sample of depressed
persons to predict thriving as a categorical outcome at follow up (see Rottenberg et al, 2018 for a
review on high functioning after depression). In fact, the smaller remaining sample with follow-
up data likely increased the possibility for Type II error in this study. It would be important to
understand the boundaries of our findings and whether there may be optimal combinations of
emotional well-being that could ultimately lead to thriving in depression. As noted above, details
about the daily positive life events were relatively generic and other descriptors such as
appraisals of the positive events were not part of the design, therefore limiting an understanding
of the exact contexts that may be beneficial among depressed persons. Finally, while end day
reports are valuable for capturing day-level data and have been shown to be valid for this
purpose, we cannot rule out recall bias as an explanation of our well-being findings. In such a
scenario, longitudinal results would reflect enhanced capacity to recall positive events rather than
actual occurrence of more positive events for depressed persons. Although such a scenario
cannot be ruled out, it appears improbable as depressed persons should, if anything, be biased
against the recall of positive events.
Despite the noted limitations, our findings add to new efforts (Rottenberg, et al, 2018;
Rottenberg et al, 2019) to understand the roots of long-term positive outcomes among persons
with depression. To expand this line of inquiry, two key future questions are to understand how
these positive outcomes (1) play out across different phases of depression (e.g., by shortening
depression episodes and/or by increase periods of recovery)? (2) play out differently across
DAILY EVENTS AND WELL-BEING IN DEPRESSION 25
persons (do some persons benefit differentially from particular changes in daily routines)? In this
work, will be valuable to move beyond categorizing people as having or not having a depression
diagnosis, to explore heterogeneous routes from symptom reduction to the onset of sustainable
V.P. (with instrumental support from A.R.D.) developed the study concept, performed the data
analysis, and drafted the manuscript All authors contributed to the conceptualization of the study,
the interpretation of results, and provided critical revisions. All authors approved the final
version of the paper for submission.
The contents of this publication do not represent the views of the Department of Veterans Affairs
or the United States Government. We thank the MIDUS study for making the data available.
Declaration of Conflicting Interests
The authors declared no conflicts of interest with respect to the authorship or the publication of
The MIDUS study was originally funded by The John D. and Catherine T. MacArthur
Foundation Research Network and by National Institute on Aging Grants P01-AG020166 and
DAILY EVENTS AND WELL-BEING IN DEPRESSION 26
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Table 1. Daily positive events as they relate to daily positive and negative affect among persons with depression (N = 121).
Model for PA
Less time with people
Number positive events
Model for NA
Less time with people
Number positive events
B = unstandardized estimate; SE = standard error;
Wald = Wald chi-square; OR = odds ratio; CI = confidence interval
*Based on Benjamini/Huchberg technique, α < .031 for significance testing.
DAILY EVENTS AND WELL-BEING IN DEPRESSION 36
Table 2. Predictors of depression status 10 years later (N = 77).
Less time with people
Number Positive Events
SE = standard error; Wald = Wald chi-square; OR = odds ratio; CI = confidence interval;
PA = positive affect; NA = negative affect.
*Based on Benjamini/Huchberg technique, α < .017 for significance testing.
DAILY EVENTS AND WELL-BEING IN DEPRESSION 37
Table 3. Daily life predictors of depression severity 10 years later (N = 77).
Baseline depression severity
Less time with people
Number Positive Events
SE = standard error; VIF = variance inflation factor; PA = positive affect; NA = negative affect.
*Based on Benjamini/Huchberg technique, α < .017 for significance testing.
DAILY EVENTS AND WELL-BEING IN DEPRESSION 38
Table 4. Daily life predictors of well-being 10 years later (N = 77).
Baseline depression severity
Less time with people
Number Positive Events
SE = standard error; VIF = variance inflation factor; PA = positive affect; NA = negative affect.
*Based on Benjamini/Huchberg technique, α < .017 for significance testing.