When searching for interventions that were successful in reducing salt intake, the researchers found 11 interventions that either had no statistically significant salt reduction or did not reduce salt intake. These interventions included self-monitoring of salt excretion (
n = 2)
[38][39][49,50] and Na:K ratio (
n = 1)
[40][51] in urine. Self-monitoring of Na:K ratio excretion reduced salt excretion without statistical significance, probably because the sample size was insufficient and baseline potassium excretion was greater than the authors had expected. Also, in interventions with self-monitoring of salt excretion there was a non-statistically significant decrease in salt, the authors reported that this was probably due to a short intervention period (4 weeks) and insufficient sample size. Although these interventions were not included as successful interventions, it is likely that if they did not have problems with the methodology, they could have been successful. Participants being able to estimate salt intake appear to be effective salt reduction strategies as mentioned in other interventions
[41][42][20,23] included in this work. A nutrition education intervention was unsuccessful in reducing salt, the intervention was to teach diabetic participants to use the nutrition information panel on food labels to choose products that comply with the Food Standards Australia New Zealand (FSANZ) guideline of <120 mg sodium/100 g food
[43][52]. This intervention was used by Ireland et al.
[44][38] in free-living adults and have successfully reduced salt. Therefore, this type of intervention is not effective in diabetics, which reinforces the importance of customizing interventions according to the population.
ThWe
researchers found three interventions that, in addition to nutrition education, used apps to reduce salt intake. Two interventions reduced salt without statistical significance and one intervention failed to reduce salt. Dorsch et al.
[45][53] described an application-based intervention that sends just-in-time contextual adaptive messages. The reduction in urinary sodium excretion was 637 mg/day, but without statistical significance. Although the authors report that there were clinically significant improvements in the intervention group compared to the control, all participants were required to have an iPhone, so the effectiveness of this intervention may be related to the socioeconomic status of the participants. Lofthouse et al.
[46][54] described an intervention that, in addition to using the app, used salt substitutes with lower sodium content, participants reduced salt excretion by 433 mg/day without statistical significance. This was a pilot study with only 11 volunteers, and these had a low baseline sodium (2342 mg) and so
thwe
researchers probably could not see the potential of this intervention to reduce salt intake. In the study by Thatthong et al.
[47][55] they described an intervention using a program that sends interactive messages about salt reduction. The study was carried out in hypertensive patients, at the end of the study, sodium excretion in the intervention group was higher than the baseline value. Although the sample size was small (
n = 50), this result indicates that this intervention is probably not effective in reducing salt intake in hypertensive patients. Nakadate et al.
[48][56] described an intervention in which they provided a salt monitoring instrument to measure the salt concentration of soup at home and low-sodium seasoning. They achieved a sodium reduction of 777 mg/day with monitoring and 413 mg/day with the low-sodium seasoning. Although the results were not statistically significant, probably due to the exploratory pilot design of the study, with sample size calculated based on provisional statistics, the results are interesting, especially the monitoring of salt in soup, in regions that have a high consumption of soup. Another study described an environmental and behavioral intervention in the workplace. They achieved an average reduction in salt intake of −0.6 g, from 8.7 g but without statistical significance. The authors reported that the cause of not achieving greater salt reduction was poor adherence to the study and programs in catering operations. The authors concluded that acceptance, effectiveness, and maintenance of workplace nutrition interventions require strong employer support
[49][57]. Therefore, it is important to only consider intervening in the workplace when the employer is motivated to reduce the salt intake of workers.
ThWe
researchers found two studies that used Salt-Restriction-Spoon in the intervention. Chen et al.
[50][58] in addition to the spoon, they provided nutritional education and informed the participants of the value of sodium excretion. At the end of the intervention, both the control group and the intervention group had decreased sodium excretion without statistical significance. Participants in both groups lived in the same place, probably causing contamination of the information for the study, the participants in the control group were informed about their sodium excretion, which may have contributed to the reduction in sodium excretion in this group. Cornélio et al.
[51][59] described an intervention in hypertensive women that, in addition to the use of the Salt-Restriction-Spoon, provided an education based on behavior modification techniques to reduce salt intake. Also, in this intervention, both the control group and the intervention group decreased sodium excretion without statistical significance. Although the authors did not mention it is possible that there was an influence to reduce salt consumption in the control group because the women were asked to assess their usual monthly salt intake, and this may have led to awareness of the amount of salt they used and led them to reduce the amount they used when cooking. Salt-Restriction-Spoon are very interesting in populations where the biggest source of salt is the addition to cooking, helping people to limit the addition of salt.
3. Conclusions
Consumer education-based interventions alone reduce salt intake, but also when combined with other strategies. Tools for estimating salt consumption and self-monitoring of its consumption are also successful in reducing it.
Herein, there is no evidence that the type of intervention analyzed is more effective in reducing salt consumption, but according to the medicine P4 approach, the researchers must analyze each revised intervention and verify in which individuals or subpopulations it is most beneficial and will lead to better results. However, the results must be interpreted with caution as the quality of the studies is mixed. In the future, it is important to develop more high-quality clinical trials, with a longer intervention time and more participants, in order to understand which interventions work best for the reduction of salt consumption according to the target population.