Effectiveness of Integrated Technology Apps for Supporting Healthy Food Purchasing and Consumption: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Searching Strategy
2.2. Eligibility Criteria
2.3. Study Selection
2.4. Data Extraction
2.5. Quality Assessment
2.6. Data Analysis
- Efficacy was defined as the ability to produce a desired or intended result [50].
- Technology apps were defined as applications for novel mobile consumer devices with a touchscreen, especially smartphones and tablet PCs [51]. In this review, the terms “app” is sued in the stricter sense with more on self-monitoring apps. Other technology interventions such as text messaging, social media (e.g., facebook), online coaching or telephone counseling were excluded in this review.
- Unhealthy foods were defined as those that were high in salt, sugar and saturated fats [2].
- Healthy foods were generally defined as the food meeting the nutrition standards promoting healthier foods that were low in sugar, salt and saturated fats, while promoting fruits and vegetables, whole grains and lean protein [2].
3. Results
3.1. Description of Included Studies
3.2. Characteristics of Included Studies
3.3. Quality Assessment
3.4. Healthy Food Purchasing and Its Measurement
3.5. Healthy Food Consumption and Its Measurement
3.6. Intervention Efficacy
4. Discussion
Strengths and Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Search Category | Search Terms |
---|---|
Apps | Application* OR app* OR smartphone* OR “smart phone*” OR “cellular phone*” OR “mobile phone*” OR “mobile telephone*” OR tablet* OR “e-learning” OR “e-health” OR “iPad*” OR “mobile health” OR “social media” |
Food purchasing and Food consumption | “food purchasing” OR “food purchase*” OR “food choice*” OR “food planning” OR “food shopping” OR “food consumption*” OR “food intake*” OR “dietary intake*” OR “healthy eating” OR “eating behaviour*” OR “healthy food” OR “food behaviour*” OR “food value*” OR “food diary” OR “food diaries” OR “nutrition assessment*” OR “diet record*” OR “diet survey*” OR “energy intake*” OR “nutrition survey*” OR “dietary assessment*” |
Intervention | Intervention* OR program* OR programme* OR “health promotion*” OR trial* OR effectiveness |
Domain | Inclusion Criteria | Exclusion Criteria |
---|---|---|
1. Publication year | Studies published between 1 January 2006 and 31 December 2020. | Studies published before or after the inclusion dates. |
2. Publication type | Original studies published in peer-reviewed journals only. | Letters, commentaries, conference proceedings, reviews, narrative articles or other materials that was not a peer-reviewed primarily study. |
3. Language | Studies limited to English-language publications. | Studies were not published in English. |
4. Targeted population | Any population. | No restriction on population. |
5. Targeted group | Adults who are aged 18 or above. | Children or adolescents below 18 years old. |
6. Research design | Quantitative studies involved experimental and cross-sectional study design. | Qualitative study or any non-experimental study designs were utilised (e.g., protocol, studies reporting prevalence or trend data, feasibility studies, measurement studies or theoretical papers). |
7. Study scope/variables | (1) Technology apps [e.g., smartphone or personal digital assistant (PDA)] in an intervention to improve healthy food purchasing and consumption for prevention. (2) Interventions could be stand-alone interventions using an app only, or multi-component interventions including an app as one of several intervention components (e.g., physical education, face-to-face counselling) with the condition that individual apps record was provided. (3) All types and units of measurements for the healthy food purchasing and consumption outcomes were acceptable (e.g., food group, self-report, servings, calories, kilograms, nutrition assessment). (4) reported data regarding efficacy for behaviour change (e.g., change in healthy food groups). (5) Interventions covering aspects of large-scale management of food (retail, restaurants, public preparation and consumption of food in school kitchens, hospitals, etc.). | (1) tracking app only but not focused on apps intervention on food purchasing or food consumption. (2) Other technology intervention than apps intervention (e.g., text messaging, apps, social media, telephone counselling or online coaching). (3) No measure of healthy food purchasing or food consumption outcomes. |
First Author (Year), Country | Study Name/Apps Name | Device | Study Design | Sample Characteristics, Mean Age | Female (%) | Grouping | Intervention Time Frame | Outcomes and Measures | Findings | Quality |
---|---|---|---|---|---|---|---|---|---|---|
Allen (2013), USA [24] | SLIM (Smart coach for LIfestyleManagement) study | smartphone | RCT | 68 obese adults | 78% | (1) intensive counseling intervention, (2) intensive counseling + smartphone intervention, (3) a less intensive counseling + smartphone intervention, and (4) smartphone intervention only | baseline and 6-month | self-reported dietary intake | Not significant. | Poor |
Alnasser (2019), Saudi Arabia [60] | Twazon app. | smartphone | pre-post single-arm pilot study | 40 overweight adult; engaged: n = 26, age = 31 years, Unengaged: n = 14, age = 31 years | 100% | engaged users (65%) and unengaged users (35%) | baseline, 2- and 4-months | Dietary intake | The daily energy consumption was decreased by >600 calories in the engaged users group compare with the unengaged group. | Poor |
Atienza (2008), USA [52] | NR | PDA | RCT | 27 healthy mid-life and older adults (≥50 years); AG: n = 16, age = 63 years; CG: n = 11, age = 58 years | AG: 69% CG: 70% | PDA program vs. control | baseline and 8 weeks | vegetable and whole-grain intake | Intervention participants reported significantly greater increases in vegetable servings and dietary fibre from grains. | Poor |
Banerjee (2020), India [59] | S Health®, Calorie Counter—MyFitnessPal®, and Calorie Counter | smartphone | prospective controlled trial | 58 healthy young adults (18–45 years); AP: n = 30; CG: n = 28 | AG: 63%; CG: 46% | apps group vs. control | baseline and 8 weeks | Food consumption | Not significant. | Poor |
Brindal (2019), Australia [57] | MotiMate | smartphone | RCT | 88 healthly adults; AG: n = 45, age = 45 years; CG: n = 43, age = 46 years | AG:75%; CG: 69% | intervention app (MotiMate) vs. control app | baseline, 4, 8, 12 and 24 weeks | healthy eating | Not significant. | Poor |
Dodd (2017), Australia [56] | SNAPP trial | smartphone | RCT | 162 healthy pregnant women; AG: n = 77, age = 31 years; CG: n = 85, age = 31 years | 100% | Lifestyle Advice + Smartphone App vs. Lifestyle Advice Only | baseline, 28 and 36 weeks | healthy eating index (HEI) | Not significant. | Good |
Eyles (2017), New Zealand [29] | SaltSwitch | smartphone | RCT | 66 adults with diagnosed cardiovascular disease; AG: n = 33, age = 64 years; CG: n = 33, age = 65 years | AG:9%; CG: 24% | SaltSwitch app vs. control group (usual care). | baseline and 4 weeks | (1) salt content of household packaged food purchases (2) saturated fat content (g/MJ), energy content (kJ/kg) and expenditure (NZ$) of household food purchases | A significant reduction in mean household purchases of salt was observed. Not significant for the second outcome. | Good |
Gill (2019), Canada [58] | HealtheSteps™ | smartphone | RCT | 118 adults at risk or diagnosed with a chronic disease; AG: n= 59, age = 57 years; CG: n = 59, age = 59 years | AG:76%; CG: 81% | HealtheSteps™ smartphone app and Healthe-Steps™ website vs. wait-list control | baseline and 18 months. | self-reported eating habits | Improved their overall healthful eating | Good |
Glanz (2006), USA [25] | NR | PDA | Intervention pilot test | 33 healthy women, 64 years | 100% | PDA diet-monitoring system | baseline and 1 month | food choice and dietary intakes | Reported total fat intake and percent energy from fat decreased significantly. | Poor |
Huberty (2019). USA [55] | Calm | smartphone | RCT | 88 healthy adult; AG: n = 41, age = 20 years; CG: n = 47, age = 22 years | AG:41%; CG: 49% | Calm app vs. wait-list control | baseline, 8 and 12 weeks | alcohol consumption and healthy eating (fruit and vegetable consumption) | Not significant. | Poor |
Inauen (2017), USA [30] | NR | smartphone | RCT | 140 healthy adult; AG: n = 70, age 27.5 years; CG: n = 70. Age = 27.5 years | 75.5% | Whatsapp support group (1. eating more fruit and vegetables 2. eating fewer unhealthy snacks) vs. control | baseline, 1- and 2-months | Self-reported healthy eating (fruits, vegetables and unhealthy snacks) | Intervention group showed a gradual increase in healthy eating over time, ate more fruits and vegetables, and less unhealthy snacks compare to the control group on Day 10. However, it is not significant at the follow ups. | Poor |
Jarvela (2018), Finland [31] | NR | smartphone | RCT | 219 healthy adult; face to face group: n = 70, age = 50 years; AG: n = 78, age = 49; CG: n = 71, age = 49 years | (1) Face-to-face: 87% (2) AG: 85% 3) CG: 82% | (1) Face-to-face (2) mobile app (3) control | baseline, 10 and 36 weeks | eating behaviour | App group showed beneficial effects on reported eating behaviour. | Poor |
Lee (2019), Korea [33] | NR | smartphone | RCT pilot test | 65 adult who diagnosis of colorectal polyps; AG: n = 32, age = 49 years; CG: n = 33, age 21 years | AG:34%; CG: 46% | intervention app vs. control (traditional mail) | baseline and 3 month | changes in dietary intake, such as that of vegetables, fruits, and fatty food. | Not significant. | Poor |
McCarroll (2015), USA [53] | LoseIt! | smartphone | Prospective intervention | 50 adult women cancer survivors, age = 58 years | 100% | web- or mobile-based apps | baseline and 4 weeks | macronutrient (carbohydrate, fat and protein) and fibre consumption | Not significant. | Poor |
Palacios (2018), USA [54] | MyNutriCart | smartphone | pilot randomised trial | 51 overweight and obese adult; AG: n = 24, age = 34 years; TG: n = 27, age = 37 years | AG:92%; TG: 89% | intervention app vs. face-to-face counseling session | baseline and 8 weeks. | healthy food choice and dietary behaviour | “MyNutriCart” app use led to significant improvements in food-related behaviours compared to baseline, with no significant differences when compared to the traditional group. | Poor |
Park (2016), Korea [28] | Strong bone, Fit body (SbFb) | smartphone | RCT | 82 young adult women with low bone mass; AG: n = 28, age = 24 years; Group education: n = 32, age = 25 years; CG: n = 22, age = 23 years | 100% | (1) apps (2) group education (3) control | baseline and 20 weeks | nutrient intake | calcium intake is higher in app and group education than control group. | Poor |
Recio-Rodriguez (2018), Spain [19] | EVIDENT II study | smartphone | RCT | 833 healthy adult; AG: n = 415, age = 51 years; CG: n = 418, age = 52 years | AG:60%; CG: 64% | intervention: counseling + application group; control: counseling group | baseline and 12-month | Macro and Micronutrients intake | The app group reported a higher percentage intake of carbohydrates, and lower percentage intakes of fats and saturated fats | Good |
Rodgers (2015), France [27] | NR | smartphone | Intervention only | 40 healthy female adults, age = 19 years | 100% | intervention: app (food journal + messages) | baseline and 3 weeks | fruit, vegetable, and sugar-sweetened beverage consumption. | Among participants with body mass index (BMI) ≥25, fruit and vegetable consumption increased with time. Among participants with BMI <21, consumption of fruit decreased, whereas the consumption of vegetables remained stable. No effects were found for sugar-sweetened beverage consumption. | Poor |
Sarcona (2017), USA [32] | NR | smartphone | cross-sectional study | 401 university students | 73% | Users and Nonusers of Mobile Health Apps | NA | healthy eating behaviour | App users were found to have more positive eating behaviours than nonusers, and the impact of using more than one type of mobile-based health app significantly improved eating behaviour. | Poor |
Turner (2013), USA [26] | Fat Secret’s Calorie Counter, My Fitness Pal, and Lose it | smartphone | RCT | 78 overweight and obese adult; AG: n = 37, age = 41 years; website: n = 24, age = 45 years; paper journal: n = 17, age = 47 years; | AG: 70%; website: 87%; paper journal: 76%; | (1) mobile app, (2) website, and (3) paper journal | baseline and 6 months | dietary intake (energy intake, fat, added sugar, fruit, vegetables) and eating behaviour | App users consumed less energy than paper journal users. No significant difference on the dietary intake and eating behaviour. | Poor |
Study | Healthy Food Purchasing | Healthy Food Consumption | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Energy Intake | Carbohydrate | Protein | Fat | Micronutrients | Grains | Vegetables | Fruits | Fibre | Added Sugar | Salt | Unhealthy Snack | Healthy Eating Index | Healthy Eating Behaviour | ||
Allen (2013) [10] | 0 | 0 | 0 | 0 | 0 | ||||||||||
Alnasser (2019) [39] | + (b) | 0 | 0 | + (b), | + (b), | + (w) | + (w) | ||||||||
Atienza (2008) [30] | + (b) | + (b) | + (b) | ||||||||||||
Banerjee (2020) [38] | 0 | 0 | 0 | ||||||||||||
Brindal (2019) [35] | 0 | 0 | 0 | 0 | 0 | ||||||||||
Dodd (2017) [34] | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||||
Eyles (2017) [15] | + (b) (salt content); 0 (saturated fat, energy content and expenditure) | ||||||||||||||
Gill (2019) [37] | 0 | 0 | 0 | 0 | + (b) | ||||||||||
Glanz (2006) [11] | + (b) | + (b) | 0 | 0 | 0 | 0 | |||||||||
Huberty (2019) [33] | 0 | 0 | |||||||||||||
Inauen (2017) [16] | + (b) (at day 10); 0 (at 2 months) | + (b) (at day 10); 0 (at 2 months) | + (b) (at day 10); 0 (at 2 months) | + (b) (at day 10); 0 (at 2 months) | |||||||||||
Jarvela (2018) [17] | + (b) | ||||||||||||||
Lee (2019) [19] | + (w) | + (w) | + (w) | ||||||||||||
McCarroll (2015) [31] | 0 | 0 | 0 | 0 | 0 | ||||||||||
Palacios (2018) [32] | + (w) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + (b) | |||||
Park (2016) [14] | 0 | 0 | 0 | 0 | + (b) | 0 | |||||||||
Recio-Rodriguez (2018) [36] | + (w) | + (b), + (w) | 0 | + (b), + (w) | 0 | 0 | 0 | 0 | 0 | 0 | |||||
Rodgers (2015), (participants with BMI ≥25) [13] | + (b) | + (b) | 0 | ||||||||||||
Rodgers (2015) (participants with BMI <21) [13] | 0 | − (b) | 0 | ||||||||||||
Sarcona (2017) [18] | + (b) | ||||||||||||||
Turner (2013) [12] | + (b) | 0 | 0 | 0 | 0 | 0 |
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Lim, S.Y.; Lee, K.W.; Seow, W.-L.; Mohamed, N.A.; Devaraj, N.K.; Amin-Nordin, S. Effectiveness of Integrated Technology Apps for Supporting Healthy Food Purchasing and Consumption: A Systematic Review. Foods 2021, 10, 1861. https://doi.org/10.3390/foods10081861
Lim SY, Lee KW, Seow W-L, Mohamed NA, Devaraj NK, Amin-Nordin S. Effectiveness of Integrated Technology Apps for Supporting Healthy Food Purchasing and Consumption: A Systematic Review. Foods. 2021; 10(8):1861. https://doi.org/10.3390/foods10081861
Chicago/Turabian StyleLim, Sook Yee, Kai Wei Lee, Wen-Li Seow, Nurul Azmawati Mohamed, Navin Kumar Devaraj, and Syafinaz Amin-Nordin. 2021. "Effectiveness of Integrated Technology Apps for Supporting Healthy Food Purchasing and Consumption: A Systematic Review" Foods 10, no. 8: 1861. https://doi.org/10.3390/foods10081861