There are several parameters which have shown (or are supposed) to influence the development of tinnitus symptoms or the success of tinnitus treatment. Within the scope of this chapter, only a few domains can be mentioned which are comparatively easy assessable with patient self-assessments or with standardized clinical interviews.

The use of the so-called item response theory (IRT) for the development of PRO tools provides a solution to many of the limitations of existing instruments. IRT methods were developed more than four decades ago (Lord 1965; Rasch 1966), and numerous attempts have been made to exploit their potential (Bech et al. 1978; Fisher 1993). Today, IRT-based tests are well established in the educational field (Haley et al. 2004; Anonyms 1988) but have just been widely introduced into health care during the past decade (Ware et al. 2003; Cella and Chang 2000; Ware et al. 2000; Bjorner et al. 2003a).

Like factor analysis, IRT models assume that the measured construct is a latent variable, referred to as the IRT score, theta, or θ, which cannot be observed directly, but can be estimated based on responses to different items measuring the construct. An IRT item bank consists of items measuring the same construct and a mathematical description of the items’ measurement properties (Bjorner et al. 2003b). The IRT model (Martin et al. 2007; Fischl and Fisher 2007) describes the probability of choosing each response on a questionnaire item as a function of theta (Embretson 2000, 2006). One important distinction of all IRT methods from classical test theory methods is that theta can be estimated from the responses to any subset of items in the bank (Bjorner et al. 2003b). Accordingly, researchers or clinicians can select items that are most relevant for a given group or an individual patient and score the responses on one common metric that is independent of the choice of items. If the item bank contains items from established questionnaires, the scores of these questionnaires can be predicted from estimates of theta even if the questionnaires themselves have not been used. Thus, comparisons of results from different questionnaires are expected to be facilitated with the introduction of comprehensive IRT item banks (Bjorner et al. 2003c). There a few of such common metrics already available for key health constructs, like depression, and some websites can assist to report theta scores or corresponding scores of similar tools (www.​common-metric.​org, www.​prosettastone.​org). Figure 7.4 shows an easy to use lookup table.

This new generation of PRO tools also promises to provide very short but still reliable assessments (Haley et al. 2004; Ware et al. 2003; Embretson 2000, 2006; Bjorner et al. 2003c; Revicki and Cella 1997). The goal of the so-called computerized adaptive test (CAT) is to select and administer only the most informative items from an IRT item bank for every individual patient according to her or his estimated theta value. After each item has been administered, an IRT score is reestimated to choose and apply the next best suited item for the current score estimate. By omitting irrelevant, uninformative items, higher measurement precision is achieved, while at the same time, respondent burden can be controlled (Cella and Chang 2000; Hambleton 2006; Hays et al. 2000). CATs generally use two different ways to end the assessment (“stopping rules”): the CAT either stops after a predefined measurement precision (confidence interval) has been achieved or after a predefined total number of items have been administered.

Whereas clinical researchers can limit their research questions to a specific setting, clinical practice applications need to be embedded within the healthcare delivery system to be useful. To connect different assessment points, one could imagine, for instance, that future patients might be asked to assess their most relevant symptoms regularly at home, e.g., on a smartphone (Fig. 7.5). If self-reported health status declines, a case manager could be automatically alerted to call the patients for further evaluation and may send them to the doctor’s office. At the office, a second test might be used to confirm the home assessment. If the patients are transferred to other settings, more comprehensive health assessments also could be applied. Each setting calls for the use of different tools. At the patients’ home, practicality and low respondent burden will be the priorities, and lower measurement precision generally will be acceptable, whereas in a clinical setting, more comprehensive tests will be favored. Ideally, all instruments used in this chain of healthcare delivery would be scalable on the same metric as described above.

There are several methods of administering standardized questionnaires to collect PROs in clinical practice. The traditional methods are face-to-face interviews and paper-and-pencil completion of questionnaires. More commonly, computerized methodologies are used, as they provide date entry, administration, analysis and, printout of data in real time, to make assessment results immediately available for the clinical encounter. Computerized technologies include traditional personal computers (PCs) (which may be situated, e.g., in the clinic waiting room), mobile tablet PCs, handheld computers, smartphones, or personal digital assistants (PDAs) given to patients for one-time use or to keep them for the duration of the evaluation (e.g., over an entire treatment or follow-up period). A variation of the computer method is the use of SmartPen technology. Instead of typing or pressing buttons, a pen-like device is used to check boxes on an individually printed paper questionnaire, which uniquely identifies the patient as well as the assessment. From the users’ perspective, this assessment mode is alike traditional paper-pencil methods but still maintaining the advantages of computerized data entry.

Whereas the mentioned technologies are primarily used for the assessment within a clinical practice setting, a new field of PRO assessments for individual case management emerges with the assessment and monitoring of the subjective health status of the patients at home; this requires different technical solutions. Telephone is still the most common way to collect PRO data at the patients’ home, either via traditional interview (i.e., a person posing questions to the patient over the telephone) or via automated telephone interviews, using interactive voice recognition (IVR) or pressing a number to select answers to questions (Fig. 7.6).

Generally, most studies comparing paper-and-pencil and computerized administration modes (PDA, online, pen, tablet, touch screen) suggest psychometric equivalence between both modes of administration (Bettinville et al. 2005; Folk et al. 2006; Norman et al. 2010; Webb et al. 1999; Heuser and Geissner 1998; Velikova et al. 1999; Cook et al. 2004; Schaeren et al. 2005; Bliven et al. 2001; Kleinman et al. 2001; Ryan et al. 2002; Saleh et al. 2002; Wilson et al. 2002), but some studies also report differences (Beebe et al. 2006; DeAngelis 2000). The literature on mode effects between paper-pencil versus phone administration is more heterogeneous. Some studies suggest no mode effects (Duncan et al. 2005; Hepner et al. 2005; de Vries et al. 2005); others report and account for them (Powers et al. 2005; Beebe et al. 2005; Kraus and Augustin 2001). Literature on mode effects using IVR technology is rare, probably due to the novelty of IVR; one large-scale study reports IVR mode effects (Rodriguez et al. 2006) and suggests to adjust for it. A significant limitation is that many studies were underpowered to detect small but meaningful clinical differences.