Computer‐assisted versus oral‐and‐written family history taking for identifying people with elevated risk of type 2 diabetes mellitus

Abstract

Background

Diabetes is a chronic illness characterised by insulin resistance or deficiency, resulting in elevated glycosylated haemoglobin A1c (HbA1c) levels. Because diabetes tends to run in families, the collection of data is an important tool for identifying people with elevated risk of type2 diabetes. Traditionally, oral‐and‐written data collection methods are employed but computer‐assisted history taking systems (CAHTS) are increasingly used. Although CAHTS were first described in the 1960s, there remains uncertainty about the impact of these methods on family history taking, clinical care and patient outcomes such as health‐related quality of life. 

Objectives

To assess the effectiveness of computer‐assisted versus oral‐and‐written family history taking for identifying people with elevated risk of developing type 2 diabetes mellitus.

Search methods

We searched The Cochrane Library (issue 6, 2011), MEDLINE (January 1985 to June 2011), EMBASE (January 1980 to June 2011) and CINAHL (January 1981 to June 2011). Reference lists of obtained articles were also pursued further and no limits were imposed on languages and publication status.

Selection criteria

Randomised controlled trials of computer‐assisted versus oral‐and‐written history taking in adult participants (16 years and older).

Data collection and analysis

Two authors independently scanned the title and abstract of retrieved articles. Potentially relevant articles were investigated as full text. Studies that met the inclusion criteria were abstracted for relevant population and intervention characteristics with any disagreements resolved by discussion, or by a third party. Risk of bias was similarly assessed independently.

Main results

We found no controlled trials on computer‐assisted versus oral‐and‐written family history taking for identifying people with elevated risk of type 2 diabetes mellitus.

Authors' conclusions

There is a need to develop an evidence base to support the effective development and use of computer‐assisted history taking systems in this area of practice. In the absence of evidence on effectiveness, the implementation of computer‐assisted family history taking for identifying people with elevated risk of type 2 diabetes may only rely on the clinicians' tacit knowledge, published monographs and viewpoint articles.

Plain language summary

Computer‐assisted versus oral‐and‐written family history taking for identifying people with elevated risk of type 2 diabetes mellitus

We know that diabetes runs in families. For this reason, healthcare professionals routinely take family histories to help them identify people who are at high risk of developing diabetes. Patient histories may be recorded manually by using oral‐and‐written methods or via a computer‐assisted history taking system. Computer‐assisted history taking systems can be used by healthcare professionals, or directly by patients, as in the case of, for example, pre‐consultation interviews. They can be used remotely, for example via the Internet, telephone  or on‐site. They draw on a range of technologies such as personal computers, personal digital assistants, mobile phones and electronic kiosks; data input can be mediated via, amongst others, keyboards, touch screens and voice‐recognition software. Although computer‐assisted history taking methods were first used in the 1960s we are still not certain about their effects on history taking in people with a high risk to develop diabetes. Therefore, we reviewed the literature to find studies that compare the effects of oral‐and‐written methods to those of computer‐assisted family history taking on the quality of collected data as well as on allowing us to identify people who are at risk of developing diabetes. In this occasion we found no randomised controlled trials that investigated the above. We therefore suggest that more primary research is required in this area to allow an informed decision to be made by physicians, patients and policymakers.

Background

Description of the condition

Diabetes mellitus, henceforth referred to as diabetes, is a metabolic disorder resulting from a defect in insulin secretion, insulin action, or both. A consequence of this is chronic hyperglycaemia (elevated levels of plasma glucose) with disturbances of carbohydrate, fat and protein metabolism. Long‐term complications of diabetes mellitus include retinopathy, nephropathy and neuropathy. The risk of cardiovascular, cerebro‐vascular and peripheral vascular diseases as well as a variety of other conditions is increased. For a detailed overview of diabetes mellitus, please see under 'Additional information' in the information on the Metabolic and Endocrine Disorders Group in The Cochrane Library (see 'About', 'Cochrane Review Groups (CRGs)’). For an explanation of methodological terms, see the main glossary in The Cochrane Library.

The World Health Organization’s (WHO) latest projections are that diabetes is expected to become one of the world’s main causes of disability and premature death within the next 25 years (WHO 2002). The prevalence of diabetes for all age groups worldwide was estimated to be 2.8% with the total number of affected people standing at 171 million. It has been estimated that the number of people suffering from this condition will increase dramatically (as a consequence of population aging, rising levels of obesity and urbanisation) reaching 366 million in 2030, representing more than 4.0% of the world’s population (Wild 2004).

Diabetes is for healthcare systems a very costly disease. Direct health care costs of diabetes range from 2.5% to 15% of annual healthcare budgets, depending on local diabetes prevalence and the sophistication of the treatments available (WHO 2002).

Type 2 diabetes is the most common form of diabetes mellitus and it has a strong tendency to cluster in families. One third of all people with type 2 diabetes are frequently not diagnosed until complications appear (ADA 2007). Given the substantial morbidity and mortality associated with diabetes, it is important that we seek ways to identify patients at high risk of developing the condition. The Diabetes Prevention Program (DPP), a clinical trial funded by the National Institutes of Health in the United States, studied over 3000 adults at high risk for developing type 2 diabetes. It was found that early diagnosis and lifestyle intervention reduced the risk of developing type 2 diabetes by 58 percent (ADA 2007).

Because family history taking reflects genetic susceptibility, it may be a useful public health tool for diagnosis at the early stages of the disease and consequently for effective management. Family history has traditionally been taken by oral‐and‐written, but it can also be taken using computer‐assisted methods.

Description of the intervention

Computer‐assisted history taking systems (CAHTS) are tools that aim to aid clinicians in gathering data from patients to inform a diagnosis, a treatment plan or both (Pringle 1998). CAHTS can be used by healthcare professionals, or directly by patients, as in the case of pre‐consultation interviews (Bachman 2003; Healthspace 2009; RelayHealth 2009). CAHTS can be used remotely, for example via the Internet, telephone or mobile phone messaging or on‐site (Appendix 1).

Bowling 2005 describes that the various CAHTS typologies depend on three interrelated factors: a) the information technology used to collect the information (e.g. personal computer, personal digital assistant, Internet, telephone); b) the mode of administration (e.g. administered by an interviewer or self‐administered); c) the channel of presentation (e.g. auditory, oral or visual). Also, a range of computer‐assisted methods are now supported via the use of the Internet. CAHTS draw on a range of technologies such as personal computers, personal digital assistants, electronic kiosks etc and data input happen via keyboard, touch screen, voice‐recognition software, among others.

With the addition of diagnostic and reminder functionalities, CAHTS may influence all stages of the patient care pathway before, during and after the consultation. For example, with the addition of a diagnostic platform such as probabilistic advice and question‐prompting, CAHTS may become instrumental in the decision‐making process.

Although CAHTS were first described in the 1960s, there is still uncertainty about the impact of these methods on family history data collection, clinical care and patient outcomes such as quality of life.

Adverse effects of the intervention

CAHTS may also cause inconvenience to patient and practitioner (Dale 2007) and they may also raise fears about privacy and confidentiality (Bowling 2005). The use of self‐administered CAHTS may lead to undetected psychosocial concerns because of lack of conduct between the patient and the physician.

How the intervention might work

CAHTS facilitate automations of history taking approaches, hence aiding the collection of data in a timely manner. CAHTS can further be administered at a time that is convenient to patient and practitioner and save time and costs (Benaroia 2007; Wolford 2008). Additionally, they promote interoperability between systems and compatibility with electronic health record templates (Llewelyn 2005). This also offers the benefit that the data collected could be linked to a computerised decision support system and the patient offered personalised feedback on their dietary intake and how to modify this to reduce their risk of developing complications. Patients who might benefit from further intervention could also be identified.

Clinician and patient‐operated CAHTS data are potentially important additions to the electronic health record as they can help to improve data quality through:

• data entry forms with data validation checks;

• encoding of data;

• legibility;

• easier access to past records;

• attribution of entries;

• greater availability;

• facilitating patient checks of their own data.

Patient‐completed diaries online allow information on dietary and self‐generated data (for example, blood glucose or urine analysis) to be made available to clinicians without the need to physically meet.

Collected data from gathered histories can also generate data sets that facilitate epidemiological research using patient level data (Bachman 2003). Studies also found that CAHTS drastically reduced the time spent on dictating and collating written records while being able to present relevant data in an easily accessible format (Tang 1995; Tang 1996).

Bachman 2003 further summarised how patient completed CAHTS may offer several advantages over more traditional approaches to obtaining the history, such as:

• patients can complete them at their own pace at home and can consult others if they have questions;

• questionnaires can serve as prompts to remind patients of things they may have forgotten;

• such questionnaires are an inexpensive means of generating a thorough account of the patient’s history;

• they bypass potential professional biases in obtaining and recording the history, and they can also overcome transcription errors, particularly if several tiers of staff are involved in recording the patient history;

• data obtained from pre‐consultation questionnaires can provide a helpful context for the subsequent clinical interview.

 Koppel 2005 found that family history was more complete in the CAHTS arm of the randomised controlled trial as opposed to the pen and paper control arm, suggesting that there may be a social desirability bias.

Why it is important to do this review

Family history taking reflects genetic susceptibility for developing type 2 diabetes and it may be a useful tool for population‐based screening programmes.

Seminal reports on quality and safety of healthcare (Auerbach 2007), invariably recognise information communication technology as one of the main vehicles for making radical improvements in delivery of healthcare. It is therefore essential that through systematic reviews of the literature we develop an evidence base that will enable health systems to translate gathered evidence into effective prevention activities.

There is a pressing need for regular and rigorous evaluations of CAHTS, analogous to techniques used in continuous quality improvement (Bowling 2005; Hogan 1997; Poissant 2005). Most of the technologies are at present supported only by face validity and modest or weak empirical evidence. This influences widespread adoption in the management of diabetes and taking of family history, hence necessitating more evaluations of CAHTS. Unless these systems are adequately studied, they may not ‘mature’ to the extent that is needed to realise their full potential when deployed in every‐day clinical settings (Auerbach 2007; Grizzle 2007).

Also, with the move from hospital care to community‐based care in many parts of the world, staff become increasingly mobile, thus requiring access to data input facilities at the point of care. The gathered information can then be shared with a multi‐disciplinary team of physicians, nurses and dieticians to plan a care package for the patient.

Cost‐effectiveness and efficiency were rarely evaluated rigorously (Sidorov 2006), even though computer‐assisted history taking systems are frequently promoted as being ‘cost‐saving’ (Chaudhry 2006; Lane 2006). Comprehensive cost‐effectiveness analyses will be required to assess the financial rationale for choosing one CAHTS over another history taking tool (Lane 2006; Quinn 2003).

Respondents may leave uncompleted or empty fields (Jaya 2008); there may also be missing data due to technical difficulties (Galliher 2008); there may also be lack of clarification for questions that may not be understood or misunderstood (Jaya 2008). CAHTS may also cause inconvenience to patient and practitioner and it may also raise fears about privacy and confidentiality (Bowling 2005).

As few randomised controlled trials have been performed so far, it has been speculated that the improvements in the volume and accuracy of the answers seen in studies (Benaroia 2007; Bachman 2003; Farzanfar 2006; Wolford 2008) may not accurately reflect the intervention. It was suggested that the effects may be attributed to novelty and performance biases whereby the behaviour of researchers and patients was influenced (Dale 2007).

Although computer‐assisted history taking systems have been available for around 30 years, successful use in routine healthcare is still variable, particularly in identification of people at higher risk of type 2 diabetes through the use of family history. This review involves an up‐to‐date literature search and detailed description of the studies on CAHTS to provide the framework for a comprehensive evaluation that will lead to an evidence base to inform policy and practice.

Objectives

To assess the effectiveness of computer‐assisted versus oral‐and‐written family history taking for identifying people with elevated risk of developing type 2 diabetes mellitus.

Methods

Criteria for considering studies for this review

Types of studies

Randomised controlled trials.

Types of participants

We considered participants who are 16 years or older at the start of the study and who underwent computer‐assisted family history taking sessions for identifying people with elevated risk of developing type 2 diabetes mellitus.

Diagnostic criteria

Typically, participants in CAHTS studies are expected to be members of the wider public. The gathering of family history information may have placed some of them in a high risk group (for example having relatives with diabetes mellitus). This, however, was related to the findings of the study and not a pre‐existing capacity allowing us to classify participants according to it.

To be consistent with changes in classification and diagnostic criteria of diabetes mellitus through the years, the diagnosis should have been established using the standard criteria valid at the time of the beginning of the trial (Alberti 1998; ADA 1999; WHO 1980; WHO 1985). Ideally, diagnostic criteria should have been described.

Types of interventions

Intervention

We have considered all computer‐assisted history taking interventions (CAHTS) for identifying people with elevated risk of developing type 2 diabetes through family history taking. The following six types of CAHTS have been considered:

1. Computer‐assisted self interviewing (CASI)

2. Audio computer‐assisted self administered interviewing (ACASI)

3. Computer‐assisted face‐to‐face interviewing (CAFI)

4. Computer‐assisted telephone interviewing (CATI)

5. Interactive voice response telephone interviewing (IVTI)

6. Internet‐based computer‐assisted history taking

Control

Oral and written methods to collect family history.

Types of outcome measures

Primary outcomes

• response rates to invitations for family history taking via the aforementioned types of CAHTS interventions;

• quality of data (error rates, completeness, accuracy, reliability);

• adverse events.

Secondary outcomes

• cost effectiveness;

• patient and provider satisfaction with the methods

Search methods for identification of studies

Electronic searches

We used the following sources for the identification of trials:

• The Cochrane Library (issue 6, 2011);

• MEDLINE (January 1985 to June 2011);

• EMBASE (January 1980 to June 2011);

• CINAHL (January 1981 to June 2011).

We have also search databases of ongoing trials: 'Current Controlled Trials' (www.controlled‐trials.com ‐ with links to other databases of ongoing trials).

For detailed search strategies please see under Appendix 2.

Where additional key words of relevance have been detected during any of the electronic or other searches, electronic search strategies have been modified to incorporate these terms.

Searching other resources

We have tried to identify additional studies by searching the reference lists of included trials and (systematic) reviews, meta‐analyses and health technology assessment reports noticed.

Data collection and analysis

Selection of studies

To determine the studies to be assessed further, two authors (Y.P., I.W.) have independently scanned the abstract, title or both sections of every record retrieved. All potentially relevant articles have been investigated as full text. Inter‐rater agreement for study selection has been measured using the kappa statistic (Cohen 1960). Differences have been marked and if these studies are later on included, the influence of the primary choice will be subjected to a sensitivity analysis. Where differences in opinion existed, they have been resolved by a third party. If resolving disagreement is not possible, the article has been added to those 'awaiting assessment' and authors were contacted for clarification. An adapted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) flow‐chart of study selection will be attached (Liberati 2009).

Data extraction and management

For studies that fulfilled inclusion criteria, two authors (Y.P., I.W) would have independently abstracted relevant population and intervention characteristics using standard data extraction templates with any disagreements to be resolved by discussion, or if required by a third party. Any relevant missing information on the trial was planned to be sought from the original author(s) of the article.

Assessment of risk of bias in included studies

Two authors (I.W., Y.P.) would have assessed each trial and performed assessment of bias independently. Possible disagreement were planned to be resolved by consensus, or with consultation of a third party (JC) in case of disagreement. Inter‐rater agreement for key bias indicators (e.g. allocation concealment, incomplete outcome data) were planned to be calculated using the kappa statistic (Cohen 1960). In cases of disagreement, the rest of the group would have been consulted and a judgement would have been made based on consensus.

We planned to assess risk of bias using the Cochrane Collaboration’s tool (Higgins 2009) using the following criteria:

• was the allocation sequence adequately generated?

• was the allocation adequately concealed?

• was knowledge of the allocated intervention adequately prevented during the study?

• were incomplete outcome data adequately addressed?

• were reports of the study free of suggestion of selective outcome reporting?

• was the study apparently free of other problems that could put it at a high risk of bias?

We planned to use individual bias items as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2009).

Measures of treatment effect

Endpoint versus change data: Where possible, endpoint data would have been presented, as change standard deviations may not be available for many studies. If both endpoint and change data were available for the same outcomes, only the former was planned to be reported in this review. If endpoint data were not available, but change data were, we planned to report the change data in the tables and text of the review. However, for inclusion of a study reporting change data in the meta‐analysis, we planned to calculate the endpoint mean from the change data given and assume that the endpoint standard deviation to be equal to the baseline standard deviation.

Unit of analysis issues

We planned to take into account the level at which randomisation occurred, such as cross‐over trials, cluster‐randomised trials and multiple observations for the same outcome.

Dealing with missing data

Relevant missing data would have been obtained from authors, where feasible. Evaluation of important numerical data such as screened, randomised patients as well as intention‐to‐treat (ITT) and per‐protocol (PP) population were planned to be carefully performed. Attrition rates, for example drop‐outs, losses to follow‐up and withdrawals were planned to be investigated. Issues of missing data and techniques to handle these (for example, last‐observation‐carried‐forward (LOCF)) were planned to be critically appraised.

Assessment of heterogeneity

In the event of substantial clinical or methodological or statistical heterogeneity, study results would not have been combined by means of meta‐analysis. Heterogeneity was planned to be identified by visual inspection of the forest plots, by using a standard Chi²‐test and a significance level of P = 0.1, in view of the low power of such tests. Heterogeneity would have been examined with the I² statistic(Higgins 2002), where I² values of 50% and more indicate a substantial level of heterogeneity (Higgins 2003). When heterogeneity was found, we planned to determine potential reasons for it by examining individual study and subgroup characteristics.

Assessment of reporting biases

Funnel plots were planned to be used to assess for the potential existence of small study bias. There are a number of explanations for the asymmetry of a funnel plot (Sterne 2001). Therefore, we planned to carefully interpreted results (Lau 2006).

Data synthesis

Data were to be summarised statistically where these were available, sufficiently similar and of sufficient quality. Statistical analysis was planned to be performed according to the statistical guidelines referenced in the newest version of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2009).

Subgroup analysis and investigation of heterogeneity

Subgroup analyses were to be mainly performed if one of the primary outcome parameters demonstrated statistically significant differences between intervention groups. In any other case, subgroup analyses would have been clearly marked as a hypothesis generating exercise.

The following subgroup analyses were planned:

• age (16 to 45 years, older than 45 years);

• socioeconomic profile;

• geographical location (at country level);

• analysis by number of repeated exposures to CAHT interventions;

• year of publication;

• type of CAHT method (self‐administered; professional‐administered).

Sensitivity analysis

We planned to perform sensitivity analyses in order to explore the influence of the following factors on effect size:

• repeating the analysis excluding unpublished studies;

• repeating the analysis taking account of risk of bias, as specified above;

• repeating the analysis excluding any very long or large studies to establish how much they dominate the results;

• repeating the analysis excluding studies using the following filters: diagnostic criteria, language of publication, source of funding (industry versus other), country.

The robustness of the results was also planned to be tested by repeating the analysis using different measures of effects size (relative risk, odds ratio etc.) and different statistical models (fixed‐effect model and random‐effects model).

Results

Description of studies

The searches found no controlled trials or randomised controlled trials assessing the effectiveness of computer‐assisted versus oral‐and‐written family history taking for identifying people with elevated risk of developing type 2 diabetes mellitus. A cluster randomised controlled trial (O'Neil 2009) evaluated a self‐administered, web‐based tool that assessed familial risk for CHD; stroke; diabetes; and colorectal, breast, and ovarian cancer, and provided a personalized prevention plan based on familial risk. This study was excluded because it did not meet our study design criteria and did not report any primary outcomes as listed in our published protocol.

Results of the search

A total of 1954 studies were retrieved via electronic database searches (Figure 1) and imported into EndNote X4 from which 276 duplicates were identified and removed by a combination and sequential use of author name, publication year, title and journal pages and checked manually by the main reviewer. From the remaining articles, 1677 were screened and deemed not relevant to our review. The reasons for exclusion of the studies were as follows: not a diabetic population; no computer‐assisted history taking systems (CAHTS) being tested; no family history taking; family history data were not presented; focused solely on how to practically use CAHTS; or the studies were not randomised controlled trials. Upon further review the one remaining article was excluded (O'Neil 2009). See Characteristics of excluded studies for details.

1.

Study flow diagram

Included studies

No studies were included in this review.

Excluded studies

One study was excluded (O'Neil 2009).

Risk of bias in included studies

No studies were included in this review.

Effects of interventions

No studies were included in this review.

Discussion

In the absence of evidence from randomised controlled trials about the effectiveness of computer‐assisted versus oral‐and‐written family history taking for identifying people with elevated risk of developing type 2 diabetes mellitus, planning and decisions in this area must rely on other research at present. A cluster randomised trial by O'Neil 2009 concludes that a self‐administered, online family history taking tool delineated a substantial burden of family‐history‐based risk for chronic diseases including diabetes in a primary care population.

Implementing self‐administered history taking in primary care has been suggested to increase available physician time and therefore reduce waiting times (Benaroia 2007; Wolford 2008), while Probst 2008 also determined this to be the case, the data have not been published as part of the study.

Authors' conclusions

Implications for practice.

In the absence of evidence of effectiveness, the implementation of computer‐assisted family history taking for identifying people with elevated risk of type 2 diabetes relies on the clinicians' tacit knowledge, published monographs and viewpoint articles. In the meantime, approximately one third of people with diabetes remain undiagnosed until complications appear (ADA 2007).

Implications for research.

There is a need to develop an evidence base to support the effective development and use of computer‐assisted history taking systems in identifying people with elevated risk of type 2 diabetes. It is essential that through interdisciplinary collaboration and by working with patients we develop those methodologies that will enable the rigorous evaluation of the accuracy as well as the cost and clinical effectiveness of patient‐completed computer‐assisted history taking systems (CAHTS) against oral‐and‐written methods for collecting family history to identify people with elevated risk for type 2 diabetes. Well designed RCTs can inform phases 1 and 2 of translational research to support sustainable screening for early detection of type 2 diabetes via CAHTS.

Appendices

Appendix 1. Data collection modes

Administration Modes	Onsite	Telephone	Internet
Professional completed	The patient is present onsite with a health practitioner who facilitates the history‐taking process with a technological system (audio presentation, oral, keyed input)	The patient responds to a telephone interview by a health practitioner who records the responses (audio presentation, oral, keyed input)	N/A
Patient completed	The patient is present onsite and conducts the history by himself/herself through a technological system, such as laptop, desktop computer, or PDA (audio & visual presentation, keyed input)	The patient responds to an automated telephone system that records the responses for access by a health practitioner (audio presentation, keyed input)	The patient is given access to an online survey to complete, which can then be accessed by a health practitioner and linked to other similar patient records (visual & audio presentation, keyed input)
Footnotes N/A: not acknowledged; PDA: personal ditigal assistant

Appendix 2. Search strategies

Search terms

Unless otherwise stated, search terms are free text terms; MeSH = Medical subject heading (Medline medical index term); exp = exploded MeSH; the dollar sign ($) or asterisk (*) stand for any character(s); the question mark (?) substitutes for one or no characters; ab = abstract; adj = adjacent; ot = original title;  pt = publication type; rn = Registry number or Enzyme Commission number; sh = MeSH; ti = title; tw = text word.   The Cochrane Library #1 MeSH descriptor Diabetes mellitus explode all trees    #2 diabet* in All Text   #3 (IDDM in All Text or NIDDM in All Text or MODY in All Text)    #4 (late in All Text and (onset in All Text near/6 diabet* in All Text) )    #5 (maturity in All Text and (onset in All Text near/6 diabet* in All Text) )    #6 (syndrom in All Text and (X in All Text near/6 diabet* in All Text) )    #7 (hyperinsulin* in All Text or (insulin in All Text and sensitiv* in All Text) )    #8 (insulin* in All Text and secret in All Text and dysfunction* in All Text)    #9 (impaired in All Text and glucose in All Text and toleran* in All Text)    #10 (glucose in All Text and intoleran* in All Text)    #11 MeSH descriptor Glucose Intolerance explode all trees    #12 (insulin* in All Text and resist* in All Text)    #13 ( (non in All Text and insulin* in All Text and depend* in All Text) or (noninsulin* in All Text and depend* in All Text) or (non in All Text and insulin?depend* in All Text) or noninsulin?depend* in All Text)    #14 MeSH descriptor Insulin resistance explode all trees    #15 ( (insulin* in All Text and depend* in All Text) or insulin?depend* in All Text)    #16 (#1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9 or #10 or #11 or #12 or #13 or #14 or #15)    #17 MeSH descriptor Diabetes insipidus explode all trees    #18 (diabet$ in All Text and insipidus in All Text)    #19 (#17 or #18)    #20 (#16 and not #19)    #21 MeSH descriptor Medical History Taking explode all trees    #22 MeSH descriptor Automatic Data processing explode all trees    #23 MeSH descriptor Data collection explode all trees with qualifiers: MT,ST,TD,IS   #24 MeSH descriptor Decision support techniques explode all trees    #25 MeSH descriptor Decision Making, computer‐assisted explode all trees    #26 MeSH descriptor Computer‐assisted instruction explode all trees    #27 MeSH descriptor Therapy, computer‐assisted explode all trees    #28 MeSH descriptor Diagnosis, computer‐assisted explode all trees    #29 MeSH descriptor Medical informatics explode all trees    #30 MeSH descriptor Telemedicine explode all trees    #31 MeSH descriptor Remote consultation explode all trees    #32 MeSH descriptor Questionnaires explode all trees with qualifiers: ST,MT,TD   #33 MeSH descriptor Interviews as topic explode all trees with qualifiers: ST,MT,PX   #34 MeSH descriptor Medical records systems, computerized explode all trees    #35 MeSH descriptor Computers explode all trees    #36 ( (computer* in All Text near/6 histor* in All Text) or ( (computer* in All Text near/6 data in All Text) and collection* in All Text) or (computer* in All Text near/6 screen* in All Text) or (computer* in All Text near/6 interview* in All Text) or (computer* in All Text near/6 inventor* in All Text) )    #37 ( (Computer* in All Text near/6 anamnes* in All Text) or (Computer* in All Text near/6 questionnair* in All Text) or (computer* in All Text near/6 assessment* in All Text) or (computer* in All Text near/6 consult* in All Text) )    #38 ( (electronic in All Text near/6 histor* in All Text) or ( (electronic in All Text near/6 data in All Text) and collection* in All Text) or (electronic in All Text near/6 screen* in All Text) or (electronic in All Text near/6 interview* in All Text) or (electronic in All Text near/6 inventor* in All Text) )    #39 ( (electronic in All Text near/6 anamnes* in All Text) or (electronic in All Text near/6 questionnair* in All Text) or (electronic* in All Text near/6 assessment* in All Text) or (electronic in All Text near/6 consult* in All Text) )    #40 ( (online in All Text near/6 histor* in All Text) or ( (online in All Text near/6 data in All Text) and collection* in All Text) or (online in All Text near/6 screen* in All Text) or (online in All Text near/6 interview* in All Text) or (online in All Text near/6 inventor* in All Text) )    #41 ( (online in All Text near/6 anamnes* in All Text) or (online in All Text near/6 questionnair* in All Text) or (online in All Text near/6 assessment* in All Text) or (online in All Text near/6 consult* in All Text) )    #42 ( (on‐line in All Text near/6 histor* in All Text) or ( (on‐line in All Text near/6 data in All Text) and collection* in All Text) or (on‐line in All Text near/6 screen* in All Text) or (on‐line in All Text near/6 interview* in All Text) or (on‐line in All Text near/6 inventor* in All Text) )    #43 ( (on‐line in All Text near/6 anamnes* in All Text) or (on‐line in All Text near/6 questionnair* in All Text) or (on‐line in All Text near/6 assessment* in All Text) or (on‐line in All Text near/6 consult* in All Text) )    #44 ( (automated in All Text near/6 histor* in All Text) or ( (automated in All Text near/6 data in All Text) and collection* in All Text) or (automated in All Text near/6 screen* in All Text) or (automated in All Text near/6 interview* in All Text) or (automated in All Text near/6 inventor* in All Text) )    #45 ( (automated in All Text near/6 anamnes* in All Text) or (automated in All Text near/6 questionnair* in All Text) or (automated in All Text near/6 assessment* in All Text) or (automated in All Text near/6 consult* in All Text) )   #46 ( (web in All Text near/6 histor* in All Text) or ( (web in All Text near/6 data in All Text) and collection* in All Text) or (web in All Text near/6 screen* in All Text) or (web in All Text near/6 interview* in All Text) or (web in All Text near/6 inventor* in All Text) )    #47 ( (web in All Text near/6 anamnes* in All Text) or (web in All Text near/6 questionnair* in All Text) or (web in All Text near/6 assessment* in All Text) or (web in All Text near/6 consult* in All Text) )    #48 ( (internet in All Text near/6 histor* in All Text) or ( (internet in All Text near/6 data in All Text) and collection* in All Text) or (internet in All Text near/6 screen* in All Text) or (internet in All Text near/6 interview* in All Text) or (internet in All Text near/6 inventor* in All Text) )    #49 ( (internet in All Text near/6 anamnes* in All Text) or (internet in All Text near/6 questionnair* in All Text) or (internet in All Text near/6 assessment* in All Text) or (internet in All Text near/6 consult* in All Text) )    #50 ( (telephon* in All Text near/6 interview* in All Text) or (telephon* in All Text near/6 inventor* in All Text) or (telephon* in All Text near/6 consult* in All Text) )    #51 ( (face‐to‐face in All Text near/6 interview* in All Text) or (face‐to‐face in All Text near/6 inventor* in All Text) or (face‐to‐face in All Text near/6 consult* in All Text) )    #52 (FFQ in All Text or (personal in All Text and digital in All Text and assistant* in All Text) )    #53 (acasi in All Text or casi in All Text or cati in All Text or cafi in All Text or ivti in All Text or kiosk* in All Text)    #54 (#21 or #22 or #23 or #24 or #25 or #26 or #27 or #28 or #29 or #30 or #31 or #32 or #33 or #34 or #35 or #36 or #37 or #38 or #39 or #40)    #55 (#41 or #42 or #43 or #44 or #45 or #46 or #47 or #48 or #49 or #50 or #51 or #52 or #53)    #56 (#54 or #55)    #57 (#20 and #56)    #58 MeSH descriptor Diet explode all trees    #59 MeSH descriptor Family explode all trees    #60 ( (family in All Text and history in All Text) or diet in All Text or screen* in All Text)    #61 (#58 or #59 or #60)    #62 (#57 and #61)      MEDLINE 1. exp Diabetes Mellitus/   2. diabet$.tw,ot.   3. (IDDM or NIDDM or MODY or T1DM or T2DM or T1D or T2D).tw,ot.   4. (non insulin$ depend$ or noninsulin$ depend$ or non insulin?depend$ or noninsulin?depend$).tw,ot.   5. (insulin$ depend$ or insulin?depend$).tw,ot.   6. exp Diabetes Insipidus/   7. diabet$ insipidus.tw,ot.   8. or/1‐5   9. 6 or 7   10. 8 not 9   11. exp Medical History Taking/   12. exp Automatic Data Processing/   13. exp Data Collection/mt, st, td, is [Methods, Standards, Trends, Instrumentation]   14. exp Decision Support Techniques/   15. exp Decision Making, Computer‐Assisted/   16. exp Computer‐Assisted Instruction/   17. exp Therapy, Computer‐Assisted/   18. exp Medical Informatics/   19. exp Telemedicine/   20. exp Remote Consultation/   21. exp Questionnaires/st, mt, td   22. exp Interviews as topic/px, mt, st   23. exp Medical records systems, computerized/   24. exp Computers/ or exp Computers, Handheld/   25. (computer* adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   26. (electronic adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   27. ((online or on‐line) adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   28. (automated adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   29. (web adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   30. (internet adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   31. (tablet* adj6 (histor* or data collection* or screen* or interview* or questionnair* or assessment* or consult*)).tw,ot.   32. ((touchscreen* or touch screen*) adj6 (histor* or data collection* or screen* or interview* or questionnair* or assessment*)).tw,ot.   33. ((telephon* or telefon* or face‐to‐face) adj6 (interview* or inventor* or consult*)).tw,ot.   34. (FFQ or personal digital assistant*).tw,ot.   35. (acasi or casi or cati or cafi or ivti or kiosk*).tw,ot.   36. or/11‐35   37. Diet/ or exp Diabetic Diet/ or exp Diet, Carbohydrate‐Restricted/ or exp Diet, Atherogenic/ or exp Diet Therapy/   38. diet.tw,ot.   39. exp Family/   40. exp Mass Screening/is, mt, td, st [Instrumentation, Methods, Trends, Standards]   41. (family history or screen* or anamnes*).tw,ot.   42. or/37‐41   43. randomized controlled trial.pt.   44. controlled clinical trial.pt.   45. randomi?ed.ab.   46. placebo.ab.   47. drug therapy.fs.   48. randomly.ab.   49. trial.ab.   50. groups.ab.   51. or/43‐50   52. Meta‐analysis.pt.   53. exp Technology Assessment, Biomedical/   54. exp Meta‐analysis/   55. exp Meta‐analysis as topic/   56. hta.tw,ot.   57. (health technology adj6 assessment$).tw,ot.   58. (meta analy$ or metaanaly$ or meta?analy$).tw,ot.   59. ((review$ or search$) adj10 (literature$ or medical database$ or medline or pubmed or embase or cochrane or cinahl or psycinfo or psyclit or healthstar or biosis or current content$ or systemat$)).tw,ot.   60. or/52‐59   61. 51 or 60   62. (comment or editorial or historical‐article).pt.   63. 61 not 62   64. 10 and 36 and 42 and 63   65. (animals not (animals and humans)).sh.   66. 64 not 65     EMBASE 1. exp Diabetes Mellitus/   2. diabet$.tw,ot.   3. (non insulin* depend* or noninsulin* depend* or non insulin?depend* or noninsulin?depend*).tw,ot.   4. (insulin* depend* or insulin?depend*).tw,ot.   5. (IDDM or NIDDM or MODY or T1DM or T2DM or T1d or T2D).tw,ot.   6. or/1‐5   7. exp Diabetes Insipidus/   8. diabet* insipidus.tw,ot.   9. 7 or 8   10. 6 not 9   11. *information processing/   12. exp decision support system/   13. exp computer assisted therapy/   14. exp computer assisted diagnosis/   15. exp automation/ or exp "automation, computers and data processing"/   16. exp computer analysis/ or exp computer program/ or exp computer simulation/ or exp electronic data interchange/ or exp human computer interaction/   17. exp telecommunication/   18. exp telemedicine/   19. exp teleconsultation/   20. exp computer interface/   21. *questionnaire/ or exp open‐ended questionnaire/ or exp structured questionnaire/   22. exp electronic medical record/   23. exp microcomputer/   24. (computer* adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   25. ((online or on‐line) adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   26. (electronic adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   27. (automated adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   28. (web adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   29. (internet adj6 (histor* or data collection* or screen* or interview* or inventor* or anamnes* or questionnair* or assessment* or consult*)).tw,ot.   30. ((telephon* or telefon* or face‐to‐face) adj6 (interview* or inventor* or consult*)).tw,ot.   31. (FFQ or personal digital assistant*).tw,ot.   32. (acasi or casi or cati or cafi or ivti or kiosk*).tw,ot.   33. (tablet* adj6 (histor* or data collection* or screen* or interview* or questionnair* or assessment* or consult*)).tw,ot.   34. ((touchscreen* or touch screen*) adj6 (histor* or data collection* or screen* or interview* or questionnair* or assessment*)).tw,ot.   35. exp personal digital assistant/   36. or/11‐35   37. exp diet/   38. exp diabetic diet/ or exp low carbohydrate diet/ or exp diet restriction/ or exp low calory diet/ or exp diet therapy/   39. diet*.tw,ot.   40. exp family history/   41. exp mass screening/   42. (family history or history tak* or anamnes* or screen*).tw,ot.   43. or/37‐42   44. exp Randomized Controlled Trial/   45. exp Controlled Clinical Trial/   46. exp Clinical Trial/   47. exp Comparative Study/   48. exp Drug comparison/   49. exp Randomization/   50. exp Crossover procedure/   51. exp Double blind procedure/   52. exp Single blind procedure/   53. exp Placebo/   54. exp Prospective Study/   55. ((clinical or control$ or comparativ$ or placebo$ or prospectiv$ or randomi?ed) adj3 (trial$ or stud$)).ab,ti.   56. (random$ adj6 (allocat$ or assign$ or basis or order$)).ab,ti.   57. ((singl$ or doubl$ or trebl$ or tripl$) adj6 (blind$ or mask$)).ab,ti.   58. (cross over or crossover).ab,ti.   59. or/44‐58   60. exp meta analysis/   61. (metaanaly$ or meta analy$ or meta?analy$).ab,ti,ot.   62. (systematic adj3 review*).tw,ot.   63. exp Literature/   64. exp Biomedical Technology Assessment/   65. hta.tw,ot.   66. (health technology adj6 assessment$).tw,ot.   67. or/60‐66   68. 59 or 67   69. (comment or editorial or historical‐article).pt.   70. 68 not 69   71. 10 and 36 and 43 and 70 CINAHL 1. mh diabetes mellitus+ 2. TI diabet* or AB diabet* 3. TI (IDDM OR NIDDM OR MODY OR T1DM OR T2DM OR T1D OR T2D) or ab (IDDM OR NIDDM OR MODY OR T1DM OR T2DM OR T1D OR T2D) 4. TI (non insulin* depend* OR noninsulin* depend* OR non insulin*depend* OR noninsulin*depend*) or AB (non insulin* depend* OR noninsulin* depend* OR non insulin*depend* OR noninsulin*depend*) 5. TI (insulin* depend* OR insulin*depend*) or AB (insulin* depend* OR insulin*depend*) 6. mh diabetes insipidus+ 7. TI (diabet* AND insipidus) or AB (diabet* AND insipidus) 8. S1 or S2 or S3 or S4 or S5 9. S6 or S7 10. S8 NOT S9 11. mh patient history taking+ 12. mh PATIENT RECORD SYSTEMS+ 13. mh MEDICAL RECORDS+ 14. mh INTERVIEWS+/MT 15. mh QUESTIONNAIRES+/mt 16. mh REMOTE CONSULTATION+ 17. mh DIAGNOSIS, COMPUTER ASSISTED+ 18. mh DECISION MAKING, COMPUTER ASSISTED+ 19. exp DATA COLLECTION, COMPUTER ASSISTED+ 20. mh MEDICAL INFORMATICS+ 21. mh TELEMEDICINE+ 22. (MH "Computers and Computerization+") or (mh "COMPUTERS, PORTABLE+") 23. TI ((computer* N6 histor*) OR (computer* N6 data collection*) OR (computer* N6 screen*) OR (computer* N6 interview*) OR (computer* N6 inventor*) OR (computer* N6 anamnes*) OR (computer* N6 questionnair*) OR (computer* N6 assessment*) OR (computer* N6 consult*)) OR AB ((computer* N6 histor*) OR (computer* N6 data collection*) OR (computer* N6 screen*) OR (computer* N6 interview*) OR (computer* N6 inventor*) OR (computer* N6 anamnes*) OR (computer* N6 questionnair*) OR (computer* N6 assessment*) OR (computer* N6 consult*)) 24. TI ((electronic N6 histor*) OR (electronic N6 data collection*) OR (electronic N6 screen*) OR (electronic N6 interview*) OR (electronic N6 inventor*) OR (electronic N6 anamnes*) OR (electronic N6 questionnair*) OR (electronic N6 assessment*) OR (electronic N6 consult*)) OR AB ((electronic N6 histor*) OR (electronic N6 data collection*) OR (electronic N6 screen*) OR (electronic N6 interview*) OR (electronic N6 inventor*) OR (electronic N6 anamnes*) OR (electronic N6 questionnair*) OR (electronic N6 assessment*) OR (electronic N6 consult*)) 25. TI ((online N6 histor*) OR (online N6 data collection*) OR (online N6 screen*) OR (online N6 interview*) OR (online N6 inventor*) OR (online N6 anamnes*) OR (online N6 questionnair*) OR (online N6 assessment*) OR (online N6 consult*)) OR AB ((online N6 histor*) OR (online N6 data collection*) OR (online N6 screen*) OR (online N6 interview*) OR (online N6 inventor*) OR (online N6 anamnes*) OR (online N6 questionnair*) OR (online N6 assessment*) OR (online N6 consult*)) 26. TI ((on‐line N6 histor*) OR (on‐line N6 data collection*) OR (on‐line N6 screen*) OR (on‐line N6 interview*) OR (on‐line N6 inventor*) OR (on‐line N6 anamnes*) OR (on‐line N6 questionnair*) OR (on‐line N6 assessment*) OR (on‐line N6 consult*)) OR AB ((on‐line N6 histor*) OR (on‐line N6 data collection*) OR (on‐line N6 screen*) OR (on‐line N6 interview*) OR (on‐line N6 inventor*) OR (on‐line N6 anamnes*) OR (on‐line N6 questionnair*) OR (on‐line N6 assessment*) OR (on‐line N6 consult*)) 27. TI ((automated N6 histor*) OR (automated N6 data collection*) OR (automated N6 screen*) OR (automated N6 interview*) OR (automated N6 inventor*) OR (automated N6 anamnes*) OR (automated N6 questionnair*) OR (automated N6 assessment*) OR (automated N6 consult*)) OR AB ((automated N6 histor*) OR (automated N6 data collection*) OR (automated N6 screen*) OR (automated N6 interview*) OR (automated N6 inventor*) OR (automated N6 anamnes*) OR (automated N6 questionnair*) OR (automated N6 assessment*) OR (automated N6 consult*)) 28. TI ((web N6 histor*) OR (web N6 data collection*) OR (web N6 screen*) OR (web N6 interview*) OR (web N6 inventor*) OR (web N6 anamnes*) OR (web N6 questionnair*) OR (web N6 assessment*) OR (web N6 consult*)) OR AB ((web N6 histor*) OR (web N6 data collection*) OR (web N6 screen*) OR (web N6 interview*) OR (web N6 inventor*) OR (web N6 anamnes*) OR (web N6 questionnair*) OR (web N6 assessment*) OR (web N6 consult*)) 29. TI ((internet N6 histor*) OR (internet N6 data collection*) OR (internet N6 screen*) OR (internet N6 interview*) OR (internet N6 inventor*) OR (internet N6 anamnes*) OR (internet N6 questionnair*) OR (internet N6 assessment*) OR (internet N6 consult*)) OR AB ((internet N6 histor*) OR (internet N6 data collection*) OR (internet N6 screen*) OR (internet N6 interview*) OR (internet N6 inventor*) OR (internet N6 anamnes*) OR (internet N6 questionnair*) OR (internet N6 assessment*) OR (internet N6 consult*)) 30. TI ((tablet N6 histor*) OR (tablet N6 data collection*) OR (tablet N6 screen*) OR (tablet N6 interview*) OR (tablet N6 questionnair*) OR (tablet N6 assessment*) OR (tablet N6 consult*)) OR AB ((tablet N6 histor*) OR (tablet N6 data collection*) OR (tablet N6 screen*) OR (tablet N6 interview*) OR (tablet N6 questionnair*) OR (tablet N6 assessment*) OR (tablet N6 consult*)) 31. TI ((touchscreen N6 histor*) OR (touchscreen N6 data collection*) OR (touchscreen N6 screen*) OR (touchscreen N6 interview*) OR (touchscreen N6 questionnair*) OR (touchscreen N6 assessment*)) OR AB ((touchscreen N6 histor*) OR (touchscreen N6 data collection*) OR (touchscreen N6 screen*) OR (touchscreen N6 interview*) OR (touchscreen N6 questionnair*) OR (touchscreen N6 assessment*)) 32. TI ((touch screen N6 histor*) OR (touch screen N6 data collection*) OR (touch screen N6 screen*) OR (touch screen N6 interview*) OR (touch screen N6 questionnair*) OR (touch screen N6 assessment*)) OR AB ((touch screen N6 histor*) OR (touch screen N6 data collection*) OR (touch screen N6 screen*) OR (touch screen N6 interview*) OR (touch screen N6 questionnair*) OR (touch screen N6 assessment*)) 33. TI ((telephon* N6 interview*) OR (telephon* N6 inventor*) OR (telephon* N6 consult*)) OR AB ((telephon* N6 interview*) OR (telephon* N6 inventor*) OR (telephon* N6 consult*)) 34. TI ((face‐to‐face N6 interview*) OR (face‐to‐face N6 inventor*) OR (face‐to‐face N6 consult*)) OR AB ((face‐to‐face N6 interview*) OR (face‐to‐face N6 inventor*) OR (face‐to‐face N6 consult*)) 35. TI (FFQ or personal digital assistant) OR AB (FFQ or personal digital assistant) 36. TI (acasi or casi or cati or cafi or ivti or kiosk*) OR AB (acasi or casi or cati or cafi or ivti or kiosk*) 37. S11 or S12 or S13 or S14 or S15 or S16 or S17 or S18 or S19 or S20 or S21 or S22 or S23 or S24 or S25 or S26 or S27 or S28 or S29 or S30 or S31 or S32 or S33 or S34 or S35 or S36 38. mh diet+ or mh diet therapy+ 39. TI diet OR AB diet 40. mh family+ 41. mh health screening+/mt 42. TI (family history or screen* or anamnes*) OR AB (family history or screen* or anamnes*) 43. S38 or S39 or S40 or S41 or S42 44. (MH "Clinical Trials+") 45. (MH "Comparative Studies") 46. (MH "Random Assignment") 47. (MH "Placebos") 48. (MH "Prospective Studies+") 49. AB ((trial* N3 clinical) OR (trial* N3 control*) OR (trial* N3 comparative) OR (trial* N3 placebo) OR (trial* N3 prospective) OR (trial* N3 randomi?ed)) OR TI ((trial* N3 clinical) OR (trial* N3 control*) OR (trial* N3 comparative) OR (trial* N3 placebo) OR (trial* N3 prospective) OR (trial* N3 randomi?ed)) 50. AB ((stud* N3 clinical) OR (stud* N3 control*) OR (stud* N3 comparative) OR (stud* N3 placebo) OR (stud* N3 prospective) OR (stud* N3 randomi?ed)) OR TI ((stud* N3 clinical) OR (stud* N3 control*) OR (stud* N3 comparative) OR (stud* N3 placebo) OR (stud* N3 prospective) OR (stud* N3 randomi?ed)) 51. TI ((random* N6 allocat*) OR (random* N6 assign*) OR (random* N6 basis*) OR (random* N6 order*)) OR AB ((random* N6 allocat*) OR (random* N6 assign*) OR (random* N6 basis*) OR (random* N6 order*)) 52. TI ((blind* N6 single) OR (blind* N6 double) OR (blind* N6 triple)) OR AB ((blind* N6 single) OR (blind* N6 double) OR (blind* N6 triple)) 53. TI ((mask* N6 single) OR (mask* N6 double) OR (mask* N6 triple)) OR AB ((mask* N6 single) OR (mask* N6 double) OR (mask* N6 triple)) 54. TI (crossover or cross over) OR AB (crossover or cross over) 55. S44 or S45 or S46 or S47 or S48 or S49 or S50 or S51 or S52 or S53 or S54 56. (MH "Meta Analysis") 57. TI (metaanaly* OR meta analy* OR meta?analy*) OR AB (metaanaly* OR meta analy* OR meta?analy*) 58. TI (systematic* N3 review*) OR AB (systematic* N3 review*) 59. TI (hta) or AB (hta) 60. TI (health technology N6 assess*) OR AB (health technology N6 assess*) 61. S56 or S57 or S58 or S59 or S60 62. S55 or S61 63. PT (comment OR editorial OR historical‐article) 64. S62 NOT S63 65. S10 and S37 and S43 and S64

Characteristics of studies

Characteristics of excluded studies [ordered by study ID]

Study	Reason for exclusion
O'Neil 2009	Did not meet study design criteria and listed primary outcomes

Differences between protocol and review

None

Contributions of authors

JOSIP CAR: Conceived the idea for the review and supervised the production

YANNIS PAPPAS: Wrote the protocol and supervised IW

IGOR WEI: Re‐wrote the protocol

AZEEM MAJEED: Supervised the production

AZIZ SHEIKH: Supervised the production

Sources of support

Internal sources

• Global eHealth Unit, Department of Primary Care and Social Medicine, Imperial College London, UK.

  JC, YP, IW, AM received salary and office space

• General Practice section Division of Community Health SciencesThe University of Edinburgh, UK.

  AS received salary and office space

External sources

• NHS CFHEP 001, UK.

• NIHR, UK.

  The Department of Primary Care & Public Health at Imperial College London is grateful for support from the NIHR Collaboration for Leadership in Applied Health Research & Care (CLAHRC) Scheme, the NIHR Biomedical Research Centre scheme, and the Imperial Centre for Patient Safety and Service Quality.

Declarations of interest

None known

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