The Relationship Between a Balanced Time Perspective and Self-monitoring of Blood Glucose Among People With Type 1 Diabetes

The Relationship Between a Balanced Time Perspective and Self-monitoring of Blood Glucose Among... Abstract Background Self-monitoring of blood glucose helps people with type 1 diabetes to maintain glycemic control and reduce the risk of complications. However, adherence to blood glucose monitoring is often suboptimal. Purpose Like many health behaviors, self-monitoring of blood glucose involves exerting effort in the present to achieve future benefits. As such, the present research explored whether individual differences in time perspective—specifically, the extent to which people have a balanced time perspective—are associated with the frequency with which people with type 1 diabetes monitor their blood glucose and, thus, maintain glycemic control. Methods Adults with type 1 diabetes completed measures of time perspective, feelings associated with monitoring, attitudes toward monitoring, and trait self-control. Objective data regarding the frequency with which participants monitored their blood glucose levels and their long-term glycemic control were extracted from their medical records. Results Hierarchical regression analyses and tests of indirect effects (N = 129) indicated that having a more balanced time perspective was associated with more frequent monitoring of blood glucose and, as a result, better glycemic control. Further analyses (N = 158) also indicated that there was an indirect relationship between balanced time perspective and monitoring of blood glucose via the feelings that participants associated with monitoring and their subsequent attitudes toward monitoring. Conclusions These findings point to the importance and relevance of time perspective for understanding health-related behavior and may help to inform interventions designed to promote self-monitoring of blood glucose in people with type 1 diabetes. Type 1 diabetes, Balanced time perspective, Self-monitoring of blood glucose, Glycemic control, HbA1c levels Introduction Diabetes mellitus is a group of metabolic disorders that are characterized by an excess of glucose circulating in the blood stream, known as hyperglycemia. Type 1 diabetes accounts for approximately 5%–10% of cases of diabetes [1] and occurs due to the destruction of insulin-producing cells that impairs the body’s ability to metabolize glucose. The management of type 1 diabetes is directed toward maintaining healthy blood glucose levels to reduce the risk of microvascular complications (e.g., damage to the eyes, kidneys, and nervous system) and macrovascular complications (e.g., heart attack, heart failure, and strokes) that can have serious and life-debilitating consequences, including loss of vision, limb amputation, and premature death [2, 3]. Self-monitoring of blood glucose has been identified as a key strategy in maintaining glycemic control [4]. Obtaining reliable information about glycemic variations enables the individual and their health care providers to make informed adjustments to their therapeutic regime (e.g., diet, exercise, insulin dosage [5]). Indeed, numerous studies have shown that frequent monitoring of blood glucose (i.e., three to four times daily) is associated with reductions in glycated hemoglobin (HbA1c; a measure of long-term glycemic control) and improved health outcomes [6, 7]. However, despite clear recommendations and the potential benefits, adherence to blood glucose monitoring is often suboptimal, with studies suggesting that 21% of adults never engage in glucose monitoring [8] and 60% monitor less frequently than recommended [9]. As such, identifying factors that are associated with adherence to glucose monitoring has become a focal point of research [10]. Factors Associated With Self-monitoring of Blood Glucose Previous research has demonstrated that demographic factors (e.g., older age, male gender, ethnic minority, low socioeconomic status, and lower levels of education) and biomedical factors (e.g., longer time since diabetes diagnosis and less intensive treatment regimens) are associated with less frequent monitoring of blood glucose [9, 10]. However, researchers have recently highlighted the importance of identifying psychological factors (e.g., locus of control and compensatory beliefs [11]) that can help to understand self-management behaviors in diabetes, especially as such research could inform health education and interventions designed to promote adherence [12]. Many psychological models of health behavior suggest that the extent to which a person values future benefits over more immediate benefits or costs is an important determinant of health behavior [13]. For example, the possible benefits of regularly monitoring blood glucose (e.g., lower likelihood of kidney failure, stroke, and heart attack) may not come to fruition for many years, while at the same time, monitoring blood glucose may involve short-term costs (e.g., inconvenience, discomfort, difficulty, or fear of a “bad” monitoring result). Furthermore, it has been suggested that some people are motivated to avoid monitoring their blood glucose as it serves as a reminder of their diabetes diagnosis [14]. Thus, although some individuals may value their future health and will take steps to ensure it, others may discount their future health in favor of more immediate benefits or to avoid short-term costs. The present research therefore suggests that time perspective may be associated with the extent to which people monitor their blood glucose. Time Perspective Time perspective refers to cognitive and affective biases that people have for the past, present, and/or future and has been found to motivate and influence behavior [15]. According to Zimbardo and Boyd [15], there are five time perspectives: (a) past-negative, reflecting an adverse view of the past, (b) past-positive, reflecting a warm and sentimental view of the past, (c) present-hedonistic, reflecting a pleasure-seeking attitude toward life, (d) present-fatalistic, reflecting the belief that much of life is determined by fate, and (e) future time perspective, reflecting a greater consideration of the effects of current actions on future outcomes. Previous research has indicated that specific time perspectives are associated with specific health behaviors (for a review, see [16]), including the health behaviors of people with diabetes. For example, studies have shown that a future time perspective is associated with more adaptive behaviors, such as medication adherence [17], weight management behaviors (e.g., eating less fatty foods and engaging in more physical activity [18]), and stronger intentions to attend a diabetes screening appointment [19]. Although this evidence seems to suggest that having a future time perspective is beneficial for engaging in health-protective behaviors, researchers have also argued that focusing on one time perspective, while excluding others, can be detrimental [20]. For example, although having a future time perspective may encourage people to set goals for the future (e.g., to achieve long-term glycemic control), it would be difficult for an individual to form plans to achieve these goals without using information from the past (e.g., past knowledge of how certain foods influence their blood glucose levels) or the present (e.g., information obtained from monitoring their blood glucose). As such, it has been suggested that having a balanced time perspective is most beneficial, where people are able to draw from multiple timeframes and switch flexibly between them to meet situational demands and achieve their goals [21, 22]. Interestingly, however, although differences in a balanced time perspective have been explored in relation to psychological well-being (e.g., happiness and life satisfaction [23]), very little research has explored the relationship between balanced time perspective and specific health behaviors. Given the importance of blood glucose monitoring for managing the symptoms of type 1 diabetes and promoting future health, the present research explored whether individual differences in a balanced time perspective were associated with the frequency with which participants monitored their blood glucose, and thus, achieved long-term glycemic control. Why Might a Balanced Time Perspective Be Associated With Blood Glucose Monitoring? There are several reasons to think that differences in the extent to which people have a balanced time perspective may be associated with the frequency with which they monitor their blood glucose levels. First, empirical research has indicated that having a more balanced time perspective is associated with higher levels of positive affect (e.g., the extent to which people tend to feel excited and determined) and lower levels of negative affect (e.g., the extent to which people tend to feel scared and ashamed [23]). Therefore, individuals with a more balanced time perspective may associate more positive feelings with monitoring (e.g., monitoring their blood glucose makes them feel relaxed and reassured) and so monitor more frequently as a result. Second, having a more balanced time perspective may be associated with people’s attitudes toward monitoring their blood glucose. Specifically, individuals with a more balanced time perspective may consider monitoring to be more beneficial and worthwhile for their future health (i.e., they have more positive attitudes toward monitoring) and so monitor more frequently as a result. In addition, given that past research has highlighted that people’s feelings toward a particular behavior (or the emotions that they associate with performing the behavior) can influence their subsequent attitudes toward that behavior (for a review, see [24]), it also seems likely that feelings and attitudes are related, such that positive feelings toward monitoring promote positive attitudes toward monitoring (i.e., these mediators may occur sequentially). Finally, previous research has demonstrated that having a more balanced time perspective is associated with greater self-control ability [25]. Furthermore, greater self-control ability has been found to be associated with better glycemic control in adolescents with type 1 diabetes [26]. Therefore, there may be an indirect relationship between balanced time perspective and blood glucose monitoring via self-control ability. In light of these considerations, the present research will explore three possible mediators of the relationship between a balanced time perspective and the frequency of blood glucose monitoring: (a) The feelings that people associate with monitoring, (b) people’s attitudes toward monitoring, and (c) self-control ability. The Present Research People with type 1 diabetes need to self-monitor their blood glucose to maintain glycemic control and reduce the risk of future health complications. The present research proposes that differences in people’s time perspective and, specifically, differences in the extent to which people hold a balanced time perspective may be associated with the frequency with which people with type 1 diabetes monitor their blood glucose, and thus, achieve long-term glycemic control. A second aim of the present research was to explore potential reasons why balanced time perspective may be associated with self-monitoring. Specifically, the following hypotheses were tested: Hypothesis 1: A more balanced time perspective will be associated with more frequent self-monitoring of blood glucose and, as a consequence, lower HbA1c levels, indicating better long-term glycemic control. Hypothesis 2: The feelings that people associate with monitoring, their attitudes toward monitoring, and their self-control ability will mediate the relationship between the extent to which people hold a balanced time perspective and the frequency with which people self-monitor their blood glucose levels. Method Study Setting and Recruitment The study was conducted in collaboration with the Adult Diabetes Outpatient Clinics at Sheffield Teaching Hospitals NHS Foundation Trust in the UK. This Trust has two diabetes centers, based at the Royal Hallamshire Hospital and the Northern General Hospital. Potential participants were identified by nurse specialists, clinicians, and research coordinators at these diabetes centers. To be eligible to participate, individuals needed to be aged 18 or older, have had a diagnosis of type 1 diabetes for at least 12 months (as assessed by the date on which they were clinically diagnosed), and have access to an electronic glucose meter to monitor their blood glucose. Eligible participants were provided with a recruitment pack that contained a letter of invite, an information sheet, a consent form, a questionnaire, and a stamped addressed envelope. This information was either sent to eligible participants via post, or it was given to them when they attended an appointment at the clinic. Participants were able to decide whether they would like to complete a paper copy of the consent form and questionnaire or whether they would prefer to provide this information online via the survey software, Qualtrics (https://www.qualtrics.com/). Participants who chose to complete a paper copy of the questionnaire were asked to return this, along with their consent form, using the envelope provided. Participants did not receive any incentives for taking part in this research. Between April 2016 and January 2017, 779 postal questionnaires were distributed. Of those contacted, 165 (21%) agreed to participate. A further 74 participants were approached at the diabetes outpatient clinics and 22 (30%) agreed to take part. Four participants (2%) were removed from the analyses because they did not meet the inclusion criteria (i.e., they did not have a diagnosis of type 1 diabetes), resulting in a final sample of 183 participants. Participant Characteristics Table 1 displays the demographic and biomedical characteristics of the sample. Participants were aged between 18 and 88 years (M = 49.95; SD = 17.18). Approximately one half of the sample were female (49%), and the majority were White British (97%). An Index of Multiple Deprivation (IMD) score was calculated using postcode data. The English IMD ranks every postcode area in England from the most deprived area (ranked 1) to the least deprived area (ranked 32,844). Due to this wide range, ranks were divided by 1,000 for ease of comprehension. The mean IMD for the present sample was 18.01 (SD = 98.00), which is slightly higher than the overall mean rank for England (16.42), suggesting that the sample was, on average, marginally more deprived than the population of England as a whole. Participants were, on average, 21 years after diagnosis at the time they completed the study (SD = 15.99; range: 1–73 years). The mean HbA1c level for the sample was 63 mmol/mol (SD = 14.71), which is higher than the recommended value (≤48 mmol/mol; [27]), indicating that the sample tended to have difficulties controlling their blood glucose levels. The mean HbA1c level for the current sample was also compared with the mean HbA1c level for other patients with type 1 diabetes under the care of Sheffield Teaching Hospitals. Research coordinators at these diabetes centers identified 1,437 patients who matched our inclusion criteria (i.e., had type 1 diabetes for longer than 12 months and were aged 18 or older). The average HbA1c level for this patient group was 68.4 mmol/mol, which is slightly higher (d = 0.37) than the average HbA1c level for our sample (i.e., 62.99 mmol/mol). Although this suggests that our sample tended to have difficulties controlling their blood glucose, they had slightly better glycemic control than the average patient under the care of Sheffield Teaching Hospitals. Table 1 Demographic and Biomedical Characteristics of the Sample Characteristic  n (missing)  %  Mean (SD)  Sex  177 (6)       Male  87  47.5     Female  90  49.2    Age (years)  177 (6)    49.95 (17.17)  Ethnicity  182 (1)       White British  178  97.3     Non-white  4  2.2    Country of birth  170 (13)       UK  166  90.7     Other  4  2.2    Education level  180 (3)       No formal education  3  1.6     Primary education  7  3.8     Secondary education  45  24.6     College/sixth form  48  26.2     Undergraduate degree  46  25.1     Postgraduate degree  22  12.0     PhD/doctorate  9  4.9    Employment status  182 (1)       Full-time  84  45.9     Part-time  24  13.1     Unemployed  8  4.4     Student  8  4.4     Retired  43  23.5     Unable to work  12  6.6     Other  3  1.6    Index of Multiple Deprivation score  177 (6)    18.01 (98.00)  Attended a DAFNE course  181 (2)       Yes  125  68.3     No  56  30.6    Time since diabetes diagnosis (years)  172 (11)    21.34 (15.99)  HbA1c value  147 (36)    62.99 (14.71)  Characteristic  n (missing)  %  Mean (SD)  Sex  177 (6)       Male  87  47.5     Female  90  49.2    Age (years)  177 (6)    49.95 (17.17)  Ethnicity  182 (1)       White British  178  97.3     Non-white  4  2.2    Country of birth  170 (13)       UK  166  90.7     Other  4  2.2    Education level  180 (3)       No formal education  3  1.6     Primary education  7  3.8     Secondary education  45  24.6     College/sixth form  48  26.2     Undergraduate degree  46  25.1     Postgraduate degree  22  12.0     PhD/doctorate  9  4.9    Employment status  182 (1)       Full-time  84  45.9     Part-time  24  13.1     Unemployed  8  4.4     Student  8  4.4     Retired  43  23.5     Unable to work  12  6.6     Other  3  1.6    Index of Multiple Deprivation score  177 (6)    18.01 (98.00)  Attended a DAFNE course  181 (2)       Yes  125  68.3     No  56  30.6    Time since diabetes diagnosis (years)  172 (11)    21.34 (15.99)  HbA1c value  147 (36)    62.99 (14.71)  DAFNE Dose Adjustment for Normal Eating; HbA1c glycated hemoglobin. View Large Table 1 Demographic and Biomedical Characteristics of the Sample Characteristic  n (missing)  %  Mean (SD)  Sex  177 (6)       Male  87  47.5     Female  90  49.2    Age (years)  177 (6)    49.95 (17.17)  Ethnicity  182 (1)       White British  178  97.3     Non-white  4  2.2    Country of birth  170 (13)       UK  166  90.7     Other  4  2.2    Education level  180 (3)       No formal education  3  1.6     Primary education  7  3.8     Secondary education  45  24.6     College/sixth form  48  26.2     Undergraduate degree  46  25.1     Postgraduate degree  22  12.0     PhD/doctorate  9  4.9    Employment status  182 (1)       Full-time  84  45.9     Part-time  24  13.1     Unemployed  8  4.4     Student  8  4.4     Retired  43  23.5     Unable to work  12  6.6     Other  3  1.6    Index of Multiple Deprivation score  177 (6)    18.01 (98.00)  Attended a DAFNE course  181 (2)       Yes  125  68.3     No  56  30.6    Time since diabetes diagnosis (years)  172 (11)    21.34 (15.99)  HbA1c value  147 (36)    62.99 (14.71)  Characteristic  n (missing)  %  Mean (SD)  Sex  177 (6)       Male  87  47.5     Female  90  49.2    Age (years)  177 (6)    49.95 (17.17)  Ethnicity  182 (1)       White British  178  97.3     Non-white  4  2.2    Country of birth  170 (13)       UK  166  90.7     Other  4  2.2    Education level  180 (3)       No formal education  3  1.6     Primary education  7  3.8     Secondary education  45  24.6     College/sixth form  48  26.2     Undergraduate degree  46  25.1     Postgraduate degree  22  12.0     PhD/doctorate  9  4.9    Employment status  182 (1)       Full-time  84  45.9     Part-time  24  13.1     Unemployed  8  4.4     Student  8  4.4     Retired  43  23.5     Unable to work  12  6.6     Other  3  1.6    Index of Multiple Deprivation score  177 (6)    18.01 (98.00)  Attended a DAFNE course  181 (2)       Yes  125  68.3     No  56  30.6    Time since diabetes diagnosis (years)  172 (11)    21.34 (15.99)  HbA1c value  147 (36)    62.99 (14.71)  DAFNE Dose Adjustment for Normal Eating; HbA1c glycated hemoglobin. View Large Design and Procedure The study employed a cross-sectional design in which participants were asked to complete measures of time perspective, the feelings that they associate with monitoring their blood glucose levels, their attitudes toward monitoring their blood glucose, and their ability to exert self-control. Permission was also obtained for the research team to access participants’ Diasend database and medical records to extract information regarding the frequency with which they monitored their blood glucose levels and their long-term glycemic control (i.e., their HbA1c level). The study was presented to participants as an investigation into the factors that influence blood glucose monitoring and glycemic control; however, no details were provided on the specific factors of interest or how they might relate to these outcomes. Measures Demographics The following demographic information was collected from participants: date of birth, gender, ethnicity, country of birth, postcode, occupation, employment status, and level of education. Participants were also asked to indicate whether they had participated in the Dose Adjustment for Normal Eating (DAFNE) training course. This course is offered to adults with type 1 diabetes across the UK and provides formal training on how to adjust insulin doses according to diet (e.g., carbohydrate intake) and lifestyle (e.g., amount of exercise). The course trains attendees to monitor their blood glucose levels before each meal to guide the calculation of their insulin dose. Time perspective Time perspective was measured using Zimbardo’s Time Perspective Inventory (ZTPI; [15]). This measure contains 56-items that assess five dimensions of time perspective: (a) past-positive (e.g., “It gives me pleasure to think about my past”), (b) past-negative (e.g., “I often think of what I should have done differently in my life”), (c) present-fatalistic (e.g., “It doesn’t make sense to worry about the future, since there is nothing I can do about it anyway”), (d) present-hedonistic (e.g., “I find myself getting swept up in the excitement of the moment”), and (e) future (e.g., “I am able to resist temptations when I know there is work to be done”). Participants were asked to respond to each of the items on a 5-point Likert scale, anchored by “very untrue of me” to “very true of me.” Cronbach’s alpha suggested that each subscale was internally reliable: past-positive (α = .75); past-negative (α = .86), present-fatalistic (α = .72), present-hedonistic (α = .80), and future (α = .80). To measure a balanced time perspective, we first computed a Deviation from a Balanced Time Perspective (DBTP) score [25] by subtracting participants’ scores for each subscale from the “optimal” score, as specified by Zimbardo and Boyd [28]. This measure was then reverse scored, so that higher scores indicated a more balanced time perspective. Affect associated with self-monitoring blood glucose How participants typically feel when they self-monitor their blood glucose was measured using the stem “Monitoring my blood glucose makes me feel…” followed by eight items: guilty, bad about myself, good about myself, relaxed, disappointed, at ease, anxious, and reassured. These items were devised for the purpose of this study and were informed by the literature and attendance at a DAFNE training course. Items were rated on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree.” Negative items were reverse coded so that higher scores indicated that participants associated monitoring with more positive affect (α = .86). Attitudes toward self-monitoring blood glucose Participants’ attitudes toward monitoring were measured with the stem “I think that monitoring my blood glucose every time that I am supposed to is…” followed by six bipolar adjectives rated on a 5-point scale: “Important—unimportant,” “easy—difficult,” “harmful—beneficial,” “worthwhile—pointless,” “unpleasant—pleasant,” and “wise—foolish.” After reverse coding negative items, the items were averaged such that higher scores indicated that participants held more positive attitudes toward self-monitoring their blood glucose levels (α = .77). We also measured the extent to which participants found their current monitoring regime effective, convenient, and intrusive using the Glucose Monitoring Experiences Questionnaire (GME-Q; [29]); however, none of these subscales mediated the relationship between balance time perspective and the frequency with which participants monitored their blood glucose (see Supplementary Material 1). Self-control Trait self-control was assessed using the 13-item Brief Self-Control Scale (BSCS; [30]). Previous studies have demonstrated that the BSCS is a valid measure of self-control (e.g., [30]), and it has been found to be associated with glycemic control in individuals with type 1 diabetes [26]. Example items include “I am good at resisting temptation” and “I often act without thinking through all the alternatives.” Items were rated on a 5-point Likert scale, anchored by “not at all” to “very much.” After reverse scoring negative items, items were averaged such that higher scores reflected greater levels of trait self-control (α = .77). Clinical Outcomes Biomedical information The following information was collected from participants’ medical records: time since diabetes diagnosis, name of consultant in charge of care, and current insulin regime (e.g., frequency of injections, insulin types, and doses). Frequency of blood glucose monitoring The frequency with which participants monitored their blood glucose was measured using Diasend software (https://diasend.com//en). Diasend is a system for recording information from electronic blood glucose meters, including the value, date, and time of each measurement. This information is uploaded by patients or their health care providers to a secure online database. To account for any effects of participation in the research on the frequency with which participants monitored their blood glucose, the data were extracted for three separate weeks: (a) the week prior to when participants completed the questionnaire (or nearest available date), (b) the week when participants completed the questionnaire, and (c) the week after the questionnaire was completed (or nearest available date). A one-way repeated-measures ANOVA was conducted to test whether there were differences in the frequency with which participants monitored their blood glucose between these weeks. Mauchly’s test of sphericity indicated that the assumption of sphericity had been violated (χ2 (2) = 9.31, p = .010), and therefore, the degrees of freedom were corrected using the Huynh-Feldt estimate of sphericity (ε = .90). There were no differences in the frequency with which participants monitored their blood glucose according to the week that the data were extracted, F(1.81, 117.44) = 1.68, p = .193; Time 1: M = 28.55, SD = 12.99; Time 2: M = 29.71, SD = 13.70; Time 3: M = 28.35, SD = 13.55. This confirms that participating in the study did not influence the frequency with which participants monitored their blood glucose. If no data were available within a year of the date required, then the data were recorded as missing. The number of times that participants monitored their blood glucose in each of these weeks (where available) was averaged to provide an objective measure of the frequency with which participants self-monitored their blood glucose during the study period. Participants also reported how often they monitored their blood glucose each week using a single item: “On average, how many times a week do you monitor your blood glucose?” If participants provided a range (e.g., 25–30), then the median value was recorded. There was a high correlation between the data extracted from the Diasend software and the self-reported frequency with which participants monitored their blood glucose (r = .75; see Table 3). As such, to reduce missing data in these variables (Nmissing = 45 and 5 for the objective and self-report measures, respectively) and to ensure sufficient power for subsequent analyses, a composite measure was created. That is, when data were available for both of these measures, an average was taken; otherwise, scores were based on either the objective or self-reported data depending on which was available. Long-term glycemic control Medical records were reviewed to extract participants’ most recent HbA1c level. HbA1c is a measure of glycosylated hemoglobin that reflects overall blood glucose levels over the previous 6–8 weeks [31]. Previous research has demonstrated a strong relationship between high levels of HbA1c and complications [2], and, as such, HbA1c is considered to be the “gold standard” measure of long-term glycemic control [32]. HbA1c levels are measured in mmol/mol, with levels exceeding 48 mmol/mol reflecting difficulties controlling blood glucose levels [27]. The HbA1c reading that most closely corresponded to the date that the participant completed the questionnaire was extracted from participants’ medical records. If a participants’ HbA1c level had not been tested within a year of the date that the questionnaire was completed, then it was recorded as missing. Analytic Strategy The aim of the present research was to investigate whether individual differences in a balanced time perspective are associated with the frequency with which people with type 1 diabetes monitor their blood glucose levels and, as a result, maintain glycemic control. To address these questions, the data were analyzed in three stages. First, the relationships between the demographic factors (e.g., age, gender, ethnicity) and biomedical factors (e.g., time since diagnosis) and the outcome variables (i.e., frequency of self-monitoring of blood glucose and HbA1c levels) were explored using correlations, t tests, and ANOVAs as appropriate. When significant relationships between these factors and the outcome variables were found, the relevant factors were controlled for in subsequent analyses. Second, hierarchical regression analyses were conducted, with balanced time perspective as the independent variable (entered in Step 2) and the frequency of blood glucose monitoring or HbA1c level as the dependent variables, controlling for any covariates identified in the first step of the analyses (entered in Step 1). These analyses were conducted using SPSS version 23 [33]. Finally, a series of mediation models were conducted using PROCESS [34]. These models explored (a) whether the relationship between balanced time perspective and long-term glycemic control was mediated by the frequency with which participants monitored their blood glucose and (b) whether the relationship between balanced time perspective and self-monitoring of blood glucose was mediated by the feelings that participants’ associated with monitoring, their attitudes toward monitoring, and/or their self-control ability. In all of the mediation models, the indirect effect was tested using a bootstrap estimation approach with 10,000 resamples. Confidence intervals excluding zero were considered statistically significant at the p < .05 level. All of the analyses used the composite measure of the frequency with which participants monitored their blood glucose to reduce missing data and increase the statistical power of these analyses. Additional analyses were also conducted to explore the relationship between the individual dimensions of time perspective and the outcome variables (i.e., frequency of self-monitoring of blood glucose and HbA1c levels), to permit comparison with previous studies that have focused on these variables. These analyses are not reported here, but can be found in Supplementary Material 2. Results Preliminary Analyses Preliminary analyses were conducted to establish whether the data met the statistical assumptions for the analyses outlined earlier. These analyses revealed the presence of outliers. Specifically, an analysis of standardized residuals indicated that four participants had outlying values (i.e., z-scores greater than ±3.29 SD from the mean) on the measure of the frequency of self-monitoring of blood glucose and one participant had an outlying HbA1c value. As such, these participants were removed from subsequent analyses involving these variables. The means, SD, and range for the key study variables (excluding the outliers identified) are presented in Table 2. Table 2 Means, SD, and Range for Key Study Variables Variable  Sample size (N)  Mean (SD)  Range  Balanced time perspective  164  2.80 (0.66)  3.98  Affect associated with monitoring  175  3.55 (0.82)  4.00  Attitudes toward monitoring  174  4.22 (0.59)  3.67  Self-control  178  3.23 (0.60)  3.15  Self-reported SMBG frequency  173  30.01 (13.91)  74.00  Objective SMBG frequency  136  27.85 (13.53)  72.00  Combined SMBG frequency  177  28.61 (13.00)  73.00  HbA1c level  142  62.94 (13.62)  82.00  Variable  Sample size (N)  Mean (SD)  Range  Balanced time perspective  164  2.80 (0.66)  3.98  Affect associated with monitoring  175  3.55 (0.82)  4.00  Attitudes toward monitoring  174  4.22 (0.59)  3.67  Self-control  178  3.23 (0.60)  3.15  Self-reported SMBG frequency  173  30.01 (13.91)  74.00  Objective SMBG frequency  136  27.85 (13.53)  72.00  Combined SMBG frequency  177  28.61 (13.00)  73.00  HbA1c level  142  62.94 (13.62)  82.00  Outliers have been excluded. HbA1c glycated hemoglobin; SMBG self-monitoring of blood glucose. View Large Table 2 Means, SD, and Range for Key Study Variables Variable  Sample size (N)  Mean (SD)  Range  Balanced time perspective  164  2.80 (0.66)  3.98  Affect associated with monitoring  175  3.55 (0.82)  4.00  Attitudes toward monitoring  174  4.22 (0.59)  3.67  Self-control  178  3.23 (0.60)  3.15  Self-reported SMBG frequency  173  30.01 (13.91)  74.00  Objective SMBG frequency  136  27.85 (13.53)  72.00  Combined SMBG frequency  177  28.61 (13.00)  73.00  HbA1c level  142  62.94 (13.62)  82.00  Variable  Sample size (N)  Mean (SD)  Range  Balanced time perspective  164  2.80 (0.66)  3.98  Affect associated with monitoring  175  3.55 (0.82)  4.00  Attitudes toward monitoring  174  4.22 (0.59)  3.67  Self-control  178  3.23 (0.60)  3.15  Self-reported SMBG frequency  173  30.01 (13.91)  74.00  Objective SMBG frequency  136  27.85 (13.53)  72.00  Combined SMBG frequency  177  28.61 (13.00)  73.00  HbA1c level  142  62.94 (13.62)  82.00  Outliers have been excluded. HbA1c glycated hemoglobin; SMBG self-monitoring of blood glucose. View Large Identification of Covariates We measured a number of demographic and biomedical factors that have previously been found to be associated with the frequency with which people monitor their blood glucose levels. However, to avoid reducing the statistical power of our main analyses, our decision as to which of the covariates to include in our analyses was determined by identifying the demographic and biomedical factors that have significant relationships with the outcome variables in the current sample. The correlations between the study variables are presented in Table 3. Neither age, time since diabetes diagnosis, nor IMD scores were significantly associated with the frequency with which participants self-monitored their blood glucose or HbA1c levels (p’s > .05). Thus, these factors were not controlled for in later analyses. Independent t tests indicated that gender was not significantly associated with either the frequency of blood glucose monitoring or HbA1c values (p’s > .05). However, there was a significant difference in the frequency of monitoring blood glucose between participants who had attended a DAFNE course and those who had not, t(174) = −3.49, p = .001. As might be expected, participants who had attended a DAFNE course tended to monitor their blood glucose levels more frequently (M = 30.91; SD = 12.70) than those who had not attended (M = 23.75; SD = 12.21). Thus, whether participants had attended a DAFNE course was controlled for in analyses exploring the relationship between time perspective and the frequency with which participants monitored their blood glucose levels. There was no difference in HbA1c levels as a function of DAFNE attendance, t(46.24) = −0.10, p = .925, and so DAFNE attendance was not controlled in the analyses focusing on HbA1c levels. Table 3 Descriptive Statistics and Pearson’s Bivariate Correlations Between Study Variables Variables  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  1. Age  .07  .44**  −.02  .29**  .28**  .35**  .06  −.05  .05  −.13  N  172  167  159  169  169  172  167  136  171  142  2. Index of Multiple Deprivation  .04  .12  −.02  .06  .09  .03  −.00  .04  −.14  N  167  159  169  169  172  167  136  171  142  3. Time since diagnosis (in years)    −.08  .18*  .07  .04  .13  .14  .13  .01  N    157  165  164  167  162  134  166  142  4. Balanced time perspective    .26**  .18*  .13  .15  .19*  .14  −.08  N    162  162  164  160  126  164  132  5. Affect associated with monitoring        .53**  .34**  .25**  .20*  .24**  −.37**  N        171  175  170  134  174  141  6. Attitudes toward monitoring          .29**  .35**  .16  .31**  −.20*  N          174  170  134  174  139  7. Self-control ability            .08**  .01  .07  −.31**  N            173  136  177  142  8. Self-reported SMBG frequency              .75**  .95**  −.18*  N              132  173  137  9. Objective SMBG frequency                .93**  −.15  N                136  122  10. Combined SMBG frequency                  −.20*  N                  141  11. HbA1c value                  –  Variables  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  1. Age  .07  .44**  −.02  .29**  .28**  .35**  .06  −.05  .05  −.13  N  172  167  159  169  169  172  167  136  171  142  2. Index of Multiple Deprivation  .04  .12  −.02  .06  .09  .03  −.00  .04  −.14  N  167  159  169  169  172  167  136  171  142  3. Time since diagnosis (in years)    −.08  .18*  .07  .04  .13  .14  .13  .01  N    157  165  164  167  162  134  166  142  4. Balanced time perspective    .26**  .18*  .13  .15  .19*  .14  −.08  N    162  162  164  160  126  164  132  5. Affect associated with monitoring        .53**  .34**  .25**  .20*  .24**  −.37**  N        171  175  170  134  174  141  6. Attitudes toward monitoring          .29**  .35**  .16  .31**  −.20*  N          174  170  134  174  139  7. Self-control ability            .08**  .01  .07  −.31**  N            173  136  177  142  8. Self-reported SMBG frequency              .75**  .95**  −.18*  N              132  173  137  9. Objective SMBG frequency                .93**  −.15  N                136  122  10. Combined SMBG frequency                  −.20*  N                  141  11. HbA1c value                  –  N represents sample size for each correlation. HbA1c glycated hemoglobin; SMBG self-monitoring of blood glucose. *p < .05, **p < .01, ***p < .001. View Large Table 3 Descriptive Statistics and Pearson’s Bivariate Correlations Between Study Variables Variables  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  1. Age  .07  .44**  −.02  .29**  .28**  .35**  .06  −.05  .05  −.13  N  172  167  159  169  169  172  167  136  171  142  2. Index of Multiple Deprivation  .04  .12  −.02  .06  .09  .03  −.00  .04  −.14  N  167  159  169  169  172  167  136  171  142  3. Time since diagnosis (in years)    −.08  .18*  .07  .04  .13  .14  .13  .01  N    157  165  164  167  162  134  166  142  4. Balanced time perspective    .26**  .18*  .13  .15  .19*  .14  −.08  N    162  162  164  160  126  164  132  5. Affect associated with monitoring        .53**  .34**  .25**  .20*  .24**  −.37**  N        171  175  170  134  174  141  6. Attitudes toward monitoring          .29**  .35**  .16  .31**  −.20*  N          174  170  134  174  139  7. Self-control ability            .08**  .01  .07  −.31**  N            173  136  177  142  8. Self-reported SMBG frequency              .75**  .95**  −.18*  N              132  173  137  9. Objective SMBG frequency                .93**  −.15  N                136  122  10. Combined SMBG frequency                  −.20*  N                  141  11. HbA1c value                  –  Variables  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  1. Age  .07  .44**  −.02  .29**  .28**  .35**  .06  −.05  .05  −.13  N  172  167  159  169  169  172  167  136  171  142  2. Index of Multiple Deprivation  .04  .12  −.02  .06  .09  .03  −.00  .04  −.14  N  167  159  169  169  172  167  136  171  142  3. Time since diagnosis (in years)    −.08  .18*  .07  .04  .13  .14  .13  .01  N    157  165  164  167  162  134  166  142  4. Balanced time perspective    .26**  .18*  .13  .15  .19*  .14  −.08  N    162  162  164  160  126  164  132  5. Affect associated with monitoring        .53**  .34**  .25**  .20*  .24**  −.37**  N        171  175  170  134  174  141  6. Attitudes toward monitoring          .29**  .35**  .16  .31**  −.20*  N          174  170  134  174  139  7. Self-control ability            .08**  .01  .07  −.31**  N            173  136  177  142  8. Self-reported SMBG frequency              .75**  .95**  −.18*  N              132  173  137  9. Objective SMBG frequency                .93**  −.15  N                136  122  10. Combined SMBG frequency                  −.20*  N                  141  11. HbA1c value                  –  N represents sample size for each correlation. HbA1c glycated hemoglobin; SMBG self-monitoring of blood glucose. *p < .05, **p < .01, ***p < .001. View Large Two one-way ANOVAs were conducted to examine whether participants’ level of education or employment status influenced the outcome variables. Given that some levels of these variables contained just a small number of participants (e.g., only three participants reported having no formal education, see Table 1), some of the groups were combined to reduce unequal group sizes and to ensure that post hoc tests could be conducted if required. Specifically, for level of education, the lowest two levels (i.e., “no formal education” and “primary education”) were combined, as were the upper two levels (i.e., “postgraduate degree” and “PhD/doctorate”). For employment status, the groups “unemployed” and “unable to work” were combined, and the group “other,” which only contained three observations, was excluded. The analyses indicated that there were no differences in HbA1c levels according to level of education or employment status (p’s > .05). Similarly, there was no difference in the frequency with which participants self-monitored their blood glucose levels according to employment status, F(4,151) = 1.78, p = .136. There was, however, a significant difference in the frequency with which participants monitored their blood glucose according to their level of education, F(4, 151) = 3.42, p = .010. Post hoc tests revealed that participants who had completed secondary education (i.e., up to GCSE level) monitored their blood glucose more frequently (M = 33.15, SE = 2.19) than those who had completed college/sixth form (i.e., up to A-level; M = 24.37, SE = 2.16, p = .038). Thus, level of education was controlled for in analyses exploring the relationship between balanced time perspective and the frequency with which participants monitored their blood glucose. As the sample in this study was predominantly White British (97.3%) and from the UK (90.7%), differences in ethnicity and country of birth could not be explored. Finally, given that our sample was recruited using two different methods (i.e., via postal questionnaires or approached in clinic), independent t tests and chi-square tests were conducted to explore whether the demographics, biomedical factors, or the outcome measures varied according to how participants were recruited. These analyses revealed that none of the variables differed according to how the sample was recruited (p’s > .05), and therefore, the method of recruitment was not considered further. Is a Balanced Time Perspective Associated With (a) the Frequency of Blood Glucose Monitoring and (b) Long-term Glycemic Control? The correlation between balanced time perspective and the frequency of blood glucose monitoring was small and not statistically significant (r = .14; p = .066), as was the correlation between balanced time perspective and HbA1c levels (r = −.08; p = .365; see Table 3). However, given that our earlier analyses indicated that whether participants had attended a DAFNE course and their level of education were significantly associated with the frequency with which they monitored their blood glucose, further tests of these relationships were conducted as planned, using hierarchical regression and mediation analyses. These analyses provide a better estimate of the relationship between balanced time perspective and the frequency with which people with type 1 diabetes monitor their blood glucose and HbA1c levels as they enable us to control for these confounding factors. Frequency of self-monitoring blood glucose levels Participants’ level of education and whether they had attended a DAFNE course were entered into Step 1 of a hierarchical regression and explained 8% of the variance in the frequency with which participants monitored their blood glucose levels (R2 = .08, adj. R2 = .07, F(2, 159) = 6.66, p = .002). Inspection of the beta weights revealed that, although attendance on a DAFNE course was a significant predictor (β = 0.28, p < .001), level of education was not (β = −0.07, p = .391). The addition of the variable representing a balanced time perspective in Step 2 led to a significant increase in the variance explained in the frequency with which participants self-monitored their blood glucose levels (R2change = .03, Fchange(1, 158) = 4.97, p = .027). The beta weight indicated that balanced time perspective was positively associated with monitoring (β = 0.18, p = .027). This suggests that the more balanced a participant’s time perspective, the more frequently they monitored their blood glucose levels. In the final model, the variables explained 11% of the variance in the frequency with which participants self-monitored their blood glucose levels, F(3, 158) = 6.21, p = .001, with DAFNE course attendance and a balanced time perspective both emerging as significant, independent predictors. Long-term glycemic control To explore whether a balanced time perspective predicted long-term glycemic control, a second regression analysis was conducted with participants’ HbA1c levels as the dependent variable and balanced time perspective as the independent variable. We did not control for DAFNE course attendance or level of education, as our initial analyses suggested that these factors were not associated with HbA1c levels. This regression analysis indicated that a balanced time perspective was not a significant, direct predictor of participants’ long-term glycemic control, F(1, 134) = 1.01, p = .317, β = −0.09, p = .317. Does Self-monitoring of Blood Glucose Mediate the Relationship Between Balanced Time Perspective and Long-term Glycemic Control? A mediation analysis was conducted to explore whether there was an indirect relationship between balanced time perspective and HbA1c levels, via the frequency with which participants monitored their blood glucose. As before, we controlled for whether participants had attended a DAFNE course and their level of education. As can be seen in Figure 1, a balanced time perspective was positively associated with the frequency with which participants monitored their blood glucose (a = 5.119, p = .004), and more frequent monitoring was negatively associated with HbA1c levels (b = −0.204, p = .034), indicating that more frequent monitoring led to better glycemic control. There was also a significant indirect effect of balanced time perspective on HbA1c levels via the frequency of blood glucose monitoring (indirect effect = −1.045, 95% confidence interval [CI]: [−2.696, −0.018]). Taken together, these findings suggest that participants with a more balanced time perspective monitored their blood glucose more frequently, which resulted in lower (and therefore healthier) HbA1c levels. In support of the regression analysis, there was not a direct relationship between balanced time perspective and HbA1c levels (c’ = −0.870, p = .657). Fig. 1. View largeDownload slide Mediation model of the relationship between a balanced time perspective and long-term glycemic control (i.e., HbA1c levels) via the frequency with which participants self-monitor their blood glucose levels (N = 129). As recommended by Hayes (2013), values represent unstandardized beta coefficients with the SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Fig. 1. View largeDownload slide Mediation model of the relationship between a balanced time perspective and long-term glycemic control (i.e., HbA1c levels) via the frequency with which participants self-monitor their blood glucose levels (N = 129). As recommended by Hayes (2013), values represent unstandardized beta coefficients with the SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Which Factors Mediate the Relationship Between a Balanced Time Perspective and the Frequency of Blood Glucose Monitoring? The final set of analyses explored whether the relationship between a balanced time perspective and the frequency with which participants monitored their blood glucose was explained by the feelings that they associate with monitoring, their attitudes toward monitoring, and/or their self-control ability. Two different predictions can be made regarding the ordering of these variables. On the one hand, it is possible that these variables mediate the relationship independently (i.e., parallel mediation). On the other hand, it is possible that the feelings that participants associate with monitoring are related to their attitudes toward monitoring that, in turn, influence the frequency with which they monitor their blood glucose (i.e., serial mediation). To test these predictions, two mediation models were tested: (a) a parallel mediation model (containing all of the potential mediators) and (b) a serial mediation model (containing feelings and attitudes associated with monitoring in series). The findings from the parallel mediation model are presented in Figure 2. Balanced time perspective was significantly related to the feelings that participants associated with monitoring their blood glucose levels (a1 = 0.343, p < .001) and their attitudes toward monitoring (a2 = 0.152, p = .021), but not participants’ self-control ability (a3 = 0.133, p = .067). The only significant predictor of the frequency with which participants monitored their blood glucose levels was their attitudes toward monitoring (b2 = 6.293, p = .004). However, tests of the indirect effects indicated that none of these factors independently mediated the relationship between balanced time perspective and the frequency with which participants monitored their blood glucose levels (see Table 4). The direct effect was also not significant (c’ = 1.594, p = .321). Table 4 Summary of Indirect Effects (N = 158) for the Parallel Mediation Model Depicted in Figure 3   Indirect effect  Variable  Effect  SE  95% CI  Lower  Upper  Affect associated with SMBG  0.591  0.503  −0.221  1.809  Attitudes toward SMBG  0.955  0.632  −0.027  2.570  Self-control ability  −0.075  0.230  −0.677  0.304  Total indirect effect  1.471  0.815  0.001  3.215    Indirect effect  Variable  Effect  SE  95% CI  Lower  Upper  Affect associated with SMBG  0.591  0.503  −0.221  1.809  Attitudes toward SMBG  0.955  0.632  −0.027  2.570  Self-control ability  −0.075  0.230  −0.677  0.304  Total indirect effect  1.471  0.815  0.001  3.215  CIs for indirect effects are based on 10,000 bootstrapped samples. CIs excluding zero are considered statistically significant at the p < .05 level. Effect unstandardized indirect effect; CI confidence interval; SMBG self-monitoring of blood glucose. View Large Table 4 Summary of Indirect Effects (N = 158) for the Parallel Mediation Model Depicted in Figure 3   Indirect effect  Variable  Effect  SE  95% CI  Lower  Upper  Affect associated with SMBG  0.591  0.503  −0.221  1.809  Attitudes toward SMBG  0.955  0.632  −0.027  2.570  Self-control ability  −0.075  0.230  −0.677  0.304  Total indirect effect  1.471  0.815  0.001  3.215    Indirect effect  Variable  Effect  SE  95% CI  Lower  Upper  Affect associated with SMBG  0.591  0.503  −0.221  1.809  Attitudes toward SMBG  0.955  0.632  −0.027  2.570  Self-control ability  −0.075  0.230  −0.677  0.304  Total indirect effect  1.471  0.815  0.001  3.215  CIs for indirect effects are based on 10,000 bootstrapped samples. CIs excluding zero are considered statistically significant at the p < .05 level. Effect unstandardized indirect effect; CI confidence interval; SMBG self-monitoring of blood glucose. View Large Fig. 2. View largeDownload slide Parallel mediation model of the relationship between a balanced time perspective and the frequency of blood glucose monitoring via the feelings that participants associate with monitoring, their attitudes toward monitoring, and self-control ability (N = 158). As recommended by Hayes [34], values represent unstandardized beta coefficients with SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Fig. 2. View largeDownload slide Parallel mediation model of the relationship between a balanced time perspective and the frequency of blood glucose monitoring via the feelings that participants associate with monitoring, their attitudes toward monitoring, and self-control ability (N = 158). As recommended by Hayes [34], values represent unstandardized beta coefficients with SE shown in parentheses. *p < .05, **p < .01, ***p < .001. The findings from the serial mediation model are presented in Figure 3. When feelings associated with monitoring and attitudes toward monitoring were placed in series, balanced time perspective significantly related to feelings associated with monitoring (a1 = 0.343, p < .001), but not attitudes toward monitoring (a2 = 0.022, p = .694). In turn, the feelings that participants’ associated with monitoring did not significantly predict the frequency with which they monitored their blood glucose levels (b1 = 1.635, p = .265), but attitudes toward monitoring did (b2 = 6.183, p = .004). Clarifying these findings, there was a significant indirect effect of balanced time perspective on the frequency with which participants monitored their blood glucose levels through the feelings that they associated with monitoring and then their attitudes toward monitoring (indirect effect = 0.800, 95% CI: [0.25, 1.86]). Furthermore, after controlling for the feelings that participants associated with monitoring and their attitudes toward monitoring, the direct effect was not significant (c’ = 1.579, p = .324). This provides support for a serial mediation model in which a balanced time perspective influences the feelings that participants associate with monitoring that, in turn, influences their attitudes toward monitoring and so the frequency with which they do so. Fig. 3. View largeDownload slide Sequential mediation model of the relationship between a balanced time perspective and the frequency of blood glucose monitoring via the feelings that participants associate with monitoring and their subsequent attitudes toward monitoring (N = 158). As recommended by Hayes (2013), values represent unstandardized beta coefficients with SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Fig. 3. View largeDownload slide Sequential mediation model of the relationship between a balanced time perspective and the frequency of blood glucose monitoring via the feelings that participants associate with monitoring and their subsequent attitudes toward monitoring (N = 158). As recommended by Hayes (2013), values represent unstandardized beta coefficients with SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Discussion The aim of the present research was to test whether time perspective was associated with the frequency with which people with type 1 diabetes monitored their blood glucose levels and, as a result, achieved long-term glycemic control. Consistent with our initial hypotheses, we found that, after controlling for participants’ level of education and whether they had attended a DAFNE course, a more balanced time perspective was associated with more frequent self-monitoring of blood glucose. Furthermore, the findings indicated that, although there was not a direct relationship between the extent to which participants had a balanced time perspective and long-term glycemic control, there was a significant indirect effect, suggesting that a more balanced time perspective is associated with better long-term glycemic control via its relationship with the frequency of blood glucose monitoring. A second aim of the present research was to identify factors that explain why the extent to which participants had a balanced time perspective was associated with self-monitoring of blood glucose. Our findings suggested that the feelings that participants associated with monitoring their blood glucose (e.g., the extent to which doing so made them feel reassured) and participants’ subsequent attitudes toward monitoring (e.g., the extent to which they believed that monitoring their blood glucose is worthwhile) mediated the relationship between a balanced time perspective and the frequency with which participants monitored their blood glucose levels. Specifically, participants with a more balanced time perspective tended to associate more positive affect with monitoring their blood glucose levels. This, in turn, was associated with more positive attitudes toward monitoring, which were associated with more frequent monitoring. These findings are important from both theoretical and practical perspectives. From a theoretical perspective, the findings are consistent with theories and past research that points to the importance of time perspective for understanding health behavior (e.g., [16]), including the self-management behaviors of people with diabetes (e.g., [17, 18]), and research that has demonstrated the importance of self-monitoring of blood glucose for maintaining glycemic control (e.g., [6, 7]). Furthermore, and in light of the findings from our serial mediation analysis, the present research also indicates that how people typically feel when they monitor their blood glucose is related to their attitudes toward monitoring. This is important because, although attitudes are commonly featured in models of health behavior (e.g., the Theory of Planned Behavior [35]), a common criticism of these models is that they assume that behavior is rational and, as such, they fail to acknowledge the role of other noncognitive determinants, such as emotions [36]. Thus, our findings provide empirical support for these criticisms and for past research that has highlighted the role of (anticipated and experienced) emotions in shaping people’s attitudes toward various behaviors [24]. The present findings also extend previous investigations in two ways. First, although previous research has highlighted the benefits of a future time perspective, the present research demonstrates the efficacy of having a balanced time perspective in promoting the performance of health-protective behaviors. This is significant as it suggests that the optimal time perspective is more nuanced than simply a focus on the future and that other dimensions of time perspective should not be ignored. Second, although previous research has explored the relationship between balanced time perspective and psychological well-being (e.g., [23]), the present research is the first study, to our knowledge, that has explored the relationship between having a more balanced time perspective and a specific health behavior—namely, the extent to which people with type 1 diabetes monitor their blood glucose levels. In contrast to previous research, the present research did not find a relationship between a balanced time perspective and self-control ability [25]. Similarly, we did not find a relationship between participants’ self-control ability and the extent to which they self-monitored their blood glucose levels. This is perhaps surprising as previous research has found that self-control is associated with a wide range of behaviors [37], including better glycemic control in adolescents with type 1 diabetes [26]. One possible explanation for the lack of relationship in the present research is that a core component of self-control is the ability to resist immediate temptation (i.e., an inhibitory response [38]), whereas self-monitoring of blood glucose is considered an active and deliberate behavior that does not necessarily require the person to overcome or resist an alternative course of action. As such, the self-regulatory challenges involved in blood glucose monitoring are likely motivational (e.g., Is this something that I want to do?) rather than volitional (e.g., I want to do this, but struggle to do so). Self-control may be more strongly associated with self-management behaviors that involve inhibiting impulses (e.g., resisting fatty foods), rather than self-management behaviors that involve deciding whether to take proactive steps to benefit future health (e.g., checking blood glucose levels). Nonetheless, the present research further highlights the need to explore psychological factors for understanding self-management behaviors in diabetes [12]. The present findings also have a number of practical implications; not least for interventions designed to promote self-monitoring of blood glucose levels. Specifically, future research could explore whether it is possible to facilitate a balanced time perspective to promote self-monitoring of blood glucose. For example, previous research with individuals with post-traumatic stress disorder has developed a therapy that involves identifying and modifying time perspective [39]. During this therapy, deviations from a balanced time perspective are identified (e.g., a high score on the past-negative subscale), and efforts are made to enhance neglected dimensions of time perspective to promote balance (e.g., by asking the individual to think about all the positive things in their past that they have previously ignored). It would be interesting to investigate whether a similar intervention could also increase the frequency with which participants with type 1 diabetes monitor their blood glucose levels. Such studies would not only be practically important, but would also represent the first experimental tests of the relationship between balanced time perspective and health outcomes. Strengths and Limitations Although the present research provides support for the significance of time perspective for understanding how frequently people with type 1 diabetes self-monitor their blood glucose levels, we acknowledge that the size of the effects found was relatively small. That is, after controlling for whether participants had attended a DAFNE course and their level of education (which together explained 8% of the variance in the frequency with which participants monitored their blood glucose), differences in time perspective only explained an additional 3% of the variance. These effects are, however, comparable to other studies exploring psychological correlates of health behavior (e.g., [40]), and variables explaining a similar percentage of variance are often included in models of health behavior (e.g., [41]). Furthermore, even small effects can have substantive implications for public health [42, 43]. However, to provide stronger support for interventions designed to modify time perspective, future research could consider context-specific measures of time perspective. For example, previous studies have demonstrated that using a measure of time perspective that is specific to the health condition being studied (e.g., using the Hypertension Temporal Orientation Scale [44]), to assess differences in time perspective in individual with hypertension), can explain a larger amount of the variance in subsequent behavior (e.g., [45]). This suggests that a diabetes-specific measure of time perspective may increase the size of the effects found, therefore providing greater support for the development of interventions designed to modify time perspective. It may also be easier to modify time perspective with respect to a specific issue, than more general perspectives. A strength of the present research was the use of an objective measure of glycemic control and the frequency with which participants self-monitored their blood glucose levels. Although this is not the first study to use HbA1c levels to measure glycemic control, it is one of the first studies to use Diasend software for research purposes. The promising findings reported here suggest that the software may be a useful way to investigate other research questions (e.g., exploring habits associated with blood glucose monitoring). The present research found a high correlation between participants’ self-reported frequency of monitoring and the objective data extracted from participants’ electronic blood glucose meters, and so these measures were combined to reduce missing data and to ensure that the analyses were sufficiently powered. Although this suggests that people are fairly accurate in reporting their blood glucose monitoring practices, future studies that use data provided by Diasend software may want to recruit larger samples to compensate for data that may not have been uploaded onto the system. There are, however, some further limitations to the present research that warrant discussion. One limitation is the cross-sectional nature of this research, which means that any inferences about the causal nature of these relationships are based on theoretical considerations that cannot be empirically verified using the present data. Although it seems reasonable to assume that time perspective (being a relatively stable individual difference [15]) is a precursor to the frequency with which people monitor their blood glucose and, in turn, outcomes such as glycemic control, future studies could and should utilize a longitudinal design—or better still, an experimental design as suggested earlier—to provide empirical support for these ideas. A second limitation of the present research was the relatively low response rate (22% of those invited to take part agreed to do so). Low response rates can introduce self-selection bias, and as a result, our sample may not be representative of individuals with type 1 diabetes. For example, given that we told participants that we were interested in blood glucose monitoring and glycemic control, it is possible that individuals who monitored their blood glucose more frequently and had better glycemic control were more likely to take part. That said, the average HbA1c level for the current sample was only slightly lower than the average HbA1c level for the 1,437 patients at Sheffield Teaching Hospitals who matched our inclusion criteria (63 mmol/mol compared with 68 mmol/mol), and the size of this effect was estimated to be small (d = 0.37). This suggests that, although the current sample had slightly better glycemic control, there was not a substantial difference between those participants who took part in this study and the larger population pool. Our sample did, however, lack ethnic diversity as 97% of the sample was White British. Given that previous research has indicated that ethnic minority groups are less likely to monitor their blood glucose [9], future studies with more ethnically diverse samples are important to ensure that the findings can be generalized. Finally, given the limited population from which participants could be recruited (i.e., adults with type 1 diabetes attending the outpatient clinics at Sheffield Teaching Hospitals) and due to missing data, the size of the sample obtained to test our hypotheses was smaller than anticipated. Therefore, it is possible that our analyses failed to detect some potentially significant associations (i.e., there was an increased chance of making a type II error). Although our sample size is comparable with similar studies conducted within this population (e.g., [26]), the findings should be interpreted with caution. Conclusion The present research found that a more balanced time perspective was associated with more frequent self-monitoring of blood glucose among adults with type 1 diabetes and, as a consequence, better long-term glycemic control. The present research also sheds light on why a balanced time perspective is associated with blood glucose monitoring. Specifically, the findings suggest that people with a more balanced time perspective monitor their blood glucose more frequently because they associate more positive feelings with monitoring and thus have more positive attitudes toward monitoring. From a theoretical standpoint, these findings suggest that future research should consider whether and how balanced time perspective influences the performance of other health behaviors. From a practical standpoint, the research suggests that a promising intervention for people with type 1 diabetes might be to try to promote a balanced time perspective to increase the frequency with which people monitor their blood glucose and thus improve glycemic control. Supplementary Material Supplementary material is available at Annals of Behavioral Medicine online. Acknowledgments The authors thank all of the participants who took part in this research. We would also thank the staff at Sheffield Teaching Hospitals NHS Foundation Trust, particularly Professor Simon Heller, Dr. Jackie Elliott, and Dr. Sharon Caunt who provided advice regarding the design of this study and assistance with data collection. This research was funded by a PhD scholarship awarded to the first author by the University of Sheffield. Compliance with Ethical Standards Conflict of Interest Authors Harriet M. Baird, Thomas L. Webb, Jilly Martin, and Fuschia M. Sirois declare that they have no conflict of interest. Authors’ Contributions H. M. Baird conducted the study and drafted the manuscript with contributions from other study authors. All authors read and approved the final manuscript. Ethical Approval This study received ethical approval from North West–Greater Manchester East Research Ethics Committee (16/NW/0039). Informed Consent Informed consent was obtained from all participants who took part in this study. References 1. 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The Relationship Between a Balanced Time Perspective and Self-monitoring of Blood Glucose Among People With Type 1 Diabetes

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Abstract

Abstract Background Self-monitoring of blood glucose helps people with type 1 diabetes to maintain glycemic control and reduce the risk of complications. However, adherence to blood glucose monitoring is often suboptimal. Purpose Like many health behaviors, self-monitoring of blood glucose involves exerting effort in the present to achieve future benefits. As such, the present research explored whether individual differences in time perspective—specifically, the extent to which people have a balanced time perspective—are associated with the frequency with which people with type 1 diabetes monitor their blood glucose and, thus, maintain glycemic control. Methods Adults with type 1 diabetes completed measures of time perspective, feelings associated with monitoring, attitudes toward monitoring, and trait self-control. Objective data regarding the frequency with which participants monitored their blood glucose levels and their long-term glycemic control were extracted from their medical records. Results Hierarchical regression analyses and tests of indirect effects (N = 129) indicated that having a more balanced time perspective was associated with more frequent monitoring of blood glucose and, as a result, better glycemic control. Further analyses (N = 158) also indicated that there was an indirect relationship between balanced time perspective and monitoring of blood glucose via the feelings that participants associated with monitoring and their subsequent attitudes toward monitoring. Conclusions These findings point to the importance and relevance of time perspective for understanding health-related behavior and may help to inform interventions designed to promote self-monitoring of blood glucose in people with type 1 diabetes. Type 1 diabetes, Balanced time perspective, Self-monitoring of blood glucose, Glycemic control, HbA1c levels Introduction Diabetes mellitus is a group of metabolic disorders that are characterized by an excess of glucose circulating in the blood stream, known as hyperglycemia. Type 1 diabetes accounts for approximately 5%–10% of cases of diabetes [1] and occurs due to the destruction of insulin-producing cells that impairs the body’s ability to metabolize glucose. The management of type 1 diabetes is directed toward maintaining healthy blood glucose levels to reduce the risk of microvascular complications (e.g., damage to the eyes, kidneys, and nervous system) and macrovascular complications (e.g., heart attack, heart failure, and strokes) that can have serious and life-debilitating consequences, including loss of vision, limb amputation, and premature death [2, 3]. Self-monitoring of blood glucose has been identified as a key strategy in maintaining glycemic control [4]. Obtaining reliable information about glycemic variations enables the individual and their health care providers to make informed adjustments to their therapeutic regime (e.g., diet, exercise, insulin dosage [5]). Indeed, numerous studies have shown that frequent monitoring of blood glucose (i.e., three to four times daily) is associated with reductions in glycated hemoglobin (HbA1c; a measure of long-term glycemic control) and improved health outcomes [6, 7]. However, despite clear recommendations and the potential benefits, adherence to blood glucose monitoring is often suboptimal, with studies suggesting that 21% of adults never engage in glucose monitoring [8] and 60% monitor less frequently than recommended [9]. As such, identifying factors that are associated with adherence to glucose monitoring has become a focal point of research [10]. Factors Associated With Self-monitoring of Blood Glucose Previous research has demonstrated that demographic factors (e.g., older age, male gender, ethnic minority, low socioeconomic status, and lower levels of education) and biomedical factors (e.g., longer time since diabetes diagnosis and less intensive treatment regimens) are associated with less frequent monitoring of blood glucose [9, 10]. However, researchers have recently highlighted the importance of identifying psychological factors (e.g., locus of control and compensatory beliefs [11]) that can help to understand self-management behaviors in diabetes, especially as such research could inform health education and interventions designed to promote adherence [12]. Many psychological models of health behavior suggest that the extent to which a person values future benefits over more immediate benefits or costs is an important determinant of health behavior [13]. For example, the possible benefits of regularly monitoring blood glucose (e.g., lower likelihood of kidney failure, stroke, and heart attack) may not come to fruition for many years, while at the same time, monitoring blood glucose may involve short-term costs (e.g., inconvenience, discomfort, difficulty, or fear of a “bad” monitoring result). Furthermore, it has been suggested that some people are motivated to avoid monitoring their blood glucose as it serves as a reminder of their diabetes diagnosis [14]. Thus, although some individuals may value their future health and will take steps to ensure it, others may discount their future health in favor of more immediate benefits or to avoid short-term costs. The present research therefore suggests that time perspective may be associated with the extent to which people monitor their blood glucose. Time Perspective Time perspective refers to cognitive and affective biases that people have for the past, present, and/or future and has been found to motivate and influence behavior [15]. According to Zimbardo and Boyd [15], there are five time perspectives: (a) past-negative, reflecting an adverse view of the past, (b) past-positive, reflecting a warm and sentimental view of the past, (c) present-hedonistic, reflecting a pleasure-seeking attitude toward life, (d) present-fatalistic, reflecting the belief that much of life is determined by fate, and (e) future time perspective, reflecting a greater consideration of the effects of current actions on future outcomes. Previous research has indicated that specific time perspectives are associated with specific health behaviors (for a review, see [16]), including the health behaviors of people with diabetes. For example, studies have shown that a future time perspective is associated with more adaptive behaviors, such as medication adherence [17], weight management behaviors (e.g., eating less fatty foods and engaging in more physical activity [18]), and stronger intentions to attend a diabetes screening appointment [19]. Although this evidence seems to suggest that having a future time perspective is beneficial for engaging in health-protective behaviors, researchers have also argued that focusing on one time perspective, while excluding others, can be detrimental [20]. For example, although having a future time perspective may encourage people to set goals for the future (e.g., to achieve long-term glycemic control), it would be difficult for an individual to form plans to achieve these goals without using information from the past (e.g., past knowledge of how certain foods influence their blood glucose levels) or the present (e.g., information obtained from monitoring their blood glucose). As such, it has been suggested that having a balanced time perspective is most beneficial, where people are able to draw from multiple timeframes and switch flexibly between them to meet situational demands and achieve their goals [21, 22]. Interestingly, however, although differences in a balanced time perspective have been explored in relation to psychological well-being (e.g., happiness and life satisfaction [23]), very little research has explored the relationship between balanced time perspective and specific health behaviors. Given the importance of blood glucose monitoring for managing the symptoms of type 1 diabetes and promoting future health, the present research explored whether individual differences in a balanced time perspective were associated with the frequency with which participants monitored their blood glucose, and thus, achieved long-term glycemic control. Why Might a Balanced Time Perspective Be Associated With Blood Glucose Monitoring? There are several reasons to think that differences in the extent to which people have a balanced time perspective may be associated with the frequency with which they monitor their blood glucose levels. First, empirical research has indicated that having a more balanced time perspective is associated with higher levels of positive affect (e.g., the extent to which people tend to feel excited and determined) and lower levels of negative affect (e.g., the extent to which people tend to feel scared and ashamed [23]). Therefore, individuals with a more balanced time perspective may associate more positive feelings with monitoring (e.g., monitoring their blood glucose makes them feel relaxed and reassured) and so monitor more frequently as a result. Second, having a more balanced time perspective may be associated with people’s attitudes toward monitoring their blood glucose. Specifically, individuals with a more balanced time perspective may consider monitoring to be more beneficial and worthwhile for their future health (i.e., they have more positive attitudes toward monitoring) and so monitor more frequently as a result. In addition, given that past research has highlighted that people’s feelings toward a particular behavior (or the emotions that they associate with performing the behavior) can influence their subsequent attitudes toward that behavior (for a review, see [24]), it also seems likely that feelings and attitudes are related, such that positive feelings toward monitoring promote positive attitudes toward monitoring (i.e., these mediators may occur sequentially). Finally, previous research has demonstrated that having a more balanced time perspective is associated with greater self-control ability [25]. Furthermore, greater self-control ability has been found to be associated with better glycemic control in adolescents with type 1 diabetes [26]. Therefore, there may be an indirect relationship between balanced time perspective and blood glucose monitoring via self-control ability. In light of these considerations, the present research will explore three possible mediators of the relationship between a balanced time perspective and the frequency of blood glucose monitoring: (a) The feelings that people associate with monitoring, (b) people’s attitudes toward monitoring, and (c) self-control ability. The Present Research People with type 1 diabetes need to self-monitor their blood glucose to maintain glycemic control and reduce the risk of future health complications. The present research proposes that differences in people’s time perspective and, specifically, differences in the extent to which people hold a balanced time perspective may be associated with the frequency with which people with type 1 diabetes monitor their blood glucose, and thus, achieve long-term glycemic control. A second aim of the present research was to explore potential reasons why balanced time perspective may be associated with self-monitoring. Specifically, the following hypotheses were tested: Hypothesis 1: A more balanced time perspective will be associated with more frequent self-monitoring of blood glucose and, as a consequence, lower HbA1c levels, indicating better long-term glycemic control. Hypothesis 2: The feelings that people associate with monitoring, their attitudes toward monitoring, and their self-control ability will mediate the relationship between the extent to which people hold a balanced time perspective and the frequency with which people self-monitor their blood glucose levels. Method Study Setting and Recruitment The study was conducted in collaboration with the Adult Diabetes Outpatient Clinics at Sheffield Teaching Hospitals NHS Foundation Trust in the UK. This Trust has two diabetes centers, based at the Royal Hallamshire Hospital and the Northern General Hospital. Potential participants were identified by nurse specialists, clinicians, and research coordinators at these diabetes centers. To be eligible to participate, individuals needed to be aged 18 or older, have had a diagnosis of type 1 diabetes for at least 12 months (as assessed by the date on which they were clinically diagnosed), and have access to an electronic glucose meter to monitor their blood glucose. Eligible participants were provided with a recruitment pack that contained a letter of invite, an information sheet, a consent form, a questionnaire, and a stamped addressed envelope. This information was either sent to eligible participants via post, or it was given to them when they attended an appointment at the clinic. Participants were able to decide whether they would like to complete a paper copy of the consent form and questionnaire or whether they would prefer to provide this information online via the survey software, Qualtrics (https://www.qualtrics.com/). Participants who chose to complete a paper copy of the questionnaire were asked to return this, along with their consent form, using the envelope provided. Participants did not receive any incentives for taking part in this research. Between April 2016 and January 2017, 779 postal questionnaires were distributed. Of those contacted, 165 (21%) agreed to participate. A further 74 participants were approached at the diabetes outpatient clinics and 22 (30%) agreed to take part. Four participants (2%) were removed from the analyses because they did not meet the inclusion criteria (i.e., they did not have a diagnosis of type 1 diabetes), resulting in a final sample of 183 participants. Participant Characteristics Table 1 displays the demographic and biomedical characteristics of the sample. Participants were aged between 18 and 88 years (M = 49.95; SD = 17.18). Approximately one half of the sample were female (49%), and the majority were White British (97%). An Index of Multiple Deprivation (IMD) score was calculated using postcode data. The English IMD ranks every postcode area in England from the most deprived area (ranked 1) to the least deprived area (ranked 32,844). Due to this wide range, ranks were divided by 1,000 for ease of comprehension. The mean IMD for the present sample was 18.01 (SD = 98.00), which is slightly higher than the overall mean rank for England (16.42), suggesting that the sample was, on average, marginally more deprived than the population of England as a whole. Participants were, on average, 21 years after diagnosis at the time they completed the study (SD = 15.99; range: 1–73 years). The mean HbA1c level for the sample was 63 mmol/mol (SD = 14.71), which is higher than the recommended value (≤48 mmol/mol; [27]), indicating that the sample tended to have difficulties controlling their blood glucose levels. The mean HbA1c level for the current sample was also compared with the mean HbA1c level for other patients with type 1 diabetes under the care of Sheffield Teaching Hospitals. Research coordinators at these diabetes centers identified 1,437 patients who matched our inclusion criteria (i.e., had type 1 diabetes for longer than 12 months and were aged 18 or older). The average HbA1c level for this patient group was 68.4 mmol/mol, which is slightly higher (d = 0.37) than the average HbA1c level for our sample (i.e., 62.99 mmol/mol). Although this suggests that our sample tended to have difficulties controlling their blood glucose, they had slightly better glycemic control than the average patient under the care of Sheffield Teaching Hospitals. Table 1 Demographic and Biomedical Characteristics of the Sample Characteristic  n (missing)  %  Mean (SD)  Sex  177 (6)       Male  87  47.5     Female  90  49.2    Age (years)  177 (6)    49.95 (17.17)  Ethnicity  182 (1)       White British  178  97.3     Non-white  4  2.2    Country of birth  170 (13)       UK  166  90.7     Other  4  2.2    Education level  180 (3)       No formal education  3  1.6     Primary education  7  3.8     Secondary education  45  24.6     College/sixth form  48  26.2     Undergraduate degree  46  25.1     Postgraduate degree  22  12.0     PhD/doctorate  9  4.9    Employment status  182 (1)       Full-time  84  45.9     Part-time  24  13.1     Unemployed  8  4.4     Student  8  4.4     Retired  43  23.5     Unable to work  12  6.6     Other  3  1.6    Index of Multiple Deprivation score  177 (6)    18.01 (98.00)  Attended a DAFNE course  181 (2)       Yes  125  68.3     No  56  30.6    Time since diabetes diagnosis (years)  172 (11)    21.34 (15.99)  HbA1c value  147 (36)    62.99 (14.71)  Characteristic  n (missing)  %  Mean (SD)  Sex  177 (6)       Male  87  47.5     Female  90  49.2    Age (years)  177 (6)    49.95 (17.17)  Ethnicity  182 (1)       White British  178  97.3     Non-white  4  2.2    Country of birth  170 (13)       UK  166  90.7     Other  4  2.2    Education level  180 (3)       No formal education  3  1.6     Primary education  7  3.8     Secondary education  45  24.6     College/sixth form  48  26.2     Undergraduate degree  46  25.1     Postgraduate degree  22  12.0     PhD/doctorate  9  4.9    Employment status  182 (1)       Full-time  84  45.9     Part-time  24  13.1     Unemployed  8  4.4     Student  8  4.4     Retired  43  23.5     Unable to work  12  6.6     Other  3  1.6    Index of Multiple Deprivation score  177 (6)    18.01 (98.00)  Attended a DAFNE course  181 (2)       Yes  125  68.3     No  56  30.6    Time since diabetes diagnosis (years)  172 (11)    21.34 (15.99)  HbA1c value  147 (36)    62.99 (14.71)  DAFNE Dose Adjustment for Normal Eating; HbA1c glycated hemoglobin. View Large Table 1 Demographic and Biomedical Characteristics of the Sample Characteristic  n (missing)  %  Mean (SD)  Sex  177 (6)       Male  87  47.5     Female  90  49.2    Age (years)  177 (6)    49.95 (17.17)  Ethnicity  182 (1)       White British  178  97.3     Non-white  4  2.2    Country of birth  170 (13)       UK  166  90.7     Other  4  2.2    Education level  180 (3)       No formal education  3  1.6     Primary education  7  3.8     Secondary education  45  24.6     College/sixth form  48  26.2     Undergraduate degree  46  25.1     Postgraduate degree  22  12.0     PhD/doctorate  9  4.9    Employment status  182 (1)       Full-time  84  45.9     Part-time  24  13.1     Unemployed  8  4.4     Student  8  4.4     Retired  43  23.5     Unable to work  12  6.6     Other  3  1.6    Index of Multiple Deprivation score  177 (6)    18.01 (98.00)  Attended a DAFNE course  181 (2)       Yes  125  68.3     No  56  30.6    Time since diabetes diagnosis (years)  172 (11)    21.34 (15.99)  HbA1c value  147 (36)    62.99 (14.71)  Characteristic  n (missing)  %  Mean (SD)  Sex  177 (6)       Male  87  47.5     Female  90  49.2    Age (years)  177 (6)    49.95 (17.17)  Ethnicity  182 (1)       White British  178  97.3     Non-white  4  2.2    Country of birth  170 (13)       UK  166  90.7     Other  4  2.2    Education level  180 (3)       No formal education  3  1.6     Primary education  7  3.8     Secondary education  45  24.6     College/sixth form  48  26.2     Undergraduate degree  46  25.1     Postgraduate degree  22  12.0     PhD/doctorate  9  4.9    Employment status  182 (1)       Full-time  84  45.9     Part-time  24  13.1     Unemployed  8  4.4     Student  8  4.4     Retired  43  23.5     Unable to work  12  6.6     Other  3  1.6    Index of Multiple Deprivation score  177 (6)    18.01 (98.00)  Attended a DAFNE course  181 (2)       Yes  125  68.3     No  56  30.6    Time since diabetes diagnosis (years)  172 (11)    21.34 (15.99)  HbA1c value  147 (36)    62.99 (14.71)  DAFNE Dose Adjustment for Normal Eating; HbA1c glycated hemoglobin. View Large Design and Procedure The study employed a cross-sectional design in which participants were asked to complete measures of time perspective, the feelings that they associate with monitoring their blood glucose levels, their attitudes toward monitoring their blood glucose, and their ability to exert self-control. Permission was also obtained for the research team to access participants’ Diasend database and medical records to extract information regarding the frequency with which they monitored their blood glucose levels and their long-term glycemic control (i.e., their HbA1c level). The study was presented to participants as an investigation into the factors that influence blood glucose monitoring and glycemic control; however, no details were provided on the specific factors of interest or how they might relate to these outcomes. Measures Demographics The following demographic information was collected from participants: date of birth, gender, ethnicity, country of birth, postcode, occupation, employment status, and level of education. Participants were also asked to indicate whether they had participated in the Dose Adjustment for Normal Eating (DAFNE) training course. This course is offered to adults with type 1 diabetes across the UK and provides formal training on how to adjust insulin doses according to diet (e.g., carbohydrate intake) and lifestyle (e.g., amount of exercise). The course trains attendees to monitor their blood glucose levels before each meal to guide the calculation of their insulin dose. Time perspective Time perspective was measured using Zimbardo’s Time Perspective Inventory (ZTPI; [15]). This measure contains 56-items that assess five dimensions of time perspective: (a) past-positive (e.g., “It gives me pleasure to think about my past”), (b) past-negative (e.g., “I often think of what I should have done differently in my life”), (c) present-fatalistic (e.g., “It doesn’t make sense to worry about the future, since there is nothing I can do about it anyway”), (d) present-hedonistic (e.g., “I find myself getting swept up in the excitement of the moment”), and (e) future (e.g., “I am able to resist temptations when I know there is work to be done”). Participants were asked to respond to each of the items on a 5-point Likert scale, anchored by “very untrue of me” to “very true of me.” Cronbach’s alpha suggested that each subscale was internally reliable: past-positive (α = .75); past-negative (α = .86), present-fatalistic (α = .72), present-hedonistic (α = .80), and future (α = .80). To measure a balanced time perspective, we first computed a Deviation from a Balanced Time Perspective (DBTP) score [25] by subtracting participants’ scores for each subscale from the “optimal” score, as specified by Zimbardo and Boyd [28]. This measure was then reverse scored, so that higher scores indicated a more balanced time perspective. Affect associated with self-monitoring blood glucose How participants typically feel when they self-monitor their blood glucose was measured using the stem “Monitoring my blood glucose makes me feel…” followed by eight items: guilty, bad about myself, good about myself, relaxed, disappointed, at ease, anxious, and reassured. These items were devised for the purpose of this study and were informed by the literature and attendance at a DAFNE training course. Items were rated on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree.” Negative items were reverse coded so that higher scores indicated that participants associated monitoring with more positive affect (α = .86). Attitudes toward self-monitoring blood glucose Participants’ attitudes toward monitoring were measured with the stem “I think that monitoring my blood glucose every time that I am supposed to is…” followed by six bipolar adjectives rated on a 5-point scale: “Important—unimportant,” “easy—difficult,” “harmful—beneficial,” “worthwhile—pointless,” “unpleasant—pleasant,” and “wise—foolish.” After reverse coding negative items, the items were averaged such that higher scores indicated that participants held more positive attitudes toward self-monitoring their blood glucose levels (α = .77). We also measured the extent to which participants found their current monitoring regime effective, convenient, and intrusive using the Glucose Monitoring Experiences Questionnaire (GME-Q; [29]); however, none of these subscales mediated the relationship between balance time perspective and the frequency with which participants monitored their blood glucose (see Supplementary Material 1). Self-control Trait self-control was assessed using the 13-item Brief Self-Control Scale (BSCS; [30]). Previous studies have demonstrated that the BSCS is a valid measure of self-control (e.g., [30]), and it has been found to be associated with glycemic control in individuals with type 1 diabetes [26]. Example items include “I am good at resisting temptation” and “I often act without thinking through all the alternatives.” Items were rated on a 5-point Likert scale, anchored by “not at all” to “very much.” After reverse scoring negative items, items were averaged such that higher scores reflected greater levels of trait self-control (α = .77). Clinical Outcomes Biomedical information The following information was collected from participants’ medical records: time since diabetes diagnosis, name of consultant in charge of care, and current insulin regime (e.g., frequency of injections, insulin types, and doses). Frequency of blood glucose monitoring The frequency with which participants monitored their blood glucose was measured using Diasend software (https://diasend.com//en). Diasend is a system for recording information from electronic blood glucose meters, including the value, date, and time of each measurement. This information is uploaded by patients or their health care providers to a secure online database. To account for any effects of participation in the research on the frequency with which participants monitored their blood glucose, the data were extracted for three separate weeks: (a) the week prior to when participants completed the questionnaire (or nearest available date), (b) the week when participants completed the questionnaire, and (c) the week after the questionnaire was completed (or nearest available date). A one-way repeated-measures ANOVA was conducted to test whether there were differences in the frequency with which participants monitored their blood glucose between these weeks. Mauchly’s test of sphericity indicated that the assumption of sphericity had been violated (χ2 (2) = 9.31, p = .010), and therefore, the degrees of freedom were corrected using the Huynh-Feldt estimate of sphericity (ε = .90). There were no differences in the frequency with which participants monitored their blood glucose according to the week that the data were extracted, F(1.81, 117.44) = 1.68, p = .193; Time 1: M = 28.55, SD = 12.99; Time 2: M = 29.71, SD = 13.70; Time 3: M = 28.35, SD = 13.55. This confirms that participating in the study did not influence the frequency with which participants monitored their blood glucose. If no data were available within a year of the date required, then the data were recorded as missing. The number of times that participants monitored their blood glucose in each of these weeks (where available) was averaged to provide an objective measure of the frequency with which participants self-monitored their blood glucose during the study period. Participants also reported how often they monitored their blood glucose each week using a single item: “On average, how many times a week do you monitor your blood glucose?” If participants provided a range (e.g., 25–30), then the median value was recorded. There was a high correlation between the data extracted from the Diasend software and the self-reported frequency with which participants monitored their blood glucose (r = .75; see Table 3). As such, to reduce missing data in these variables (Nmissing = 45 and 5 for the objective and self-report measures, respectively) and to ensure sufficient power for subsequent analyses, a composite measure was created. That is, when data were available for both of these measures, an average was taken; otherwise, scores were based on either the objective or self-reported data depending on which was available. Long-term glycemic control Medical records were reviewed to extract participants’ most recent HbA1c level. HbA1c is a measure of glycosylated hemoglobin that reflects overall blood glucose levels over the previous 6–8 weeks [31]. Previous research has demonstrated a strong relationship between high levels of HbA1c and complications [2], and, as such, HbA1c is considered to be the “gold standard” measure of long-term glycemic control [32]. HbA1c levels are measured in mmol/mol, with levels exceeding 48 mmol/mol reflecting difficulties controlling blood glucose levels [27]. The HbA1c reading that most closely corresponded to the date that the participant completed the questionnaire was extracted from participants’ medical records. If a participants’ HbA1c level had not been tested within a year of the date that the questionnaire was completed, then it was recorded as missing. Analytic Strategy The aim of the present research was to investigate whether individual differences in a balanced time perspective are associated with the frequency with which people with type 1 diabetes monitor their blood glucose levels and, as a result, maintain glycemic control. To address these questions, the data were analyzed in three stages. First, the relationships between the demographic factors (e.g., age, gender, ethnicity) and biomedical factors (e.g., time since diagnosis) and the outcome variables (i.e., frequency of self-monitoring of blood glucose and HbA1c levels) were explored using correlations, t tests, and ANOVAs as appropriate. When significant relationships between these factors and the outcome variables were found, the relevant factors were controlled for in subsequent analyses. Second, hierarchical regression analyses were conducted, with balanced time perspective as the independent variable (entered in Step 2) and the frequency of blood glucose monitoring or HbA1c level as the dependent variables, controlling for any covariates identified in the first step of the analyses (entered in Step 1). These analyses were conducted using SPSS version 23 [33]. Finally, a series of mediation models were conducted using PROCESS [34]. These models explored (a) whether the relationship between balanced time perspective and long-term glycemic control was mediated by the frequency with which participants monitored their blood glucose and (b) whether the relationship between balanced time perspective and self-monitoring of blood glucose was mediated by the feelings that participants’ associated with monitoring, their attitudes toward monitoring, and/or their self-control ability. In all of the mediation models, the indirect effect was tested using a bootstrap estimation approach with 10,000 resamples. Confidence intervals excluding zero were considered statistically significant at the p < .05 level. All of the analyses used the composite measure of the frequency with which participants monitored their blood glucose to reduce missing data and increase the statistical power of these analyses. Additional analyses were also conducted to explore the relationship between the individual dimensions of time perspective and the outcome variables (i.e., frequency of self-monitoring of blood glucose and HbA1c levels), to permit comparison with previous studies that have focused on these variables. These analyses are not reported here, but can be found in Supplementary Material 2. Results Preliminary Analyses Preliminary analyses were conducted to establish whether the data met the statistical assumptions for the analyses outlined earlier. These analyses revealed the presence of outliers. Specifically, an analysis of standardized residuals indicated that four participants had outlying values (i.e., z-scores greater than ±3.29 SD from the mean) on the measure of the frequency of self-monitoring of blood glucose and one participant had an outlying HbA1c value. As such, these participants were removed from subsequent analyses involving these variables. The means, SD, and range for the key study variables (excluding the outliers identified) are presented in Table 2. Table 2 Means, SD, and Range for Key Study Variables Variable  Sample size (N)  Mean (SD)  Range  Balanced time perspective  164  2.80 (0.66)  3.98  Affect associated with monitoring  175  3.55 (0.82)  4.00  Attitudes toward monitoring  174  4.22 (0.59)  3.67  Self-control  178  3.23 (0.60)  3.15  Self-reported SMBG frequency  173  30.01 (13.91)  74.00  Objective SMBG frequency  136  27.85 (13.53)  72.00  Combined SMBG frequency  177  28.61 (13.00)  73.00  HbA1c level  142  62.94 (13.62)  82.00  Variable  Sample size (N)  Mean (SD)  Range  Balanced time perspective  164  2.80 (0.66)  3.98  Affect associated with monitoring  175  3.55 (0.82)  4.00  Attitudes toward monitoring  174  4.22 (0.59)  3.67  Self-control  178  3.23 (0.60)  3.15  Self-reported SMBG frequency  173  30.01 (13.91)  74.00  Objective SMBG frequency  136  27.85 (13.53)  72.00  Combined SMBG frequency  177  28.61 (13.00)  73.00  HbA1c level  142  62.94 (13.62)  82.00  Outliers have been excluded. HbA1c glycated hemoglobin; SMBG self-monitoring of blood glucose. View Large Table 2 Means, SD, and Range for Key Study Variables Variable  Sample size (N)  Mean (SD)  Range  Balanced time perspective  164  2.80 (0.66)  3.98  Affect associated with monitoring  175  3.55 (0.82)  4.00  Attitudes toward monitoring  174  4.22 (0.59)  3.67  Self-control  178  3.23 (0.60)  3.15  Self-reported SMBG frequency  173  30.01 (13.91)  74.00  Objective SMBG frequency  136  27.85 (13.53)  72.00  Combined SMBG frequency  177  28.61 (13.00)  73.00  HbA1c level  142  62.94 (13.62)  82.00  Variable  Sample size (N)  Mean (SD)  Range  Balanced time perspective  164  2.80 (0.66)  3.98  Affect associated with monitoring  175  3.55 (0.82)  4.00  Attitudes toward monitoring  174  4.22 (0.59)  3.67  Self-control  178  3.23 (0.60)  3.15  Self-reported SMBG frequency  173  30.01 (13.91)  74.00  Objective SMBG frequency  136  27.85 (13.53)  72.00  Combined SMBG frequency  177  28.61 (13.00)  73.00  HbA1c level  142  62.94 (13.62)  82.00  Outliers have been excluded. HbA1c glycated hemoglobin; SMBG self-monitoring of blood glucose. View Large Identification of Covariates We measured a number of demographic and biomedical factors that have previously been found to be associated with the frequency with which people monitor their blood glucose levels. However, to avoid reducing the statistical power of our main analyses, our decision as to which of the covariates to include in our analyses was determined by identifying the demographic and biomedical factors that have significant relationships with the outcome variables in the current sample. The correlations between the study variables are presented in Table 3. Neither age, time since diabetes diagnosis, nor IMD scores were significantly associated with the frequency with which participants self-monitored their blood glucose or HbA1c levels (p’s > .05). Thus, these factors were not controlled for in later analyses. Independent t tests indicated that gender was not significantly associated with either the frequency of blood glucose monitoring or HbA1c values (p’s > .05). However, there was a significant difference in the frequency of monitoring blood glucose between participants who had attended a DAFNE course and those who had not, t(174) = −3.49, p = .001. As might be expected, participants who had attended a DAFNE course tended to monitor their blood glucose levels more frequently (M = 30.91; SD = 12.70) than those who had not attended (M = 23.75; SD = 12.21). Thus, whether participants had attended a DAFNE course was controlled for in analyses exploring the relationship between time perspective and the frequency with which participants monitored their blood glucose levels. There was no difference in HbA1c levels as a function of DAFNE attendance, t(46.24) = −0.10, p = .925, and so DAFNE attendance was not controlled in the analyses focusing on HbA1c levels. Table 3 Descriptive Statistics and Pearson’s Bivariate Correlations Between Study Variables Variables  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  1. Age  .07  .44**  −.02  .29**  .28**  .35**  .06  −.05  .05  −.13  N  172  167  159  169  169  172  167  136  171  142  2. Index of Multiple Deprivation  .04  .12  −.02  .06  .09  .03  −.00  .04  −.14  N  167  159  169  169  172  167  136  171  142  3. Time since diagnosis (in years)    −.08  .18*  .07  .04  .13  .14  .13  .01  N    157  165  164  167  162  134  166  142  4. Balanced time perspective    .26**  .18*  .13  .15  .19*  .14  −.08  N    162  162  164  160  126  164  132  5. Affect associated with monitoring        .53**  .34**  .25**  .20*  .24**  −.37**  N        171  175  170  134  174  141  6. Attitudes toward monitoring          .29**  .35**  .16  .31**  −.20*  N          174  170  134  174  139  7. Self-control ability            .08**  .01  .07  −.31**  N            173  136  177  142  8. Self-reported SMBG frequency              .75**  .95**  −.18*  N              132  173  137  9. Objective SMBG frequency                .93**  −.15  N                136  122  10. Combined SMBG frequency                  −.20*  N                  141  11. HbA1c value                  –  Variables  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  1. Age  .07  .44**  −.02  .29**  .28**  .35**  .06  −.05  .05  −.13  N  172  167  159  169  169  172  167  136  171  142  2. Index of Multiple Deprivation  .04  .12  −.02  .06  .09  .03  −.00  .04  −.14  N  167  159  169  169  172  167  136  171  142  3. Time since diagnosis (in years)    −.08  .18*  .07  .04  .13  .14  .13  .01  N    157  165  164  167  162  134  166  142  4. Balanced time perspective    .26**  .18*  .13  .15  .19*  .14  −.08  N    162  162  164  160  126  164  132  5. Affect associated with monitoring        .53**  .34**  .25**  .20*  .24**  −.37**  N        171  175  170  134  174  141  6. Attitudes toward monitoring          .29**  .35**  .16  .31**  −.20*  N          174  170  134  174  139  7. Self-control ability            .08**  .01  .07  −.31**  N            173  136  177  142  8. Self-reported SMBG frequency              .75**  .95**  −.18*  N              132  173  137  9. Objective SMBG frequency                .93**  −.15  N                136  122  10. Combined SMBG frequency                  −.20*  N                  141  11. HbA1c value                  –  N represents sample size for each correlation. HbA1c glycated hemoglobin; SMBG self-monitoring of blood glucose. *p < .05, **p < .01, ***p < .001. View Large Table 3 Descriptive Statistics and Pearson’s Bivariate Correlations Between Study Variables Variables  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  1. Age  .07  .44**  −.02  .29**  .28**  .35**  .06  −.05  .05  −.13  N  172  167  159  169  169  172  167  136  171  142  2. Index of Multiple Deprivation  .04  .12  −.02  .06  .09  .03  −.00  .04  −.14  N  167  159  169  169  172  167  136  171  142  3. Time since diagnosis (in years)    −.08  .18*  .07  .04  .13  .14  .13  .01  N    157  165  164  167  162  134  166  142  4. Balanced time perspective    .26**  .18*  .13  .15  .19*  .14  −.08  N    162  162  164  160  126  164  132  5. Affect associated with monitoring        .53**  .34**  .25**  .20*  .24**  −.37**  N        171  175  170  134  174  141  6. Attitudes toward monitoring          .29**  .35**  .16  .31**  −.20*  N          174  170  134  174  139  7. Self-control ability            .08**  .01  .07  −.31**  N            173  136  177  142  8. Self-reported SMBG frequency              .75**  .95**  −.18*  N              132  173  137  9. Objective SMBG frequency                .93**  −.15  N                136  122  10. Combined SMBG frequency                  −.20*  N                  141  11. HbA1c value                  –  Variables  2.  3.  4.  5.  6.  7.  8.  9.  10.  11.  1. Age  .07  .44**  −.02  .29**  .28**  .35**  .06  −.05  .05  −.13  N  172  167  159  169  169  172  167  136  171  142  2. Index of Multiple Deprivation  .04  .12  −.02  .06  .09  .03  −.00  .04  −.14  N  167  159  169  169  172  167  136  171  142  3. Time since diagnosis (in years)    −.08  .18*  .07  .04  .13  .14  .13  .01  N    157  165  164  167  162  134  166  142  4. Balanced time perspective    .26**  .18*  .13  .15  .19*  .14  −.08  N    162  162  164  160  126  164  132  5. Affect associated with monitoring        .53**  .34**  .25**  .20*  .24**  −.37**  N        171  175  170  134  174  141  6. Attitudes toward monitoring          .29**  .35**  .16  .31**  −.20*  N          174  170  134  174  139  7. Self-control ability            .08**  .01  .07  −.31**  N            173  136  177  142  8. Self-reported SMBG frequency              .75**  .95**  −.18*  N              132  173  137  9. Objective SMBG frequency                .93**  −.15  N                136  122  10. Combined SMBG frequency                  −.20*  N                  141  11. HbA1c value                  –  N represents sample size for each correlation. HbA1c glycated hemoglobin; SMBG self-monitoring of blood glucose. *p < .05, **p < .01, ***p < .001. View Large Two one-way ANOVAs were conducted to examine whether participants’ level of education or employment status influenced the outcome variables. Given that some levels of these variables contained just a small number of participants (e.g., only three participants reported having no formal education, see Table 1), some of the groups were combined to reduce unequal group sizes and to ensure that post hoc tests could be conducted if required. Specifically, for level of education, the lowest two levels (i.e., “no formal education” and “primary education”) were combined, as were the upper two levels (i.e., “postgraduate degree” and “PhD/doctorate”). For employment status, the groups “unemployed” and “unable to work” were combined, and the group “other,” which only contained three observations, was excluded. The analyses indicated that there were no differences in HbA1c levels according to level of education or employment status (p’s > .05). Similarly, there was no difference in the frequency with which participants self-monitored their blood glucose levels according to employment status, F(4,151) = 1.78, p = .136. There was, however, a significant difference in the frequency with which participants monitored their blood glucose according to their level of education, F(4, 151) = 3.42, p = .010. Post hoc tests revealed that participants who had completed secondary education (i.e., up to GCSE level) monitored their blood glucose more frequently (M = 33.15, SE = 2.19) than those who had completed college/sixth form (i.e., up to A-level; M = 24.37, SE = 2.16, p = .038). Thus, level of education was controlled for in analyses exploring the relationship between balanced time perspective and the frequency with which participants monitored their blood glucose. As the sample in this study was predominantly White British (97.3%) and from the UK (90.7%), differences in ethnicity and country of birth could not be explored. Finally, given that our sample was recruited using two different methods (i.e., via postal questionnaires or approached in clinic), independent t tests and chi-square tests were conducted to explore whether the demographics, biomedical factors, or the outcome measures varied according to how participants were recruited. These analyses revealed that none of the variables differed according to how the sample was recruited (p’s > .05), and therefore, the method of recruitment was not considered further. Is a Balanced Time Perspective Associated With (a) the Frequency of Blood Glucose Monitoring and (b) Long-term Glycemic Control? The correlation between balanced time perspective and the frequency of blood glucose monitoring was small and not statistically significant (r = .14; p = .066), as was the correlation between balanced time perspective and HbA1c levels (r = −.08; p = .365; see Table 3). However, given that our earlier analyses indicated that whether participants had attended a DAFNE course and their level of education were significantly associated with the frequency with which they monitored their blood glucose, further tests of these relationships were conducted as planned, using hierarchical regression and mediation analyses. These analyses provide a better estimate of the relationship between balanced time perspective and the frequency with which people with type 1 diabetes monitor their blood glucose and HbA1c levels as they enable us to control for these confounding factors. Frequency of self-monitoring blood glucose levels Participants’ level of education and whether they had attended a DAFNE course were entered into Step 1 of a hierarchical regression and explained 8% of the variance in the frequency with which participants monitored their blood glucose levels (R2 = .08, adj. R2 = .07, F(2, 159) = 6.66, p = .002). Inspection of the beta weights revealed that, although attendance on a DAFNE course was a significant predictor (β = 0.28, p < .001), level of education was not (β = −0.07, p = .391). The addition of the variable representing a balanced time perspective in Step 2 led to a significant increase in the variance explained in the frequency with which participants self-monitored their blood glucose levels (R2change = .03, Fchange(1, 158) = 4.97, p = .027). The beta weight indicated that balanced time perspective was positively associated with monitoring (β = 0.18, p = .027). This suggests that the more balanced a participant’s time perspective, the more frequently they monitored their blood glucose levels. In the final model, the variables explained 11% of the variance in the frequency with which participants self-monitored their blood glucose levels, F(3, 158) = 6.21, p = .001, with DAFNE course attendance and a balanced time perspective both emerging as significant, independent predictors. Long-term glycemic control To explore whether a balanced time perspective predicted long-term glycemic control, a second regression analysis was conducted with participants’ HbA1c levels as the dependent variable and balanced time perspective as the independent variable. We did not control for DAFNE course attendance or level of education, as our initial analyses suggested that these factors were not associated with HbA1c levels. This regression analysis indicated that a balanced time perspective was not a significant, direct predictor of participants’ long-term glycemic control, F(1, 134) = 1.01, p = .317, β = −0.09, p = .317. Does Self-monitoring of Blood Glucose Mediate the Relationship Between Balanced Time Perspective and Long-term Glycemic Control? A mediation analysis was conducted to explore whether there was an indirect relationship between balanced time perspective and HbA1c levels, via the frequency with which participants monitored their blood glucose. As before, we controlled for whether participants had attended a DAFNE course and their level of education. As can be seen in Figure 1, a balanced time perspective was positively associated with the frequency with which participants monitored their blood glucose (a = 5.119, p = .004), and more frequent monitoring was negatively associated with HbA1c levels (b = −0.204, p = .034), indicating that more frequent monitoring led to better glycemic control. There was also a significant indirect effect of balanced time perspective on HbA1c levels via the frequency of blood glucose monitoring (indirect effect = −1.045, 95% confidence interval [CI]: [−2.696, −0.018]). Taken together, these findings suggest that participants with a more balanced time perspective monitored their blood glucose more frequently, which resulted in lower (and therefore healthier) HbA1c levels. In support of the regression analysis, there was not a direct relationship between balanced time perspective and HbA1c levels (c’ = −0.870, p = .657). Fig. 1. View largeDownload slide Mediation model of the relationship between a balanced time perspective and long-term glycemic control (i.e., HbA1c levels) via the frequency with which participants self-monitor their blood glucose levels (N = 129). As recommended by Hayes (2013), values represent unstandardized beta coefficients with the SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Fig. 1. View largeDownload slide Mediation model of the relationship between a balanced time perspective and long-term glycemic control (i.e., HbA1c levels) via the frequency with which participants self-monitor their blood glucose levels (N = 129). As recommended by Hayes (2013), values represent unstandardized beta coefficients with the SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Which Factors Mediate the Relationship Between a Balanced Time Perspective and the Frequency of Blood Glucose Monitoring? The final set of analyses explored whether the relationship between a balanced time perspective and the frequency with which participants monitored their blood glucose was explained by the feelings that they associate with monitoring, their attitudes toward monitoring, and/or their self-control ability. Two different predictions can be made regarding the ordering of these variables. On the one hand, it is possible that these variables mediate the relationship independently (i.e., parallel mediation). On the other hand, it is possible that the feelings that participants associate with monitoring are related to their attitudes toward monitoring that, in turn, influence the frequency with which they monitor their blood glucose (i.e., serial mediation). To test these predictions, two mediation models were tested: (a) a parallel mediation model (containing all of the potential mediators) and (b) a serial mediation model (containing feelings and attitudes associated with monitoring in series). The findings from the parallel mediation model are presented in Figure 2. Balanced time perspective was significantly related to the feelings that participants associated with monitoring their blood glucose levels (a1 = 0.343, p < .001) and their attitudes toward monitoring (a2 = 0.152, p = .021), but not participants’ self-control ability (a3 = 0.133, p = .067). The only significant predictor of the frequency with which participants monitored their blood glucose levels was their attitudes toward monitoring (b2 = 6.293, p = .004). However, tests of the indirect effects indicated that none of these factors independently mediated the relationship between balanced time perspective and the frequency with which participants monitored their blood glucose levels (see Table 4). The direct effect was also not significant (c’ = 1.594, p = .321). Table 4 Summary of Indirect Effects (N = 158) for the Parallel Mediation Model Depicted in Figure 3   Indirect effect  Variable  Effect  SE  95% CI  Lower  Upper  Affect associated with SMBG  0.591  0.503  −0.221  1.809  Attitudes toward SMBG  0.955  0.632  −0.027  2.570  Self-control ability  −0.075  0.230  −0.677  0.304  Total indirect effect  1.471  0.815  0.001  3.215    Indirect effect  Variable  Effect  SE  95% CI  Lower  Upper  Affect associated with SMBG  0.591  0.503  −0.221  1.809  Attitudes toward SMBG  0.955  0.632  −0.027  2.570  Self-control ability  −0.075  0.230  −0.677  0.304  Total indirect effect  1.471  0.815  0.001  3.215  CIs for indirect effects are based on 10,000 bootstrapped samples. CIs excluding zero are considered statistically significant at the p < .05 level. Effect unstandardized indirect effect; CI confidence interval; SMBG self-monitoring of blood glucose. View Large Table 4 Summary of Indirect Effects (N = 158) for the Parallel Mediation Model Depicted in Figure 3   Indirect effect  Variable  Effect  SE  95% CI  Lower  Upper  Affect associated with SMBG  0.591  0.503  −0.221  1.809  Attitudes toward SMBG  0.955  0.632  −0.027  2.570  Self-control ability  −0.075  0.230  −0.677  0.304  Total indirect effect  1.471  0.815  0.001  3.215    Indirect effect  Variable  Effect  SE  95% CI  Lower  Upper  Affect associated with SMBG  0.591  0.503  −0.221  1.809  Attitudes toward SMBG  0.955  0.632  −0.027  2.570  Self-control ability  −0.075  0.230  −0.677  0.304  Total indirect effect  1.471  0.815  0.001  3.215  CIs for indirect effects are based on 10,000 bootstrapped samples. CIs excluding zero are considered statistically significant at the p < .05 level. Effect unstandardized indirect effect; CI confidence interval; SMBG self-monitoring of blood glucose. View Large Fig. 2. View largeDownload slide Parallel mediation model of the relationship between a balanced time perspective and the frequency of blood glucose monitoring via the feelings that participants associate with monitoring, their attitudes toward monitoring, and self-control ability (N = 158). As recommended by Hayes [34], values represent unstandardized beta coefficients with SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Fig. 2. View largeDownload slide Parallel mediation model of the relationship between a balanced time perspective and the frequency of blood glucose monitoring via the feelings that participants associate with monitoring, their attitudes toward monitoring, and self-control ability (N = 158). As recommended by Hayes [34], values represent unstandardized beta coefficients with SE shown in parentheses. *p < .05, **p < .01, ***p < .001. The findings from the serial mediation model are presented in Figure 3. When feelings associated with monitoring and attitudes toward monitoring were placed in series, balanced time perspective significantly related to feelings associated with monitoring (a1 = 0.343, p < .001), but not attitudes toward monitoring (a2 = 0.022, p = .694). In turn, the feelings that participants’ associated with monitoring did not significantly predict the frequency with which they monitored their blood glucose levels (b1 = 1.635, p = .265), but attitudes toward monitoring did (b2 = 6.183, p = .004). Clarifying these findings, there was a significant indirect effect of balanced time perspective on the frequency with which participants monitored their blood glucose levels through the feelings that they associated with monitoring and then their attitudes toward monitoring (indirect effect = 0.800, 95% CI: [0.25, 1.86]). Furthermore, after controlling for the feelings that participants associated with monitoring and their attitudes toward monitoring, the direct effect was not significant (c’ = 1.579, p = .324). This provides support for a serial mediation model in which a balanced time perspective influences the feelings that participants associate with monitoring that, in turn, influences their attitudes toward monitoring and so the frequency with which they do so. Fig. 3. View largeDownload slide Sequential mediation model of the relationship between a balanced time perspective and the frequency of blood glucose monitoring via the feelings that participants associate with monitoring and their subsequent attitudes toward monitoring (N = 158). As recommended by Hayes (2013), values represent unstandardized beta coefficients with SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Fig. 3. View largeDownload slide Sequential mediation model of the relationship between a balanced time perspective and the frequency of blood glucose monitoring via the feelings that participants associate with monitoring and their subsequent attitudes toward monitoring (N = 158). As recommended by Hayes (2013), values represent unstandardized beta coefficients with SE shown in parentheses. *p < .05, **p < .01, ***p < .001. Discussion The aim of the present research was to test whether time perspective was associated with the frequency with which people with type 1 diabetes monitored their blood glucose levels and, as a result, achieved long-term glycemic control. Consistent with our initial hypotheses, we found that, after controlling for participants’ level of education and whether they had attended a DAFNE course, a more balanced time perspective was associated with more frequent self-monitoring of blood glucose. Furthermore, the findings indicated that, although there was not a direct relationship between the extent to which participants had a balanced time perspective and long-term glycemic control, there was a significant indirect effect, suggesting that a more balanced time perspective is associated with better long-term glycemic control via its relationship with the frequency of blood glucose monitoring. A second aim of the present research was to identify factors that explain why the extent to which participants had a balanced time perspective was associated with self-monitoring of blood glucose. Our findings suggested that the feelings that participants associated with monitoring their blood glucose (e.g., the extent to which doing so made them feel reassured) and participants’ subsequent attitudes toward monitoring (e.g., the extent to which they believed that monitoring their blood glucose is worthwhile) mediated the relationship between a balanced time perspective and the frequency with which participants monitored their blood glucose levels. Specifically, participants with a more balanced time perspective tended to associate more positive affect with monitoring their blood glucose levels. This, in turn, was associated with more positive attitudes toward monitoring, which were associated with more frequent monitoring. These findings are important from both theoretical and practical perspectives. From a theoretical perspective, the findings are consistent with theories and past research that points to the importance of time perspective for understanding health behavior (e.g., [16]), including the self-management behaviors of people with diabetes (e.g., [17, 18]), and research that has demonstrated the importance of self-monitoring of blood glucose for maintaining glycemic control (e.g., [6, 7]). Furthermore, and in light of the findings from our serial mediation analysis, the present research also indicates that how people typically feel when they monitor their blood glucose is related to their attitudes toward monitoring. This is important because, although attitudes are commonly featured in models of health behavior (e.g., the Theory of Planned Behavior [35]), a common criticism of these models is that they assume that behavior is rational and, as such, they fail to acknowledge the role of other noncognitive determinants, such as emotions [36]. Thus, our findings provide empirical support for these criticisms and for past research that has highlighted the role of (anticipated and experienced) emotions in shaping people’s attitudes toward various behaviors [24]. The present findings also extend previous investigations in two ways. First, although previous research has highlighted the benefits of a future time perspective, the present research demonstrates the efficacy of having a balanced time perspective in promoting the performance of health-protective behaviors. This is significant as it suggests that the optimal time perspective is more nuanced than simply a focus on the future and that other dimensions of time perspective should not be ignored. Second, although previous research has explored the relationship between balanced time perspective and psychological well-being (e.g., [23]), the present research is the first study, to our knowledge, that has explored the relationship between having a more balanced time perspective and a specific health behavior—namely, the extent to which people with type 1 diabetes monitor their blood glucose levels. In contrast to previous research, the present research did not find a relationship between a balanced time perspective and self-control ability [25]. Similarly, we did not find a relationship between participants’ self-control ability and the extent to which they self-monitored their blood glucose levels. This is perhaps surprising as previous research has found that self-control is associated with a wide range of behaviors [37], including better glycemic control in adolescents with type 1 diabetes [26]. One possible explanation for the lack of relationship in the present research is that a core component of self-control is the ability to resist immediate temptation (i.e., an inhibitory response [38]), whereas self-monitoring of blood glucose is considered an active and deliberate behavior that does not necessarily require the person to overcome or resist an alternative course of action. As such, the self-regulatory challenges involved in blood glucose monitoring are likely motivational (e.g., Is this something that I want to do?) rather than volitional (e.g., I want to do this, but struggle to do so). Self-control may be more strongly associated with self-management behaviors that involve inhibiting impulses (e.g., resisting fatty foods), rather than self-management behaviors that involve deciding whether to take proactive steps to benefit future health (e.g., checking blood glucose levels). Nonetheless, the present research further highlights the need to explore psychological factors for understanding self-management behaviors in diabetes [12]. The present findings also have a number of practical implications; not least for interventions designed to promote self-monitoring of blood glucose levels. Specifically, future research could explore whether it is possible to facilitate a balanced time perspective to promote self-monitoring of blood glucose. For example, previous research with individuals with post-traumatic stress disorder has developed a therapy that involves identifying and modifying time perspective [39]. During this therapy, deviations from a balanced time perspective are identified (e.g., a high score on the past-negative subscale), and efforts are made to enhance neglected dimensions of time perspective to promote balance (e.g., by asking the individual to think about all the positive things in their past that they have previously ignored). It would be interesting to investigate whether a similar intervention could also increase the frequency with which participants with type 1 diabetes monitor their blood glucose levels. Such studies would not only be practically important, but would also represent the first experimental tests of the relationship between balanced time perspective and health outcomes. Strengths and Limitations Although the present research provides support for the significance of time perspective for understanding how frequently people with type 1 diabetes self-monitor their blood glucose levels, we acknowledge that the size of the effects found was relatively small. That is, after controlling for whether participants had attended a DAFNE course and their level of education (which together explained 8% of the variance in the frequency with which participants monitored their blood glucose), differences in time perspective only explained an additional 3% of the variance. These effects are, however, comparable to other studies exploring psychological correlates of health behavior (e.g., [40]), and variables explaining a similar percentage of variance are often included in models of health behavior (e.g., [41]). Furthermore, even small effects can have substantive implications for public health [42, 43]. However, to provide stronger support for interventions designed to modify time perspective, future research could consider context-specific measures of time perspective. For example, previous studies have demonstrated that using a measure of time perspective that is specific to the health condition being studied (e.g., using the Hypertension Temporal Orientation Scale [44]), to assess differences in time perspective in individual with hypertension), can explain a larger amount of the variance in subsequent behavior (e.g., [45]). This suggests that a diabetes-specific measure of time perspective may increase the size of the effects found, therefore providing greater support for the development of interventions designed to modify time perspective. It may also be easier to modify time perspective with respect to a specific issue, than more general perspectives. A strength of the present research was the use of an objective measure of glycemic control and the frequency with which participants self-monitored their blood glucose levels. Although this is not the first study to use HbA1c levels to measure glycemic control, it is one of the first studies to use Diasend software for research purposes. The promising findings reported here suggest that the software may be a useful way to investigate other research questions (e.g., exploring habits associated with blood glucose monitoring). The present research found a high correlation between participants’ self-reported frequency of monitoring and the objective data extracted from participants’ electronic blood glucose meters, and so these measures were combined to reduce missing data and to ensure that the analyses were sufficiently powered. Although this suggests that people are fairly accurate in reporting their blood glucose monitoring practices, future studies that use data provided by Diasend software may want to recruit larger samples to compensate for data that may not have been uploaded onto the system. There are, however, some further limitations to the present research that warrant discussion. One limitation is the cross-sectional nature of this research, which means that any inferences about the causal nature of these relationships are based on theoretical considerations that cannot be empirically verified using the present data. Although it seems reasonable to assume that time perspective (being a relatively stable individual difference [15]) is a precursor to the frequency with which people monitor their blood glucose and, in turn, outcomes such as glycemic control, future studies could and should utilize a longitudinal design—or better still, an experimental design as suggested earlier—to provide empirical support for these ideas. A second limitation of the present research was the relatively low response rate (22% of those invited to take part agreed to do so). Low response rates can introduce self-selection bias, and as a result, our sample may not be representative of individuals with type 1 diabetes. For example, given that we told participants that we were interested in blood glucose monitoring and glycemic control, it is possible that individuals who monitored their blood glucose more frequently and had better glycemic control were more likely to take part. That said, the average HbA1c level for the current sample was only slightly lower than the average HbA1c level for the 1,437 patients at Sheffield Teaching Hospitals who matched our inclusion criteria (63 mmol/mol compared with 68 mmol/mol), and the size of this effect was estimated to be small (d = 0.37). This suggests that, although the current sample had slightly better glycemic control, there was not a substantial difference between those participants who took part in this study and the larger population pool. Our sample did, however, lack ethnic diversity as 97% of the sample was White British. Given that previous research has indicated that ethnic minority groups are less likely to monitor their blood glucose [9], future studies with more ethnically diverse samples are important to ensure that the findings can be generalized. Finally, given the limited population from which participants could be recruited (i.e., adults with type 1 diabetes attending the outpatient clinics at Sheffield Teaching Hospitals) and due to missing data, the size of the sample obtained to test our hypotheses was smaller than anticipated. Therefore, it is possible that our analyses failed to detect some potentially significant associations (i.e., there was an increased chance of making a type II error). Although our sample size is comparable with similar studies conducted within this population (e.g., [26]), the findings should be interpreted with caution. Conclusion The present research found that a more balanced time perspective was associated with more frequent self-monitoring of blood glucose among adults with type 1 diabetes and, as a consequence, better long-term glycemic control. The present research also sheds light on why a balanced time perspective is associated with blood glucose monitoring. Specifically, the findings suggest that people with a more balanced time perspective monitor their blood glucose more frequently because they associate more positive feelings with monitoring and thus have more positive attitudes toward monitoring. From a theoretical standpoint, these findings suggest that future research should consider whether and how balanced time perspective influences the performance of other health behaviors. From a practical standpoint, the research suggests that a promising intervention for people with type 1 diabetes might be to try to promote a balanced time perspective to increase the frequency with which people monitor their blood glucose and thus improve glycemic control. Supplementary Material Supplementary material is available at Annals of Behavioral Medicine online. Acknowledgments The authors thank all of the participants who took part in this research. 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Annals of Behavioral MedicineOxford University Press

Published: May 10, 2018

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