Here, we summarise the studies on the use of mobile health technology (m-Health) that were indexed in PubMed in February 2016.
Mobile health technology includes the use of smartphones, telemonitoring, mobile apps and similar online tools and devices to educate patients, caregivers and health professionals about disease, to promote healthy living in the general public, and to provide an interactive platform to aid communication and feedback between individuals and those helping them manage their disease. Although evidence of clinical benefit from such technologies is relatively sparse, there is a growing evidence base on the topic.
In this blog, we are building on previous summaries of the evidence on this topic by reviewing papers indexed in PubMed in February 2016 reporting results of studies evaluating m-Health technologies.
Key studies this month:
- Eleven studies found that m-Health interventions were as effective as face-to-face, traditional care in improving symptoms or quality of life or assisting clinicians with disease management, with some also demonstrating time savings for patients and/or clinicians.
Several studies found benefits from m-Health interventions compared with usual care:
- A systematic review of 5 studies found that SMS text messages may improve adherence with contraception Smith et al. 2015
- A systematic review found telemedicine interventions can promote positive behaviour changes and help manage type 2 diabetes Jalil et al. 2015
- A systematic review of 11 studies found that most reported greater weight loss and increased physical activity in obese adults using electronic activity monitoring systems than control groups Lewis et al. 2015
- Telephone-based counselling with CBT and work coaching improved productivity and depression in US employees significantly more than usual care Lerner et al. 2015
- SMS messaging, advice and tele-coaching improved exercise capacity and quality of life compared with usual care in Belgian cardiac rehabilitation patients Frederix et al. 2015
However, 2 studies found that outcomes might be worse with telemedicine:
- Telemedicine surveillance by a trained nurse led to worse outcomes and greater healthcare resource use than outpatient follow-up in Dutch patients with COPD Berkhof et al. 2015
- Computerised alerts to the receiving hospital reduced door-to-balloon time for percutaneous coronary angiography in the UK, but there was a non-significant trend to higher mortality compared with the previous system Brown et al. 2015
Papers looking at m-Health to promote healthy living in the general public
|Population||m-Health technology used||Outcome||Clinically important outcome relating to m-Health intervention?||Reference|
|Systematic review of 11 studies of overweight or obese adults receiving interventions to increase physical activity||Electronic activity monitoring systems||Most studies found significant improvements in physical activity or weight loss by the end of the study with good adherence||Yes||Lewis et al. 2015|
|Patients with type 2 diabetes or chronic obstructive pulmonary disease (COPD)who are insufficiently active, registered at 24 family practices||It’s Life! mobile and web-based monitoring and feedback tool plus self management support program vs support program alone vs usual care for 6 months||Significantly more physical activity in the It’s Life! group at end of the study and for following 3 months compared with other two groups, no significant difference between other 2 groups||No||van der Weegen et al. 2015|
|110 insufficiently active adults||Facebook app web-based social networking physical activity intervention with pedometer vs waiting list control||Significantly greater moderate to vigorous activity in intervention group at study end at 8 weeks, no difference by 20 weeks||Significantly greater moderate to vigorous activity in intervention group at study end at 8 weeks, no difference by 20 weeks||Maher et al. 2015|
|11,651 general public users of the program||10,000 Steps Australia physical activity promotion App vs website vs both||50% of users stopped logging activity data by 30 days; attrition lower with users of app vs web only||No||Guertler et al. 2015|
|US families||Utahfamilymeals.org web site and social media campaign using Facebook, Twitter for healthy eating||After 1 month, campaign had reached 10-12% of target population, cost 0.2 cents/person||No||Lister et al. 2015|
|Review of literature||Smartphone wearable ambulatory monitorsto sense human movement||Overview of sensors and algorithms to classify human movement||No||del Rosario et al. 2015|
|7 healthy volunteers||Smartphone monitoring of different types of physical activity||Best decision-tree classifier has 89.6% accuracy for detecting different movements||No||Miao et al. 2015|
|28 university students||ActiGraph accelerometer to detect different types of physical activity vs direct researcher observation||Accelerometer metrics had 95-98% accuracy, inclinometer accuracy 71%||No||Peterson et al. 2015|
|175 older women||Accelerometer to be worn 24 hours a day for 9 consecutive days, daily logs||Analysis algorithm and visual inspection of data had 74% and 81% agreement with logs on accelerometer usage||No||Rillamas-Sun et al. 2015|
|111 elderly adults aged 70-79 years||Actigraph accelerometer threshold determination compared with VO2max maximal oxygen intake||Thresholds for moderate intensity activity 669-3,367 counts/minute (CPM) for women, 834-4,048 CPM for men; vigorous intensity 1,625-4,868 CPM for women, 2,012-5,423 CPM for men||No||Zisko et al. 2015|