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This is a comprehensive review of Open Science (preservation and sharing of data, the responsibilities of researchers, institutions and funders) in various countries, including Canada. Very helpfully, the report summarizes the problems the policy causes for researchers/scientists (though these were not based on interviews with scientists). Perhaps it would be useful for the authors to compare their findings with what the authors of the UK report found. Some points made here resonate with the UK study, but others stand in contrast to it. Is this because of the specific context of the country, or perhaps the context of this particular institution that appears to be head of the game in the implementation of Open Science policies?
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Thank you for these helpful references. We have added citations to this report throughout the Results and Discussion. We felt that the issues it notes were largely consistent with our findings, but we have highlighted one or our findings that diverges from Fecher, Friesike and Hebings’ 2015 work and from the recent Wouters and Haak 2017 CWTS, University Leiden and Elsevier study.
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3b) I think the paper could be substantially strengthened and made more generally applicable through reference to existing frameworks and similar efforts. I am thinking particularly of the work of Fecher and Friesikie (a number of articles including the important "Open Science: One term, five schools of thought" but also recent work on barriers to data sharing in Germany). Christine Borgman's work on data sharing (Big Data, Little Data…) and more broadly dissections of incentives (European Commission Expert Group report on role of research evaluation in progress to Open Science, Metric Tide report) and potentially the literature around choices to public Open Access as well could be valuable. At the moment I feel that the article reinforces what we have seen in other cases, which is valuable. I think it would be stronger and more valuable if these issues were developed in those broader contexts.
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| 99.8 |
As noted directly above, we have added reference to Fecher, Freisike and Hebing, 2015 throughout the Results and Discussion, and to European Commission report, Wilsdon et al., 2017, and Borgman, 2015, to the Discussion. We do feel that our results are largely consistent with those reported by others. In the discussion however, we highlight the unique context of the MNI’s open science initiative, and reflect on how this may influence some of our findings.
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| 99.8 |
3c) (From Cameron Neylon). Something from my own recent work that I feel would be worth exploring is the extent to which group level dynamics help or hinder adoption. What is unique about the effort at the Neuro is the way it operates at a departmental level. Does being part of a group shift the dynamics of issues? Does identity of being in the department vs identity of being part of external communities contribute to engagement, or hinder it?
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3d) The article would benefit from a more thorough literature review that looks at barriers to the adoption of open practices. While some aspects of Open Science are not as well studied, Open Access (OA) has been written about extensively in the literature, including studies about opinions and attitudes, and some explicitly about barriers to change. There have also been efforts to document barriers to adoption of Open Data. A few references are below; a number of publishers have also conducted surveys of researchers' awareness of and attitudes towards both open access and open data.
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However, more important from my perspective would be to relate this other work to what has already been written in the Discussion section. For example, researcher's misgivings based on a lack of awareness and understanding of openness has been previously documented. Similarly, the endless studies on the OA Citation Advantage speaks to the author's point that the OS community has attempted to find evidence of the benefits to researchers.
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Harley, D., Acord, S. K., Earl-Novell, S., Lawrence, S., & King, C. J. (2010). Assessing the Future Landscape of Scholarly Communication: An Exploration of Faculty Values and Needs in Seven Disciplines. Center for Studies in Higher Education. Retrieved from http://escholarship.org/uc/item/15x7385g
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Peekhaus, W., & Proferes, N. (2016). An examination of North American Library and Information Studies faculty perceptions of and experience with open-access scholarly publishing. Library & Information Science Research, 38(1), 18-29. https://doi.org/10.1016/j.lisr.2016.01.003
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other
| 99.94 |
Retaining physical and psychological function in later life is an important part of ‘active ageing’ (World Health Organisation, 2002) through the ability to maintain independence in activities of daily living (McCusker, Kakuma, & Abrahamowicz, 2002). Remaining physically active helps prevent the age-related decline in physical (Paterson & Warburton, 2010) and cognitive (Carvalho et al., 2014, Blondell et al., 2014) function, and associated loss of independence (Paterson, Govindasamy, Vidmar, Cunningham, & Koval, 2004). However, we become less physically active as we age, particularly during the transition into retirement where increased leisure time activity typically does not compensate the loss of work-based activity (Zantinge, van den Berg, Smit, & Picavet, 2014). Early intervention is necessary to encourage physical activity before the process of functional decline begins (Hebert, 1997).
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Outdoor recreation, including walking, jogging and cycling, may be the best source of physical activity for older people, as it can be incorporated in daily life (Ogilvie et al., 2007), has been shown to lead to a decrease in all-cause mortality and chronic disease (Zhao et al., 2015), it facilitates social contact (World Health Organisation, 2002), can result in higher levels of physical activity (Kerr et al., 2012) and may provide additional health benefits over engaging in activity indoors (Thompson Coon et al., 2011). However, maintaining outdoor mobility may be a challenge in later life, as individuals are at increased risk of sensory or physical impairment with age, and may be subject to environmental barriers (Mollenkopf et al., 2004, Yeom et al., 2008).
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| 99.7 |
Physical activity levels are determined by individual characteristics and shared factors such as the natural and built environment (McCormack & Shiell, 2011). One key aspect of the natural environment is both presence of, and access to, green spaces which may encourage higher levels of physical activity for recreation and transport (Paquet et al., 2013, Van Cauwenberg et al., 2011). Mobility and function in older adults has been associated with proximity to (Rosso, Auchincloss, & Michael, 2011), and quality of greenspace and green infrastructure in the built environment (Tzoulas et al., 2007), such as the presence of recreational facilities and clean environments (Wu, Prina, & Brayne, 2015), spaces that are designed according to the expressed need of individuals (Ward Thompson, 2013, Kerr et al., 2012), and factors of urban planning and design (Durand, Andalib, Dunton, Wolch, & Pentz, 2011). The relationship between physical activity and greenspace has been shown to be independent of preferences in self-selection of home location (Handy, Cao, & Mokhtarian, 2006). Whilst there is some cross-sectional evidence of a positive association between greenspace, its use for physical activity and health, findings are generally equivocal in the literature. This may in part be due to a lack of prospective studies of physical activity trajectories over time (Lee & Maheswaran, 2011). In addition, few studies have focused on specific domains of physical activity that may be associated with exposure to greenspace (Lachowycz & Jones, 2011). A particular example is recreational walking which makes an important contribution to overall physical activity in older people (Tse, Wong, & Lee, 2015). Finally, the mechanisms and moderators, including personal, social and environmental factors which help to explain the relationship between the environment and physical activity have not been well evaluated (Van Cauwenberg et al., 2011, Annear et al., 2014). For example, dog walkers are more likely to achieve higher levels of physical activity than others (Cutt, Giles-Corti, & Knuiman, 2008), and as dog walking often occurs in greenspace (Richards, McDonough, Edwards, Lyle, & Troped, 2013), it may be one mechanism that explains higher levels of physical activity and sense of community in greener areas (Lachowycz and Jones, 2013, Toohey et al., 2013). This lack of understanding limits our ability to provide greenspace or physical activity interventions that are most supportive of active ageing.
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This analysis evaluates the role of greenspace in protecting against decline in physical activity over time in older adults, and considers potential mechanisms. It uses the European Prospective Investigation of Cancer (EPIC)-Norfolk cohort study in the UK, which provides data on a wide range of health and lifestyle factors, obtained over a 7.5 year follow-up period in a population-based sample of more than 25,000 adults (Ward Thompson, 2013).
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The initial survey for EPIC-Norfolk was conducted between 1993 and 1997 (First Health Check, 1HC), recruiting 25,639 residents of the region of East Anglia, attending 35 general practice surgeries situated in the county of Norfolk (Day et al., 1999). The sample for this analysis included 15,672 participants with self-reported measures of physical activity from the Second Health Check conducted between 1998 and 2000 (2HC, Follow-up 2, from here referred to as ‘baseline’ for the purposes of this analysis) and a postal questionnaire administered between 2006 and 2009 (from here referred to as ‘follow-up’). This allowed the examination of change in physical activity over time.
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Physical activity at baseline and follow-up was self-reported in the validated Physical Activity Questionnaire (EPAQ2) (EPIC-Norfolk, 2016, Wareham et al., 2002). Participants reported the number of times and average duration over the past year which they engaged in different activities, within the domains of recreational, household, transport and occupational activity. Weekly energy expenditure was estimated by multiplying the time spent in each activity (number of hours per week) by the metabolic equivalent cost (MET) of each activity (Ainsworth et al., 2011). Overall physical activity was calculated by summing energy expenditure over all four domains. For this analysis, three measures of physical activity were used: overall and recreational activity plus a third category of activities that we hypothesised might take place outdoors in greenspace – walking, cycling and jogging. Absolute change in each measure of physical activity was calculated by subtracting values at baseline from those at follow-up.
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The main explanatory variable was the percentage of land cover in the participant’s home neighbourhood that was classified as greenspace. This was measured at baseline, unless participants were known to be at a different address by the time of follow-up. In these cases, as information on the exact date of moves was unavailable, we measured the average neighbourhood greenness for the two addresses. The ArcGIS 10.1 geographic information system (GIS) software (ESRI, 2012), was used to delineate neighbourhood boundaries around participants’ home locations defined according to their home postcode (zip code). Every postcode was geo-located using the UK Ordnance Survey Code-Point® database (Ordnance Survey, 2014), which provides a set of coordinates depicting the average latitude and longitude of all mail delivery locations within each postcode. On average, each postcode contains 15 addresses.
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Neighbourhoods are typically defined as the area within 800 m (approximating to a ten minute walk) of a home location (Dalton, Jones, Panter, & Ogilvie, 2013). However, recent research from studies employing global positioning systems to track movement suggests that 800m may be overly conservative (Boruff, Nathan, & Nijenstein, 2012), and that individuals typically travel greater distances to access resources and be physically active (Hurvitz & Moudon, 2012). Indeed, Hillsdon, Coombes, Griew, and Jones (2015) suggest that most activity is undertaken outside of the proximal home environment (800 m), even for older adults (56.3%), noting that there was little variation according to age. Given that information on actual movement patterns for the participants of EPIC-Norfolk was not available, the sensitivity of findings to neighbourhood definition was examined by employing three neighbourhood measures: 800 m, 3 km and 5 km. To compute each measure, a circular buffer was used to measure the proportion of the area of each circle that was greenspace.
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The estimates of neighbourhood greenspace were generated using data from the Centre for Ecology and Hydrology Land Cover Map of the UK (2007) (Centre for Ecology & Hydrology (CEH), 2013), which is derived from satellite images and digital cartography. It records the dominant land use type, based on a 23 class typology, in 25 m by 25 m size grid cells with greenspace being classified as cells that contain broadleaved and coniferous woodland, arable land, improved grassland, semi-natural grassland, mountain, heath and bog for the purposes of this analysis. All of these types of greenspace are potentially accessible locations for activity participation. In addition to the use of public paths, the ‘right to roam’ in the UK grants people the right to use open access land, which includes common and privately owned land in potentially any of the above land uses. In addition, greenspace in the home neighbourhood may not need to be accessible for it to benefit health, as its presence may inspire individuals to engage in physical activity outside of the home environment. Each participant’s neighbourhood exposure was computed by overlaying the mapped greenspace with the participant’s neighbourhood boundary in the GIS software.
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Demographic, lifestyle, health and anthropometric characteristics, collected using the Health and Lifestyle Questionnaire at the initial survey, baseline and follow-up, were chosen for this analysis based on empirical evidence and theoretical relevance of associations with physical activity and greenspace. Covariates included age, sex and BMI at initial survey. The relationship between greenspace and physical activity might be confounded by socio-economic status (SES) (Lee & Maheswaran, 2011), at both the individual and neighbourhood level. Employment derived social class was used at the individual level, obtained at the initial survey, classed as manual (skilled manual, semi-skilled, unskilled) and non-manual (professional, managerial and technical, skilled non-manual). At the neighbourhood level, we used the Townsend Index, a measure of relative deprivation based on information about area employment, car ownership, home ownership and household overcrowding from the UK Census (Townsend, Phillimore, & Beattie, 1988), derived at initial survey. Marital status (Trost, Owen, Bauman, Sallis, & Brown, 2002) at baseline and the presence of mobility limitations (difficulty walking half a mile) at follow-up were also included in the analysis. Dog walking, measured at follow-up, was tested as a potential mediator in the relationship between exposure to greenspace and decline in physical activity (Lachowycz and Jones, 2013, Toohey et al., 2013). Ethnicity has been found to be associated with physical activity (Gill, Celis-Morales, & Ghouri, 2014), but it was not included in this analysis as 99.7% of the sample (N=15,529) were white, reflecting the population of Norfolk, which was 98.5% according to the 2001 Census (Office for National Statistics, 2001).
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Baseline characteristics of the sample were compared for participants living in the greenest 25% (quartile) of neighbourhoods versus the least green 25%, using one-way analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. The primary outcome, change in physical activity, followed a normal distribution, therefore parametric tests were used. Change in physical activity between baseline and follow-up according to quartile of greenspace exposure in the home neighbourhood was explored using error bar plots and ANOVA. Multivariable regression models were used to explore the association between exposure to greenspace, divided into quartiles, and change in physical activity between baseline and follow-up. The reference category was individuals living in the least green home neighbourhoods (quartile 1). Models were adjusted for physical activity at baseline, age, sex, BMI, SES and marital status. Mediation analysis was conducted to test dog walking as a potential mediator on the causal path between exposure to greenspace and change in physical activity. While the test cannot prove causation, it can indicate whether the data fit with the presumed causal structure (de Vries, van Dillen, Groenewegen, & Spreeuwenberg, 2013), namely that greenspace facilitates dog walking and thereby physical activity. The product of the coefficients method developed by Preacher and Hayes was followed, to calculate total, direct and indirect effects with bootstrapped, bias corrected, standard errors (Preacher & Hayes, 2008). All analyses were conducted using Stata version 13 (Stata Corp, 2013).
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Of the 15,672 participants recruited in the initial survey, we excluded four who did not have a valid postcode that allowed their residential location to be determined. We also excluded a small number of participants (n=32) who had moved far from the study area by the time of the follow-up. A total of 15,636 individuals were included in the analysis with a mean age of 62 years at our baseline. The average length of follow-up was 7.5 years. Our sample therefore represent a cohort that were starting to reach older age in the UK, where state pensionable age was 60 years for women and 65 years for men, at the time of survey (now 63 years for women) (Gov.uk, 2016). There were statistically significant differences (P<0.001) between participant characteristics and quartile of greenspace in terms of age, social class, neighbourhood deprivation, marital status, mobility limitations, dog ownership and walkers, and the urban/rural nature of the home location (Table 1). The larger magnitude of differences were observed for those with professional and managerial occupations, for those who were married, for people owning and walking dogs, for people living in affluent areas, and for people living in urban versus rural locations. Of the total sample, we know that 393 people (2.5%) had moved house by the time of follow-up.Table 1Baseline characteristics of participants in EPIC Norfolk, according to percentage of greenspace (least green 25% and most green 25%) in their home neighbourhood.Table 1.CharacteristicAllLeast green 25%Most green 25%Difference, least and most green*PAge at baseline (years)62.2±9.1 (15,632)62.6±9.1 (3909)61.2±9.0 (3908)1.4<0.001Waist/hip ratio0.85±0.09 (14,848)0.85±0.09 (3647)0.85±0.09 (3726)0.00.558BMI (kg/m2)26.7±4.0 (15,464)26.7±4.1 (3878)26.6±3.9 (3875)0.10.374Social class (count)<0.001 Professional7.4 (1134)6.8 (260)8.2 (314)1.4 Managerial38.8 (5951)34.6 (1322)46.2 (1170)11.6 Skilled non manual16.7 (2559)17.3 (660)13.2 (504)4.1 Skilled manual21.5 (3303)24.1 (923)18.5 (710)5.6 Semi-skilled12.6 (1929)13.3 (507)11.6 (443)1.7 Unskilled3.1 (479)3.9 (150)2.4 (91)1.5Neighbourhood deprivation (count)<0.001 Relatively affluent85.1 (13,299)67.3 (2632)95.7 (3741)28.4 Relatively deprived14.9 (2337)32.7 (1279)4.3 (168)28.4Marital status (count)<0.001 Single4.3 (662)5.9 (231)3.5 (137)2.4 Married79.9 (12,429)74.2 (2882)84.5 (3284)10.3 Separated or divorced6.5 (1009)9.1 (352)4.8 (185)4.3 Widowed9.4 (1455)10.8 (418)7.2 (31)3.6Mobility: limited walking half a mile (count)0.001 Yes (a lot)9.1 (1115)10.2 (306)7.9 (248)2.3 Yes (limited)12.5 (1532)13.3 (397)11.8 (369)1.5 No78.4 (9617)76.5 (2285)80.3 (2517)3.8 Dog owners (count)18.4 (1992)11.9 (317)28.7 (780)16.8<0.001Dog walking (count)<0.001 Not applicable, don’t own a dog77.9 (8427)83.3 (2212)68.2 (1853)15.0 Never4.4 (475)4.6 (123)5.0 (137)0.4 Sometimes, but not every day5.6 (606)3.8 (100)8.0 (218)4.3 Once a day6.3 (687)4.9 (130)9.6 (261)4.7 More than once a day5.8 (628)3.5 (92)9.1 (247)5.6Urban/rural location (count)<0.001 Urban45.3 (7079)81.5 (3188)0.3 (12)81.2 Town and fringe22.3 (3483)14.5 (567)2.3 (90)12.2 Village23.3 (3649)2.5 (98)69.3 (2707)66.7 Hamlet/isolated dwelling9.1 (1425)1.5 (58)28.1 (1100)26.7Greenspace (%)56.6 (31.4)15.2 (9.1)95.7 (3.5)80.5Results are % or mean±SD (n). *P-values from ANOVA and chi square, testing for significant differences between all four quartiles of greenspace and each characteristic.
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Participants in the greenest quartile of home neighbourhoods were more physically active overall at baseline than those in the least green quartile (mean 117.0 versus 107.2 MET hours per week, P<0.001, Fig. 1). Participants experienced a decline in physical activity between baseline and follow-up of 12.6 MET hours per week (hrs/wk) overall. This decline was significantly different according to quartile of greenspace (P=0.041). Decline was less for those in greener neighbourhoods, as physical activity declined by 12.6 MET h/wk in the most green areas, but 14.2 MET hrs/wk in the least green. For activity in outdoor locations, participants overall experienced an average decline of 1.0 MET h/wk between baseline and follow-up. Again, the decline was less in greener neighbourhoods, with a 0.5 MET h/wk reduction in the most green areas but a larger 1.8 MET h/wk decline in the least green (P=0.042, Fig. 2). Conversely, recreational physical activity increased slightly between baseline and follow-up, and this increase was greatest for those in the greenest neighbourhoods (mean 2.0 MET hours per week) compared to those in the least green (mean 0.5 MET hours per week) (Fig. 3), although this difference was not statistically significant (P=0.377).Fig. 1Mean (95% CI) overall physical activity energy expenditure (PAEE) at baseline and follow-up by quartile of greenspace in the home neighbourhood.Fig. 1.Fig. 2Mean (95% CI) outdoor physical activity energy expenditure (PAEE) at baseline and follow-up by quartile of greenspace in the home neighbourhood.Fig. 2.Fig. 3Mean (95% CI) recreational physical activity energy expenditure (PAEE) at baseline and follow-up by quartile of greenspace in the home neighbourhood.Fig. 3.
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Table 2 presents the results of the regression analysis for change in overall physical activity according to greenspace in the home neighbourhood, adjusted for baseline physical activity. After adjustment for baseline physical activity, participants living in the greenest areas experienced a slower decline in overall physical activity, with a difference of 5.6 MET h/wk compared to those living in the least green (Model 1). Higher baseline physical activity was associated with a greater mean difference in physical activity change. The trend across greenspace quartiles was highly statistically significant with the greenest areas being most protective of decline (P<0.001). When adjusted for additional significant covariates of age, sex, BMI, social class and marital status (Model 2), living in the greenest home neighbourhoods at baseline was protective against decline, with these participants showing a difference in overall physical activity of 4.2 MET h/wk (trend P=0.001) from those in the least green neighbourhoods. The adjusted R-squared values indicated that the fully adjusted model explained just over a quarter (26.5%) of the variance in change in overall physical activity.Table 2Regression models for change in overall physical activity between baseline and follow-up, according to quartile of greenspace.Table 2.Model 1 Adjusted for baseline PA (n=10997, adjusted R2 19.4%)Model 2 Adjusted for baseline PA, age, sex, BMI, social class and marital status (n=10785, adjusted R2 26.5%)95% CI95% CICoeff.LowerUpperPP trendCoeff.LowerUpperPP trendQuartile of greenspace: quartile 1 (least green, ref)1.00<0.0011.000.001 quartile 21.19−1.493.870.3841.51−1.074.090.251 quartile 34.011.336.690.0033.070.485.660.020 quartile 4 (most green)5.562.888.24<0.0014.211.606.810.002Baseline PA (MET h/wk)−0.46−0.48−0.44<0.001−0.58−0.60−0.56<0.001Age at 2HC (years)−1.84−1.96−1.72<0.001Sex (ref=female)−3.51−5.39−1.64<0.001BMI (kg/m2)−0.48−0.71−0.24<0.001Social class (ref=non-manual)1.16−0.763.070.236Marital status (ref=not married)3.961.516.410.002Constant37.7535.0040.50<0.001174.49164.09184.88<0.001
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Models of change in recreational physical activity (Table 3) followed a similar pattern to overall activity, where participants living in the greenest areas presented a mean difference of 4.9 MET h/wk more than participants living in the least green when adjusted for baseline activity (Model 1), reducing to 4.0 MET h/wk when adjusted for age, sex, BMI, social class and marital status (Model 2). Participants living in the greenest areas also experienced a slower decline in outdoor physical activity (Table 4) with a difference of 1.7 MET h/wk compared to those in the least green areas (Model 1), reducing to 1.3 MET h/wk when adjusted for age, sex, BMI, social class and marital status (Model 2). Neighbourhood deprivation was not statistically significantly associated with outcomes in any of the models.Table 3Regression models for change in recreational physical activity between baseline and follow-up, according to quartile of greenspace.Table 3.Model 1 Adjusted for baseline PA (n=10852, adjusted R2 19.3%)Model 2 Adjusted for baseline PA, age, sex, BMI, social class and marital status (n=10649, adjusted R2 21.7%)95% CI95% CICoeff.LowerUpperPP trendCoeff.LowerUpperPP trendQuartile of greenspace: quartile 1 (least green, ref)1.00<0.0011.00<0.001 quartile 20.92−0.752.590.2790.87−0.792.520.306 quartile 32.020.353.690.0181.53−0.133.190.071 quartile 4 (most green)4.893.226.56<0.0014.032.365.71<0.001Baseline PA (MET h/wk)−0.52−0.54−0.50<0.001−0.53−0.55−0.51<0.001Age at 2HC (years)−0.50−0.57−0.43<0.001Sex (ref=female)6.164.947.39<0.001BMI (kg/m2)−0.28−0.43−0.13<0.001Social class (ref=non-manual)−0.74−1.970.480.233Marital status (ref=not married)2.330.763.910.004Constant16.7915.4518.13<0.00151.1045.1957.00<0.001Table 4Regression models for change in outdoor physical activity between baseline and follow-up, according to quartile of greenspace.Table 4.Model 1 Adjusted for baseline PA (n=15636, adjusted R2 19.8%)Model 2 Adjusted for baseline PA, age, sex, BMI, social class and marital status (n=15116, adjusted R2 19.8%)95% CI95% CICoeff.LowerUpperPP trendCoeff.LowerUpperPP trendQuartile of greenspace: quartile 1 (least green, ref)1.00<0.0011.000.007 quartile 20.73−0.141.610.1010.77−0.131.670.095 quartile 31.070.201.950.0170.85−0.061.750.066 quartile 4 (most green)1.740.862.62<0.0011.280.382.190.006Baseline PA (MET h/wk)−0.74−0.76−0.71<0.001−0.74−0.76−0.71<0.001Age at 2HC (years)−0.19−0.22−0.15<0.001Sex (ref=female)0.53−0.121.180.112BMI (kg/m2)−0.16−0.24−0.08<0.001Social class (ref=non-manual)−0.71−1.37−0.050.036Marital status (ref=not married)1.030.201.860.015Constant3.643.004.29<0.00119.1115.9322.30<0.001
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To put these results into context, in fully adjusted analysis, the model coefficients predict that the average participant experienced a decline of 8.0 MET h/wk for overall activity if they lived in the greenest neighbourhoods against a predicted decline of 12.1 MET h/wk for participants in the least green neighbourhoods. Corresponding values were an increase of 1.7 versus a decline of 0.02 MET h/wk for recreational physical activity, and an increase of 7.3 versus a decline of 0.4 MET h/wk for outdoor physical activity. A value of 3.5 MET h/wk is equivalent to an hour of walking at a moderate pace on a firm, level surface.
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Mediation analysis suggested that dog walking partially mediated the association between exposure to greenspace and change in physical activity (Table 5). For overall physical activity, 22.6% of the total effect is mediated, accounting for baseline activity, age, sex, BMI, social class and marital status. The mediated percentage increased to 28.1% for recreational physical activity and 50.0% for outdoor physical activity.Table 5Total, direct, and indirect effect, via the mediator of dog walking, of exposure to green space on change in physical activity.Table 5.95% CIEffect (on PA change) Ref=least green quartileCoef.LowerUpperSt. errorPOverall physical activity (n=10573)aTotal effect8.331.9014.773.280.011Direct effect6.450.0112.893.280.050Indirect effect (through dog walking)1.881.252.780.35<0.001 Recreational physical activity (n=10446)bTotal effect6.202.0610.342.110.003Direct effect4.460.338.582.110.034Indirect effect (through dog walking)1.741.222.350.27<0.001 Outdoor physical activity (n=10616)cTotal effect4.161.087.241.570.008Direct effect2.08-0.975.140.280.182Indirect effect (through dog walking)2.081.502.740.28<0.001Least green quartile versus all other quartiles of home neighbourhoods. Coefficients with 95% confidence intervals (bias corrected for indirect effects) and significance values (P). All models are adjusted for baseline physical activity, age, sex, BMI, social class and marital status.aPercent mediated 22.6%.bPercent mediated 28.1%.cPercent mediated 50.0%.
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Least green quartile versus all other quartiles of home neighbourhoods. Coefficients with 95% confidence intervals (bias corrected for indirect effects) and significance values (P). All models are adjusted for baseline physical activity, age, sex, BMI, social class and marital status.
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Sensitivity analysis (Supplemental File 1) suggested that the size of neighbourhood used to explore exposure to greenspace did not affect the associations with change in physical activity. The effect of dog walking as a mediator remained statistically significant at P<0.001 across all neighbourhood sizes, and the effect size reduced only slightly with increasing neighbourhood size.
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Greener local neighbourhoods appear to be protective against decline in overall, outdoor and recreational physical activity in the EPIC-Norfolk cohort, supporting the findings of previous studies (Paquet et al., 2013, Van Cauwenberg et al., 2011, Rosso et al., 2011). There was a strong association between physical activity change during the mean 7.5 years between baseline and follow-up in the cohort and how green the home neighbourhood was during this period, taking into account physical activity at baseline. Participants living in the greenest home neighbourhoods at baseline experienced a significantly slower decline in physical activity of over 4 MET h/wk for overall and recreational physical activity, and 1.3 MET h/wk for outdoor activity, when compared to those in the least green areas. The relationship did not change substantially after adjustment for covariates of age, sex, BMI, social class and marital status.
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| 100.0 |
Dog walking was found to quite strongly mediate the relationship between exposure to greenspace and physical activity change, particularly for outdoor activity, where 50% of the relationship was via this pathway. These findings support prior evidence that dog walking may be a way to facilitate regular physical activity (Cutt et al., 2008) and social interactions (Knight & Edwards, 2008), particularly in older populations (Toohey et al., 2013). However, the results from this cross sectional analysis cannot provide causality and further investigation is required to establish if the hypothesised mechanism - that greenspace facilitates dog walking and thereby physical activity – is in fact operating. If so, designing interventions to promote and support dog walking, perhaps through education and social support to increase self-efficacy (Richards et al., 2013), may be advantageous. Promoting dog ownership may be a further strategy to deliver social and psychological benefits, although alternatives, such as dog-sharing (e.g. www.BorrowMyDoggy.com), fostering, or companion animal policies such as dog walking programs (Johnson & Meadows, 2010), may need to be available to those who cannot look after an animal all of the time (Knight & Edwards, 2008).
|
study
| 99.44 |
Other causal mechanism(s) behind the observed association(s) between exposure to greenspace and change in physical activity exist. For example, older people may be active in greenspace due to participation in group activities and social interactions (Lachowycz & Jones, 2013), such as walking groups, which have been shown to increase physical activity particularly for adults over 60 years of age (Kassavou, Turner, & French, 2013). Further, where greenspace is not easily accessible, innovative solutions may be necessary, such as mall-walking, which has been shown to improve the health of older people through increasing physical activity (Farren et al., 2015).
|
study
| 98.5 |
The research has a number of strengths. EPIC-Norfolk provided a large sample (n=15,672), with two sets of detailed physical activity measurements an average of 7.5 years apart. The sample was drawn from a variety of urban and rural locations across the county for high exposure heterogeneity. We used information about the specific domains of physical activity likely to be conducted outside, including recreational walking, jogging and cycling. Whilst we had no information about use of greenspace amongst our participants, using these specific domains is progress towards addressing limitations outlined in previous analyses, whereby aggregating domains of activity may obscure relationships between specific types of activity and environmental characteristics (Van Cauwenberg et al., 2011). We used mediation analysis to investigate the possible causal mechanism of dog walking with the observed associations. Home neighbourhood buffers were computed based on the home address of individuals. As it is unclear if greenspace needs to be publicly accessible or just visible to encourage physical activity, circular buffers were used to represent the level of green around the home location rather than zones delineated according to road network distances.
|
study
| 100.0 |
In terms of limitations, we were not able to assess quality of greenspaces within the neighbourhood, despite some research suggesting that more attractive, larger spaces, with certain amenities encourage higher levels of physical activity (Ward Thompson, 2013, Lachowycz and Jones, 2011). In addition, we were not able to identify which greenspace was publicly accessible in the data. Nevertheless, greenspace in the home neighbourhood may not need to be accessible for it to benefit health, as its presence may inspire individuals to engage in physical activity outside of the home environment. In the absence of data on quality or accessibility of greenspace, we used detailed land cover information with circular buffers to objectively indicate a potential maximum accessible greenspace in neighbourhoods. We also tested different classifications of exposure to greenspace by running the models on different neighbourhood buffer sizes, based on evidence that people may roam further than their immediate home neighbourhood (Hillsdon et al., 2015), which did not strongly affect the association between exposure and decline in physical activity. However, it is noteworthy that evidence as to what is an appropriate ‘neighbourhood’ for older adults is unclear, and they may tend to stay closer to home than younger individuals (Jansen et al., 2016, Prins et al., 2014). The nature, meaning and use of greenspace may differ between urban and rural areas, with green urban areas particularly tending to be accessible and managed. However, stratification by urban-rural status revealed no evidence of moderation effects in this dataset, although this may be due to the fact that just 12 of our participants lived in urban neighbourhoods that fell within the top quartile of greenness.
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study
| 99.94 |
We did not have the exact house location for participants, so we used postcodes to classify exposure. Therefore, potential error in measurement exists due to the difference in location between postcode centroids and the exact address, the magnitude of which may be greater in rural postcode zones which cover larger areas on average (15.6 ha) than urban (1.3 ha). However, even in rural settings errors in the estimates of neighbourhood greenness are likely to be small given that our 800m based neighbourhoods are 201 ha in size. Potential bias may exist in this study, as certain characteristics, such as those owning dogs or those wishing to be more active, may intentionally self-select neighbourhoods with greater availability of neighbourhood greenspace (McCormack & Shiell, 2011). Nevertheless, there is empirical evidence to suggest that any effect of self-selection in studies of the built environment and health may not be large (James et al., 2015) and may in fact tend to bias associations towards the null rather than produce false-positives (Boone-Heinonen, Guilkey, Evenson, & Gordon-Larsen, 2010). As our primary exposure was predominantly measured at a single time point, we are limited in our ability to ascribe causality to the associations detected.
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study
| 99.94 |
We measured physical activity with self-reported data, which may be subject to error (Prince et al., 2008), and the use of METs introduces a potential source of error, as it is based on an average individual. Nevertheless, the measurement tool and methodology we used has been shown to be both valid and repeatable (The InterAct Consortium, 2012, Wareham et al., 2003). It is possible that our assumed outdoor activities of walking, jogging and cycling, could be undertaken indoors, for example using gym equipment. However, we believe that participants would be more likely to record in a separate category of the questionnaire covering ‘Conditioning exercises e.g. exercise bikes or rowing machines’. One other limitation was that the study was conducted in an English county and may not be representative of other areas. In particular, there was a lack of ethnic heterogeneity in the sample, with as over 99% of the participants stating their ethnicity were white. Additionally, the self-report nature of physical activity is a limitation, although it allowed us look at types of activity that may particularly be undertaken in greenspace, which is not easy to ascertain from accelerometery.
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study
| 99.94 |
Greener home neighbourhoods appear to offer protection against decline in physical activity in older people. Dog walking explains half of this association for outdoor physical activity, so our findings suggest it should be actively promoted to facilitate regular physical activity and maintenance of mobility in later life. Future research should explore other potential mechanisms that may elucidate the unexplained components of the association between greenspace and change in physical activity over time.
|
study
| 81.2 |
Recent achievements in public health have resulted in a 25-year increase in average life expectancy in the United States (1). These advances were the result of changes in the public health system, including improved surveillance systems, advocacy for effective health policies, and epidemiologic studies which improved decision-making capabilities (2). However, declining public health resources and complex health threats may make it difficult for advances of the past century to be sustained.
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other
| 99.9 |
Public health frameworks have neither changed in response to such threats nor adapted in the face of technological and cultural shifts. For example, public health’s utilization of social media is inferior to fields such as business and marketing; while health departments have attempted to incorporate social media in practice, studies suggest that health professionals’ capacity for using these tools to engage populations is low (3–5). Indeed, it has been suggested that the current public health system has “neither the organization nor the incentive to comprehensively address population-centered, primary prevention health services that are evidence-based or linked to improved health outcomes” (6).
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other
| 99.56 |
The inability, or reluctance, to adopt major advancements and reconstruct frameworks in public health may be attributed to the legacy concept. This concept is the tendency for a successful organization to believe it is entitled to continued success; as a result, the organization can fail to seek new opportunities, hampering continued success (7). Contemporary public health problems “require a different set of tools which will only be used if the legacy concept in public health is replaced by a new attitude that encourages innovation, risk-taking, and the building of new partnerships” (6).
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other
| 99.9 |
To combat increasingly complex public health threats, public health leaders should pursue new processes and implement innovative solutions. In particular, traditional public health planning models do not explicitly encourage innovation. While the private sector conventionally resorts to innovative thinking, experimentation, and risk-taking in the face of threats, this approach is not yet embraced in public health’s program planning models. A new public health framework, which incorporates successful processes of the private sector and maximizes the strengths of the public sector, may be a major key to significant improvements in our most pressing and complex public health threats.
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other
| 99.9 |
In traditional public health planning models (see Figure 1), key characteristics are as follows: Steps are linear, and solutions are often evidence based and preconceived before beginning the planning process. In these models, the goal of the health professional is not to generate novel solutions but to implement prescribed solutions in varying contexts.Funding usually comes from government and public sources, such as the Prevention and Public Health Fund and state general funds (8). Public funding opportunities are often limited in availability and scope; consequently, practitioners may be constrained in the type and cost of programs they can implement.Program outcomes are not strongly linked to funding allocation; while granting organizations do take into account program effectiveness, funding is usually allocated to organizations on a regular basis, independent of program outcomes.
|
other
| 99.9 |
Steps are linear, and solutions are often evidence based and preconceived before beginning the planning process. In these models, the goal of the health professional is not to generate novel solutions but to implement prescribed solutions in varying contexts.
|
other
| 99.9 |
Funding usually comes from government and public sources, such as the Prevention and Public Health Fund and state general funds (8). Public funding opportunities are often limited in availability and scope; consequently, practitioners may be constrained in the type and cost of programs they can implement.
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other
| 99.94 |
Traditional public health planning models have been successful for many public health problems in the past. However, reliance on evidence-based practice and public funding, as well as neglecting to attend to program outcomes in allocating funding, can result in the de-emphasis of health outcomes in program implementation, and the long-term implementation of ineffective programs.
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other
| 99.9 |
A new planning model must incorporate a mechanism for generating innovative solutions. Design thinking (see Figure 2), a problem-solving technique widely embraced in the private sector, is one such process. It is an approach to solving problems that starts with the customer and is human centered, research based, collaborative and multidisciplinary, and iterative (10).
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other
| 99.9 |
Design thinking is said to find more innovative solutions to problems in less time and with less expense than traditional methods by initiating a continuous joint discovery cycle between the client and practitioner. Shortly after this process, an inexpensive, rapid prototyping cycle is initiated that creates “touchable” solutions that the client can test. These two interconnected cycles are repeated until a desirable and viable (as determined by the client) solution is found. Only then does the practitioner implement the new solution (10, 11).
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other
| 99.9 |
While design thinking has been implemented by a number of fields in the private sector, it is less commonly utilized in the public sector, including in public health. Public health may be better able to incorporate innovation in practice with the adoption of principles from design thinking. Indeed, Trowbridge (12) suggests that “public health is well-positioned to expand application of design thinking to include health promotion.”
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other
| 99.9 |
Distinct in a new model must be the addition of public health start-up funding from businesses and other private parties. Private funding is best allocated in the Prototype (or Implementation) mode (see Figure 2), in which practitioners propose and iterate small-scale program plans. Funders can then go on to implement larger scale iterations of the most promising ideas proposed by practitioners.
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other
| 99.94 |
Seeking funding from public sources may be a challenging shift for health practitioners, as the leading health issues may not be the primary interests of private funders. Questions associated with balancing the power and interests, including conflicts of interests, are important to consider when partnering with private funders. Leveraging appropriate resources using important precautions can be taken to weigh benefits with any risks that could exist. Examples of well-cited precautions include at least three papers that note key tests to balance the power and interests of public–private partnerships while also promoting the benefits, minimizing the risks associated with leveraging increasingly sustainable partnerships in communities (13–15). These partnerships in public health already exist, including in health product development (16, 17) and the strengthening of health services (18). More clearly linking funding to outcomes may also be helpful to find better ways for valuating and monetizing prevention.
|
review
| 99.44 |
Public health practitioners must learn to procure additional funding from private entities as public health is scaled up to address an increasing variety of health needs among diverse populations. Public health practitioners can leverage the corporate social responsibility (CSR) component of private entities to advance public health programs. CSR has been defined by the European Commission as “a concept whereby companies decide voluntarily to contribute to a better society and a cleaner environment” (19). Examples of CSR include Ben and Jerry’s Caring Dairy program (a sustainability program for dairy farms) (20), Levi Strauss & Co’s Water‹Less™ process (which has saved one billion liters of water since 2011) (21), and TOMS One for One® model (a model TOMS follows to provide shoes, sight, water, and safe birth services in return for every purchased product) (22).
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other
| 99.9 |
Orlitzky et al. conducted a meta-analysis to understand the relationship between corporate social performance (CSP) and corporate financial performance (CFP) (23). Their findings suggest that there is a positive relationship between CSP and CFP. In other words, businesses that support social or environmental causes benefit though increased profits (23). In the context of public health, private organizations will likely provide continued funding to programs that help to fulfill their need for CSR and ultimately CFP.
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other
| 99.75 |
The PHIM merges traditional public health planning models with lessons learned from the private sector (see Figure 3). The PHIM accomplishes this by integrating design thinking “modes” with traditional program planning stages, leveraging the use of private sectors resources, and focusing more closely on program outcomes.
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other
| 99.94 |
The successful adoption of private sector strengths into public health planning models requires public health to adopt several key strategies that include cross-collaboration, community buy-in, autonomy, and creativity. A discussion of these strategies and steps of the PHIM are discussed in more detail below. Examples of innovative public health approaches that incorporate these principles in practice can be found in Table 1.
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other
| 99.9 |
In the capacity-building stage of traditional models (Figure 1), public health agencies develop informal partnerships with stakeholders to form decision-making teams. In the PHIM, the commitment stage incorporates two design thinking components (cross-collaboration and community buy-in) that can help to formalize partnerships over time: Cross-Collaboration: Public health has recognized the importance of coalition building and interorganizational networks to not only improve health but also obtain resources and buy-in (31). However, the PHIM suggests that a stronger emphasis should be placed on the importance of cross-silo collaborations through the application of systems thinking. Incorporating systems thinking requires (1) attention to relationships and an understanding of people, (2) specialized study to understand the parts of the public health system, (3) transcending traditional academic boundaries, and (4) matching public health problems to the appropriate method for studying them (32). While it may not be possible to completely eliminate silos within public health, systems thinking has helped public health practitioners recognize that it is “essential to link [silos] … and recognize that they represent components of a larger system” (33).Community Buy-in: Generating passion to solve community problems is a tenet of both design thinking and public health. When community members assemble together to tackle problems, the power of mobilization and local solutions begins to take place. Such grassroots efforts are typically more sustainable than top–down strategies employed by experts with little community involvement. Public health practitioners who are committed to community mobilization have learned to balance the use of best practice evidence while allowing for local innovation and creativity (34, 35). While community buy-in has been successfully implemented in recent health interventions (36, 37), successful long-term assessment is uncommon.
|
review
| 99.7 |
Cross-Collaboration: Public health has recognized the importance of coalition building and interorganizational networks to not only improve health but also obtain resources and buy-in (31). However, the PHIM suggests that a stronger emphasis should be placed on the importance of cross-silo collaborations through the application of systems thinking. Incorporating systems thinking requires (1) attention to relationships and an understanding of people, (2) specialized study to understand the parts of the public health system, (3) transcending traditional academic boundaries, and (4) matching public health problems to the appropriate method for studying them (32). While it may not be possible to completely eliminate silos within public health, systems thinking has helped public health practitioners recognize that it is “essential to link [silos] … and recognize that they represent components of a larger system” (33).
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other
| 99.8 |
Community Buy-in: Generating passion to solve community problems is a tenet of both design thinking and public health. When community members assemble together to tackle problems, the power of mobilization and local solutions begins to take place. Such grassroots efforts are typically more sustainable than top–down strategies employed by experts with little community involvement. Public health practitioners who are committed to community mobilization have learned to balance the use of best practice evidence while allowing for local innovation and creativity (34, 35). While community buy-in has been successfully implemented in recent health interventions (36, 37), successful long-term assessment is uncommon.
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other
| 99.9 |
When approaching community buy-in using the PHIM, two principles can be adopted to increase success. First, public health can learn from businesses’ success in creating demand. Businesses typically achieve success by identifying a pain and then addressing that pain in such a way that the public becomes enthusiastic enough about the solution that they are willing to pay for it. Public health relies heavily on the free distribution of services, regardless of demand. Although this is unsurprising, as populations served are often economically disadvantaged, generating solutions to health pains similar to private organizations can be implemented by public health organizations. While there is much debate globally concerning the efficacy of charging for preventive health services, particularly in lower and middle income countries, in some cases, underserved populations may not view free services as valuable (38) and charging small, reasonable fees for health services may not negatively affect demand (39, 40). While there are limitations to charging for health services and instances in which this is inappropriate, creating the kind of demand typical in the private sector should be attempted more frequently.
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other
| 99.9 |
Second, public health organizations should study and incorporate business models of innovation into practice, especially the Diffusion of Innovation Theory (41). This model seeks to explain how ideas gain momentum and diffuse through populations. The model achieves this by categorizing individuals into adoption stages (e.g., innovators, early adopters) and illustrating factors that influence the adoption of an innovation (e.g., relative advantage, compatibility) (41). Health practitioners can use this and similar models to design services and marketing efforts to increase appeal for targeted communities.
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other
| 99.9 |
In traditional models, after capacity building, health practitioners typically enter the Assessment stage, gathering data and input from the target population, often in the form of a community health assessment. The corresponding design thinking mode is the Empathy mode. This mode involves the “effort to understand the way [populations] … do things and why, their physical and emotional needs, how they think about the world, and what is meaningful to them” (10). While there is overlap between Empathy and Assessment, to better adopt the Empathy approach, public health practitioners may consider combining a human-centered approach with traditional assessment.
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other
| 99.8 |
While data-driven approaches are crucial in community health assessments, a human-centered approach helps health practitioners to become more invested in the target population by promoting connection and more intimate interactions between health practitioners and those they serve. Such an approach can yield crucial insights into health problems that would not be possible with more formal approaches. Human-centered assessment may include more frequent face-to-face interactions with the target population, observing populations in their natural settings, approaching individuals with the intent to elicit stories as opposed to conduct interviews, and checking cultural biases.
|
other
| 99.9 |
In design thinking, the Define and Ideate modes correspond with Planning. The Define mode entails defining the right challenge to address based on new understanding of populations; it is “an endeavor to synthesize … scattered findings into powerful insights” (10). Closely related is the Ideate mode, in which practitioners ideate potential solutions for the target population, often through brainstorming and other activities. In the Ideate mode, practitioners attempt to “step beyond obvious solutions,” “harness the collective perspectives and strengths of … teams,” “create fluency (volume) and flexibility (variety) in … innovation options,” and “get obvious solutions out of [team members’] … heads” (10). The Define and Ideate stage in the PHIM requires health practitioners to encourage autonomy and creativity in team members.
|
other
| 99.9 |
Autonomy is a certain degree of freedom to test solutions and make decisions without fear of failure. The processes commonly used in the traditional public health planning model do not typically encourage creativity. First, public health professionals usually do not receive specific training to think creatively and innovatively. A reliance on evidence-based practice, while well meaning and useful in addressing familiar health challenges, is not appropriate when addressing the new or unfamiliar. Second, the evidence-based practice paradigm is generally based on the assumption that if a solution works in a handful of communities, it will work anywhere; more troubling still, many of the studies provided by organizations responsible for recommending evidence-based practice are out-of-date or infrequently updated.
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other
| 99.9 |
In the traditional public health model, Planning is followed by Implementation. The corresponding design thinking mode is Prototyping. Prototyping is “the iterative generation of artifacts intended to answer questions that get you closer to your final solution” and includes creating “low-resolution prototypes that are quick and cheap to make … but can elicit useful feedback from users and colleagues” (10).
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other
| 99.94 |
In the Prototype mode, practitioners implement potential solutions with the goal to discover how they can improve their current model or program; in traditional Implementation, practitioners usually implement their programs full scale. The benefit of adopting the Prototype mode is that it allows health practitioners to better manage the solution-building process by breaking down problems and cheaply and quickly testing ideas (10). To adopt principles of the Prototype stage, public health practitioners must understand and implement the rapid experimentation and failure cycle characteristic of design thinking.
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other
| 99.9 |
Rapid experimentation and failure are principles of success commonly found in the private sector, but not embraced in the current public health landscape due to limited funding opportunities. Intuit, a software company famous for their rapid experimentation framework, exemplifies the kinds of principles public health has the resources to implement on a microscale. At Intuit, employees are encouraged to generate innovative, even outrageous, ideas through building teams, gathering solutions, and creating and testing hypotheses. The key to Intuit’s success lies in employees’ ability to talk about ideas, test them quickly without spending exorbitant amounts of money, and have a healthy tolerance for failure (42).
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other
| 99.94 |
In rapid prototyping, innovators iterate on theoretical and virtual prototypes until a “minimum awesome product” that “nails the pain” is created, as opposed to creating full-scale, error-free products that are expensive and require long development cycles (43). Despite differences between the products, audiences, and even motivations of the private and public sector, a mutually beneficial partnership between both sectors can develop on the basis of CSR. As stated previously, various private entities are motivated to engage in CSR for economic and ethical reasons. Public health practitioners can leverage the CSR component of the private sector for funds to initiate and sustain programs over time.
|
other
| 99.9 |
Furthermore, in public health, nailing the pain entails creating a health intervention or community plan that has enthusiastic buy-in from the community and is shown to change health outcomes. In traditional public health, such “prototyping” programs may take the form of pilot testing new ideas and conducting consumer research, but arguably, this is infrequently done in favor of evidence-based and traditional interventions, which are often required by granting organizations (44). Rapid prototyping allows for the testing of new ideas on a small-scale level and without extensive funding.
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other
| 99.9 |
After program implementation, health practitioners move to the Evaluation stage, which usually includes program impact and outcome evaluations. The corresponding design thinking stage is the Test mode, in which practitioners solicit feedback about the prototypes they created previously to refine prototypes, learn more about their target population, and refine their problem statement. The end goal of Testing is to get closer to an ideal solution. Results from the Test mode often prompt practitioners to go back to the Empathize, Define, Ideate, and Prototype modes to refine solutions, which is distinct from traditional public health models.
|
other
| 99.9 |
Evaluation is one of the essential skills needed for innovation (45). In design thinking, outcome evaluation, or whether a program or prototype elicited a significant change in outcomes, is the most important method of determining whether a prototype or program was successful. While process and impact evaluations in public health can be useful in determining program success, focusing too much time on this type of assessment may detract resources from evaluations that most clearly demonstrate success. The end goal is to begin to make public health entities more accountable to the programs they produce. While public health is already concerned with program evaluation, the PHIM promotes dispersing the final funding allocation after outcomes have been assessed and programs have proven to be successful. These key structural changes to the funding structure emphasize the importance of achieving measurable outcomes and perpetuating programs that are successful, while eliminating programs that fail to make significant changes.
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other
| 99.9 |
A common evaluation approach used with the traditional public health planning model is the use of logic modeling to demonstrate how inputs result in outcomes. Although innovation typically occurs through cross-collaboration, “a simple input-output or cause-and-effect model of evaluation is not appropriate.” (46) Newer, more sophisticated evaluation tools can be used when approaching evaluation from a systems thinking perspective. These tools can help with monitoring the interaction and connection between collaborators rather than simply the additive effects of inputs on outputs. Keane (47) has developed a tool to use “interactive” logic modeling to assess the impact of relationships (47). Capacity to conduct these types of evaluations will also continue to grow as more big data sources, such as electronic medical records, become available to public health.
|
other
| 99.44 |
Innovation is not intended to replace public health best-practices or planning models but is available to enhance those practices and tailor interventions to meet local needs. Traditional public health planning models are useful, but practitioners are more likely to promote innovation by allowing opportunities for building commitment, empathy, ideation, and prototyping. Further, it is feasible that more challenging issues, such as persistent chronic or infectious diseases, can be better addressed through innovation-driven creativity and greater cooperation.
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other
| 99.94 |
The aim of learning effective innovations can only come when there is a reasonable willingness to accept failures as essential for making improvements. The notion of “good failures” can be difficult for practitioners and stakeholders to accept because failure is often viewed as the antithesis of success. However, the key to good failure is that it can accelerate the learning process. The value of the PHIM is its ability to identify the hypothetical 1 strategy out of 10 that works. Evaluation of innovation requires a different perspective and should be viewed as a learning opportunity to identify what really works rather than implementing a well-intentioned approach that ultimately may not achieve an impact, which commonly occurs in practice today. Even when only 1 approach in 10 demonstrates success, that 1 approach can certainly help to inform future practice and lead to more impactful intervention.
|
other
| 99.9 |
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. HP and CL, former students at BYU, are now employees of Epic. The article was written while they were students in the MPH program at BYU. All other authors declare no competing interests.
|
other
| 99.94 |
The ocean is the elixir of life. Its composition is an excellent resource to be tapped for drug discovery. The marine environment is complex with variations in pressure, salinity, temperature and biological habitats. The marine organisms have unique therapeutic properties. These have been explored and are yet to be proved . Approximately one half of the total global biodiversity is represented by marine organisms, which are the reservoirs of active natural products . The organisms living in oceans are unique with richest sources of new drug leads. Marine sponges are said to be the gold mines for the past 50 years, with respect to the diversity of secondary metabolites. Sponges produce wide array of compounds with varying carbon skeleton, by which the diseases can be suppressed at different points on focusing specific targets. The secondary metabolites produced are biologically active molecules not directly involved in normal functions of the organisms, which includes growth, reproduction or development [2, 3].
|
review
| 99.9 |
The pharmaceutical interest in sponges arouse in the early 1950s with the discovery of spongothymidine and spongouridine nucleosides from marine sponge cryptotethia crypta These were the basis for the synthesis of Ara-C which is the first marine derived anticancer compound and Ara- A the antiviral drug [5, 6]. Ara-c is used for the treatment of leukaemia and lymphoma, the derivatives of Ara-C is used for various cancer types. It has been found that the lipid components such as fatty acids, sterols and other unsaponifiable compounds occur in lower invertebrates than higher animals.
|
review
| 98.56 |
In olden days, sponges were soaked with wine and put on the left side for heartaches, and sponges soaked in urine are used for the treatment of bites of poisonous animals. In 18th century, the physicians used sponges in powdered form for lung diseases, which comprise of various types of sponges mixed together and powdered. The sponge Spongia officinalis is used as syrup for dry and asthmatic cough in western parts of the world. Manoalide, the sesterterpenoids isolated from marine sponge Luffariella variabilis , is found to be an antibiotic and analgesic. There are around 5300 different products discovered from sponges. The ability to stimulate the production of secondary metabolites by sponges is an important consideration when one wants to harvest compounds from sponges for the production of potential novel therapeutics. The molecular mode of action is not thoroughly investigated, whereas the mechanism by which the sponges interfere with compounds have been reported , through which the bioactive compounds can be transformed into new medicines.
|
review
| 99.7 |
Here, in this study, the marine sponge Aurora globostellata is considered based on its importance in pharmaceutical applications (manuscript communicated). The compounds isolated have been characterized in detail for breast cancer. Their bioactivity is explored in in-vitro and in-vivo studies. The attempt has been undertaken to evaluate the mode of action and druggability of the metabolites isolated and characterized. The discovery of number of bioactive compounds from sponges has been increasing day by day. The natural source would overcome the existing synthetic drugs in mode of action and also reduce the side effects caused by the commercial compounds. Based on the 3D structure of the receptors, modern methods of discovering new leads from natural source are on the rise. The present study focuses on the in-silico analysis of the naturally isolated compounds from marine sponges and compared with the results for the commercial drugs: Afinitor, Halaven, Ixabipilone, Lapatinib, Letrozole, Palbocilib, Raloxifene, and Tamoxifen. The in-silico approach enables one to screen for ADMET properties of vast number of molecules within a few minutes thus reducing the time and is a non- expensive and non-tedious process with great accuracy, which is not possible in standard experimental methods [9, 10, 11]. A comparative analysis of the compounds using Glide Schrodinger package is used to find the common binding residues in HER2, the breast cancer target from among the ten considered compounds. This can confirm the quality of the natural compounds with high binding affinity than the commercial drugs [12, 13].
|
study
| 99.94 |
The marine sponges are collected from Rameswaram Coast, Tamil Nadu by SCUBA diving and they are extracted using hexane solvent. The compounds are isolated using column chromatography and the identification of the isolated compounds is accomplished using spectroscopic methods like GCMS and NMR. The compounds are confirmed as Stigmasterol and Oleic acid; these two compounds are considered to be the ligands for docking analysis against Human Epidermal Growth Factor Receptor 2 (RCSB PDB code 1N8Z). A comparison of the docking results of the breast cancer drugs with the natural compounds isolated from marine sponges Aurora globostellata, against the HER2 has been carried out to estimate the quality of the isolated compounds to act as drug like molecules equivalent to that of the commercial drugs.
|
study
| 100.0 |
HER-2 / neu have been widely studied in breast cancer. The HER-2/ neu oncogene encodes a transmembrane tyrosine kinase receptor with extensive homology to the epidermal growth factor receptor 2. HER2 receptors consists of four transmembrane tyrosine receptors, they are HER1, also called as ErbB1, HER2 (ErbB2), HER3 (ErbB3) and HER4 (ErbB4) . HER2 is a gene responsible for breast cancer, it is also called as ERBB2 (Erb-B2 receptor tyrosine kinase). The over expression of HER2 protein makes the uncontrollable growth and division of cancer cells. The HER2 is found to be over expressed in 20-25% cases. The ErbB receptors contains four plasma membrane receptor tyrosine kinase and all these members of the family contain extra cellular domains, the dimerization site and the ligand binding site where the synthetic molecule binds [15, 16].
|
review
| 99.44 |
The protein three-dimensional crystal structure of Human Epidermal Growth Factor Receptor 2 (PDB ID 1N8Z) is obtained from Protein Data bank and is prepared for the analysis, using protein preparation wizard. In the protein preparation step, protein minimization, grid generation and docking of ligands were done using Glide Schrodinger package . The Hydrogen atoms were added to the protein for maintaining the ionization and tautomeric state of Asp, Glu, Ser, His and Arg amino acids. The missing side chains and atoms are corrected, followed by the protein structure minimization using force fields to minimize the steric clashes in the structure. This protein structure was used for the grid generation in further docking analysis.
|
study
| 99.94 |
The commercial compounds Afinitor, Ixabipilone, Letrozole, Halaven, Lapatinib, Palbociclib, Raloxifene and Tamoxifen and the natural compounds isolated from Aurora globostellata, Stigmasterol and Oleic acid are considered as the ligands against the target HER-2. The ligand structures are downloaded from Pubchem. The ligands have been segregated into three groups; the first group consisting of the five commercial compounds Afinitor, Ixabipilone, Letrozole, Halaven, Lapatinib; the second group representing the next three potential commercial compounds (Palbociclib, Raloxifene and Tamoxifen) and the last group as the isolated compounds (Stigmasterol and Oleic acid). Ligprep was used for ligand preparation. It generates various structures with ionization states at pH 7.0±2.0 with ionizer. The force field Merck Molecular Force field MMFF94 is used for the optimization, producing low energy conformation of the ligand .
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study
| 100.0 |
The package Maestro from SchrÖdinger used here has various merits, where it supports various file formats as structural input, featured tool in creating molecular models and has shown to possess a high visualization capability in viewing small to large complex molecules .
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other
| 99.94 |
Glide focuses towards the orientation of the molecule, its position and the conformation, which screen large libraries. Glide docking applies three different scoring functions; they are Standard precision docking (SP), High throughput virtual screening (HTVS) and Extra precision docking (XP). Both HTVS and SP docking use the same scoring function. The HTVS minimize the immediate conformations throughout docking, and reduces the torsional refinement and more suitable for screening more ligands. XP docking is found to be superior to SP docking in terms of sampling. XP docking reduces the false positive and has more additional terms than SP. In the docking methodology, initially Glide uses hierarchical filters for finding the active site regions for ligand binding in the receptor molecules. Poses means the alignment, position and conformation with respect to the receptor. The next step is the ligand screening, which is an exhaustive search based on torsion angle space. After the ligand screening, it is minimized using molecular mechanics energy function, which is said to be a reasonable model in prediction of binding modes . The best poses are given by E-model score which deals with the van der Waals and electrostatic forces. Glide score represents the buried polar groups and steric clashes, which ranks different ligand poses, where the more negative value represents the tighter binding affinity .
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study
| 99.94 |
The lead compounds from natural resources fail to enter into the market due to the poor pharmacokinetic properties. So, designing ligands satisfying the Adsorption, Distribution, Metabolism and Elimination (ADME) properties will go through the market as a good drug. The drugs should be orally absorbed and distributed to the site of action and eliminated from the body without leaving any traces, which produces adverse effects. Hence, the tools and computer-aided methods, nowadays, have become popular in identifying good drug candidate molecule [21, 22, 23].
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other
| 95.6 |
QikProp, the package in Schrodinger is used for calculating molecular descriptors in predicting ADMET properties . The following parameters are considered here with their ranges given specifically; Polar Surface Area (PSA) that is related to oral bioavailability with the area less than 140A2; Rule of Five indicating the molecules suitability for oral administration; QPlog BB- Blood Brain Barrier that provides an access for the central nervous system with a range lies between -3.0 to 1.0; QPlogPo/w that calculates the hydrophobicity of the molecule with a range of 2.0-6.5; QPlogHERG, the experimental IC50 value for HERG K+ channel blockage, with a range below -5.0; QPPCaco and QPPMDCK, the respective cell permeabilities with a value of >500 nm/sec .
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study
| 98.9 |
Molecular docking approach helps us in identifying best binding ligands with the protein target and helps in exploring new small molecular leads from natural sources with higher binding affinities. These lead molecules enter into the higher phases of drug development and may end up as a good drug candidate. The protein ligand interactions were carried out using Schrodinger (GLIDE) commercial software. The target protein, the crystal structure of extracellular domain of human HER2, complexed with Herceptin Fab, was considered for this analysis. The Herceptin Fab domain was removed for the docking of commercial drugs with HER2. The ligands considered are: the commercial drugs, Afinitor, Halaven, Ixabipilone, Lapatinib, Letrozole, Palbociclib, Raloxifene and Tamoxifen and the natural compounds Oleic acid and Stigmasterol isolated from marine sponge Aurora globostellata. In this study, XP Docking procedure was used. It ranks the best conformations based on the ligand binding to the receptor molecules.
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study
| 100.0 |
The docking results of the ten ligands including the natural compounds have been listed in Table 1. The Gscore is a scoring function that predicts the binding energy of the ligand; it ranks the different poses of the ligands. The higher the negative score shows the higher and tight binding affinity. From this study, the compounds are ranked as follows based on their binding energies: Afinitor > Ixabipilone > Letrozole > Halaven > Palbocilib > Oleic acid > Raloxifene > Lapatinib > Stigmasterol > Tamoxifen. From the comparison of the docking energetics, it is observed that the Gscore values are all in the same range, indicating that they all can be grouped into a single family. Except Afinitor, Halavan, Ixabipilone and Letrozole, all others form a cluster to be like a drug. This indicates that the natural compounds, Oleic Acid and Stigmasterol behave like a drug like molecules. The same grouping is confirmed from the point of view of van der Waals, electrostatics, internal, hydrogen bonding and binding energies as well. Similar residues seem to have hydrogen bonding, indicate the closeness in the grouping.
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study
| 100.0 |
As per Lipinski's rule, the parameters for the drug-like property for the ligands have been listed and compared Figure 1. The first four commercial drugs have a higher molecular weight by not obeying the rule. Afinitor, Lapatinib, Letrozole and Palbociclib show higher donor HB and hence do not obey the rule. The first four and Palbociclib show a negative red band indicating their non-drug like behavior. Surprisingly, except Stigmasterol all the others show a positive drug-like property of QlogPo/w. In the Overall sense, Rule of Five shows a non-drug like nature for the first four commercial drugs. PSA is negative for Afinitor. Likewise, QPlog BB shows the same trend. The ADME properties, Qplog HERG, QPP Caco and QPPMDCK show a nondrug like nature for all the ligands except Raloxifene, Tamoxifen, Stigmasterol and Oleic acid. Thus, the comparison of the ligands based on the Lipinski's rule and ADME properties indicate a strong drug likeness for the best commercial drugs Raloxifene and Tamoxifen that have been observed with the green blocks for all the properties in the tabular diagram, Figure 1. Coincidently, the isolated compounds, Stigmasterol and Oleic acid show the same nature as these two commercial drugs and expected to behave in the same manner as the drug like molecules. This would be evaluated by the in-silico method by the interaction studies with the target, as discussed in the following section.
|
study
| 99.94 |
The docking interactions map between HER2 with all the ten ligands provides the information about the interacting residues and their mode and type of interactions with the ligands under consideration. The interaction maps are shown only for three ligands, namely Tamoxifen, Oleic acid and Stigmasterol shown in Figure 2.
|
study
| 100.0 |
The residues of HER2 that have closer interactions with the ligands are highlighted in different colored circles based on the type of interactions such as hydrogen bonds, Van der Waals forces, ionic bonds. These were the residues responsible for the ligand-HER2 interactions respectively. All these interacting residues for all the ligands are identified and the common interacting residues are obtained to figure out the pharmacophore / functional interacting pattern. To obtain this, the ligands are grouped into three categories: the first five of the commercial drugs, the next three - the most potent commercial drugs and lastly, the isolated compounds from Sponge, namely Stigmasterol and Oleic acid. For all the three groups, their respective binding site residues are identified. Then a Venn diagram has been drawn, which is as shown in Figure 3.
|
study
| 100.0 |
The first group contains 32 residues, the second groups have 28 residues and the third groups have 12 residues in common among the ligands in the groups. The Venn diagram shows 9 residues as common among all the groups / ligands and 23 residues as common among any two of the three groups. The isolated compounds contain almost all of their interacting residues with HER2. The following 9 residues are common among all the ligands: Thr5, Asp8, Asn37, Gln84, Leu291, Val292, Arg412, Ile413 and Gly417.
|
study
| 100.0 |
We plotted a scatter diagram (Figure 4) of the cumulative occurrences of these ligands interacting with the residues of HER2 (plotted against the sequence number of HER2). The resultant plot clearly indicates that there are five different clusters in HER2 that are so closer to one among them spatially, in the folded form, thus forming the pocket of interaction. The clusters are colored differently so as to differentiate them on the structure. These clusters are distributed among the three domains: Nterminal Domain, middle elongated domain and the third helical domain. The N-terminal domain consists of first three of the clusters (residues T5, G6, T7 and D8; residues Q35, G36, N37 and G38; residues T83 and Q84); middle domain has the cluster 4 (residues T290, L291 and V292), and the helical domain 3 has the helical cluster (residues G411, R412, I413, L414, H415, N416 and G417). It has been observed that the domain 1 consists majorly of polar residues and domain 2 is observed with hydrophobic residues. The helical domain 3 has both hydrophobic and hydrophilic residues and the helical wheel plot segregates both the groups for specific interactions.
|
study
| 100.0 |
When these 23 residues that are common among either two of the groups are highlighted on the structure of HER2, it clearly shows (Figure 5a), the converged region / binding pocket on the structure wherein these ligands bind with HER2. The cluster group residues are colored the same way as in Figure 3. When the pocket has been filled with only the 9 common residues (Figure 5b), it clearly indicates the functional groups that form the pocket to which the drug is expected to bind. The designing of a drug is based on these functional groups and is to be optimized so as to get a high affinity molecule with lowest binding constant.
|
study
| 100.0 |
The observation of the pocket of interaction that is common among the ligands confirms the binding pocket, which when optimized to design a molecule that fits well within the pocket forms the initiation of the design of a candidate molecule. The isolated compound Stigmasterol agrees well with the binding residues and thus can be optimized further to arrive at a molecule that has a high binding affinity and low binding constant.
|
study
| 99.94 |
Thus, the results of the docking studies carried out on HER2 corroborate to the findings that the most suitable drug like properties are possessed by Stigmasterol. In comparison with Oleic acid it is a better bet as oleic acid is more lipophilic commonly present in sponges, which is relatively less druggable. This provides evidence of how a marine sponge can be a source of potential anti- cancer agent. Further in-vivo studies need to be performed in future to validate the wet lab results. The preclinical studies will pave way for a potential anti-cancer compound.
|
study
| 99.94 |
Nanomedicine is a promising field that applies nanotechnologies to healthcare with the ultimate aim of developing innovative medicines to improve the diagnosis and treatment of human diseases. Several materials have been proposed to prepare nanomaterials, such as inorganic materials , lipids and polymers . In the pharmaceutical domain, nanomaterials have found interesting applications in drug delivery as they can modify the fate of the drug in the body, assure control of the release, protect the drug from enzymatic and chemical degradation and improve efficacy and/or reduce toxicity of drugs. Recently, bioinspired, smart and stimuli-responsive nanomaterials have been developed to further boost the efficacy of these materials, allowing specific and targeted delivery to diseased tissues and cells .
|
review
| 99.9 |
Topical preparations containing corticosteroids are widely used for the treatment of inflammatory skin diseases due to their anti-inflammatory effects. However, repeated or long-term application of topical corticosteroids may lead to numerous adverse events, including atrophy, striae, telangiectasia, purpura, acneiform eruption and perioral rosacea-like dermatitis . One of the options to reduce the adverse effects of topical steroids is to enhance their retention in the skin without augmenting the amount permeated, to reduce the applied dose. Clobetasol-17-propionate (CP) is considered the most potent of the currently available topical corticosteroids, as its vasoconstriction activity is 1800 times higher than hydrocortisone , but the incidence of unfavorable side effects is greater than other related compounds , limiting its clinical applicability . Some approaches to improve clobetasol administration have been recently described; they include the use of foam or spray formulations , the application of ultrasounds and of different kinds of nanocarriers such as microemulsions , nanogels , nanoparticles, nanocapsules, nanoemulsions and nanostructured lipid carriers .
|
review
| 99.9 |
Our group has developed and evaluated in vitro two innovative formulations, a sodium-deoxycholate (Na-DOC) gel containing 0.05% w/w CP and CP loaded nanoparticles (NP), composed of lecithin and chitosan, dispersed in a chitosan gel. In particular, lecithin-chitosan nanoparticles have been previously proposed as a self-assembled nanometric delivery system for oral , topical and nasal delivery .
|
study
| 99.94 |
Both the formulations developed showed positive profiles in terms of the skin accumulation of CP in vitro. The Na-DOC gel accumulated a much higher drug amount in the skin with respect to a commercial cream used as reference with the same CP concentration (0.05%) . On the other hand, the CP-loaded NP formulation accumulated the same drug amount as the commercial cream, but starting from a ten-fold lower CP concentration, i.e., 0.005% w/w .
|
study
| 100.0 |
The topical application of nanomedicines is still a topic of much debate, regarding their real benefit over more traditional formulations, their specific mechanism of action in the skin and the safety of potentially biopersistent nanomaterials and their by-products within the skin layers . In the present study, we aimed to evaluate if the in vitro accumulation of clobetasol-17-propionate evidenced with the NP formulation would improve the efficacy in vivo. Furthermore, the skin tolerability of the formulation was investigated and the possible drug release mechanism of the nanoparticles discussed, with the aim to rule out that tissue structure modifications could be the cause of the enhanced efficacy shown when using the nanoformulation.
|
study
| 99.94 |
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