Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Nýlega bætt við
Evaluating the Reliability and Validity of Predictive Anthropometric Equations for Estimating Fat Mass, Lean Mass and the Role of Maturity Offset in Lean Mass Prediction Within Professional, Academy Soccer Players from the United Kingdom
(2026-03) Efstathiou, Elena; Wilson, Laura J.; Dickinson, Brent; Curtis, Christopher; Department of Sport Science
The reliability and validity of anthropometric equations remain uncertain in young athletes experiencing biological maturation. This study assessed the reliability and validity of anthropometric equations against dual-energy X-ray absorptiometry (DXA)-derived fat mass (FM) and lean mass (LM) values and examined the influence of maturity offset within academy soccer players. Twenty-five male academy soccer players (age: 18.6 ± 0.8 years, height: 182.7 cm ± 5.9 cm, BM: 79.3 kg ± 7.6 kg) completed skinfold and DXA assessments. FM and LM were estimated using commonly adopted anthropometric equations. Reliability and validity were assessed. Linear regression examined the influence of maturity offset. Acceptable agreement for the equations of Wilmore & Behnke and Oliver et al. for LM and FM was observed (FM; ICC: 0.858–0.891, CV%: 8.1–8.8 ± 4.6–6.4, LoA: 2.62–3.06 to −1.33–−1.62, ES: 0.27–0.47, Z = −2.257–−3.150; LM: ICC: 0.886–0.905, CV%: 2.9–3.3 ± 1.3, LoA: 5.17–5.62 to 0.54–0.78, ES: 0.42–0.48, both p < 0.001). Bland–Altman inspection showed mean bias and wide LoA for all equations. Maturity offset modestly predicted LM for all equations. Observed anthropometric equations have limited validity vs. DXA-derived FM and LM in academy soccer players. Maturity offset warrants consideration for maturity-sensitive, population-specific equations to avoid systematic errors.
Evaluating the Efficacy of Electronic Screening, Brief Intervention, and Referral to Treatment (e-SBIRT) for Gambling : An Online Pilot Randomised Trial
(2026-10) Wright, Simon; Quigley, Martyn; Dymond, Simon; Department of Psychology
Objectives: To investigate the efficacy of electronic screening, brief intervention, and referral to treatment (e-SBIRT) at improving gambling outcomes and increasing help-seeking. Methods: We conducted a two-arm, randomised online pilot trial (n = 83) comparing an e-SBIRT intervention with an active control over 12 weeks. The brief intervention was informed by motivational interviewing and incorporated personalised normative feedback, information provision, and relapse-prevention components. Eligible participants were aged 18 or older, resided in the UK and had scores indicating at least moderate severity gambling. Results: Participants (54 [65.1%] male; mean [SD] age = 40.58 [12.75] years, mean [SD] PGSI = 7.16 [5.58]) in the e-SBIRT and control conditions showed improvements in gambling harms (p = 0.033) and perceived ability to control gambling (p = 0.029). No significant effects of condition assignment or condition x time interactions were observed. However, exploratory analyses of individual model coefficients suggested greater improvement in perceived ability to control gambling among participants receiving e-SBIRT at 12 weeks (p = 0.043). Exploratory analyses also suggested higher rates of help-seeking at 12-week follow-up among participants receiving e-SBIRT. Conclusion: Overall, e-SBIRT did not demonstrate clear advantages over assessment and information provision alone. Further research should prioritise refining intervention components and evaluating SBIRT approaches in settings that better reflect its opportunistic delivery model.
Consumer-Based Retailer Brand Equity Across Different Shopping Needs : Experimentally Analysing Sub-Brands
(2026-02) Sigurdsson, Valdimar; Larsen, Nils Magne; Folwarczny, Michał; Maulana, Huda K.; Fagerstrøm, Asle; Menon, R. G.Vishnu; Department of Business and Economics
The current research extends the consumer-based retailer brand equity literature by examining consumers' willingness-to-shop at various sub-brands during different types of shopping trips. We experimentally tested the predictive validity of a modified scale using sub-brands from two major retailer brands. Study 1 focused on United Kingdom consumers and examined sub-brands of Tesco. The findings revealed a strong correlation between the scale's index and consumers' willingness-to-shop for both major and minor shopping trip types, as well as at Tesco Express and Extra sub-brands. The scale's eight facets were nearly always significant across these conditions. Study 2 was conducted on consumers from the United States, and used Walmart's sub-brands: Neighborhood Markets and Supercenters. The findings replicated those of Study 1. However, in Study 2 all eight facets of the scale were significant across all conditions. The experimental data confirms the academic relevance of the scale as a single-factor measure across different consumer shopping goals, supporting its practical application as a standalone measurement for assessing retailer brand portfolio equity. Furthermore, all facets are essential and can provide meaningful insights for managers. The data also show that consumer-based retailer brand equity can mediate the effects of different shopping trip types when there is a large difference in willingness-to-shop at the retailer sub-brand level.
Does the Intent Match the Output : Aligning Development Goals With Training Load in Youth Basketball
(2026-03) Lever, Jonathon R.; Duffield, Rob; Murray, Andrew; Bill, Holland A.; Bartlett, Jonathan D.; Fullagar, Hugh H.K.; Department of Sport Science
This study investigated the alignment between external training load metrics and coach-prescribed development goals in an elite youth basketball setting. External training load data from training drills were collected over two years from 25 elite male youth basketball players in a full-time residential academy. At the start of each term, coaches developed and assigned individual developmental goals (IDGs) for each player. Using inductive thematic analysis, these IDGs were retrospectively grouped into four overarching development goal categories (defensive, offensive, skill, and physical) and 16 specific goal types (e.g., cutting, shooting, and load tolerance). Separately, external load metrics were recorded during all on-court training sessions using Catapult Vector S7 devices. To align IDGs with external load metrics, two multinomial logistic regression models were developed to classify (1) development goal category and (2) specific goal type (SGT) using per-minute external load metrics. Both models achieved 66% classification accuracy (Kappa = 0.60). Key predictors, such as high-intensity deceleration counts, vertical PlayerLoad, and high-speed running distances, were retained in both models following stepwise selection. Model performance was strong, with large reductions in AIC (ΔAIC = 1224.1 and 2540.7, respectively), demonstrating that coach-assigned IDGs were associated with distinct external load profiles. Additionally, accumulated training time differed significantly across specific goal types, reflecting systematic variation in emphasis across the season. These findings demonstrate that external training load metrics reflect the structure of coach-assigned development goals, offering a data-driven framework to evaluate alignment between training design and physical demands in youth basketball.
Determining Cluster-Specific Differences in the Number of Days Required to Reliably Predict Habitual Physical Activity : Intraclass Correlation Resampling Analysis
(2026) Murphy, Conor Jordan; Jouan, Gabriel M.; Friðgeirsdóttir, Katrin Y.; Islind, Anna Sigridur; Saavedra, Jose M.; Óskarsdóttir, María; Arnardóttir, Erna Sif; Department of Engineering; Department of Sport Science; Department of Computer Science
Background: Previous research has attempted to determine the minimum number of days of accelerometry required to reliably reflect an individual’s physical activity. However, human behaviors on a day-to-day basis can be highly variable. As a consequence, the number of days required to reliably predict habitual physical activity is dependent on the variability that exists within an individual. There is a concern that adopting generic recommendations from previous research could provide unreliable estimates by failing to represent individuals with specific physical activity patterns. Objective: The main aim of this study was to identify clusters of individuals with distinct physical activity patterns and to determine if the number of days of accelerometry data required to reliably estimate short- (7 days) and medium-term (28 days) physical activity differed between each unique cluster. Methods: Accelerometry data were retrieved from 2 independent research studies. Participants during each study had their physical activity recorded using a Withings Scanwatch (Withings Health Solutions). Following a data eligibility process, agglomerative hierarchical clustering was used to identify clusters of individuals based on their physical activity. The clusters were determined using 4 dimensions; mean, SD, skewness, and kurtosis of the step count data. Intraclass correlation coefficients (ICCs) of step count were then calculated within each physical activity cluster. A series of ICCs were computed by separately comparing the average step count across the full periods (7 and 28, for the short- and medium-term analysis, respectively) to a series of averaged subsamples (ranging from 1-6 days and 1-27 days, for the short- and medium-term analysis, respectively). For each subsample, 500 random combinations were generated and compared, providing a distribution of ICCs for each subsample. An ICC of ≥0.80 identified when the subsample of days was sufficient to achieve appropriate reliability. Results: Of 258 participant datasets, 149 were eligible for the short-term analysis and 64 were eligible for the medium-term analysis. Following agglomerative hierarchical clustering, 4 and 3 clusters of sufficient size (n≥12) were identified in the short-term and medium-term analyses, respectively. When considering the short-term analysis, to achieve a mean ICC score greater than or equal to 0.80, using all randomized combinations, the number of days ranged from 2 to 6 days depending on the physical activity cluster. For the medium-term analysis, the number of days required to achieve a mean ICC score greater than or equal to 0.80 ranged from 6 to 11 days. The short-term analysis clusters displayed more diversity in physical activity patterns than the medium-term analysis. Conclusions: Physical activity patterns influence the number of days required to estimate habitual physical activity. Thus, to avoid unreliable estimates of physical activity, which could significantly impact the interpretation of results, researchers should be mindful of the physical activity patterns of their sample before adopting generic recommendations.
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