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Maguire et al. (2000)

Last updated 22 Mar 2021

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Navigation-related structural change in the hippocampi of taxi drivers.

Background information: According to Maguire, the role of the hippocampus is to facilitate spatial memory, in the form of navigation. From previous studies (pre-Maguire) it was impossible to know whether differences in brain anatomy are predetermined, or whether the brain is susceptible to plastic changes, in response to environmental stimulation – in this case driving a taxi.

Taxi drivers undergo extensive training, known as ‘The Knowledge’ and therefore make an ideal group for the study of spatial navigation.

Aim: To examine whether structural changes could be detected in the brain of people with extensive experience of spatial navigation.

Method: Structural MRI scans were obtained. 16 right-handed male London taxi drivers participated; all had been driving for more than 1.5 years. Scans of 50 healthy right-handed males who did not drive taxis were included for comparison. The mean age did not differ between the two groups.

Results: 1) Increased grey matter was found in the brains of taxi drivers compared with controls in two brain regions, the right and left hippocampi. The increased volume was found in the posterior (rear) hippocampus.

2)Changes with navigation experience – A correlation was found between the amount of time spent as a taxi driver and volume in the right posterior hippocampus.

Conclusion : The results provide evidence for structural differences between the hippocampi of London taxi drivers and control participants, therefore suggesting that extensive practice with spatial navigation affects the hippocampus.

Evaluation:

Could this particular arrangement of hippocampal grey matter predispose individuals to professional dependence on navigational skills? This notion was tested directly, by examining a correlation between hippocampi volume and the amount of time spent as a taxi driver. Right posterior hippocampal volume positively correlated with the amount of time spent as a taxi driver and therefore suggests that changes in hippocampal volume are acquired.

As such, the finding indicates the possibility of local plasticity in the structure of the healthy adult human brain, as a function of increased exposure to an environmental stimulus. The results suggest that a mental map of London is stored in the posterior hippocampus and is accommodated by an increase in tissue volume.

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Brain Plasticity (Neuroplasticity): How Experience Changes the Brain

Eagle Gamma

Freelance Writer

Eagle Gamma writes about science & technology for the BBC, the US Department of Energy, and popular magazines.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

The brain’s capacity to reorganize and adapt after damage is known as neuroplasticity or brain plasticity.

Take-home Messages

• Brain plasticity, also known as neuroplasticity, is the brain’s biological, chemical, and physical capacity to reorganize its structure and function.
• Neuroplasticity occurs due to learning, experience, and memory formation or due to damage to the brain.
• Learning and new experiences cause new neural pathways to strengthen, whereas neural pathways used infrequently become weak and eventually die. This process is called synaptic pruning.
• Although traditionally associated with changes in childhood, recent research indicates that mature brains continue to show plasticity due to learning.
• Neuroplasticity provides protective effects in managing traumas during human development (Cioni et al., 2011). Also, learning music or second languages can increase neuroplasticity (Herholtz & Zatorre, 2012).
• Plasticity allows the brain to cope better with the indirect effects of brain damage resulting from inadequate blood supply following a stroke.
• Fundamentally, the nervous system needs to rearrange itself to adapt to its unfolding situation. The genes program the body to have neuroplasticity so that animals can survive in unpredictable environments.

Neuroplasticity, also called brain plasticity, refers to the capacity of the brain to change and adapt in structure and function in response to learning and experience.

The brain possesses a remarkable ability to rewire itself. These changes range from individual neuron pathways making new connections to systematic adjustments like cortical remapping.

This occurs in all healthy people, especially children, after various problems like brain injuries.

Early Theories

Early experimental work on neuroplasticity was conducted by an eighteenth-century Italian scientist, Michele Malacarne, who discovered that animals made to learn tasks would develop larger brain structures (Rosenzweig, 1996).

The first theoretical notions of neural plasticity were developed in the nineteenth century by William James , a psychology pioneer. James wrote about this topic in his 1890 book The Principles of Psychology (James, 1890).

In the twentieth century, influential neuroscientist Santiago Ramón y Cajal proposed that neurons in adults break down and rebuild (Fuchs & Flügge, 2014).

Modern Theories

Progress of the idea – modern theories: Modern experimental instruments like imaging tools have yielded enough information to develop improved theories.

Scientists now think that neuroplasticity occurs throughout all life stages, with extensive capacities from childhood development to healing diseases (Doidge, 2007).

The brain can rearrange itself in terms of the functions it carries out and the basic underlying structure (Zilles, 1992).

Functional Plasticity

Functional recovery after brain trauma.

Functional plasticity is the brain’s ability to move functions from a damaged area of the brain after trauma, to other undamaged areas. Existing neural pathways that are inactive or used for other purposes take over and carry out functions lost because of the injury.

After a brain injury, such as an accident or stroke, the unaffected brain areas can adapt and take over the functions of the affected parts. This process varies in speed, but it can be fast in the first few weeks (phase of spontaneous recovery) then it becomes slower.

It can be helped by rehabilitation, and the nature of rehabilitation programs varies with the type of injury, from retraining some types of movement to speech therapy.

There are ways through which brain plasticity can enable brain-damaged people to regain some of their past capacities. Each of the approaches through which the nervous system adapts its functionality has differences in how it occurs and in which patients it occurs.

Axonal sprouting

Functional plasticity can occur through a process termed axonal sprouting, where undamaged axons grow new nerve endings to reconnect the neurons, whose links were severed through damage.

Undamaged axons can also sprout nerve endings and connect with other undamaged nerve cells, thus making new links and new neural pathways to accomplish what was a damaged function.

Homologous Area Adaptation

Although each brain hemisphere has its own functions, if one brain hemisphere is damaged, the intact hemisphere can sometimes take over some of the functions of the damaged one.

In homologous area adaptation, brain-behavior becomes active in the equivalent part on the opposite side of the brain from where it usually occurs (Grafman, 2000). If it normally occurs on the right side, it would move to the left side, and vice versa.

This functional neuroplasticity occurs more often in children than in adults. Shifting over a module to the opposite side displaces some of the functionality that was originally there.

As a result, the two functions may become less effective, contaminating each other.

Cross-Modal Reassignment

Cross-modal reassignment occurs when the brain uses an area that would normally process a certain type of sensory information (such as sight) for a different type of sensory information instead (such as sound).

When a brain region does not receive sensory data as expected, say because a person has become blind, this brain region may become repurposed for another sense, like touch.

This can enable blind people to “see” Braille text with their fingers (Grafman, 2000).

Also, some blind people learn to reuse their visual centers for hearing sounds, thus becoming capable of “echolocation” to navigate around environments (Thaler & Goodale, 2010).

Map Expansion

In map expansion, the brain notices that a certain area gets extensive use, so it expands this area (Grafman, 2000). This is comparable to how the body can notice that certain muscles get more use (such as those involved in an often-played sport), then grows those muscles larger.

When a person often engages in an activity or experience, this produces enlargement of the associated brain region. Brain growth occurs right away, so neuroscientists can detect it through brain imaging technologies while it occurs (Grafman, 2000).

Compensatory Masquerade

Compensatory masquerade involves the brain reusing a component to conduct a mental operation other than what it would typically do.

For example, suppose a person suffers a brain injury with some functionality lost. In that case, this person may be able to reuse a different method behind the scenes, like finding one’s way by remembering directions instead of by sense of location (Grafman, 2000).

Evidence For Functional Plasticity

Homologous area adaptation:.

Case studies of stroke victims who have experienced brain damage and thus lost some brain functions have shown that the brain has the ability to re-wire itself with undamaged brain sites taking over the functions of damaged brain sites.

Thus, neurons next to damaged brain sites can take over at least some of the functions that have been lost.

A youth with a right parietal lobe injury wound up with the left parietal lobe taking over some functions normally occurring on the right side. The youth then had difficulty with tasks normally occurring on the left side because some right-side equivalents had taken over left-side brain resources (Grafman, 2000).

Neuronal Unmasking:

Wall (1977) noticed the brain contained ‘dormant synapses’ – neural connections which have no function.

However, when brain damage occurs, these synapses can become activated and open up connections to regions of the brain that are not normally active and take over the neural function that has been lost due to damage.

Structural Plasticity

How experience changes brain plasticity.

Structural neuroplasticity is the brain’s ability to change its physical structure as a result of learning, involving reshaping individual neurons (nerve cells).

During infancy, the brain experiences rapid growth in the number of synaptic connections .

As each neuron matures, it sends out multiple branches; this increases the number of synaptic contacts from neuron to neuron. At birth, each neuron in the cerebral cortex has approximately 2,500 synapses.

By the time a child is three years old, the number of synapses is approximately 15,000 (Gopnick et al. 1999).

As we mature, the connections we do not use are deleted, and the ones we use frequently are strengthened, called neural pruning (Purcell & Zukerman, 2011). This process continues throughout our life.

While plasticity occurs throughout life, it is especially relevant during the early “critical years,” when brain plasticity enables the senses, language, and other skills to develop.

Developmental plasticity

Part of the development of the vision system is genetically hardwired. However, another part of this development depends on neuroplasticity. As a child grows, the incoming information from light sources, such as light reflected off the faces of caregivers, provides necessary cues for the brain to adjust its growth patterns.

The equivalent plasticity-based growth also occurs with the other senses, calibrating the young person to local conditions.

The development of language reveals even more about neuroplasticity. Again, part of this functionality is genetically hardwired, but part depends on environmental feedback. An individual has certain nerve cells programmed to become grammar modules.

For these to function correctly, they require the input of specific grammatical rules from a culture, such as the rules of English or Spanish. Thus, neuroplasticity enables the brain to process language.

How Does Neuroplasticity Work?

At the most basic level, it starts with the production of a new nerve cell ( neurogenesis ). Then, individual neurons develop new connections with each other.

A neuron works by sending or receiving electrochemical signals from other neurons in the brain.

The way that individual neurons connect to each other controls how the signals get sent, like the routing of messages over the internet or of instructions codes in a computer processor.

As each neuron develops connections to others, this results in growing clusters of cells. The neurons can adjust the level or strength of the signal with connecting neurons.

This ongoing process provides fine-tuning of the neural architecture. Neuroplasticity uses cascades of electrochemical signals that unfold as a result of the expression of genetic codes through cell signal molecules (Flavell & Greenberg, 2008).

Rewiring larger regions, reorganizing the nervous system at multiple levels

Neurons work together at several different levels. Not only individual cells but even clumps within brain regions can grow in greater or lower density.

As cells grow or die in different regions, the relative densities vary. Such variations can provide an even broader adjustment or neuroplasticity in the brain than individual nerve cell connections.

When nerve bundles become broken, through injury or surgery, the brain can regrow these elements (Doidge, 2007). Surprisingly, the brain can reconnect itself in an efficient manner even to deal with sizable upsets. It operates like a plant, able to regrow around lost parts.

The brain can regrow after injury, starting instantly at the molecular level in the injured area (Wall & Wang, 2002). Gradually, the repairs extend through subcortical layers, reaching larger-scale cortical levels of the brain.

This growth occurs throughout the nervous system, including the spine and distributed branches, not only in the brain.

Recurring synaptic connections grow more efficient (cell assembly theory)

Nerve cells work by producing electrochemical activity in the “synapses,” which are gaps connecting the cells together.

As a synaptic connection fires more often, it grows more efficient in what is known as “cell assembly theory.” A phrase describing this phenomenon is “cells that fire together wire together” (Lowel, 1992).

The nerve connections grow stronger when one cell fires before the other rather than when they both fire simultaneously. Sequential firing produces a causal relationship, enabling the nervous system to learn.

As a comparison, internet search engines track which sites link directly to which other sites. The combined directional links of billions of sites produce an efficient map of the internet.

The combined directional links of billions of neurons produce an efficient map of the body and its environment.

Evidence for Structural Plasticity

The famous study by Maguire et al., 2000 demonstrates brain plasticity. She studied 16 London taxi drivers and found an increase in the volume of grey matter in the posterior hippocampus compared to a control group. This area of the brain is involved in short-term memory and spatial navigation.

Further support comes from Mechelli et al. (2004), who found that learning a second language increases the density of grey matter in the left inferior parietal cortex and that the degree of structural reorganization in this area is influenced by the fluency attained and the age at which the second language was learned.

With age, neuroplasticity decreases; however, Mahncke et al. (2006) used a computer training program with older adults with memory impairment and found a significant improvement compared to the control group.

This has potential benefits for society as a brain-plasticity-based intervention targeting normal age-related cognitive decline may delay the time when these people need support in their everyday life.

Learning and new experiences cause new neural pathways to strengthen, whereas neural pathways which are used infrequently become weak and eventually die. Thus brains adapt to changing environments and experiences. Boyke (2008) found that even at 60+, learning a new skill (juggling) resulted in increased neural growth in the visual cortex.

Kuhn (2014) found that playing video games for 30+ minutes per day resulted in increased brain matter in the cortex, hippocampus, and cerebellum . Thus, the complex cognitive demands of mastering video games caused the formation of new synaptic connections in brain sites that control spatial navigation, planning, decision-making, etc.

Davidson (2004) matched eight experienced practitioners of Tibetan Buddhist meditation against 10 participants with no meditation experience. Levels of gamma brain waves were far higher in the experienced meditation group both before and during meditation. Gamma waves are associated with the coordination of neural activity in the brain.

This implies that meditation can increase brain plasticity and cause permanent and positive changes to the brain.

Kempermann (1998) found that rats housed in more complex environments showed an increase in neurons compared to a control group living in simple cages. Changes were particularly clear in the hippocampus – associated with memory and spatial navigation.

A similar phenomenon was shown in a study of London taxi drivers. MRI scans revealed that the posterior portion of the hippocampus was significantly larger than the control group, and the size of the difference was positively correlated with the amount of time spent as a taxi driver (i.e., greater demands on memory = more neurons in this portion of the hippocampus).

Critical Evaluation

Neuroplasticity can explain a broad range of facts about the structure and function of the brain. This notion does, however, have some constraints.

These involve the gradual decline of neuroplasticity with age and certain restrictions regarding how much neural plasticity is possible, even in young, healthy people. Also, scientists have yet to learn many critical aspects of neuroplasticity.

The limits of brain plasticity (decline with age, biological constraints)

Neuroplasticity can only go so far. Non-human animals show many areas of brain plasticity. However, their brains cannot reshape themselves enough to learn a human language or perform advanced mathematics.

Neuroplasticity works on biologically available material, which imposes limitations like only adjusting the specific neural substrate of a cognitive function or adapting a brain function somewhat for a season.

In people whose brains reuse large regions for different operations, such as blind people whose vision centers become useful for touch or sound, this capacity can only work for specific types of processing.

Even people blind from birth would not be able to reuse their color-detection brain cells for touch because, unlike geometry-detection brain cells, these have hard coding for visual input (Grafman, 2000).

Even in healthy individuals, neuroplasticity declines with age (Lu et al., 2004). Over the years, as the body becomes less flexible, so does the brain.

Much neuroplasticity is geared towards enabling younger people to develop an understanding and capacity to act within their surroundings. This stabilizes to some extent in adulthood, even declining in the elderly.

One can see the decline of neuroplasticity in how older people become more fixed in their ways while younger people learn rapidly.

What don’t we know about brain plasticity?

Neuroplasticity has grown over recent centuries into a topic of considerable interest to scientists but remains poorly understood. The brain imaging tools for conducting studies on this topic are still young. As such, much knowledge has yet to be found.

Scientists remain unsure about many of the mechanisms underlying brain plasticity (Grafman, 2000). While a few of the processes have been studied in molecular detail, others have not, and the conceptual understanding of how they take place is a source of ignorance.

Therefore, the technical underpinnings of neuroplasticity represent an area of active interest in which further investigations are being carried out.

Neuroplasticity, the freedom of the brain to readjust its routing to enable more effective calculation in response to injury or normal challenges, has shown itself to occur across a range of animals.

However, we still only know some of the brain regions in which it occurs, some of the mechanisms through which it happens, and some of the costs and advantages that it offers.

Animal adaptations

Animals other than humans exhibit neuroplasticity as much as humans do, or in some cases, even more.

Animals evolved to survive over periods of years through recurring cycles of weather and other environmental conditions. As such, some species exhibit neuroplasticity in cyclical patterns (Nottebohm, 1981).

Brain regions that navigate the environment, like the hippocampus, often grow during mating season (Nottebohm, 1981). Also, some birds’ brain centers for singing mating songs grow during this period.

Other animals’ brains develop differently depending on the season, such as adjusting when to lay more eggs or become pregnant (Wayne et al., 1998).

Behavioral or environmental modification

Several alterations to one’s behaviors or environmental conditions can affect the brain’s structure. These include sports or other physical activities, meditation, using drugs, or pollution.

When a person becomes physically active, this affects the brain and the rest of the body. Aerobic activities like running or bicycling increase the brain’s rate of producing neurons (Gomez-Pinilla & Hillman, 2013). This results in better awareness of one’s surroundings, decision-making, affect, and other mental functions.

Meditation practices, which involve focusing attention, can also invoke neuroplasticity (Lutz et al., 2004). In this case, the brain increases its capacity for controlling emotions or awareness.

Drugs, including alcohol, illicit drugs, and certain medications, can affect the brain (Ganguly & Poo, 2013). These substances often produce chemical adjustments in the brain, which can last well beyond the duration of drug use, even for decades.

In some cases, like heavy drinking, entire brain regions can restructure themselves to recover lost functionality.

The brain responds to the environment as the main part of its normal function. As such, the brain can also undergo harm from environmental problems.

Air pollution, heavy metals, and other pollutants can inhibit brain development. For example, air pollution can destroy brain cells, reducing cognitive function (Calderón-Garcidueñas, 2002).

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Changes in London taxi drivers' brains driven by acquiring 'the Knowledge'

Acquiring 'the Knowledge' -- the complex layout of central London's 25 000 streets and thousands of places of interest -- causes structural changes in the brain and changes to memory in the capital's taxi drivers, new research funded by the Wellcome Trust has shown.

The study, published in the journal Current Biology , supports the increasing evidence that even in adult life, learning can change the structure of the brain, offering encouragement for lifelong learning and the potential for rehabilitation after brain damage.

To qualify as a licensed London taxi driver, a trainee must acquire 'the Knowledge' of the capital's tens of thousands of streets and their idiosyncratic layout. This training typically takes between three and four years, leading to a stringent set of examinations that must be passed to obtain an operating licence; only around half of trainees pass. This comprehensive training and qualification procedure is unique among taxi drivers anywhere in the world.

Previous studies of qualified London taxi drivers, led by Professor Eleanor Maguire from the Wellcome Trust Centre for Neuroimaging at UCL (University College London), have shown a greater volume of grey matter -- the nerve cells in the brain where processing takes place -- in an area known as the posterior hippocampus and less in the anterior hippocampus relative to non-taxi drivers.

The studies also showed that although taxi drivers displayed better memory for London-based information, they showed poorer learning and memory on other memory tasks involving visual information, suggesting that there might be a price to pay for acquiring the Knowledge. The research suggested that structural brain differences may have been acquired through the experience of navigating and to accommodate the internal representation of London.

To test whether this was the case, Professor Maguire and colleague Dr Katherine Woollett followed a group of 79 trainee taxi drivers and 31 controls (non-taxi drivers), taking snapshots of their brain structure over time using magnetic resonance imaging (MRI) and studying their performance on certain memory tasks. Only 39 of the group passed the tests and went on to qualify as taxi drivers, giving the researchers the opportunity to divide the volunteers into three groups for comparison: those that passed, those that trained but did not pass, and the controls who never trained.

The researchers examined the structure of the volunteers' brains at the start of the study, before any of the trainees had begun their training. They found no discernible differences in the structures of either the posterior hippocampus or the anterior hippocampus between the groups, and all groups performed equally well on the memory tasks.

Three to four years later -- when the trainees had either passed the test or had failed to acquire the Knowledge -- the researchers again looked at the brain structures of the volunteers and tested their performance on the memory tasks. This time, they found significant differences in the posterior hippocampus -- those trainees that qualified as taxi drivers had a greater volume of grey matter in the region than they had before they started their training.

This change was not apparent in those who failed to qualify or in the controls. Interestingly, there was no detectable difference in the structure of the anterior hippocampus, suggesting that these changes come later, in response to changes in the posterior hippocampus.

On the memory tasks, both qualified and non-qualified trainees were significantly better at memory tasks involving London landmarks than the control group. However, the qualified trainees -- but not the trainees who failed to qualify -- were worse at the other tasks, such as recalling complex visual information, than the controls.

"The human brain remains 'plastic', even in adult life, allowing it to adapt when we learn new tasks," explains Professor Maguire, a Wellcome Trust Senior Research Fellow. "By following the trainee taxi drivers over time as they acquired -- or failed to acquire -- the Knowledge, a uniquely challenging spatial memory task, we have seen directly and within individuals how the structure of the hippocampus can change with external stimulation. This offers encouragement for adults who want to learn new skills later in life.

"What is not clear is whether those trainees who became fully fledged taxi drivers had some biological advantage over those who failed. Could it be, for example, that they have a genetic predisposition towards having a more adaptable, 'plastic' brain? In other words, the perennial question of 'nature versus nurture' is still open."

In the research paper, Professor Maguire and Dr Woollett speculate on the biological mechanisms that may underpin the changes to the brain they observed.

One theory, supported by studies in rodents, is that when learning that requires cognitive effort takes place and is effective, there is an increase in the rate at which new nerve cells are generated and survive. The hippocampus is one of the few brain areas where the birth of new nerve cells is known to take place.Alternatively, it could be that the synapses, or connections, between existing nerve cells grew stronger in the trainees who qualified.

Dr John Williams, Head of Neuroscience and Mental Health at the Wellcome Trust, says: "The original study of the hippocampi of London taxi drivers provided tantalising hints that brain structure might change through learning, and now Eleanor's follow-up study, looking at this directly within individual taxi trainees over time, has shown this is indeed the case. Only a few studies have shown direct evidence for plasticity in the adult human brain related to vital functions such as memory, so this new work makes an important contribution to this field of research."

• Intelligence
• Neuroscience
• Brain Injury
• Educational Psychology
• Brain-Computer Interfaces
• Social cognition
• Memory-prediction framework
• What is knowledge?
• Theory of cognitive development
• Limbic system

Story Source:

Materials provided by Wellcome Trust . Note: Content may be edited for style and length.

Journal Reference :

• Katherine Woollett, Eleanor A. Maguire. Acquiring “the Knowledge” of London's Layout Drives Structural Brain Changes . Current Biology , 2011; DOI: 10.1016/j.cub.2011.11.018

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Psychology IBDP: Localisation of function – Relevant research studies

Read our latest blog-post for students currently studying psychology for the IBDP from Pamoja teacher, Peter Anthony

Localisation of function is the second topic area of the Biological Approach to understanding behaviour.

You need to understand the theory that behaviour, emotion, and/ or thoughts originate in specific regions of the brain.

Maguire (2000) is relevant to this topic as well as techniques used to study the brain in relation to behaviour .

This is an excellent example of how you can economise on the number of studies you need to learn during the course. Maguire (2000) is highly recommended for any response to an SAQ on this topic.

For an ERQ, you would need to supplement Maguire (2000) with another study, either Draganski et al. (2001) or Tierney et al. (2001). Draganski is recommended as you have already encountered this investigation in your study of brain imaging technology.

Links to original studies: Maguire (2000) Draganski et al. (2004)

Find out more about the IBDP Psychology SL and IBDP Psychology HL with Pamoja.

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Effect of high-risk pregnancy on prenatal stress level: a prospective case-control study

• Open access
• Published: 11 May 2024

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• Hülya Türkmen   ORCID: orcid.org/0000-0001-6187-9352 1 ,
• Bihter Akın   ORCID: orcid.org/0000-0002-3591-3630 2 &
• Yasemin Erkal Aksoy   ORCID: orcid.org/0000-0002-7453-1205 2  

The study aimed to determine the effects of high-risk pregnancy on prenatal stress levels. The study was conducted with a case-control design in Turkey in September-December 2019. The sample included pregnant women diagnosed with high-risk pregnancy and were at their 36th or later gestational weeks as the case group ( n  = 121) and healthy pregnant women as the control group ( n  = 245). The Antenatal Perceived Stress Inventory (APSI) and the Revised Prenatal Distress Questionnaire (NUPDQ-17 Item Version) were used to assess the stress levels of the participants in the study. It was determined that high-risk pregnancy was associated with higher rates of prenatal stress (APSI: p  < 0.001, effect size = 0.388; NUPDQ: p  = 0.002, effect size = 0.272) compared to the control group. The results of the linear regression analysis showed that high-risk pregnancy affected APSI (R 2  = 0.043, p  < 0.001) and NUPDQ (R 2  = 0.033, p  = 0.009) scores, but education levels, number of pregnancies, and number of abortions did not affect APSI and NUPDQ scores. According to the results of this study, high-risk pregnant women are in a risk group for stress. It is of great importance for the course of a pregnancy that healthcare professionals assess the stress levels of pregnant women in the high-risk pregnancy category and provide psychological support to pregnant women who have high stress levels or are hospitalized.

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Introduction

A high-risk pregnancy is a significant health problem that threatens the health of the pregnant woman, the health of her fetus, and ultimately the health of her newborn, increases the risk of morbidity and mortality, and has physiological, psychological, social, and economic aspects (Cincioğlu et al., 2020 ; Gözüyeşil & Düzgün, 2021 ; Sinaci et al., 2020 ). Chronic diseases existing before pregnancy and problems that arise during pregnancy can make a pregnancy risky. Pregnant women with gestational diabetes mellitus, preeclampsia/eclampsia, potential threat of preterm labor, cervical insufficiency, premature rupture of membranes, vaginal bleeding, Rh incompatibility, intrauterine growth retardation, and infections are in the high-risk category (Gözüyeşil & Düzgün, 2021 ; Sinaci et al., 2020 ; ACOG, 2019 ; Soğukpınar et al., 2018 ; Üzar-Özçetin & Erkan, 2019 ; ACOG, 2018 ).

Approximately 10% of all pregnancies in the world are considered to be in the high-risk category (Cincioğlu et al., 2020 ; Gourounti et al., 2015a , b ; Göüyeşil & Düzgün, 2021 ; Sinaci et al., 2020 ; Soğukpınar et al., 2018 ; Üzar-Özçetin & Erkan, 2019 ). According to Turkey Demographic and Health Survey (TNSA) 2018 data, 35% of pregnancies in Turkey are in the high-risk category (Hacettepe University Institute of Population Studies, 2019 ).

Good mental health during pregnancy is important for the health of both the pregnant woman and her fetus (Gümüşdaş et al., 2014 ). In a high-risk pregnancy, the normal outcome of the pregnancy and the birth of a healthy baby are threatened. These pregnant women have a variety of health needs that must be met. If these needs are not met, the mother may experience extreme stress and anxiety (Ölçer & Oskay, 2015 ). In the case of intense stress caused by the risks of pregnancy, the elevation of catecholamines such as cortisol and epinephrine may increase the possibility of pregnancy complications (e.g., preeclampsia) and adversely affect pregnancy outcomes (e.g., intrauterine growth retardation) (Atasever & Çelik, 2018 ; Cetin et al., 2017 ; Deshpande, 2016 ; Gözüyeşil & Düzgün, 2021 ; Riggin, 2020 ; Traylor et al., 2020 ; Yüksel et al., 2013 ). Moreover, newborns exposed to extreme stress during the intrauterine period may have permanent health problems later in their lives (Graignic-Philippe et al., 2014 ; MacKinnon et al., 2018 ; Van De Loo et al., 2016 ).

It is seen that distress in pregnancy has a high prevalence ranging between 11.9% and 63.5% in studies conducted in Turkey (Yüksel et al., 2013 ; Çapık et al., 2015 ; Gözüyeşil & Düzgün, 2021 ). It is very important that health professionals identify pregnant women at risk of stress to ensure a healthy pregnancy process and protect the fetus and newborn from the harmful effects of stress. This way, more careful monitoring of pregnant women at risk of stress can be ensured, and the negative consequences of stress can be prevented with appropriate interventions (Williamson et al., 2023 ; Atasever & Çelik, 2018 ; Pinar et al., 2022 ). It is reported in the international literature that mental problems such as anxiety and stress are more common in high-risk pregnancies than in healthy pregnancies (Byatt et al., 2014 ; Abedian et al., 2015 ; Gourounti et al., 2015a , b ). In Turkey, there are few studies examining the prenatal stress levels of high-risk pregnant women. However, in these studies, the prenatal stress levels of women with healthy and high-risk pregnancies were not compared, and only the stress levels of high-risk pregnancies were determined (Gözüyeşil & Düzgün, 2021 ; Üzar-Özçetin & Erkan, 2019 ). Current evidence indicates that studies describing the concept of prenatal stress in high-risk pregnancies with different diagnoses are needed to learn more about the complex aspects of prenatal stress and identify the sociodemographic and obstetric factors that may lead to high-risk pregnancies for early diagnosis (Pinar et al., 2022 ; Hung et al., 2021 ; Gözüyeşil & Düzgün, 2021 ; Mete et al., 2020 ; Üzar-Özçetin & Erkan, 2019 ; Atasever & Çelik, 2018 ). For this reason, it is thought that this study will guide healthcare professionals who provide care for women with high-risk pregnancies about the services they will provide.

This study aims to prospectively determine the effects of high-risk pregnancies on prenatal stress levels compared to healthy pregnant women, using two different measurement instruments.

Materials and methods

Research questions.

Does high-risk pregnancy have an impact on prenatal stress?

What are the factors affecting the prenatal distress levels of women diagnosed with high-risk pregnancy?

Is there a difference in prenatal stress levels among different high-risk pregnancy diagnoses?

Design and settings

This case-control study was conducted to determine the difference between the stress levels of women with healthy and high-risk pregnancies. In other words, it was aimed to determine the suspected causal effect of high-risk pregnancy on prenatal stress levels. This case-control study was carried out between September and December 2019 at the Obstetrics and Gynecology Inpatient and Outpatient Clinics of Atatürk City Hospital in the Balıkesir province of Turkey. The case and control groups included women who were selected from the same hospital.

The hospital where the study was conducted hosted a total of 4,152 deliveries in 2018. Approximately 10% of all pregnancies are considered to be in the high-risk category (Sinaci et al., 2020 ). The sample size required to conduct the study was calculated as 134 high-risk pregnant women using the Epi Info StatCalc program based on an assumed population size of 4152, prevalence of 10%, margin of error of 5%, and in a 95% confidence interval. The study was completed with a total of 384 pregnant women, including 134 high-risk pregnant women and 250 healthy pregnant women, who accepted to participate in the study and filled out the consent form. However, the data of 13 pregnant women in the case group and 5 pregnant women in the control group were excluded from the study because they filled out the data collection forms incompletely. For the case group ( n  = 121), the post hoc power analysis (G*Power 3.1) revealed a medium effect size and a power of 0.421.

The inclusion criteria of the study were being in a gestational week further than 36 weeks, not having a psychiatric diagnosis, and being 18 years old or order. Pregnant women who were hospitalized in the Obstetrics and Gynecology Inpatient Clinic, were diagnosed with high-risk pregnancy by a physician, and met the inclusion criteria were included in the high-risk pregnancy group. In the control group, pregnant women who were healthy, were at or above their 36th gestational week, and visited the Outpatient Clinics were included. Women who wanted to leave the study or responded incompletely to the data collection forms were excluded. After those who met the inclusion criteria were included, no pregnant women withdrew from the study by their own accord. However, 18 pregnant women were excluded from the study because they filled out the forms incompletely.

High-risk pregnancies were defined as the presence of one or more of the following: pre-existing chronic diseases, preeclampsia, gestational diabetes mellitus (GDM), vaginal bleeding, placenta previa, threat of preterm labor, premature rupture of membranes, intrauterine growth retardation (IUGR), fetal anomaly/distress, multiple pregnancy, polyhydramnios/oligohydramnios, Rh incompatibility, and infectious diseases (Cincioğlu et al., 2020 ; Gözüyeşil & Düzgün, 2021 ; Sinaci et al., 2020 ; Üzar-Özçetin & Erkan, 2019 ).

Data collection

A Personal Information Form, the Antenatal Perceived Stress Inventory, and the Prenatal Distress Questionnaire were administered to the participants. The participants were informed about the study, the purpose of the study was explained to them, and their written consent was obtained. The data collection forms were administered to the participants by the first author. The data were collected based on the self-reports of the participants. The data collection period was between September and December 2019. The interviews lasted about 15 min for each participant.

Personal information form

The form which was prepared by the researchers in line with the literature consisted of a total of 20 questions on some characteristics of the participants, including their sociodemographic characteristics and obstetric history (Gözüyeşil & Düzgün, 2021 ; Mete et al., 2020 ; Üzar-Özçetin & Erkan, 2019 ; Atasever & Çelik, 2018 ).

Antenatal perceived stress inventory (APSI)

The Turkish validity and reliability study of the inventory developed by Razurel et al. ( 2014 ) to assess perceived stress in the prenatal period was performed by Atasever and Çelik ( 2018 ). The inventory is applied to pregnant women at the 36th -39th gestational weeks. It is a 5-point Likert-type scale (very much = 5 points, much = 4 points, quite = 3 points, a little = 2 points, none = 1 point) and consists of 12 items and 3 dimensions. Its dimensions are Medical and Obstetric Risks/Fetal Health, Psychosocial Changes during Pregnancy, and Prospect of Childbirth. The minimum and maximum scores that can be obtained from the inventory are 12 and 60. High scores indicate high levels of stress perceived by pregnant women. In the study conducted by Atasever and Çelik, Cronbach’s alpha internal consistency coefficient for APSI was found to be 0.70, while this coefficient was found as 0.81 in this study.

Revised prenatal distress questionnaire ((NUPDQ)-17 Item Version)

The questionnaire developed by Yali and Lobel ( 1999 ) to determine the levels of stress experienced by women regarding pregnancy-related issues was revised by Lobel ( 2008 ). The Turkish validity and reliability study of the questionnaire was performed by Yüksel et al. ( 2011 ). The questionnaire is in the form of a 3-point Likert-type scale (very much = 2 points, a little = 1 point, none = 0 point) and consists of 17 items. The questionnaire is unidimensional. The minimum and maximum scores that can be obtained from the questionnaire are 0 and 34. High scores indicate that pregnant women have high levels of prenatal distress. Cronbach’s alpha internal consistency coefficient for NUPDQ was found to be 0.85 in the study performed by Yüksel et al., while it was found as 0.82 in this study.

Statistical analysis

Frequency, percentage, mean, and standard deviation values were used in the data analyses. Whether the data had normal distribution was tested using the Kolmogorov-Smirnov test. The Chi-squared test and independent-samples t-test methods were used to identify the differences between groups in terms of the sociodemographic and obstetric information of the participants. The Mann-Whitney U Test was used to determine the differences between the case and control groups in terms of their total APSI, APSI subscale, and total NUPDQ scores. The Type I error level was accepted as p  < 0.05. A Cohen’s d value of 0.20 is considered to indicate a small effect size, a value of 0.50 is considered to show a medium effect size, and a value of 0.80 or greater is interpreted as a large effect size (Özsoy & Özsoy, 2013 ). Since education, number of pregnancies, and number of miscarriages, which are thought to have an impact on stress levels, may be confounding factors, the linear regression analysis in this study was carried out to determine whether these factors or high-risk pregnancy affected the stress levels of the participants (Models 1 and 2). As a result of the Mann-Whitney U Test, a significant difference was found between high-risk pregnancies and healthy pregnancies in terms of “psychosocial changes during pregnancy” and “prospect of childbirth”. For this reason, APSI dimensions were collected in a single model, and a linear regression analysis was performed for the further analysis of the relationship between high-risk pregnancy and the APSI dimension scores of the participants (Model 3). The variable with the highest β coefficient was considered the relatively most significant independent variable. Multicollinearity was ignored in case of Tolerance > 0.20 and variance inflation factors (VIF) < 10. R 2 shows what percentage of the dependent variable is explained by the independent variables. According to Cohen, R 2 values of 0.0196, 0.1300, and 0.2600 are the lower thresholds for small, medium, and large effect sizes, respectively (Özsoy & Özsoy, 2013 ). One-Way MANOVA was also conducted to see whether there was a difference in stress levels during pregnancy between the case and control groups. The comparison of the stress levels of the participants in the case group based on their diagnoses was conducted with the Kruskal-Wallis test.

Ethical considerations

For the study to be carried out, approval was obtained from the Clinical Research Ethics Committee of the Faculty of Medicine of the University, and written permission was obtained from the institution where the study would be conducted (2019/123). The purpose of the study was explained to the pregnant women who agreed to participate, and they were informed that their identifying information would be kept confidential. The written consent of the participants was obtained with the Volunteer Information Form. The participants in both the case and control groups who were thought to need counseling were referred to a specialist for psychological support.

Table  1 shows the sociodemographic and obstetric characteristics of the participants. There was no significant difference between the women in the case and control groups in terms of age, whether they had an income-generating job, income status, place of residence, parity, number of living children, status of having a planned pregnancy, and smoking status ( p  > 0.05). It was determined that the participants in the case group had significantly lower education levels than those in the control group ( p  < 0.001). Moreover, the number of pregnancies ( p  = 0.036) and the number of abortions ( p  = 0.012) in the case group were found significantly higher than those in the control group. These results supported the hypothesis that some sociodemographic and obstetric characteristics of women are associated with high-risk pregnancies.

According to the Kolmogorov-Smirnov test results, the total APSI and NUPDQ scores of the participants were not normally distributed ( p  < 0.001). Table  2 shows the stress levels of the participants compared based on their scale scores. The total APSI ( p  < 0.001, Cohen’s d = 0.388) and total NUPDQ ( p  = 0.002, Cohen’s d = 0.272) scores of the participants in the case group were significantly higher than those of the participants in the control group. The APSI Psychosocial Changes during Pregnancy ( p  < 0.001, Cohen’s d = 0.473) and Prospect of Childbirth ( p  < 0.001, Cohen’s d = 0.314) dimension scores of the participants in the case group were also significantly higher than those of the participants in the control group. These results supported the hypothesis that prenatal stress levels are higher in high-risk pregnancies than in healthy pregnancies.

Table  3 shows the stress levels of the participants with different diagnoses in the case group. In terms of risky pregnancies, the most frequently observed diagnoses were the threat of preterm labor in 37.2% of the participants in the case group, vaginal bleeding/placenta previa in 20.7%, and gestational diabetes mellitus in 10.7%. No statistically significant correlation was found between the diagnoses of the participants in the case group and their total APSI or NUPDQ scores ( p  > 0.05). This result did not support the hypothesis that there is a difference in prenatal stress levels based on differences in high-risk pregnancy diagnoses.

In the linear regression analysis, Model 1 included APSI on education, gravidity, number of abortions, and high-risk pregnancy, and it was determined that there was a significant relationship between APSI and high-risk pregnancies, where the former explained 4.3% of the total variance in the latter (R 2  = 0.043) ( p  < 0.001). Model 2 included NUPDQ on education, gravidity, number of abortions, and high-risk pregnancy, and it was determined that there was a significant relationship between NUPDQ and high-risk pregnancies, where the former explained 3.3% of the total variance in the latter (R 2  = 0.033) ( p  = 0.009). Model 3 included APSI dimensions, and it was determined that there was a significant relationship between psychosocial changes during pregnancy and high-risk pregnancy, where the former explained 5.7% of the total variance in the latter ( R  = 0.057) ( p  < 0.001) (Table  4 ).

As a result of the MANOVA, it was determined that having a high-risk or a pregnancy was associated with significant differences in the combined set of dependent variables (APSI and NUPDQ total scores), F = 6.231, p  = 0.002, Wilk’s Lambda = 0.967. There were significant differences between the case and control groups in terms of their APSI psychosocial changes during pregnancy and prospect of childbirth dimension scores, F = 7.258, p  < 0.001, Pillai’s Trace = 0.057. (Table  5 ).

This study determined the stress levels of pregnant women with high-risk and healthy pregnancies. Stress experienced in high-risk pregnancies can have negative effects in terms of the pregnancy process and maternal and fetal health (Atasever & Çelik, 2018 ; Gözüyeşil & Düzgün, 2021 ; Riggin, 2020 ; Traylor et al., 2020 ; Yüksel et al., 2011 ). Therefore, it is thought that the results of this study will contribute to the literature.

It was determined in this study that the education levels of the high-risk pregnant women, who constituted the case group, were lower compared to the healthy pregnant women in the control group. Other studies in the literature have shown that risk factors in pregnancy are at higher rates in women with low educational levels (Annagür et al., 2014 ; Soğukpınar et al., 2018 ; Topalahmetoğlu et al., 2017 ; Türkmen, 2019 ). It is thought that as education levels increase, the knowledge levels of pregnant women about the management of risk factors in pregnancy increase, and these situations are intervened with in the early period. Moreover, high education levels can also prevent factors that may cause a high-risk pregnancy such as malnutrition, ill-advised exercise practices, and lack of antenatal care. For this reason, considering the results of our study, it is recommended that health professionals provide education to pregnant women with low education levels about prenatal care, proper nutrition, exercise, and antenatal follow-ups.

The numbers of pregnancies and abortions among the participants in the case group in this study were significantly higher compared to those in the control group. In the study by Orbay et al. ( 2017 ), the number of pregnancies among pregnant women with GDM was lower than the number of pregnancies among those without GDM. Cincioğlu et al. ( 2020 ) found the mean number of abortions among pregnant women with risky pregnancies to be 1.31 ± 0.71. As the number of pregnancies increases, the potential risks of pregnancy also increase. More frequent monitoring of pregnant women with a high number of abortions by health professionals and providing information about family planning methods to women with a high number of pregnancies will prevent high-risk pregnancies.

High-risk pregnancies consist of many obstetric pathologies including maternal chronic diseases. Every pathologic condition experienced during pregnancy can affect the women’s stress levels (Sinaci et al., 2020 ). In this study, two different scales were used to measure the stress levels of the participants, and the stress levels of the participants in the case group were found to be higher than the stress levels of those in the control group. Gözübebek and Düzgün (2021) stated that 63.5% of pregnant women diagnosed with risky pregnancies experienced distress. Üzar-Özçetin and Erkan ( 2019 ) reported high perceived stress levels in high-risk pregnant women. In their meta-analysis study, Amiri and Behnezhad ( 2019 ) revealed that diabetes during pregnancy was a risk factor for anxiety symptoms, and diabetes increased the risk of anxiety by up to 48%. Other studies in the literature have shown that high-risk pregnant women also have high anxiety levels (Byatt et al., 2014 ; Denis et al., 2012 ; Gourounti et al., 2015a , b ; McDonald et al., 2021 ; Orbay et al., 2017 ; Sinaci et al., 2020 ; Hung et al., 2021 ).

It was reported that low education levels, having a history of abortion, and a high number of pregnancies may cause prenatal stress (Atasever & Çelik, 2018 ). In this study, lower education levels and higher numbers of pregnancies and abortions were found in the case group than in the healthy control group. Since low education levels and high numbers of pregnancies and abortions were thought to be potential confounding factors in terms of prenatal stress, further analyses tests were performed, and it was determined that these factors did not affect stress levels to a significant extent. The research in this field may benefit from a more in-depth exploration of potential implications and confounding factors related to education.

In the studies performed by Üzar-Özçetin and Erkan ( 2019 ) and Yüksel et al. ( 2013 ), it was found that the stress levels of pregnant women who had experienced hospitalization due to any risk during pregnancy were high. It is considered that diagnosis methods, treatment methods, symptoms, complications, and their effects on the fetus for high-risk pregnancies cause great stress in pregnant women, and the fact that some pregnant women spend this process in the hospital increases their stress levels even further. For this reason, considering the results of our study, it is recommended that healthcare professionals inform the pregnant woman about her diagnosis and symptoms and explain each procedure to be performed, and that healthcare institutions provide more comfortable hospital rooms.

In this study, it was seen that the prenatal stress levels of the participants were high in terms of psychosocial changes during pregnancy. Intense stress can cause a sense of helplessness and hopelessness by depleting the energy of individuals, as well as negatively affecting their physical and mental health (Sharma & Rush, 2014 ). For this reason, healthcare professionals have an important role in the care of pregnant women with high-risk pregnancies. They should take an active role in the early diagnosis of at-risk pregnant women through qualified home visits and by initiating and continuing treatment. Advanced clinical guidelines and case management models should be developed for women having high-risk pregnancies.

In the study by Pinar et al. ( 2022 ), women with high-risk pregnancies were given training on stress management. After the training program, it was determined that 51.4% of the women in the intervention group and 75.7% in the control group experienced stress. Based on these results, it is recommended that healthcare professionals provide training, to ensure the active participation of the pregnant woman and her partner, on stress management to reduce the perceived stress, anxiety, and hopelessness levels of women in high-risk pregnancy cases. Additionally, these pregnant women should be provided with methods to cope with stress such as breathing exercises, relaxation exercises, appropriate physical exercises, visualization/yoga, massage therapy, music therapy, explanations about social support factors, and practices strengthening their spirituality (Ölçer & Oskay, 2015 ).

In this study, no significant difference was found in the prenatal stress levels of the participants in the case group based on their obstetric diagnoses of high-risk pregnancy. In the study by Byatt et al. ( 2014 ), no significant difference was identified between obstetric diagnoses in terms of anxiety levels in pregnant women. A high-risk pregnancy causes high stress levels in pregnant women due to similar diagnostic tests, treatment methods, hospitalization, complications, and fetal outcomes (Kent et al., 2015 ).

It is thought that high stress levels are not associated with obstetric diagnoses, and similar stress levels are experienced by all pregnant women aware of any risky situation during their pregnancies. Nevertheless, an increase in stress levels in risky pregnancies such as cases of preeclampsia may cause a further aggravation in the clinical status of women. Healthcare professionals should be aware of the higher stress levels of these pregnant women, they should help the pregnant woman express her feelings and thoughts by providing a reassuring communication environment, and plan appropriate consultancy, intervention, and care routines to help reduce their stress levels. These professionals can also coach high-risk pregnant women in terms of stress reduction and coping mechanisms. Pregnant women with high stress levels should be referred to a specialist for psychological support and therapy.

Strengths and limitations

The strength of this study was its prospective design with a control group. In this study, the use of two similar scales in terms of measuring the stress levels of pregnant women provided rigor and transparency compared to data obtained in previous studies. Since these scales were included in studies in Turkey only in the context of testing their validity and reliability in Turkish, it was decided to use them in the study as they measure stress levels in the prenatal period. A limitation of the study was that the pre-pregnancy stress levels of the participants, which would affect their present condition, were not measured in the study. Women with high stress levels before pregnancy have a higher risk of high-risk pregnancy. In other words, instead of high-risk pregnancy increasing stress levels, high stress levels may have affected the high-risk pregnancy statuses of the women. The results of the study also revealed a significant difference in education levels between the case and control groups. However, as a result of further analyses, it was determined that education did not affect prenatal stress levels.

In this study, it was determined that high-risk pregnancy affected prenatal stress. Moreover, it was found that the participants in the case group who had high-risk pregnancies had lower education levels and higher numbers of pregnancies and abortions compared to the participants in the control group with healthy pregnancies. This is why healthcare professionals are recommended to bear in mind that pregnant women with low education levels and a high number of pregnancies and abortions are at risk of high-risk pregnancies and monitor these pregnant women more frequently and carefully.

It is of great importance for the course of a pregnancy that healthcare professionals assess the stress levels of pregnant women in the high-risk pregnancy category, provide psychological support to pregnant women who have high stress levels or are hospitalized, offer them counseling and training opportunities (e.g., relaxation exercises, breathing exercises, practices strengthening their spirituality, music therapy), take appropriate precautions, and refer these pregnant women to specialists if needed. Moreover, since the stress levels of these pregnant women will increase even more during childbirth, alternative methods to reduce the fear of childbirth and childbirth pain should be explained.

It is recommended to organize educational programs such as trainings, seminars, and conferences on stress management during pregnancy for health professionals working in family health centers, community health centers, and gynecology departments.

Consequently, more studies with larger sample sizes are needed to compare diagnostic stress levels in high-risk pregnancies. In addition to prenatal stress and childbirth fear levels, future studies should also determine the stress levels of women before pregnancy for similar comparisons between high-risk and healthy pregnancies.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors express thanks to the mothers for participation in the study.

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK). No funding was received to conduct the study.

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK).

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Hülya Türkmen

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Study conception and design: Hülya TÜRKMEN.  Data collection:  Hülya TÜRKMEN.  Data analysis and interpretation:  Hülya TÜRKMEN.  Drafting of the article:  Hülya TÜRKMEN, Bihter AKIN, Yasemin ERKAL AKSOY.  Critical revision of the article:  Hülya TÜRKMEN, Bihter AKIN, Yasemin ERKAL AKSOY.

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Türkmen, H., Akın, B. & Erkal Aksoy, Y. Effect of high-risk pregnancy on prenatal stress level: a prospective case-control study. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-05956-z

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Accepted : 28 March 2024

Published : 11 May 2024

DOI : https://doi.org/10.1007/s12144-024-05956-z

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