Physical activity interventions and nutrition‐based interventions for children and adolescents with type 1 diabetes mellitus
Abstract
Objectives
This is a protocol for a Cochrane Review (intervention). The objectives are as follows:
To assess the effects of physical activity interventions and nutrition‐based interventions for children and adolescents with type 1 diabetes mellitus.
Background
Between 1990 and 2008, the prevalence of children and adolescents diagnosed with type 1 diabetes mellitus (T1DM) worldwide increased from 2.8% to 4.0% per year (Patterson 2012). There are stark differences in the incidence and mortality rates of children and adolescents with T1DM in different geographical regions and country income groups (Patterson 2019). The highest number of incident and prevalent cases can be found in Europe, North America, and Caribbean regions; however, mortality rates related to T1DM in these regions are among the lowest worldwide. This contrasts with the African region, which, despite low incidence rates, has the largest number of T1DM related deaths, suggesting high infant mortality rates. Meanwhile, countries in the South East Asia region have notably large numbers of deaths in those with T1DM, and India has the highest number of incident cases in the world. Additionally, based on the World Bank income group aggregation, it is evident that the upper‐middle‐income and high‐income countries have the highest number of T1DM incident and prevalent cases, but the lowest T1DM death rates (Patterson 2019). This trend is the opposite for low‐ and low‐middle income countries, where high numbers of T1DM deaths are reported despite low numbers of incident and prevalent cases.
The impact of a disease is often determined by its mortality rate. However, as medical advances have led to better treatments and delayed mortality, it is necessary to assess health outcomes not only from a medical perspective but to include other aspects of health, such as health‐related quality of life. The assessment of an individual's health‐related quality of life offers an overview of the perceived impact of the disease on the individual's life, including physical, psychological, socioeconomic, cultural satisfaction, and well‐being (CDC 2001). Children and adolescents with T1DM tend to have lower general health‐related quality of life, compared to healthy matched children and adolescents (Kalyva 2011). Factors often associated with low health‐related quality of life among children with T1DM — especially in terms of physical well‐being — include being older, female, and having late‐onset T1DM (AlBuhairan 2016; Murillo 2017). On the other hand, children with less diabetic complications, better glycosylated haemoglobin A1c (HbA1c) control, better treatment adherence and treatment satisfaction, have relatively higher health‐related quality of life (Lopez‐Bastida 2019; Tumminia 2015). Overall, measuring health‐related quality of life can provide insights into its relationship with associated risk factors, and can guide interventions to improve situations for certain subgroups. It can also help to monitor the progress and efficacy of interventions on the individual's overall well‐being (CDC 2001). Therefore, it is necessary to examine the impacts of interventions on health‐related quality of life.
The management of T1DM includes a combination of pharmacological and non‐pharmacological interventions. The pharmacological management of T1DM includes the administration of short‐acting, intermediate‐ and long‐acting human or animal insulin or insulin analogues to keep the blood glucose level under control (WHO 2016). Non‐pharmacological interventions, generally used in addition to pharmacological interventions, include behavioural, educational, family, and psychosocial interventions (Grey 2000; Hilliard 2016). Both types of interventions are equally important in the management of T1DM in children and adolescents, as too much emphasis on either type could lead to poor outcomes among children and adolescents (Acerini 2016).
Description of the condition
Type 1 diabetes mellitus was previously known as juvenile diabetes or insulin‐dependent diabetes. It is a disease of disordered immune function in genetically susceptible people involving the destruction of beta cells (ß cells) that secrete insulin in the pancreas (ADA 2010; Barclay 2010). Predisposition to T1DM can also be attributed to the interplay of an individual's genes and exposure to exogenous triggers, e.g. environment, family history, nutrition, or viral factors (Knip 2005; Majeed 2011; Rewers 2016). Environmental triggers may exist even at the prenatal stage, whereby maternal enteroviral infection and older maternal age have been associated with risk factors of T1DM development in the child (Rewers 2016). Other likely promoters of progression to T1DM include infant weight gain and serious life events, whereas higher consumption of omega‐3 fatty acids may be a potential protective factor (Rewers 2016). Interestingly, white people of non‐Hispanic descent in Europe and the USA seem to be more prone to T1DM than other ethnic groups (Bell 2009; Knip 2005; Lorenzi 1985), but reasons accounting for such differences remain unknown.
People with T1DM monitor their blood glucose by pricking their fingertip and testing the blood with a blood glucose meter. HbA1c is the measurement of glucose control over two to three months (Sherwani 2016). Since individuals with T1DM have no or very minimal endogenous insulin, they are dependent on daily administrations of exogenous insulin through needle injections or an insulin pump (Barclay 2010; Herold 2013). The rate of ß‐cell destruction in T1DM varies according to age groups, with ß‐cell destruction being rapid among infants and children but slow among adults (ADA 2010). Children diagnosed with T1DM are especially at risk for diabetic ketoacidosis and severe hypoglycaemia (Rewers 2002). Diabetic ketoacidosis is a life‐threatening condition that results from high levels of blood acids, called ketones, in the body, which occurs when blood glucose levels are too high or if insulin treatment is insufficient. On the contrary, severe hypoglycaemia occurs when blood sugar levels are too low due to excess insulin in the body. Severe hypoglycaemia can happen in situations when the child is more physically active than usual after lunch one day, or if they consume less food than usual during lunch. Such changes in daily schedules could complicate children's blood glucose management while at school (Schwartz 2010). Hyperglycaemia may lead to long‐term complications of T1DM, including acute cardiovascular events (e.g. myocardial infarction and stroke), kidney failure, leg amputation, diabetic retinopathy, nerve damage, and increased risks of mortality (WHO 2016). While these manifestations usually do not appear until adulthood, the pathogenesis of these complications begins in childhood.
Treatment of T1DM is especially difficult in the adolescence period. During adolescence, there are marked hormonal, metabolic, cognitive, and psychosocial changes that are associated with puberty (Kroger 2017). While hormonal changes can reduce sensitivity to insulin (Kelsey 2016), other factors (psychosocial, communication‐related, peer influence, self‐identity) may cause poor glycaemic control among adolescents (Babler 2015; Ciranka 2019; Datye 2015; King 2017; Schwartz 2010). The struggle to manage T1DM during this turbulent phase may prove challenging; therefore, interventions targeting children and adolescents with T1DM should consider psychosocial and 'lifestyle' factors to improve their health‐related quality of life.
Description of the intervention
Behaviour change interventions for children and adolescents with T1DM are designed to address knowledge and skills, increase self‐efficacy, and promote healthy behaviours (Hampson 2000). Behaviour change interventions are wide‐ranging and can target more than one behavioural component, such as diet choices, meal frequency, physical activities, sedentary behaviours, glucose monitoring, and insulin compliance (Mead 2017). In our review, the focus of behaviour change interventions is limited to physical activity interventions and nutrition‐based interventions.
Based on the guidelines and position statements of the International Society for Pediatric and Adolescent Diabetes (ISPAD), the European Association for the Study of Diabetes (EASD), the American Diabetes Association (ADA), the American Association for Clinical Endocrinology, and the Endocrine Society, children and adolescents with T1DM should participate in physical activities (Reusch 2018). Physical activity interventions focus on improving the well‐being of children and adolescents with T1DM; they aim to maintain safe levels of HbA1c, improve lipoprotein profiles, and increase maximal aerobic capacities or VO2 max (D'hooge 2011; MacMillan 2014; Nadella 2017; Naughton 2014; Salem 2010; Seeger 2011).Recommended physical activities are primarily divided into three groups:
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aerobic exercise of low to vigorous intensity, defined as exercise that uses large muscle groups in dynamic activities and results in increases in heart rate and energy expenditure (e.g. swimming, jogging, and cycling);
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flexibility and balance exercise (e.g. stretching, Pilates, and gymnastics); and
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resistance exercise, defined as anaerobic training that increases muscular strength, power, and endurance (e.g. sprints, and jumping rope (also known as skipping)) (Adolfsson 2018; MacMillan 2014; Reusch 2018; Robertson 2009).
A broader way to conceptualise physical activity is a reduction of sedentary behaviours. There are now emerging studies looking at evidenced‐based strategies to decrease sedentary time, including motivational interviewing (Harvey 2015; Powell 2014; Quirk 2018), cognitive restructuring (Harvey 2015), and electronic health tools (Harvey 2015; Hilliard 2016; Knox 2019). Our review will include behavioural therapies that increase young individuals’ motivation to participate in physical activities and reduce sedentary activities.
Nutrition is one of the most important aspects of diabetes management. Medical nutrition therapies for children and adolescents with T1DM usually focus on avoiding hyperglycaemia and achieving near‐normal blood glucose levels without severe hypoglycaemia. One major aim is to achieve normal growth and development, keeping metabolic parameters within normal limits (Bantle 2008). Interventions to achieve good glycaemic control are often educational, including counting carbohydrates, nutrition counselling, and diet adjustment using individualised meal plans, printed diet charts, food plates, or food pyramids (Rani 2017; Smart 2014; Tascini 2018). The major emphases on diet prescriptions for children with T1DM are on carbohydrate content and blood glucose values, while balancing the nutritional content of selected protein, carbohydrates, and fats. The timing of mealtime insulin is also a crucial part of nutrition therapy to avoid high or low postprandial blood glucose (Abdelghaffar 2015; Smart 2014). Overall, nutrition‐based interventions focus on improving glycaemic control and supporting healthy eating patterns by emphasising a variety of nutrient‐dense foods in appropriate portion sizes to improve overall health.
Adverse effects of the intervention
Children and adolescents with T1DM who use insulin react differently to physical activity interventions and are at risk of hypoglycaemia. Therefore, these interventions must be carefully calibrated and co‐ordinated between medical teams and family members to determine adequate deliveries of exogenous insulin (Reidy 2018; Taleb 2016). Exogenous insulin does not have the dynamic adaptability of the normal physiological system of non‐diabetic people. Any excess or insufficient amount can alter glucose uptake, hepatic glucose production and lipolysis, glucagon secretion, hormone release, and catecholamine responses (Ridell 2006). Hence, closely monitoring blood glucose patterns and maintaining strict regulation of insulin before, during, and following exercise is warranted (Bussau 2006; Iscoe 2011; McMahon 2007; Yardley 2012; Yardley 2013). Adopting individualised approaches before initiating more intense exercise regimens will give clues to whether special attention needs to be paid or certain precautions need to be taken. For example, children with decreased hypoglycaemia awareness may require prompting from adults to eat a snack during an exercise regimen or to adjust the basal and pre‐exercise meal bolus insulin beforehand (Robertson 2014). The order of different types of exercises also needs to be explained to families. For example, it is known from the literature that a short anaerobic exercise before an aerobic exercise is more favourable for glucose control than an aerobic exercise prior to an anaerobic exercise (Bussau 2006; Yardley 2012).
Nutrition‐based interventions may result in weight loss, linear growth effects, or changes in insulin sensitivity or requirements, especially with a change in carbohydrate intake, which could adversely affect the physical and neurocognitive development of a child or adolescent (Dong 2017; Franz 2014; Roberts 2017). Hence, these diet modifications also necessitate more frequent blood glucose monitoring, at least at the beginning of any changes in diet (ADA 2015).
How the intervention might work
In the meta‐analysis by Quirk and colleagues (Quirk 2014) physical activity interventions for children and adolescents with T1DM were generally found to have good engagement rates, and were effective in increasing physical activity, improving cardiovascular health, and decreasing HbA1c levels. Nutrition‐based interventions that educate and equip children and adolescents with necessary skills to better manage T1DM were reported to increase their sense of self‐efficacy and adherence to diabetes regimens such as self‐monitoring of blood glucose, and following prescribed medications (Jiang 2019; Smart 2014). Both intervention types aim to improve diabetes‐related outcomes by targeting glycaemic control and reducing adverse events — such as hypoglycaemia, diabetic ketoacidosis, and hospital admissions — through self‐management and self care (Bantle 2008; MacMillan 2014; Nadella 2017). Studies have shown the effectiveness of these interventions in reducing adverse events and ultimately enhancing health‐related quality of life (Nunes‐Silva 2017; Tumminia 2015). It is of value that these interventions target children and adolescents: when children learn health‐enhancing behaviours in their childhood, they are more likely to continue practising these health behaviours when they emerge into adulthood (Garcia 2013). Therefore, improving the self‐management and self‐care of T1DM at a young age is both crucial and beneficial.
Why it is important to do this review
Current systematic reviews focusing on physical activity and nutrition‐based interventions in the management of T1DM in children and adolescents demonstrate mixed results based on age groups and sex, and are not up‐to‐date with research findings from the past five years (Aman 2009; Beraki 2014; MacMillan 2014; Quirk 2014). Reviews on the importance of physical activity on glycaemic control among adults with T1DM generally reveal minimal reductions in HbA1c (Chimen 2012; Kennedy 2013; Yardley 2014). However, physical activity interventions have been shown to reduce HbA1c significantly in individuals aged 18 years and below with T1DM (Beraki 2014; Herbst 2006; MacMillan 2014). In addition to glycaemic control, physical activity improves VO2 max and is associated with a beneficial cardiovascular risk profile (Seeger 2011; Herbst 2007). Prior reviews have focused on more short‐term outcomes, except for one study from the 1980s (LaPorte 1986). A thorough review is warranted to examine more long‐term outcomes throughout the life course, especially the impact of childhood interventions on adult outcomes (Garber 2011).
Reviews on the effects of nutrition‐based interventions for children or adolescents with T1DM are severely lacking. Existing nutrition‐based intervention reviews combine samples of adults and children, include both individuals with T1DM and type 2 diabetes mellitus, and mainly focus on the effects of low‐carbohydrate or low‐glycaemic‐index diets (Brand‐Miller 2003; Thomas 2010; Turton 2018). Overall, these reviews reveal that low‐glycaemic‐index diets can significantly decrease HbA1c, suggesting improvements in glycaemic control (Brand‐Miller 2003; Thomas 2010). However, a more holistic review on the effects of nutrition‐based interventions, not just low‐glycaemic‐index diets, targeting children and adolescence with T1DM is needed.
There are two published Cochrane Reviews relevant to this review. One focuses on a similar population, children with T1DM, but assesses the effects of routine hospital admission versus the outpatient or home care of children with T1DM (Clar 2007). The review found no difference between the home‐management group and the hospitalised group for psychosocial and behavioural outcomes and rates of acute diabetic complications within two years. Another review assessed the effects of low‐glycaemic‐index diets or low glycaemic loads on glycaemic control in people with diabetes. There were fewer reported episodes of hypoglycaemia and significant decreases in HbA1c among participants who had low‐glycaemic‐index diets (Thomas 2009); however, the focus of this review was not on the paediatric population.
We did not find any review providing comprehensive evidence on evaluating the effects of physical activity and nutrition‐based interventions on the management of T1DM among children and adolescents aged up to 18 years. Beyond insulin and recent novel therapies for T1DM, this review will specifically add to the current literature by synthesising information on non‐pharmacological interventions that could enhance the health‐related quality of life of children and adolescents with T1DM.
Objectives
To assess the effects of physical activity interventions and nutrition‐based interventions for children and adolescents with type 1 diabetes mellitus.
Methods
Criteria for considering studies for this review
Types of studies
We will include randomized controlled trials (RCTs).
Types of participants
The Cochrane Child Health Field defines infants, children and youth as individuals aged from 0 to 18 years. Thus, we will include children or adolescents who were between the ages of 1 to 18 years old and diagnosed with T1DM at the time of study.
Diagnostic criteria for diabetes mellitus
In order to be consistent with changes in the classification of and diagnostic criteria for diabetes mellitus over the years, the diagnosis should be established using the standard criteria valid at the time of the trial commencing (for example, ADA 2003; ADA 2017; WHO 1999). Ideally, the diagnostic criteria should have been described. We will use the study authors’ definition of diabetes mellitus if necessary.
According to the standards of medical care in diabetes (ADA 2017), diabetes may be diagnosed based on plasma glucose criteria or HbA1c criteria. A person is diagnosed with T1DM if any one of the following conditions is met.
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Fasting plasma glucose ≥ 126 milligrams per decilitre (mg/dL), or 7.0 millimoles per litre (mmol/L), on more than one occasion, when fasting (defined as no caloric intake for at least eight hours).
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Two‐hour plasma glucose ≥ 200 mg/dL (11.1 mmol/L) after an oral glucose tolerance test (OGTT) load of 1.75 g/kg (maximum of 75 g).
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HbA1c ≥ 6.5% (48 millimoles per mole (mmol/mol)). The test should be performed in a laboratory using a method that is National Glycohemoglobin Standardization Program‐certified and standardised to the Diabetes Control and Complications Trial (DCCT) assay.
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In a person with classic symptoms of hyperglycaemia or hyperglycaemic crisis, random plasma glucose ≥ 200 mg/dL (11.1 mmol/L).
The results of conditions one to three above should be confirmed by repeat‐testing in the absence of unequivocal hyperglycaemia.
Currently, it is uncertain whether the use of HbA1c criteria and its recommended HbA1c cut‐off should be used to diagnose diabetes in children and adolescents, as the epidemiological studies that guided the basis for the use of HbA1c criteria included only the adult population (ADA 2017). This review will include trials that used plasma glucose criteria or HbA1c criteria for the diagnosis of T1DM among children and adolescents. Changes in diagnostic criteria may have produced significant variability in the clinical characteristics of the participants included as well as in the results obtained, which will be investigated through a sensitivity analysis.
Types of interventions
We plan to investigate the following comparisons of intervention versus control/comparator.
Intervention
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Physical activity interventions, including exercise interventions and reduction in sedentary behaviours
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Nutrition‐based interventions
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Physical activity interventions and nutrition‐based interventions
Comparisons
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Placebo
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Usual care
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No intervention
Usual care includes, but is not limited to, access to outpatient appointments with an endocrinologist and/or support from a diabetes team consisting of a diabetes nurse educator, dietician, and social worker.
Concomitant interventions have to be the same in both the intervention and comparator groups, in order to establish fair comparisons. If a trial includes multiple arms, we will include any arm that meets the review inclusion criteria.
Minimum duration of the intervention
A minimal duration of at least four weeks of administration of the intervention will be required of eligible studies.
Minimum duration of follow‐up
The duration of follow‐up will not be restricted in this review. Data collection time points from post‐intervention and beyond will be included.
We will define any follow‐up periods going beyond the original time frame for the primary outcome measure (as specified in the power calculation of the trial’s protocol) as an extended follow‐up period (also called open‐label extension study) (Buch 2011; Megan 2012).
Summary of specific exclusion criteria
We will exclude trials examining interventions of interest among children and adolescents with T1DM with other comorbidities such as cardiovascular disease, and/or other chronic conditions (e.g. asthma, epilepsy, inflammatory bowel disease, and a variety of developmental conditions, e.g. autism spectrum disorder and attention‐deficit hyperactivity disorder).
Types of outcome measures
We will not exclude a trial if it fails to report one or several of our primary or secondary outcome measures. If none of our primary or secondary outcomes is reported in the trial, we will not include the trial but will provide some basic information in an additional table. We will investigate the following outcomes using the methods and time points specified below.
Primary outcomes
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Health‐related quality of life
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HbA1c
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Adverse events
Secondary outcomes
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Morbidity
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All‐cause mortality
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Adherence to nutrition‐based interventions
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Adherence to physical activity interventions
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Diabetes knowledge
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Diabetes self‐efficacy
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Societal costs
Method of outcome measurement
The following primary and secondary outcomes will be assessed and measured by means of self‐reported data, and investigator‐assessed.
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Health‐related quality of life: evaluated by a validated instrument such as, but not exclusive to, the Pediatric Quality of Life Inventory (PedsQL), Diabetes Quality of Life Measure (DQOL), KINDL (KiddyKINDL, KidKINDL, KiddoKINDL), or DISABKIDS.
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HbA1c: glycaemic control evaluated through blood glucose laboratory tests and measured in per cent or mmol/mol.
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Adverse events: hypoglycaemic episodes (number of times and glucose levels, if documented) and events other than hypoglycaemic episodes, such as hyperglycaemic events and diabetic ketoacidosis leading to hospitalisation.
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Morbidity: diabetic complications (e.g. retinopathy, nephropathy, and neuropathy).
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All‐cause mortality: defined as death from any cause.
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Adherence to treatment: measured by self‐reported/parent‐reported observations (e.g. exercise length and food diary) and/or objective measures such as the use of actigraphy.
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Diabetes knowledge: evaluated by a validated instrument such as the Diabetes Knowledge Questionnaire.
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Diabetes self‐efficacy: evaluated by a validated instrument such as the Diabetes Empowerment Scale and the Diabetes Self‐Efficacy Scale.
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Societal costs: such as direct costs (defined as admission or readmission rates, average length of stay, visits to general practitioners, accident or emergency visits, medication consumption), and indirect costs (defined as resources lost due to illness by the participants or their family member, reported by the participants).
The list of instruments used in measuring the individual outcomes is not exhaustive and the inclusion of other validated instruments used in outcome measurement is expected in our review.
Timing of outcome measurement
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Adverse events and all‐cause mortality will be measured at any time after participants were randomized to the intervention/comparator groups.
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All other outcomes will be measured at all time points after the completion of the intervention.
Search methods for identification of studies
Electronic searches
We will search the following sources from the inception of each database to the date of search and will place no restrictions on the language of publication:
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Cochrane Central Register of Controlled Trials (CENTRAL) via the Cochrane Register of Studies Online (CRSO);
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Ovid MEDLINE (from 1946 onwards);
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ClinicalTrials.gov (www.clinicaltrials.gov);
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World Health Organization International Clinical Trials Registry Platform (ICTRP) (www.who.int/trialsearch/); and
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CINAHL EBSCO (Cumulative Index to Nursing and Allied Health Literature).
We will not include Embase in our search, as RCTs indexed in Embase are now prospectively added to CENTRAL via a highly sensitive screening process (Cochrane 2020). For detailed search strategies, see Appendix 1.
Searching other resources
We will attempt to identify other potentially eligible studies or ancillary publications by searching the reference lists of included studies, systematic reviews, meta‐analyses, and health technology assessment reports. We will also contact the authors of included studies to obtain additional information on the retrieved studies and establish whether we may have missed further studies. We define grey literature as studies identified by searching the trials registers named above and theses and dissertations identified via CINAHL.
We will not use abstracts or conference proceedings for data extraction unless full data are available from the study authors because this information source does not fulfil the CONSORT requirements, which consist of "an evidence‐based, minimum set of recommendations for reporting randomised trials" (CONSORT 2018; Scherer 2018). We will present information on abstracts or conference proceedings in the 'Characteristics of studies awaiting classification' table.
Data collection and analysis
Selection of studies
Two review authors (EN and SS) will independently screen the abstracts, titles, or both, of every record retrieved by the literature searches to determine which trials we should assess further. We will obtain the full texts of all potentially relevant records. We will resolve any disagreements through consensus or by recourse to a third review author (EL). If we cannot resolve a disagreement, we will categorise the trial as a ‘Study awaiting classification’ and will contact the trial authors for clarification. We will present an adapted PRISMA flow diagram to show the process of the trial selection (Liberati 2009). We will list all articles excluded after full‐text assessments in a 'Characteristics of excluded studies’ table and will provide the reasons for exclusion.
Data extraction and management
For trials that fulfil our inclusion criteria, two review authors (EN and SS) will independently extract key participant and intervention characteristics. We will describe interventions according to the ‘template for intervention description and replication’ (TIDieR) checklist (Hoffmann 2014; Hoffmann 2017).
We will report data on efficacy outcomes and adverse events using standardised data extraction sheets from the Cochrane Metabolic and Endocrine Disorders (CMED) Group. We will resolve disagreements by discussion or, if required, by consultation with a third review author (EL).
We will provide information for potentially relevant ongoing trials, including the trial identifiers, in the ‘Characteristics of ongoing studies’ table and in a joint appendix ‘Matrix of study endpoint (publications and trial documents)'. We will try to find the protocol for each included study and will report in a joint appendix regarding the primary, secondary, and other outcomes in comparison with other data in publications.
We will email all authors of the included trials to enquire whether they will be willing to answer questions regarding their trials. We will present the results of this survey in an appendix. We will thereafter seek relevant missing information on the trial from the primary study author(s), if required.
Dealing with duplicate and companion publications
In the event of duplicate publications, companion documents, or multiple reports of a primary study, we will maximise the information yield by collating all available data, and we will use the most complete data set aggregated across all known publications. We will list duplicate publications, companion documents, multiple reports of a primary study, and trial documents of the included studies (such as trial registry information) as secondary references under the study ID of the included study. We will do the same for duplicate publications, companion documents, multiple reports of a trial, and trial documents of the excluded studies.
Data from clinical trials registers
If data from the included studies are available as study results in clinical trials registers, such as ClinicalTrials.gov or similar sources, we will make full use of the information and extract the data. If there is also a full publication of the study, we will collate and critically appraise all available data. If an included study is marked as a completed trial in a clinical trial register but no additional information (study results, publication, or both) is available, we will add this study to the ‘Characteristics of studies awaiting classification’ table.
Assessment of risk of bias in included studies
Two review authors (EN and WT) will independently assess the risk of bias for each included study. We will resolve disagreements by consensus or by consulting a third review author (SS). In case of any disagreements, we will consult the remainder of the review author team and make a judgement based on consensus. If adequate information is not available from the publications, trial protocols, or other sources, we will contact the study authors to request missing data on ‘Risk of bias’ items.
We will use the Cochrane ‘Risk of bias’ assessment tool, which involves assigning judgements of low, high, or unclear risks of bias (for details, see Appendix 2; Appendix 3) (Higgins 2019b). We will evaluate individual bias items as described in the Cochrane Handbook for Systematic Reviews of Interventions, according to the criteria and associated categorisations contained therein (Higgins 2019b).
Summary assessment of risk of bias
We will present a 'Risk of bias' graph and a 'Risk of bias' summary figure. We will distinguish between self‐reported and investigator‐assessed and adjudicated outcome measures.
We will consider the following self‐reported outcomes.
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Health‐related quality of life
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Adverse events
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Diabetes knowledge
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Diabetes self‐efficacy
We will consider the following outcomes to be investigator‐assessed.
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HbA1c
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Adverse events
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Morbidity
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All‐cause mortality
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Adherence to treatment
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Societal costs
Risk of bias for a trial across outcomes
Some 'Risk of bias' domains, such as selection bias (sequence generation and allocation sequence concealment), affect the risk of bias across all outcome measures in a study. In case of a high risk of selection bias, we will mark all end points investigated in the associated study as being at high risk. Otherwise, we will not perform a summary assessment on the risk of bias across all outcomes for a study.
Risk of bias for an outcome within a study and across domains
We will assess the risk of bias for an outcome measure by including all entries relevant to that outcome (i.e. both study‐level entries and outcome‐specific entries). We consider a low risk of bias to denote a low risk of bias for all key domains, an unclear risk to denote an unclear risk of bias for one or more key domains, and a high risk to denote a high risk of bias for one or more key domains.
Risk of bias for an outcome across studies and across domains
We will define outcomes as being at low risk of bias when most information comes from studies at low risk of bias, unclear risk when most information comes from studies at low or unclear risk of bias, and high risk when a sufficient proportion of information comes from studies at high risk of bias. These are the main summary assessments that we will incorporate into our judgements about the quality of evidence in the ‘Summary of findings’ tables.
Measures of treatment effect
When at least two included studies are available for a comparison of a given outcome, we will try to express dichotomous data as a risk ratio (RR) with 95% confidence intervals (CIs). For continuous outcomes measured on the same scale (e.g. HbA1c), we will estimate the intervention effect using the mean difference (MD) with 95% CIs. For continuous outcomes that measure the same underlying concept (e.g. health‐related quality of life) but use different measurement scales, we will calculate the standardised mean difference (SMD). We will express time‐to‐event data as a hazard ratio (HR) with 95% CIs.
Unit of analysis issues
We will take into account the level at which randomisation occurred, such as cross‐over studies, cluster‐randomised trials, and multiple observations for the same outcome. If more than one comparison from the same study is eligible for inclusion in the same meta‐analysis, we will either combine groups to create a single pair‐wise comparison, or we will appropriately reduce the sample size so that the same participants do not contribute data to the meta‐analysis more than once (splitting the 'shared' group into two or more groups). Although the latter approach offers some solution for adjusting the precision of the comparison, it does not account for correlation arising from inclusion of the same set of participants in multiple comparisons (Higgins 2019a).
We will attempt to re‐analyse cluster‐RCTs that have not appropriately adjusted for potential clustering of participants within clusters in their analyses. Variance of the intervention effects will be inflated by a design effect. Calculation of a design effect involves estimation of an intracluster correlation coefficient (ICC). We will obtain estimates of ICCs by contacting study authors, or by imputing ICC values using either estimates from other included studies that report ICCs or external estimates from empirical research (e.g. Bell 2013). We plan to examine the impact of clustering by performing sensitivity analyses.
Dealing with missing data
If possible, we will obtain missing data from the authors of the included studies. We will carefully evaluate important numerical data, such as randomly assigned participants after screening, as well as intention‐to‐treat and as‐treated and per‐protocol populations. We will investigate attrition rates (e.g. dropouts, losses to follow‐up, and withdrawals), and we will critically appraise issues concerning missing data and the use of imputation methods (e.g. last observation carried forward).
When the included studies do not report means and standard deviations but median and range or interquartile range for outcomes, and we do not receive requested information from the study authors, we will impute these values by estimating the mean and the variance from the median, the range, and the size of the sample (Hozo 2005).
Assessment of heterogeneity
In the event of clinical or methodological heterogeneity, we will not report study results as the pooled effect estimate in a meta‐analysis. We will identify heterogeneity (inconsistency) by visually inspecting the forest plots and by using a standard Chi² test with a significance level of α = 0.1 (Deeks 2019). In view of the low power of this test, we will also consider the I² statistic, which quantifies inconsistency across studies, to assess the impact of heterogeneity on the meta‐analysis (Higgins 2002; Higgins 2003). When we identify heterogeneity, we will attempt to determine possible reasons for this by examining individual characteristics of the study and subgroups.
Assessment of reporting biases
If we include 10 or more studies that investigate a particular outcome, we will use funnel plots to assess small‐study effects. Several explanations may account for funnel plot asymmetry, including the true heterogeneity of the effect with respect to study size, poor methodological design (and hence bias of small studies), and publication bias (Sterne 2017). Therefore, we will interpret the results carefully (Sterne 2019).
Data synthesis
We plan to undertake (or display) a meta‐analysis only if we judge participants, interventions, comparisons, and outcomes to be sufficiently similar to ensure an answer that is clinically meaningful. We will interpret random‐effects meta‐analyses with due consideration for the whole distribution of effects and will present a prediction interval (Borenstein 2017a; Borenstein 2017b; Higgins 2009). A prediction interval requires at least three studies to be calculated and specifies a predicted range for the true treatment effect in an individual study (Riley 2013). For rare events such as event rates below 1%, we will use the Peto odds ratio method, provided there is no substantial imbalance between intervention and comparator group sizes and intervention effects are not exceptionally large. In addition, we will perform statistical analyses according to the statistical guidelines presented in the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2019).
In the event where a meta‐analysis cannot be carried out for all of the included studies due to the lack of required information, a narrative summary will be conducted according to each of the outcome measures.
Subgroup analysis and investigation of heterogeneity
We expect the following characteristics to introduce clinical heterogeneity, and we plan to carry out the following subgroup analyses for these, including investigation of interactions (Altman 2003).
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Types of intervention (physical activity intervention versus nutrition‐based intervention)
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Method of delivery for the interventions (technology‐based versus non‐technology based)
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Providers of interventions (healthcare professional or a lay person)
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Target audience (individual versus group)
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Age groups (children versus adolescents)
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Different intensities of the interventions (e.g. once a week, several times a week, daily)
Sensitivity analysis
When applicable, we plan to explore the influence of important factors on effect sizes, by performing sensitivity analyses in which we restrict the analyses to the following.
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Published studies
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Studies with low risk of bias, as specified in the Assessment of risk of bias in included studies section
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Very long or large studies (to establish the extent to which they dominate the results)
Furthermore we will use of the following filters to perform sensitivity analyses, if applicable: diagnostic criteria, imputation used, language of publication (English versus other languages), source of funding (industry versus other), or country (depending on data).
We will test the robustness of results by repeating analyses using different measures of effect size (i.e. RR, OR, etc.) and different statistical models (fixed‐effect and random‐effects models).
Summary of findings and assessment of the certainty of the evidence
Certainty of the evidence
We will present the overall certainty of the evidence for each outcome specified below, according to the GRADE approach, which takes into account issues related not only to internal validity (risk of bias, inconsistency, imprecision, and publication bias) but also to external validity, such as the directness of the results. Two review authors (EN and WT) will independently rate the certainty of the evidence for each outcome. We will resolve differences in assessment by discussion or by consultation with a third review author (SS).
We will include an appendix entitled ‘Checklist to aid consistency and reproducibility of GRADE assessments’ to help with the standardisation of the ‘Summary of findings’ tables (Meader 2014). Alternatively, we will use the GRADEpro Guideline Development Tool (GDT) software and will present evidence profile tables as an appendix (GRADEproGDT 2015). We will present the results for the outcomes as described in the Types of outcome measures section. If a meta‐analysis is not possible, we will present the results in a narrative format in the ‘Summary of findings’ table. We will justify all decisions to downgrade the certainty of the evidence, and we will record this information in the footnotes of the tables; we will also make comments to aid the reader’s understanding of the Cochrane Review when necessary.
'Summary of findings' tables
We will present a summary of the evidence in ‘Summary of findings’ tables. This will provide key information about the best estimate of the magnitude of effect in relative terms and as absolute differences for each relevant comparison of alternative management strategies; numbers of participants and studies addressing each important outcome; and a rating of overall confidence in effect estimates for each outcome. We will create the ‘Summary of findings’ tables using the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2019) along with the Review Manager 5 software and online version (Review Manager 2020; RevMan Web 2020).
Interventions presented in the 'Summary of findings' tables will be physical activity interventions including exercise interventions and reduction in sedentary behaviours; nutrition‐based interventions; physical activity interventions and nutrition‐based interventions. Comparators will be placebo, usual care or no intervention.
We will report the following outcomes, listed according to priority.
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Health‐related quality of life
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Morbidity
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All‐cause mortality
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Diabetes self‐efficacy
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Adverse events
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HbA1c
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Societal costs
