Internet-delivered obesity treatment improves symptoms of and risk for depression

Effectiveness and Components of Web-Based Interventions on Weight Changes in Adults Who Were Overweight and Obese: A Systematic Review with Meta-Analyses

Overweight and obesity have reached epidemic proportions worldwide. The COVID-19 pandemic resulted in an increased need for remote implementation of weight-loss interventions; therefore, the effectiveness of web-based interventions needed to be assessed. This study aimed to examine the effectiveness of web-based interventions and their potency in facilitating weight changes in adults who were overweight or obese. We searched PubMed and Ichu-shi Web from the first year of inclusion in each database until the search date (30 September 2020). Among 1466 articles retrieved from the two databases and manual search, 97 were selected to undergo qualitative analysis and 51 articles were subjected to quantitative analysis. Qualitative analysis of 97 articles demonstrated that articles showing significant effectiveness mostly used the following components: social support, self-monitoring for behavior, self-monitoring for the outcome (weight), behavioral goal setting, information about health consequences, and outcome goal setting. Quantitative analysis of 51 articles showed a significant effectiveness of web-based intervention (standardized mean difference, -0.57; 95% confidence interval, -0.75 to -0.40). This study demonstrated the effectiveness of web-based interventions on weight change in adults with overweight and obesity. Subgroup meta-analyses identified personalized information provision and expert advice to be remarkably effective components.

Does Internet Use Impact the Health Status of Middle-Aged and Older Populations? Evidence from China Health and Retirement Longitudinal Study (CHARLS)

In the context of both rapid technological development and increasing aging, the relationship between technological development and the health of the middle-aged and older population is gradually receiving academic attention. This study empirically examined the health consequences of the Internet for the middle-aged and older population in China using data from the 2018 China Health and Retirement Longitudinal Study. The results indicated that Internet use was effective in improving the self-assessed health and chronic disease status of the middle-aged and older population. However, the effect of Internet use on the improvement of chronic disease conditions in this population was more pronounced than self-assessed health. In the heterogeneity analysis, the effect of Internet use on the health of female and middle-aged adults was more significant than that of male and older adults aged >60 years. This paper also used a propensity score matching model to eliminate the endogeneity problem caused by sample selectivity bias. The results revealed that the propensity score matching model analysis was more robust. Moreover, if sample selectivity bias was not eliminated, the effect of Internet use on the improvement of self-assessed health in the middle-aged and older population would be underestimated, whereas the effect of Internet use on the chronic disease status of the middle-aged and older adults would be overestimated.

Depression and obesity among females, are sex specificities considered?

This study aimed to systematically review the relationship of obesity-depression in the female sex. We carried out a systematic search (PubMed, MEDLINE, Embase) to quantify the articles (controlled trials and randomized controlled trials) regarding obesity and depression on a female population or a mixed sample. Successively, we established whether the sex specificities were studied by the authors and if they reported on collecting data regarding factors that may contribute to the evolution of obesity and depression and that could be responsible for the greater susceptibility of females to those conditions. After applying the inclusion and exclusion criteria, we found a total of 20 articles with a female sample and 54 articles with a mixed sample. More than half of all articles (51.35%, n = 38) evaluated the relationship between depression and obesity, but only 20 (27.03%) evaluated this relationship among females; still, 80% of those (n = 16) presented supporting results. However, few articles considered confounding factors related to female hormones (12.16%, n = 9) and none of the articles focused on factors responsible for the binomial obesity-depression in the female sex. The resulting articles also supported that depression (and related impairments) influencing obesity (and related impairments) is a two-way road. This systematic review supports the concurrency of obesity-depression in females but also shows how sex specificities are ultimately under-investigated. Female sex specificity is not being actively considered when studying the binomial obesity-depression, even within a female sample. Future studies should focus on trying to understand how the female sex and normal hormonal variations influence these conditions.

Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study

BACKGROUND

New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps.

OBJECTIVE

This study aimed to reveal the profiles of users of a mental health app through machine learning techniques.

METHODS

We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login.

RESULTS

The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features.

CONCLUSIONS

User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.

Early Engagement is Associated with Better Weight Loss in Emerging Adults

Objectives: Predictors of success among emerging adults (EAs; ages 18-25) within behavioral weight loss (BWL) trials are largely unknown. We examined whether early program engagement predicted overall engagement and weight loss in EAs. Methods: Data were pooled from 2 randomized controlled pilot trials in EAs. Participants (N = 99, 80% female, BMI = 33.7±5.1 kg/m²) received a 3-month BWL intervention. Weight was objectively assessed at 0 and 3 months; engagement was tracked weekly; retention was assessed at 3 months. Results: Greater engagement during the initial 4 weeks of treatment predicted greater weight loss (p = .001). Compared to those who did not engage in all 4 initial weeks, participants meeting this threshold experienced greater overall engagement (9.6 vs 4.2 weeks, p < .001), weight losses (intent-to-treat = -3.8% vs -1.3%, p = .004), and retention (78% vs 53%, p = .012). Conclusions: Early engagement in BWL is associated with better outcomes among EAs. Monitoring engagement in real-time during the initial 4 weeks of treatment may be necessary to intervene effectively. Early engagement did not vary by sex or race; future work should identify characteristics associated with poor early engagement.

Assessment of Use and Preferences Regarding Internet-Based Health Care Delivery: Cross-Sectional Questionnaire Study

Background

There has been an incremental increase in the use of technology in health care delivery. Feasibility, acceptability, and efficacy of interventions based on internet technologies are supported by a growing body of evidence.

Objective

The aim of this study was to investigate use and preferences in the general adult population in Germany for remote, internet-based interaction (eg, email, videoconferencing, electronic medical records, apps).

Methods

A nationwide cross-sectional questionnaire survey in adults that was representative in terms of age, sex and educational level was carried out.

Results

A total of 22.16% (538/2428) of survey participants reported not using the internet for work or private use. The nonuser phenotype can be described as being older, having lower educational and income status, and living in less populated areas. The majority of participants within the cohort of internet users reported that they would not consider using electronic medical records (973/1849, 52.62%), apps (988/1854, 53.29%), or emails to report symptoms (1040/1838, 56.58%); teleconference with one (1185/1852, 63.98%) or more experts (1239/1853, 66.86%); or participate in video psychotherapy (1476/1853, 79.65%) for the purpose of medical consultation or treatment. Older age and lower educational level were the most robust predictors of assumed future denial of use.

Conclusions

Our results point toward low use and preference rates among the general population for the use of telemedicine. It also seems that those who might benefit from telemedical interventions the most, are, in fact, those who are most hesitating. These low use and preference rates of eHealth should be considered prior to designing and providing future telemedical care, supporting the need for easy-to-use, data secure solutions.