Table 3 Time period, Intervention tools, quality of included studies in the reviews, and recommendations for future research

Böhm et al. 2019 [47]

Systematic review

January 2012 to June 2018 (2014–2016)

Mobile phones, smartphones, tablets, or wearables

Tool:

Cochrane Handbook for Systematic Reviews of Interventions

Risk of bias:

2/5 (40%) medium

3/5 (60%) high

1) PA intervention programs for children/adolescents with a greater BMI z-score

2) intervention programs with a longer period of time (≥6 months)

3) sufficiently large number of participants (≥250)

4) bypass self-reported measurements

5) implement theoretical frameworks and BCTs

6) follow-up beyond postintervention

7) age- and sex-specific interventions

8) engagement of children and adolescents with wearable activity trackers

9) impact of social support (school/family)

10) multicomponent interventions

11) cost-effectiveness analyses

Buckingham et al. 2019

[53]

Systematic review

January 2007 to February 2018

(2009–2018)

mHealth interventions:

mobile phone, smartphone apps, personal digital assistants, tablets, wearable activity monitors/ trackers

Tool:

Effective Public Health Practice Project

Quality rating:

1/25 (4%) strong,

9/25 (36%) moderate,

15/25 (60%) weak

1) larger samples and more diverse workspace settings

2) report intervention components and outcomes in greater detail

3) SB in addition to PA, and bypass self-report

4) no-intervention control or a reliable baseline measurement

5) wider impact on health and wellbeing

6) mixed and qualitative methods

7) adverse events associated with mHealth use

8) mHealth vs multi-component interventions

9) subgroup differences

Direito et al. 2017 [52]

Systematic review and Meta-Analysis of RCTs

From earliest availableto January 2015

(2007–2014)

mHealth interventions:

mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants

Tool:

Cochrane Collaboration’s tool

No total rating:

High Risk of Bias for blinding, unclear allocation, other biases were low for most studies

1) long-term effectiveness and cost-effectiveness of mHealth interventions

2) dose-response relationship between intervention exposure and outcomes

3) report intervention components and outcomes in greater detail

4) efficacy of more advanced technology than SMS

Ferrer et al. 2017 [51]

Systematic review

not specified (2010–2014)

Facebook based interventions

Not assessed

1) no-intervention control

2) target a broader diversity of participants

3) attrition rates for varying durations of interventions

4) theory-based content and measure the effects of those mediators

5) effectivity of social support

6) validate self-report measures against device-measured outcomes of PA

7) match the PA assessment method to the stated goals and outcomes of the intervention

8) long term follow-up

Hamel et al. 2011 [56]

Systematic review

1998 to 2010 (1999–2009)

Computer- and web-based interventions

Tool:

Critical Appraisal Skills Programme of the Public Health Resource Unit

Quality rating:

No summary presented

1) bypass self-report

2) sex specific interventions

3) involve support persons (e.g. parents or peers) and analyze effectivity

4) integrate into existing school curriculum

5) include a theoretical framework

6) individual tailoring

McIntosh et al. 2017 [50]

Systematic review

2010 to July 2016

(2010–2014)

Web-based or eHealth interventions

Tool:

based on the critical appraisal for public health checklist

Quality rating:

3/10 (30%) high

7/10 (70%) moderate

1) longer follow-up

2) address bias incorporated with self-reporting methods

3) utilize theoretical foundation for eHealth interventions

4) relationship of confounding facets to effectiveness

5) conduct power analysis of studies

6) scale up interventions

Muellemann et al. 2018 [49]

Systematic review

from earliest available

to April 2017 (1997–2017)

eHealth interventions:

computer, telephone

smartphone, or tablet

Tool:

Cochrane Collaboration’s tool for assessing risk of bias

Risk of bias:

1/20 (95%), low

19/20 (95%) moderate to high

1) eHealth interventions vs non-eHealth interventions promoting PA in older adults

Nour et al. 2016 [54]

Systematic review and Meta-Analysis

1990 to August 2015 (2007–2014)

eHealth- and mHealth-based interventions: texting, email, mobile phone apps, phone calls, or websites

Tool:

Cochrane Collaboration’s tool for assessing risk of bias

Risk of bias rating:

majority of the studies unclear to high risk (attrition bias)

2/14 (14%) studies additionally high detection bias

1) longer follow-up in intervention

2) secondary outcomes (e.g.) weight and indicators of cardiovascular health)

3) focus primarily on vegetables

4) combine efficacious strategies and repeat exposure at a later date

5) develop validated tools for measuring vegetable intake in young adults

6) quantify a serving of vegetables

7) implement Biomarkers (e.g. vitamin C and beta-carotene)

8) more diverse samples

9) cost effectiveness for upscaling interventions

10) conduct process evaluations

Rocha et al. 2019 [55]

Meta-Analysis

1999 to July 2018

(1999–2017)

eHealth interventions: mobile devices (apps, text messages via cellphone), web or internet-based programs, computer-based programs (non-Internet based), and video games.

Tool:

guided by the Cochrane’s Risk of Bias Tool for RCTs

Quality rating:

5/19 (26%) good

12/19 (63%) fair

2/19 (11%) poor

1) tailor based on distal correlates and proximal determinants of dietary habits

2) link the types of BCTs implemented in the eHealth interventions to effectiveness

3) develop validated tools for measuring FVI

4) report intervention components and outcomes in greater detail

5) use of the CALO-RE taxonomy for uniformity in the reporting of BCTs

Schoeppe et al. 2016 [57]

Systematic review

January 2006 to November 2016

(2010–2016)

mHealth (App interventions):

stand-alone intervention using apps only, or a multi-component intervention including apps

Tool:

25-point criteria adapted from the CONSORT checklists

Quality rating:

11/27 (40%) high

8/27 (30%) fair

8/27 (30%) low

1) test the efficacy of specific app features and BCTs

2) efficacy of stand-alone app intervention vs multi-component app interventions

3) efficacy of app vs website, print-based and face-to-face interventions

4) utilize larger sample sizes

5) tailor app interventions to specific population groups with high app usage (e.g., women, young people)

6) report app usage statistics using device and self-report measures

7) optimal duration and intensity of app interventions

8) user engagement and retention in app interventions

9) relationship between user engagement and intervention efficacy (considering socio-demographic and psychosocial facets)

Stephenson et al. 2017 [48]

Systematic Review and Meta-analysis

from earliest available to June 2016 (2012–2016)

Computer, mobile or wearable technology

Tool:

Cochrane Collaboration’s risk of bias tool

Risk of bias:

1/17 (6%) low

3/17 (18%) unclear

13/17 (76%) high

1) focus on attrition rates

2) improve reporting of BCTs

3) improve detection bias by using objective measurement tools of SB

4) conduct extended follow-up

5) include outcome measures that will be of interest to workplaces and policy makers 6) use adaptive interventions