Nonusage Attrition of Adolescents in an mHealth Promotion Intervention and the Role of Socioeconomic Status: Secondary Analysis of a 2-Arm Cluster-Controlled Trial

Laura Maenhout, Carmen Peuters, Greet Cardon, Geert Crombez, Ann DeSmet, Sofie Compernolle, Laura Maenhout, Carmen Peuters, Greet Cardon, Geert Crombez, Ann DeSmet, Sofie Compernolle

Abstract

Background: Mobile health (mHealth) interventions may help adolescents adopt healthy lifestyles. However, attrition in these interventions is high. Overall, there is a lack of research on nonusage attrition in adolescents, particularly regarding the role of socioeconomic status (SES).

Objective: The aim of this study was to focus on the role of SES in the following three research questions (RQs): When do adolescents stop using an mHealth intervention (RQ1)? Why do they report nonusage attrition (RQ2)? Which intervention components (ie, self-regulation component, narrative, and chatbot) prevent nonusage attrition among adolescents (RQ3)?

Methods: A total of 186 Flemish adolescents (aged 12-15 years) participated in a 12-week mHealth program. Log data were monitored to measure nonusage attrition and usage duration for the 3 intervention components. A web-based questionnaire was administered to assess reasons for attrition. A survival analysis was conducted to estimate the time to attrition and determine whether this differed according to SES (RQ1). Descriptive statistics were performed to map the attrition reasons, and Fisher exact tests were used to determine if these reasons differed depending on the educational track (RQ2). Mixed effects Cox proportional hazard regression models were used to estimate the associations between the use duration of the 3 components during the first week and attrition. An interaction term was added to the regression models to determine whether associations differed by the educational track (RQ3).

Results: After 12 weeks, 95.7% (178/186) of the participants stopped using the app. 30.1% (56/186) of the adolescents only opened the app on the installation day, and 44.1% (82/186) stopped using the app in the first week. Attrition at any given time during the intervention period was higher for adolescents from the nonacademic educational track compared with those from the academic track. The other SES indicators (family affluence and perceived financial situation) did not explain attrition. The most common reasons for nonusage attrition among participants were perceiving that the app did not lead to behavior change, not liking the app, thinking that they already had a sufficiently healthy lifestyle, using other apps, and not being motivated by the environment. Attrition reasons did not differ depending on the educational track. More time spent in the self-regulation and narrative components during the first week was associated with lower attrition, whereas chatbot use duration was not associated with attrition rates. No moderating effects of SES were observed in the latter association.

Conclusions: Nonusage attrition was high, especially among adolescents in the nonacademic educational track. The reported reasons for attrition were diverse, with no statistical differences according to the educational level. The duration of the use of the self-regulation and narrative components during the first week may prevent attrition for both educational tracks.

Trial registration: ClinicalTrials.gov NCT04719858; https://ichgcp.net/clinical-trials-registry/NCT04719858.

Keywords: adolescents; mHealth; mobile phone; nonusage attrition; socioeconomic status.

Conflict of interest statement

Conflicts of Interest: None declared.

©Laura Maenhout, Carmen Peuters, Greet Cardon, Geert Crombez, Ann DeSmet, Sofie Compernolle. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 10.05.2022.

Figures

Figure 1
Figure 1
Attrition pattern of the #LIFEGOALS intervention.
Figure 2
Figure 2
Kaplan-Meier plots according to socioeconomic status indicator (educational track).
Figure 3
Figure 3
Kaplan-Meier plots according to socioeconomic status indicator. FAS: Family Affluence Scale.
Figure 4
Figure 4
Kaplan-Meier plots according to socioeconomic status indicator (perceived financial situation).

References

    1. Direito A, Carraça E, Rawstorn J, Whittaker R, Maddison R. mHealth technologies to influence physical activity and sedentary behaviors: behavior change techniques, systematic review and meta-analysis of randomized controlled trials. Ann Behav Med. 2017 Apr;51(2):226–39. doi: 10.1007/s12160-016-9846-0.10.1007/s12160-016-9846-0
    1. Fedele DA, Cushing CC, Fritz A, Amaro CM, Ortega A. Mobile health interventions for improving health outcomes in youth: a meta-analysis. JAMA Pediatr. 2017 May 01;171(5):461–9. doi: 10.1001/jamapediatrics.2017.0042. 2611946
    1. Rathbone AL, Prescott J. The use of mobile apps and SMS messaging as physical and mental health interventions: systematic review. J Med Internet Res. 2017 Aug 24;19(8):e295. doi: 10.2196/jmir.7740. v19i8e295
    1. Mclaughlin M, Delaney T, Hall A, Byaruhanga J, Mackie P, Grady A, Reilly K, Campbell E, Sutherland R, Wiggers J, Wolfenden L. Associations between digital health intervention engagement, physical activity, and sedentary behavior: systematic review and meta-analysis. J Med Internet Res. 2021 Feb 19;23(2):e23180. doi: 10.2196/23180.
    1. Flores Mateo G, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res. 2015 Nov 10;17(11):e253. doi: 10.2196/jmir.4836. v17i11e253
    1. Romeo A, Edney S, Plotnikoff R, Curtis R, Ryan J, Sanders I, Crozier A, Maher C. Can smartphone apps increase physical activity? Systematic review and meta-analysis. J Med Internet Res. 2019 Mar 19;21(3):e12053. doi: 10.2196/12053. v21i3e12053
    1. mHealth: New Horizons for Health Through Mobile Technologies: Second Global Survey on eHealth. Geneva: World Health Organization; 2011.
    1. McIntosh JR, Jay S, Hadden N, Whittaker PJ. Do E-health interventions improve physical activity in young people: a systematic review. Public Health. 2017 Jul;148:140–8. doi: 10.1016/j.puhe.2017.04.001.S0033-3506(17)30138-5
    1. Lau PW, Lau EY, Wong DP, Ransdell L. A systematic review of information and communication technology-based interventions for promoting physical activity behavior change in children and adolescents. J Med Internet Res. 2011 Jul 13;13(3):e48. doi: 10.2196/jmir.1533. v13i3e48
    1. eHealth Research, Theory and Development A Multidisciplinary Approach. London, United Kingdom: Routledge; 2018.
    1. Turner T, Spruijt-Metz D, Wen CK, Hingle MD. Prevention and treatment of pediatric obesity using mobile and wireless technologies: a systematic review. Pediatr Obes. 2015 Dec;10(6):403–9. doi: 10.1111/ijpo.12002.
    1. Gomez Quiñonez S, Walthouwer MJ, Schulz DN, de Vries H. mHealth or eHealth? Efficacy, use, and appreciation of a web-based computer-tailored physical activity intervention for Dutch adults: a randomized controlled trial. J Med Internet Res. 2016 Nov 09;18(11):e278. doi: 10.2196/jmir.6171. v18i11e278
    1. Laura L. Technology as a platform for improving healthy behaviors and weight status in children and adolescents: a review. Obesity. 2015;1(3) doi: 10.16966/2380-5528.109.
    1. Rose T, Barker M, Maria Jacob C, Morrison L, Lawrence W, Strömmer S, Vogel C, Woods-Townsend K, Farrell D, Inskip H, Baird J. A systematic review of digital interventions for improving the diet and physical activity behaviors of adolescents. J Adolesc Health. 2017 Dec;61(6):669–77. doi: 10.1016/j.jadohealth.2017.05.024. S1054-139X(17)30253-7
    1. Clarke AM, Kuosmanen T, Barry MM. A systematic review of online youth mental health promotion and prevention interventions. J Youth Adolesc. 2015 Jan;44(1):90–113. doi: 10.1007/s10964-014-0165-0.
    1. Neil AL, Batterham P, Christensen H, Bennett K, Griffiths KM. Predictors of adherence by adolescents to a cognitive behavior therapy website in school and community-based settings. J Med Internet Res. 2009 Feb 23;11(1):e6. doi: 10.2196/jmir.1050. v11i1e6
    1. Christensen H, Reynolds J, Griffiths KM. The use of e-health applications for anxiety and depression in young people: challenges and solutions. Early Interv Psychiatry. 2011 Feb;5 Suppl 1:58–62. doi: 10.1111/j.1751-7893.2010.00242.x.
    1. Eysenbach G. The law of attrition. J Med Internet Res. 2005 Mar 31;7(1):e11. doi: 10.2196/jmir.7.1.e11. v7e11
    1. Lubans DR, Smith JJ, Skinner G, Morgan PJ. Development and implementation of a smartphone application to promote physical activity and reduce screen-time in adolescent boys. Front Public Health. 2014;2:42. doi: 10.3389/fpubh.2014.00042. doi: 10.3389/fpubh.2014.00042.
    1. 21% of users abandon an app after one use. Localytics. [2022-04-12]. .
    1. Guertler D, Vandelanotte C, Kirwan M, Duncan MJ. Engagement and nonusage attrition with a free physical activity promotion program: the case of 10,000 steps Australia. J Med Internet Res. 2015 Jul 15;17(7):e176. doi: 10.2196/jmir.4339. v17i7e176
    1. Edney S, Ryan JC, Olds T, Monroe C, Fraysse F, Vandelanotte C, Plotnikoff R, Curtis R, Maher C. User engagement and attrition in an app-based physical activity intervention: secondary analysis of a randomized controlled trial. J Med Internet Res. 2019 Nov 27;21(11):e14645. doi: 10.2196/14645. v21i11e14645
    1. Meyerowitz-Katz G, Ravi S, Arnolda L, Feng X, Maberly G, Astell-Burt T. Rates of attrition and dropout in app-based interventions for chronic disease: systematic review and meta-analysis. J Med Internet Res. 2020 Sep 29;22(9):e20283. doi: 10.2196/20283. v22i9e20283
    1. Vandelanotte C, Duncan MJ, Kolt GS, Caperchione CM, Savage TN, Van Itallie A, Oldmeadow C, Alley SJ, Tague R, Maeder AJ, Rosenkranz RR, Mummery WK. More real-world trials are needed to establish if web-based physical activity interventions are effective. Br J Sports Med. 2019 Dec 03;53(24):1553–4. doi: 10.1136/bjsports-2018-099437.bjsports-2018-099437
    1. Shin Y, Kim SK, Lee M. Mobile phone interventions to improve adolescents' physical health: a systematic review and meta-analysis. Public Health Nurs. 2019 Nov;36(6):787–99. doi: 10.1111/phn.12655.
    1. Egilsson E, Bjarnason R, Njardvik U. Usage and weekly attrition in a smartphone-based health behavior intervention for adolescents: pilot randomized controlled trial. JMIR Form Res. 2021 Feb 17;5(2):e21432. doi: 10.2196/21432. v5i2e21432
    1. Dugas M, Gao GG, Agarwal R. Unpacking mHealth interventions: a systematic review of behavior change techniques used in randomized controlled trials assessing mHealth effectiveness. Digit Health. 2020;6:2055207620905411. doi: 10.1177/2055207620905411. 10.1177_2055207620905411
    1. Michie S, West R, Sheals K, Godinho CA. Evaluating the effectiveness of behavior change techniques in health-related behavior: a scoping review of methods used. Transl Behav Med. 2018 Mar 01;8(2):212–24. doi: 10.1093/tbm/ibx019. 4825845
    1. Direito A, Dale LP, Shields E, Dobson R, Whittaker R, Maddison R. Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? BMC Public Health. 2014 Jun 25;14:646. doi: 10.1186/1471-2458-14-646. 1471-2458-14-646
    1. Schroé H, Van Dyck D, De Paepe A, Poppe L, Loh WW, Verloigne M, Loeys T, De Bourdeaudhuij I, Crombez G. Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention. Int J Behav Nutr Phys Act. 2020 Oct 07;17(1):127. doi: 10.1186/s12966-020-01001-x. 10.1186/s12966-020-01001-x
    1. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2017 Jun;7(2):254–67. doi: 10.1007/s13142-016-0453-1. 10.1007/s13142-016-0453-1
    1. Hinyard LJ, Kreuter MW. Using narrative communication as a tool for health behavior change: a conceptual, theoretical, and empirical overview. Health Educ Behav. 2007 Oct 30;34(5):777–92. doi: 10.1177/1090198106291963.1090198106291963
    1. Slater MD, Rouner D. Entertainment-education and elaboration likelihood: understanding the processing of narrative persuasion. Commun Theory. 2002 May;12(2):173–91. doi: 10.1111/j.1468-2885.2002.tb00265.x.
    1. Murphy ST, Frank LB, Chatterjee JS, Baezconde-Garbanati L. Narrative versus non-narrative: the role of identification, transportation and emotion in reducing health disparities. J Commun. 2013 Feb 07;63(1):116–37. doi: 10.1111/jcom.12007.
    1. Perski O, Crane D, Beard E, Brown J. Does the addition of a supportive chatbot promote user engagement with a smoking cessation app? An experimental study. Digit Health. 2019;5:2055207619880676. doi: 10.1177/2055207619880676. 10.1177_2055207619880676
    1. Western MJ, Armstrong ME, Islam I, Morgan K, Jones UF, Kelson MJ. The effectiveness of digital interventions for increasing physical activity in individuals of low socioeconomic status: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2021 Nov 09;18(1):148. doi: 10.1186/s12966-021-01218-4. 10.1186/s12966-021-01218-4
    1. Arsenijevic J, Tummers L, Bosma N. Adherence to electronic health tools among vulnerable groups: systematic literature review and meta-analysis. J Med Internet Res. 2020 Feb 06;22(2):e11613. doi: 10.2196/11613. v22i2e11613
    1. Fadhil A, Gabrielli S. Addressing challenges in promoting healthy lifestyles: the al-chatbot approach. Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare; PervasiveHealth '17: Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare; May 23 - 26, 2017; Barcelona Spain. 2017.
    1. Richter M, Erhart M, Vereecken CA, Zambon A, Boyce W, Nic Gabhainn S. The role of behavioural factors in explaining socio-economic differences in adolescent health: a multilevel study in 33 countries. Soc Sci Med. 2009 Aug;69(3):396–403. doi: 10.1016/j.socscimed.2009.05.023.S0277-9536(09)00320-7
    1. Sigmundová D, Sigmund E, Tesler R, Ng KW, Hamrik Z, Mathisen FK, Inchley J, Bucksch J. Vigorous physical activity in relation to family affluence: time trends in Europe and North America. Int J Public Health. 2019 Sep;64(7):1049–58. doi: 10.1007/s00038-019-01271-8. 10.1007/s00038-019-01271-8
    1. Omorou AY, Manneville F, Langlois J, Legrand K, Böhme P, Muller L, Guillemin F, Briançon S, Lecomte E, PRALIMAP-INÈS Trial Group Physical activity rather than sedentary behaviour is socially determined in French adolescents with overweight and obesity. Prev Med. 2020 May;134:106043. doi: 10.1016/j.ypmed.2020.106043.S0091-7435(20)30067-0
    1. Jiménez Pavón D, Ortega FP, Ruiz JR, España Romero V, García Artero E, Moliner Urdiales D, Gómez Martínez S, Vicente Rodríguez G, Manios Y, Béghin L, Répasy J, Sjöstrom M, Moreno LA, González Gross M, Castillo MJ, HELENA Study Group Socioeconomic status influences physical fitness in European adolescents independently of body fat and physical activity: the HELENA study. Nutr Hosp. 2010;25(2):311–6.S0212-16112010000200017
    1. Elgar FJ, Pförtner T, Moor I, De Clercq B, Stevens GW, Currie C. Socioeconomic inequalities in adolescent health 2002–2010: a time-series analysis of 34 countries participating in the Health Behaviour in School-aged Children study. The Lancet. 2015 May;385(9982):2088–95. doi: 10.1016/s0140-6736(14)61460-4.
    1. Spotlight on adolescent health and well-being. Findings from the 2017/2018 Health Behaviour in School-aged Children (HBSC) survey in Europe and Canada. International report. Volume 1. Key findings 14. Denmark: World Health Organization; 2020.
    1. Maenhout L, Peuters C, Cardon G, Compernolle S, Crombez G, DeSmet A. The association of healthy lifestyle behaviors with mental health indicators among adolescents of different family affluence in Belgium. BMC Public Health. 2020 Jun 18;20(1):958. doi: 10.1186/s12889-020-09102-9. 10.1186/s12889-020-09102-9
    1. Latulippe K, Hamel C, Giroux D. Social health inequalities and eHealth: a literature review with qualitative synthesis of theoretical and empirical studies. J Med Internet Res. 2017 Apr 27;19(4):e136. doi: 10.2196/jmir.6731. v19i4e136
    1. Onderzoeksrapport Apestaartjaren : de digitale leefwereld van jongeren. Mediaraven. [2022-04-13]. .
    1. van Lenthe FJ, de Bourdeaudhuij I, Klepp K, Lien N, Moore L, Faggiano F, Kunst AE, Mackenbach JP. Preventing socioeconomic inequalities in health behaviour in adolescents in Europe: background, design and methods of project TEENAGE. BMC Public Health. 2009 May 08;9(1):125. doi: 10.1186/1471-2458-9-125. 1471-2458-9-125
    1. Velten J, Lavallee KL, Scholten S, Meyer AH, Zhang X, Schneider S, Margraf J. Lifestyle choices and mental health: a representative population survey. BMC Psychol. 2014 Dec 23;2(1):58. doi: 10.1186/s40359-014-0055-y. 55
    1. Walsh R. Lifestyle and mental health. Am Psychol. 2011 Oct;66(7):579–92. doi: 10.1037/a0021769.2011-01021-001
    1. Biddle SJ, Asare M. Physical activity and mental health in children and adolescents: a review of reviews. British J Sports Med. 2011 Aug 01;45(11):886–95. doi: 10.1136/bjsports-2011-090185.bjsports-2011-090185
    1. Rodriguez-Ayllon M, Cadenas-Sánchez C, Estévez-López F, Muñoz NE, Mora-Gonzalez J, Migueles JH, Molina-García P, Henriksson H, Mena-Molina A, Martínez-Vizcaíno V, Catena A, Löf M, Erickson KI, Lubans DR, Ortega FB, Esteban-Cornejo I. Role of physical activity and sedentary behavior in the mental health of preschoolers, children and adolescents: a systematic review and meta-analysis. Sports Med. 2019 Sep 16;49(9):1383–410. doi: 10.1007/s40279-019-01099-5.10.1007/s40279-019-01099-5
    1. Schwarzer R. Modeling health behavior change: how to predict and modify the adoption and maintenance of health behaviors. Applied Psychol. 2008 Jan;57(1):1–29. doi: 10.1111/j.1464-0597.2007.00325.x.
    1. Communication and Persuasion Central and Peripheral Routes to Attitude Change. New York: Springer; 1986.
    1. Oinas-Kukkonen H, Harjumaa M. Persuasive systems design: key issues, process model, and system features. Commun Assoc Inf Syst. 2009;24(1):28. doi: 10.17705/1cais.02428.
    1. Maenhout L, Peuters C, Cardon G, Compernolle S, Crombez G, DeSmet A. Participatory development and pilot testing of an adolescent health promotion chatbot. Front Public Health. 2021 Nov 11;9:724779. doi: 10.3389/fpubh.2021.724779. doi: 10.3389/fpubh.2021.724779.
    1. Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, Posner S. Socioeconomic status in health research: one size does not fit all. JAMA. 2005 Dec 14;294(22):2879–88. doi: 10.1001/jama.294.22.2879.294/22/2879
    1. Gezondheid en welzijn. Health Behaviour in School-Aged Children. [2022-04-13].
    1. Hartley JE, Levin K, Currie C. A new version of the HBSC Family Affluence Scale - FAS III: Scottish qualitative findings from the international FAS development study. Child Indic Res. 2016 Aug 2;9(1):233–45. doi: 10.1007/s12187-015-9325-3. 9325
    1. Health Behaviour in School-aged Children (Hbsc) Study Protocol: Background, Methodology and Mandatory Items for the 2017/18 Survey. St Andrews: CAHRU; 2018.
    1. Currie C, Molcho M, Boyce W, Holstein B, Torsheim T, Richter M. Researching health inequalities in adolescents: the development of the health Behaviour in School-Aged Children (HBSC) family affluence scale. Soc Sci Med. 2008 Mar;66(6):1429–36. doi: 10.1016/j.socscimed.2007.11.024.S0277-9536(07)00607-7
    1. Torsheim T, Cavallo F, Levin KA, Schnohr C, Mazur J, Niclasen B, Currie C, FAS Development Study Group Psychometric validation of the revised family affluence scale: a latent variable approach. Child Indic Res. 2016;9:771–84. doi: 10.1007/s12187-015-9339-x. 9339
    1. Boyce W, Torsheim T, Currie C, Zambon A. The family affluence scale as a measure of national wealth: validation of an adolescent self-report measure. Soc Indic Res. 2006 Apr 20;78(3):473–87. doi: 10.1007/s11205-005-1607-6.
    1. Factsheet demografische context. HSBC. [2022-04-13]. .
    1. Technisch verslag JOP-schoolmonitor 2018. jeugdonderzoeksplatform. [2022-04-13]. .
    1. Postel MG, de Haan HA, ter Huurne ED, van der Palen J, Becker ES, de Jong CA. Attrition in web-based treatment for problem drinkers. J Med Internet Res. 2011 Dec 27;13(4):e117. doi: 10.2196/jmir.1811. v13i4e117
    1. Van der Mispel C, Poppe L, Crombez G, Verloigne M, De Bourdeaudhuij I. A self-regulation-based eHealth intervention to promote a healthy lifestyle: investigating user and website characteristics related to attrition. J Med Internet Res. 2017 Jul 11;19(7):e241. doi: 10.2196/jmir.7277. v19i7e241
    1. Schroé H, Crombez G, De Bourdeaudhuij I, Van Dyck D. Investigating when, which, and why users stop using a digital health intervention to promote an active lifestyle: secondary analysis with a focus on health action process approach-based psychological determinants. JMIR Mhealth Uhealth. 2022 Jan 31;10(1):e30583. doi: 10.2196/30583. v10i1e30583
    1. Rich JT, Neely JG, Paniello RC, Voelker CC, Nussenbaum B, Wang EW. A practical guide to understanding Kaplan-Meier curves. Otolaryngol Head Neck Surg. 2010 Sep;143(3):331–6. doi: 10.1016/j.otohns.2010.05.007. S0194-5998(10)00710-2
    1. Clark TG, Bradburn MJ, Love SB, Altman DG. Survival analysis part I: basic concepts and first analyses. Br J Cancer. 2003 Jul 21;89(2):232–8. doi: 10.1038/sj.bjc.6601118. 6601118
    1. Bradburn MJ, Clark TG, Love SB, Altman DG. Survival analysis part II: multivariate data analysis--an introduction to concepts and methods. Br J Cancer. 2003 Aug 04;89(3):431–6. doi: 10.1038/sj.bjc.6601119. 6601119
    1. Michie S, Yardley L, West R, Patrick K, Greaves F. Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop. J Med Internet Res. 2017 Jun 29;19(6):e232. doi: 10.2196/jmir.7126. v19i6e232
    1. Chan A, Kow R, Cheng JK. Adolescents’ perceptions on smartphone applications (Apps) for health management. J Mobile Technol Med. 2017 Aug;6(2):47–55. doi: 10.7309/jmtm.6.2.6.
    1. Strömmer S, Shaw S, Jenner S, Vogel C, Lawrence W, Woods-Townsend K, Farrell D, Inskip H, Baird J, Morrison L, Barker M. How do we harness adolescent values in designing health behaviour change interventions? A qualitative study. Br J Health Psychol. 2021 Nov 04;26(4):1176–93. doi: 10.1111/bjhp.12526.
    1. Geraghty AW, Torres LD, Leykin Y, Pérez-Stable EJ, Muñoz RF. Understanding attrition from international internet health interventions: a step towards global eHealth. Health Promot Int. 2013 Sep;28(3):442–52. doi: 10.1093/heapro/das029. das029
    1. Christensen H, Griffiths KM, Mackinnon AJ, Brittliffe K. Online randomized controlled trial of brief and full cognitive behaviour therapy for depression. Psychol Med. 2006 Dec;36(12):1737–46. doi: 10.1017/S0033291706008695.S0033291706008695
    1. Ludden GD, van Rompay TJ, Kelders SM, van Gemert-Pijnen JE. How to increase reach and adherence of web-based interventions: a design research viewpoint. J Med Internet Res. 2015 Jul 10;17(7):e172. doi: 10.2196/jmir.4201. v17i7e172
    1. Kirkova-Bogdanova A. Standards in mobile health and fitness applications. Knowl Int J. 2021;46(3):441–4.
    1. Nicholas J, Proudfoot J, Parker G, Gillis I, Burckhardt R, Manicavasagar V, Smith M. The ins and outs of an online bipolar education program: a study of program attrition. J Med Internet Res. 2010 Dec 19;12(5):e57. doi: 10.2196/jmir.1450. v12i5e57
    1. Christensen H, Mackinnon A. The law of attrition revisited. J Med Internet Res. 2006 Sep 29;8(3):e20; author reply e21. doi: 10.2196/jmir.8.3.e20. v8i3e20
    1. Sieverink F. Opening the Black Box of eHealth: A Mixed Methods Approach for the Evaluation of Personal Health Records. Enschede: University of Twente; 2017.
    1. Yardley L, Spring BJ, Riper H, Morrison LG, Crane DH, Curtis K, Merchant GC, Naughton F, Blandford A. Understanding and promoting effective engagement with digital behavior change interventions. Am J Prev Med. 2016 Nov;51(5):833–42. doi: 10.1016/j.amepre.2016.06.015.S0749-3797(16)30243-4
    1. Torous J, Lipschitz J, Ng M, Firth J. Dropout rates in clinical trials of smartphone apps for depressive symptoms: a systematic review and meta-analysis. J Affect Disord. 2020 Feb 15;263:413–9. doi: 10.1016/j.jad.2019.11.167.S0165-0327(19)32606-0
    1. Mendonça G, Cheng LA, Mélo EN, de Farias Júnior JC. Physical activity and social support in adolescents: a systematic review. Health Educ Res. 2014 Oct;29(5):822–39. doi: 10.1093/her/cyu017.cyu017
    1. Hagquist CE. Health inequalities among adolescents: the impact of academic orientation and parents' education. Eur J Public Health. 2007 Feb 14;17(1):21–6. doi: 10.1093/eurpub/ckl087.ckl087
    1. Svedberg P, Nygren JM, Staland-Nyman C, Nyholm M. The validity of socioeconomic status measures among adolescents based on self-reported information about parents occupations, FAS and perceived SES; implication for health related quality of life studies. BMC Med Res Methodol. 2016 Apr 29;16:48. doi: 10.1186/s12874-016-0148-9. 10.1186/s12874-016-0148-9
    1. Bannink R, Broeren S, Joosten-van Zwanenburg E, van As E, van de Looij-Jansen P, Raat H. Effectiveness of a web-based tailored intervention (E-health4Uth) and consultation to promote adolescents' health: randomized controlled trial. J Med Internet Res. 2014 May 30;16(5):e143. doi: 10.2196/jmir.3163. v16i5e143
    1. Kohl LF, Crutzen R, de Vries NK. Online prevention aimed at lifestyle behaviors: a systematic review of reviews. J Med Internet Res. 2013 Jul 16;15(7):e146. doi: 10.2196/jmir.2665. v15i7e146
    1. Richter M, Vereecken CA, Boyce W, Maes L, Gabhainn SN, Currie CE. Parental occupation, family affluence and adolescent health behaviour in 28 countries. Int J Public Health. 2009;54(4):203–12. doi: 10.1007/s00038-009-8018-4.
    1. Richter M, Kuntsche E, de Looze M, Pförtner TK. Trends in socioeconomic inequalities in adolescent alcohol use in Germany between 1994 and 2006. Int J Public Health. 2013 Oct 9;58(5):777–84. doi: 10.1007/s00038-013-0486-x.
    1. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 1) J Epidemiol Community Health. 2006 Jan 01;60(1):7–12. doi: 10.1136/jech.2004.023531. 60/1/7
    1. Missinne S, Neels K, Bracke P. Reconsidering inequalities in preventive health care: an application of cultural health capital theory and the life-course perspective to the take-up of mammography screening. Sociol Health Illn. 2014 Nov;36(8):1259–75. doi: 10.1111/1467-9566.12169.
    1. Gagné T, Frohlich KL, Abel T. Cultural capital and smoking in young adults: applying new indicators to explore social inequalities in health behaviour. Eur J Public Health. 2015 Oct;25(5):818–23. doi: 10.1093/eurpub/ckv069.ckv069
    1. Cushing CC, Bejarano CM, Ortega A, Sayre N, Fedele DA, Smyth JM. Adaptive mHealth intervention for adolescent physical activity promotion. J Pediatr Psychol. 2021 Jun 03;46(5):536–46. doi: 10.1093/jpepsy/jsaa125.6114715
    1. van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, Seydel ER. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res. 2011 Dec 05;13(4):e111. doi: 10.2196/jmir.1672. v13i4e111
    1. Vandelanotte C, Müller AM, Short CE, Hingle M, Nathan N, Williams SL, Lopez ML, Parekh S, Maher CA. Past, present, and future of eHealth and mHealth research to improve physical activity and dietary behaviors. J Nutrition Educ Behav. 2016 Mar;48(3):219–28.e1. doi: 10.1016/j.jneb.2015.12.006.S1499-4046(15)00806-4

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren