Habits are an essential part of our daily lives. They help us perform actions effortlessly without requiring conscious thinking, allowing us to deal with more complex problems at the same time. Habits are defined as automatic behaviours that are triggered by cues (Aarts et al., 1998; Verplanken and Orbell, 2003; Verplanken, 2006; de Wit et al., 2009; Balleine and O’Doherty, 2010; Gardner et al., 2012; Dolan and Dayan, 2013; Galla and Duckworth, 2015; Wood and Runger, 2016; Ersche et al., 2017). The automaticity of habits means that they are performed without awareness, are difficult to control, and are mentally efficient (Bargh, 1994, 1996; Verplanken and Orbell, 2003). Habitual behaviours are triggered very quickly, which is advantageous in many situations (Schneider and Chein, 2003). Because habitual behaviours are triggered by cues, they are not activated based on current goals or outcome value. A cue to a habit triggers the behaviour regardless of the consequence (Tricomi et al., 2009; Balleine and O’Doherty, 2010; Ersche et al., 2017) and is therefore difficult to resist. Habits are very stable and highly inflexible (Schneider and Chein, 2003; de Wit et al., 2009; Dolan and Dayan, 2013; Ersche et al., 2017). Habits are not only defined by the frequency of performing an action, but the construct also encompasses the strength and automaticity of the habitual behaviour (Verplanken and Orbell, 2003; Verplanken, 2006).

Unlike habitual behaviour, goal-directed behaviour is motivated and driven by its consequences. One engages in goal-directed behaviour when one wants to achieve a desired outcome and receive the rewarding consequences associated with that action (Dolan and Dayan, 2013). Goal-directed behaviour is characterised by active deliberation, high computational effort, and the ability to change that behaviour in response to a changing environment and contextual demands (Dayan, 2009). A key difference between habitual and goal-directed behaviour is that habitual behaviour is known as stimulus-response (S-R) action, meaning that a stimulus triggers a particular behaviour and will consistently lead to that behaviour (Redgrave et al., 2010; Dolan and Dayan, 2013; Robbins and Costa, 2017). Goal-directed behaviour, on the other hand, is referred to as response-outcome (R-O) action. This means engaging in a specific response to obtain a specific outcome (Redgrave et al., 2010; Dolan and Dayan, 2013).

Research on animals and humans provided evidence that habitual behaviours, as well as goal-directed behaviours are mediated by the cortico-basal ganglia-thalamo-cortical circuits (Wood and Runger, 2016; Knowlton and Patterson, 2018; Patterson and Knowlton, 2018). While the associative circuit, which also supports working memory and is connected to the prefrontal cortex, is considered to mediate goal-directed behaviours, the sensorimotor circuit underlies automatic, habitual behaviours and is connected to the somatosensory and motor cortex and supplementary motor area via the medial and posterior putamen (Wood and Runger, 2016; Knowlton and Patterson, 2018). Indeed, lesions of the dorsolateral striatum, which includes the putamen in addition to the caudate nucleus, in rodents can lead to a task braking pattern of activity during habitual runs in a maze in rodents (Smith and Graybiel, 2013). In humans, practicing movements through repetition leads to decreased activation in areas associated with goal-directed behaviour, such as the prefrontal cortex and anterior cingulate and associative parts of the basal ganglia, and to an increase in activity in the sensorimotor cortex (Wood and Runger, 2016). The activity of the sensorimotor circuit is modulated by extended brain regions. For example, dopaminergic projections from the substantia nigra to the dorsal striatum modulate habit plasticity, and in rats a lesion of the nigrostriatal pathway leads to impaired habit formation (Wood and Runger, 2016). It is therefore reasonable to expect that, for example, patients with Parkinson’s disease (PD) with nigrostriatal dopamine deficiency. will perform normally habitual behaviours such as walking in a goal-directed manner due to the impairment of their habit system (Graybiel and Rauch, 2000; Graybiel, 2008; Redgrave et al., 2010).

Habitual behaviour needs to be objectively measurable. This is important because there are many situations in which measuring habits would be particularly useful. A habit scale would also be useful for the assessment of people with PD as well as individuals with other basal ganglia disorders such as addiction or obsessive-compulsive disorder (OCD). In addition, it could be used to determine whether and how changes in daily habits are related to the development of harmful habits such as impulse control disorders (ICD) in PD, addictions, or compulsions in OCD and whether changes in daily habits can be used to assess or predict development of such atypical and harmful habits.

There has been a debate on how to measure habit strength (Hagger et al., 2015; Labrecque and Wood, 2015). Based on Gardner’s definition of habits ‘as a process by which a stimulus automatically generates an impulse toward action, based on learned stimulus-response associations’ (Gardner, 2015); different criteria for measurement of habits can be considered, including automaticity, frequency of past performance and test of cue-behaviour cognitive associations. Gardner has emphasised the automaticity component of habits, although this automaticity of a habit has a low predictive validity (Labrecque and Wood, 2015). A habit strength should predict future behaviours and strong habits should reduce the impact of consciousness on performance (Labrecque and Wood, 2015). Over the years, several scales have been developed to measure habits. The two most successful of these are the Self-Report Habit Index (SRHI) (Verplanken and Orbell, 2003) and the Creature of Habits Scale (COHS) (Ersche et al., 2017). However, both scales have a number of limitations and there remains a need for a valid and reliable scale that measures the range of habits in daily life. A crucial limitation of the SRHI is that it only measures a particular behaviour at a particular time, i.e., the administrator of the scale selects the behaviour in which he/she is interested. Even though this measure has been designed to measure the experience of automaticity and frequency of past performance, it has recently been suggested to measure automaticity only (Gardner et al., 2012; Gardner, 2015). This means that the SRHI cannot measure the frequency or strength of engagement in behavioural habits as a whole, but only a single habitual behaviour. This is problematic because some individuals may not engage in the selected behaviour, so they are excluded from the analysis or do not receive a score. Therefore, it is not possible to use the SRHI as a measure of daily habits in general. In addition, the SRHI does not consider all aspects that define habitual behaviours. For example, it does not measure whether the behaviour is performed automatically, without attention, or without regard to consequences, which are the core characteristics of habits. This suggests that the SRHI is of limited value in accurately measuring the nature, frequency, and strength of habits that people engage in on a daily basis.

The COHS also has some important limitations. The COHS consists of two subscales: routine and automatic. However, habitual behaviours are characterised by more than just these two factors. Therefore, the COHS, like the SRHI, may not accurately measure habits because some important characteristics are not captured. In addition, the COHS was designed to provide a measure of personality that considers the extent to which individuals engage in the same habitual behaviours over time, with a focus on eating behaviours. This is a clear drawback that could be overcome by a new scale that focuses on measuring multitude of habits in daily life and would provide a better understanding of a range of basic habits.

With these limitations in mind, the Daily Habit Scale (DHS) was developed to provide a more valid and reliable measure of daily habitual behaviours. The DHS aims to obtain an index of habits in daily life. As such, the DHS measures both the types of habits that individuals engage in and the strength of those habits, while measuring the defining criteria of a habit (Verplanken and Orbell, 2003; Verplanken, 2006; Gardner et al., 2012; Gardner, 2015): (i) Frequency of engagement, (ii) Automaticity of performance, (iii) Difficulty in resisting doing it, and (iv) Unconcern about consequences. In this paper, we report on the development and validation of the DHS.

Ethical approval for the study was obtained from the local ethics committee. Participants gave informed consent. Participants were recruited via the online participant pools Prolific and Call for Participants. Data were collected using the Qualtrics online system and subsequently stored in an electronic database.

The aim of this study was to develop and validate the DHS. We developed a 38-item scale. The results showed that the DHS is a valid and reliable scale for measuring daily life habits. Factors analysis of the 38 items, showed its construct validity with the main variance captured by eight key factors, with high internal consistency and test-retest reliability.

Two scales relating to “normal” and pathological habits, namely the COHS and the QUIP, were used to assess the convergent validity of the DHS. There was a significant positive correlation between the two subscales of the COHS, routine and automaticity, and the DHS. This was as expected since both scales measure habitual behaviours. There were also significant correlations between two of the subscales of the QUIP, particularly impulsive eating, and gambling, and the DHS. The QUIP also measures habitual behaviours, albeit pathological habits such as gambling, and impulsive eating; thus, these significant correlations were also expected and predicted. However, it is worth noting that the correlations between the DHS and the COHS and QUIP were low, although significant. This may be due in part to the fact that each measure reflects specific aspects of habits, e.g., eating habits for COHS and abnormal and pathological habits for QUIP.

As expected, there was a significant correlation between the HADS anxiety subscale and the DHS. Namely, previous evidence suggests that anxious individuals engage in more habitual behaviours than people without anxiety (Alvares et al., 2014; Ersche et al., 2017). However, these previous findings were based on the use of different habit scales and some focused on social anxiety rather than general anxiety. Nonetheless, the present study confirms that higher levels of general anxiety lead to higher engagement in everyday daily habits. This relationship could also be explained by the results of animal studies, which suggest that anxiety leads to frontal lobe degeneration, which in turn results in disruption of the formation and maintenance of goal-directed behaviours (Dias-Ferreira et al., 2009). With lower engagement in goal-directed behaviours, habitual behaviours must take over, which would lead people with anxiety to engage in more habitual behaviours than people without anxiety. This result also confirms the convergent validity of the DHS. Moreover, the correlation of the DHS with the HADS depression subscale was negative and significant, as expected, suggesting that individuals with lower levels of depression have a significantly higher level of daily habit behaviours. This can be explained by the fact that lack of interest and motivation, characteristic symptoms of depression, reduce engagement in goal-directed behaviours and thus impair engagement in habitual behaviours. However, the evidence to date for a link between depression and habits is rather indirect and contradictory. There is some evidence for differences in striatal dopamine in depressed patients (Willner, 1983; Bowden et al., 1997; Santiago et al., 2014). As striatal dopamine is likely to be a key factor in habitual behaviours, this could have implications for daily habits. However, evidence also exists that striatal dopamine activity does not differ in depressed patients (Hirvonen et al., 2008). Indeed, in our study lower level of depression resulted in significantly higher engagements in some (Household activities, Common daily activities, and Sport-related activities), but not all DHS subscales The DHS and the HADS depression subscale measure different constructs and therefore it is important to note that they were not positively correlated with each other. This confirmed the divergent validity of the DHS.

There was a significant negative correlation between age and the total DHS score. More specifically, age negatively correlated with certain subscales, Hygiene and self-care activities Leisure activities, Unhealthy habits, Technology and internet use and Sleep-related activities. Younger people would be expected to engage more in technology and internet use as they are typically more technologically knowledgeable than older populations (Olson et al., 2011). It also makes sense that younger people engaged in more leisure activities as this subscale involved items such as playing games which are likely to appeal more to the younger population and are perhaps more available to them. The majority of virtual games are targeted at younger people, and they are likely to have access to them on different devices and game consoles. Younger people also spend more time in self-care and hygiene activities. Unhealthy habits such as eating chocolate, eating savoury snacks, and fast food, as well as smoking and spending money are more common among younger people. This subscale also included an item on sexual intercourse, which is not an unhealthy habit in itself but, if not protected, for example can be risky and lead to health complications and loaded within this subscale. Research has shown that younger people engage in more sexual intercourse than older people (Pearlman and Kobashi, 1972; Pfeiffer and Davis, 1972). Finally, the reason for younger people engaging in more sleep-related habits may be because the sleep cycle changes with age (Feinberg, 1974; Cajochen et al., 2006) and so sleep duration is shorter and is more interrupted in older individuals and which could reduce the habits related to sleep. On the other hand, the results suggest that older people engage in more Household cleaning and Common daily activities, including taking medication. The latter would fit with expectations and the available data, as older people tend to have more medical issues and are therefore on more prescription medication than younger people and may also have to take more pain killers. Older people engaging in more household cleaning than younger people could be due to several reasons. One main reason could be because a larger proportion of younger people may still live with their parents who are likely to be in charge of the household activities.

We found that females engaged more in daily habit behaviours than males. This is in concordance to previous research that suggested that the neural correlates behind habits, i.e., levels of striatal dopamine activity, were stronger in females than males (Mozley et al., 2001; Laakso et al., 2002; Haaxma et al., 2007). More specifically, females were more engaged in Hygiene and self-care activities, Household activities, Common daily activities and Technology and internet use than males. Household cleaning is stereotypically perceived as more of a female role, and the higher score of females on this scale seems to reflect higher engagement in this. Similarly, females tend to spend more time on self-care activities, such as make-up and brushing hair, than males. Even when it comes to common daily activities, like shopping and taking medication, the odds are in favour of females, the explanation for which seems reasonable, because females use contraceptive pill or hormone replacement therapy and are more fond of shopping than males (Merrell, 2003). At a first glance, it looks like technology and internet use may be more common among males. However, this subscale included the use of mobile phone, checking e-mails and internet social networking, all of which are likely to be more prevalent among females. This could be due to gender differences in sociability, as females tend to be more sociable and enter relationships more easily than males. For example, females perceive same-sex social interactions more rewarding than males, and activation of oxytocin receptors in the ventral tegmental area is critical for social reward in both females and males (Borland et al., 2019). On the other hand, males engaged in more Leisure activities and Sleep-related activities. Males play more games than females (López-Fernández et al., 2021), and they are more prone to sedentary type of daily activities (Segrin, 2000), including watching television than females. In addition, gender differences in sleep patterns with females having longer sleep times, shorter sleep-onset latency and higher sleep efficiency (Krishnan and Collop, 2006) could explain the gender differences in Sleep-related activities (encompassing the items Sleeping late and Waking up early) in favour of men.

The DHS can be potentially used in different populations. Habitual behaviours are strongly affected in PD (Knowlton et al., 1996; Witt et al., 2002; Wilkinson et al., 2008, 2009). The DHS will be useful for assessing people with PD. It has been suggested that people with PD perform normally habitual behaviours such as walking in a goal-directed manner due to the overuse and impairment of their habitual system (Redgrave et al., 2010; Hernandez et al., 2019). Thus, the DHS can be used in the prodromal phase of PD or in individuals with rapid eye movement sleep disorder who then develop PD (Postuma et al., 2012) to determine gradual overuse and decline of the habitual system over time. Researchers could use the DHS to understand whether and. how habits change during the progression of PD. This could provide crucial clues to how PD evolves and changes the balance between goal-directed and habitual behaviours. This is an excellent starting point to better understand the mechanisms behind PD and its links with the pattern of dopamine loss that begins in the sensorimotor dorsal striatum and is associated with habits (Wood and Runger, 2016; Hernandez et al., 2019). In such longitudinal studies, the DHS can also be used as a validated questionnaire to understand how engagement in daily habits changes with initial onset and further development and progression of symptoms of PD. Harmful habits in the form of ICDs are relatively common in PD treated with dopamine medications, affecting 15-20% of these patients (Weintraub et al., 2015). ICDs are also common in the general population. There is evidence of a lifetime prevalence of 9.2% (de Graaf et al., 2012). Therefore, DHS can be used to quantify daily habits and determine if any changes in these, may relate to development of ICDs.

The DHS can also be used as a questionnaire to assess and understand other disorders in which habits may be affected, such as addictions (Belin et al., 2013), Gilles da la Tourette syndrome (GTS) (Martino and Hedderly, 2019), or OCD (Gillan and Robbins, 2014; Hadjas et al., 2019). The scale could be used to assess change in symptoms such as tics and compulsions which represent habitual behaviours and potentially provide new information about the development of such harmful habits and the course of these disorders. DHS can be a useful tool in changing certain maladaptive behaviours. For example, a previous study suggested the need for a habit scale to implement behavioural strategies for injury prevention (Nilsen et al., 2008). To develop a behavioural strategy, it is important to understand the nature and strength of current habits. Therefore, in such situations, the DHS could be a very useful tool to gain a better understanding of the mechanisms behind the formation and maintenance of habits in health and disease.

The DHS could also be used more generally to develop a better understanding of habit formation, maintenance, and strength in the general population. Therefore, in addition to being used in a clinical setting, the DHS can be very useful for future research.

Analysis of the influence of key factors provides some useful insights into the study of habits. The results suggest that age and anxiety have the greatest influence on habitual behaviours, while gender and depression have no influence. These findings could be useful when using the DHS with clinical populations such as patients with PD, OCD, addictions, or GTS, as clinicians and researchers will have a better idea of what factors do not influence habits and what factors may need to be controlled when using the DHS. It is also useful to know that of the variables tested, age is the greatest predictor of daily habits. Future research could also examine other factors that might influence daily habitual behaviour. These could include variables such as personality, presence/absence of ICDs, and other neurological or neurological disorders. This would help to understand the impact of these factors on daily habits. It would also be useful to investigate other factors to improve the predictive model for DHS. The inclusion of other psychological, social, and environmental factors would likely increase the amount of variance in DHS that could be explained. This in turn could lead to a better understanding of the nature and determinants of natural daily habits in health and disease.

This study is subject to certain limitations. For example, some important habitual activities, such as use of transportation, identified in previous work (Wood et al., 2002) have not been included. Nevertheless, we believe that the fact that the DHS includes 38 items across nine different factors makes the scale sufficiently broad to reflect a wide range of daily habitual behaviours. In addition, certain items that one would expect to be grouped into a single factor, such as Internet social networking, Socialising, and Calling particular people were distributed across different factors, namely Technology and internet use and Sport-related activities. It is easy to understand that Internet social networking was loaded on the Technology and internet use factor, since social networking nowadays depends heavily on the use of cell phones and computers. On the other hand, Socialising and Calling particular people, together with Participating in sports and Exercising grouped together with to the factor Sports-related activities, which can be explained by the fact that participation in sports activities usually requires contact with certain people who are interested in the same sport. There were also inconsistencies in other item-factor loadings, such as Drinking water in Hygiene and self-care activities that could have been expected to be loaded to Common daily activities, or Emptying bowls in Household activities instead of Common daily activities or Hygiene and Self-related activities. In addition, Having sex loaded to Unhealthy habits. As explained earlier, Having sex itself is not an unhealthy habit, but it could lead to health problems if it is not practiced properly, such as when it is done unprotected. Nevertheless, the overall structure of the DHS is congruent, and explanations can be found even for items that are counterintuitive and load to unexpected factors. The DHS does not account for performance context, which is a central component of habit formation and habit activation, because repeated performance in different contexts requires decision making and thus do not reflect habit. Although performance context may be theoretically important, particularly for habit formation, we did not consider it relevant for measuring the strength of daily habits. Performance context cannot be easily or meaningfully operationalized within the context of a scale.

In conclusion, the present study developed and validated the DHS. The results show that the 38 item DHS consists of eight factors. The DHS was found to have high construct validity, internal consistency, test-retest reliability, convergent validity, and divergent validity. Therefore, considering certain limitations of the DHS, such as not considering the context of performance of habits, and the absence of certain items, e.g., transportation use, the DHS can be used by both clinicians and researchers as a measure of everyday daily habits.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by University College London. The patients/participants provided their written informed consent to participate in this study.

DG, MJ, RC, and PL had substantial contributions to the conception or design of the work, the acquisition, analysis or interpretation of data for the work, provided approval for publication of the content, agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy, or integrity of any part of the work are appropriately investigated and resolved. MT and RS contributed substantially to the conception of the work and interpretation of the data for the work and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy, or integrity of any part of the work are appropriately investigated and resolved. DG and MJ drafted the work or revisited it critically for important intellectual content. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.