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Workforce resilience in pharmacy is required to ensure the practice, education, and administrative systems remain viable and sustainable over time and when facing challenges. Whether it is addressing burnout of pharmacists or students, or the structure and policies/procedures of employment and professional organizations, working to increase resilience across all individuals and sectors is essential to relieve pressure and promote better well-being, especially during the recent pandemic. The purpose of this article is to describe the development of a community of practice global group focused on development of resilience within the pharmacy workforce that is inclusive of students, pharmacy interns/preregistration and registered pharmacists. The steering group meets monthly and has representation of 24 members across eight countries. Members meet to discuss pertinent issues they are facing in practice, as well as to share and progress ideas on education, research, and practice initiatives. To date, members have collectively implemented resilience training in pharmacy education, researched burnout and resilience in both students and pharmacists, and facilitated international collaborations both within and outside core group members. Future activities will focus on strengthening the community of practice in order to harness the power of the collective.

Physical behavior (ie, physical activity, sedentary behavior, and sleep) is a crucial lifestyle factor for preventing and managing diseases across the lifespan. However, less is known about potential work-related psychological and cognitive outcomes such as productivity. The present study examined within-person associations between physical behavior and self-perceived work ability. To investigate the degree to which physical behavior parameters influence self-perceived work ability in everyday life, we conducted an Ambulatory Assessment study in 103 university students over 5 days. Physical behavior was assessed continuously via a multi-sensor system. Self-perceived work ability was assessed repeatedly up to six times per day on smartphones. We employed multilevel modeling to analyze the within-person effects of physical behavior on self-perceived work ability. Physical activity intensity (MET) (β = 0.15 ± 0.06, t = 2.59, p = 0.012) and sit-to-stand transitions (β = 0.07 ± 0.03, t = 2.44, p = 0.015) were positively associated with self-perceived work ability. Sedentary bouts (≥20 min) (β = −0.21 ± 0.08, t = −2.74, p = 0.006) and deviation from a recommended sleep duration (ie, 8 h) (β = −0.1 ± 0.04, t = −2.38, p = 0.018) were negatively associated with self-perceived work ability. Exploratory analyses supported the robustness of our findings by comparing various time frames. Total sedentary time and sleep quality were not associated with self-perceived work ability. Regular sleep durations, breaking up sedentary time through sit-to-stand transitions, and higher intensities of physical activity may be important for the regulation of self-perceived work ability in university students’ daily lives.

Additional file 2: The TIDieR (Template for Intervention Description and Replication) ChecklistDescription: Populated TIDieR Checklist.

Additional file 3: Table S1. Additional data extraction details specific to this study. Table S2. Baseline characteristics of participants included (n = 196) and excluded (n = 35) in this study. Table S3. Compliance and amount of work at baseline and 3 months within intervention (n = 114) and control participants (n = 81). Table S4. Adjusted mean changes in sitting and activity outcomes within control (n = 81) and intervention (n = 114) groupsa. Table S5. Between group differences (intervention - control) in workplace sitting time by subjective time (hours since starting work) and clock time. Table S6. Variation over time in hourly sitting among the intervention (n = 82) and control groups (n = 114) at baseline and 3-month follow up. Description: Supplemental tables presenting additional data relevant to this study.

Additional file 1: Table S1. CONSORT 2010 checklist of information to include when reporting a cluster randomised trial. Description: Populated CONSORT Extension for Cluster-Randomised Trials 2012 Checklist.

Table S1. Strategies by category for each of the intervention targets. This table reports all unique strategies nominated by intervention participants (nâ =â 134) for each intervention target, within each strategy category. (DOCX 20 kb)

Figure S9. Associations (95% CI) of context-specific sitting time (h/day) with cardiometabolic risk biomarkers, adjusted for potential confounders, total sitting time and further for waist circumference. Associations are plotted standardised (β) and shown in text unstandardised (b). (PDF 66 kb)

Directed acyclic graphs of causal assumptions and confounding. (PDF 1728 kb)

CONSORT 2010 checklist of information to include when reporting a cluster randomised trial. Populated CONSORT Extension for Cluster-Randomised Trials 2012 Checklist. (DOCX 29 kb)

Sitting time questions from AusDiab3. (PDF 112 kb)

AF 1 IJBNPA AusDiab3 sitting questionnaire validity.pdf; Details of activity monitor (activPAL) data collection and processing. (PDF 67 kb)

AF 3 IJBNPA AusDiab3 sitting questionnaire validity.pdf; Comparison of baseline (AusDiab1) characteristics between AusDiab1 participants who ultimately were included (n = 700) or not included (n = 10547) in the present study, due to sub-study non-selection/non-participation/lack of data, or loss to follow-up (AusDiab1–AusDiab3). (PDF 58 kb)

AF 4 IJBNPA AusDiab3 sitting questionnaire validity.pdf; Sitting time in various contexts recalled over the past seven days and relative validity against total sitting time assessed by activPAL™ within participant in paid work (n = 410) and not in paid work (n = 284). (PDF 47 kb)

Objectives: To examine associations between self-reported television (TV) viewing time and overall sitting time with the metabolic syndrome and its components. Design: Cross-sectional. Setting: Population-based sample of older men and women living in Australia. Participants: One thousand nine hundred fifty-eight participants from the Australian Diabetes, Obesity and Lifestyle (AusDiab) study (aged≥60, mean age 69, 54% women). Measurements: Self-reported television viewing time and overall sitting time were collected using an interviewer-administered questionnaire. The metabolic syndrome was defined according to the revised International Diabetes Federation criteria. Results: Compared with those in the lowest quartile, the odds ratios (ORs) of the metabolic syndrome in the highest quartile of television viewing time were 1.42 (95% confidence interval (CI)=0.93–2.15) for men and 1.42 (95% CI=1.01–2.01) for women and in the highest quartile of overall sitting time were 1.57 (95% CI=1.02–2.41) for men and 1.56 (95% CI=1.09–2.24) for women. Television viewing time was associated with lower high-density lipoprotein cholesterol (HDL-C) levels and glucose intolerance in women. Overall sitting time was detrimentally associated with greater risk of high triglyceride levels in men and women, abdominal obesity in women, and low HDL-C levels in men. All models were adjusted for age, education, physical activity, self-rated health, employment, diet, smoking, and alcohol intake and for hormone replacement therapy and estrogen use in women. Conclusion: For older adults, high levels of sedentary behavior were associated with greater prevalence of the metabolic syndrome; reducing prolonged overall sitting time may be a feasible way to improve metabolic health.

Context Emerging evidence suggests that sedentary behavior (i.e., time spent sitting) may be negatively associated with health. The aim of this study was to systematically review the evidence on associations between occupational sitting and health risks. Evidence acquisition Studies were identified in MarchApril 2009 by literature searches in PubMed, PsycINFO, CENTRAL, CINAHL, EMBASE, and PEDro, with subsequent related-article searches in PubMed and citation searches in Web of Science. Identified studies were categorized by health outcome. Two independent reviewers assessed methodologic quality using a 15-item quality rating list (score range 015 points, higher score indicating better quality). Data on study design, study population, measures of occupational sitting, health risks, analyses, and results were extracted. Evidence synthesis 43 papers met the inclusion criteria (21% cross-sectional, 14% casecontrol, 65% prospective); they examined the associations between occupational sitting and BMI (n=12); cancer (n=17); cardiovascular disease (CVD, n=8); diabetes mellitus (DM, n=4); and mortality (n=6). The median study-quality score was 12 points. Half the cross-sectional studies showed a positive association between occupational sitting and BMI, but prospective studies failed to confirm a causal relationship. There was some casecontrol evidence for a positive association between occupational sitting and cancer; however, this was generally not supported by prospective studies. The majority of prospective studies found that occupational sitting was associated with a higher risk of DM and mortality. Conclusions Limited evidence was found to support a positive relationship between occupational sitting and health risks. The heterogeneity of study designs, measures, and findings makes it difficult to draw definitive conclusions at this time.

Table S1. Characteristics of eligible participants who were included or excluded due to missing data. Table S2. Associations of context-specific sitting time (h/day) with biomarkers of cardiometabolic risk: stratified by gender. (DOCX 28 kb)

Background High volumes of sitting time are associated with an elevated risk of type 2 diabetes and cardiovascular disease, and with adverse cardiometabolic risk profiles. However, previous studies have predominately evaluated only total sitting or television (TV) viewing time, limiting inferences about the specific cardiometabolic health impacts of sitting accumulated in different contexts. We examined associations of sitting time in four contexts with cardiometabolic risk biomarkers in Australian adults. Methods Participants (n = 3429; mean ± SD age 58 ± 10 years) were adults without clinically diagnosed diabetes or cardiovascular disease from the 2011–2012 Australian Diabetes, Obesity and Lifestyle (AusDiab) study. Multiple linear regressions examined associations of self-reported context-specific sitting time (occupational, transportation, TV-viewing and leisure-time computer use) with a clustered cardiometabolic risk score (CMR) and with individual cardiometabolic risk biomarkers (waist circumference, BMI, resting blood pressure, triglycerides, HDL- and LDL-cholesterol, and fasting and 2-h post-load plasma glucose). Results Higher CMR was significantly associated with greater TV-viewing and computer sitting time (b [95%CI] = 0.07 [0.04, 0.09] and 0.06 [0.03, 0.09]), and tended to be associated with higher occupational and transport sitting time (0.01 [− 0.01, 0.03] and 0.03 [− 0.00, 0.06]), after adjustment for potential confounders. Furthermore, keeping total sitting time constant, accruing sitting via TV-viewing and computer use was associated with significantly higher CMR (0.05 [0.02, 0.08] and 0.04 [0.01, 0.06]), accruing sitting in an occupational context was associated with significantly lower CMR (− 0.03 [− 0.05, − 0.01]), while no significant association was seen for transport sitting (0.00 [− 0.03, 0.04]). Results varied somewhat between the respective biomarkers; however, higher sitting time in each domain tended to be associated detrimentally with individual biomarkers except for fasting glucose (non-significant associations) and systolic blood pressure (a beneficial association was observed). Overall, associations were stronger for TV-viewing and computer use, and weaker for occupational sitting. Conclusions Higher context-specific sitting times tended to be detrimentally associated, albeit modestly, with CMR and several cardiometabolic risk biomarkers. There was some evidence suggesting that the context in which people sit is relevant above and beyond total sitting time. Methodological issues notwithstanding, these findings may assist in identifying priorities for sitting-reduction initiatives, in order to achieve optimal cardiometabolic health benefits.

The TIDieR (Template for Intervention Description and Replication) Checklist. Populated TIDieR Checklist. (DOCX 29 kb)

The TIDieR (Template for Intervention Description and Replication) Checklist. Populated TIDieR Checklist. (DOCX 29 kb)

Table S1. Characteristics of eligible participants who were included or excluded due to missing data. Table S2. Associations of context-specific sitting time (h/day) with biomarkers of cardiometabolic risk: stratified by gender. (DOCX 28 kb)

In developed and developing countries, social, economic, and environmental transitions have led to physical inactivity and large amounts of time spent sitting. Research is now unraveling the adverse public health consequences of too much sitting. We describe improvements in device-based measurement that are providing new insights into sedentary behavior and health. We consider the implications of research linking evidence from epidemiology and behavioral science with mechanistic insights into the underlying biology of sitting time. Such evidence has led to new sedentary behavior guidelines and initiatives. We highlight ways that this emerging knowledge base can inform public health strategy: First, we consider epidemiologic and experimental evidence on the health consequences of sedentary behavior; second, we describe solutions-focused research from initiatives in workplaces and schools. To inform a broad public health strategy, researchers need to pursue evidence-informed collaborations with occupational health, education, and other sectors. Refereed/Peer-reviewed

Background: An Advanced Pharmacy Practice Framework for Australia (the ‘APPF’) was published in October 2012. Further to the release of the APPF, the Advanced Pharmacy Practice Framework Steering Committee planned to develop an advanced practice recognition model for Australian pharmacists. Aim: To gauge the perspectives of the pharmacy profession relating to advanced practice, via an online survey, in order to inform the design of the model. Method: A survey was developed and administered to Australian pharmacists through SurveyMonkey. The survey content was based on findings from a review of national and international initiatives for recognition of advanced practice in pharmacy and other health disciplines, including medicine and nursing. Results: The results of the survey showed that a high proportion of respondents considered they were already working at, or working towards achieving, an advanced level of practice. The responses relating to the assessment methods showed a clear preference for ‘submission of a professional portfolio’. A ‘written examination’ had a low level of support and in relation to an ‘oral examination by a panel’ there was a marked preference for a panel of multidisciplinary health professionals over a panel of pharmacists. Conclusion: The survey outcomes will inform the development of an advanced pharmacy practice recognition model for Australian pharmacists, particularly in relation to the assessment methods. Survey outcomes also demonstrated that there is scope to further enhance the application of the APPF in the development and recognition of advanced practitioners, and to build greater awareness of the breadth of competencies encompassed by ‘advanced practice’. Refereed/Peer-reviewed

Sedentary behavior is a growing public health concern. Engaging in sedentary behavior is detrimentally associated with a number of health risks and higher mortality. On average, adults spend over half of their waking hours sedentary while engaged in leisure, transport, and work. Over the past 50 years, sedentary time appears to have increased particularly through television viewing, travel by car, and use of technology; however, recent trends in car use may be stable or reversing in some developed countries. Segments of the population that engage in more sedentary leisure pursuits include older adults, non-workers, and those with lower educational attainment. In transport-related and occupational sitting, office workers who commute by car are more sedentary. The environments in which people live (their neighborhoods) and the work they do (desk bound or not) affect the amount of time people spend in sedentary behavior. Light-intensity physical activity seems to be a key behavioral determinant of sedentary time. Initiatives or settings that encourage light-intensity activity may help to reduce sedentary behavior. Further research examining prevalence, trends, and correlates of specific sedentary behaviors is needed in a wide range of countries to inform interventions aimed at reducing sedentary time.

CLARK, B. K., G. N. HEALY, E. A. H. WINKLER, P. A. GARDINER, T. SUGIYAMA, D. W. DUNSTAN, C. E. MATTHEWS, and N. OWEN. Relationship of Television Time with Accelerometer-Derived Sedentary Time: NHANES. Med. Sci. Sports Exerc., Vol. 43, No. 5, pp. 822-828, 2011. Purpose: To examine the relationship of self-reported television (TV) viewing time with accelerometer-derived total sedentary time and to determine whether it differs by subgroup. Methods: Using data for adults (>= 20 yr) from the 2003-2004 and 2005-2006 nationally representative US National Health and Nutrition Examination Surveys (NHANES; n = 5738), linear regression models examined the associations of categories of self-reported TV viewing time (< 1, 1, 2, 3, 4, and 95 h.d(-1)) with accelerometer-derived sedentary time (< 100 counts per minute; h.d(-1)). Spearman rho assessed the correlation between participants' rankings on the two measures. Analyses were stratified by gender, age, race/ethnicity, and, in the 2003-2004 NHANES cycle, by work status among working-aged adults (20-65 yr, n = 2069). Results: TV viewing time was significantly associated with sedentary time, with positive associations for all gender, age, race/ethnicity groups, and for those not working or working part-time, but not for those in full-time work. However, correlations between rankings of the measures were only "fair" overall (rho = 0.22) and were similar for all gender and racial/ethnic groups and for those of mid- and older age but not for those of younger age (20-39 yr, rho = 0.05). In the working-aged subgroup, there was also a fair correlation between the measures for those not working (rho = 0.22) but no significant correlation for those in part-time (rho = 0.14) or full-time work (rho = 0.03). Conclusions: Associations of TV viewing time with accelerometer-derived total sedentary time were statistically significant, but correlations were of only fair magnitude, and the strength of the relationship was not consistent across all population subgroups. These findings suggest that TV viewing time has an influence on overall sedentary time at a population level; however, measurement of sedentary time in other domains is also important.

Background:Sedentary behaviors, particularly television viewing (TV) time, are associated with adverse health outcomes in adults, independent of physical activity levels. These associations are stronger and more consistent for women than for men.Methods:Multivariate regression models examined the sociodemographic correlates of 2 categories of TV time (≥2 hours/day and ≥4 hours/day); in a large, population-based sample of Australian adults (4950 men, 6001 women; mean age 48.1 years, range 25–91) who participated in the 1999/2000 Australian Diabetes, Obesity, and Lifestyle (AusDiab) study.Results:Some 46% of men and 40% of women watched ≥ 2 hours TV/day; 9% and 6% respectively watched ≥ 4 hours/day. For both men and women, ≥2 hours TV/day was associated with less than tertiary education, living outside of state capital cities, and having no paid employment. For women, mid and older age (45−64 and 65+) were also significant correlates of ≥2 hours TV/day. Similar patterns of association were observed in those viewing ≥4 hours/day.Conclusions:Prolonged TV time is associated with indices of social disadvantage and older age. These findings can inform the understanding of potential contextual influences and guide preventive initiatives.

Time spent in non‐occupational sedentary behaviours (particularly television viewing time) is associated with excess adiposity and an increased risk of metabolic disorders among adults; however, there are no reviews of the validity and reliability of assessing these behaviours. This paper aims to document measures used to assess adults' time spent in leisure‐time sedentary behaviours and to review the evidence on their reliability and validity. Medline, CINAHL and Psych INFO databases and reference lists from published papers were searched to identify studies in which leisure‐time sedentary behaviours had been measured in adults. Sixty papers reporting measurement of at least one type of leisure‐time sedentary behaviour were identified. Television viewing time was the most commonly measured sedentary behaviour. The main method of data collection was by questionnaire. Nine studies examined reliability and three examined validity for the questionnaire method of data collection. Test–retest reliabilities were predominantly moderate to high, but the validity studies reported large differences in correlations of self‐completion questionnaire data with the various referent measures used. To strengthen future epidemiological and health behaviour studies, the development of reliable and valid self‐report instruments that cover the full range of leisure‐time sedentary behaviour is a priority. Refereed/Peer-reviewed