Direct, Indirect and Mixed Methods of Health Education By Nurse and Its Impact on Type 2 Diabetes Patients: A Literature review

Background: Diabetes Mellitus (DM) is one of the chronic non-communicable diseases that has currently been very common, in particular Diabetes Mellitus type 2 that threatens public health. It has been included in the category of the six biggest causes of worldwide death, but self-control of treatment and obedience to self-care is still low. One of the influencing factors is related to the lack of knowledge. Providing ongoing health education is one of the solutions or efforts to strengthen knowledge in type 2 diabetes. The aim was to identify the various health education methods currently used by nurses and their impacts on type 2 DM. The method used is a literature review. The literature was searched on data based on Scopus, Web of Science, SAGE, CINAHL with the keywords methods or interventions, education, health, nursing, and type 2 Diabetes Mellitus. Fifteen (15) pieces of literature were considered to meet the criteria inclusion. Results: Three (3) types of health education methods used by nurses were identified: 1). Direct health education refers to providing education by nurses to patients through training, coaching, interviews, Focus Group Discussion (FGD), and home visits; 2). Indirect Health Education - using mobile phones; 3). The mix of direct health education and the use of mobile phone-based applications is carried out with the application of mobile health technology and nurse health coaching. The impact of providing health education by nurses to type 2 diabetic patients: increasing of knowledge, behavioral change on preventing diabetes complications, increasing self-efficacy, increasing self-care activities (diet management, physical activity, monitoring blood sugar levels, and foot care). Conclusion: various methods of health education carried out by nurses currently have a positive impact on improving and increasing self-care management and efforts to prevent complications in type 2 diabetes.

IoT-assisted physical education training network virtualization and resource management using a deep reinforcement learning system

The Internet of Things (IoT) development made it possible for technology to communicate physical education by connecting cost-effective heterogeneous devices and digital applications to uncontrolled and accessible environments. The traditional physical education monitoring environment creates crucial manual efforts on athletes' activity observations and tracking consistently. Similarly, remote monitoring and assessment of athletes in sports training seem to be barriers to physical education monitoring and training. It creates various chances to improve training and education through technology advancements like IoT and deep learning. Students can efficiently monitor their physical behavior to increase their physical and psychological benefits. The IoT-assisted physical activity monitoring device is proposed to track students' physical activity and enhance outcomes. The management ability allows students to organize and increase speed their physical activity in a wellness manner. In addition, this study examines the connections between monitoring ability which is an essential component for sports activities and physical activity. This system collects essential information from IoT-based wearable devices that interact with the data in real time by virtualizing the device. The IoT network includes several device activities and monitors the heartbeat and physical body temperature of a person. The analysis of specific studies and student feedback shows that the designed virtual system of physical educations is effective in its application and implementation and provides a reliable guide for developing student physical educational systems. The experimental analysis is evaluated; the solution offered is developing and supporting physical education and training approaches in reality and creates healthy environment systems to solve the health monitoring challenges posed by IoT devices. The proposed method has achieved extraordinary physical activity monitoring compared to the conventional systems, as shown by experimental findings. The simulation analysis of physical education can help students and improve the associated aspects of physical abilities with high accuracy ratio (98.3), prediction ratio (96.5%), interaction ratio (94.4%), performance ratio (95.1%), the efficiency ratio (93.2), F-score (92.2%), and reduce error rate (17.5%) and physical activity patterns.

Five–year changes in objectively measured cardiorespiratory fitness, physical activity, and sedentary time in mid-to-late adulthood

This study examined five-year changes in cardiorespiratory fitness, physical activity, and sedentary time in mid-to-late aged adults. Fifty-seven participants completed baseline and follow-up treadmill exercise tests and physical activity monitoring. We observed a 14% decline in fitness (p<.001), 12% decrease in physical activity (p= 0.010), and non-significant increase in sedentary time (p=.196). Age was negatively associated with five-year change in physical activity (r=-.31; p=.02) and this decline was strongest among APOE ε4 carriers (g=-0.75). Novelty: • Cardiorespiratory fitness and physical activity significantly declined from mid-to-late adulthood, these findings were most pronounced among older adults and those with genetic risk for Alzheimer’s disease.

Successes and Challenges of Implementing Teleprehabilitation for Onco-Surgical Candidates and Patients’ Experience: A Retrospective Pilot-Cohort Study

Purpose: This study aimed to document the successes and challenges of teleprehabilitation programs for cancer patients undergoing surgery.Method: This pilot-cohort study included adults scheduled for elective cancer surgery, referred to the prehabilitation clinic to engage in physical activity and received a teleprehabilitation program between August 1st 2020 and February 28th 2021. Using a technology platform that included a tablet and was wearable, data were acquired through virtual physical activity monitoring in addition to patient charts.Results: Ten patients (8 males and 2 females; mean age: 68.3 years, SD: 11.96) diagnosed with various thoraco-abdominal malignancies were included in the current descriptive study. The successes identified were related to recruitment and assessment, improvement in functional capacity, clinic scheduling and interventions, and optimal medical follow-up. The challenges identified were related to the adoption of the technologies by patients and the multidisciplinary team, the accurate acquisition of patient physical activity data, and the initial costs to acquire the new technologies. Patients were satisfied with the teleprehabilitation program (i.e., services delivered; average appreciation: 96%), and they perceived the technologies provided to be 90% user-friendly.Conclusion: The findings of the current study are paramount in view of the current international health paradigm changes prioritizing remote interventions facilitated through digital communication technologies. It provides important insight into the clinical application of telehealth in elderly populations, notably in the context of acute preoperative cancer care. This article may provide guidance for other cancer care facilities aiming to implement teleprehabilitation programs.

Research on physical activity variability and changes of metabolic profile in patients with prediabetes using Fitbit activity trackers data

BACKGROUND: Monitoring physical activity with consumers wearables is one of the possibilities to control a patient’s self-care and adherence to recommendations. However, clinically approved methods, software, and data analysis technologies to collect data and make it suitable for practical use for patient care are still lacking. OBJECTIVE: This study aimed to analyze the potential of patient physical activity monitoring using Fitbit physical activity trackers and find solutions for possible implementation in the health care routine. METHODS: Thirty patients with impaired fasting glycemia were randomly selected and participated for 6 months. Physical activity variability was evaluated and parameters were calculated using data from Fitbit Inspire devices. RESULTS: Changes in parameters were found and correlation between clinical data (HbA1c, lipids) and physical activity variability were assessed. Better correlation with variability than with body composition changes shows the potential to include nonlinear variability parameters analysing physical activity using mobile devices. Less expressed variability shows better relationship with control of prediabetic and lipid parameters. CONCLUSIONS: Evaluation of physical activity variability is essential for patient health, and these methods used to calculate it is an effective way to analyze big data from wearable devices in future trials.

A Comparison of Physical Activity Between Home-Based and Centre-Based Pulmonary Rehabilitation: A Randomised Controlled Secondary Analysis

Background: Pulmonary rehabilitation (PR) is a highly effective intervention for individuals with chronic obstructive pulmonary disease (COPD). Physical activity (PA) has been shown to increase after a centre-based programme, yet it is not clear if a home-based programme can offer the same benefit. This study aimed to evaluate the effect of home-based PR compared with the centre-based PR on the PA levels post 7 weeks of PR and 6 months follow-up.Method: In this study, 51 participants with COPD, of them, 36 (71%) men completed physical activity monitoring with a SenseWear Armband, at three time points (baseline, 7 weeks, and 6 months). The participants were randomly assigned to either centre-based supervised PR (n = 25; 69 ± 6 years; FEV1 55 ± 20% predicted) or home-based PR (n = 26; 68 ± 7 years; FEV1 42 ± 19% predicted) programmes lasting 7 weeks. The home-based programme includes one hospital visit, a self-management manual, and two telephone calls. The PA was measured as step count, time in moderate PA (3–6 metabolic equivalent of tasks [METs]) in bouts of more than 10 min and sedentary time (&lt;2 METs).Results: Home-based PR increased step count significantly more than the centre-based PR after 7 weeks (mean difference 1,463 steps: 95% CI 280–2,645, p = 0.02). There was no difference in time spent in moderate PA was observed (mean difference 62 min: 95% CI −56 to 248, p = 0.24). Sedentary behaviour was also significantly different between the centre and home-based groups. The home group spent 52 min less time sedentary compared with the centre-based (CI −106 to 2, p = 0.039). However, after 6 months, the step count and time spent in moderate PA returned to baseline in both the groups.Conclusion: This study provides an important insight into the role of home-based PR which has the potential to be offered as an alternative to the centre-based PR. Understanding who may best respond from the centre or home-based PR warrants further exploration and how to maintain these initial benefits for the long-term.Trial Registry: ISRCTN: No.: ISRCTN81189044; URL: isrctn.com.

Effect of personal activity intelligence (PAI) monitoring in the maintenance phase of cardiac rehabilitation: a mixed methods evaluation

Background Personal activity intelligence (PAI) is a single physical activity metric based upon heart rate responses to physical activity. Maintaining 100 PAI/week is associated with a 25% risk reduction in cardiovascular disease mortality and 50 PAI/week provides 60% of the benefits. The effect of utilising this metric within a cardiac population has not been previously investigated. The aim of this study was to determine the effect of PAI monitoring on the amount and/or intensity of physical activity for people in the maintenance phase of cardiac rehabilitation and to explore participants’ perceptions of this approach. Methods A concurrent mixed methods approach was undertaken. Participants in the maintenance phase of cardiac rehabilitation monitored PAI for six weeks via a wearable physical activity monitoring device (WPAM). In the first three weeks participants were blinded to their PAI score. A quality-of-life questionnaire (EQ-5D-5L) was completed, and semi-structured interviews conducted to investigate attitudes to PAI monitoring. Daily PAI data was collected throughout the 6-week period. Results Twenty participants completed the trial. PAI earned/day was increased after participants could view their data (mean difference: 2.1 PAI/day (95% CI 0.3, 4.0), p = 0.027). The median change in percentage of days participants achieved a Total PAI score of 25 (p = 0.023) and 50 (p = 0.015) were also increased. The mean change in total scores for the EQ-5D-5L and EQVAS were improved after 6 weeks (0.6 ± 1.05; 95% CI (0.11–1.09); p = 0.019); (5.8/100; 95% CI (2.4–9.2); p = 0.002 respectively). Thematic framework analysis identified three global themes (perceptions on the WPAM, PAI and factors affecting exercise). Most participants stated motivation to exercise increased after they could view their PAI data. Many of the participants believed they would continue to use PAI long-term. Others were undecided; the latter primarily due to technical issues and/or preferring devices with greater functionality and attractiveness. All participants would recommend PAI. Conclusion This exploratory study showed monitoring PAI via a WPAM increased the amount and/or intensity of physical activity within the cardiac population. Participants found PAI interesting, beneficial, and motivating. If technical issues, aesthetics, and functionality of the WPAM were improved, participants may continue to use the approach long-term. PAI may be a viable strategy to assist people with cardiac disease maintain physical activity adherence.

Evaluation of the Diet Tracking Smartphone Application Keenoa™: A Qualitative Analysis

Keenoa™ is a novel Canadian diet application (app) currently used by Canadian dietitians to collect diet-related data from clients. The goal of this study was to evaluate Keenoa™ based on user feedback and compare it to a conventional pen and paper method. One hundred and two participants were recruited and randomly assigned to record their diets using this application for 3 nonconsecutive days. Following this, participants were invited to complete an online “exit” survey. Seventy-two subjects responded, with 50 completing an open-ended question asking for general feedback about the app. Data were reviewed and 3 main themes emerged: strengths, challenges, and future recommendations. Strengths associated with the app consisted of picture recognition software, the additional commentary feature, and the overall pleasant data collection process. Challenges that were identified included inconsistencies with the barcode scanning features, the limited food database, time to enter food details, and software issues. Future recommendations included using a larger food database, pairing dietary intake with physical activity monitoring, and having accessible nutritional data. Despite these limitations, participants preferred using mobile apps to record diet compared with traditional written food diaries.

Wearable Movement Sensors in Osteoarthritis: Narrative Review (Preprint)

BACKGROUND The objective of this study is to review and summarize recent developments in wearable technology detailing the key enabling technologies (i.e., sensor components) and applications of wearable technology as they relate to lower extremity osteoarthritis. OBJECTIVE The objective of this study is to review and summarize recent developments in wearable technology detailing the key enabling technologies (i.e., sensor components) and applications of wearable technology as they relate to lower extremity osteoarthritis. METHODS A literature search was performed in March 2021 using the PubMed and EMBASE databases for publications on wearable movement technologies in lower-limb OA. Papers published within the previous 5 years were identified. The search was limited to original research studies published in English. Duplicate studies, systematic reviews, conference abstracts, and study protocols were removed. Sample keywords and their combinations included: (osteoarthritis OR TKA OR total knee arthroplasty OR total knee replacement) AND (wearable* OR sensor). RESULTS From the literature, 72 studies were determined relevant and subsequently included in this review. Wearable technology has successfully been implemented for gait assessment, movement pattern training using feedback, assessment of intervention outcomes, and physical activity monitoring. Additionally, some studies demonstrated algorithms or measurement systems that could be used for movement pattern training with feedback in future implementations. Study participants identified appearance and comfort during use as key aspects for the acceptance of wearable technology, and enjoyed seeing both quantitative sensor data as well as qualitative patient-reported outcomes. CONCLUSIONS Advancements in wearable sensor technology allow for data collection and analysis in both accurate and unobtrusive ways. The technology can be used to passively collect data, implement exercise interventions, or actively retrain movement patterns. Future opportunities remain to have more efficient, smaller systems and provide biofeedback for new, previously unused metrics.

Politik zur Bewegungsförderung in Deutschland

Zusammenfassung Hintergrund Da Bewegungsmangel eine entscheidende Ursache für nichtübertragbare Erkrankungen ist, wurden sowohl von der Weltgesundheitsorganisation (WHO) als auch von Wissenschaftlerinnen und Wissenschaftlern weltweit verschiedene Tools zum Monitoring und Audit bewegungsfördernder Politik entwickelt. Allerdings wurden diese Tools bisher noch nicht genutzt, um systematisch und umfassend Daten zu bewegungsfördernder Politik in Deutschland zu erheben und zu analysieren. Ziel der Arbeit Die Studie hat zum Ziel, die politischen Anstrengungen zur Bewegungsförderung in Deutschland in einem systematischen Überblick darzustellen. Material und Methoden Die Erhebung wurde im Rahmen des Projekts „Policy Evaluation Network“ (www.jpi-pen.eu) unter Nutzung des Health-Enhancing Physical Activity (HEPA) Policy Audit Tools (PAT) der WHO durchgeführt. Datenquellen waren Erhebungen der WHO für das European Union Physical Activity Monitoring Framework, eigene Recherchearbeiten sowie eine Expertenbefragung. Ergebnisse Die Ergebnisse zeigen das Spektrum der relevanten Akteure und bieten einen Überblick über aktuelle politische Maßnahmen sowie die Bereiche Surveillance, Evaluation und finanzielle Förderung. Darüber hinaus identifizieren sie wichtige Erfolge der aktuellen deutschen Politik sowie bestehende Herausforderungen. Diskussion Im internationalen Vergleich fällt auf, dass andere Länder im Gegensatz zu Deutschland messbare nationale Ziele für die Förderung von Bewegung formuliert haben. Hervorzuheben ist, dass Deutschland zu der Minderheit der Staaten mit spezifischen Bewegungsempfehlungen für Menschen mit nichtübertragbaren Erkrankungen zählt. Eine besonders hohe Relevanz hat die Weiterentwicklung von Strukturen für Bewegungsförderung in Deutschland.