Quality Assessment of Smartphone Fitness Apps Used to Increase Physical Activity Level and Improve General Health in Adults: A Systematic Review

Overview

Journal Digit Health

Specialty Medical Informatics

Date 2022 Nov 24

PMID 36420320

Authors

Alexandher Negreiros

Roberto B T Maciel

Bianca Carvalho de Barros

Rosimeire Simprini Padula

Affiliations

Soon will be listed here.

Abstract

Introduction: Mobile exercise apps for smartphones have been used with intervention measures to increase physical activity. This study aimed to identify and evaluate the quality of fitness apps for smartphones that were used to increase the level of physical activity and improve the overall health of healthy adults.

Methods: The systematic review was performed in five electronic databases EMBASE, MEDLINE, CINAHL, Academic Research Premier e Cochrane Reviews, and Trials. The search terms were grouped into three categories according to the principles of population, intervention, comparison, and outcomes. The following includes examples of the group terms: population (healthy adults), intervention (smartphone apps), and outcomes (physical activity level).

Results: Of the 3924 potential articles, 74 were read for full-text analysis. Only seven studies were included in the review. The methodological evaluation of the studies and the apps' quality showed that only one study and one app were evaluated with good quality. All studies used a type of application to improve the level of physical activity (measured by the number of daily steps), reporting an increase and improvement in some general health indices (calorie expenditure, weight, BMI) in healthy adults, regardless of frequency and duration of intervention and applications.

Conclusion: We cannot say that the use of smartphone applications improves the level of physical activity and general health. The low methodological quality of the studies and the possibility to evaluate the applications used (Mars Scale) due to the lack of technical standardization presented in the studies, despite the app used showing positive results in all studies.

Citing Articles

Typical everyday movements cause specific patterns in heart rate.

Heidelbach M, Cysarz D, Edelhauser F Front Physiol. 2024; 15:1379739.

PMID: 39129753 PMC: 11310120. DOI: 10.3389/fphys.2024.1379739.

Infusing behavior science into large language models for activity coaching.

Hegde N, Vardhan M, Nathani D, Rosenzweig E, Speed C, Karthikesalingam A PLOS Digit Health. 2024; 3(4):e0000431.

PMID: 38564502 PMC: 10986996. DOI: 10.1371/journal.pdig.0000431.

References

Rollo S, Antsygina O, Tremblay M . The whole day matters: Understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. J Sport Health Sci. 2020; 9(6):493-510. PMC: 7749249. DOI: 10.1016/j.jshs.2020.07.004. View

Garcia-Ortiz L, Recio-Rodriguez J, Agudo-Conde C, Patino-Alonso M, Maderuelo-Fernandez J, Repiso Gento I . Long-Term Effectiveness of a Smartphone App for Improving Healthy Lifestyles in General Population in Primary Care: Randomized Controlled Trial (Evident II Study). JMIR Mhealth Uhealth. 2018; 6(4):e107. PMC: 5948409. DOI: 10.2196/mhealth.9218. View

Mehra S, Visser B, Dadema T, van den Helder J, Engelbert R, Weijs P . Translating Behavior Change Principles Into a Blended Exercise Intervention for Older Adults: Design Study. JMIR Res Protoc. 2018; 7(5):e117. PMC: 5956161. DOI: 10.2196/resprot.9244. View

Schoeppe S, Alley S, Van Lippevelde W, Bray N, Williams S, Duncan M . Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act. 2016; 13(1):127. PMC: 5142356. DOI: 10.1186/s12966-016-0454-y. View

Lunde P, Nilsson B, Bergland A, Kvaerner K, Bye A . The Effectiveness of Smartphone Apps for Lifestyle Improvement in Noncommunicable Diseases: Systematic Review and Meta-Analyses. J Med Internet Res. 2018; 20(5):e162. PMC: 5960039. DOI: 10.2196/jmir.9751. View

Wong F . Influence of Pokémon Go on physical activity levels of university players: a cross-sectional study. Int J Health Geogr. 2017; 16(1):8. PMC: 5322678. DOI: 10.1186/s12942-017-0080-1. View

Harries T, Eslambolchilar P, Rettie R, Stride C, Walton S, van Woerden H . Effectiveness of a smartphone app in increasing physical activity amongst male adults: a randomised controlled trial. BMC Public Health. 2016; 16:925. PMC: 5010703. DOI: 10.1186/s12889-016-3593-9. View

Stoyanov S, Hides L, Kavanagh D, Zelenko O, Tjondronegoro D, Mani M . Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015; 3(1):e27. PMC: 4376132. DOI: 10.2196/mhealth.3422. View

Lee M, Lee S, Kim T, Yoo H, Kim S, Suh D . Feasibility of a Smartphone-Based Exercise Program for Office Workers With Neck Pain: An Individualized Approach Using a Self-Classification Algorithm. Arch Phys Med Rehabil. 2016; 98(1):80-87. DOI: 10.1016/j.apmr.2016.09.002. View

10.

King A, Hekler E, Grieco L, Winter S, Sheats J, Buman M . Effects of Three Motivationally Targeted Mobile Device Applications on Initial Physical Activity and Sedentary Behavior Change in Midlife and Older Adults: A Randomized Trial. PLoS One. 2016; 11(6):e0156370. PMC: 4924838. DOI: 10.1371/journal.pone.0156370. View

11.

Tudor-Locke C, Han H, Aguiar E, Barreira T, Schuna Jr J, Kang M . How fast is fast enough? Walking cadence (steps/min) as a practical estimate of intensity in adults: a narrative review. Br J Sports Med. 2018; 52(12):776-788. PMC: 6029645. DOI: 10.1136/bjsports-2017-097628. View

12.

Fanning J, Roberts S, Hillman C, Mullen S, Ritterband L, McAuley E . A smartphone "app"-delivered randomized factorial trial targeting physical activity in adults. J Behav Med. 2017; 40(5):712-729. DOI: 10.1007/s10865-017-9838-y. View

13.

Ding D, Lawson K, Kolbe-Alexander T, Finkelstein E, Katzmarzyk P, van Mechelen W . The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016; 388(10051):1311-24. DOI: 10.1016/S0140-6736(16)30383-X. View

14.

Haque M, Kangas M, Jamsa T . A Persuasive mHealth Behavioral Change Intervention for Promoting Physical Activity in the Workplace: Feasibility Randomized Controlled Trial. JMIR Form Res. 2020; 4(5):e15083. PMC: 7235808. DOI: 10.2196/15083. View

15.

Egger G, Binns A, Rossner S . The emergence of "lifestyle medicine" as a structured approach for management of chronic disease. Med J Aust. 2009; 190(3):143-5. DOI: 10.5694/j.1326-5377.2009.tb02317.x. View

16.

Glynn L, Hayes P, Casey M, Glynn F, Alvarez-Iglesias A, Newell J . Effectiveness of a smartphone application to promote physical activity in primary care: the SMART MOVE randomised controlled trial. Br J Gen Pract. 2014; 64(624):e384-91. PMC: 4073723. DOI: 10.3399/bjgp14X680461. View

17.

Oftedal S, Vandelanotte C, Duncan M . Patterns of Diet, Physical Activity, Sitting and Sleep Are Associated with Socio-Demographic, Behavioural, and Health-Risk Indicators in Adults. Int J Environ Res Public Health. 2019; 16(13). PMC: 6651368. DOI: 10.3390/ijerph16132375. View

18.

Wang Y, Su C, Ouyang Y, Jia X, Zhang B, Wang Z . Secular trends in sedentary behaviors and associations with weight indicators among Chinese reproductive-age women from 2004 to 2015: findings from the China Health and Nutrition Survey. Int J Obes (Lond). 2020; 44(11):2267-2278. DOI: 10.1038/s41366-020-00684-3. View

19.

Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W . The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013; 46(1):81-95. DOI: 10.1007/s12160-013-9486-6. View

20.

Bardus M, van Beurden S, Smith J, Abraham C . A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. Int J Behav Nutr Phys Act. 2016; 13:35. PMC: 4785735. DOI: 10.1186/s12966-016-0359-9. View