BackgroundMobile phone health apps may now seem to be ubiquitous, yet much remains unknown with regard to their usage. Information is limited with regard to important metrics, including the percentage of the population that uses health apps, reasons for adoption/nonadoption, and reasons for noncontinuance of use.ObjectiveThe purpose of this study was to examine health app use among mobile phone owners in the United States.MethodsWe conducted a cross-sectional survey of 1604 mobile phone users throughout the United States. The 36-item survey assessed sociodemographic characteristics, history of and reasons for health app use/nonuse, perceived effectiveness of health apps, reasons for stopping use, and general health status.ResultsA little over half (934/1604, 58.23%) of mobile phone users had downloaded a health-related mobile app. Fitness and nutrition were the most common categories of health apps used, with most respondents using them at least daily. Common reasons for not having downloaded apps were lack of interest, cost, and concern about apps collecting their data. Individuals more likely to use health apps tended to be younger, have higher incomes, be more educated, be Latino/Hispanic, and have a body mass index (BMI) in the obese range (all P<.05). Cost was a significant concern among respondents, with a large proportion indicating that they would not pay anything for a health app. Interestingly, among those who had downloaded health apps, trust in their accuracy and data safety was quite high, and most felt that the apps had improved their health. About half of the respondents (427/934, 45.7%) had stopped using some health apps, primarily due to high data entry burden, loss of interest, and hidden costs.ConclusionsThese findings suggest that while many individuals use health apps, a substantial proportion of the population does not, and that even among those who use health apps, many stop using them. These data suggest that app developers need to better address consumer concerns, such as cost and high data entry burden, and that clinical trials are necessary to test the efficacy of health apps to broaden their appeal and adoption.

Objective Computer-tailored interventions have become increasingly common for facilitating improvement in behaviors related to chronic disease and health promotion. A sufficient number of outcome studies from these interventions are now available to facilitate the quantitative analysis of effect sizes, permitting moderator analyses that were not possible with previous systematic reviews. Method The present study employs meta-analytic techniques to assess the mean effect for 88 computer-tailored interventions published between 1988 and 2009 focusing on four health behaviors: smoking cessation, physical activity, eating a healthy diet, and receiving regular mammography screening. Effect sizes were calculated using Hedges g. Study, tailoring, and demographic moderators were examined by analyzing between-group variance and meta-regression. Results Clinically and statistically significant overall effect sizes were found across each of the four behaviors. While effect sizes decreased after intervention completion, dynamically tailored interventions were found to have increased efficacy over time as compared with tailored interventions based on one assessment only. Study effects did not differ across communication channels nor decline when up to three behaviors were identified for intervention simultaneously. Conclusion This study demonstrates that computer-tailored interventions have the potential to improve health behaviors and suggests strategies that may lead to greater effectiveness of these techniques.

The transtheoretical model, in general, and the stages of change, in particular, have proven useful in adapting or tailoring treatment to the individual. We define the stages and processes of change and then review previous meta-analyses on their interrelationship. We report an original meta-analysis of 39 studies, encompassing 8,238 psychotherapy patients, to assess the ability of stages of change and related readiness measures to predict psychotherapy outcomes. Clinically significant effect sizes were found for the association between stage of change and psychotherapy outcomes (d = .46); the amount of progress clients make during treatment tends to be a function of their pretreatment stage of change. We examine potential moderators in effect size by study outcome, patient characteristics, treatment features, and diagnosis. We also review the large volume of behavioral health research, but scant psychotherapy research, that demonstrates the efficacy of matching treatment to the patient's stage of change. Limitations of the extant research are noted, and practice recommendations are advanced.

Quality of life (QOL) is a multidimensional construct that includes physical, psychological, and relationship well-being. We conducted a systematic review and meta-analysis of randomized controlled studies published between 1980 and 2012 of interventions conducted with both cancer patients and their partners that were aimed at improving QOL. Using bibliographic software and manual review, two independent raters reviewed 752 articles with a systematic process for reconciling disagreement, yielding 23 articles for systematic review and 20 for meta-analysis. Most studies were conducted in breast and prostate cancer populations. Study participants (N=2645) were primarily middle-aged (Mean = 55 years old) and white (84%). For patients, the weighted average effect size (g) across studies was 0.25 (95% CI = 0.12-0.32) for psychological outcomes (17 studies), 0.31 (95% CI = 0.11-0.50) for physical outcomes (12 studies), and 0.28 (95% CI = 0.14-0.43) for relationship outcomes (10 studies). For partners, the weighted average effect size was 0.21 (95% CI = 0.08-0.34) for psychological outcomes (12 studies), and 0.24 (95% CI = 0.6 - 0.43) for relationship outcomes (7 studies). Therefore, couple-based interventions had small but beneficial effects in terms of improving multiple aspects of QOL for both patients and their partners. Questions remain regarding when such interventions should be delivered and for how long. Identifying theoretically based mediators and key features that distinguish couple-based from patient-only interventions may help to strengthen their effects on patient and partner QOL.

This review offers promising findings on the impact of cancer care coordination on increasing value and reducing healthcare costs in the USA.

Introduction Erectile function recovery (EFR) rates after radical prostatectomy (RP) vary greatly based on a number of factors, such as erectile dysfunction (ED) definition, data acquisition means, time-point postsurgery, and population studied. Aim To conduct a meta-analysis of carefully selected reports from the available literature to define the EFR rate post-RP. Main Outcome Measures EFR rate after RP. Methods An EMBASE and MEDLINE search was conducted for the time range 1985–2007. Articles were assessed blindly by strict inclusion criteria: report of EFR data post-RP, study population ≥50 patients, ≥1 year follow-up, nerve-sparing status declared, no presurgery ED, and no other prostate cancer therapy. Meta-analysis was conducted to determine the EFR rate and relative risks (RR) for dichotomous subgroups. Results A total of 212 relevant studies were identified; only 22 (10%) met the inclusion criteria and were analyzed (9,965 RPs, EFR data: 4,983 subjects). Mean study population size: 226.5, standard deviation = 384.1 (range: 17–1,834). Overall EFR rate was 58%. Single center series publications (k = 19) reported a higher EFR rate compared with multicenter series publications (k = 3): 60% vs. 33%, RR = 1.82, P = 0.001. Studies reporting ≥18-month follow-up (k = 10) reported higher EFR rate vs. studies with <18-month follow-up (k = 12), 60% vs. 56%, RR = 1.07, P = 0.02. Open RP (k = 16) and laparoscopic RP (k = 4) had similar EFR (57% vs. 58%), while robot-assisted RP resulted in a higher EFR rate (k = 2), 73% compared with these other approaches, P = 0.001. Patients <60 years old had a higher EFR rate vs. patients ≥60 years, 77% vs. 61%, RR = 1.26, P = 0.001. Conclusions These data indicate that most of the published literature does not meet strict criteria for reporting post-RP EFR. Single and multiple surgeon series have comparable EFR rates, but single center studies have a higher EFR. Younger men have higher EFR and no significant difference in EFR between ORP and LRP is evident.

Psychosocial interventions had medium-size effects on both pain severity and interference. These robust findings support the systematic implementation of quality-controlled psychosocial interventions as part of a multimodal approach to the management of pain in patients with cancer.

BackgroundMobile apps hold promise for serving as a lifestyle intervention in public health to promote wellness and attenuate chronic conditions, yet little is known about how individuals with chronic illness use or perceive mobile apps.ObjectiveThe objective of this study was to explore behaviors and perceptions about mobile phone–based apps for health among individuals with chronic conditions.MethodsData were collected from a national cross-sectional survey of 1604 mobile phone users in the United States that assessed mHealth use, beliefs, and preferences. This study examined health app use, reason for download, and perceived efficacy by chronic condition.ResultsAmong participants, having between 1 and 5 apps was reported by 38.9% (314/807) of respondents without a condition and by 6.6% (24/364) of respondents with hypertension. Use of health apps was reported 2 times or more per day by 21.3% (172/807) of respondents without a condition, 2.7% (10/364) with hypertension, 13.1% (26/198) with obesity, 12.3% (20/163) with diabetes, 12.0% (32/267) with depression, and 16.6% (53/319) with high cholesterol. Results of the logistic regression did not indicate a significant difference in health app download between individuals with and without chronic conditions (P>.05). Compared with individuals with poor health, health app download was more likely among those with self-reported very good health (odds ratio [OR] 3.80, 95% CI 2.38-6.09, P<.001) and excellent health (OR 4.77, 95% CI 2.70-8.42, P<.001). Similarly, compared with individuals who report never or rarely engaging in physical activity, health app download was more likely among those who report exercise 1 day per week (OR 2.47, 95% CI 1.6-3.83, P<.001), 2 days per week (OR 4.77, 95% CI 3.27-6.94, P<.001), 3 to 4 days per week (OR 5.00, 95% CI 3.52-7.10, P<.001), and 5 to 7 days per week (OR 4.64, 95% CI 3.11-6.92, P<.001). All logistic regression results controlled for age, sex, and race or ethnicity.ConclusionsResults from this study suggest that individuals with poor self-reported health and low rates of physical activity, arguably those who stand to benefit most from health apps, were least likely to report download and use these health tools.