engineers), especially during Covid lucas2021mindful , we are also interested
in working with the self-connection scale by barrett2015validation .

##  7 Conclusion

In this article, we presented the results of an intervention with live group
breathing practice to deepen the participants’ connection to themselves,
framed with a weekly self-development topic. Awareness raising is happening on
a neurological/unconscious level by breathwork, and on a mental/rational level
by the topic presentations and reflecting upon them in group conversation as
well as in personal practice with proposed tools. The quantitative and
qualitative results indicate that this intervention may be helpful in
improving participants’ mindfulness attention awareness, well-being, and self
efficacy.
There is a wide selection of wellness classes available outside of work for
the person looking, while at work there may be a few generic offerings that
work on a content level, but often not on a neurophysiological or embodied
level.
Software engineers have a strong background in rational thinking and work with
empirical evidence, so there is a need for programs with adequate language
such that software engineers who feel overwhelmed are attracted - science-
based and in a safe space, brought to them by someone who can relate to their
specific work experiences. This may help sway hesitant software engineers to
try out a relaxation and recovery technique, benefitting their personal
resilience and well-being and, in turn, their work performance and job
satisfaction (important for retention). Consequently, we see three ways of
potential impact by our study: 1) to inform and raise awareness in the
research community as well as in practice, 2) to train further cohorts of
software engineers and software engineering researchers and educators in
restorative practices, 3) to develop tailored programs for companies and
higher education that teach these techniques and frame them science-based
while still focusing on the embodiment component to increase self-connection.
The main challenge that remains is that the pace of work life is artificially
high because of a perceived need for constant competition (e.g. time to
market, to offer better service, to increase our skills, etc.) as remarked by
several participants in our study to the point where they felt they didn’t
“have time” for restorative practices. The speeding-up of life we have been
witnessing over the past decades has consequences for health. In a certain
pattern, physical stress is healthy and makes sure that we get certain things
done - and those phases of stress needs to be taking turns with phases of
recovery (beyond sleeping 6 hours per night). When recovery is not
sufficiently given, stress wears on our physical (adrenal fatigue), mental
(burn-out), and emotional health (depression and anxiety). Restorative
practices can help us recover more quickly and become more resilient - they do
not change the underlying systemic misalignments.
Our vision is that restorative and contemplative practices can support us in
recovering a stronger connection to self, such that we have the mental and
emotional capacity to reflect on our values and how we live into them. We get
to decide every day how we want to continue, and the constraints can be
shifted, some immediately, some over time. There are systems with unhealthy
dynamics in place, yes, and we can change them – because we humans are the
ones that created them. If we don’t like the constant stress and time
pressure, let’s change the systems and societal structures that create them.
Part of that is acknowledging the tendency of the human mind to always want
more (and we see how it plays out in our economy), and developing our own
practice to stay present with that dass1971here . The first step towards that
from the perspective of our research is: Let’s normalise taking care of our
nervous systems as much as brushing our teeth, and thereby improve our
physical, mental, and emotional health. There could be a start of every
meeting with a deep breath to become present, someone teaching peers an
emergency breathing technique to relax and focus before a presentation, there
a well-being course that teaches breathing practices (or other restorative
techniques) twice a year at a company, a weekly meditation group that provides
community support in addition to daily personal practice (when it comes to
personal practice, 5 minutes is always better than nothing). The options are
many, the prioritization is an individual choice.

We leave you with a quote from a journal entry that sums up results reflected
for a number of participants and that seem worth acknowledging:

> Today was the last day of the 12 weeks. I took away a whole new world, that
> I am still trying to reconcile with. (…) Anyway, learnings: be conscious
> where you put your attention, and hence your energy, what the wonder
> precious moment is, that I am not different—I am unique, to put intentions
> to things, how meditation with a intention/visualization can change your
> day, that breathing can “make you float” and have psychedelic experiences,
> the forgotten joy of dancing, the power of gratefulness and that I am
> grateful for the bad stuff that happened to me (!), how important it is to
> love and be kind to oneself, to surrender to feelings rather than pushing
> them away, the power of small routines (as well as the difficulty of keeping
> them), that I am not my thoughts or my emotions (what the f*+@?!?!), (…)
> What else can I say, really? THANK YOU!!! \- participant 75, run 1, journal,
> Dec 10 2020

##  8 Data Availability

To support open science, the replication package including the raw
quantitative data is available on Zenodo https://zenodo.org/record/5082388,
which links to a Github repository https://github.com/torkar/rise2flow.

The qualitative responses are not available as many of them reveal very
personal experiences, deep emotions, and individual life circumstances that
might involuntarily disclose identifiable information.

###### Acknowledgements.

We thank the participants of Rise 2 Flow 1 and 2 for their trust in us to
support them in cultivating a personal practice for increased well-being, for
their dedication, and for their generous feedback. The first author thanks
Robert Feldt for a helpful discussion of available survey instruments during
the design phase of this study, and Sabine and Fritz Penzenstadler for helpful
input in conversation and action. We thank Francisco Gomes de Oliveira Neto
and Leticia Duboc for thoughtful feedback on earlier versions of this
manuscript. We thank the anonymous reviewers who gave very thorough and
thoughtful feedback on an earlier version (shout-out to especially Reviewer
1). We appreciate you. The computations were enabled by resources provided by
the Swedish National Infrastructure for Computing (SNIC), partially funded by
the Swedish Research Council through grant agreement no. 2018-05973. Part of
this research is financed by the Area of Advance ICT at Chalmers University of
Technology under no. C-2019-0299.

## Conflict of interest

The authors declare that they have no conflict of interest.

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##  Appendix A Survey Instruments

###  A.1 The MAAS instrument

The Mindfulness Attention Awareness Scale (MAAS) is replicated from
brown2003benefits .

Instruction MAAS:

> Below is a collection of statements about your everyday experience. Using
> the 1 (almost never) - 6 (almost always) scale below, please indicate how
> frequently or infrequently you currently have each experience. Please answer
> according to what *really reflects* your experience rather than what you
> think your experience should be. Please treat each item separately from
> every other item.

| 1 | 2 | 3 | 4 | 5 | 6
---|---|---|---|---|---|---
I could be experiencing some emotion and not be conscious of it until some time later. |  |  |  |  |  |
I break or spill things because of carelessness, not paying attention, or thinking of something else. |  |  |  |  |  |
I find it difficult to stay focused on what’s happening in the present. |  |  |  |  |  |
I tend to walk quickly to get where I’m going without paying attention to what I experience along the way. |  |  |  |  |  |
I tend not to notice feelings of physical tension or discomfort until they really grab my attention. |  |  |  |  |  |
I forget a person’s name almost as soon as I’ve been told it for the first time. |  |  |  |  |  |
It seems I am “running on automatic,” without much awareness of what I’m doing. |  |  |  |  |  |
I rush through activities without being really attentive to them. |  |  |  |  |  |
I get so focused on the goal I want to achieve that I lose touch with what I’m doing right now to get there. |  |  |  |  |  |
I do jobs or tasks automatically, without being aware of what I’m doing. |  |  |  |  |  |
I find myself listening to someone with one ear, doing something else at the same time. |  |  |  |  |  |
I drive places on ‘automatic pilot’ and then wonder why I went there. |  |  |  |  |  |
I find myself preoccupied with the future or the past. |  |  |  |  |  |
I find myself doing things without paying attention. |  |  |  |  |  |
I snack without being aware that I’m eating. |  |  |  |  |  |
Table 6: The Mindfulness Attention Awareness Scale (MAAS) brown2003benefits

###  A.2 The instruments SPANE, PWB, and PTS

Diener et al. diener2009new  proposed a set of related instruments in ‘New
measures of well-being’ that includes the Scale of Positive And Negative
Experience (SPANE), the scale of Psychological Well-being (PWB), and the scale
of Positive Thinking (PTS).

Instruction SPANE:

> Please think about what you have been doing and experiencing during the past
> four weeks. Then report how much you experienced each of the following
> feelings, using the scale below. For each item, select a number from 1 (Very
> rarely or never) to 5 (Very often or always).

| 1 | 2 | 3 | 4 | 5
---|---|---|---|---|---
Positive |  |  |  |  |
Negative |  |  |  |  |
Good |  |  |  |  |
Bad |  |  |  |  |
Pleasant |  |  |  |  |
Unpleasant |  |  |  |  |
Happy |  |  |  |  |
Sad |  |  |  |  |
Afraid |  |  |  |  |
Joyful |  |  |  |  |
Angry |  |  |  |  |
Contented |  |  |  |  |
Table 7: The Scale of Positive and Negative Experiences (SPANE) diener2009new

Instruction PWB:

> Below are 8 statements with which you may agree or disagree. Using the 1
> (Strongly disagree) – 7 (Strongly agree) scale below, indicate your
> agreement with each item by indicating that response for each statement.

| 1 | 2 | 3 | 4 | 5 | 6 | 7
---|---|---|---|---|---|---|---
I lead a purposeful and meaningful life. |  |  |  |  |  |  |
My social relationships are supportive and rewarding. |  |  |  |  |  |  |
I am engaged and interested in my daily activities |  |  |  |  |  |  |
I actively contribute to the happiness and well-being of others |  |  |  |  |  |  |
I am competent and capable in the activities that are important to me |  |  |  |  |  |  |
I am a good person and live a good life |  |  |  |  |  |  |
I am optimistic about my future |  |  |  |  |  |  |
People respect me |  |  |  |  |  |  |
Table 8: The Psychological Well-Being (PWB) diener2009new

Instruction PTS:

> The following items are to be answered “Yes” or “No.” Write an answer next
> to each item to indicate your response.

| Yes | No
---|---|---
I see my community as a place full of problems. |  |
I see much beauty around me. |  |
I see the good in most people. |  |
When I think of myself, I think of many shortcomings. |  |
I think of myself as a person with many strengths. |  |
I am optimistic about my future. |  |
When somebody does something for me, I usually wonder if they have an ulterior motive. |  |
When something bad happens, I often see a “silver lining,” something good in the bad event. |  |
I sometimes think about how fortunate I have been in life. |  |
When good things happen, I wonder if they might have been even better. |  |
I frequently compare myself to others. |  |
I think frequently about opportunities that I missed. |  |
When I think of the past, the happy times are most salient to me. |  |
I savor memories of pleasant past times. |  |
I regret many things from my past. |  |
When I see others prosper, even strangers, I am happy for them. |  |
When I think of the past, for some reason the bad things stand out. |  |
I know the world has problems, but it seems like a wonderful place anyway. |  |
When something bad happens, I ruminate on it for a long time. |  |
When good things happen, I wonder if they will soon turn sour. |  |
When I see others prosper, it makes me feel bad about myself. |  |
I believe in the good qualities of other people. |  |
Table 9: The Positive Thinking Scale

###  A.3 Self Efficacy

The instrument was developed by Jerusalem et al. jerusalem1999skala  and based
on Bandura et al.’s bandura1999self  self-efficacy model. It is used to assess
the individual stress resilience of the participants and encompasses ten items
that offer a positively phrased statement on change, challenges or unexpected
circumstances which the participant has to rate as “Not true” (1), “Hardly
true” (2), “Rather true” (3) or “Exactly true” (4).

Instruction:

> Please rate the following statements on the basis of the given scale and
> tick as appropriate:

| 1 | 2 | 3 | 4
---|---|---|---|---
When problems arise, I find ways to carry through. |  |  |  |
I always succeed in solving difficult problems, if I try. |  |  |  |
It does not give me any difficulty to realize my intentions and goals. |  |  |  |
In unexpected situations I always know how to behave. |  |  |  |
Even with surprising events, I believe that I can handle them well. |  |  |  |
I can easily face difficulties because I can always trust my abilities. |  |  |  |
Whatever happens, I’ll be fine. |  |  |  |
For every problem I can find a solution. |  |  |  |
When a new thing comes to me, I know how to handle it. |  |  |  |
If a problem arises, I can do it on my own. |  |  |  |
Table 10: Self efficacy instrument by Jerusalem et al. jerusalem1999skala

###  A.4 Perceived Productivity

The HPQ232323http://www.hcp.med.harvard.edu/hpq measures perceived
productivity in two ways: First, it uses an eight-item scale (summative,
multiple reversed indicators), that assesses overall and relative performance,
and second, it uses an eleven-point list of general ratings of participants’
own performance as well as typical performance of similar workers.

Instructions PP:

> The next questions are about the time you spent during your hours at work in
> the past 4 weeks (28 days). Select the one response for each question that
> comes closest to your experience from “None of the time” (1) to “All of the
> time” (5).

| 1 | 2 | 3 | 4 | 5
---|---|---|---|---|---
How often was your performance higher than most workers on your job? |  |  |  |  |
How often was your performance lower than most workers on your job? |  |  |  |  |
How often did you do no work at times when you were supposed to be working? |  |  |  |  |
How often did you find yourself not working as carefully as you should? |  |  |  |  |
How often was the quality of your work lower than it should have been? |  |  |  |  |
How often did you not concentrate enough on your work? |  |  |  |  |
How often did health problems limit the kind or amount of work you could do? |  |  |  |  |
Table 11: Perceived Productivity from the HPQ

  * •

On a scale from 0 to 10 where 0 is the worst job performance anyone could have
at your job and 10 is the performance of a top worker, how would you rate the
usual performance of most workers in a job similar to yours?

  * •

Using the same 0-to-10 scale, how would you rate your usual job performance
over the past year or two?

  * •

Using the same 0-to-10 scale, how would you rate your overall job performance
on the days you worked during the past 4 weeks (28 days)?

  * •

How would you compare your overall job performance on the days you worked
during the past 4 weeks (28 days) with the performance of most other workers
who have a similar type of job?

    * –

You were a lot better than other workers

    * –

You were somewhat better than other workers

    * –

You were a little better than other workers

    * –

You were about average

    * –

You were a little worse than other workers

    * –

You were somewhat worse than other workers

    * –

You were a lot worse than other workers

###  A.5 The WHO-5 instrument

The 5-item World Health Organization Well-Being Index (WHO-5, see Tab. 12) is
a short and generic global rating scale measuring subjective well-being.
Because the WHO considers positive well-being to be another term for mental
health jahoda , the WHO-5 only contains positively phrased items, and its use
is recommended by bech1999health .

Instruction:

> Please indicate for each of the five statements which is closest to how you
> have been feeling over the last week from “At no time” (1) to “All of the
> time” (6). Over the last week:

| 1 | 2 | 3 | 4 | 5 | 6
---|---|---|---|---|---|---
I have felt cheerful and in good spirits. |  |  |  |  |  |
I have felt calm and relaxed. |  |  |  |  |  |
I have felt active and vigorous. |  |  |  |  |  |
I woke up feeling fresh and rested. |  |  |  |  |  |
My daily life has been filled with things that interest me. |  |  |  |  |  |
Table 12: WHO-5

##  Appendix B Model designs

###  B.1 Gaussian Process model

Below is the model specification for modeling the weekly or daily trends using
a Gaussian Process.

$\displaystyle\left[\begin{array}[]{c}\mathrm{Q}1_{i}\\\ \vdots\\\
\mathrm{Q}5_{i}\\\ \end{array}\right]$
$\displaystyle\sim\mathrm{Cumulative}\left(\left[\begin{array}[]{c}\phi_{\mathrm{Q}1,i}\\\
\vdots\\\ \phi_{\mathrm{Q}5,i}\end{array},\mathbf{S}\right]\right)$
$\displaystyle\mathrm{[likelihood]}$
$\displaystyle\operatorname{logit}(\phi_{\mathrm{Q}\\{1,\ldots,5\\},i})$
$\displaystyle=\gamma_{\mathrm{\scriptscriptstyle{TIME}}[i]}+\alpha_{\mathrm{\scriptscriptstyle{ID}}[i]}$
[linear model] $\displaystyle\left[\begin{array}[]{c}\gamma_{1}\\\ \vdots\\\
\gamma_{n}\end{array}\right]$
$\displaystyle\sim\mathrm{MVNormal}\left(\left(\begin{array}[]{c}0\\\
\vdots\\\ 0\end{array}\right),\mathbf{K}\right)$ [prior Gaussian process]
$\displaystyle\mathbf{K}_{ij}$
$\displaystyle=\tau^{2}\exp(-T^{2}_{ij}/2\rho^{2})$
$\displaystyle\text{[covariance matrix }\mathcal{GP}\text{]}$
$\displaystyle\tau$ $\displaystyle\sim\mathrm{Weibull}(2,1)$
$\displaystyle\text{[prior std dev }\mathcal{GP}\text{]}$ $\displaystyle\rho$
$\displaystyle\sim\text{Inv-Gamma}(4,1)$ $\displaystyle\text{[prior length-
scale }\mathcal{GP}\text{]}$ $\displaystyle\mathbf{S}$
$\displaystyle=\left(\begin{array}[]{ccccc}\sigma_{\text{Q}1}&0&0&0&0\\\
0&\sigma_{\text{Q}2}&0&0&0\\\ 0&0&\sigma_{\text{Q}3}&0&0\\\
0&0&0&\sigma_{\text{Q}4}&0\\\ 0&0&0&0&\sigma_{\text{Q}5}\\\
\end{array}\right)\mathbf{R}\left(\begin{array}[]{ccccc}\sigma_{\text{Q}1}&0&0&0&0\\\
0&\sigma_{\text{Q}2}&0&0&0\\\ 0&0&\sigma_{\text{Q}3}&0&0\\\
0&0&0&\sigma_{\text{Q}4}&0\\\ 0&0&0&0&\sigma_{\text{Q}5}\\\
\end{array}\right)$ [covariance matrix]
$\displaystyle\sigma_{\text{Q}1},\ldots,\sigma_{\text{Q}5}$
$\displaystyle\sim\text{Weibull}(2,1)$ [prior std dev among questions]
$\displaystyle\mathbf{R}$ $\displaystyle\sim\mathrm{LKJ}(2)$ [prior
correlation matrix] $\displaystyle\alpha_{\mathrm{\scriptscriptstyle{ID}}[i]}$
$\displaystyle\sim\mathrm{Normal}(\bar{\alpha},\sigma_{\mathrm{\scriptscriptstyle{ID}}})$
[adaptive prior] $\displaystyle\bar{\alpha}$
$\displaystyle\sim\mathrm{Normal}(0,2)$ [hyperprior avg ID]
$\displaystyle\sigma_{\mathrm{\scriptscriptstyle{ID}}}$
$\displaystyle\sim\mathrm{Weibull}(2,1)$ [hyperprior std dev of IDs]

For the weekly trend, on Line $1$ we assume a Cumulative likelihood where we
model all questions’ covariance using a covariance matrix $\mathbf{S}$. The
linear model on the next line uses a $\operatorname{logit}$ link function as
is default, and then models the time, $\gamma$, with a Gaussian Process
($\mathcal{GP}$), with a varying intercept $\alpha$ for subjects.

Line $3$ places a multivariate normal distribution as prior for the
$\mathcal{GP}$, while Lines $4$–$6$ declares a covariance matrix, a prior for
the standard deviations, and a prior for the length-scale argument of the
$\mathcal{GP}$.

On Line $7$ a covariance matrix is declared for $\mathbf{S}$. Then priors for
the standard deviations among questions and the correlation matrix
$\mathbf{R}$ are declared (Lines $8$–$9$).

Finally, Lines $10$–$12$ declare an adaptive prior for the varying intercept
among subjects, and hyperpriors for the average subject (Line $11$) and the
standard deviation of subjects (final line).

For the daily trend the same model can be used. However, for the daily trend
there was only one question asked. This means that the covariance between
questions does not need to be modeled and, hence, Lines $7$–$9$ can be
removed. Additionally, a suitable prior for the daily data concerning length-
scale is $\text{Inv-Gamma}(1.6,0.1)$.

As is evident from the reproducibility package, prior predictive checks were
conducted and the combination of priors were uniform on the outcome scale.

###  B.2 Dummy variable regression model

Recall, that for the dummy variable regression models (DVRMs) each instrument
(MAAS, SPANE, etc.) was modeled separately with the time ($t_{0}$ vs. $t_{1}$)
used as an indicator (predictor). Four population-level effects (age, gender,
occupation, and living conditions) and one group-level effect (subject) were
used as predictors.

$\displaystyle\left[\begin{array}[]{c}\mathrm{Q}1_{i}\\\ \vdots\\\
\mathrm{Q}n_{i}\\\ \end{array}\right]$
$\displaystyle\sim\mathrm{Cumulative}\left(\left[\begin{array}[]{c}\phi_{\mathrm{Q}1,i}\\\
\vdots\\\ \phi_{\mathrm{Q}n,i}\end{array},\mathbf{S}\right]\right)$
$\displaystyle\mathrm{[likelihood]}$ $\displaystyle\mathbf{S}$
$\displaystyle=\left(\begin{array}[]{ccc}\sigma_{\text{Q}1}&0&0\\\
0&\ddots&0\\\
0&0&\sigma_{\text{Q}n}\end{array}\right)\mathbf{R}\left(\begin{array}[]{ccc}\sigma_{\text{Q}1}&0&0\\\
0&\ddots&0\\\ 0&0&\sigma_{\text{Q}n}\end{array}\right)$ [covariance matrix]
$\displaystyle\sigma_{\text{Q}1},\ldots,\sigma_{\text{Q}n}$
$\displaystyle\sim\text{Weibull}(2,1)$ [prior std dev among questions]
$\displaystyle\mathbf{R}$ $\displaystyle\sim\mathrm{LKJ}(2)$ [prior
correlation matrix]
$\displaystyle\operatorname{logit}(\phi_{\mathrm{Q}\\{1,\ldots,n\\},i})$
$\displaystyle=\alpha\cdot\text{AGE}+\gamma\cdot\text{GENDER}+\omega\cdot\text{OCCUPATION}$
$\displaystyle+\lambda\cdot\text{LIVING}+\tau\cdot\text{TIME}+\iota_{\mathrm{\scriptscriptstyle{ID}}[i]}$
[linear model] $\displaystyle\alpha,\gamma,\omega,\lambda,\tau$
$\displaystyle\sim\mathrm{Normal}(0,3)$ [priors population-level effects]
$\displaystyle\iota_{\mathrm{\scriptscriptstyle{ID}}[i]}$
$\displaystyle\sim\mathrm{Normal}(\bar{\alpha},\sigma_{\mathrm{\scriptscriptstyle{ID}}})$
[adaptive prior] $\displaystyle\bar{\alpha}$
$\displaystyle\sim\mathrm{Normal}(0,2)$ [hyperprior avg ID]
$\displaystyle\sigma_{\mathrm{\scriptscriptstyle{ID}}}$
$\displaystyle\sim\mathrm{Weibull}(2,1)$ [hyperprior std dev of IDs]

For each instrument we assumed a Cumulative likelihood where all questions’
covariance was modeled by a covariance matrix $\mathbf{S}$. On Line $2$ the
covariance matrix is declared for $\mathbf{S}$ and priors for the standard
deviations among questions and the correlation matrix $\mathbf{R}$ are
declared on Lines $3$–$4$).

The linear model on the next two lines uses a $\operatorname{logit}$ link
function as is default, and then declares five population-level parameters and
a varying intercept $\iota$ for subjects. On Line $7$ priors for the
population-level parameters are declared.

Finally, Lines $8$–$10$ an adaptive prior with hyperpriors is declared for the
varying intercept $\iota$.

The only thing that differs between the instruments are the number of
questions asked. This implies that the covariance matrix $\mathbf{S}$ differs
in size depending on number of questions.

Additionally, for one instrument, SE, there were two questions modeled with a
$\mathsf{Bernoulli}$ likelihood due to responses on two levels.

As is evident from the reproducibility package, prior predictive checks were
conducted and the combination of priors were uniform on the outcome scale.

##  Appendix C Detailed Findings: Significant Effects of Other Predictors

To show that the experiments of run 1 and run 2 confirm the general
tendencies, we confirm the underlying latent scale in Fig. 11. The similar
curves with similar centers of the peak show that there is no threat to
validity given by the two different lengths of the experiment. In addition,
combining the two runs gives the model more certainty, which makes the results
more reliable. Had we taken the results of both runs separately, there would
be more uncertainty in both individual models, but this was not necessary
given the present latency.

Figure 11: Underlying latent scale for outcome, given experimental session X

###  C.1 Mindfulness Attention Awareness Scale

The MAAS instrument (App. A.1) consisted of $15$ statements to agree or
disagree with. Eleven of the ratings indicated a significant difference at
$t_{0}$ vs. $t_{1}$: Q$1$–$8$, $11$–$12$, and $14$. In all the above cases the
effect was negative, i.e., the responses were higher at $t_{0}$ than at
$t_{1}$ (please see Fig. 6. If we look at the other predictors, age and gender
did not have a significant effect, while occupation was significant (negative)
for Q$2$, i.e., “I break or spill things because of carelessness, not paying
attention, or thinking of something else.”

Figure 12: MAAS Density plots computed from posterior draws. The densities are
cut off at 95% and the shaded area is the 50% uncertainty interval. We can see
a number of questions not crossing zero (no effect observed).

Additionally, the predictor living condition was significant (negative) in
Q$1$–$3$, $8$, and $12$ (items listed in App. A.1).

###  C.2 Scale of Positive And Negative Experiences

For the SPANE items, see App. A.2. The results for the predictor time are in
Fig. 9.

Below we summarize the significant effects of the other predictors. In all the
following tables for predictors, a $+$ means that the item was rated higher
for that variable, and a $-$ means that the item was rated lower for that
variable.
For gender, a $-$ means that females rated themselves more negatively than
males, and a $+$ means that females rated themselves more positively. This is
not visible directly from the table below, but requires to know how the data
was coded inside the model. For this specific reason, we moved these tables
into the appendix, as they are not relevant to understand the narrative of the
article, but can be considered interesting observations.

Question | Age | Gender | Occupation |  Living conditions
---|---|---|---|---
Q$3$ |  | $-$ |  |
Q$6$ |  | $-$ |  |
Q$7$ |  | $-$ |  |
Q$9$ | $+$ |  |  |

In summary for this table, the higher the age, the higher the response in
Q$9$. Concerning gender, males answered with higher values in Q$3$, Q$6$, and
Q$7$.

###  C.3 Psychological Well-Being

Figure 13: The effects of $t$ for the PWB instrument. The temporal variable
$t$ clearly has an effect (positive) in all questions except Q3.

Figure 13 shows the effects for the predictor time. The temporal variable $t$
clearly has an effect (positive) in all questions except Q3.

Below we summarize the significant effects of the other predictors for PWB
(for the items, see App. A.2). The same logic applies here as in the previous
table; however, one new effect is present, i.e, occupation. In Q$3$ (I am
engaged and interested in my daily activities.), participants with occupation
student replied with higher responses compared to others.

Question | Age | Gender | Occupation |  Living conditions
---|---|---|---|---
Q$1$ |  |  |  |  $+$
Q$2$ | $-$ | $-$ |  |
Q$3$ | $+$ |  | $-$ |
Q$4$ |  | $-$ |  |
Q$7$ |  |  |  |  $-$

###  C.4 Positive Thinking Scale

For the PTS items, see App. A.2. The results for the predictor time are given
below in Fig. 14.

Figure 14: The PTS results for the predictor time.

Below we summarize the significant effects of the other predictors. Please
refer to the appendix for the respective survey items.

Question | Age | Gender | Occupation |  Living conditions
---|---|---|---|---
Q$1$ |  |  | $-$ |  $-$
Q$3$ |  |  | $+$ |
Q$11$ | $-$ | $-$ |  |
Q$16$ |  |  |  |  $+$
Q$17$ |  |  | $-$ |  $-$
Q$19$ |  |  |  |  $-$

###  C.5 Self Efficacy

The SE instrument (App. A.3) consisted of ten questions (Likert $1$–$4$).
Questions $6$, $7$, and $9$ showed a significant effect (positive), i.e.,
higher responses at $t_{1}$, see Fig. 15.

Figure 15: SE effects for predictor time.

  * Q$6$

I can easily face difficulties because I can always trust my abilities.

  * Q$7$

Whatever happens, I’ll be fine.

  * Q$9$

When a new thing comes to me, I know how to handle it.

Concerning the other predictors, no significant effects were present, i.e., it
is not clear which predictors drove the significant difference between $t_{0}$
and $t_{1}$.

###  C.6 Perceived Productivity

The HPQ part consisted of eleven questions (with Likert scales varying, going
up to $5$, $7$, or $10$, depending on the question, see App. A.4). The results
for the predictor time are given in Fig. 16. Only Q$1$ (How often was your
performance higher than most workers on your job?) shows a significant
difference when moving from $t_{0}$ to $t_{1}$ (lower responses at $t_{1}$).

Figure 16: The PP results for the predictor time.

Below we summarize the significant effects of the other predictors, i.e. Q$3$
(How often did you do no work at times when you were supposed to be working?)
showing a higher score for gender female, and Q$5$ (How often was the quality
of your work lower than it should have been?) showing a lower score when the
living condition was shared with partner or family as opposed to living by
oneself.

Question | Age | Gender | Occupation |  Living conditions
---|---|---|---|---
Q$3$ |  | $+$ |  |
Q$5$ |  |  |  |  $-$

###  C.7 Predictor Number of Sessions

The following Table 13 shows an overview of all significant effects for total
number of sessions as predictor. The first column is an ID, the rowname
indicates the variable of the instrument, e.g. MAASQ116_total_sessions refers
to MAAS question 1 (Likert scale 1 -6) for total sessions attended. The next
two columns indicate the estimate and the estimation error. Please note that
for SPANE, the results seem to be alternating, but looking back at the
instrument (see Sec. A.2), half of the items were scored reversely in exactly
the pattern that is reflected here.

| rowname | Estimate | Est.Error | Q2.5 | Q97.5
---|---|---|---|---|---
1 | $MAASQ116_{t}otal_{s}essions$ | -0.3155496 | 0.1216486 | -0.5569970 | -0.08184895
2 | $MAASQ216_{t}otal_{s}essions$ | -0.3804576 | 0.1273390 | -0.6411205 | -0.13837388
3 | $MAASQ316_{t}otal_{s}essions$ | -0.2634123 | 0.1231561 | -0.5100075 | -0.02042572
5 | $MAASQ516_{t}otal_{s}essions$ | -0.3689709 | 0.1167109 | -0.6023460 | -0.14523413
6 | $MAASQ616_{t}otal_{s}essions$ | -0.2894895 | 0.1305140 | -0.5477286 | -0.03658702
7 | $MAASQ716_{t}otal_{s}essions$ | -0.3647491 | 0.1231923 | -0.6058283 | -0.12399760
8 | $MAASQ816_{t}otal_{s}essions$ | -0.2611191 | 0.1214209 | -0.5011597 | -0.02438610
10 | $MAASQ1016_{t}otal_{s}essions$ | -0.2886498 | 0.1174016 | -0.5226733 | -0.05928175
11 | $MAASQ1116_{t}otal_{s}essions$ | -0.4540885 | 0.1211362 | -0.6957715 | -0.21564968
12 | $MAASQ1216_{t}otal_{s}essions$ | -0.2509503 | 0.1246984 | -0.4957514 | -0.01287479
14 | $MAASQ1416_{t}otal_{s}essions$ | -0.4358311 | 0.1179180 | -0.6693166 | -0.20832100
1 | $SPANEQ115_{t}otal_{s}essions$ | 0.4662730 | 0.1511756 | 0.1756023 | 0.77767102
2 | $SPANEQ215_{t}otal_{s}essions$ | -0.5187067 | 0.1341723 | -0.7911272 | -0.25860345
3 | $SPANEQ315_{t}otal_{s}essions$ | 0.4918396 | 0.1524530 | 0.2054288 | 0.80508272
4 | $SPANEQ415_{t}otal_{s}essions$ | -0.4509748 | 0.1308125 | -0.7134680 | -0.20059290
5 | $SPANEQ515_{t}otal_{s}essions$ | 0.3955807 | 0.1311677 | 0.1416188 | 0.65872865
6 | $SPANEQ615_{t}otal_{s}essions$ | -0.2643148 | 0.1243299 | -0.5096883 | -0.01981721
7 | $SPANEQ715_{t}otal_{s}essions$ | 0.5689896 | 0.1411704 | 0.3003263 | 0.84980113
8 | $SPANEQ815_{t}otal_{s}essions$ | -0.3191885 | 0.1221512 | -0.5628583 | -0.08297879
9 | $SPANEQ915_{t}otal_{s}essions$ | -0.4594716 | 0.1445001 | -0.7530686 | -0.18126877
10 | $SPANEQ1015_{t}otal_{s}essions$ | 0.3753050 | 0.1239305 | 0.1374020 | 0.61918997
11 | $SPANEQ1115_{t}otal_{s}essions$ | -0.2855759 | 0.1255116 | -0.5319962 | -0.04022932
1 | $PWBQ117_{t}otal_{s}essions$ | 0.3232594 | 0.1505071 | 0.03253444 | 0.6231016
2 | $PWBQ217_{t}otal_{s}essions$ | 0.2971393 | 0.1408516 | 0.02316987 | 0.5816784
4 | $PWBQ417_{t}otal_{s}essions$ | 0.3391010 | 0.1257622 | 0.09843479 | 0.5881914
5 | $PWBQ517_{t}otal_{s}essions$ | 0.2659871 | 0.1345689 | 0.01074883 | 0.5332659
6 | $PWBQ617_{t}otal_{s}essions$ | 0.3150326 | 0.1478417 | 0.02922867 | 0.6087226
7 | $PWBQ717_{t}otal_{s}essions$ | 0.3061679 | 0.1298780 | 0.05548536 | 0.5639220
8 | $PWBQ817_{t}otal_{s}essions$ | 0.3378056 | 0.1378315 | 0.07209965 | 0.6095512
9 | $PSTQ901_{t}otal_{s}essions$ | 1.9809234 | 0.9627086 | 0.470679200 | 4.20554675
12 | $PSTQ1201_{t}otal_{s}essions$ | -0.5350738 | 0.2776467 | -1.103002250 | -0.01894271
17 | $PSTQ1701_{t}otal_{s}essions$ | -0.9643101 | 0.3736942 | -1.751491250 | -0.28321947
18 | $PSTQ1801_{t}otal_{s}essions$ | 0.6554668 | 0.3499538 | 0.009657123 | 1.38352275
7 | $SEQ714_{t}otal_{s}essions$ | 0.4327188 | 0.1503527 | 0.1492953 | 0.736231
6 | $PPHQ615_{t}otal_{s}essions$ | -0.2617817 | 0.1271908 | -0.5070381 | -0.01792041
Table 13: Significant effects for total number of sessions as predictor

The following Table 14 shows an overview of all significant effects for number
of sessions live and recorded as predictor.

| rowname | Estimate Est. | Error | Q2.5 | Q97.5
---|---|---|---|---|---
1 | $MAASQ116_{l}ive_{s}essions$ | -0.2939231 | 0.1254551 | -0.5422444 | -0.04608940
3 | $MAASQ216_{l}ive_{s}essions$ | -0.2663390 | 0.1292418 | -0.5273328 | -0.01924983
9 | $MAASQ516_{l}ive_{s}essions$ | -0.3714334 | 0.1238394 | -0.6162243 | -0.13673993
13 | $MAASQ716_{l}ive_{s}essions$ | -0.3463737 | 0.1278613 | -0.5996803 | -0.10126535
19 | $MAASQ1016_{l}ive_{s}essions$ | -0.2683671 | 0.1265510 | -0.5189259 | -0.02028169
21 | $MAASQ1116_{l}ive_{s}essions$ | -0.3519825 | 0.1277744 | -0.6044776 | -0.10675305
22 | $MAASQ1116_{r}ecorded_{s}essions$ | -0.2729695 | 0.1309911 | -0.5282038 | -0.01542422
27 | $MAASQ1416_{l}ive_{s}essions$ | -0.4692584 | 0.1242340 | -0.7120738 | -0.22517830
1 | $SPANEQ115_{l}ive_{s}essions$ | 0.3714696 | 0.1564535 | 0.07600304 | 0.68959905
3 | $SPANEQ215_{l}ive_{s}essions$ | -0.4590504 | 0.1376351 | -0.73415957 | -0.19549265
5 | $SPANEQ315_{l}ive_{s}essions$ | 0.3926880 | 0.1523264 | 0.10560065 | 0.70054970
7 | $SPANEQ415_{l}ive_{s}essions$ | -0.3858643 | 0.1326585 | -0.65485220 | -0.13025333
9 | $SPANEQ515_{l}ive_{s}essions$ | 0.4090131 | 0.1388661 | 0.14721700 | 0.69743993
13 | $SPANEQ715_{l}ive_{s}essions$ | 0.6621751 | 0.1569447 | 0.36908735 | 0.98411335
15 | $SPANEQ815_{l}ive_{s}essions$ | -0.2616480 | 0.1268337 | -0.51337150 | -0.01369651
17 | $SPANEQ915_{l}ive_{s}essions$ | -0.3424188 | 0.1480671 | -0.63512853 | -0.05596512
19 | $SPANEQ1015_{l}ive_{s}essions$ | 0.4365976 | 0.1324461 | 0.17729748 | 0.69863018
7 | $PWBQ417_{l}ive_{s}essions$ | 0.3380077 | 0.1354551 | 0.07777354 | 0.6092814
9 | $PWBQ517_{l}ive_{s}essions$ | 0.2920369 | 0.1449304 | 0.01599527 | 0.5898144
16 | $PWBQ817_{r}ecorded_{s}essions$ | 0.3250622 | 0.1521206 | 0.03104731 | 0.6273903
2 | $PSTQ101_{r}ecorded_{s}essions$ | -0.8114198 | 0.4127656 | -1.70676300 | -0.08103662
8 | $PSTQ401_{r}ecorded_{s}essions$ | -0.6589501 | 0.3618867 | -1.41424650 | -0.01133453
17 | $PSTQ901_{l}ive_{s}essions$ | 3.1336475 | 1.5388280 | 0.76178103 | 6.67567475
22 | $PSTQ1101_{r}ecorded_{s}essions$ | -1.2048545 | 0.4597776 | -2.20838500 | -0.40500393
28 | $PSTQ1401_{r}ecorded_{s}essions$ | 1.5137920 | 0.9497095 | 0.02391954 | 3.70106175
33 | $PSTQ1701_{l}ive_{s}essions$ | -0.7760812 | 0.3848592 | -1.59662025 | -0.07400772
36 | $PSTQ1801_{r}ecorded_{s}essions$ | 1.0777616 | 0.6296321 | 0.02372501 | 2.47252725
44 | $PSTQ2201_{r}ecorded_{s}essions$ | 2.0895201 | 1.2682835 | 0.10201318 | 4.99020650
13 | $SEQ714_{l}ive_{s}essions$ | 0.4491137 | 0.158366 | 0.1545877 | 0.7680788
22 | $PPOQ117_{r}ecorded_{s}essions$ | -0.2526645 | 0.1271695 | -0.5066494 | -0.003687787
Table 14: Significant effects for number of live and recorded sessions as
predictor