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Mobile health and medical apps weekly is out!

Read our newspaper about mobile health and medical applications!

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Calculate risk of stroke on Apple Watch

Read a review of our stroke app on iMedicalApps! via iMedicalApps.

New version of MedCalc for Apple Watch

We just released a new version of our Medical for Apple Watch application!

Version 2.2 introduces new scores and tools:

  • Ideal body weight calculation is now included with BMI and BSA
  • Corticosteroid equivalence calculator
  • CAGE questionnaire (screening of alcoholism)
  • Centor Score (probability of strep pharyngitis)

Medical for Apple Watch is a great tool for physicians and medical students. Previous versions already contained several tools including:

  • BMI, BSA calculation
  • eGFR
  • Cardiovascular scores:
    ABCD2, CHA2DS2 VASc, corrected QT, HEART Score, PE probability

It is fully optimized for Watch OS2!

The price ($0.99) remains unchanged! So don’t hesitate to take a look and try by yourself by clicking here

 

 

Emergency medicine and internal medicine trainees’ smartphone use in clinical settings in the United States

Purpose:
Smartphone technology offers a multitude of applications (apps) that provide a wide range of functions for healthcare professionals. Medical trainees are early adopters of this technology, but how they use smartphones in clinical care remains unclear. Our objective was to further characterize smartphone use by medical trainees at two US academic institutions, as well as their prior training in the clinical use of smartphones.
Methods:
In 2014, we surveyed 347 internal medicine and emergency medicine resident physicians at the University of Utah and Brigham and Women’s Hospital about their smartphone use and prior training experiences. Scores (0%–100%) were calculated to assess the frequency of their use of general features (email, text) and patient-specific apps, and the results were compared according to resident level and program using the Mann-Whitney U test.
Results:
A total of 184 residents responded (response rate, 53.0%). The average score for using general features, 14.4/20 (72.2%) was significantly higher than the average score for using patient-specific features and apps, 14.1/44(33.0%; P < 0.001). The average scores for the use of general features, were significantly higher for year 3–4 residents, 15.0/20 (75.1%) than year 1–2 residents, 14.1/20 (70.5%; P=0.035), and for internal medicine residents, 14.9/20 (74.6%) in comparison to emergency medicine residents, 12.9/20 (64.3%; P = 0.001). The average score reflecting the use of patient-specific apps was significantly higher for year 3–4 residents, 16.1/44 (36.5%) than for year 1–2 residents, 13.7/44 (31.1%; P = 0.044). Only 21.7% of respondents had received prior training in clinical smartphone use.
Conclusions:
Residents used smartphones for general features more frequently than for patient-specific features, but patient-specific use increased with training. Few residents have received prior training in the clinical use of smartphones.

via J Educ Eval Health Prof

Exercise and rehabilitation delivered through exergames in older adults: An integrative review of technologies, safety and efficacy

Abstract

Background

There has been a rapid increase in research on the use of virtual reality (VR) and gaming technology as a complementary tool in exercise and rehabilitation in the elderly population. Although a few recent studies have evaluated their efficacy, there is currently no in-depth description and discussion of different game technologies, physical functions targeted, and safety issues related to older adults playing exergames.

Objectives

This integrative review provides an overview of the technologies and games used, progression, safety measurements and associated adverse events, adherence to exergaming, outcome measures used, and their effect on physical function. Methods: We undertook systematic searches of SCOPUS and PubMed databases. Key search terms included “game”, “exercise”, and “aged”, and were adapted to each database. To be included, studies had to involve older adults aged 65 years or above, have a pre-post training or intervention design, include ICT-implemented games with weight-bearing exercises, and have outcome measures that included physical activity variables and/or clinical tests of physical function.

Results

Sixty studies fulfilled the inclusion criteria. The studies had a broad range of aims and intervention designs and mostly focused on community-dwelling healthy older adults. The majority of the studies used commercially available gaming technologies that targeted a number of different physical functions. Most studies reported that they had used some form of safety measure during intervention. None of the studies reported serious adverse events. However, only 21 studies (35%) reported on whether adverse events occurred. Twenty-four studies reported on adherence, but only seven studies (12%) compared adherence to exergaming with other forms of exercise. Clinical measures of balance were the most frequently used outcome measures. PEDro scores indicated that most studies had several methodological problems, with only 4 studies fulfilling 6 or more criteria out of 10. Several studies found positive effects of exergaming on balance and gait, while none reported negative effects.

Conclusion

Exergames show promise as an intervention to improve physical function in older adults, with few reported adverse events. As there is large variability between studies in terms of intervention protocols and outcome measures, as well as several methodological limitations, recommendations for both practice and further research are provided in order to successfully establish exergames as an exercise and rehabilitation tool for older adults.

via International Journal of Medical Informatics

mActive: A Randomized Clinical Trial of an Automated mHealth Intervention for Physical Activity Promotion

Background

We hypothesized that a fully automated mobile health (mHealth) intervention with tracking and texting components would increase physical activity.

Methods and Results

mActive enrolled smartphone users aged 18 to 69 years at an ambulatory cardiology center in Baltimore, Maryland. We used sequential randomization to evaluate the intervention’s 2 core components. After establishing baseline activity during a blinded run‐in (week 1), in phase I (weeks 2 to 3), we randomized 2:1 to unblinded versus blinded tracking. Unblinding allowed continuous access to activity data through a smartphone interface. In phase II (weeks 4 to 5), we randomized unblinded participants 1:1 to smart texts versus no texts. Smart texts provided smartphone‐delivered coaching 3 times/day aimed at individual encouragement and fostering feedback loops by a fully automated, physician‐written, theory‐based algorithm using real‐time activity data and 16 personal factors with a 10 000 steps/day goal. Forty‐eight outpatients (46% women, 21% nonwhite) enrolled with a mean±SD age of 58±8 years, body mass index of 31±6 kg/m2, and baseline activity of 9670±4350 steps/day. Daily activity data capture was 97.4%. The phase I change in activity was nonsignificantly higher in unblinded participants versus blinded controls by 1024 daily steps (95% confidence interval [CI], −580 to 2628; P=0.21). In phase II, participants receiving texts increased their daily steps over those not receiving texts by 2534 (95% CI, 1318 to 3750; P<0.001) and over blinded controls by 3376 (95% CI, 1951 to 4801; P<0.001).

Conclusions

An automated tracking‐texting intervention increased physical activity with, but not without, the texting component. These results support new mHealth tracking technologies as facilitators in need of behavior change drivers.

via Journal of the American Heart Association