Lifelong learning in community healthcare: Testing competence after learning activities in a blended learning space.

AIMS AND OBJECTIVES: This study reports from a municipality in Norway that implemented a competence enhancement programme for all its institutional nursing staff during the COVID-19 pandemic to fill identified competence gaps. BACKGROUND: Many Norwegian municipalities are experiencing a demand for expanded community healthcare services due to an increase in elderly patients and patients with extensive and complex needs. At the same time, most municipalities are striving to recruit and keep competent health personnel. New ways of organising and increasing the competence of the workforce may help ensure that the healthcare delivered corresponds to patients' changing needs. DESIGN AND METHODS: Nursing staff were encouraged to complete targeted competence enhancing activities with the aim of enhancing their competence in identified areas. The learning activities were blended and consisted of e-learning courses, lectures, supervision, vocational training and meetings with a superior. Competence was measured before and after the competence enhancing activities (n = 96). The STROBE checklist was applied. RESULTS: The results provide insight into the competence development of registered nurses and assistant nurses in institutional community health services. They show that the implementation of a workplace-based blended learning programme improved competence significantly, especially for assistant nurses. CONCLUSIONS: Offering workplace-based competence enhancing activities seems to be a sustainable way of facilitating lifelong learning among nursing staff. Facilitation of learning activities in a blended learning space may enhance accessibility and increase the potential for participation. A combination of reorganisation of roles and simultaneous competence enhancing activities can ensure that both managers and nursing staff prioritise filling competence gaps.

A Mapping Review of Physical Activity Recordings Derived From Smartphone Accelerometers.

BACKGROUND: Smartphones with embedded sensors, such as accelerometers, are promising tools for assessing physical activity (PA), provided they can produce valid and reliable indices. The authors aimed to summarize studies on the PA measurement properties of smartphone accelerometers compared with research-grade PA monitors or other objective methods across the intensity spectrum, and to report the effects of different smartphone placements on the accuracy of measurements. METHODS: A systematic search was conducted on July 1, 2019 in PubMed, Embase, SPORTDiscus, and Scopus, followed by screening. RESULTS: Nine studies were included, showing moderate-to-good agreements between PA indices derived from smartphone accelerometers and research-grade PA monitors and/or indirect calorimetry. Three studies investigated measurement properties across smartphone placements, with small differences. Large heterogeneity across studies hampered further comparisons. CONCLUSIONS: Despite moderate-to-good agreements between PA indices derived from smartphone accelerometers and research-grade PA monitors and/or indirect calorimetry, the validity of smartphone monitoring is currently challenged by poor intermonitor reliability between smartphone brands/versions, heterogeneity in protocols used for validation, the sparsity of studies, and the need to address the effects of smartphone placement.

Comparison of three activity monitors for estimating sedentary time among children.

BACKGROUND: Time spent sedentary appears to be associated with several health outcomes in adults, but findings are inconsistent in children. Further, the assessment of sedentary time represents a major challenge. The objectives of the present study were to determine whether 1) ActiGraph GT3X+, ActivPAL and SenseWear Armband Pro3 (SWA) provide comparable estimates of sedentary time in 9-12-year-old children, 2) these devices are valid compared with direct observation, and 3) ActivPAL discriminates between sitting and standing behavior. METHODS: The sample was 67 children. Data were collected during three consecutive days in November 2012. To test the activity monitors in contexts related to physical and sedentary activities commonly performed by children, the children participated in sessions of activity while sitting (watching television, playing video games and tossing a ball while sitting) and standing (musical chairs, active video gaming and tossing a ball) while wearing three different activity monitors at the same time. All activity sessions were observed by two researchers. Differences between monitors were determined using Friedman's two-way analysis of variance by rank order. RESULTS: Minutes of estimated sedentary time differed across device brands during combined sitting activities: SWA vs. ActiGraph GT3X+ (P = 0.048), SWA vs. ActivPAL, (P < 0.001) and ActiGraph GT3X+ vs. ActivPAL (P = 0.002). Out of 12 min in total of combined recorded sitting activity, SWA reported a median of 6 min (95 % Confidence Interval [CI] = 5.0, 7.0), ActiGraph GT3X+ 7 min (7.0, 8.0) and ActivPAL 10 min (8.6, 10.8) as sedentary time. ActivPAL recorded 3.7 (2.4, 4.0) minutes of the non-sitting activities 'musical chairs', 4.0 (4.0, 4.0) minutes in 'standing ball toss'; and 4.0 (2.7, 4.0) minutes in 'active video gaming' as sitting time. CONCLUSION: Recorded sedentary time varied among the monitors GT3X+, SWA and ActivPAL, and misclassification of standing activities as sitting activities were apparent for ActivPAL in certain activities.