ABSTRACT Figuring out which symptoms are central for symptom escalation during the COVID-19 pandemic is important for targeting prevention and intervention. Previous studies have contributed

to the understanding of the course of psychological distress during the pandemic, but less is known about key symptoms of psychological distress over time. Going beyond a pathogenetic

pathway perspective, we applied the network approach to psychopathology to examine how psychological distress unfolds in a period of _maximum stress_ (pre-pandemic to pandemic onset) and a

period of _repeated stress_ (pandemic peak to pandemic peak). We conducted secondary data analyses with the Understanding Society data (_N_ = 17,761), a longitudinal probability study in the

(2) a _first incidence peak to second incidence peak network_, and (3) a _second incidence peak to third incidence peak network_. All networks were highly consistent over time. _Loneliness_

and _thinking of self as worthless_ displayed a high influence on other symptoms. _Feeling depressed_ and _not overcoming difficulties_ had many incoming connections, thus constituting an

end-product of symptom cascades. Our findings highlight the importance of loneliness and self-worth for psychological distress during COVID-19, which may have important implications in

therapy and prevention. Prevention and intervention measures are discussed, as single session interventions are available that specifically target loneliness and worthlessness to alleviate

mental health problems. SIMILAR CONTENT BEING VIEWED BY OTHERS COMPARISON OF NETWORKS OF LONELINESS, DEPRESSIVE SYMPTOMS, AND ANXIETY SYMPTOMS IN AT-RISK COMMUNITY-DWELLING OLDER ADULTS

BEFORE AND DURING COVID-19 Article Open access 26 June 2024 DYNAMIC EFFECTS OF PSYCHIATRIC VULNERABILITY, LONELINESS AND ISOLATION ON DISTRESS DURING THE FIRST YEAR OF THE COVID-19 PANDEMIC

Article Open access 09 January 2025 COVID-19 LOCKDOWN HAS ALTERED THE DYNAMICS BETWEEN AFFECTIVE SYMPTOMS AND SOCIAL ISOLATION AMONG OLDER ADULTS: RESULTS FROM A LONGITUDINAL NETWORK

ANALYSIS Article Open access 19 July 2021 INTRODUCTION The coronavirus disease 2019 (COVID-19) pandemic and related government-imposed lockdown measures have affected social life and mental

health worldwide [1, 2]. In large prospective-longitudinal studies including the Understanding Society Study, a nationally representative, longitudinal household study from the UK, it has

been demonstrated that (a) nearly 40% of the examined population experienced a significant increase in psychological distress during the pandemic, (b) psychological distress peaked during

all lockdown periods with its maximum in the third lockdown and (c) mental health problems decreased during easing of lockdown restrictions [3,4,5,6,7]. This pattern has been observed

consistently across countries [8,9,10,11,12,13]. The existing studies have helped to identify different mental health trajectories and psychological or sociodemographic factors (e.g., being

female, young, lonely) related to the deterioration of mental health during the pandemic (e.g, ref. [6]). However, the studies typically obtained composite scores to examine interindividual

differences or intraindividual changes in mental health which can obscure the identification of important subtleties in dynamic symptom changes and interactions between symptoms over time.

For instance, the importance of single symptoms like enjoying day-to-day activities, loneliness, the ability to concentrate, sleeping problems, decision-making problems, self-confidence, or

happiness and their interaction in light of governmental restrictions remain masked. Moreover, investigating temporal dynamics of complex symptom networks is critical to identify central

symptoms responsible for the escalation of other symptoms over time, e.g., during the COVID-19 pandemic, which, then, can be potentially targeted in intervention and prevention [14]. To

elucidate the complex and dynamic interactions among symptoms, network analytical approaches have been proposed [15]. From the perspective of the network approach to psychopathology [16, 17]

co-occurring psychopathological symptoms, referred to as nodes, are connected with each other to a greater or lesser extent (these connections between symptoms are also referred to as

edges). Thus, it is assumed that symptoms mutually influence and possibly reinforce each other, with central symptoms characterized by a substantial influence on other symptoms. These

symptoms may prove clinically relevant because, in theory, they lead to an activation of the entire network, thus increasing symptom activation [18]. In the context of the COVID-19 research,

loneliness has been identified as a central relevant symptom. In repeated cross-sectional studies, there is first indication that loneliness was the most central variable in mental health

networks (i.e., depression, anxiety, stress, and poor sleep [19]). In addition, the partial correlations (i.e., symptom connectivity in network analysis) between the affective symptoms and

social isolation increased during lockdown, potentially indicating that socially isolated groups are more vulnerable to affective disorders in the face of this population stressor [20].

However, in a network analysis in an Irish sample, loneliness was not centrally related to anxiety and depression [21]. In addition, studies have revealed a central influence of

worthlessness within mental health networks. Worthlessness exhibited the highest influence in a network of pandemic-related mental health conditions and symptoms of major depressive disorder

and generalized anxiety disorder [22]. In addition, worthlessness functioned as bridge symptom in a cross-sectional network, exerting a high influence on the activation of the relationship

between of depression and parental stress [23]. Loneliness and worthlessness appear to be particularly relevant to be studied in a longitudinal approach, as these cross-sectional studies

provide a limited perspective and cannot unravel unfolding dynamic symptom changes, which is particularly relevant for prolonged stressors as pandemics. Accordingly, these studies do not

distinguish whether a symptom was central because it had a strong influence on others or whether it was strongly influenced by others. In terms of symptom network activation, it is important

to identify which symptoms lead to symptom activation as these may be central in intervention and prevention to counteract a symptom exacerbation at an early stage. Therefore, in line with

the network theory, these symptoms may be viable targets to stop symptom activation in the network [15, 18, 24]. A longitudinal network analysis is clinically relevant as it enables a

fine-grained identification of pre-COVID-19 symptoms that predispose symptom escalation during the lockdown as well as symptoms that emerge during COVID-19 and reinforce symptom

manifestation. This may inform prevention and intervention programs at an early stage of network activation tailored to specific time-points in the pandemic. Only one study that used

ecological momentary assessments compared the temporal network structure during a lockdown stage with the network structure of a no-lockdown stage in a German convenience sample (August

2020–March 2021), covering a “no-lockdown” and a “lockdown” stage [25]. In this study, loneliness centrally influenced other symptoms only during the lockdown but not before the lockdown.

However, this study had a small sample, and did not examine how pre-COVID symptoms unfold and operate in a network during a lockdown. Network analyses that include data before the pandemic

onset and at highly relevant time points across the entire pandemic (e.g., lockdowns), in particular disentangling ingoing and outgoing symptom connections, are currently lacking. To close

this research gap, we analyzed data from the Understanding Society Study, a representative prospective-longitudinal household study in adults from UK, using temporal network analysis. These

networks can be used to identify dynamic symptom changes and, in particular, which symptoms have a central influence on the network at a subsequent time point. To pinpoint how symptoms

unfold during a period of _maximum stress_ for individuals, we examined dynamical change in symptom networks from the last pre-COVID survey (2019) to the first COVID wave in April 2020

(pre-COVID to first incidence peak network). To examine the effects of _repeated stress_, we therefore selected two further waves (November 2020, January 2021), as these time points can be

seen as repeated stressors for individuals (a first incidence peak to second incidence peak network, and a second incidence peak to third incidence peak network). Based on prior literature,

we expected that loneliness constitutes a symptom that is centrally influencing other symptoms [19, 20], thus triggering psychological distress symptom network activation. Given the lack of

longitudinal studies, we exploratorily tested how symptoms like day-to-day activities, the ability to concentrate, sleeping problems, decision-making problems, self-confidence, or happiness

operate and unfold in the networks without specifying further a priori hypotheses. METHODS PARTICIPANTS AND STUDY DESIGN We conducted a secondary data analysis by drawing on data from the

_Understanding Society UK Household Longitudinal Study_, a nationally representative, prospective cohort study in the United Kingdom (UK) with about 40,000 households assessed since 2009

[26, 27]. Since April 2020, all members of the main Understanding Society samples aged 16 years and older in April 2020 were invited to participate, if they participated in at least one of

the last two waves of data collection investigating various questions pertaining to individuals’ life’s during COVID-19 (_Understanding Society: COVID-19 Study)_. Until now, data for eight

consecutive COVID-19 waves were collected in April, May, June, July, September, November 2020, as well as January 2021, and March 2021. All participants of the _Understanding Society:

COVID-19 Study_ subsample were assessed using online and telephone surveys. In line with our research questions, we focused on the analysis of the most recent measurement occasion before the

onset of the pandemic (2019/2020), the first time-point during the pandemic (April 2020, wave 1, _n_ = 17,761), and the subsequent peaks, i.e., wave 6 (November 2020, _n_ = 12,035) and wave

7 (January 2021, _n_ = 11,968), as defined by COVID-19 incidence and stringency index (see supplement Fig. S18–S20 for the course of the incidence of COVID-19 cases, for confirmed deaths,

and for the degree of the stringency of lockdown measures). In total, 17,761 participants participated in the first wave during the COVID-19 pandemic of the _Understanding Society: COVID-19_

and were the target of the present analyses. Among the 34,318 participants who participated in the last assessment of the pre-COVID-19 survey (i.e. before the COVID-19 pandemic), the

response to the first wave during COVID-19 was ~51,75%. This is consistent with the individual-level online response levels of the main study, which was 50–55%, which increased to 85–90%

through face-to-face surveys [28]. Full demographic characteristics of our sample including the sample sizes for each sampling time are depicted in Table 1. Ethical approval was granted by

the University of Essex Ethics Committee (ETH1920-1271). The data are openly available to researchers via the UK Data service (https://ukdataservice.ac.uk). Detailed information on the

_Understanding Society UK Household Longitudinal Study and Understanding Society: COVID-19 Study_, including the sample structure, subsamples, and panel attrition, has been previously

presented [6, 29]. All procedures and measures collected in the _Understanding Society Study_ are described at https://www.understandingsociety.ac.uk/about/study-content. For our secondary

data analysis additional ethical approval was not required. The authors assert that all procedures contributing to this work comply with the Helsinki Declaration of 1975, as revised in 2013.

QUESTIONNAIRE MEASURES GENERAL HEALTH QUESTIONNAIRE 12 (GHQ-12) The General Health Questionnaire 12 (GHQ-12) is a commonly applied measurement tool assessing mental health symptoms during

the past 2 weeks [30]. Participants responded to each of the twelve items on a four-point Likert scale ranging from 0 “_not at all_” to 3 “_completely_”. Negative items were recoded so that

higher GHQ 12 scores indicate poorer symptom expression. Internal consistency was good (α = 0.90 − 0.92 for all sampling times). A cutoff score of >11 has been proposed as an indicator of

mental distress, as established by Goldberg et al. [31], replicated and validated among others by Ruiz et al. [32]. During the pre-COVID-19 survey, 37.86 % (_n_ = 6725) suffered mental

health deterioration above threshold, increasing to 50.08% (_n_ = 8894) in wave 1, 53.43 % (_n_ = 9489) in wave 6 and 53.95 % (_n_ = 9582) in wave 7, based on the imputed sample. LONELINESS

Loneliness was assessed with a single item from the Government Statistical Service (GSS) harmonized principle of loneliness

(https://gss.civilservice.gov.uk/guidances/harmonized-standards-guidance/); see also ref. [33]. Before COVID-19, the item read: “How often do you feel lonely?”. For the eight waves during

COVID-19, the wording was: “In the last 4 weeks, how often did you feel lonely?”. In all sampling times (pre-COVID-19 and during COVID-19), the item was assessed on a 3-point Likert scale

ranging from 1 = “_Hardly ever or never_”, 2 = “_Some of the time_” to 3 = “_Often_”. MISSINGNESS ANALYSIS AND DATA IMPUTATION Sample characteristics of the participants who dropped out of

the study between April 2020 and January 2021 compared to those who stayed have been described elsewhere (see supplemental material in refs. [6, 29]). Briefly, characteristics that were

associated with missingness were ethnicity (non-white had higher values of missingness than white participants), sex (males had higher levels of missingness than females), and age (older

participants displayed higher levels of missingness than younger participants). Data were thus treated as missing at random (MAR) [34]. Excluding participants that did not complete all

relevant questions would result in a final sample of _N_ = 7815 complete cases. Thus, missing data were imputed. As multiple imputations [35] are not compatible with the present network

analytical approach, we used one imputed dataset as recommended under such circumstances [36, 37]. To this end, we imputed the data with the MICE package in R [38] by using predictive mean

matching [34]. In our imputation model, demographic characteristics that were associated with missingness, i.e. sex and ethnicity, were included as auxiliary variables. We performed a

sensitivity analysis using full-cases only (see Supplemental Figs. S1 and S2). The results essentially remained the same compared to the imputed data set, increasing confidence in our

results and their interpretation. DATA ANALYSIS We performed all analyses with the R Version 4.0.3 [39]. The R code to reproduce the current results is openly available on the OSF

(https://osf.io/cusf3/?view_only=4edd973d5da24a20bbc2895a8288e2bd). We used cross-lagged panel network models (CLPN) to analyze the unfolding networks over time [37]. We analyzed three

longitudinal networks: (1) _pre-COVID survey to wave 1_, (2) _wave 1 to wave 6_, (3) _wave 6 to wave 7_. In a first step, we calculated the regression coefficients for the models: We

computed autoregressive pathways, where a symptom at one time-point predicts itself at the next time-point while adjusting for all other symptoms. Then, we computed cross-lagged pathways,

where a symptom predicts another symptom while adjusting for the autoregressive effects and all other symptoms. For the estimation of the regression coefficients, we applied the least

absolute shrinkage and selection operator (LASSO), which applies a penalization to avoid estimating spurious edges [40, 41]. We calculated our directional CLPNs from pre-measurement to first

incidence peak and incidence peak to incidence peak using the _glmnet_ package [37, 42]. The _qgraph_ package [43] was used for the visualization of the networks using an average layout for

the three different networks over time. To quantify the centrality of symptoms in our directed CLPNs, we examined two centrality indices: in- and out-expected-influence. These indices are

calculated by all outgoing symptom associations one respective item has with all other items in the following wave. This centrality measure is coined out-expected-influence (the degree to

which a symptom predicts other symptoms). The other centrality metric is quantified by the ingoing symptom associations one item has with all other items and is labeled in-expected-influence

(the degree to which a symptom is predicted by other symptoms). The edge weight difference test and centrality difference tests were used to determine possible significant differences in

edges and centrality indices, respectively [44]. RESULTS DESCRIPTIVE STATISTICS The GHQ items with the highest mean values (i.e., >1 in each sampling time) were _able to concentrate_,

_playing a useful part_, _capable of making decisions_, _enjoy normal activities_, _can face up to problems_, and _feeling reasonably happy_ (see Table 2). GHQ values increased from the

pre-COVID-19 assessment compared to the first wave during COVID-19 in April 2020, _t_(15,464) = −11.22, _p_ < .001, Hedges’s _g_ = 0.185, indicating a deterioration of mental health.

Comparing incidence peak to incidence peak, mental health problems in November 2020 (wave 6) were significantly higher than in April 2020 (wave 1), _t_(10,892) = −6.91, _p_ < .001,

Hedges’s _g_ = 0.027 but January 2021 (wave 7) was not significantly different from November 2020 (wave 6), _t_(9,738) = −1.90, _p_ = 0.058. Detailed descriptive statistics can be found in

Table 2. ACCURACY AND STABILITY The bootstrapped confidence intervals around the edge weights are shown in Figs. S3–S5 (see supplemental material). The bootstrapped confidence intervals were

small to moderate, indicating good accuracy of our networks. In addition, the case-drop bootstrapping results indicate a high stability of the centrality indices (Figs. S6– S8 in the

supplemental material). NETWORK COMPARISON The edge lists of all networks are presented in the supplemental materials _edg1_. The number of non-zero edges was consistent across networks

[range: 125 (pre-COVID to first incidence peak) – 130 (second incidence peak to third incidence peak)]. The correlation of the edge lists between the networks was moderate to strong (range:

_r_ = 0.75 – _r_ = 0.83). The overall correlation of the out-expected-influence (_r_ = 0.89) and the overall correlation of the in-expected-influence (_r_ = 0.84) between networks was high.

All three CLPN networks are depicted in Fig. 1: The symptoms _loneliness, feeling unhappy and depressed, thinking of self as worthless, losing confidence, could not overcome difficulties,

under stress_, and _lost much sleep_ formed a consistent cluster with strong connections over time. The symptoms _can face up to problems, capable of making decisions, feeling reasonably

happy, able to concentrate_, and _enjoy normal activities_ were less consistently connected across time. Table 3 depicts the five strongest edges for all three networks. Overall, the

strongest edge connection in the three networks was the connection _thinking of self as worthless_ → _losing confidence_ (range: _β_ = 0.15 – 0.20). In all three networks, the edge

_loneliness_ → _feeling unhappy and depressed_ displayed the second strongest edge connection (range: _β_ = 0.12 – 0.17). In the first network, the connection _loneliness_ → _losing

confidence_ was the third strongest one, while it was the fifth strongest in the second network, and not as strong in the third CLPN network (range: _β_ = 0.08 – 0.15). The connection

_loneliness_ → _thinking of self as worthless_ was the fourth strongest in the first network, the third strongest in the second and the fifth strongest in the third CLPN network (range: _β_ 

= 0.10 – 0.14). Moreover, the connection _thinking of self as worthless_ → _could not overcome difficulties_ was the fifth strongest in the first, the seventh strongest in the second and the

third strongest connection in the third network (range: _β_ = 0.08 – 0.13). Finally, the connection _losing confidence_ → _thinking of self as worthless_ was the fourth strongest in the

second network, but not as strong in the first and third network (range: _β_ = 0.06 – 0.10). The edge weights difference tests for each of the 3 networks indicated significantly stronger

connections between the aforementioned edges compared to most other edges (see Figs. S9–S11 in the supplemental material). SYMPTOM CENTRALITY The standardized centrality parameters revealed

consistent patterns over time and can be seen in Fig. 2: We found the strongest in-expected-influence for _feeling unhappy and depressed_, followed by _could not overcome difficulties_ and

_losing confidence_, showing a significantly higher in-expected influence than many other symptoms across sampling times. Lower in-expected-influence values were found for the symptoms

_capable of making decisions_, _enjoy normal activities_ and _can face up to problems_ in all networks and _loneliness_ in the CLPN network second incidence peak to third incidence peak (see

supplemental material Figs. S12–S14 for in-expected-influence difference tests). The strongest out-expected-influence was found for _loneliness_ and _thinking of self as worthless_. The

lowest out-expected-influence was found for _feeling reasonably happy_, _can face up to problems_, _playing a useful part_ (see supplemental material Figs. S15–S17 for out-expected influence

difference tests). RESULTS SUMMARY We found consistent patterns of symptom associations over time as _thinking of self as worthless_ → _losing confidence_ and _loneliness_ → _feeling

unhappy and depressed_ displayed the strongest connections in all networks. Similarly, the standardized centrality parameters indicated that _feeling unhappy and depressed_ and _could not

overcome difficulties_ and _losing confidence_ had the strongest in-expected-influence, and _loneliness_ and _thinking of self as worthless_ had the strongest out-expected-influence in all

networks. DISCUSSION Drawing on national, probability-sampled longitudinal data in adults from the UK, this study aimed to disentangle whether mental health symptoms influenced other

symptoms or were influenced by other symptoms over time in one network with _maximum psychosocial stress_ (pre-COVID to first incidence peak) and in two networks with _repeated, sustained

psychosocial stress_ (first incidence peak to second incidence peak and second incidence peak to third incidence peak). Loneliness and worthlessness emerged as most central symptoms that

lead to the activation of further symptom cascades. As feeling depressed was activated by other symptoms (i.e., high in-expected centrality), this indicates that it is a recipient symptom in

the network, constituting a central downstream product of the symptom cascade. Given their influence on other symptoms, low-threshold interventions focusing on self-worth and loneliness may

help reduce the spread of network activation. The present study is the first to analyze a large sample in a longitudinal network design during the COVID-19, which is highly relevant when

facing a population stressor like a pandemic. In line with previous studies, psychological distress increased over time from the pre-COVID-19 assessment to the pandemic assessments. Over

time, the three resulting networks were consistent as reflected in high correlations between connections among symptoms and the centrality of symptoms. The feeling of worthlessness was among

the symptoms with the highest influence on other symptoms (i.e., out-going centrality). In general, self-worth is transdiagnostically associated with mental health [45], but during the

pandemic loss of self-worth may be attributable to a variety of reasons such as inability to engage in hobbies or being socially less active [46, 47]. In all three networks loneliness was

also among the symptoms with the highest influence on other symptoms in subsequent time-points. Loneliness exerts a strong influence on mental health symptoms of the UK population, which

aligns with the findings of Pierce et al. [6] and Kikuchi et al. [11], that during COVID-19 pandemic living alone is associated with poorer mental health, with Groarke et al. [48], that

loneliness predicts higher depressive symptoms later in pandemic, and with the cross-sectional network analyses of Wong et al. [19] and Yu and Mahendran [20]. In addition, we extend previous

findings by including a longitudinal perspective on individual symptoms. In line with previous findings before the pandemic that loneliness has an impact on depressive symptoms (for

meta-analysis see [49]), the symptoms _feeling unable to overcome difficulties_ and _feeling depressed_ were affected by loneliness in the present analysis. Intervening on these dynamics

appears highly relevant because these patterns could instigate a vicious circle as unhappiness and depressive symptoms are likely associated with less activities and more social withdrawal

[50]. Our findings indicate that loneliness before lockdown centrally affects the network during lockdown. This suggests a vulnerability-stress model in that pre-pandemic loneliness leads to

the initiation of a symptoms cascade in the presence of a major population stressor. The importance of loneliness and self-worth and their connections among each other (e.g., _loneliness_ →

 _thinking of self as worthless_) align with the sociometer theory of self-esteem that self-esteem functions as an internal proxy of social inclusion [51]. Thus, these symptoms can be

understood as the cluster sense of belonging. Maintaining and restoring this basic human need of belonging during lockdowns is thus of paramount importance. Surprisingly, enjoying day to day

activities did not emerge as key symptom despite substantial restrictions to engage in hobbies or social activities. This indicates that the cognitive evaluation and general coping

mechanisms may be decisive for initiating symptom dynamics and not the restrictions per se [52]. In line with this, cognitive reappraisal interventions have been found to strengthen

resilience against deterioration in positive mood caused by being alone [53]. CLINICAL IMPLICATIONS The connections and the central symptoms with their connections can be used to

investigate, both through experimental and clinical studies, whether these are indeed causal for symptom escalation under stress. Subsequently, it is to be examined whether this symptom

escalation, i.e., this symptom spread can be prevented by addressing these nodes [14, 54, 55]. Together with previous findings on loneliness [48, 56, 57], our study reemphasizes the

importance of loneliness for the escalation of other mental health symptoms, thus constituting a key target for intervention [58]. Prevention and intervention programs could be implemented

on societal level (e.g., in educational institutions) as regular courses on stress reduction, emotional regulation, empathy, and self-compassion after proving their effectiveness. On an

individual level, home visits and daily contact programs were proposed to target loneliness [58]. Hence, at any time in the pandemic, especially at the relevant times of onset and incidence

peaks, it is advisable and important to address the social connectedness and potential social isolation of individuals and to implement appropriate programs to counteract the impairment of

mental health at an early stage. For instance, when implementing contact restrictions, policy makers need to consider how to adequately enable social support groups or small in-person group

activities to stop the manifestation of loneliness. For example, single-session growth mindset interventions for adolescent anxiety and depression could be helpful to reduce mental health

deterioration [59]. The results of our study revealed that, prior to the COVID-19 pandemic, GHQ-12 mean levels were slightly above the clinical cut-off of 11 as established by Goldberg et

al. [31]. In the following three waves during the pandemic, the threshold was likewise exceeded with an increase of one point from the pre-COVID-19 survey (see [60] for a more detailed

analysis on clinical change in this sample). Despite the small effect sizes of the observed changes, it is important to note that this is a population-based study and small effects can have

a significant impact on a large population [61]. The present study provides insights into the dynamics of symptom levels, and sheds light on the activation of symptom cascades that may have

been involved in such population level shifts. It appears that the symptom cascades and the findings as a whole have a high relevance in our sample, and we found that this relevance is not

only for the subjects who were already in the pathological range (measured with the GHQ-12), but also for the subjects with subclinical mental health issues. In addition, it is important for

prevention and intervention to disentangle how to address the needs for individuals who were already lonely before the pandemic compared to persons who became lonely in light of the

restrictions [56]. From our findings it can be deduced that loneliness does not have to be addressed only during the pandemic, but already before, so that one is better prepared for such

stressors [58]. As loneliness was an important variable during all stages of the pandemic, it needs to be investigated whether different groups of individuals felt lonely during the

pandemic. As several studies found younger individuals to be at a higher risk (e.g. [57, 62]), prevention and intervention programs should address these groups in particular. Li & Wang

[63] analyzed the first wave of the Understanding Society dataset with weighted univariate analyses. They found significantly higher scores in GHQ-12 and for loneliness among women, younger

people, participants without a partner, and unemployed individuals. These findings suggest that these subgroups represent populations at higher risk, warranting the implementation of

specific intervention and preventive measures tailored to these populations. Intervention programs that take place online are already available for this purpose (e.g. [64, 65]), reducing

loneliness and ameliorating feelings of belonging (for an extended review see ref. [66]). Single-session interventions are also available, and first studies found evidence of reducing

hopelessness and increasing agency, among other outcomes [67]. There are also targeted programs aimed at reducing loneliness, such as the digital intervention “Happify”, which has been shown

to have a significant positive impact on feelings of loneliness [68]. Also, group interventions conducted via Zoom have shown positive significant effects on loneliness and depression [69].

Similarly, interventions can be implemented that can reduce worthlessness and enhance self-worth [70]. Overall, there are different opportunities to implement appropriate specific

interventions to counteract the deterioration of symptom cascades at an early stage. These could be implemented as early interventions or, at the onset of a pandemic, in terms of indicated

prevention as a preventive measure for potential at-risk groups. Targeting specific reasons of loss of self-worth may be advisable for intervention efforts, as loss of self-worth may be

attributable to a variety of reasons such as inability to engage in hobbies or being socially less active [46, 47]. The question of appropriate interventions here is a particularly

individual one that depends on the cause of the loss of self-worth. In the case of the loss of employment, support for existential issues would be more likely helpful. The inability to

engage in hobbies might be more likely to benefit from motivation to resume previous hobbies or to take up new ones. Reduced social activity with its negative impact on self-worth can be

addressed closely in conjunction with the interventions mentioned above for loneliness. STRENGTHS AND LIMITATIONS A major advantage of our study is the use of a representative, large dataset

that includes both pre-pandemic surveys and waves during the pandemic. It is particularly advantageous that these waves took place during pandemic-relevant assessment intervals, so that,

based on the stringency index, particularly relevant time points could be examined. The network analytic approach allowed us to perform an analysis with a high degree of resolution, as we

can describe dynamic changes over time and differentiate these changes in incoming and outgoing connections separately (influence of vs. on a symptom). This is important, as the common cause

perspective of mean value differences of GHQ-12 suggests targeting psychological distress holistically, which can be complicated, while we provide specific target points by identifying

constitutive elements of psychological distress. One limitation results from the necessary use of imputation. Missingness was a concern in the present study. Our conclusions hold only under

the missing at random (MAR) assumptions, which is based on our own missingness analyses and previous studies on this dataset that identified important variables associated with missingness

(e.g. [6]). Although our results point to plausible intervention targets, within- and between person variance remains entangled in the present study [37], and intervention studies are needed

first to showcase the effects of targeting central symptoms (e.g. refs. [14, 54]). The results obtained here relate specifically to pandemic events and conditions in the UK. Because the

pandemic event was a country-specific and individual event for which country-specific measures were taken, our results cannot be reliably extrapolated to other countries or populations. As

the GHQ-12 is primarily tailored to depressive symptoms, it would be useful to broaden the range of symptoms in further studies by including anxiety symptoms or more fine-grained loneliness

measures to unravel specific dynamics among these symptoms. In the Understanding Society study, loneliness was assessed with a single item, limiting the construct validity of loneliness.

While this item presents an indicator of the subjective feeling of loneliness, important nuances underlying loneliness and their role in an evolving network remains masked. We cannot discern

differential effects of important aspects of social loneliness like a lack of social contact and emotional loneliness or a lack of emotional closeness and intimacy within a network

perspective. These other facets of loneliness should be considered in future studies, for instance by the means of the UCLA 3-item loneliness scale as an approximate measure or other more

extensive scales [71, 72]. Furthermore, it is important to note that the GHQ-12 is a screening tool to indicate caseness but does not possess psychiatric diagnostic-specific validity.

Nevertheless, the GHQ-12 is useful for capturing important transdiagnostic symptoms commonly found across several mental disorders. This allowed us to examine important associations between

these transdiagnostic symptoms and loneliness. However, the change in survey method from face-to-face surveys to online surveys or telephone surveys must be critically considered [73].

Dropout in 2020 was particularly higher in the older age groups, among people living alone and among people with a lower level of education. In addition to the reduced response rate, it

cannot be ruled out that changes in the survey format induced response bias. However, a thorough psychometric analysis of the GHQ-12 in the present sample indicated longitudinal measurement

invariance across time before and during COVID-19 [73]. CONCLUSION Our study provides important insights concerning the influence of symptoms on future symptoms and symptom constellations of

psychological distress. The present findings suggest that feelings of loneliness and worthlessness increase the risk of mental health deterioration and facilitate later cascades of symptom

escalation under chronic stressors such as repeated lockdowns, thus constituting clinically relevant targets for prevention and intervention. Feeling depressed and not overcoming

difficulties had many incoming connections, thus constituting an end-product of symptom cascades. Further research is needed to investigate whether and how our findings can be translated

into clinical practice and how they may inform policy makers. The associations we found can be used to investigate, through experimental and through clinical studies, whether these nodes are

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Society). Psychological Assessment. In press. https://doi.org/10.1037/pas0001237. Download references FUNDING Open access funding provided by the Open Access Publishing Fund of

Philipps-Universität Marburg with support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). Open Access funding enabled and organized by Projekt DEAL. AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * University of Marburg, Marburg, Germany Michael Odenthal, Christoph Benke & Christiane A. Pané-Farré * University of Cambridge, Cambridge, UK

Pascal Schlechter Authors * Michael Odenthal View author publications You can also search for this author inPubMed Google Scholar * Pascal Schlechter View author publications You can also

search for this author inPubMed Google Scholar * Christoph Benke View author publications You can also search for this author inPubMed Google Scholar * Christiane A. Pané-Farré View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS MO: conceptualization; data curation; formal analysis; investigation; methodology; visualization;

roles/writing—original draft; writing—review and editing. PS: conceptualization; data curation; formal analysis; investigation; methodology; visualization; roles/writing—original draft;

writing—review and editing. CB: conceptualization; investigation; methodology; visualization; supervision; roles/writing—original draft; writing—review and editing. CAP-F: conceptualization;

investigation; methodology; visualization; supervision; roles/writing—original draft; writing—review and editing. CORRESPONDING AUTHOR Correspondence to Michael Odenthal. ETHICS

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http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Odenthal, M., Schlechter, P., Benke, C. _et al._ Temporal dynamics in mental health

symptoms and loneliness during the COVID-19 pandemic in a longitudinal probability sample: a network analysis. _Transl Psychiatry_ 13, 162 (2023). https://doi.org/10.1038/s41398-023-02444-z

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