Too much coffee, excessive smartphone preoccupation, or hours of Internet surfing. It is often difficult to draw the line between what are commonly referred to as “bad habits” and addictions. In particular, the increasing discussion around behavioral addictions (for a review, see Potenza, 2022) and a possible pathologization of normal behaviors (Billieux et al., 2015b), as well as the inclusion of substance-unrelated addictions in diagnostic manuals (e.g., gambling disorder in DSM-5 and additionally pathological gaming in ICD-11), challenge the classical understanding of addictions (Robbins & Clark, 2015). Thus, while addictions are no longer naturally attributed to the physiological effects of substances and physical dependence, psychological mechanisms of addiction are once again coming into focus. In particular, brain imaging techniques have been used to demonstrate neuronal parallels regarding the development and maintenance of mechanisms that facilitate addiction in non-substance-related behaviors (Holden, 2001, 2010).

One of the psychological mechanisms that was originally considered causative of addictive behavior even before the perspective of substance abuse, for example in the period of behaviorists, is habit (Berridge, 2021). Habits are defined as “memory-based propensities to respond automatically to specific cues, which are acquired by the repetition of cue-specific behaviors in stable contexts” (Verplanken, 2018, p. 4). For example, each time we are at the bus station (cue) to drive to work (stable context), we automatically grab out the smartphone (cue-specific behavior), because we have done this for all our working life (repetition). These habitual behaviors are highly automatic and are mainly performed implicitly (Wood et al., 2002).

With regard to addictive behavior, such cue-specific behaviors in stable contexts play a crucial role for the maintenance of symptoms. In his model of cue reactivity in addictions, Tiffany (1995) already emphasized the importance of such cues and stimulus–response relationships derived from them. This has recently been empirically shown to be important for behavioral addictions as well (Starcke et al., 2018). Lewis (2018) also postulated in his theory of addiction that addictive behavior can be significantly explained by very deep learning processes and mechanisms based on classical S-R learning and that the corresponding neural processes are involved in the development of addictions and contributes to compulsive, addictive behavior.

However, the role of habits in the development of addictions has been discussed again controversially (see, e.g., Berridge, 2021; Epstein, 2020; Hogarth, 2020; Lamb & Ginsburg, 2018; Singer et al., 2018; Vandaele & Ahmed, 2021). A quintessence of this theoretical and conceptual high-level discussion presumably already lies in different definitions of the construct of habit (Berridge, 2021). Nevertheless, the discussion repeatedly reveals that basal underlying mechanisms of habit formation share neurophysiological commonalities with addiction formation. A perspective that has been less considered in the discussion so far is the individual’s own subjective perception of the addictive behavior as a multifaceted construct. In line with this, Sjoerds et al. (2014) already proposed that improved measurements of habits in addictive disorders should be developed to provide opportunities for a closer examination of the underpinnings of addiction and differentiate between individuals with addictive, or solely habitual behaviors.

For a better understanding of the role of habits in demarking, for example, health-risk behaviors from addictive behaviors, it is important to provide valid measures for their assessment. The Self-Report Habit Index (SRHI, Verplanken & Orbell, 2003) constitutes the most widely used instrument that characterizes habit on a 12-item scale by three aspects: automatic activation, behavioral frequency, and relevance to self-identity. The original version was validated in four studies referring to travel mode choices (Verplanken & Orbell, 2003). Despite of its multiple use, the SRHI has been criticized over the years, and several debates about the factorial structure of the SRHI (one single factor of habit vs. a three-dimensional solution, comprising lack of awareness, lack of control, and behavioral repetition) and the habit construct emerged (Reyes Fernández et al., 2019; Sniehotta & Presseau, 2012). One advantage of the SRHI is that it can measure habit strength of different behaviors, since it is adaptable to specific target behaviors. The SRHI has frequently been used in the field of health psychology, especially regarding eating and exercise behavior. In their meta-analysis, Gardner et al. (2011) found a high internal consistency (α = 0.92 for nutrition and α = 0.93 for physical activity). This was replicated in a study by Thurn et al. (2014), where the SRHI was translated into German for the domains of everyday physical activity and sports. The SRHI was further used to assess a variety of other, less frequently investigated behaviors, like texting while driving (Bayer & Campbell, 2012), following speed limits (Bordarie, 2019), or medication-taking habits (Durand et al., 2018). For each behavior, habit strength showed strong associations to the actual target behavior.

However, to our best knowledge, no published validated German version that can be used to study substance-related or potential health-risk behaviors exits yet. Since non-validated translations into western European language belong to the “worst offenders” (Clark & Watson, 2019) of the existing replication crisis, we aimed at translating and validating the SRHI for further use in research on health-risk behaviors with different degrees of consensus regarding their addictive properties. Here, we assessed the habitual patterns of caffeine consumption (as a substance-related form of consumption with addictive properties but less consensus regarding the definition as an addiction) and smartphone/tablet use (as an everyday activity which still might have properties of a behavioral addiction but with low awareness of the addictive potential). Our intention was, on the one hand, to perform a sound validation of the scale version translated into German and to test its applicability in the context of potentially addictive health-risk behaviors. In addition, we were interested in correlations of the SRHI with scales for the assessment of addictive consumption/behavior in the context of the collection of measures for congruent validation. Additionally, as part of capturing convergent validity and in light of current debates about the role of habits in the context of addiction, we were interested in assessing correlations of the SRHI as an established habit measure and questionnaires measuring corresponding addictive behaviors. In study 2, we delineated this correlation in addition to established measures of construct validation (behavioral frequency).

Study 1

Method

Study Design and Participants

In study 1 (August to November 2020), German-speaking participants (N = 608) completed an online-questionnaire, assessing two SRHI scales (one adapted for caffeine consumption, one adapted for smartphone/tablet use) and scales to measure addictive behaviors and health states. The online survey was conducted using the web-based software Unipark (Questback GmbH, Germany). Inclusion criteria were legal age (≥ 18 years) and proficiency in the German language. Recruitment took place in the virtual laboratory of the Distance University of Hagen, where students have the opportunity to participate in online studies and online experiments. In addition, the link was disseminated in student forums and cooperating colleges and universities. Participants received course credit for participation. The study was conducted in line with the requirements of the Helsinki Declaration. The ethics committee of the Distance University of Hagen approved the study (registered: EA_273_2020).

Measures

Unless reported otherwise, the internal consistencies given are from the present study data.

Sociodemographic Data

To characterize the sample, we assessed age, gender, educational level, and current employment status.

Self-Report Habit Index (SRHI)

The SRHI (Verplanken & Orbell, 2003) was adapted for the two health-related behaviors after translation into German by two different German native speakers with fluency in English. Possible differences were discussed in a consensus conference. With the heading “Behavior X is something…,” each item could be scored from (1) agree to (7) disagree on a 7-point Likert scale (see Table 7 in the Appendix). A preceding filter was inserted for the two versions, leading only those participants to the subsequent section who stated to generally drink caffeine-based beverages (coffee, black tea, energy drinks), or own a smartphone/tablet. Instructions for each behavior were as follows: “Drinking caffeine-based beverages is something…” for consuming caffeine (SRHI-CAF) and “Grabbing my smartphone / tablet is something…” for smartphone/tablet use (SRHI-PHO). Habit strength was calculated as a mean value of all items in each specific scale.

Convergent Validity Measures

Caffeine Expectancy Questionnaire—Dependency Subscale (CED-DS)

The Dependency Subscale of the German version of the CED-DS (Schott et al., 2016) consists of 12 items that can be answered on 6-point Likert scales ranging from (1) very unlikely to (6) very likely. Internal consistency was excellent (α = 0.96).

Young’s Internet Addiction Test adapted for Smartphone- and Tablet-Use (sIAT)

The Internet Addiction Test (Young, 1998) was originally developed to measure presence and severity of internet addiction, but has been adapted for related behaviors, like binge-watching (Steins-Loeber et al., 2020). We adapted the sIAT for smartphone/tablet addiction and participants answered 12 items on a 5-point Likert scale ranging from (1) rarely to (5) often. Internal consistency was excellent (α = 0.91).

Discriminant Validity Measure

Somatic Symptom Scale (SSS-8)

The SSS-8 (Gierk et al., 2014) is an 8-item scale to assess constraints in physical well-being during the past 7 days including pain, fatigue, and cardiopulmonary burden on a 5-point Likert scale ranging from (0) not at all to (4) very much. The mean score indicated the overall constraints on physical well-being. Internal consistency was acceptable (α = 0.78).

Data Analysis

To assure data quality, we screened the data sets for missing values and no variance in answers options (click-through) and had to exclude two participants.

To ensure that subjects performed the respective behavior, we asked in advance whether participants consumed caffeinated beverages or used a smartphone/tablet. If subjects indicated that they did not consume coffee or did not own a smartphone/tablet, they were not presented within the respective subset. We also excluded subjects listwise who had more than one missing value per questionnaire. After subjects were excluded, the percentage of missing values comprised less than 1% of the data set. According to Mirzaei et al. (2022), this amount of missing data (< 5%) is negligible and can be handled by single imputations. Therefore, we replaced the missing values with ipsative mean imputation. All analyses were calculated based on the subsamples that reported each respective behavior. Resulting subsample sizes for SRHI-CAF were n = 473 and for SRHI-PHO were n = 549.

For each SRHI version, we assessed normality, skewness, and kurtosis via Shapiro–Wilk-tests and graphical analyses. Although the assumption of normality was violated in the Shapiro–Wilk tests, critical values for skewness (> 2) and kurtosis (> 7) were not surpassed (Curran et al., 1996).

We assessed item ranges, difficulties, and item selectivity for each SRHI version, as well as inter-item correlations. We conducted reliability analyses for each SRHI version (Cronbach’s α), with values > 0.80 indicating good and values > 0.90 indicating excellent internal consistency (Cohen, 1988).

We performed exploratory factor analyses (EFA) with Promax rotations for each scale, based on correlation of factors and oblique rotation used by Reyes Fernández et al. (2019). For factor analysis extraction, we used the maximum likelihood method. Parallel analysis and Velicer’s revised MAP test (O'Connor, 2000) served as criteria to identify separate factors after checking for sufficient scores on the Kaiser–Meyer–Olkin criterion (> 0.80) and the Bartlett test (> 0.80). We used oblique Promax rotation (Bühner, 2021), and aimed at avoiding redundant residuals and secondary factor loadings > 0.30.

Convergent and discriminant validity were determined using Pearson’s correlation coefficients. Statistically significant differences between the convergent and discriminant coefficients were assessed by means of Fisher’s z-test (Steiger, 1980), using the calculator by Hemmerich (2017). Overall, values of p < 0.05 indicated statistical significance. Analyses were conducted using SPSS version 26.

Results

Sample Characteristics

The final analysis sample consisted of 606 participants. The majority of the sample was female (83.8%) with a mean age of 30.58 years (SD = 10.10). Approximately half of the sample was students, and the majority of remaining participants were employed. Approximately half of the sample reported a school leaving qualification or higher (see Table 1).

Table 1 Sociodemographic characteristics of participants for total and subgroup samples in study 1 and study 2
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Analyses of sociodemographic differences between the subsamples showed no significant differences in age. A χ2-test revealed that the SRHI-PHO sample included more students/apprentices than the rest of the full sample (χ2(8) = 22.07, p = 0.010).

SRHI Item and Scale Characteristics

Table 2 provides an overview on the item characteristics of the two SRHI versions. For all items, the range from 1 to 7 was sufficiently covered with mean values for each scale ranging in the middle area of the scales (2 to 5). Difficulties were satisfying and mainly ranged between 20 and 80%. The selectivity exceeded 0.30 in each item. Skewness and excess scores ranged in the expected spectrum. Internal consistency of each SRHI scale was excellent (SRHI-CAF: α = 0.92 and SRHI-PHO: α = 0.94).

Table 2 Item statistics for Self-Report Habit Index (SRHI) items for the investigated habits (study 1)
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Exploratory Factor Analyses

For both SRHI scales, the EFA indicated a clear three-factorial structure consisting of the factors “Lack of Awareness” (LA), “Lack of Control” (LC), and “Behavioral Repetition” (BR) (see Table 3) with the same loading pattern. For most of the items, the communalities were satisfying with h2 > 0.60. Overall, the explained variance of the subscales ranged between 6 and 60%. Correlations of the SRHI-CAF subscales were as follows: LA-LC r = 0.51; LA-BR r = 0.70; LC-BR r = 0.65. For the SRHI-PHO subscales, correlations took the values: LA-LC r = 0.76; LA-BR r = 0.73; LC-BR r = 0.65.

Table 3 Results from exploratory factor analyses (EFA) of the Self-Report Habit Index (SRHI) for the investigated habits (study 1)
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Convergent and Discriminant Validity

For convergent validity with measures of addictive behaviors, both SRHI scales showed significant correlations (all ps < 0.01) with large effect sizes (see Table 4).

Table 4 Descriptive statistics and correlations for SRHI and SRHI subscales for the investigated habits, behavior-specific convergent validity, and validity measures (study 1)
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All correlations of the SRHI scales with constructs to assess discriminant validity were significantly smaller than the correlations with convergent constructs. Only habits of smartphone/tablet use were associated with subjective impairments in physical health. In addition, both SRHI scales showed significantly stronger correlations with addiction measures (Fisher’s z: SRHI-CAF – CED-DS: zSteiger = 12.23, p < 0.001; SRHI-PHO – sIAT: zSteiger = 9.62, p < 0.001) than with subjective impairments in physical health.

The pattern of correlations of the SRHI subscales with validity measures did not deviate from the correlations with the overall scale.

Discussion

In sum, the results of study 1 showed good item characteristics and internal consistencies of the German SRHI versions assessing both health-risk behaviors. Regarding the factorial structure of the German SRHI scales, we found evidence for a three-factorial structure in line with the results of Reyes Fernández et al. (2019).

Study 2

Study 2 aimed at testing the factor structure of the German SRHI scales on an independent sample and construct validation based on more established measures. We therefore followed the suggestions by Verplanken (2018) and assessed the behavior frequency of each target behavior for convergent validity. For discriminant validity, we now assessed mindfulness, a non-habit-related reflective mental process, to contrast automatic behavioral tendencies (Kang et al., 2013).

Method

In study 2 (N = 702, August to October 2021), participants answered the SRHI as well as different convergent and discriminant validity measures. Procedure and inclusion criteria were the same as in study 1.

Measures

Regarding sociodemographic data and SRHI scales, study 2 applied the same instruments as reported in study 1. The selection of subsamples for both health-risk behaviors was performed accordingly. Unless reported otherwise, the internal consistencies given are from the present study data.

Convergent Validity

For convergent validity with corresponding addictive behaviors in study 2, the CED-DS and sIAT were applied as reported in study 1.

Behavior Frequency

Behavior frequency was measured by means of one single item (“How often did you XY on an ordinary day last week”).

Discriminant Validity

Mindful Attention and Awareness Scale (MAAS)

The MAAS (Brown & Ryan, 2009) measures mindfulness with 15 self-referred statements assessing the tendency to act deliberately, be in the present moment, and not to be judgemental. In the German version (Michalak et al., 2008), participants are asked to rate these statements on a 6-point Likert scale ranging from (1) nearly always to (6) almost never. Internal consistency was excellent (α = 0.90).

Data Analysis

The quality check of the data and item analyses were identical to study 1. Due to click-through criteria, we had to exclude three participants. For the assessment of the scale structure, we conducted maximum likelihood confirmatory factor analyses (CFA) using R version 3.6.2 (R Core Team, 2021), the lavaan (v0.6–7; Rosseel, 2012), the lavaanPlot (v0.5.1; Lishinski, 2018), and the MVN (v5.8; Korkmaz et al., 2014) packages. Codes are available upon request from the authors. Other analyses were conducted with SPSS version 27.

Due to violation of the multinomial normality assumption (West et al., 1995), we ran the CFA based on a reflective model with robust standard errors, using the Satorra-Bentler statistic. The approximated model fit was determined based on the χ2-statistic, χ2/df-quotient, robust comparative fit index (CFI), robust Tucker-Lewis index (TLI), robust root mean square error of approximation (RMSEA), and root mean square residual (SRMR). According to Schermelleh-Engel et al. (2008), values < 2 for χ2/df, ≥ 0.95 for CFI and TLI, ≤ 0.06 (N > 250) respectively ≤ 0.08 (N ≤ 250) for RMSEA, and ≤ 0.06 for SRMR indicate a good model fit. Values < 3 for χ2/df, and ≤ 0.11 for SRMR indicate an acceptable model fit.

Identical to study 1, analyses were calculated based on the subsamples that reported each respective behavior and evaluation of validity measures underwent the same procedure.

Results

Sample and Item Characteristics

The final analysis sample consisted of 699 participants with differing sample sizes for the SRHI-scale subgroups. Sample characteristics were similar to the study 1 sample (see Table 1).

Confirmatory Factor Analysis

For both SRHI scales, the CFA indicated a better model fit for a three-factor solution (“Lack of Awareness,” “Lack of Control,” “Behavioral Repetition”) compared to a one-dimensional structure (see Table 5, Fig. 1). Due to the large sample sizes, all χ2-values reached statistical significance. The χ2/df-quotient was acceptable for the SHRI-CAF, while the SRHI-PHO exceeded the acceptable value of 3. However, CFI and TLI values exceeded 0.95 for the SRHI-CAF and SRHI-PHO three-factor models, indicating a good model fit. Although none of the three-factor models met the RMSEA criterion for good model fit in relation to the sample sizes, the SRMR values indicated a good fit for each scale.

Table 5 Results of confirmatory factor analyses of the SRHI for the investigated habits for the relationship among three facets of habit behavior
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Fig. 1
figure 1

Confirmatory factor analysis for SRHI-CAF and SRHI-PHO (study 2). Items are numbered in the order presented in Table 7. All modeled correlations and path coefficients are significant (p < .05)

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Convergent and Discriminant Validity

For convergent validity, behavior frequency was significantly and positively associated with habit strength for both health-risk behaviors. However, the effect size of the convergent measures varied from small (r = 0.28 for SRHI-CAF) to medium (r = 0.35 for SRHI-PHO).

In addition, Fisher’s z-tests for both SRHI scales showed significantly stronger correlations with addiction measures than with the respective measures of behavior frequency (SRHI-CAF: zSteiger =  − 9.44, p < 0.001; SRHI-PHO: zSteiger =  − 7.98, p < 0.001).

The discriminant measure (MAAS) had a lower and non-significant association with SRHI-CAF but was significantly correlated with SRHI-PHO (see Table 6). Still, Fisher’s z-tests showed that SRHI-CAF correlated significantly stronger with convergent compared to discriminant measures. This was not the case for the SRHI-PHO scale.

Table 6 Descriptive statistics and correlations for SRHI and SRHI subscales for the investigated habits and correlations with validity measures (study 2)
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The pattern of correlations of the SRHI subscales with validity measures did not deviate from the correlations with the overall scale.

Discussion

The results of study 2 showed that a three-factorial model structure of the SRHI scales accounted for satisfactory model fit indices for both health-risk behaviors. Altogether, a three-factorial structure was superior to a one-factor structure for the SRHI.

We found significant correlations of SRHI measures with behavior frequency as a commonly assessed measure in habit research. However, for both behaviors, the size of these correlations was low to moderate only and significantly lower compared to the correlations with addiction measures. For the latter ones, however, all correlation coefficients were higher than r = 0.50, indicating strong effect sizes (Cohen, 1988). In the light of the debate about the role of habits in describing and measuring addictive behaviors, this finding is important. It thus supports the assumption that habits indeed are closely related to addictive behaviors (Berridge, 2021; Lewis, 2018).

General Discussion

The present studies aimed at validating a German version of the SRHI for two different possible health-risk behaviors: caffeine consumption and smartphone/tablet use. It further intended to shed light onto the role of habit in (potentially) addictive behaviors that can only be sufficiently assessed with well-validated scales. Therefore, we performed two subsequent cross-sectional validation studies to assess item characteristics and factorial structure as well as discriminant and convergent validity. For the latter, we additionally examined whether the correlation of SHRI scales for the (potentially) addictive health-risk behaviors showed stronger associations with scales for addictive behaviors than with behavioral frequency as a common measure of habit strength.

Overall, we found that item and scale characteristics of the German SRHI versions were good and comparable across the different behaviors, characterizing the German SRHI as a reliable, valid, and flexible assessment instrument that can be adapted to the measurement of different health-risk behaviors in German-speaking populations, extending the mere use to assess habits of physical activity (Thurn et al., 2014).

Exploratory and confirmatory factor analyses indicated that a three-factor solution of both German SRHI versions with the subscales “Lack of Awareness,” “Lack of Control,” and “Behavioral Repetition” provided a better model fit than a one-dimensional solution. This result is in line with a previous study using the SRHI in another language (Reyes Fernández et al., 2019).

The amount of explained variance by the factor Lack of Awareness is consistent with findings of Gardner et al. (2011), who claimed automaticity as the active ingredient in the construct of habit strength.

With regard to the role of habits in the context of addiction research, the present studies show an interesting picture: The correlations of the SRHI scales with addiction measures are not only highly significant but also very strong for both targeted areas of behavior, a substance-related behavior like caffeine consumption as well as behavioral addictive behaviors, such as smartphone and tablet use. In addition, these correlations were more pronounced than correlations with a more established convergent validation measure in habit research, i.e., behavioral frequency (Verplanken, 2018).

On the one hand, this emphasizes that measuring self-reported habits is an important and often neglected component of research on addictive behaviors (Sjoerds et al., 2014) and that the SRHI could be well used for this purpose. On the other hand, it emphasizes that habits are indeed likely to be important components in explaining behavior, in line with the assumptions of Lewis (2018) and Berridge (2021). Since habit formation can be explained with the help of classical S-R learning theories, corresponding processes of behavior change, e.g., through extinction learning, offer approaches to change addictive behavior again, to reduce excessive behavior, or to promote abstinence (e.g., Everitt, 2014).

However, awareness should be raised to the point that the frequency or amount of smartphone use does not necessarily equate to the strength of bad habit or addictive tendencies, other than in the case with caffeine consumption. For example, smartphone use can be helpful for practical/utilitarian activities (such as e-banking, emails, participating in meetings, orientation and navigation) or serve on the other hand as means for recreational and entertainment use (e.g., social media use, browsing without a purpose, or playing games) (see e.g., Akdim et al., 2022; Billieux et al., 2015a). Therefore, a careful distinction must be made between those utilitarian vs. hedonic/entertainment motifs of smartphone applications or browsing behavior, especially in terms of addictive tendencies. For instance, recent research shows hedonic and non-utilitarian smartphone use rather than utilitarian use was associated with smartphone addiction (Vujić & Szabo, 2022), and adverse effects of well-being were more pronounced within hedonic purposes of smartphone use (Moqbel et al., 2022). Since we did not differentiate between these two types of smartphone use, future research should address the relationship of habitual use and addictive tendencies in more detail.

Furthermore, it has to be noted that potentially addictive tendencies of smartphone use may in large parts overlap with general internet addiction tendencies, since the smartphone today is merely a device to use internet-based applications and virtual worlds (such as social media). Thus, future studies could assess both internet addiction tendencies and problematic/addictive smartphone use, to identify the specific addictive components.

With regard to behavioral frequency, it remains questionable at what frequency a behavior actually constitutes a habit (Rebar et al., 2018). In our studies, we focused on behaviors that—despite their different recognition as (potentially) addictive behaviors—are performed several times a day in the rules. Nevertheless, it is possible that a person who consumes caffeine only occasionally would still describe this behavior as a personal habit. This could contribute to the variance in the measures of behavioral frequency being significantly larger and the correlations with the SRHI being smaller as a result.

The reason for potentially different attributions of behaviors as habits lies in the nature of the construct of habit. Therefore, it would be interesting to also measure other aspects of the definition of habit in the future for a more precise differentiation between addictive behavior and habitual behavior, e.g., context stability or correlations with specific cues (Verplanken, 2018).

Strengths and Limitations

To our best knowledge, the present studies are the first to assess the psychometric properties of the SRHI in German and across different health-risk behaviors with a high degree of automaticity. We conducted extensive analyses on the psychometric properties in two separate studies and used validated measures for the assessment of the construct validity. However, the study is subject to several limitations.

The general limitations of online surveys apply. In addition, most of the sample was female, so that future studies should aim at recruiting a gender-balanced, more representative sample.

To this, we only analyzed self-reported behavior frequency per day as a convergent measure in study 2 and omitted response frequency, behavior duration, intention, or objective measures of behavior as possible additional variables. Although correlations between the SRHI scales and behavior frequency were significant, their size depended on the specific behavior. With regard to the use of validation scales for addictive smartphone use, we must also point out that at the time the study was conducted, besides the IAT (which was not specifically designed for smartphone use), hardly any field-tested German-language inventories were available for validation. For further investigation of the influence of habitual smartphone use and its potential for problematic use up to behavioral addiction, future research should therefore refer to the recently developed smartphone-specific measurement instruments on this research field (e.g., the Smartphone Application-Based Addiction Scale; Csibi et al., 2018).

Furthermore, the target behaviors in the present studies differed in the degree of automatization and in their frequency during the day. For example, for smartphone/tablet use, it may be difficult to estimate the actual behavior frequency per day in contrast to estimating the number of caffeinated beverages. Therefore, especially for the more unconsciously performed habit of smartphone/tablet use, objective behavioral measures (e.g., screen time as assessed by the respective device) should be included in future studies on the link between habit strength and actual use.

Since smartphone use also differs between utilitarian and hedonic as well as potentially problematic use with regard to the various usage scenarios named above, tracking the apps used on the smartphone in particular and assessing the factual logged times as more objective measures would give a better overview of the type and actual extent of usage behavior here (see also James et al., 2022).

The SRHI is an instrument for measuring habit strength which showed strong associations to addictive tendencies in the present studies. However, it should be noted that the original purpose of the SRHI was not to measure or characterize excessive or pathological behaviors. With regard to the measurement of excessive or pathological smartphone use, other scales are more suitable (for an overview of scales that measure problematic smartphone use, see Harris et al., 2020). Another more general critique targets that the SRHI lacks the measurement of context cues (behavior X in context Y is something I do) (Norman & Cooper, 2011; Sniehotta and Presseau, 2012). However, health-risk behaviors might be triggered by different categories of cues like internal cues (stress, tiredness), external cues (waiting for the bus and grab the smartphone), or social cues (coffee break with colleagues). Therefore, cue specificity of the habit should be assessed with a separate tool and adapted to the specific behaviors in question.

Conclusion

The German version of the SRHI has good psychometric properties and can be used to assess habit strength of different health-risk behaviors. In general, the SRHI might also be an important and economic measure for research on the role of habits in the etiology and maintenance of substance-related and behavioral addictions.