What type of interventions improve adherence to recommendations on nutritional intake?


Interventions to improve nutritional status


What type of interventions improve adherence to recommendations on nutritional intake?

Read the study article and the additional research article to answer the above question, according to the topic chosen, in a 3 full page (not including title and reference page) summary (use the two articles to answer the question to support your statement).

The Summary must include the following headings

1. Introduction and Key Points

Defines the Topic and Question

Importance of the topic

Why did you pick the topic and why should we as nurses’ care about this topic?

States why it is a problem

2. Article Search (only for additional article)

Current and credible resource

Database search-terms and methods (key terms used; how you found the article)

Number of articles located (how you narrowed the search, how you ultimately selected the article that you selected).

3. Article Findings (for the two articles)

How it addresses the topic

Type of Research conducted (quantitative or qualitative)

Findings of research (who was studied, how they were studied, who the population was, what tools were used for the study, what were the important findings / results)

4. Evidence for Practice

Summary of evidence

How it will improve nursing practice of the bedside

How will this evidence decrease a gap in practice?

Any concerns or weaknesses located in the evidence

5. Sharing of Evidence

Who would you share the information with?

How would you share this information (e.g through PowerPoint, brochures, community meetings, etc)?

What resources would you need to accomplish this sharing of evidence (e.g a community space for a community meeting, a projector for a PowerPoint presentation, etc)?

Why would it be important to share this evidence with the nursing profession?

6. Conclusion

Summarizes the theme of the paper.

Information presented in logical sequence.

All key points addressed

Note: Conclusion shows depth of understanding.

Writing Requirements

3 full pages (not less, not more please).

APA style used properly for in- text citations, references, and quotation.

All references are cited, and all citations have references (the references are the two articles used)

Telehealth methods to deliver dietary interventions in adults with chronic disease: a systematic review and meta-analysis1,2

Jaimon T Kelly,3 Dianne P Reidlinger,3 Tammy C Hoffmann,4 and Katrina L Campbell3,5*

3Faculty of Health Sciences and Medicine, 4Centre for Research in Evidence Based Practice, Bond University, Gold Coast, Australia; and 5Nutrition and

Dietetics Department, Princess Alexandra Hospital, Brisbane, Australia

ABSTRACT Background: The long-term management of chronic disease re- quires the adoption of complex dietary recommendations, which

can be facilitated by regular coaching to support behavioral changes.

Telehealth interventions can overcome patient-centered barriers to

accessing face-to-face programs and provide feasible delivery methods,

accessible regardless of geographic location. Objective: This systematic review assessed the effectiveness of telehealth dietary interventions at facilitating dietary change in chronic disease. Design: A structured systematic search was conducted for all ran- domized controlled trials evaluating multifactorial dietary interven-

tions in adults with chronic disease that provided diet education in

an intervention longer than 4 wk. Meta-analyses that used the ran-

dom-effects model were performed on diet quality, dietary adher-

ence, fruit and vegetables, sodium intake, energy, and dietary fat

intake. Results: A total of 25 studies were included, involving 7384 participants. The telehealth dietary intervention was effec-

tive at improving diet quality [standardized mean difference

(SMD): 0.22 (95% CI: 0.09, 0.34), P = 0.0007], fruit and veg-

etable intake [mean difference (MD) 1.04 servings/d (95% CI:

0.46, 1.62 servings/d), P = 0.0004], and dietary sodium intake

[SMD: 20.39 (20.58, 20.20), P = 0.0001]. Single nutrients (total fat and energy consumption) were not improved by tele-

health intervention; however, after a telehealth intervention, impor-

tant clinical outcomes were improved, such as systolic blood pressure

[MD: 22.97 mm Hg (95% CI: 25.72, 20.22 mm Hg), P = 0.05], total cholesterol [MD:20.08 mmol/L (95% CI:20.16,20.00 mmol/L), P = 0.04], triglycerides [MD: 20.10 mmol/L (95% CI: 20.19, 20.01 mmol/L), P = 0.04], weight [MD: 20.80 kg (95% CI: 21.61, 0 kg), P = 0.05], and waist circumference [MD: 22.08 cm (95% CI: 23.97, 20.20 cm), P = 0.03]. Conclusions: Telehealth-delivered dietary interventions targeting whole foods and/or dietary patterns can improve diet quality, fruit

and vegetable intake, and dietary sodium intake. When applicable,

they should be incorporated into health care services for people with

chronic conditions. This review was registered at http://www.crd.

york.ac.uk/PROSPERO/ as CRD42015026398. Am J Clin Nutr 2016;104:1693–702.

Keywords: telehealth, diet quality, dietary, diet, fruit, vegetables, chronic disease


Chronic diseases are the leading cause of ill health, accounting for .68% of all deaths worldwide (1). Chronic diseases are characterized by a multifactorial etiology (1), including obesity, heart disease, diabetes mellitus, hypertension, stroke, and renal disease, which is often diet related (2). Self-management and the adoption of a healthy lifestyle, such as improved dietary habits, increased physical activity, and other health-related behaviors (e.g., smoking cessation) are considered essential for the man- agement of chronic diseases (3, 4). Standard chronic disease care models, however, are followed by only a minority of pa- tients for many reasons, including poor compliance and high patient burden (5). This suggests that the long-term maintenance of dietary behaviors cannot be facilitated with traditional models of care.

Individuals with multiple risk factors for cardiovascular dis- ease (CVD)6 and other chronic diseases have been identified as having higher levels of nonattendance in face-to-face (FTF) consultations (6, 7). Patient-centered barriers, including limited transport and geographical isolation, working hours, and for- getting about appointments, can contribute to appointment nonattendance (8). Additional health care barriers that can hin- der access to traditional FTF care include administrative error, poor access to clinic facilities, limited parking, and unfavorable operating hours of clinics (7).

Telehealth technologies can be used to provide education and self-management support to facilitate and sustain lifestyle changes and have several advantages over traditional FTF models

1 The authors reported no funding received for this study. JTK is supported

by an Australian Post Graduate Award scholarship through Bond University. 2 Supplemental Figures 1 and 2 and Supplemental Tables 1–3 are available

from the “Online Supporting Material” link in the online posting of the

article and from the same link in the online table of contents at http://ajcn.


*To whom correspondence should be addressed. E-mail: kcampbel@bond.


Received April 11, 2016. Accepted for publication October 4, 2016.

First published online November 9, 2016; doi: 10.3945/ajcn.116.136333.

6 Abbreviations used: BP, blood pressure; CVD, cardiovascular disease;

DBP, diastolic blood pressure; FTF, face-to-face; HbA1c, glycated hemoglo-

bin; HF, heart failure; MD, mean difference; RCT, randomized controlled

trial; SBP, systolic blood pressure; SMD, standardized mean difference;

TIDieR, Template for Intervention Description and Replication.

Am J Clin Nutr 2016;104:1693–702. Printed in USA. � 2016 American Society for Nutrition 1693

at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from

36333.DCSupplemental.html http://ajcn.nutrition.org/content/suppl/2016/11/09/ajcn.116.1 Supplemental Material can be found at:


of care (9). Telehealth strategies may assist patients with chronic disease to achieve dietary behavioral change (9–11) and are flexible in time and location, with the potential to offer intensive interventions that may not be feasible with traditional care models. According to the WHO (12), telehealth refers to the delivery of health care services from a distance synchronously (i.e., same time, different location) and/or asynchronously (i.e., different time, different location), by use of information and communication technologies to exchange health in- formation (12). A telehealth lifestyle intervention may offer flexibility in delivery mode involving the provision of health education or counseling individuals or groups remotely via the telephone (13), computer or Internet (14–16), video (17), e-mail (18), and/or mobile applications, including text and photo messaging (19, 20).

Although a number of systematic reviews have covered different combinations of telehealth interventions in healthy (21–23) and chronic disease (24–27) populations, none have specifically evaluated interventions that attempt to change dietary patterns or target multiple dietary changes simultaneously (e.g., multiple food groups, nutrients). These diet interventions represent the dietary advice that is typically provided to chronic disease pop- ulations (28). This systematic review, therefore, aimed to assess the overall effectiveness of telehealth dietary interventions for facilitating multifactorial dietary change in adults with chronic disease.


We followed a prespecified review protocol, published elsewhere (29), that detailed our rationale, purpose, and methodology. It was prospectively registered in the In- ternational Prospective Register of Systematic Reviews as CRD42015026398.

Literature search

We performed a literature search in the electronic databases MEDLINE (http://www.ovid.com), EMBASE (http://www.embase. com), CINAHL (https://www.ebscohost.com), and PsychINFO (http://www.ovid.com) (from inception to November 2015) us- ing a variety of subject headings and free text terms and syno- nyms relevant to the review in consultation with an experienced systematic review search librarian, and published in the protocol (29). There was no date or language restriction in our search strategy. A multistep search approach was taken to retrieve relevant studies through use of forward-and-backward citation searching; expert correspondence; search of conference abstracts, theses, and dissertations (ProQuest); and the International Clinical Trials Register search portal and clinicaltrials.gov to identify ongoing trials. This review follows the format recommended in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (30). Two review authors screened articles independently, with disagreements in judgment re- solved by consensus or a third reviewer.

Study selection

All of the search results were merged into EndNote (Thomson Reuters) and deduplicated before screening. Studies were in- cluded in the review if they met all of the following criteria:

1) randomized controlled trial (RCT), cluster RCT, or quasi-RCT; 2) adult participants (.18 y); 3) conducted in a population with an established diet-related chronic disease defined as obesity [BMI (in kg/m2) $30], diabetes mellitus, heart disease, hyper- tension, stroke, or kidney disease (29, 31); 4) provision of mul- tifactorial dietary education in which the dose of total intervention contact hours and/or the total number of interaction contacts was $50% delivered by $1 telehealth strategy; 5) developed or de- livered by a qualified health care professional (e.g., nurse, di- etitian, physician); and 6) reported on any measure of dietary intake at baseline and $4 wk later at follow-up.

We defined a multifactorial dietary intervention as targeting more than a singular nutrient and/or food group. Multifactorial dietary interventions included those aimed at overall diet quality (assessed as any outcome that objectively scores adherence to dietary guidelines) (32–34) and patterns, such as the Mediter- ranean diet (35) and/or the Dietary Approaches to Stop Hyper- tension diet (36), or those that educate patients about $2 dietary components (nutrients and/or food groups) simultaneously. Studies that targeted $2 diet changes within the same nutrient (e.g., manipulation of categories of fatty acids) were excluded because the dietary components related to only 1 nutrient, and thus were not classified as multifactorial.

Studies were included if they compared a telehealth in- tervention to usual care (as defined by the trial authors); to dietary education in a FTF or group-based environment with no tele- health component; or via methods for which ,50% of the intervention was delivered by telehealth; or to a nondietary- focused intervention.

The primary outcome was dietary intake (any measure), with secondary outcomes relating to clinical outcomes such as all- cause mortality, cardiovascular mortality, hospitalizations, and clinical markers of chronic disease progression [e.g., blood pressure (BP), weight, blood lipid profiles].

Data extraction and management

The following data were extracted from included studies: in- tervention details [following the components outlined in the Template for Intervention Description and Replication (TIDieR) checklist] (37), participant characteristic (chronic disease, age, and sex), attrition, sample size, and study design and duration. Risk of bias was assessed by 2 review authors independently by use of Cochrane methodology (38) to categorize selection bias, perfor- mance bias, detection bias, attrition bias, and reporting bias in each study as low, unclear, or high risk of bias. Means, SDs, SEs, or 95% CIs for all prespecified primary and secondary outcome data that were reported at baseline and follow-up were extracted for analysis. When a study presented adjusted and unadjusted values, the most adjusted value was extracted for analysis.

Statistical analysis

To calculate the overall treatment effect on primary and secondary outcomes, the difference between the intervention and comparison groups’ change scores from baseline to the end of follow-up was extracted. If change from baseline values was not available, then end-of-intervention values were extracted, with the assumption that the baseline values were similar. The vari- ance was calculated from the SD or SE from the difference


at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from


between baseline and follow-up, or from the CI when these values were not available (38). When interventions and associ- ated outcomes were assessed as sufficiently homogeneous and when sufficient information was available from the studies, quantitative data were pooled into Revman (version 5.3; Co- chrane Collaboration) for meta-analysis through use of the DerSimonian-Laird random-effects model (39) and checked with the fixed-effects model to ensure robustness and suscepti- bility to potential outliers. The I2 statistic was used to assess the inconsistencies between studies and describe the percentage of variability in effect. Heterogeneity was considered substantial if the I2 statistic was $50%.

Effect sizes (for combined fruit and vegetable servings, energy intake, blood pressure, weight, and lipid profiles) were converted to standard units and calculated as mean differences (MDs). Effect sizes for diet quality scores, dietary adherence, sources of dietary fat, and sources of dietary sodium intake were calculated as standardized MDs (SMDs) because of the variability in outcome measures. We imputed missing SDs for 1 study in the dietary sodium and systolic BP (SBP) analysis (40) with data from an included study that used similar methods and sample sizes (41, 42), as recommended (43). Egger’s plot was explored to assess po- tential publication bias. Sensitivity analyses were conducted to investigate study results that appeared to be heterogeneous from the results of other analyzed studies, including large studies and high-risk-of-bias studies. Subgroup analyses also were conducted on different chronic health conditions (e.g., diabetes mellitus, CVD); studies that used different telehealth strategies; studies that targeted specified single food groups or nutrients (e.g., modifying sodium, fat, fruit, and/or vegetable interventions) compared with dietary patterns, and multiple lifestyle education interventions compared with solely dietary education interventions.


Characteristics of included studies

The flow of study identification and selection is detailed in the Preferred Reporting Items for Systematic Reviews and Meta- Analysis flowchart (Supplemental Figure 1). The search identi- fied 6967 studies. After duplicates were removed and nonrelevant studies (n = 5608) were excluded, 370 studies were subject to full text review. After this, 345 studies were excluded, leaving 25 for inclusion, involving 7384 participants. Supplemental Table 1 details the characteristics of the included studies, including the frequency of contact, delivery provider, and type of dietary edu- cation. In all but 4 studies (42, 44–46), dietary education was delivered as part of a multifactorial lifestyle intervention. Studies were conducted in CVD (15 studies) (40–42, 46–57), diabetes mellitus (5 studies) (44, 58–61), end-stage kidney disease (2 studies) (45, 62), obesity (1 study) (14), and a mix of CVD and diabetes mellitus (2 studies) (13, 63). The duration of studies ranged from 8 wk (46, 48, 56) to 8 y (61). Telehealth delivery methods varied from the telephone (13 studies) (13, 40–42, 48, 50, 54, 56, 57, 59, 61–63), short message service (4 studies) (45, 49, 51, 58), the Internet (3 studies) (14, 53, 55), video (1 study) (47) or video- conferencing (1 study) (60), and a mix of telehealth methods (3 studies) (44, 46, 50). The percentage of the interventions de- livered by telehealth methods varied from 66% to 100% across the included studies.

All of the included studies reported measures of dietary intake at baseline and follow-up. Studies varied in dietary outcome measures used and included diet quality (3 studies), dietary adherence (7 studies), energy intake (3 studies), measures of dietary fat (8 studies), dietary sodium (7 studies), and intake of fruits and/or vegetables (9 studies). We were unable to statisti- cally pool 10 studies into the meta-analysis, and these are therefore presented narratively (Supplemental Table 2). A table of excluded dietary studies that did not report dietary outcomes are provided in Supplemental Table 3.

Effect of telehealth interventions on dietary change

Diet quality

Three studies involving 992 participants measured diet quality (40, 41, 59) through the use of a diet quality score (40), the Australian Healthy Eating Index (59), and the Dietary Ap- proaches to Stop Hypertension diet score (41). The telehealth intervention improved diet quality [SMD: 0.22 (95% CI: 0.09, 0.34), P = 0.0007, I2 = 0%] compared with nontelehealth comparators (Figure 1). The results remained significant in 2 trials with 12- (40) and 24- (59) mo follow-up [SMD: 0.18 (95% CI: 0.02, 0.33), P = 0.02, I2 = 0%].

Diet adherence

Dietary adherence outcomes (7 studies) (45, 48, 52, 56, 58, 60, 62) could not be pooled into the meta-analysis because of the variation in outcome reporting statistics, which could not be standardized, and therefore are presented narratively in Supple- mental Table 2. Overall, telehealth intervention substantially im- proved diet adherence compared with nontelehealth comparators, as reported by the trial authors in 57% (n = 4) of the studies.

Fruit and vegetable intake

A total of 9 studies reported on fruit and vegetable intake (13, 14, 44, 46, 49–51, 53, 54). Telehealth interventions increased fruit and vegetable intake by 1.04 servings/d [(95% CI: 0.46, 1.62 servings/d), P = 0.009, I2 = 70%] in 4 studies with 5 comparisons (2147 participants), and by 2.94 servings/wk [(95% CI: 0.91, 4.97 servings/wk), P = 0.005, I2 = 84%] in 2 studies with 4 comparisons (1682 participants) (Figure 2). Three studies could not be pooled statistically and are reported in Supplemental Table 2. The trial with longer-term 12-mo (13) follow-up sup- ported this finding [MD: 0.65 (95% CI: 0.02, 1.28), P = 0.04].

In the per-day analysis, 4 of the 5 comparisons used the tele- phone and 1 used the Internet (14); a sensitivity analysis showed that telephone telehealth intervention resulted in increased fruit and vegetable intake by 0.77 servings/d [(95%CI: 0.39, 1.14), P = 0.0001], with heterogeneity reduced (I2 = 35%) in the 4 com- parisons (2057 participants). The dose of intervention showed that weekly contact led to a higher intake [MD: 1.32 servings/d (95% CI: 0.38, 2.26 servings/d), P = 0.006, I2 = 85%] in 3 studies (46, 50, 53) than did monthly contact [MD: 0.27 servings/d (95% CI: 0.02, 0.52 servings/d), P = 0.03, I2 = 0%] (13, 44). The trial with longer-term 12-mo (14) follow-up was not statistically significant.

Measures of dietary sodium intake

A total of 7 studies measured dietary sodium intake. Five studies were pooled (570 participants) (42, 50, 56, 57, 63),


at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from


showing that telehealth interventions reduced dietary sodium intake on urinary and/or self-assessed scores [SMD:20.39 (95% CI: 20.58, 20.20), P = 0.0001, I2 = 19%] compared with nontelehealth interventions (Figure 1). All 7 studies used the telephone as the telehealth delivery method and were conducted in participants with CVD. Two studies used nonvalidated tools for determining daily sodium intake, measuring tea- spoons of salt (1 teaspoon being w6 g NaCl or 2300 mg Na) (50), and determining a salted food score (56). Exclusion of these studies from the analysis resulted in 3 studies that re- ported on millimoles per liter of urinary sodium per day (42, 57, 63), which did not reach significance [MD: 28.27 mmol (95% CI: 217.34, 0.79 mmol/L), P = 0.07, I2 = 24%]. We could not statistically pool 2 studies; these are reported in Supplemental Table 2. The results remained significant in 2

trials with 12-mo (42, 63) follow-up [MD: 26.00 (95% CI: 210.41, 21.59), P = 0.008, I2 = 0%].

Energy intake

A total of 3 studies [2172 participants, all with long-term durations (12 mo–4 y)] measured energy intake (59, 61, 63). The telehealth intervention did not significantly reduce energy intake [MD: 210.48 kcal (95% CI: 267.20, 46.25 kcal), P = 0.72, I2 = 15%] compared with usual care. All 3 studies used the tele- phone as the telehealth delivery method and were conducted in participants with diabetes mellitus.

Sources of dietary fat intake

A total of 8 studies reported on measures of dietary fat intake (13, 44, 46, 54–56, 61, 63), including total fat per day (13, 61, 63),

FIGURE 1 Forest plot of the effect of telehealth dietary intervention on diet quality and dietary sodium. The effectiveness of telehealth is presented by using random effects. The means and SDs of changes from baseline are reported for trials. Effects of trials are presented as weights (percentages) and std. mean differences (95% CIs). IV, inverse variance; std., standardized.

FIGURE 2 Forest plot of the effect of telehealth dietary intervention on servings of fruit and vegetable intake. The means and SDs of changes from baseline are reported for trials. Effects of trials are presented as weights (percentages) and mean differences (95% CIs). IV, inverse variance.


at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from


percentage of calories from total fat (55), saturated fat intake per day (13, 56, 63), percentage of calories from saturated fat (55), and the Kristal Fat and Fiber Behavior Scale (44). Telehealth interventions did not significantly reduce total dietary fat intake [MD:20.10 g (95% CI:21.90, 1.70 g), P = 0.91, I2 = 76%] in 4 studies (2427 participants), but did significantly reduce saturated fat compared with nontelehealth comparators [MD: 20.93 g (95% CI: 21.51, 20.32 g), P = 0.002, I2 = 0%] in 2 studies (572 participants). Three studies could not be pooled statistically and are reported in Supplemental Table 2. Long-term trials (12 mo–4 y) were not statistically significant (55, 61, 63).

Telehealth dietary intervention and clinical outcomes

A total of 21 studies measured clinical outcomes that we were able to pool into the meta-analysis.


The telehealth intervention significantly reduced SBP by MD 22.64 mm Hg [(95% CI: 25.12, 20.16 mm Hg), P = 0.04, I2= 83%] in 12 studies with a median duration of 6 mo (4202 participants) (40, 41, 46, 48–51, 53, 55, 57, 59, 61) (Figure 3).

A subgroup analysis by chronic disease condition showed that the result for SBP was more pronounced in people with diabe- tes mellitus (59, 61) [MD: 25.91 mm Hg (95% CI: 211.14, 20.68 mm Hg), P = 0.03, I2= 69%] than in people with CVD (40, 41, 46, 48–51, 53, 57) [MD: 21.31 mm Hg (95% CI: 23.39, 0.77 mm Hg), P = 0.22, I2 = 60%]. Diastolic BP (DBP) was not significantly reduced following telehealth interventions [MD: 21.60 mm Hg (95% CI: 23.42, 0.22 mm Hg), P = 0.1, I2= 87%] in 10 studies (3512 participants) (46, 48–51, 53, 55, 57, 59, 61). Subgroup analysis by chronic disease condition did not alter the result for DBP (data not shown). Four long-term trials (durations 12–24 mo) did not result in significant change in both SBP and DBP (40, 55, 59, 61).

Weight, BMI, and waist circumference

Weight was significantly reduced by dietary telehealth in- terventions [MD: 20.80 kg (95% CI: 21.61, 0.00 kg), P = 0.05, I2= 79%] in 8 studies (974 participants) (14, 42, 44, 46, 53, 57, 59, 63) (Figure 3). Seven studies reported nonsignificant changes in BMI (data not shown) in 3560 participants (14, 42, 50, 51, 54, 55, 60, 61, 63), and 5 studies reported a significant

FIGURE 3 Effects of telehealth dietary interventions on systolic blood pressure (in milligrams of mercury), weight (in kilograms), and waist circum- ference (in centimeters). The means and SDs of changes from baseline are reported for trials. Effects of trials are presented as weights (percentages) and mean differences (95% CIs). IV, inverse variance.


at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from


reduction in waist circumference in 1659 participants (49, 55, 59, 60, 63) ofMD22.08 cm (95%CI:23.97,20.20 cm; P = 0.03, I2 = 84%) (Figure 3). BMI and weight were not changed significantly in longer-term studies; however, waist circumference remained significant in 4 trials with durations of 12–24 mo (55, 59, 60, 63) [MD:20.51 cm (95% CI:20.73,20.29 cm), P = 0.0001, I2 = 0%].

Serum lipids and glycated hemoglobin

Changes in serum lipids and glycated hemoglobin (HbA1c) were reported in 11 studies. Telehealth interventions significantly reduced total cholesterol (44, 46, 49–51, 53, 55, 56, 59, 61, 63) in 11 studies of 3697 participants [MD 20.08 mmol/L (95% CI: 20.16, 20.00 mmol/L), P = 0.04, I2 = 52%] (Figure 4). No changes in LDL cholesterol (49–51, 53, 56, 59, 63), HDL cho- lesterol (49, 51, 53, 56, 59, 63), or HbA1c (44, 59, 61) were ob- served (data not shown). Triglycerides were significantly reduced following dietary telehealth interventions compared with usual care [MD 20.10 mmol/L (95% CI: 20.19, 20.01 mmol/L), P = 0.04, I2 = 70%] in 7 studies encompassing 3268 participants (46, 49, 50, 56, 59, 61, 63) (Figure 4). Sensitivity analyses excluding durations of$2 y of follow-up (2 studies) of 2 (59) and 8 (61) y reduced the heterogeneity and resulted in an MD of 20.16 mmol/L [(95% CI: 20.26, 20.06 mmol/L), P = 0.001, I2 = 39%]. Long-term trials (durations of 12 mo–4 y) did not result in significant changes in biochemical outcomes (55, 59, 61, 63).

Mortality and hospitalizations

Two studies reported clinical endpoints and rates of hospi- talizations (47, 52). Ferrante and colleagues (52) conducted

a 16-mo telephone intervention in patients with heart failure (HF) with contacts determined by severity of condition. The study was assessed as a low risk of bias and led to significantly reduced all- cause mortality in the telehealth group (15.3%) compared with usual care (16.1%; P , 0.05), HF admission (16.8% and 22.3%, respectively; P , 0.005), CV admission (24.1% and 30.1%, respectively; P , 0.006), and all-cause admission (34.3% and 39.1%, respectively; P , 0.05). In the study by Albert et al. (47), however, a 3-mo multifactorial lifestyle video and tele- phone-based intervention in HF did not significantly reduce rates of HF hospitalizations between groups and was a moderate risk of bias study.

Risk of bias

Supplemental Figure 2 shows the risk of bias of the included studies. The risk of bias was low-to-moderate across the in- cluded studies. The majority (92%) of studies had adequate randomization; however, concealed allocation was reported in only 48% of the included studies, suggesting potential selection bias. Double blinding was achieved in only 1 study (49), and blinding of participants to treatment arm only was done in 1 study (41); however, intervention staff was aware of treatment allocation. All other trials were not able to blind participants, given the nature of dietary intervention to facilitate and modify behavior change. This means that detection bias was high in 80% of the dietary measures because they were self-reported. Attrition bias (through high loss to follow-up and no explanation of how such data were addressed) was judged to be high in 32% of studies. Furthermore, there appears to be reporting bias in

FIGURE 4 Effects of telehealth dietary interventions on total cholesterol and triglycerides (in millimoles per liter). The means and SDs of changes from baseline are reported for trials. Effects of trials are presented as weights (percentages) and mean differences (95% CIs). IV, inverse variance.


at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from


$20% of the included studies because they did not report out- comes that were stated in methods or protocol articles; 6 were rectified on contact with corresponding authors. When the Grading of Recommendations Assessment, Development and Evaluation (directness, precision, consistency, and study lim- itations) recommendations were considered, the evidence quality for diet quality and fruit and vegetable intake was believed to be moderate, given that the dietary intake data were self-reported.


This systematic review assessed the effectiveness of complex telehealth dietary interventions for facilitating dietary change in adults with chronic disease. The primary finding was that dietary interventions delivered by telehealth effectively improved dietary adherence on a moderate scale and made small improvements in diet quality (38). Single macronutrients were less likely to be modified by telehealth intervention, including energy and total dietary fat intake, which were not significantly different between groups in the pooled analysis.

Telehealth interventions resulted in a significant reduction in dietary sodium intake compared with nontelehealth interventions (SMD 20.39, P = 0.0001), which is considered a moderate effect statistically (38). All dietary sodium outcomes showed improved compliance with a reduced sodium intervention by use of telehealth. This has clinical implications for telehealth be- cause it may provide a useful tool in future intervention studies, given that sodium modification is hampered by poor patient compliance in clinical practice (64). The finding that fruits and vegetables increased by 1 serving/d following telehealth in- terventions supports the potential for the use of these in- terventions to improve diet quality, which is a finding similar to a systematic review in telephone-based interventions in the general population (65). Although significant heterogeneity was observed, subanalysis by type of technology reduced the effect size to 0.8 servings/d, but remained significant. Fruit and veg- etable consumption is a strong predictor of mortality, and the magnitude of change that we found may reduce the risk of all- cause mortality (66). The European Prospective Investigation into Cancer and Nutrition-Heart study estimated that an increase in every 80-g portion of fruit and vegetables was associated with a 4% reduction in risk of death (67).

Whole-of-diet approaches, as opposed to isolated single- nutrient interventions, reflect how foods within the diet are con- sumed and have an impact on other nutrients when manipulated (e.g., a change in fat intake often results in a concomitant increase in the proportion of energy derived from carbohydrates). De- velopments in nutrition science recognize the effect of the complexity of foods consumed in combination on long-term CVD risk, weight regulation, and disease progression (68). Such approaches appreciate the interaction of foods and nutrients, which may be more relevant to chronic disease when examining the cumulative effect over time of food on health (69, 70). This shift in nutrition focus suggests that single-nutrient interventions will be used less often clinically and in public health practice (68), which is an important consideration for emerging telehealth interventions in the design of dietary educational content. Al- though we observed only small changes in diet quality, the magnitude of change demonstrated may result in a substantial public health benefit if adopted for secondary prevention of

chronic disease (71). For example, higher-quality diet has been linked to a reduced risk of all-cause mortality in people .65 y (72). Although a stronger association was found when com- paring the highest with the lowest quartiles, the risk decreases at each quartile above the lowest-quality diet (72). Furthermore, improving fruit and vegetable intake to better reflect dietary guidelines and therefore overall diet quality may reduce the complications of chronic disease (33), which have been dem- onstrated to result in a reduction in all-cause mortality of 21% during a 15-y follow-up period (73).

Dietary change through telehealth interventions can translate to small improvements in some clinical outcomes, such as a re- duction in SBP of 3 mmHg. Although this is slightly less than the larger-scale meta-analyses of all dietary sodium interventions in hypertension of 25 mm Hg, the effect size is similar to that in people without hypertension (74). A sustained change in SBP of 2–3 mm Hg in a period .6 mo has been suggested to translate into a reduced risk of cardiovascular events (75). Other risk factors such as weight, waist circumference, total cholesterol, and triglycerides improved significantly in some studies following telehealth interventions, whereas LDL, HDL, and HbA1c did not. The long-term studies (.12 mo) included were not adequately powered to detect BP and weight change at final follow-up, and the inclusion criteria meant that some larger trials were excluded because of the lack of reporting of dietary outcomes. For exam- ple, the Practice-based Opportunities for Weight Reduction study (76) observed a between-group change in SBP of 21.8 mm Hg and weight change of 24 kg after 24 mo in the telephone coaching group, but it was not possible to include it. This well- designed trial supports the effects of telehealth interventions on surrogate CVD outcomes in chronic disease.

A previous systematic review demonstrated that technologies such as telephone and video contact improved adherence to diet (31), which we have extended to improved dietary quality, im- proved fruit and vegetable intake, and reduced sodium intake when compared to usual chronic disease care. Telehealth may facilitate compliance with the diet via a variety of mechanisms beyond the provision of regular review and behavior prompting, although it is likely the most influential on sustained dietary change in our review (9, 77). Telehealth strategies offer feasible ways to facilitate dietary changes and offer advantages over FTF consultations, including flexibility (78) and a high rate of patient acceptability (20, 79). Smartphones are used by .75% of the population (80, 81), highlighting the widespread potential that telehealth interventions provide to improve access, coverage, and implantation methods, particularly in low socioeconomic populations (24). Because primary health care may hold the key to the dissemination of telehealth dietary delivery interventions, evaluation studies are needed. Process evaluation frameworks should be incorporated into future telehealth trials, reporting attendance and participation, acceptability, and costs, which will ultimately assist in their translation into clinical practice (82).

To our knowledge, our study is the first systematic review of telehealth strategies for facilitating multifactorial dietary change. Its strengths are the comprehensive search strategy used, the use throughout the review of 2 independent review authors, and the use of Cochrane methodology to appraise the risk of bias. The TIDieR checklist (37) was used to extract data, which allowed multiple sources of heterogeneity and intervention reporting inadequacies to be identified.


at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from


Our review has several limitations, including that multifaceted dietary changes usually were a component of multiple lifestyle recommendations (22 of the 25 included studies), making it difficult to delineate the effect of isolated dietary intervention on clinical outcomes. Half of the included studies used telephone delivery methods; other telehealth technologies such as smart- phone applications and Internet use were underrepresented and may reduce the generalizability of the findings.

This review highlights the need for future trials that aim to change dietary behavior with telehealth interventions other than telephone, such as mobile, Internet, or videoconference methods. The application of the TIDieR checklist highlights a need for better reporting of telehealth interventions, because many trials did not report important logistical data relating to intervention conduct. Finally, although favorable changes were reported in some surrogate clinical outcomes such as BP and lipid profiles following telehealth interventions, this may not translate to hard clinical endpoints. Because of a lack of studies, conclusions cannot be drawn about effects on mortality and hospitalizations. Future well-designed intervention studies with adequate length of follow-up are required to assess these important endpoints.

In conclusion, adults with a diet-related chronic disease may experience improvements in diet quality, fruit and vegetable intake, and dietary sodium intake if they are provided with dietary interventions through telehealth. Although large variations in findings may be explained by differences in intervention conduct, it is difficult to explore as a result of suboptimal reporting of dietary interventions. Single macronutrients (calories and total dietary fat) were not significantly modified following telehealth interventions, which may highlight the benefit of delivering complex dietary intervention that target the quality of the diet and/or dietary patterns facilitated by convenient telehealth technologies. The results of this review support the changes in nutrient focus and may inform the future development of evidence- based telehealth programs, which, ideally, can be tailored to specific chronic disease conditions.

We thank David Honeyman and Justin Clark for their assistance in the de-

velopment of the search strategy.

The authors’ responsibilities were as follows—JTK: assisted in the con-

ceptualization of the review, conducted the initial literature search, assessed

the risk of bias, conducted the analysis, drafted the manuscript, and had

primary responsibility for the final content; DPR: assisted in the conceptu-

alization of the review and analysis interpretation and revised the drafted

manuscript; TCH: participated in the design of the study, provided method-

ological and clinical expertise, and reviewed the drafted manuscript; KLC:

conceived the review, conducted the literature search, assessed the risk of

bias, assisted in analysis interpretation, and revised the drafted manuscript;

and all authors: read and approved the final manuscript. The authors reported

no conflicts of interest related to the study.

REFERENCES 1. Mendis S, Armstrong T, Bettcher D, Branca F, Lauer J, Mace C,

Poznyak V, Riley L, Silva V, Stevens G. Global status report on non- communicable diseases 2014. Geneva: World Health Organization; 2014.

2. World Health Organization. Diet, nutrition and the prevention of chronic diseases. WHO Tech Rep Ser 2003(916):1–60.

3. Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L, Lichtenstein AH, Kumanyika S, Kraus WE, Fleg JL, Redeker NS. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association. Circulation 2010;122: 406–41.

4. Celler BG, Lovell NH, Basilakis J. Using information technology to improve the management of chronic disease. Med J Aust 2003;179: 242–6.

5. Jerant AF, von Friederichs-Fitzwater MM, Moore M. Patients’ per- ceived barriers to active self-management of chronic conditions. Pa- tient Educ Couns 2005;57:300–7.

6. Whelan ME, Goode AD, Eakin EG, Veerman J, Winkler EA, Hickman IJ, Reeves MM. Feasibility, effectiveness and cost-effectiveness of a telephone-based weight loss program delivered via a hospital out- patient setting. Transl Behav Med 2016;6:386–95.

7. Paterson BL, Charlton P, Richard S. Non-attendance in chronic disease clinics: a matter of non-compliance? J Nurs Healthc Chronic Illn 2010; 2:63–74.

8. Murdock A, Rodgers C, Lindsay H, Tham TCK. Why do patients not keep their appointments? Prospective study in a gastroenterology outpatient clinic. J R Soc Med 2002;95:284–6.

9. Goode AD, Reeves MM, Eakin EG. Telephone-delivered interventions for physical activity and dietary behavior change: an updated sys- tematic review. Am J Prev Med 2012;42:81.

10. Direito A, Dale LP, Shields E, Dobson R, Whittaker R, Maddison R. Do physical activity and dietary smartphone applications incorporate evidence-based behavior change techniques? BMC Public Health 2014;14:646.

11. Norman GJ, Zabinski MF, Adams MA, Rosenberg DE, Yaroch AL, Atienza AA. A review of ehealth interventions for physical activity and dietary behavior change. Am J Prev Med 2007;33:336–45.

12. World Health Organization. Telemedicine: opportunities and de- velopments in member states: report on the second global survey on ehealth. Global Observatory for eHealth Series–Volume 2. Geneva (Switzerland): World Health Organization; 2010.

13. Eakin E, Reeves M, Lawler S, Graves N, Oldenburg B, Del Mar C, Wilke K, Winkler E, Barnett A. Telephone counseling for physical activity and diet in primary care patients. Am J Prev Med 2009;36: 142–9.

14. McConnon A, Kirk SF, Cockroft JE, Harvey EL, Greenwood DC, Thomas JD, Ransley JK, Bojke L. The Internet for weight control in an obese sample: results of a randomized controlled trial. BMC Health Serv Res 2007;7:206.

15. Tate DF, Wing RR, Winett RA. Using Internet technology to deliver a behavioral weight loss program. JAMA 2001;285:1172–7.

16. Touger-Decker R, Denmark R, Bruno M, O’Sullivan-Maillet J, Lasser N. Workplace weight loss program: comparing live and internet methods. J Occup Environ Med 2010;52:1112–8.

17. Cook RF, Billings DW, Hersch RK, Back AS, Hendrickson A. A field test of a web-based workplace health promotion program to improve dietary practices, reduce stress, and increase physical activity: ran- domized controlled trial. J Med Internet Res 2007;9:e17.

18. Dorough AE, Winett RA, Anderson ES, Davy BM, Martin EC, Hedrick V. DASH to wellness: emphasizing self-regulation through E-health in adults with prehypertension. Health Psychol 2014;33:249–54.

19. Morikawa N, Yamasue K, Tochikubo O, Mizushima S. Effect of salt reduction intervention program using an electronic salt sensor and cellular phone on blood pressure among hypertensive workers. Clin Exp Hypertens 2011;33:216–22.

20. Nundy S, Razi RR, Dick JJ, Smith B, Mayo A, O’Connor A, Meltzer DO. A text messaging intervention to improve heart failure self- management after hospital discharge in a largely African-American population: before-after study. J Med Internet Res 2013;15:e53.

21. Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, Patel V, Haines A. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med 2013;10:e1001362.

22. Goode AD, Winkler EA, Lawler SP, Reeves MM, Owen N, Eakin EG. A telephone-delivered physical activity and dietary intervention for type 2 diabetes and hypertension: does intervention dose influence outcomes? Am J Health Promot 2011;25:257–63.

23. Harris J, Felix L, Miners A, Murray E, Michie S, Ferguson E, Free C, Lock K, Landon J, Edwards P. Adaptive e-learning to improve dietary behavior: a systematic review and cost-effectiveness analysis. Health Technol Assess 2011;15:1–160.

24. Beratarrechea A, Lee AG, Willner JM, Jahangir E, Ciapponi A, Rubinstein A. The impact of mobile health interventions on chronic disease outcomes in developing countries: a systematic review. Telemed J E Health 2014;20:75–82.


at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from


25. Cassimatis M, Kavanagh DJ. Effects of type 2 diabetes behavioural telehealth interventions on glycaemic control and adherence: a sys- tematic review. J Telemed Tel 2012;18:447–50.

26. Murray E, Burns JSST, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease.. Cochrane Database Syst Rev 2005;4:CD004274.

27. Pal K, Eastwood SV, Michie S, Farmer AJ, Barnard ML, Peacock R, Wood B, Inniss JD, Murray E. Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus. Cochrane Database Syst Rev 2013;3:CD008776.

28. Hu FB. Dietary pattern analysis: a new direction in nutritional epide- miology. Curr Opin Lipidol 2002;13:3–9.

29. Kelly JT, Reidlinger DP, Hoffmann TC, Campbell KL. Telehealth methods to deliver multifactorial dietary interventions in adults with chronic disease: a systematic review protocol. Syst Rev 2015;4:185–7.

30. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 2009;151:264–9.

31. Desroches S, Lapointe A, Ratte S, Gravel K, Legare F, Turcotte S. Interventions to enhance adherence to dietary advice for preventing and managing chronic diseases in adults. Cochrane Database Syst Rev 2013;2:CD008722.

32. Cespedes EM, Hu FB. Dietary patterns: from nutritional epidemiologic analysis to national guidelines. Am J Clin Nutr 2015;101:899–900.

33. Reidlinger DP, Darzi J, Hall WL, Seed PT, Chowienczyk PJ, Sanders TA. How effective are current dietary guidelines for cardiovascular disease prevention in healthy middle-aged and older men and women? A randomized controlled trial. Am J Clin Nutr 2015;101:922–30.

34. US Department of Agriculture. Scientific report of the 2015 Dietary Guidelines Advisory Committee [monograph on the Internet]. Wash- ington (DC): USDA; 2015 [cited 2016]. Available from: https://health. gov/dietaryguidelines/2015-scientific-report/PDFs/Scientific-Report-of-the- 2015-Dietary-Guidelines-Advisory-Committee.pdf.

35. Panagiotakos DB, Pitsavos C, Arvaniti F, Stefanadis C. Adherence to the Mediterranean food pattern predicts the prevalence of hypertension, hypercholesterolemia, diabetes and obesity, among healthy adults; the accuracy of the MedDietScore. Prev Med 2007;44:335–40.

36. Karanja NM, Obarzanek E, Lin PH, McCullough ML, Phillips KM, Swain JF, Champagne CM, Hoben KP, DASH Collaborative Research Group. Descriptive characteristics of the dietary patterns used in the Dietary Approaches to Stop Hypertension trial. J Am Diet Assoc 1999; 99:S19–27.

37. Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, Altman DG, Barbour V, Macdonald H, Johnston M. Better reporting on interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ 2014;348:g1687.

38. Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions [Internet]. London: The Cochrane Collaboration. c2011 [cited 2016]. Available from: http://handbook.cochrane.org/.

39. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177–88.

40. Migneault JP, Dedier JJ, Wright JA, Heeren T, Campbell MK, Morisky DE, Rudd P, Friedman RH. A culturally adapted telecommunication system to improve physical activity, diet quality, and medication ad- herence among hypertensive African-Americans: a randomized con- trolled trial. Ann Behav Med 2012;43:62–73.

41. Friedberg JP, Rodriguez MA, Watsula ME, Lin I, Wylie-Rosett J, Allegrante JP, Lipsitz SR, Natarajan S. Effectiveness of a tailored be- havioral intervention to improve hypertension control: primary out- comes of a randomized controlled trial. Hypertension 2015;65:440–6.

42. Philipson H, Hagelind SSE, Swedberg KS, Ekman IE, Schaufelberger MS. Salt and water restriction is effective in patients with chronic heart failure. Eur J Heart Fail 2011;10(Suppl):S202–3.

43. Wiebe N, Vandermeer B, Platt RW, Klassen TP, Moher D, Barrowman NJ. A systematic review identifies a lack of standardization in methods for handling missing variance data. J Clin Epidemiol 2006;59:342–53.

44. Glasgow RE, Toobert DJ. Brief, computer-assisted diabetes dietary self-management counseling: effects on behavior, physiologic out- comes, and quality of life. Med Care 2000;38:1062–73.

45. Welch JL, Astroth KS, Perkins SM, Johnson CS, Connelly K, Siek KA, Jones J, Scott LL. Using a mobile application to self-monitor diet and fluid intake among adults receiving hemodialysis. Res Nurs Health 2013;36:284–98.

46. Miller ER, Cooper LA, Carson KA, Wang N-Y, Appel LJ, Gayles D, Charleston J, White K, You N, Weng Y. A dietary intervention in urban African Americans: results of the “Five Plus Nuts and Beans” ran- domized trial. Am J Prev Med 2016;50:87–95.

47. Albert NM, Buchsbaum R, Li J. Randomized study of the effect of video education on heart failure healthcare utilization, symptoms, and self-care behaviors. Patient Educ Couns 2007;69:129–39.

48. Chiu CW, Wong FKY. Effects of 8 weeks sustained follow-up after a nurse consultation on hypertension: a randomized trial. Int J Nurs Stud 2010;47:1374–82.

49. Chow CK, Redfern J, Hillis GS, Thakkar J, Santo K, Hackett ML, Jan S, Graves N, de Keizer L, Barry T, et al. Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: a randomized clinical trial. JAMA 2015;314: 1255–63.

50. Cicolini G, Simonetti V, Comparcini D, Celiberti I, Di Nicola M, Capasso LM, Flacco ME, Bucci M, Mezzetti A, Manzoli L. Efficacy of a nurse-led email reminder program for cardiovascular prevention risk reduction in hypertensive patients: a randomized controlled trial. Int J Nurs Stud 2014;51:833–43.

51. Pfaeffli Dale L, Whittaker R, Jiang Y, Stewart R, Rolleston A, Maddison R. Text message and Internet support for coronary heart disease self- management: results from the Text4Heart randomized controlled trial. J Med Internet Res 2015;17:e237.

52. Ferrante D, Varini S, Macchia A, Soifer S, Badra R, Nul D, Grancelli H, Doval H. Long-term results after a telephone intervention in chronic heart failure: DIAL (Randomized Trial of Phone Intervention in Chronic Heart Failure) follow-up. J Am Coll Cardiol 2010;56:372–8.

53. Green BB, Anderson ML, Cook AJ, Catz S, Fishman PA, McClure JB, Reid RJ. e-Care for heart wellness: a feasibility trial to decrease blood pressure and cardiovascular risk. Am J Prev Med 2014;46:368–77.

54. Hawkes A, Patrao T, Atherton J, Ware R, Taylor C, O’Neil A, Foreman R, Oldenburg B. Effect of a telephone-delivered coronary heart disease secondary prevention program (ProActive Heart) on quality of life and health behaviours: primary outcomes of a randomized controlled trial. Int J Behav Med 2013;20:413–24.

55. Lear SA, Singer J, Banner-Lukaris D, Horvat D, Park JE, Bates J, Ignaszewski A. Improving access to cardiac rehabilitation using the internet: a randomized trial. Stud Health Technol Inform 2015;209: 58–66.

56. Mok VK, Sit JW, Tsang AS, Chair SY, Cheng TL, Chiang CS. A controlled trial of a nurse follow-up dietary intervention on maintaining a heart-healthy dietary pattern among patients after myocardial in- farction. J Cardiovasc Nurs 2013;28:256–66.

57. Woollard J, Beilin L, Lord T, Puddey I, MacAdam D, Rouse I. A controlled trial of nurse counselling on lifestyle change for hyperten- sives treated in general practice: preliminary results. Clin Exp Phar- macol Physiol 1995;22:466–8.

58. Arora S, Burner E, Lam J, De Santos R, Meeks A, Menchine M. Trial to examine text-message based mhealth in ed patients with diabetes (TExT-MED). Acad Emerg Med 2013;20:S180.

59. Eakin EG, Winkler EA, Dunstan DW, Healy GN, Owen N, Marshall AM, Graves N, Reeves MM. Living well with diabetes: 24-month outcomes from a randomized trial of telephone-delivered weight loss and physical activity intervention to improve glycemic control. Di- abetes Care 2014;37:2177–85.

60. Izquierdo R, Lagua CT, Meyer S, Ploutz-Snyder RJ, Palmas W, Eimicke JP, Kong J, Teresi JA, Shea S, Weinstock RS. Telemedicine intervention effects on waist circumference and body mass index in the IDEATel Project. Diabetes Technol Ther 2010;12:213–20.

61. Sone H, Tanaka S, Iimuro S, Tanaka S, Oida K, Yamasaki Y, Oikawa S, Ishibashi S, Katayama S, Yamashita H, et al. Long-term lifestyle in- tervention lowers the incidence of stroke in Japanese patients with type 2 diabetes: a nationwide multicentre randomised controlled trial (the Japan Diabetes Complications Study). Diabetologia 2010;53:419–28.

62. Wong FK, Chow SK, Chan TM. Evaluation of a nurse-led disease management programme for chronic kidney disease: a randomized controlled trial. Int J Nurs Stud 2010;47:268–78.

63. Woollard J, Burke V, Beilin LJ, Verheijden M, Bulsara MK. Effects of a general practice-based intervention on diet, body mass index and blood lipids in patients at cardiovascular risk. J Cardiovasc Risk 2003; 10:31–40.

64. Luft FC, Morris CD, Weinberger MH. Compliance to a low-salt diet. Am J Clin Nutr 1997;65(2 Suppl):698S–703S.


at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from


65. Vanwormer JJ, Boucher JL, Pronk NP. Telephone-based counseling improves dietary fat, fruit, and vegetable consumption: a best-evidence synthesis. J Am Diet Assoc 2006;106:1434–44.

66. Wang X, Ouyang Y, Liu J, Zhu M, Zhao G, Bao W, Hu FB. Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ 2014;349:g4490.

67. Crowe FL, Roddam AW, Key TJ, Appleby PN, Overvad K, Jakobsen MU, Tjonneland A, Hansen L, Boeing H, Weikert C, et al. Fruit and vegetable intake and mortality from ischaemic heart disease: results from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Heart study. Eur Heart J 2011;32:1235–43.

68. Mozaffarian D. Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation 2016;133: 187–225.

69. Schwingshackl L, Hoffmann G. Diet quality as assessed by the Healthy Eating Index, the Alternate Healthy Eating Index, the Dietary Ap- proaches to Stop Hypertension Score, and Health Outcomes: a sys- tematic review and meta-analysis of cohort studies. J Acad Nutr Diet 2015;115:780–800.

70. US Department of Agriculture. A series of systematic reviews on the relationship between dietary patterns and health outcomes. [mono- graph on the Internet]. Washington (DC): USDA; 2014 [cited 2016]. Available from: http://www.nel.gov/vault/2440/web/files/DietaryPatterns/ DPRptFullFinal.pdf.

71. de Lorgeril M, Salen P. Mediterranean diet in secondary prevention of CHD. Public Health Nutr 2011;14:2333.

72. McNaughton SA, Bates CJ, Mishra GD. Diet quality is associated with all-cause mortality in adults aged 65 years and older. J Nutr 2012;142: 320–5.

73. Russell J, Flood V, Rochtchina E, Gopinath B, Allman-Farinelli M, Bauman A, Mitchell P. Adherence to dietary guidelines and 15-year risk of all-cause mortality. Br J Nutr 2013;109:547–55.

74. He FJ, Li J, MacGregor GA. Effect of longer term modest salt re- duction on blood pressure: Cochrane systematic review and meta- analysis of randomised trials. BMJ 2013;346:f1325.

75. Hooper L, Bartlett C, Davey SG, Ebrahim S. Advice to reduce dietary salt for prevention of cardiovascular disease. Cochrane Database Syst Rev 2004;1:CD003656.

76. Appel LJ, Clark JM, Yeh H-C, Wang N-Y, Coughlin JW, Daumit G, Miller ER III, Dalcin A, Jerome GJ, Geller S. Comparative effective- ness of weight-loss interventions in clinical practice. N Engl J Med 2011;365:1959–68.

77. Renders CM, Valk GD, Griffin SJ, Wagner E, van Eijk JM, Assendelft WJJ. Interventions to improve the management of diabetes mellitus in primary care, outpatient and community settings. Cochrane Database Syst Rev 2001;1:CD001481.

78. White LAE, Krousel-Wood MA, Mather F. Technology meets health- care: distance learning and telehealth. Ochsner J 2001;3:22–9.

79. Yehle KS, Chen AM, Plake KS, Yi JS, Mobley AR. A qualitative analysis of coronary heart disease patient views of dietary adherence and web-based and mobile-based nutrition tools. J Cardiopulm Rehabil Prev 2012;32:203–9.

80. Communications report 2012–13. Sydney (Australia): Australian Communications and Media Authority; 2013.

81. Mackay MM. Australian Mobile Phone Lifestyle Index 8th edition– special topic: impact of tablets on mobile phone use. Docklands (Australia): AIMIA; 2012.

82. Glasgow RE, Emmons KM. How can we increase translation of re- search into practice? Types of evidence needed. Annu Rev Public Health 2007;28:413–33.


at E B


O host on D

ecem ber 1, 2016

ajcn.nutrition.org D

ow nloaded from


Copyright of American Journal of Clinical Nutrition is the property of American Society for Nutrition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use.