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Impact of online learning on sense of belonging among first year clinical health students during COVID-19: student and academic perspectives | BMC Medical Education

Impact of online learning on sense of belonging among first year clinical health students during COVID-19: student and academic perspectives | BMC Medical Education

Online student cross-sectional survey

Demographic characteristics

A total of 179 out of the possible 663 students (27{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} completion) completed the online survey in June 2020. Median age of students was 19 years (IQR 18–28 years) and there were approximately three times as many females as males (Table 1), reflective of the undergraduate health sciences cohort (70{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} female). Student numbers were also reflective of the broader enrolment numbers in the programs (i.e., occupational therapy is the largest program). Just over half (53{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}; n = 94) of students had no prior experience in undertaking a Bachelor degree, and 76{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students had not completed any online courses prior to enrolment.

Table 1 Demographic characteristics

Quantitative results to the sense of belonging questionnaire

In terms of students’ sense of belonging to the university, the majority felt ‘quite’ or ‘extremely’ happy with their choice of university (74{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) and felt ‘quite’ or ‘extremely’ welcomed by the university (68{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}). While most students felt respected by both staff (70{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) and students (60{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) at the university, students reported less connectiveness (23.5{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) to the university. Only 20{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students reported they felt they were understood as an individual, and only 13{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} felt they ‘quite’ or ‘extremely’ mattered to others at the university (Table 2).

Table 2 Online learning and Sense of Belonging to the University [1]

Table 3 shows how the online learning experiences impacted on students’ perception of the course; 27{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students felt ‘quite’ or ‘extremely’ connected to staff while 16{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students felt ‘quite’ or ‘extremely’ connected to other students. While 49{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students rated 4 and above for the level of respect that they received from other students and their contribution towards the subject, students who had prior higher education felt less respected than students who had no prior higher education (p = 0.03). When asked how the online subject had contributed to understanding, knowledge/skills in their chosen health profession, about half of the students rated the online subject highly (rating 4 and above). Students who had prior higher education indicated higher ratings of understanding and knowledge/skills compared to students without prior higher education (p = 0.07 and p = 0.03 respectively). There was also a significantly higher proportion of students with no prior higher education who identified the online learning experience as either ‘quite’ or ‘extremely’ likely to impact their intention to continue with their current course (p = 0.001).

Table 3 Impact of online profession-specific subject on perception of the course

Qualitative results

Qualitative findings provided insight into experiences of staff and students during the rapid, unplanned transition to online learning. Student questionnaire responses included two open-ended questions expanding on enablers and barriers to sense of belonging. These yielded 145 enablers and 254 barriers to students’ feeling a sense of belonging. Data were subjected to qualitative content analysis by two authors and categories are presented in Additional file 1.

Three focus groups were conducted: two student sessions, each with two students enrolled in Speech Pathology and Paramedicine, and one academic session with five participants. Four full time academics and one casual academic participated from a total population of nine eligible academics. Using the processes described in the methods, focus group analysis was compared with the survey content analysis and the authors identified synergies between them. Findings were then integrated under a global theme, underpinned by organising and basic themes. The following themes reflect triangulation between academic and student focus group data in addition to survey responses.

Global theme—navigating belonging during the COVID-19 crisis: a shared responsibility

“We are in this together…making the best of this”

This theme explores sense of belonging creation during this period as a shared process, where participants perceived they worked together to get through the crisis. Students and academics encountered many challenges as they transitioned to online learning but despite hard times, were able to engage positively. The global theme revealed students and academics were navigating belonging during the COVID-19 crisis, and this journey was a shared responsibility. Both groups were working to achieve positive student engagement that would in turn create a sense of belonging in first-year students. A strong commitment of working hard to make the best out of this was mutually acknowledged.

Students perceived academics had done “a really good job at making sure we belonged…in those first few weeks that we were on campus but even more so probably while we were in Zoom” (Student-Astrid-Focus Group). Academics perceived students were actively engaged in making online learning work and were collegial and collaborative.

The shared experiences about navigating belonging during the COVID-19 crisis, have been captured under four organising themes: dimensions of belonging, individual experiences and challenges, reconceptualising teaching and learning, and relationships are central to belonging. Within each organising theme, basic themes were identified that provide depth to the organising theme (Fig. 1). Additional files 1 and 2 present a summary of the quotes obtained from the open-ended surveys and focus groups respectively, that contribute to the themes in Fig. 1.

Fig. 1
figure 1

Pictorial representation of the global, organising, and basic themes

Organising theme: dimensions of belonging

This theme outlines that belonging is a multidimensional experience with several facets underpinning participants’ experiences. Students and academics identified several dimensions of belonging in relation to first year students’ experiences, as illustrated by two basic themes that sit under the organising theme: what it means to belong, and layers of belonging.

Basic theme: what it means to belong

This theme explores the idea that belonging at university is underpinned by feeling valued and connected. Academics and students agreed that having a sense of being valued by the university and a desire to have an active connection across all aspects of university life was important for students.

Belonging as a student was gained through a connection with the “vocation” (Student-Claire-Focus Group) or the course and career, and with people who will “be there” (Student-Claire-Focus Group) for them. Furthermore, support of academics was critical to gaining a sense of belonging. It was noted by academics and students, that when students feel they belong at university, they are actively engaged in their learning, and this sense of belonging in turn shapes their overall identity. Students can then “actually sort of relax and become themselves” (Staff-Brooke).

Belonging to their cohort, their course, their future profession, and their university was important for students. One academic noted that the “concept of acceptance” is part of the sense of belonging and goes “both ways” (Staff-Brooke).

Both academics and students agreed that the rapid change to online learning due to COVID-19, meant that developing a sense of belonging was challenged.

Basic theme: layers of belonging

This theme identified layers of belonging reflected in participants’ experiences. Peer, academic and professional layers each contributed to an overall sense of belonging and key examples are provided below.

Peers

Belonging to peers was described as “having that connection to someone that’s going through exactly the same thing as what you’re going through” (Student-Astrid-Focus Group). Students were concerned that when learning moved online that this sense of belonging would be jeopardised by less opportunities for in-person interaction.

Academics

Being connected to academics was perceived by students as directly impacting learning, with one student commenting: “…when they’re not connecting with the teacher, they’re not connecting with the content, they’re not connecting with the feedback. That’s when you develop this sense of feeling like you just don’t belong” (Student-Emily-Focus Group).

Academics perceived it was also important for students to develop a sense of belonging to the university community.

Profession

Belonging to a profession was identified as an important feature of belonging by academics and students. Studying a degree with a clear professional identity facilitated first year students to feel they belonged compared to those undertaking general health science degrees which may have multiple pathways and career options less directly aligned to first year studies.

One academic actively encouraged first year students to belong to their professional association as a way of fostering belonging in first years.

Organising theme—Individual experiences and challenges

This theme outlines that while there are similarities in participants’ experiences, individuals have unique contexts and factors shaping their experiences. Academics and students reflected upon personal impacts of the COVID-19 pandemic on their teaching or learning and how they responded as individuals to the ensuing challenges. Two basic themes emerged: Challenges of transition and recognising different learning preferences.

Basic
theme
—challenges of transition

This theme explored the significant challenges of transitioning to online teaching and learning. For some students, the transition to online learning offered potential benefits of flexibility and reduced travel time. Two of the four students in the focus groups opted for online learning opportunities available in other subjects of study prior to the pandemic to efficiently manage their study and external commitments. Nonetheless, the pandemic brought a raft of personal challenges that diminished these expected benefits. Covid-related changes to family employment, reduced access to childcare support and non-optional home schooling presented new concerns.

Clearly, students missed the opportunity to focus attention on their learning needs when balancing childcare demands and home-schooling during lockdowns.

Unlike a conventional online courses where students choose or plan to be online, the sudden, unexpected, and unplanned move to online study was prefaced by a short period (four weeks) of in-person class time. This initial in-person time was identified as being key to relationship building.

Academics identified positive experiences and challenges during the transition to online learning. The rapid change presented a problem to be solved and individuals could “embrace it and to work effectively…as a team” (Staff-Jane). Quickly strategizing and responding to the demands of online learning required team knowledge, experience, and support. Hence, enhanced team culture was a further positive for academics, being “present for each other” (Staff-Brooke).

Basic
theme
:
recognising different learning preferences

This theme identifies experiences of online learning influenced by personal attributes, individual expectations and learning preferences. Such key factors impacted students’ capacity to maintain focus on academic goals after the rapid change to online learning. Some students reflected that barriers were not solely a feature of online learning environments, reporting that competing priorities, including work commitments and limited contact time with staff as pre-existing challenges to belonging. However, some students directly attributed their limited engagement and reduced motivation to the online learning environment.

Students suggested that active engagement “comes down to personality” (Student-Astrid- Focus Group). If a student was not shy they were comfortable to come forward and participate online. Some students perceived clear links between personal discipline, engagement, commitment, and achievement in online learning environments.

Further, students perceived effective (and ineffective) online group functioning reflected personalities of individual members, with some groups/personalities seen as being able to organise whilst other groups lacked leadership and cohesion.

Students who perceived themselves as active engagers reported being drawn towards other students who demonstrated motivation to interact and learn. Other students perceived their personalities or learning preferences were misaligned with the expectations of belonging in online learning environments and focussed upon tasks rather than connection.

Academics recognised student diversity and a need to reflect and re-evaluate expectations of students in online environments. They accepted that some students may be quietly engaging and learning to belong, but this was harder to observe in online compared to in-person learning environments.

Organising theme—relationships are central to belonging

This theme identified the relationship between all parties as a fundamental aspect of creating a sense of belonging. Two basic themes were influential in shaping perceptions of how relationships and connections contribute to belonging: collaboration with peers is fundamental, and effective and regular communication with staff is necessary.

Basic
theme
—collaboration with peers is fundamental

This theme revealed collaboration with student peers was a key element of creating a sense of belonging. The degree of social interaction with student peers and opportunities to create friendships contributed to feelings of belonging. Accordingly, students found it problematic when peers neglected to turn cameras on during classes, making interaction very difficult. Visualisation of peers and use of cameras in online classes impacted students’ opportunities to get to know each other.

Challenges posed by online learning were further highlighted in the student survey through a focus on non-academic aspects of university and campus life. Typically, university campuses offer interactional opportunities through clubs, sport, and shared spaces to learn and socialise. Campus life, students suggested, may facilitate learning and personal development. Absence of this type of interaction was linked to barriers in developing friendships and consequently a lesser sense of belonging as reflected in Additional file 1.

Basic theme—
communication
with academics is necessary

This theme outlined that communicating with academics was a key component of creating a sense of belonging. With less opportunities for peer support, there was stronger reliance on the academic-student connection, although students reported positive and negative interactions with academics during online learning.

Positive interactions and individualised communication with academics enhanced student sense of satisfaction and belonging. Furthermore, students in the focus groups reported a feeling of trust and a bond created by a shared challenge. Survey responses echoed this sentiment, noting that academics were “non-judgmental and supportive” (Student Survey 18) and created a sense of camaraderie. However, when students perceived impersonal communication from academics, they felt less connected or believed that teaching had become a “transaction” (Student-Astrid- Focus Group). Perceived levels of enthusiasm and engagement from academics influenced student’s perceptions of connection and belonging.

Students identified the online environment as a barrier to communication with academics. While systematic and university level communication was perceived as a useful source of information, students prioritised individualised communication from academic staff as key to belonging.

Academics concurred that effective communication was challenged in online environments, missing non-verbal cues and responsivity that characterises a classroom environment. Although the online learning environment provides an opportunity for academics to connect professionally with students, there were students who left their cameras off, with one academic noting they didn’t push this issue because there are many reasons for students choosing this option.

Organising theme: reconceptualising teaching and learning

This theme reveals how academics and students reconceptualised their expectations and modes of teaching and learning, to manage the crisis. It was not easy for academics or students, and many strategies were employed to make it work, with two basic themes emerging: challenges to online teaching and learning, and strategies to engage and connect.

Basic theme:
challenges
of online teaching and learning: “how do I make this work?”

This theme outlined many challenges faced by both academics and students during a rapid change to online mode. With the rapid change to online learning, academics asked themselves, ‘How do I make this work?’.

Managing workload

Academics reported their workload increased significantly, and they “found it a juggling act” (Staff-Louise) to meet their teaching requirements. Administrative loads consequently increased when reduced in-person contact with students led to more electronic communication. Academics needed to up-skill in online teaching in a short time frame and perceived this responsibility as all encompassing.

The rapid switch to online learning attracted significant academic workload, implementing and adapting content to see how material “might play out in a Zoom environment…[where]…everything takes longer” (Staff-Natalie).

Some students noticed a temptation to disengage from online learning, which meant balancing their workload and study demands became a challenge as they also faced significant workload and stressors in their personal lives due to COVID-19.

Class dynamics

Academics and students spoke about the change to classroom dynamics. The online environment was noted as being one in which it was difficult to read the room to see how students were progressing with their work. Others tried to use humour to enliven a class, only to have the Zoom frame freeze, killing the mood they were trying to create. Hence, staff felt teaching online was less conversational, flexible and responsive compared to face-to-face. Moreover, academics missed hands-on practical elements; a big shift for some programs.

Technological challenges

Academics learnt new skills quickly, but often these skills would be challenged when technology failed. Some academics reported a sense of vulnerability due to technological ineptitude but acknowledged that making mistakes in front of students could humanise the experience. Academics also acknowledged that some students did not have adequate technological resources to meet changes in their learning requirements when classes were placed online.

Basic theme: strategies to engage and connect

This theme reflected the strategies academics and students employed to remain engaged and connected. Academics worked hard to enhance online learning and hoped to connect with students and engage them in activities. Students too were active and appreciated academics’ efforts to facilitate engagement and connection. Underlying many of the strategies adopted by academics was a deep concern for student welfare during this time. Therefore, many academics aimed to ensure students were engaged and connected with each other and with the academic team. Academics built in small group opportunities during online teaching so students could connect, learn, and socialise.

Staff also spoke about informing students they could contact staff for support. One staff member described crossing the divide and actively discouraging a ‘them and us’ dynamic between students and staff.

A variety of teaching tools were identified by staff to build connection and promote engagement. Such tools included interactive quizzes, ice breakers activities, integrating reflective practices into activities and ‘drop in’ sessions. Staff also encouraged students to establish social media groups or other group experiences outside the classroom. Some staff members arrived early to zoom classes and left late to enable students to connect informally.

Students appreciated staff attempts to provide these activities. Students found these initiatives helpful, recognising staff placed effort into knowing students personally and focussing on student wellbeing and achievement. Students cited examples of provision of extra resources, mini-lectures, additional question and answer sessions, and fast response times to student queries. Students also initiated their own engagement strategies, including using group and personal messaging over platforms such as Facebook messenger.

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Healthy lifestyle behaviors, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: A cohort study

Healthy lifestyle behaviors, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: A cohort study

Abstract

Methods and findings

This retrospective cohort study included 15,104 patients with T2D free of macro- and microvascular complications at baseline (2006 to 2010) from the UK Biobank. Healthy lifestyle behaviors included noncurrent smoking, recommended waist circumference, regular physical activity, healthy diet, and moderate alcohol drinking. Outcomes were ascertained using electronic health records. Over a median of 8.1 years of follow-up, 1,296 cases of the composite microvascular complications occurred, including 558 diabetic retinopathy, 625 diabetic kidney disease, and 315 diabetic neuropathy, with some patients having 2 or 3 microvascular complications simultaneously. After multivariable adjustment for sociodemographic characteristics, history of hypertension, glycemic control, and medication histories, the hazard ratios (95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} confidence intervals (CIs)) for the participants adhering 4 to 5 low-risk lifestyle behaviors versus 0 to 1 were 0.65 (0.46, 0.91) for diabetic retinopathy, 0.43 (0.30, 0.61) for diabetic kidney disease, 0.46 (0.29, 0.74) for diabetic neuropathy, and 0.54 (0.43, 0.68) for the composite outcome (all Ps-trend ≤0.01). Further, the population-attributable fraction (95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CIs) of diabetic microvascular complications for poor adherence to the overall healthy lifestyle (<4 low-risk factors) ranged from 25.3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} (10.0{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}, 39.4{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) to 39.0{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} (17.7{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}, 56.8{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}). In addition, albumin, HDL-C, triglycerides, apolipoprotein A, C-reactive protein, and HbA1c collectively explained 23.20{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} (12.70{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}, 38.50{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) of the associations between overall lifestyle behaviors and total diabetic microvascular complications. The key limitation of the current analysis was the potential underreporting of microvascular complications because the cases were identified via electronic health records.

Author summary

Citation: Geng T, Zhu K, Lu Q, Wan Z, Chen X, Liu L, et al. (2023) Healthy lifestyle behaviors, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: A cohort study. PLoS Med 20(1):
e1004135.

https://doi.org/10.1371/journal.pmed.1004135

Academic Editor: Weiping Jia, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, CHINA

Received: June 8, 2022; Accepted: October 26, 2022; Published: January 10, 2023

Copyright: © 2023 Geng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The UK Biobank data are available on application to the UK Biobank (www.ukbiobank.ac.uk/). This research has been conducted using the UK Biobank Resource under Application Number 68307.

Funding: GL is supported by grants from National Nature Science Foundation of China (82073554), the Hubei Province Science Fund for Distinguished Young Scholars (2021CFA048), and the Fundamental Research Funds for the Central Universities (2021GCRC076). AP is supported by grants from National Nature Science Foundation of China (81930124, 82021005, and 82192902), and the Fundamental Research Funds for the Central Universities (2021GCRC075). The Funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations:
ALP,
alkaline phosphatase; ALT,
alanine aminotransferase; AST,
aspartate aminotransferase; BMI,
body mass index; CI,
confidence interval; CRIC,
Chronic Renal Insufficiency Cohort; CRP,
C-reactive protein; CVD,
cardiovascular disease; DBP,
diastolic blood pressure; DIY,
do-it-yourself; eGFR,
estimated glomerular filtration rate; GGT,
gamma glutamyltransferase; HDL-C,
high-density lipoprotein cholesterol; HR,
hazard ratio; LDL-C,
low-density lipoprotein cholesterol; MET,
metabolic equivalent of task; ONTARGET,
Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial; PAF,
population-attributable fraction; SBP,
systolic blood pressure; TC,
total cholesterol; T2D,
type 2 diabetes; WC,
waist circumference

Introduction

Diabetes is a global public health crisis affecting greater than 0.5 billion adults worldwide [1]. Diabetic microvascular complications including diabetic retinopathy, diabetic neuropathy, and diabetic kidney disease have placed a significant health and economic burden borne by individuals, families, and health systems [2,3]. For example, diabetic retinopathy, the leading cause of vision loss, is present in nearly 30{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of patients with diabetes [4]. Furthermore, both diabetic kidney disease and diabetic neuropathy may develop in approximately 50{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of patients with diabetes [5,6]. Therefore, it is paramount to identify cost-effective strategies to prevent and delay the development of microvascular complications in patients with diabetes.

Beyond the glucose control by medications, the American Diabetes Association guideline has highlighted that both caregivers and patients should focus on how to optimize lifestyle behaviors to improve diabetes care [7]. Although lifestyle behaviors that are generally recommended, e.g., normal weight, no smoking, moderate alcohol drinking, healthy diet, and physically active, have been associated with lower risks of microvascular complications [8–14], to our best knowledge, the magnitudes of the joint association of multiple lifestyle factors with the development of microvascular complications in diabetes have not yet been quantified, which may have substantial public health implications on translating epidemiological findings to meaningful public health actions. In addition, several studies have linked lifestyle behaviors with a range of intermediate variables including lipid profile [15,16], liver function biomarkers [15,17–19], renal function biomarkers [20,21], blood pressure indices [22], glucose metabolism measures [23], and systemic inflammatory factors [15,16]; however, whether and the extent to which these metabolic biomarkers could mediate the association between lifestyle behaviors and diabetic microvascular complications remains unclear.

To shed light on the potential favorable association of overall lifestyle behaviors on microvascular complications in patients with diabetes, we examined the joint association of multiple lifestyle behaviors, including waist circumference (WC), smoking status, habitual diet, physical activity, and alcohol intake with risks of total microvascular complications, diabetic retinopathy, diabetic neuropathy, and diabetic kidney disease among patients with type 2 diabetes (T2D) who participated in the UK Biobank study. In addition, we also comprehensively evaluated the effect of a series of blood biomarkers on mediating the relationship between lifestyle behaviors and diabetic microvascular complications.

Methods

Study population

The UK Biobank is a large community-based prospective cohort study for common diseases of middle and older adults including over 500,000 participants aged 37 to 73 years from 22 sites across England, Scotland, and Wales between March 2006 and October 2010. Extensive data were obtained through touchscreen questionnaires, physical measurements, and biological samples at recruitment. Specific methods of data collection have been described previously [24,25].

Our sample of 15,104 was generated by including patients with T2D identified by using the algorithms method developed by the UK Biobank study [26] and excluding participants with prevalent macro- or microvascular complication cases, had incomplete information on lifestyle behaviors, or withdrawal from the study. The flowchart of patients included in the current study is present in S1 Fig.

The study was approved by the North West Multi-Centre Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland. All participants provided written informed consent. In the current analysis, we employed the UK Biobank study to test a priori hypothesis; we did not publish an analysis plan before conducting analyses between January 2022 and March 2022. The associations between lifestyle factors and the risk of microvascular complications in participants without excluding those with macrovascular complications and stratified analysis by preexisting cardiovascular disease (CVD) status were performed in response to peer review in July 2022. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).

Measurements of lifestyle behaviors

Five lifestyle behaviors, namely, WC, smoking status, physical activity, habitual diet, and alcohol intake, were evaluated in the current analysis. We used WC instead of body mass index (BMI) to avoid the potential obesity paradox [27,28] as evidence found an obesity paradox when obesity was measured by BMI but not when measured by WC in patients with diabetes [29]. WC was measured using the Wessex nonstretchable sprung tape measurement, and low-risk WC was defined as <80 cm for women and <94 cm for men [30,31]. Data on smoking status were self-reported, and noncurrent smoking was defined as low-risk behavior. The frequency of all types of alcohol intake was reported using 6 predefined categories, between never to daily or almost daily. For participants who reported to drink alcohol, data on the average monthly or weekly alcohol intake from 6 types of alcohol beverages were collected. We calculated the average units of alcohol intake using the abovementioned information and defined low-risk drinking as moderate drinking (1 to 14 g/day for women or 1 to 28 g/day for men). Data on the type and duration of physical activity were derived from the questionnaire. Leisure-time physical activity score based on the 5 activities undertaken in the last 4 weeks was computed by multiplying the metabolic equivalent of task [MET] score of each activity by the minutes performed [32,33]. Light DIY (do-it-yourself), walking for pleasure, other exercises (e.g., swimming, cycling, keep fit, bowling), heavy DIY, and strenuous sports were given 1.5, 3.5, 4.0, 5.5, and 8.0 METs, respectively [34]. The midpoints of the frequency and duration of physical activities were used to calculate the time spent on each activity. We then classified the top third of the physical activity score as the low-risk group. In addition, we generated a dietary score to reflect the overall diet quality including 10 components, namely, fruits, vegetables, whole grains, fish, dairy, vegetable oils, refined grains, processed meat, unprocessed meat, and sugar-sweetened beverages. Low-risk diet was defined as meeting 5 or more ideal diet components [35]. Participants with each low-risk behavior were assigned 1 point; otherwise, 0 points. The overall healthy lifestyle score was the sum of individual score of the 5 lifestyle behaviors, ranging from 0 to 5, with higher score indicating healthier lifestyle.

Assessment of the circulating biomarkers

Blood samples were collected from consenting participants at recruitment, separated by components and stored at UK Biobank (−80°C and LN2) until analysis. Blood biomarkers were externally validated with stringent quality control in the UK Biobank; full details on assay performance have been given elsewhere [36]. We selected the potential biological biomarkers mediating the association between lifestyle factors and microvascular complications based on knowledge of potential pathways, including glycemic control determined by glycated hemoglobin (HbA1c), lipid profile (total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], triglycerides, apolipoprotein A, apolipoprotein B, and lipoprotein A), liver function (alanine aminotransferase [ALT], alkaline phosphatase [ALP], aspartate aminotransferase [AST], gamma glutamyltransferase [GGT], total bilirubin, total protein, and albumin), renal function (cystatin C, creatinine, urate, and urea), inflammation (C-reactive protein [CRP], and white blood cell count), and blood pressure indices (systolic blood pressure [SBP] and diastolic blood pressure [DBP]).

Statistical analysis

Comparisons of baseline characteristics across the categories of the overall healthy lifestyle score were made using ANOVA or chi-squared test. We also compared the differences between patients included in the current analysis and those who were excluded due to missing values. Person-years were calculated from the date of recruitment to the date of death, first endpoint, lost to follow-up, or the end of follow-up, whichever came first. The lost to follow-up variable in the UK Biobank has been created by amalgamating data from 5 possible sources: (1) Death reported to UK Biobank by a relative; (2) NHS records indicate they are lost to follow-up; (3) NHS records indicate they have left the UK; (4) UK Biobank sources report they have left the UK; (5) Participant has withdrawn consent for future linkage. The end of follow-up dates were 1 April 2017, 17 September 2016, and 1 November 2016, for centers in England, Wales, and Scotland, respectively. Cox proportional hazards regression models were used to calculated hazard ratios (HRs) and 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} confidence intervals (CIs) for the associations of individual lifestyle behaviors and overall healthy lifestyle score with risks of total and individual microvascular complications in patients with T2D. We imputed the missing values of covariates (≤7{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) using multiple imputations by chained equations with 5 imputations (SAS PROC MI with a fully conditional specification method and PROC MIANALYZE). Linear regression model and logistic regression model with all the covariates in the fully adjusted model were used to impute continuous variables and categorical variables, respectively. The percentage of missing values are present in S1 Table.

Three models were built. In Model 1, we adjusted for age (continuous, years), sex (male, female), Townsend Deprivation Index (continuous), and race/ethnicity (White, others). In Model 2, we further adjusted for education attainment (college or university degree, A/AS levels or equivalent or O levels/GCSEs, NVQ or HND or HNC or equivalent or other professional qualifications, none of the above), sleep duration (<6, 6 to 8, or ≥9 hours/day), family history of CVD (yes, no), family history of hypertension (yes, no), and prevalence of hypertension (yes, no). Finally, in Model 3, diabetes duration (continuous, years), HbA1c (continuous, mmol/mol), use of diabetes medication (none, only oral medicine, insulin, and others), use of antihypertensive medication (yes, no), use of lipid-lowing medication (yes, no), and use of aspirin (yes, no) were additionally adjusted. Further, restricted cubic spline analysis was applied to test dose–response relationships between the healthy lifestyle score and risks of outcomes. We also calculated the population-attributable fractions (PAFs) using the {e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}par SAS Macro (https://www.hsph.harvard.edu/donna-spiegelman/software/par/) to estimate the proportion of microvascular complications that could theoretically be avoided if all participants adhered to 4 or more low-risk lifestyle behaviors.

Mediation effects of biomarkers on the associations of overall lifestyle score with risks of total and individual microvascular complications were evaluated using mediation package in R. Indirect, direct, and total effects for each mediator were computed via combining the mediator and outcome models with the adjustment of all the covariates in Model 3. Nonparametric bootstrap resampling was used to compute the CIs of the proportions of mediations. We selected the available biomarkers from the UK Biobank for the mediation analyses based on knowledge of potential causal pathways to predisposing to microvascular complications or mortality [19,37–40]. The selected biomarkers were considered as potential mediators following two-step analysis. First, we assessed the associations of all biomarkers with the overall lifestyle score using the multivariable-adjusted linear regression models. Second, we evaluated the associations of biomarkers that were significantly associated with the overall lifestyle score, with risks of all the outcomes using the multivariable-adjusted Cox regression model. We then chose the biomarkers significantly associated with each outcome for the mediation analysis accordingly.

In addition, stratified analyses were conducted by age (≤60, >60 years), sex (female, male), education (less than college, college, or above), diabetes duration (≤3, >3 years), use of diabetes medication (yes, no), and HbA1c (≤53, >53 mmol/mol). Interactions between the overall healthy lifestyle score and stratified factors on the risk of outcomes were examined using the likelihood ratio test by adding product terms in the multivariable-adjusted Cox models. Further, we examined the associations of different combinations of low-risk lifestyle behaviors with outcomes.

Several sensitivity analyses were conducted to test the robustness of our results. First, to minimize the potential reverse causation, we performed the analysis among patients with T2D after excluding the cases that occurred within 2 years of follow-up. Second, we generated the overall lifestyle score using low-risk drinking defined as moderate alcohol drinking and never drinking and repeated the main analysis using the new lifestyle score. Third, we constructed the healthy lifestyle score using BMI or waist-to-hip ratio instead of WC. Fourth, we generated a weighted healthy lifestyle score and examined the associations of the weighted healthy lifestyle score with risks of outcomes. Fifth, we investigated the association between the overall lifestyle score and risk of diabetic kidney disease, and mediation analysis for diabetic kidney disease with additional adjustment for kidney function biomarkers. Sixth, we performed the analysis via including the patients with CVD (n = 3,397) at baseline and stratified the associations by preexisting CVD status. Finally, given the potential competing risk of death highlighted during the peer review process, we assessed the associations of healthy lifestyle score with risks of microvascular complications using both the cause-specific hazard model and Fine and Gray subdistribution methods.

We used SAS V.9.4 and R software version 4.0.2 (R Foundation for Statistical Computing) for all statistical analyses. A two-tailed P < 0.05 was considered to be statistically significant.

Results

Baseline characteristics

Among 15,104 participants with T2D (60.3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} male; mean age, 59.3 years), there were 3,406 (22.6{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}), 6,080 (40.3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}), 4,062 (26.9{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}), 1,556 (10.3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) having 0 or 1, 2, 3, and 4 or 5 low-risk lifestyle behaviors, respectively. The baseline characteristics are shown in Table 1. Participants with more low-risk lifestyle behaviors were more likely to be men, White, less deprived, highly educated, sleep recommended hours, have a lower level of HbA1c, and have a lower prevalence of hypertension. They were less likely to use aspirins and medications for diabetes, dyslipidemia, and hypertension. In addition, compared the participants who were excluded due to missing values, those included in the current analysis were more likely to be men, White, less deprived, highly educated, noncurrent smokers, physically active, moderate alcohol drinkers, and eat healthier (S2 Table).

Lifestyle behaviors and outcomes

During 117,445 person-years of follow-up (median 8.1 years; interquartile range 7.3 to 8.8 years; maximum 11.9 years), there occurred 1,639 (10.9{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) deaths and 1,296 (8.6{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) composite microvascular complications cases, including 558 (3.7{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) diabetic retinopathy, 625 (4.1{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) diabetic kidney disease, and 315 (2.1{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) diabetic neuropathy. Among all the cases, one case of diabetic kidney disease was uniquely identified from death records. S3 Table shows the associations between individual lifestyle behaviors and all the outcomes. Being physically active, with lower WC, and moderate alcohol intake were associated with a lower risk of microvascular complications, while noncurrent smoking and healthy diet were not. The overall healthy lifestyle score was associated with lower risks of all the outcomes in a dose–response manner (all Ps for linear trend ≤0.01; Table 2 and Figs 1 and S2). Compared with participants with 0 to 1 low-risk lifestyle behavior, participants with 4 to 5 low-risk lifestyle behaviors had HRs (95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CIs) of 0.65 (0.46, 0.91) for diabetic retinopathy, 0.43 (0.30, 0.61) for diabetic kidney disease, 0.46 (0.29, 0.74) for diabetic neuropathy, and 0.54 (0.43, 0.68) for the composite microvascular complications, respectively. For each number increment in low-risk lifestyle behavior, there was a 13{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} lower risk of diabetic retinopathy (HR, 0.87; 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CI: 0.80, 0.95), 22{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} lower risk of diabetic kidney disease (HR, 0.78; 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CI: 0.72, 0.85), 27{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} lower risk of diabetic neuropathy (HR, 0.73; 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CI: 0.65, 0.83), and a 18{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} lower risk of the composite microvascular complications (HR, 0.82; 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CI: 0.77, 0.87).

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Fig 1. Dose–response relationship of the healthy lifestyle score with risk of microvascular complications among individuals with T2D.

X-axis showed the numbers of low-risk lifestyle behaviors, and y-axis showed the HRs of the composite microvascular complications (A), diabetic retinopathy (B), diabetic kidney disease (C), and diabetic neuropathy (D). Black curves were HRs, and grey zones were 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CIs. Multivariable-adjusted models were adjusted for age (continuous, years), sex (male, female), ethnicity (White, others), education attainment (college or university degree, A/AS levels or equivalent or O levels/GCSEs or equivalent or other professional qualifications, or none of the above), Townsend Deprivation Index (continuous), sleep duration (<6, 6–8, or ≥9 hours/day), family history of CVD (yes, no), family history of hypertension (yes, no), prevalence of hypertension (yes, no), diabetes duration (continuous, years), HbA1c (continuous, mmol/mol), use of diabetes medication (none, only oral medication pills, or insulin or others), use of antihypertensive medication (yes, no), use of lipid-lowing medication (yes, no), and use of aspirin (yes, no). All P-nonlinearity were ≥0.09 and all P for overall association were <0.001 (except for diabetic retinopathy: P for overall association = 0.008). CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio; T2D, type 2 diabetes.


https://doi.org/10.1371/journal.pmed.1004135.g001

In addition, the estimated PAFs of nonadherence to 4 or more low-risk lifestyle factors were 39.0{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} (17.7{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}, 56.8{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) for diabetic kidney disease and 25.3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} (10.0{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}, 39.4{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) for the composite microvascular complications (Table 2).

Mediation analysis

All the biomarkers were significantly associated with the overall lifestyle score except for total protein, lipoprotein A, and SBP (S4 Table). The associations between the selected biomarkers and all outcomes are shown in S5 Table. Six significant mediators were detected on the associations of lifestyle score with risk of the composite microvascular complications and diabetic kidney disease, namely, albumin, HDL-C, triglycerides, apolipoprotein A, CRP, and HbA1c. The relationship between the lifestyle behaviors and risk of diabetic neuropathy was mediated by cystatin C, GGT, total bilirubin, albumin, HDL-C, triglycerides, apolipoprotein A, CRP, and HbA1c with the proportion of mediation effect ranging from 3.22{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} to 11.35{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}. Collectively, the mediators explained 23.20{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}, 24.40{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}, and 31.90{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the associations of overall lifestyle behaviors with composite microvascular complications, diabetic kidney disease, and diabetic neuropathy, respectively. In addition, our data showed that among all the potential biomarkers, only HbA1c was a significant mediator that explained 15.26{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the relationship between the overall lifestyle score and risk of diabetic retinopathy (Table 3).

Secondary analysis and sensitivity analysis

Consistent results were observed when analyses were stratified by age, sex, education, diabetes duration, use of hypoglycemic medication, and HbA1c level. No significant interaction was observed between the healthy lifestyle score and the stratified factors on the outcomes considering multiple comparisons (S3 Fig). Further, the results of different combinations of low-risk lifestyle factors showed that the increased numbers of low-risk lifestyle factors were associated with graded lower risks of diabetic retinopathy, diabetic kidney disease, diabetic neuropathy, and the composite microvascular complications (S6 Table).

In the sensitivity analyses, the results were generally robust when excluding patients with events that occurred within the first 2 years of follow-up, defining low-risk alcohol intake as moderate drinking and nondrinking, generating the lifestyle score using BMI or waist-to-hip ratio instead of WC, or generating the overall lifestyle score as a weighted score (S7–S10 Tables). The association between overall lifestyle behaviors and risk of diabetic kidney disease was slightly attenuated when estimated glomerular filtration rate (eGFR) was additionally adjusted, and the results of mediation analysis for diabetic kidney disease were largely unchanged with the additional adjustment of eGFR (S11 and S12 Tables). Further, we observed similar results when patients with preexisting CVD were included and in patients with preexisting CVD, although diabetic retinopathy did not reach statistical significance in patients with preexisting CVD probably due to the insufficient power (S13 and S14 Tables). Finally, consistent results were demonstrated when we used 2 competing risk models accounting for the death (S15 Table).

Discussion

In this retrospective cohort study of patients with T2D, adherence to a greater number of healthy lifestyle behaviors, including recommended WC, noncurrent smoking, physically active, healthy diet, and moderate alcohol drinking, was inversely associated with lower risks of diabetic retinopathy, diabetic kidney disease, diabetic neuropathy, and the composite microvascular complications. For each number increment in low-risk lifestyle behavior, there was an 18{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} lower risk of developing diabetic microvascular complications. Moreover, the results of PAFs suggested that 25.3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the diabetic microvascular complications could have been avoided if the patients with T2D had 4 or more healthy lifestyle behaviors. In addition, the mediators collectively explained 23.20{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the associations between the overall healthy lifestyle score and diabetic microvascular complications. Specifically, CRP, albumin, HbA1c, and lipids profile (HDL-C, triglycerides, and apolipoprotein A) could explain 4.44{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} to 10.69{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of the association between overall lifestyle behaviors and the total diabetic microvascular complications.

Our study contributes to the literature regarding the influence of combined healthy lifestyle behaviors on the risk of diabetic microvascular complications. To date, many studies have been performed to evaluate the relationship between individual lifestyle behaviors and risk of diabetic microvascular complications; however, the joint association of multiple lifestyle behaviors with microvascular complications remains unknown. For example, the Irish Longitudinal Study showed that a history of smoking was associated with a higher risk of developing microvascular complications [8]. The Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET) studies demonstrated that adherence to a healthy dietary pattern (the Alternate Healthy Eating Index) [9], being physically active, and moderate alcohol consumption [12] were associated with a lower risk of incident chronic kidney disease among patients with T2D. Furthermore, general obesity and abdominal obesity were associated with higher risks of diabetic kidney disease [41], diabetic retinopathy [13], and diabetic neuropathy [42].

However, the results of lifestyle interventions on microvascular complications among patients with diabetes or impaired glucose tolerance in clinical trials were inconsistent. The Steno-2 randomized trial including 160 patients with T2D and persistent microalbuminuria showed pharmacological therapies in combination with lifestyle behavior modifications, including adopting a healthy diet, engaging regular physical activity, and participating in smoking cessation courses, significantly reduced the risk of diabetic nephropathy, retinopathy, and neuropathy [43]. Further, the China Da Qing Diabetes Prevention Study including 577 participants with impaired glucose tolerance reported that healthy diet and exercise interventions in combination resulted in a 47{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} reduction in the diabetic retinopathy incidence, but no beneficial effects were observed for diabetic nephropathy or neuropathy [44]. In addition, the Look AHEAD trial consisting of 5,145 overweight or obese patients with T2D, which focused on weight management through increased energy deficit and physical activity, resulted in a significant decrease in chronic kidney disease [45], but not diabetic neuropathy measured by physical examinations [46]. Notably, microvascular complications were not predefined primary outcomes in these trials and small numbers of cases might partially explained the heterogeneities in these findings (e.g., 296 cases of very-high-risk chronic kidney disease in the Look AHEAD trial). Further trials with proper designs are needed to corroborate our findings in the future.

Our mediation analyses contribute to better understanding the lower risk of microvascular complications associated with lifestyle behaviors. Our data showed that the associations of overall lifestyle behaviors with diabetic kidney disease, diabetic neuropathy, and total microvascular complications may be explained by the improvement in glycemic control, liver function, lipid profile, and systemic inflammation, with lifestyle behaviors related lower risk of diabetic neuropathy might be additionally explained by kidney function amelioration. However, our data showed that the association between lifestyle and diabetic retinopathy was mainly through the glycemic control rather than other pathways. Our results corroborate prior findings from the observational studies. For example, intensive lifestyle intervention including physical activity and healthy diet recommendations could benefit glycemic control [47]. Adherence to a combined healthy lifestyle score including healthy diet, physically active, nonsmoking, healthy sleep, and social support were associated with lower concentrations of inflammatory markers [48]. Chronic Renal Insufficiency Cohort (CRIC) Study showed that combined healthy lifestyle characterized as physically active, nonsmoking, and BMI ≥25 kg/m2 were associated with lower risks of atherosclerotic events and kidney function decline among patients with chronic kidney disease [20]. Furthermore, lifestyle modifications including promoting healthy diet, physical activity, and weight loss could significantly improve liver function, renal function, lipid profile, endothelial dysfunction, and reduce systemic inflammation in interventional studies [49–54].

The current study is among the first to investigate the relationship between the overall lifestyle behaviors and diabetic microvascular complications. The strengths of this study included the large sample size, long period of follow-up, and extensive collection of data on clinical biomarkers, which allowed us to comprehensively evaluate the potential mechanisms underlying the observed associations. Despite the strengths, this study should be interpreted in the light of its potential limitations. First, as the microvascular complications were identified via hospital inpatient records and death registries, there might be underreporting of the cases, for example, primary care data were not completely available currently. Second, the self-reported and one-time assessment of lifestyle behaviors data are susceptible to measurement errors. In addition, information on lifestyle behaviors was collected at recruitment and the behaviors may change over time; hence, the observed associations might be attenuated due to nondifferential misclassification bias. Third, mediation analysis assumes causality between lifestyles behaviors and biological biomarkers, although both the lifestyle behaviors and biological mediators were assessed at the same time in the UK Biobank. Future studies with repeatedly measured data are required to replicate our findings. Fourth, our study is limited in terms of ethnic diversity (>85{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} Whites); our results may not be directly generalized to other ethnic groups. Fifth, our study was based on a retrospective sampling from the UK Biobank study; hence, the causality should be interpreted with caution. Sixth, the UK Biobank is not representative of the general population of the UK, particularly relating to socioeconomic deprivation, lifestyles, and noncommunicable disease, with evidence of the healthy volunteer selection bias. Finally, residual or unknown confounding could not be excluded due to the observational study design, although we have in our effort to adjust for the potential confounding factors.

Supporting information

S2 Fig. Dose–response relationship of the healthy lifestyle score with risk of microvascular complications among individuals with T2D (unadjusted model).

X-axis showed the numbers of low-risk lifestyle behaviors, and y-axis showed the HRs of the composite microvascular complications (A), diabetic retinopathy (B), diabetic kidney disease (C), and diabetic neuropathy (D). Black curves were HRs, and grey zones were 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CIs. All P-nonlinearity were ≥0.10, and all P for overall association were <0.001 (except for diabetic retinopathy: P for overall association = 0.005). CI, confidence interval; HR, hazard ratio; T2D, type 2 diabetes.

https://doi.org/10.1371/journal.pmed.1004135.s003

(TIF)

S3 Fig. Stratified analyses of the associations of overall healthy lifestyle score with microvascular complications.

Model 1: unadjusted model. Model 2: age (continuous, years), sex (male, female), race (White, others), education attainment (college or university degree, A/AS levels or equivalent or O levels/GCSEs or equivalent or other professional qualifications, or none of the above), Townsend Deprivation Index (continuous), sleep duration (<6, 6–8, or ≥9 hours/day), family history of CVD (yes, no), family history of hypertension (yes, no), prevalence of hypertension (yes, no), diabetes duration (continuous, years), use of diabetes medication (none, only oral medication pills, or insulin or others), HbA1c (continuous, mmol/mol), use of antihypertensive medication (yes, no), use of lipid-lowing medication (yes, no), and use of aspirin (yes, no), with exception of stratifying factors. Interactions were tested based on Model 2.

https://doi.org/10.1371/journal.pmed.1004135.s004

(TIF)

S7 Table. HRs (95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CIs) of microvascular complications according to the overall lifestyle score among individuals with T2D after excluding the cases that occurred within first 2 years of follow-up.

CI, confidence interval; HR, hazard ratio; T2D, type 2 diabetes.

https://doi.org/10.1371/journal.pmed.1004135.s011

(DOCX)

S8 Table. HRs (95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CIs) of microvascular complications according to the healthy lifestyle score using moderate drinking and nondrinking as the low-risk behavior in individuals with T2D.

CI, confidence interval; HR, hazard ratio; T2D, type 2 diabetes.

https://doi.org/10.1371/journal.pmed.1004135.s012

(DOCX)

S9 Table. HRs (95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CIs) of microvascular complications according to the healthy lifestyle score using optimal BMI or optimal WHR as the low-risk behavior in individuals with T2D.

BMI, body mass index; CI, confidence interval; HR, hazard ratio; T2D, type 2 diabetes; WHR, waist-to-hip ratio.

https://doi.org/10.1371/journal.pmed.1004135.s013

(DOCX)

S11 Table. HRs (95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CIs) of diabetic kidney disease according to the healthy lifestyle score among individuals with T2D with additional adjustment for eGFR.

CI, confidence interval; eGFR, estimated glomerular filtration rate; HR, hazard ratio; T2D, type 2 diabetes.

https://doi.org/10.1371/journal.pmed.1004135.s015

(DOCX)

S12 Table. Possible mediators of the associations between the healthy lifestyle score and diabetic kidney disease among individuals with T2D with additional adjustment for eGFR.

eGFR, estimated glomerular filtration rate; T2D, type 2 diabetes.

https://doi.org/10.1371/journal.pmed.1004135.s016

(DOCX)

S13 Table. HRs (95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} CIs) of microvascular complications according to the healthy lifestyle score in 18,501 individuals with T2D including patients with preexisting macrovascular disease.

CI, confidence interval; HR, hazard ratio; T2D, type 2 diabetes.

https://doi.org/10.1371/journal.pmed.1004135.s017

(DOCX)

Acknowledgments

We are grateful to all the participants of UK Biobank and all the people involved in building the UK Biobank study.

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Sleep quality among inpatients of Spanish public hospitals

Sleep quality among inpatients of Spanish public hospitals

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    Homeschooling, online learning among factors in D51’s declining enrollment | Western Colorado

    Homeschooling, online learning among factors in D51’s declining enrollment | Western Colorado

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    Attendance in physical education classes, sedentary behavior, and different forms of physical activity among schoolchildren: a cross-sectional study | BMC Public Health

    Attendance in physical education classes, sedentary behavior, and different forms of physical activity among schoolchildren: a cross-sectional study | BMC Public Health

    Participants

    Schoolchildren (7–12 years-old) from 2nd to 5th-grade in part-time public schools in Feira de Santana (Bahia) participated in this cross-sectional study. Feira de Santana is in the Northeast region of Brazil (inhabitants: 624,107; Human Development Index: 0.712). Data collection covered weekdays (Tuesday to Friday), from March to October of the year 2019 and included a probability sample of students from 2nd to 5th-grade, from public schools in the urban area, with broadband Internet. The sample size was defined based on the following parameters: a population of 15,920 students enrolled in the education system, according to data from the Municipal Department of Education; expected prevalence of outcomes of 50{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}; confidence limit of three percentage points; design effect (deff) of 2.0; and 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} confidence interval (95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}CI). Based on these parameters, the sample size was calculated at 2,000 students. A further 20{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} was added to make up for presumed losses, resulting in a sample of 2,400 students (Fig. 1).

    Fig. 1
    figure 1

    The cluster sampling process was carried out in three stages: I) all schools in the municipal network were stratified according to the 11 geographic and administrative centers of the Department of Education (clusters); II) one school from each center was randomly drawn; III) all classrooms from 2nd to 5th grade within each school were selected (159 classrooms), and all subjects within the selected classrooms were invited to participate in the study. All methods were carried out in accordance with relevant guidelines and regulations of ethical standards set out in Resolution No. 466/2012 of Brazil’s National Research Ethics Council. Informed consent was obtained from all participants involved in the study and their parents/guardians provided authorization in writing. The study protocol was approved by the Research Ethics Council of the State University of Feira de Santana (Approval No. 02307918.5.0000.0053, Opinion No.: 3.116.495). The Municipal Department of Education provided information regarding the sex, age, and school shift of participants.

    Measurement of sedentary behaviors and physical activities

    The participants self-reported the SB and physical activity on the Food Intake and Physical Activity of Schoolchildren (Web-CAAFE) questionnaire. The Web-CAAFE is a previously validated self-report questionnaire [27], completed on the internet and based on the previous-day recall. The instrument collects information on weight status, food consumption, physical activity, and SB and includes screens to evaluate physical education classes and to investigate modes of commuting to school.

    Participants choose up to 32 items, out of a total of 50 stored in the system, which they had performed the day before across three periods (morning, afternoon, evening). The list contains five SB icons (one for academic tasks, e.g. reading, writing, drawing, painting; four electronic devices, e.g. TV, video game, computer, and cell phone), and 27 physical activity icons classified into: Active play (Play with a ball, Play catch, Soccer, Dance, Marbles, Jump rope, Gymnastics, Elastics, Play in the park, Play in the water/Swim, Ride a bicycle, Rollerblade/Skateboard/Ride a scooter, Fly a kite, Dodgeball, Hide and seek, Play with a dog, Hopscotch), Non-active play (Board games, Playing with dolls/action figures, Playing with toy cars, Spinning top/Bayblade, Listen to music, Play musical instrument), Structured physical activity (Ballet, Fight Sports), and Household chores (Wash the dishes, Sweep). Information on the weekly frequency of participation in physical education classes is assessed through the question “How many times a week do you take part in physical education classes?” (none, 1, 2 3, 4, every day of the week). The closed list of leisure activities, sports, home chores, and sedentary activities was compiled based on results from focal groups, previous instruments for this age range, and the 7-day recall completed by 180 schoolchildren [28].

    Participants completed the Web-CAAFE at the school, after receiving verbal explanations about how the software works and how to complete the questionnaire. Students were instructed not to interact during the task and the research team helped when requested, without inducing responses.

    Anthropometric measurements

    The study included weight and height measurements to calculate the Body Mass Index (BMI), measured by trained researchers, following recommended standardization [29]. Weight was measured using an AVAnutri® digital scale with graduation every 100 g and a maximum capacity of 200 kg. Height was measured using a portable stadiometer, detachable, with a square platform, Seca® brand, with a 205 cm maximum height and graduation every 1 mm. The students were barefoot, wearing school uniform, and with no headwear during measurements. Age-and sex-specific BMI z-scores were calculated according to the International Obesity Task Force (IOTF) [30]. The weight status was categorized into non-overweight (underweight and normal weight), overweight, and obesity according to IOTF reference values [30].

    Classification of economic level

    Socioeconomic status was investigated based on the analysis of possession of items, education level of the head of the household, and access to public services, according to the Brazilian Economic Classification Criteria [31]. The socioeconomic status was classified into classes, related to the average household income in Reais (R$): A (R$25,554.33), B-C (R$1,748.59 to R$11,279.14), and D-E (R$719.81). Based on the average dollar exchange rate between March and October 2019, income ranges in these classes were: A (US$ 6,485.87), B-C (US$ 443.80 to 2,862.72), and D-E (US$ 182.69).

    Data processing and analysis

    The weekly attendance in PE was the main exposure analyzed (0/week; 1/week; ≥ 2/week). Daily frequencies of active play, non-active play, and structured physical activity were the main outcomes (count outcomes). These frequencies were obtained by summing all reports in the morning, afternoon, and night. For example, if a participant reported riding a bike in the morning period, playing with a ball in the afternoon, and playing with a dog in the evening, then their sum was 3 counts of active play. SB frequency was obtained by summing the daily reports of academic tasks and screen use. DPA frequency was obtained by summing the daily reports of all physical activities.

    Students with intellectual disabilities and ages outside the age group of seven to 12 years participated in the study but were excluded from the statistical analyses. Descriptive statistics are used to present the study variables. Variables without normal distribution after verification of the histograms and the Shapiro–Wilk test are described by median and interquartile range values. Differences in non-normally distributed continuous variables were evaluated using the non-parametric Mann–Whitney test (U). Categorical variables are described as absolute and relative values and compared using Pearson’s chi-square test (Χ2).

    The associations between weekly attendance in PE and frequencies of active play, non-active play, and structured physical activity were analyzed using the values of prevalence ratios (PR) and respective 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}CI estimated via multiple Negative Binomial Regression, with adjustment for age (7–9 years; ≥ 10 years), school shift (morning; afternoon), and BMI z-scores, adopting a robust variance estimation method. Negative Binomial models analyzing the association between weekly attendance in PE and DPA and SB were also adjusted by the daily frequency of household chores. The group of household chores was not included in the present analysis as an outcome because there is no evidence of an association with attendance in PE.

    The Negative Binomial distribution is suitable for fitting count data susceptible to overdispersion. In addition, it showed higher linearity in the comparison between observed and predicted values of the outcome. The zero-inflation between the factors was assumed to be constant. Although the negative binomial regression models provide a measure of association such as Incidence-Rate Ratios (IRR), we adopted the prevalence ratio (PR) as the most appropriate way to present our results, considering the cross-sectional design of the study. Statistical significance was assessed using p value < 0.05. Effect modification was tested using interaction terms between weekly attendance in PE and sex, age, school shift, and BMI z-scores. Interactions that showed statistical significance at the critical value of p < 0.05 were described.

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    Home schooling grows among Chicago’s Black families

    Home schooling grows among Chicago’s Black families

    Illinois saw a 3{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} fall in public faculty enrollment in the 2020-21 faculty 12 months from 2019-20, with kindergarten and elementary faculties seeing the steepest declines, in accordance to facts from the state’s Board of Schooling, the board’s once-a-year report and the Condition Report Card analyzed by Progress Illinois, an unbiased firm that promotes public education.

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    “It’s attainable that some of all those kids are staying household-schooled,” Robin Steans, president of Progress Illinois, mentioned for the duration of a Town Club of Chicago training party in August. “The reality is we do not do a great job of gathering all of that information and bringing it up to the point out degree. We don’t know.”

    Illinois is a person of the handful of states that isn’t going to need home-education households to register with the point out or community district.

    The pandemic’s impact on instruction gave moms and dads and caretakers a closer watch of their children’s day-to-working day educational practical experience. And some were being underwhelmed.

    “They bought a likelihood to see particularly what the little ones have been becoming taught,” suggests Joyce Burges, CEO and co-founder of Nationwide Black Household Educators, a nationwide membership dwelling-schooling business. “And a good deal of these families have claimed to me that they did not like what they had been taught or how they have been staying taught.”

    But there were other variables that contribute to the selection to residence-school.

    Hardy’s son has special requirements and requires “a minor little bit a lot more notice in specified places,” she suggests. She felt the curriculum at CPS wasn’t letting learners the time and the room to grow the natural way. Property education makes it possible for that, she provides.

    A scientific therapist, Hardy satisfies with shoppers in the evening so that she can oversee her son’s training all through the day.

    Burges claims the pandemic’s change to remote and versatile do the job has permitted extra Black family members to look at house education for the to start with time. She also witnessed additional moms and dads gravitate to in-house studying for the reason that they felt Black record and views had been absent in their children’s mainstream education and learning.

    During the pandemic, Black moms and dads “observed the whitewashing in some of the historical past textbooks that their kids were applying,” Burges states. “They did not see their history—their foreparents and forefathers (contributing) at all to the generating of this nation.”

    Jaleesa Smith integrates lessons and actions that reflect her students’ identities in her residence-schooling system. The mother and educator runs Close friends of Cabrini, a Chicago-primarily based co-op that provides unschooling online, a type of property education wherever youngsters guideline their have mastering. Smith’s pupils have accomplished geography classes on the continent of Africa and practiced multiplication and division in Swahili. She finds textbooks with Latino and Black people. You will find even been a Black Heritage Month coding venture.

    Even however the pandemic is receding, Burges thinks the Black property-education movement is going to continue to mature.

    “We just woke (up) to the fact that our children were not studying what is vital to us,” she suggests. “Mom and dad are not standing on the sidelines anymore.”

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