Accelerometry and physical activity questionnaires - a systematic review

Abstract Background The aim of this study is to review accelerometer wear methods and correlations between accelerometry and physical activity questionnaire data, depending on participant characteristics. Methods We included 57 articles about physical activity measurement by accelerometry and questionnaires. Criteria were to have at least 100 participants of at least 18 years of age with manuscripts available in English. Accelerometer wear methods were compared. Spearman and Pearson correlation coefficients between questionnaires and accelerometers and differences between genders, age categories, and body mass index (BMI) categories were assessed. Results In most investigations, requested wear time was seven days during waking hours and devices were mostly attached on hips with waist belts. A minimum of four valid days with wear time of at least ten hours per day was required in most studies. Correlations (r = Pearson, ρ = Spearman) of total questionnaire scores against accelerometer measures across individual studies ranged from r = 0.08 to ρ = 0.58 (P < 0.001) for men and from r = −0.02 to r = 0.49 (P < 0.01) for women. Correlations for total physical activity among participants with ages ≤65 ranged from r = 0.04 to ρ = 0.47 (P < 0.001) and from r = 0.16 (P = 0.02) to r = 0.53 (P < 0.01) among the elderly (≥65 years). Few studies investigated stratification by BMI, with varying cut points and inconsistent results. Conclusion Accelerometers appear to provide slightly more consistent results in relation to self-reported physical activity among men. Nevertheless, due to overall limited consistency, different aspects measured by each method, and differences in the dimensions studied, it is advised that studies use both questionnaires and accelerometers to gain the most complete physical activity information

Needs assessment towards research data management at the Medical Faculty of the University of Freiburg – Data of the BE-KONFORM study

In order to investigate employees’ needs of the Medical Faculty of the University of Freiburg regarding research data management, the BE-KONFORM study was performed in a two-step approach. First, guideline-based qualitative video interviews with four researchers were performed to identify key constructs of relevance. Second, a standardized online survey was conducted from 1st to 15th of November 2020 based on e-mail invitation by the dean and a faculty newsletter. The questionnaire was provided bilingual (English and German) using a backward-forward translation method, no reminders and incentives were used to increase the response rate. The online survey was programmed in REDCap and was accessible via online link. The target population were members of the Medical Faculty (listed in the newsletter mailing list) regardless of the type of working contract signed. The final dataset contains 236 complete cases (90% German and 10% English). The study includes a randomised module asking for data publication (group A) or not (group B). 113 cases were randomized into group A and 99% of them consented to the publication of the collected research data in anonymized form (n=112). The dataset comprised questions about work-related characteristics (professional status, working experience, scientific field of work), data management-related items (definition of research data management, type of data used, type of storage used for saving data, use of electronic laboratory notebooks), experience and attitudes towards data publication in data repositories, as well as needs and preferences regarding research data management support. The produced data offers the possibility to connect with other data collected in this field in other contexts (faculties or universities)

Barriers and facilitators for implementation of a complex health services intervention in long-term care homes: a qualitative study using focus groups

Background!#!With rising numbers of elderly people living in nursing homes in Germany, the need for on-site primary care is increasing. A lack of primary care in nursing homes can lead to unnecessary hospitalization, higher mortality, and morbidity in the elderly. The project CoCare ('coordinated medical care') has therefore implemented a complex health intervention in nursing homes, using inter alia, regular medical rounds, a shared patient medical record and medication checks, with the aim of improving the coordination of medical care. This study reports upon the results of a qualitative study assessing the perceived barriers and facilitators of the implementation of CoCare by stakeholders.!##!Methods!#!Focus group interviews were held between October 2018 and November 2019 with nurses, general practitioners and GP's assistants working or consulting in a participating nursing home. A semi-structured modular guideline was used to ask participants for their opinion on different aspects of CoCare and which barriers and facilitators they perceived. Focus groups were analyzed using qualitative content analysis.!##!Results!#!In total, N = 11 focus group interviews with N = 74 participants were conducted. We found six themes describing barriers and facilitators in respect of the implementation of CoCare: understaffing, bureaucracy, complexity, structural barriers, financial compensation, communication and collaboration. Furthermore, participants described the incorporation of the intervention into standard care.!##!Conclusion!#!Barriers perceived by stakeholders are well known in the literature (e.g. understaffing and complexity). However, CoCare provides a good structure to overcome barriers and some barriers will dissolve after implementation into routine care (e.g. bureaucracy). In contrast, especially communication and collaboration were perceived as facilitators in CoCare, with the project being received as a team building intervention itself.!##!Trial registration!#!WHO UTN: U1111-1196-6611; DRKS-ID: DRKS00012703 (Date of Registration in DRKS: 2017 Aug 23)

Impact of a complex health services intervention in long-term care nursing homes on 3-year overall survival: results from the CoCare study

Abstract Background The Coordinated medical Care (CoCare) project aimed to improve the quality of medical care in nursing homes by optimizing collaboration between nurses and physicians. We analyze the impact of the CoCare intervention on overall survival. Methods The effect of time-varying treatment on 3-year overall survival was analyzed with treatment as time-varying covariate within the entire cohort. To reduce bias due to non-random assignment to treatment groups, regression adjustment was applied. Therefore, age, sex, and level of care were used as potential confounders. Results The study population consisted of 8,893 nursing home residents (NHRs), of which 1,330 participated in the CoCare intervention. The three-year overall survival was 49.8% in the entire cohort. NHRs receiving the intervention were associated with a higher survival probability compared to NHRs of the control group. In a univariable cox model with time-dependent treatment, the intervention was associated with a hazard ratio of 0.70 [95%CI 0.56–0.87, p = 0.002]. After adjustment for age, sex and level of care, the hazard ratio increased to 0.82 but was still significant [95%CI 0.71–0.96, p = 0.011]. Conclusion The analysis shows that optimizing collaboration between nurses and physicians leads to better survival of NHRs in Germany. This adds to the already published favorable cost-benefit ratio of the CoCare intervention and shows that a routine implementation of optimized collaboration between nurses and physicians is highly recommended

The improved physical activity index for measuring physical activity in EPIC Germany.

In the European Investigation into Cancer and Nutrition study (EPIC), physical activity (PA) has been indexed as a cross-tabulation between PA at work and recreational activity. As the proportion of non-working participants increases, other categorization strategies are needed. Therefore, our aim was to develop a valid PA index for this population, which will also be able to express PA continuously. In the German EPIC centers Potsdam and Heidelberg, a clustered sample of 3,766 participants was re-invited to the study center. 1,615 participants agreed to participate and 1,344 participants were finally included in this study. PA was measured by questionnaires on defined activities and a 7-day combined heart rate and acceleration sensor. In a training sample of 433 participants, the Improved Physical Activity Index (IPAI) was developed. Its performance was evaluated in a validation sample of 911 participants and compared with the Cambridge Index and the Total PA Index. The IPAI consists of items covering five areas including PA at work, sport, cycling, television viewing, and computer use. The correlations of the IPAI with accelerometer counts in the training and validation sample ranged r = 0.40-0.43 and with physical activity energy expenditure (PAEE) r = 0.33-0.40 and were higher than for the Cambridge Index and the Total PA Index previously applied in EPIC. In non-working participants the IPAI showed higher correlations than the Cambridge Index and the Total PA Index, with r = 0.34 for accelerometer counts and r = 0.29 for PAEE. In conclusion, we developed a valid physical activity index which is able to express PA continuously as well as to categorize participants according to their PA level. In populations with increasing rates of non-working people the performance of the IPAI is better than the established indices used in EPIC

Additional file 1 of Impact of a complex health services intervention in long-term care nursing homes on 3-year overall survival: results from the CoCare study

Supplementary material

Prediction of activity-related energy expenditure under free-living conditions using accelerometer-derived physical activity

The purpose of the study was to develop prediction models to estimate physical activity (PA)-related energy expenditure (AEE) based on accelerometry and additional variables in free-living adults. In 50 volunteers (20-69 years) PA was determined over 2 weeks using the hip-worn Actigraph GT3X + as vector magnitude (VM) counts/minute. AEE was calculated based on total daily EE (measured by doubly-labeled water), resting EE (indirect calorimetry), and diet-induced thermogenesis. Anthropometry, body composition, blood pressure, heart rate, fitness, sociodemographic and lifestyle factors, PA habits and food intake were assessed. Prediction models were developed by context-grouping of 75 variables, and within-group stepwise selection (stage I). All significant variables were jointly offered for second stepwise regression (stage II). Explained AEE variance was estimated based on variables remaining significant. Alternative scenarios with different availability of groups from stage I were simulated. When all 11 significant variables (selected in stage I) were jointly offered for stage II stepwise selection, the final model explained 70.7% of AEE variance and included VM-counts (33.8%), fat-free mass (26.7%), time in moderate PA + walking (6.4%) and carbohydrate intake (3.9%). Alternative scenarios explained 53.8-72.4% of AEE. In conclusion, accelerometer counts and fat-free mass explained most of variance in AEE. Prediction was further improved by PA information from questionnaires. These results may be used for AEE prediction in studies using accelerometry

Studies on the effects of public policy on formation of large-scale landscapes : Focusing on Sapporo and Obihiro regions in Hokkaido [an abstract of dissertation and a summary of dissertation review]

<p>Spearman correlation coefficients and 95% confidence intervals (95% CI) between accelerometer counts, Physical Activity Energy Expenditure (PAEE), Physical Activity Level (PAL), Moderate and Vigorous Physical Activity (MVPA), sedentary time, the Improved Physical Activity Index (IPAI) (continuous and in categories), the Cambridge Index, and the Total Physical Activity Index in 911 participants of the EPIC Germany study validation sample.</p

Spearman correlation coefficients and 95% confidence intervals (95% CI) between accelerometer counts, Physical Activity Energy Expenditure (PAEE), Physical Activity Level (PAL), Moderate and Vigorous Physical Activity (MVPA), sedentary time, the Improved Physical Activity Index (IPAI) (continuous and in categories), the Cambridge Index, and the Total Physical Activity Index in 699 non-working participants of the EPIC Germany sub-study.

<p>Spearman correlation coefficients and 95% confidence intervals (95% CI) between accelerometer counts, Physical Activity Energy Expenditure (PAEE), Physical Activity Level (PAL), Moderate and Vigorous Physical Activity (MVPA), sedentary time, the Improved Physical Activity Index (IPAI) (continuous and in categories), the Cambridge Index, and the Total Physical Activity Index in 699 non-working participants of the EPIC Germany sub-study.</p