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Introduction: Smartphone apps to support individuals in recovery from substance use disorders (SUDs) are increasingly available. Although many people with SUDs express interest in recovery support apps, few try them or use them long-term. Strategies like gamification and contingency management are increasingly being considered to sustain engagement. This study sought to describe features of a recovery support app called the Addiction version of the Comprehensive Health Enhancement Support System (A-CHESS) that are most used by individuals in SUD recovery and what makes individuals more likely to use these apps. Methods: A total of 202 peoplewithA-CHESS accounts completed an online survey assessing their experiences using A-CHESS between April and June 2021. We described app features reported to be most beneficial for managing anxiety, loneliness, and isolation during COVID-19; reasons for not using A-CHESS; and suggested app features for future recovery support apps. Results: Respondents had a mean age of 41 years, 85% were White, and 61% were female. Respondents reported that app features related to messaging (ie, open discussion boards and private messaging) and informational or motivational resources were the most useful for managing isolation, anxiety, and loneliness. Reasons for not using A-CHESS were not knowing how to use the app and the app not being part of a personalized treatment plan. The most common suggested components for future apps were rewards for meeting goals and a support meeting locator. Conclusions: Ensuring that health apps are intuitive and include features that appeal to patients and educating patients about features apps already include that help them meet goals may enhance engagement with recovery apps. [ABSTRACT FROM AUTHOR]

Objectives To determine whether an online support tool can impact anxiety in women experiencing an abnormal mammogram. Materials and methods We developed an online support system using the Comprehensive Health Enhancement Support System (CHESS) designed for women experiencing an abnormal mammogram as a model. Our trial randomized 130 of these women to online support (the intervention group) or to a list of five commonly used Internet sites (the comparison group). Surveys assessed anxiety and breast cancer worry, and patient satisfaction at three important clinical time points: when women were notified of their abnormal mammogram, at the time of diagnostic imaging, and at the time of biopsy (if biopsy was recommended). Results Study participants in the intervention group showed a significant decrease in anxiety at the time of biopsy compared to the comparison group ( p = 0.017). However, there was no significant difference in anxiety between the intervention group and the comparison group at the time of diagnostic work-up. We discontinued assessment of patient satisfaction after finding that many women had substantial difficulty answering the questions that referenced their physician, because they did not understand who their physician was for this process of care. Conclusion The combination of the inability to identify the physician providing care during the mammography work-up and anxiety effects seen only after an interaction with the breast imaging team may indicate that online support only decreases the anxiety of women in concert with direct interpersonal support from the healthcare team. [ABSTRACT FROM AUTHOR]

Objective: Informal caregivers (family and friends) of people with cancer are often unprepared for their caregiving role, leading to increased burden or distress. Comprehensive Health Enhancement Support System (CHESS) is a Web-based lung cancer information, communication, and coaching system for caregivers. This randomized trial reports the impact on caregiver burden, disruptiveness, and mood of providing caregivers access to CHESS versus the Internet with a list of recommended lung cancer websites. Methods: A total of 285 informal caregivers of patients with advanced nonsmall cell lung cancer were randomly assigned to a comparison group that received Internet or a treatment group that received Internet and CHESS. Caregivers were provided a computer and Internet service if needed. Written surveys were completed at pretest and during the intervention period bimonthly for up to 24 months. Analyses of covariance (ANCOVAs) compared the intervention's effect on caregivers' disruptiveness and burden (CQOLI-C), and negative mood (combined Anxiety, Depression, and Anger scales of the POMS) at 6 months, controlling for blocking variables (site, caregiver's race, and relationship to patient) and the given outcome at pretest. Results: Caregivers randomized to CHESS reported lower burden, r(84) = 2.36, p = .021, d = .39, and negative mood, r(86) = 2.82, p = .006, d = .44, than those in the Internet group. The effect on disruptiveness was not significant. Conclusions: Although caring for someone with a terminal illness will always exact a toll on caregivers, eHealth interventions like CHESS may improve caregivers' understanding and coping skills and, as a result, ease their burden and mood. [ABSTRACT FROM AUTHOR]

Importance: Patients leaving residential treatment for alcohol use disorders are not typically offered evidence-based continuing care, although research suggests that continuing care is associated with better outcomes. A smartphone-based application could provide effective continuing care.Objective: To determine whether patients leaving residential treatment for alcohol use disorders with a smartphone application to support recovery have fewer risky drinking days than control patients.Design, Setting, and Participants: An unmasked randomized clinical trial involving 3 residential programs operated by 1 nonprofit treatment organization in the Midwestern United States and 2 residential programs operated by 1 nonprofit organization in the Northeastern United States. In total, 349 patients who met the criteria for DSM-IV alcohol dependence when they entered residential treatment were randomized to treatment as usual (n = 179) or treatment as usual plus a smartphone (n = 170) with the Addiction-Comprehensive Health Enhancement Support System (A-CHESS), an application designed to improve continuing care for alcohol use disorders.Interventions: Treatment as usual varied across programs; none offered patients coordinated continuing care after discharge. A-CHESS provides monitoring, information, communication, and support services to patients, including ways for patients and counselors to stay in contact. The intervention and follow-up period lasted 8 and 4 months, respectively.Main Outcomes and Measures: Risky drinking days--the number of days during which a patient's drinking in a 2-hour period exceeded 4 standard drinks for men and 3 standard drinks for women, with standard drink defined as one that contains roughly 14 g of pure alcohol (12 oz of regular beer, 5 oz of wine, or 1.5 oz of distilled spirits). Patients were asked to report their risky drinking days in the previous 30 days on surveys taken 4, 8, and 12 months after discharge from residential treatment.Results: For the 8 months of the intervention and 4 months of follow-up, patients in the A-CHESS group reported significantly fewer risky drinking days than did patients in the control group, with a mean of 1.39 vs 2.75 days (mean difference, 1.37; 95% CI, 0.46-2.27; P = .003).Conclusions and Relevance: The findings suggest that a multifeatured smartphone application may have significant benefit to patients in continuing care for alcohol use disorders.Trial Registration: clinicaltrials.gov Identifier: NCT01003119. [ABSTRACT FROM AUTHOR]

IMPORTANCE; Patients leaving residential treatment for alcohol use disorders are not typically offered evidence-based continuing care, although research suggests that continuing care is associated with better outcomes. A smartphone-based application could provide effective continuing care. OBJECTIVE: To determine whether patients leaving residential treatment for alcohol use disorders with a smartphone application to support recovery have fewer risky drinking days than control patients. DESIGN, SETTING, AND PARTICIPANTS: An unmasked randomized clinical trial involving 3 residential programs operated by 1 nonprofit treatment organization in the Midwestern United States and 2 residential programs operated by 1 nonprofit organization in the Northeastern United States. In total, 349 patients who met the criteria for DS/W-/Valcohol dependence when they entered residential treatment were randomized to treatment as usual (n = 179) or treatment as usual plus a smartphone (n = 170) with the Addiction-Comprehensive Health Enhancement Support System (A-CHESS), an application designed to improve continuing care for alcohol use disorders. INTERVENTIONS: Treatment as usual varied across programs; none offered patients coordinated continuing care after discharge. A-CHESS provides monitoring, information, communication, and support services to patients, including ways for patients and counselors to stay in contact. The intervention and follow-up period lasted 8 and 4 months, respectively. MAIN OUTCOMES AND MEASURES: Risky drinking days-the number of days during which a patient's drinking in a 2-hour period exceeded 4 standard drinks for men and 3 standard drinks for women, with standard drink defined as one that contains roughly 14 g of pure alcohol (12 oz of regular beer, 5 oz of wine, or 1.5 oz of distilled spirits). Patients were asked to report their risky drinking days in the previous 30 days on surveys taken 4,8, and 12 months after discharge from residential treatment. RESULTS: For the 8 months of the intervention and 4 months of follow-up, patients in the A-CHESS group reported significantly fewer risky drinking days than did patients in the control group, with a mean of 1.39 vs 2.75 days (mean difference, 1.37; 95% CI, 0.46-2.27; P = .003). CONCLUSIONS AND RELEVANCE: The findings suggest that a multifeatured smartphone application may have significant benefit to patients in continuing care for alcohol use disorders. [ABSTRACT FROM AUTHOR]

The article reports on research which was conducted to evaluate an online intervention system of resources for adult children of alcoholics called the Comprehensive Health Enhancement Support System (CHESS). Researchers evaluated the intervention with 23 subjects. They found that the subjects who used the intervention had a higher attendance rate in group therapy sessions but concluded that their sample size was too small to lead to definitive conclusions.

Abstract: Purpose: The Internet offers a potential medium for delivering smoking cessation treatment to adolescents. However, few Internet-based cessation programs for adolescents have been evaluated. We describe adolescent use of a home-based Internet intervention to stop smoking (Stomp Out Smokes [SOS]) and explore baseline characteristics associated with SOS use. Methods: Participants were 70 adolescent smokers aged 12–18 years (50% female, 90% Caucasian) randomized to receive the SOS intervention for 24 weeks as part of a larger clinical trial. SOS comprised 40 components, of which eight were primarily interactive (e.g., discussion support group, ask an expert, quit plan) and 32 were primarily informational (e.g., managing withdrawal, medications to stop smoking). SOS use data were captured electronically, including total logins to the site, and type of SOS components used defined by page hits on the interactive and information components. Results: A total of 7,708 SOS website pages (6825 interactive and 883 informational) were accessed over the 24 weeks. The highest proportion of page hits was for the discussion support group (35%) and quit plan (30%). Interactive pages were significantly more likely to be used than informational pages (median 65 vs. 6, p < .001). Males accessed fewer interactive pages compared with females (p = .04). No other baseline characteristics were univariately associated with total logins or use of informational or interactive pages. Conclusions: Adolescent smokers most often used a discussion support group and other interactive Internet-based cessation components. Future studies designed to increase adolescent use, and efficacy of, Internet-based cessation programs are warranted. [Copyright &y& Elsevier]

Psychological interventions for women coping with breast cancer (BC) have embraced the idea that the open expression of negative emotions is beneficial. This study asks if there is a difference in the amount of negative emotions expressed by members of professional-led compared to self-directed on-line support groups. To answer this question we examined the support group messages written by participants of 11 Internet delivered BC support groups. Seven were facilitated by professionals and four were self-directed. We used two text analysis software programs, LWIC and PCAD, to categorize the interaction. Groups conducted by professionals expressed significantly more negative emotions, anxiety, hostility, and depression and fewer positive emotions than self-directed groups. These differences suggest what leaders encourage, reinforce, model, and may influence members' behaviors. Professionals encourage the further expression of such emotions, whereas lay leaders more often respond with support and reassurance to members who express painful emotions. [ABSTRACT FROM AUTHOR]

Presents the results of telephone interviews of 23 health care leaders to understand what managers need to adopt total quality management (TQM). TQM principles; Innovation and improvement literature; Discussion and conclusions.

Background: Online discussion forums allow those in addiction recovery to seek help through text-based messages, including when facing triggers to drink or use drugs. Trained staff (or "moderators") may participate within these forums to offer guidance and support when participants are struggling but must expend considerable effort to continually review new content. Demands on moderators limit the scalability of evidence-based digital health interventions.Objective: Automated identification of recovery problems could allow moderators to engage in more timely and efficient ways with participants who are struggling. This paper aimed to investigate whether computational linguistics and supervised machine learning can be applied to successfully flag, in real time, those discussion forum messages that moderators find most concerning.Methods: Training data came from a trial of a mobile phone-based health intervention for individuals in recovery from alcohol use disorder, with human coders labeling discussion forum messages according to whether or not authors mentioned problems in their recovery process. Linguistic features of these messages were extracted via several computational techniques: (1) a Bag-of-Words approach, (2) the dictionary-based Linguistic Inquiry and Word Count program, and (3) a hybrid approach combining the most important features from both Bag-of-Words and Linguistic Inquiry and Word Count. These features were applied within binary classifiers leveraging several methods of supervised machine learning: support vector machines, decision trees, and boosted decision trees. Classifiers were evaluated in data from a later deployment of the recovery support intervention.Results: To distinguish recovery problem disclosures, the Bag-of-Words approach relied on domain-specific language, including words explicitly linked to substance use and mental health ("drink," "relapse," "depression," and so on), whereas the Linguistic Inquiry and Word Count approach relied on language characteristics such as tone, affect, insight, and presence of quantifiers and time references, as well as pronouns. A boosted decision tree classifier, utilizing features from both Bag-of-Words and Linguistic Inquiry and Word Count performed best in identifying problems disclosed within the discussion forum, achieving 88% sensitivity and 82% specificity in a separate cohort of patients in recovery.Conclusions: Differences in language use can distinguish messages disclosing recovery problems from other message types. Incorporating machine learning models based on language use allows real-time flagging of concerning content such that trained staff may engage more efficiently and focus their attention on time-sensitive issues. [ABSTRACT FROM AUTHOR]

Background: Despite the near ubiquity of mobile phones, little research has been conducted on the implementation of mobile health (mHealth) apps to treat patients in primary care. Although primary care clinicians routinely treat chronic conditions such as asthma and diabetes, they rarely treat addiction, a common chronic condition. Instead, addiction is most often treated in the US health care system, if it is treated at all, in a separate behavioral health system. mHealth could help integrate addiction treatment in primary care.Objective: The objective of this paper was to report the effects of implementing an mHealth system for addiction in primary care on both patients and clinicians.Methods: In this implementation research trial, an evidence-based mHealth system named Seva was introduced sequentially over 36 months to a maximum of 100 patients with substance use disorders (SUDs) in each of three federally qualified health centers (FQHCs; primary care clinics that serve patients regardless of their ability to pay). This paper reports on patient and clinician outcomes organized according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.Results: The outcomes according to the RE-AIM framework are as follows: Reach-Seva reached 8.31% (268/3226) of appropriate patients. Reach was limited by our ability to pay for phones and data plans for a maximum of 100 patients per clinic. Effectiveness-Patients who were given Seva had significant improvements in their risky drinking days (44% reduction, (0.7-1.25)/1.25, P=.04), illicit drug-use days (34% reduction, (2.14-3.22)/3.22, P=.01), quality of life, human immunodeficiency virus screening rates, and number of hospitalizations. Through Seva, patients also provided peer support to one another in ways that are novel in primary care settings. Adoption-Patients sustained high levels of Seva use-between 53% and 60% of the patients at the 3 sites accessed Seva during the last week of the 12-month implementation period. Among clinicians, use of the technology was less robust than use by patients, with only a handful of clinicians using Seva in each clinic and behavioral health providers making most referrals to Seva in 2 of the 3 clinics. Implementation-At 2 sites, implementation plans were realized successfully; they were delayed in the third. Maintenance-Use of Seva dropped when grant funding stopped paying for the mobile phones and data plans. Two of the 3 clinics wanted to maintain the use of Seva, but they struggled to find funding to support this.Conclusions: Implementing an mHealth system can improve care among primary care patients with SUDs, and patients using the system can support one another in their recovery. Among clinicians, however, implementation requires figuring out how information from the mHealth system will be used and making mHealth data available in the electronic health (eHealth) record. In addition, paying for an mHealth system remains a challenge. [ABSTRACT FROM AUTHOR]

Background: Symptom distress in patients toward the end of life can change rapidly. Family caregivers have the potential to help patients manage those symptoms, as well as their own stress, if they are equipped with the proper resources. Electronic health (eHealth) systems may be able to provide those resources. Very sick patients may not be able to use such systems themselves to report their symptoms but family caregivers could.Objective: The aim of this paper was to assess the effects on cancer patient symptom distress of an eHealth system that alerts clinicians to significant changes in the patient's symptoms, as reported by a family caregiver.Methods: A pooled analysis from two randomized clinical trials (NCT00214162 and NCT00365963) compared outcomes at 12 months for two unblinded groups: a control group (Comprehensive Health Enhancement Support System [CHESS]-Only) that gave caregivers access to CHESS, an online support system, and an experimental group (CHESS+CR [Clinician Report]), which also had CHESS but with a CR that automatically alerted clinicians if symptoms exceeded a predetermined threshold of severity. Participants were dyads (n=235) of patients with advanced lung, breast, or prostate cancer and their respective family caregivers from 5 oncology clinics in the United States of America. The proportion of improved patient threshold symptoms was compared between groups using area-under-the-curve analysis and binomial proportion tests. The proportion of threshold symptoms out of all reported symptoms was also examined.Results: When severe caregiver-reported symptoms were shared with clinicians, the symptoms were more likely to be subsequently reported as improved than when the symptoms were not shared with clinicians (P<.001). Fewer symptom reports were completed in the group of caregivers whose reports went to clinicians than in the CHESS-Only group (P<.001), perhaps because caregivers, knowing their reports might be sent to a doctor, feared they might be bothering the clinician.Conclusions: This study suggests that an eHealth system designed for caregivers that alerts clinicians to worrisome changes in patient health status may lead to reduced patient distress.Trial Registration: Clinicaltrials.gov NCT00214162; https://clinicaltrials.gov/ct2/show/NCT00214162 (Archived by WebCite at http://www.webcitation.org/6nmgdGfuD) and Clinicaltrials.gov NCT00365963; https://clinicaltrials.gov/ct2/show/NCT00365963 (Archived by WebCite at http://www.webcitation.org/6nmh0U8VP). [ABSTRACT FROM AUTHOR]

Background: eHealth interventions appear and change so quickly that they challenge the way we conduct research. By the time a randomized trial of a new intervention is published, technological improvements and clinical discoveries may make the intervention dated and unappealing. This and the spate of health-related apps and websites may lead consumers, patients, and caregivers to use interventions that lack evidence of efficacy.Objective: This paper aims to offer strategies for increasing the speed and usefulness of eHealth research.Methods: The paper describes two types of strategies based on the authors' own research and the research literature: those that improve the efficiency of eHealth research, and those that improve its quality.Results: Efficiency strategies include: (1) think small: conduct small studies that can target discrete but significant questions and thereby speed knowledge acquisition; (2) use efficient designs: use such methods as fractional-factorial and quasi-experimental designs and surrogate endpoints, and experimentally modify and evaluate interventions and delivery systems already in use; (3) study universals: focus on timeless behavioral, psychological, and cognitive principles and systems; (4) anticipate the next big thing: listen to voices outside normal practice and connect different perspectives for new insights; (5) improve information delivery systems: researchers should apply their communications expertise to enhance inter-researcher communication, which could synergistically accelerate progress and capitalize upon the availability of "big data"; and (6) develop models, including mediators and moderators: valid models are remarkably generative, and tests of moderation and mediation should elucidate boundary conditions of effects and treatment mechanisms. Quality strategies include: (1) continuous quality improvement: researchers need to borrow engineering practices such as the continuous enhancement of interventions to incorporate clinical and technological progress; (2) help consumers identify quality: consumers, clinicians, and others all need to easily identify quality, suggesting the need to efficiently and publicly index intervention quality; (3) reduce the costs of care: concern with health care costs can drive intervention adoption and use and lead to novel intervention effects (eg, reduced falls in the elderly); and (4) deeply understand users: a rigorous evaluation of the consumer's needs is a key starting point for intervention development.Conclusions: The challenges of distinguishing and distributing scientifically validated interventions are formidable. The strategies described are meant to spur discussion and further thinking, which are important, given the potential of eHealth interventions to help patients and families. [ABSTRACT FROM AUTHOR]

The Institute of Medicine defines a good death a "one that is free from avoidable death and suffering for patients, families and caregivers in general accordance with the patients' and families' wishes.". The current system creates barriers to reducing the stress and suffering that accompany a patient's end of life. Data and eHealth technology, if it were more accessible, could help patients, families, and caregivers to cope with end of life issues. [ABSTRACT FROM AUTHOR]