Although governments are investing heavily in big data analytics, reports show mixed results in terms of performance. Whilst big data analytics capability provided a valuable lens in business and seems useful for the public sector, there is little knowledge of its relationship with governmental performance. This study aims to explain how big data analytics capability led to governmental performance. Using a survey research methodology, an integrated conceptual model is proposed highlighting a comprehensive set of big data analytics resources influencing governmental performance. The conceptual model was developed based on prior literature. Using a PLS-SEM approach, the results strongly support the posited hypotheses. Big data analytics capability has a strong impact on governmental efficiency, effectiveness, and fairness. The findings of this paper confirmed the imperative role of big data analytics capability in governmental performance in the public sector, which earlier studies found in the private sector. This study also validated measures of governmental performance.
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Purpose: This study examined the effects of a giant (4×3 m) exercising board game intervention on ambulatory physical activity (PA) and a broader array of physical and psychological outcomes among nursing home residents. Materials and methods: A quasi-experimental longitudinal study was carried out in two comparable nursing homes. Ten participants (aged 82.5±6.3 and comprising 6 women) meeting the inclusion criteria took part in the 1-month intervention in one nursing home, whereas 11 participants (aged 89.9±3.1 with 8 women) were assigned to the control group in the other nursing home. The giant exercising board game required participants to per-form strength, flexibility, balance and endurance activities. The assistance provided by an exercising specialist decreased gradually during the intervention in an autonomy-oriented approach based on the self-determination theory. The following were assessed at baseline, after the intervention and after a follow-up period of 3 months: PA (steps/day and energy expenditure/day with ActiGraph), cognitive status (mini mental state examination), quality of life (EuroQol 5-dimensions), motivation for PA (Behavioral Regulation in Exercise Questionnaire-2), gait and balance (Tinetti and Short Physical Performance Battery), functional mobility (timed up and go), and the muscular isometric strength of the lower limb muscles. Results and conclusion: In the intervention group, PA increased from 2,921 steps/day at baseline to 3,358 steps/day after the intervention (+14.9%, P=0.04) and 4,083 steps/day (+39.8%, P=0.03) after 3 months. Energy expenditure/day also increased after the intervention (+110 kcal/day, +6.3%, P=0.01) and after 3 months (+219 kcal/day, +12.3%, P=0.02). Quality of life (P<0.05), balance and gait (P<0.05), and strength of the ankle (P<0.05) were also improved after 3 months. Such improvements were not observed in the control group. The preliminary results are promising but further investigation is required to confirm and evaluate the long-term effectiveness of PA interventions in nursing homes.
Introduction: There is a lack of effective interventions available for Pediatric Physical Therapists (PPTs) to promote a physically active lifestyle in children with physical disabilities. Participatory design methods (co-design) may be helpful in generating insights and developing intervention prototypes for facilitating a physically active lifestyle in children with physical disabilities (6–12 years). Materials and methods: A multidisciplinary development team of designers, developers, and researchers engaged in a co-design process–together with parents, PPTs, and other relevant stakeholders (such as the Dutch Association of PPTs and care sports connectors). In this design process, the team developed prototypes for interventions during three co-creation sessions, four one-week design sprint, living-lab testing and two triangulation sessions. All available co-design data was structured and analyzed by three researchers independently resulting in themes for facilitating physical activity. Results: The data rendered two specific outcomes, (1) knowledge cards containing the insights collected during the co-design process, and (2) eleven intervention prototypes. Based on the generated insights, the following factors seem important when facilitating a physically active lifestyle: a) stimulating self-efficacy; b) stimulating autonomy; c) focusing on possibilities; d) focusing on the needs of the individual child; e) collaborating with stakeholders; f) connecting with a child's environment; and g) meaningful goal setting. Conclusion: This study shows how a co-design process can be successfully applied to generate insights and develop interventions in pediatric rehabilitation. The designed prototypes facilitate the incorporation of behavioral change techniques into pediatric rehabilitation and offer new opportunities to facilitate a physically active lifestyle in children with physical disabilities by PPTs. While promising, further studies should examine the feasibility and effectivity of these prototypes.
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Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
