. 2017 Feb 11;24(4):867–879. doi: 10.1093/jamia/ocw166

Table 2.

Primary and secondary results of reviews, by technology type

Author (year)	Primary review results	Secondary review results
Mobile health interventions
Bort-Roig et al. (2014)¹¹	Good user perceptions of smartphone interventions’ usability and usefulness.	Smartphone strategies to influence PA were ad hoc, not theory-based. Intervention effects modest at best.
O’Reilly (2013)¹²	In all, 75% reported significant PA, sedentary behavior changes. These studies employed SMS communication to promote PA (n = 4), PA self-monitoring through mobile journaling (n = 4), or SMS and journaling.	Usability mixed; 58% agreed easy to use. No long-term follow-up.
Stephens (2013)¹³	In all, 71% reported significant results in at least one outcome, physical inactivity and/or weight.	High acceptability of text messaging and smartphone applications.
⁺Fanning et al. (2012)¹⁴	Significant moderate effects for mHealth interventions.	Moderate to large effect for pedometer steps. Nonsignificant effects for moderate-vigorous PA duration.
⁺Lyzwinski et al. (2014)¹⁵	Medium significant effects favoring mHealth interventions compared to controls.	Reduced BMI, waist circumference, body fat %; improved dietary intake and self-reported physical activity.
Computer and web-based interventions
⁺Civljak (2014)¹⁶	Several reported success of smoking cessation ≥6 months.	Programs tailored to individual responses had higher quit rates than UC. Internet may add benefit when used with nicotine pharmacotherapy.
Aneni et al. (2014)¹⁷	No effect on PA, dietary outcomes, lipid profiles, or hypertension. Modest improvements observed in weight.	Successful interventions included “human contact” and environmental modification, or targeted specific disease entities, eg, hypertension.
⁺Pal et al. (2013)¹⁸	Small effect of BG control, with a larger effect in the mobile phone group.	Little evidence for improving depression, health-related QoL, or weight.
Ramadas et al. (2011)¹⁹	Goal-setting, personalized coaching, interactive feedback, online peer support all successful.	Strong theoretical basis, longer intervention duration increased success, ie, only relatively longer studies (12 weeks) reported positive ﬁndings.
⁺Angeles et al. (2011)²⁰	Web-based tools better than UC for HbA1c and LDL-C.	Heterogeneity among studies with 12-month intervention.
Pietrzak et al. (2014)²¹	Majority of studies reported improvement in blood pressure and HbA1c in patients with T2DM.	Fewer CVD events and lower weight, improved lipid profile, eating habits, increased physical activity.
Pereira et al. (2015)²²	Effective at improving BG control and diabetes knowledge compared with UC.	Interventions with a human element seen as more attractive to users.
Levine et al. (2014)²³	Technology-assisted weight loss interventions compare favorably to other modalities.	Twelve (75%) interventions achieved weight loss (range: 0.08–5.4 kg) compared to controls, while 5%–45% of patients lost at least 5% of baseline weight.
⁺Lustria et al. (2013)²⁴	Tailored websites and programs more effective.	Targeting general populations more effective than specific groups.
⁺Reed et al. (2012)²⁵	Computer group lost significantly more weight.	Substitution studies: no difference between intervention and control.
van Vugt et al. (2013)²⁶	Nine saw improvements in depression, diabetes distress, well-being, self-efficacy, stress, communication.	Seven grounded in theoretical model; self-regulation theory, social learning theory most common.
Vegting et al. (2013)²⁷	Four had significant difference in BMI/weight; 2 had significant difference in SBP; 2 had significant difference in DBP.	Multiple modifiable lifestyle behavior. Internet interventions in primary or secondary care not superior to UC for CVD risk factors.
Yu et al. (2011)²⁸	Few tools met criteria for effectiveness, usability, usefulness, and sustainability.	Need to identify strategies to minimize website attrition and enable patients and clinicians to make informed decisions about website choice.
⁺Harris et al. (2011)²⁹	E-learning no more effective than other behavior change approaches to diet, reducing obesity or weight.	Heterogeneity of studies meant no firm conclusions could be drawn.
⁺Foster et al. (2013)³⁰	Positive, moderate-sized effects on increasing self-reported PA and cardiorespiratory fitness at 12 months.	Effectiveness of interventions supported by moderate-high quality studies
Buhi et al. (2013)³¹	In all, 35% of studies focusing on diabetes and improving diabetes management reported statistically significant improvements in BG.	Using SMS with longer intervention duration led to greater improvements in BG, BP, weight, smoking; 76.5% did not use theoretical framework, most had more than 300 participants.
Social media/social networking interventions
⁺Toma (2014)³²	Compared to controls, interventions reduced HbA1c, systolic and diastolic BP, triglycerides, TC.	Subgroup analysis: T2DM had greater HbA1c reduction than T1DM.
Telehealth and/or telemedicine
⁺Verhoeven et al. (2010)³³	Few studies showed significant differences between usual care and intervention groups.	High degree of heterogeneity and few quality studies.
⁺Merriel et al. (2014)³⁴	No evidence for overall CVD risk reduction.	Weak evidence for reduction of BP and total cholesterol, and no change in HDL or smoking rates.
Munro et al. (2013)³⁵	Home-based CR as effective as hospital-based. May produce longer-term gains via maintenance of PA.	Results positive with regard to patient outcomes and feedback.
⁺Omboni et al. (2012)³⁶	HBPT improved the physical component of QoL.	No difference was observed in the risk of adverse events.
Cassimatis et al. (2012)³⁷	Half reported significant improvements in BG control.	In total, 5/8 studies on dietary adherence, 5/8 on physical activity, 4/9 on BG self-monitoring, 3/8 on medication taking reported significant effects.
Combination of technologies
Connelly et al. (2013)³⁸	All reported an increase in physical activity: Web (n = 9), mHealth (n = 3), CD-ROM (n = 2), computer-based (n = 1); n = 9 reported a significant increase.	Promoting participant adherence leads to better outcomes. Logbooks, phone calls, and e-mails increased behavior change.
⁺Wieland et al. (2012)³⁹	Effective compared to no or minimal (pamphlets, UC) intervention.	Smaller effect (weight loss, lower levels of maintenance) compared to in-person interventions. Only one study examined 12-month outcomes.
Chang et al. (2013)⁴⁰	Social media use inconsistently reported.	Social media incorporated in online weight management interventions via message boards and chat rooms with unclear benefits.
⁺Saffari et al. (2014)⁴¹	Effect of interventions on glycemic control greater for text messaging and Internet (86%) than texting alone (44%).	Age, sample size, diabetes duration, period of intervention, level of HbA1c, and type of intervention may have implications for effectiveness.
Bacigalupo et al. (2013)⁴²	Strong evidence across several high-quality RCTs of short-term weight loss due to mHealth interventions.	Moderate evidence for medium-term outcomes, none >12 months.
Cotterez et al. (2014)⁴³	Two showed improvements in diet and/or PA; 2 had improvements in glycemic control compared to control.	Successful studies were theory-based, had interactive components with tracking and personalized feedback, opportunities for peer support.

+ = meta-analysis conducted; PA = physical activity; SMS = short messaging service; BG = blood glucose; UC = usual care; HbA1c = hemoglobin A1c; LDL-C = low-density lipoprotein; T2DM = type 2 diabetes mellitus; BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; TC = triglycerides; CR = cardiac rehabilitation; QoL = quality of life; RCT = randomized controlled trial; CVD = cardiovascular disease; T2D1 = type 1 diabetes mellitus; HDL-C = high-density lipoprotein; HBPT = Home Blood Pressure TeleMonitoring.