Wellbeing is a Public Health Issue

Well-being interventions are an important tool in helping populations cultivate positive feelings,

behaviors, or thoughts. More importantly, identifying those that work well and for whom is

equally important. Our paper builds on Heintzelman et al. (2023). We randomly assigned people

to work on PPIs that were either matched to their strengths, matched their weaknesses, or self-

selected. We assessed well-being before and after a 4-week PPIs and again three to four weeks

later. This endeavor involved Prolific Academic, an online platform widely used by researchers

interested in working with public participants. The survey was advertised as “Person-Activity Fit

for Online Happiness Practices”. As predicted, out to post-intervention, the weaknesses condition

improved in well-being significantly more than the strengths condition. Likewise, the self-

selection condition improved in well-being significantly more than the strengths condition out to

post-intervention. Additionally, the weaknesses and self-selection conditions did not have

significantly different trajectories of well-being from the strengths condition out to follow-up.

The desire for well-being is a universal one. It’s a topic discussed in legislative chambers,

academia, and amongst healthcare professionals. Given this knowledge, how well are we

identifying what contributes to well-being, source interventions designed to improve well-being,

and more importantly, of those identified, how effective are they at generalizing to the public.

For this study well-being constitutes two approaches, subjective well-being (SWB) and

psychological well-being (PWB). The two inform positive psychological interventions (PPIs)

either independent of each other or in this study’s case, together as well. The SWB model can be

explained as traits such as cheerful disposition that may help mitigate negative life experiences.

The PWB model is understood to involve personal growth measures such as personal growth.

The Koydemir et al. (2021) study identified interventions that target both SWB and PWB

together as showing greater promise evidenced by the effect size and sustained long term

temporal benefits exhibited in adult participants. Their results substantiate the claim that PPIs

improve well-being. Nevertheless, there are limitations and suggestions to consider. There are

three additional points to touch on for future research, that of reliability, validity, and to consider

measures other than self-report methods (Koydemir et al., 2021). Additionally, to address the

three, socioeconomic status (SES) should be a consideration as it possibly mitigates the self-

report concern as well as provide an opportunity to generalize to the greater population, which is

a prerequisite for validity.

We witness individuals with means accessing resources to help them address concerns

that have contributed to a less than optimum mental and physical well-being. Then you have

those who do not have the means to access the same resources. Additionally, with the notion that

research is conducted to drill down on issues in the hope of informing stakeholders interested in

either ensuring well-being or methods designed to mitigate poor well-being, the research being

conducted when dealing with the topic of public health must have internal and external validity.

Therefore, it is imperative that socio-economic status (SES) factors are considered. Efforts to

mitigate factors or variables that are a hindrance to well-being are devised and measured to

gauge efficacy in the hopes of deploying to the general at large.

From research conducted under the Positive Psychology discipline, a Meta-Analysis

study involving 68 randomized controlled peer-reviewed studies tested the effect of PPIs versus

control on Subjective Well-Being (SWB) and Psychological Well-Being (PWB) examining for

efficacy in measuring SWB and PWB or a combination of both measures (Koydemir et al.,

2021). The present study cast a wide net by incorporating an analysis of past studies, focusing on

Well-Being, Duration of Intervention and length of Sessions, Age, and Delivery Method (online

or traditional). As the study sought to make use of past and recent experimental research, they

made use of four online databases which garnered 223 studies with 16,085 participants, resulting

with findings of where participants fell in regard to SWB and PWB. The results of this study

indicated that PPIs effectively increased SWB and PWB over control groups. Their analysis

showed that interventions on a short-term (3 months) and long-term (6-months) approach

favored long term, especially when it came to older participants. Whereas younger participants

benefitted better from short term interventions. Additionally, participants generally benefited

more when interventions were a combination of both PWB and SWB. When considering the

Koydemir et al. (2021) paper in comparison to the Heintzelman et al. (2023) paper, there are

different measurements to consider. The Heintzelman et al. (2023) paper has a Positive

Engagement Model, Medical Model, Strengths-Based Model, and Autonomy Model.

The significant difference between the two is that the Koydemir et al. (2021) study does not

incorporate clinical (Medical Model). Nevertheless, the overarching research is similar. They are

both attempting to show which approaches or measures work best to help participants achieve

well-being.

Heintzelman et al. (2023) explain person activity fit as an optimal match between an

activity and the individual performing it. Bearing in mind individual character traits and trusting

a participant to choose activities that meet their needs, this approach helps ensure participants

experience greater success when affording a degree of autonomy.

As for strategies employed, Heintzelman et al. (2023) primary goal was to test the

efficacy of different PPIs personalization strategies for improving well-being. In one experiment,

participants were randomly assigned to one of three positive intervention conditions manipulated

to assess different types of person-activity fit (Heintzelman et al., 2023). In the first condition,

participants were assigned to work on five (out of ten possible) happiness skills that addressed

their weaknesses. In the second condition, participants self-selected their targeted happiness

skills (again, five out of ten). Finally, in the third condition, participants were randomly assigned

to complete five out of ten possible happiness skills. All participants focused on one happiness

skill per week for five weeks and then had five weeks of booster material for their topics. Before

and after the 10-week intervention, participants reported their life satisfaction, positive affect,

and negative affect. Participants in the self-selection condition decreased in negative affect to a

greater extent than the random condition but did not increase more in positive affect.

Alternatively, participants in the weaknesses condition increased in positive affect to a greater

extent than the random condition but did not decrease more in negative affect. There were no

significant condition differences on changes in life satisfaction. These data demonstrate that

personalization of positive interventions (i.e., weaknesses and self-selection conditions) can

show greater improvements in well-being than random approaches. Our experiment will build on

this work by once again testing the efficacy of the weaknesses and self-selection conditions to

improve well-being, and also exploring how assigning participants to work on their strengths

compares to the weaknesses and self-selection approaches.

Research by Gander et al. (2013) conducted a strength-based intervention and found that

such interventions can enhance happiness. Gander et al. also emerged with the questions of

whether personality factors should be considered and whether interventions should be

personalized. Finally, the Centers for Disease Control and Prevention (CDC) defines public

health as promoting, improving, and protecting the health of our society through making healthy

lifestyles more accessible, epidemiological research, and infectious disease control (Darrow,

2015). As numerous studies above have alluded to, there is a concerted effort to better

understand previous interventions, participants’ diversity, methods, and outcome. This is in large

part because a nation’s health and productivity are a reflection of the collective subjective and

psychological health of the population. Therefore, a national concern and public health issue.

The article written by Barger et al. (2009) conducted a multivariate analysis utilizing data

from two previous national surveys involving approximately 330,247 American participants

combined. The research investigated life satisfaction disparities for White, Black, and Hispanic

respondents and assessed the relative contributions of socio-economic status (SES), health status,

and social relationships to said disparities. Barger et al. (2009) found that Black and Hispanic

respondents, as compared to their White counterparts, were less likely to report being very

satisfied and Black respondents were more likely to report being dissatisfied. Controlling for

SES and health status explained some of the discrepancy between White and Black participants

on life satisfaction and explained all of the difference between Hispanic and White participants

in life satisfaction. The purpose of this research was to better understand the underlying reasons

for well-being, and associated disparities, amongst Americans of various racial/ethnic identities

as this may help policy makers better understand how the nation is doing under the public health

umbrella, identify themes, and develop interventions to mitigate issues. To do so on a

comprehensive level, research needs to incorporate all racial and ethnic populations and factors

that contribute to better life outcomes as well as hindrances, and to what extent the two outcomes

inform education choices, which is a determinant for employment and health, for example. As

Barger et al. (2009) make note of the need for subsequent research to be inclusive and

incorporate SES into its measures. This is further alluded to in the Gander et al. (2013) paper as

they refer to the need for societal epidemiological mitigation measures.

Participants were randomly assigned to work on PPIs that were matched to experimental

conditions strengths, weaknesses, or self-selected conditions. We assessed well-being on a post

intervention and follow-up timetable, before and after a 4-week PPIs and again three to four

weeks later. I predict that the weaknesses condition will report higher well-being at post-

intervention and follow-up than the strengths condition. As previously reported in the

Heintzelman et al. (2022) paper, participants who were able to choose self-selection, they were

more likely to opt for weakness-based activities. The belief is that participants would be more

likely to commit to a weakness of their choosing. Additionally, I predict that the self-selection

condition will report similar well-being at post-intervention and follow-up than the strengths

condition as Self-selection equates to autonomy. Participants who feel empowered are likely to

be more motivated. As previously mentioned, authors have advised to include a broader, more

representative sample, considering the potential benefits said interventions may offer society as a

whole. Thus, my additional hypothesis involves socioeconomic status. If SES is accounted for in

the creation of a positive psychology intervention it will increase the efficacy of said

interventions for participants. Between-condition studies involve an individual and a specific

condition. SES is individual based. By isolating the participant and their associated SES

specifics, the resulting outcomes can serve as a model applicable to them as well as those within

similar parameters. This hypothesis is supported by previous analysis that showed that 6-9% of

the variance seen in life satisfaction is accounted for by SES. Furthermore, as the Gander et al.

(2013) study suggests, strengths should be included as a moderator. SES in itself is an

informative moderator as well as resilience. Due to this, it is important to consider whether PPIs

are more effective among those of lower SES who may need support for their well-being. In

addition, it is important to consider whether PPIs that have historically excluded SES, generalize

to this population.

Participants from the United States (N = 850) were recruited from Prolific Academic

(https://www.prolific.com/) an online platform that provides researchers with a means of

working with interested participants while conducting research. The study was advertised as

“Person-Activity Fit for Online Happiness Practices”. Participants were financially compensated

for their time spanning six stages of the study, incrementally beginning at $3.00, $3.25, $3.50,

$3.75, $4.00, and $4.25, resulting in a total compensation of $21.75 if all parts were completed.

Participants ages ranged from 18 to 81 years (M = 38.86, SD = 12.80), and 52.54% of the sample

identified as Male, with 45.53% identifying as Female, 1.65% identifying as Nonbinary, 0.35%

of the sample Prefer not to state, 0.71% Middle Eastern, 0.59% Native American, 0.12%

Hawaiian/Pacific Islander, 0.12% as Other. Socioeconomic status was measured on a 7-point

scale (1 = very bottom, 7 = very top) asking participants to compare their status to others (M =

3.79, SD = 1.17).

We advertised our six-part study as “Person-Activity Fit for Online Happiness Practices”

and interested participants from Prolific Academic signed up for the study and provided consent

before completing any measures. We told all participants that they would be engaging in

research-tested ways to increase their happiness, but that they would be assigned happiness

practices with different matching strategies so we could test hypotheses about person-activity fit.

All aspects of the study were completed online via Qualtrics.

First, participants completed various measures indicating the degree to which they

exhibited various character virtues (e.g., gratitude, mindfulness, resilience). Next, participants

were randomly assigned to one of three conditions: strengths (n = 285), weaknesses (n = 279), or

self-selection (n = 280). Participants in the strengths and weaknesses conditions then ranked five

of the character virtues (gratitude, mindfulness, empathy, social connection, and resilience)

according to which one was their strongest to which one was their least strong attribute.

Alternatively, participants in the self-selection condition ranked these same five virtues

according to which one they would most to least want to practice. Regardless of condition,

every participant was assigned to work on one happiness practice per week for four weeks.

Participants were matched to their happiness practice according to their condition and their

responses to the ranking questions. Specifically, participants in the strengths condition were

assigned to work on happiness practices that matched their top-ranked virtue. For example, if

they ranked gratitude as their top virtue, they would be assigned to weekly activities that practiced

gratitude (Gratitude Journal, Gratitude Letter, Gratitude Meditation, and Three Good Things).

Participants in the weaknesses condition wereassigned to work on happiness practices that matched

their bottom-ranked virtue. Lastly, participants in the self-selection condition were assigned to work on

the virtue that they indicated they would most want to practice (their top ranked virtue).

Before being assigned a happiness practice for the first week, participants reported their

well-being with three items, “I feel satisfied with my life.” “I have a sense of meaning and

purpose in my life,” and “I generally feel happy,” on a 5-point scale (Not at all, Slightly,

Somewhat, Mostly, and Completely). These items were consistent with one another, so we

averaged them into a baseline well-being composite (Cronbach’s ⍺ = .91, M = 3.26, SD =

1.09).

Next, participants were assigned their first happiness practice for the week according to

their condition and virtue rankings. The following week, participants were invited to take part in

the second time point in which they completed weekly well-being measures (not used in the

current study) and were assigned their second happiness practice. Participants were invited

in the same way to take part in the third and fourth time points in which they received their

third and fourth happiness practices. One week after their fourth happiness practice instructions,

participants were invited back to complete post-intervention measures.

Specifically, they once again report their well-being with the previously mentioned three-

item composite (Cronbach’s ⍺ = .94, M = 3.38, SD = 1.08). Three to four weeks later,

participants reported their well-being once more to assess the longevity of the effects

(Cronbach’s ⍺ = .94, M = 3.42, SD = 1.06). Finally, participants answered open-ended questions

about their experience in the study and were debriefed as to the purpose of the study.

First, we analyzed changes in well-being from baseline to post-intervention and from

baseline to follow-up with paired samples t-tests across the entire samples. We found significant

changes in well-being out to post-intervention, t(807) = 5.94, p < .001, d = .21, 95% CI [0.14,

0.28], and follow-up, t(783) = 7.71, p < .001, d = .28, 95% CI [0.20, 0.35].

To address our between-condition hypothesis, we conducted linear regression analysis

with post-intervention or follow-up well-being as the dependent variables and centered baseline

well-being and dummy-coded condition variables as the predictors (see Table 1). Specifically,

the strengths condition was the reference group (0), and we entered the weakness and self-

selection conditions as dummy-coded (1) predictor variables.

As predicted, out to post-intervention, the weaknesses condition improved in well-being

significantly more than the strengths condition. Contrary to my prediction, the self-selection

condition was not similar in well-being. In addition, as predicted, the weaknesses and self-

selection conditions did not have significantly different trajectories of well-being from the

strengths condition out to follow-up.

Additionally, we conducted an exploratory analysis incorporating socioeconomic status

(SES) in the model to explore the degree to which SES related to changes in well-being in each

condition (Figure 1). This graph depicts the model-predicted post-intervention well-being by

condition and level of SES, controlling for pre-intervention well-being. Specifically, in the linear

regression model, we added centered SES to the model to explore the effect of SES in the

strengths condition, and we also added interaction terms to explore whether the effect of SES

was different in the weaknesses condition or self-selection condition then it was in the strengths

condition.

The between-condition differences on post-intervention well-being that we documented

remained, but we also found SES to moderate the effects. First, as indicated by our centered SES

parameter estimate, higher SES predicted higher gains in well-being out to post-intervention in

the strengths condition, b = 0.076, SE = 0.031, 95% CI [0.015, 0.138], t(807) = 2.427 p = 0.015.

The Weakness X SES interaction term was not significant, indicating that higher SES was also

predictive of higher well-being gains in the weakness condition (as it was in the strengths

condition), b = -0.024, SE = 0.042, 95% CI [-0.106, 0.058], t(807) = -0.573, p = 0.566. Finally,

the Self-selection X SES interaction term was significant; whereas higher SES was associated

with larger gains in well-being in the strengths condition, it was not in the self-selection

condition, b = -0.075, SE = 0.041, 95% CI [-0.155, 0.006], t(807) = -1.825 p = 0.068. A plot of

the model-predicted post-intervention means demonstrates that participants in the self-selection

condition had higher well-being than those in the other conditions across levels of SES and the

line is relatively flat indicating that post-intervention well-being was consistent across levels of

SES in the self-selection condition (Figure 1). The significant post-intervention SES effects were

no longer significant at follow-up well-being.

Weakness and self-selection conditions both significantly increased in well-being in pre-

to post- intervention more than the strengths condition. Both weakness and self-selection

conditions no longer had statistically significantly higher increases than the strengths condition at

follow-up. Self-selection is predicting higher levels of post-intervention well-being across levels

of SES. The weaknesses condition was less effective than self-selection and actually worked

better for those with relatively higher SES. Similarly, the strengths condition was less effective

than the self-selection condition and actually worked better for those with relatively higher SES.

Figure 1 clearly demonstrates that among those who self-selected their PPIs, SES did not really

matter for them. There were good effects witnessed across SES levels. Whereas for the strengths

and weaknesses conditions, higher SES was helpful.

The self-selection condition is equally effective across SES. Our main condition findings

were that weaknesses and self-selection remained the same. However, we did find effects of

SES. Those in higher SES benefited more from the strength condition as opposed to lower in

SES. This was also the case for the weaknesses condition as it was not significantly different

from the strengths condition. For the SES and self-select, there was no effect in the self-select

condition. It was equally effective across SES levels. Upon running a linear regression

incorporating SES, C_SES indicates b = 0.076, SE = 0.031, 95% CI [0.015, 0.138], t(807) =

2.427 p = 0.015. SESXWE indicates b = -0.024, SE = 0.042, 95% CI [-0.106, 0.058], t(807) = –

0.573, p = 0.566. SESXSS indicates b = -0.075, SE = 0.041, 95% CI [-0.155, 0.006], t(807) = –

1.825 p = 0.068. As predicted, the weaknesses condition out to post-intervention, the weaknesses

condition improved in well-being significantly more than the strengths condition. Likewise, the

self-selection condition improved in well-being significantly more than the strengths condition

out to post-intervention. In addition, as predicted, the weaknesses and self-selection conditions

did not have significantly different trajectories of well-being from the strengths condition out to

follow-up.

The Heintzelman et al. (2023) study was able to show potential when “personalizing PPIs

activities” for participants, regardless of “self-select or weakness-based assignments” (p13). The

most significant statistical findings were with negative affect from baseline to post-test.

As for limitations, because our participants originated from an online platform, the

assumption is that participants have internet access and are familiar with online surveys. There is

a distinct possibility that certain participants from lower SES backgrounds would be excluded,

resulting in external validity issues. Secondly, as the participant population was

disproportionately White, this poses a problem for generalizability. The last limitation to

consider would be a control group. This study lacked a neutral control group, as its conditions

were all happiness interventions. Control groups are important when conducting experiments.

When considering future directions, crowdsourcing research work by multiple labs would

increase the number of participants. As the paper alludes to, well-being is a global issue. With

that, comes cultural sensitivities. If you were to approach this research on a global scale, the PPIs

would need to ensure you measure for collectivist versus individualist values. Another

consideration would be open-ended questions about participants’ greatest weaknesses in regard

to their happiness and then ask them to work on that weakness. Additionally, as these

interventions are intended to gauge effectiveness of activities that lend themselves to greater

happiness, this is a universal one in which SES needs to be incorporated as well as randomized

amongst a broader more inclusive population. Lastly, a limitation and possible future direction

would be to capture individual participant differences. Resilience is considered a trait that typically

helps people better tackle and cope with issues in life and may very well aid in tackling

PPIs challenges.

From the numerous reports cited it is apparent that Positive Psychological Interventions

(PPIs) work, but consideration must be given to the personalization factor. Self-selection of

happiness activities was effective across all levels of SES whereas working on strengths and

weakness were less effective among those with lower SES. Given the importance of PPIs for the

improvement of individual well-being, it is important to account for the benefit of personalizing

the intervention to the needs of the person in favor of other approaches, particularly those that

have been proven ineffective for a given certain SES.

Barger, S. D., Donoho, C. J., & Wayment, H. A. (2009). The relative contributions of

race/ethnicity, socioeconomic status, health, and social relationships to life satisfaction in

the United States. Quality of Life Research, 18(2), 179-89.

https://doi.org/10.1007/s11136-008-9426-2

Darrow, W. W. (2015). The Decline and disorganization of public health in the United States.

International Journal of Social Quality, 5(2), 29–45.

https://doi.org/10.3167/IJSQ.2015.050203

Gander, F., Proyer, R., Ruch, W., & Wyss, T. (2013). Strength-based positive interventions:

further evidence for their potential in enhancing well-being and alleviating depression.

Journal of Happiness Studies, 14(4), 1241–1259. https://doi.org/10.1007/s10902-012-

9380-0

Heintzelman, S. J., Kushlev, K., & Diener, E. (2023). Personalizing a positive psychology

intervention improves well-being. Applied Psychology: Health and Well-Being, 1-22.

https://doi.org/10.1111/aphw.12436

Koydemir, S., Sökmez, A. B., & Schütz, A. (2021). A meta-analysis of the effectiveness of

randomized controlled positive psychological interventions on subjective and

psychological well-being. Applied Research in Quality of Life, 16, 1145–1185.

https://doi.org/10.1007/s11482-019-09788-z